Optical flow evaluation results: R5.0 angle error

Optical flow evaluation results	Statistics: Average SD R2.5 R5.0 R10.0 A50 A75 A95 Error type: endpoint angle interpolation normalized interpolation
Show images: below table above table

R5.0 angle error	avg.	Army (Hidden texture) GT im0 im1			Mequon (Hidden texture) GT im0 im1			Schefflera (Hidden texture) GT im0 im1			Wooden (Hidden texture) GT im0 im1			Grove (Synthetic) GT im0 im1			Urban (Synthetic) GT im0 im1			Yosemite (Synthetic) GT im0 im1			Teddy (Stereo) GT im0 im1
	rank	all	disc	untext	all	disc	untext	all	disc	untext	all	disc	untext	all	disc	untext	all	disc	untext	all	disc	untext	all	disc	untext
RAFT-it+_RVC [198]	2.7	7.80 3	25.6 4	2.77 1	5.39 4	19.9 1	5.35 13	4.02 2	13.8 2	1.39 2	0.86 2	9.09 2	0.01 1	11.4 1	16.3 4	4.51 1	3.05 1	13.0 1	1.89 1	1.46 3	11.5 3	0.05 1	1.08 4	3.98 7	0.02 1
RAFT-it [194]	9.2	9.16 12	28.0 12	3.69 5	6.15 9	22.7 3	5.47 15	4.36 3	14.6 3	2.64 7	0.76 1	8.31 1	0.15 3	13.4 9	18.7 11	6.14 6	5.09 3	13.6 2	5.33 4	4.89 25	23.7 74	0.67 6	0.87 2	3.19 2	0.05 2
MS_RAFT+_RVC [195]	10.9	9.48 16	25.6 4	3.00 2	11.0 64	24.1 7	12.7 95	5.23 5	16.1 5	4.55 24	0.97 3	10.2 4	0.14 2	11.8 4	16.2 2	5.30 3	4.33 2	13.8 3	4.19 3	1.06 1	9.70 1	0.22 3	0.70 1	2.13 1	0.42 7
NNF-Local [75]	16.3	7.69 2	26.2 7	3.54 4	7.19 19	30.7 33	6.11 27	5.88 8	19.3 12	4.53 22	4.01 15	23.9 25	2.00 18	11.7 2	16.4 5	5.52 5	10.4 22	29.7 14	9.30 15	5.25 32	20.0 30	2.61 22	1.88 13	6.47 35	0.19 5
MDP-Flow2 [68]	18.0	10.3 24	30.4 22	6.57 28	5.28 2	23.9 5	4.13 3	5.46 6	17.6 6	3.58 12	4.49 24	25.3 31	2.11 21	15.8 28	21.4 28	10.2 36	10.6 24	29.9 15	9.87 25	4.44 16	19.3 22	2.81 23	1.39 8	4.82 12	1.11 11
OFLAF [78]	19.2	8.96 10	27.9 11	4.57 12	7.36 23	26.4 13	6.68 30	4.80 4	14.9 4	3.37 11	4.43 23	21.2 15	2.81 37	13.4 9	19.0 13	7.08 11	11.4 34	28.0 8	9.28 13	5.35 34	19.1 20	3.15 26	2.47 25	5.73 26	5.50 49
NN-field [71]	20.6	8.65 6	28.3 13	4.00 7	8.38 35	33.1 50	7.44 36	5.86 7	19.0 10	4.53 22	3.15 6	21.4 17	1.25 8	12.0 6	16.9 7	5.41 4	6.58 5	20.2 4	3.45 2	8.64 71	23.6 71	2.88 24	2.47 25	8.48 53	0.20 6
RAFT-TF_RVC [179]	22.0	13.0 56	35.5 51	4.17 9	8.26 33	28.0 18	7.49 37	7.92 37	23.2 27	7.98 66	1.09 4	9.43 3	0.18 4	15.1 21	21.3 23	6.37 7	8.49 7	22.6 5	8.43 11	4.54 17	23.1 64	0.69 7	1.31 7	4.62 10	0.10 4
CoT-AMFlow [174]	25.5	10.1 20	30.9 25	6.87 31	6.06 7	25.8 11	5.30 12	5.99 10	19.1 11	4.65 27	4.70 29	25.8 37	2.42 27	16.0 31	21.3 23	12.2 56	10.5 23	31.1 19	9.77 20	5.57 36	21.3 45	3.87 40	2.08 16	5.30 18	4.37 37
PMMST [112]	27.1	12.9 54	32.6 35	8.45 54	10.2 50	30.5 30	10.7 67	7.39 24	22.2 19	6.31 42	3.87 14	13.5 5	2.73 33	14.0 13	18.7 11	7.52 12	10.9 30	28.9 11	9.95 26	4.99 28	20.8 37	3.18 28	1.52 9	3.87 6	1.12 12
HAST [107]	30.3	7.13 1	21.8 1	3.37 3	7.28 21	26.1 12	6.10 26	3.86 1	12.2 1	0.97 1	3.85 13	21.3 16	1.50 9	11.7 2	16.5 6	4.64 2	15.1 79	35.5 41	14.0 82	19.4 117	36.3 123	39.0 148	1.24 6	3.55 4	1.32 13
ComponentFusion [94]	31.0	8.86 9	28.7 15	5.91 21	6.30 10	24.2 8	5.98 22	6.79 17	21.6 16	4.99 30	4.11 16	24.4 26	2.04 20	16.2 37	22.0 35	11.3 48	13.4 54	40.4 70	12.4 67	7.66 63	21.3 45	5.22 55	2.05 15	5.21 16	3.61 28
ALD-Flow [66]	31.3	8.44 5	27.6 10	4.09 8	6.49 12	27.2 16	5.04 11	7.66 29	24.1 32	3.72 14	4.58 27	27.1 45	2.01 19	16.0 31	22.7 43	8.55 17	9.39 14	33.3 28	8.46 12	7.21 58	18.5 14	17.3 115	4.06 65	11.1 68	5.94 59
nLayers [57]	32.2	8.66 7	25.4 3	4.54 10	13.0 94	32.1 43	13.7 103	7.74 31	21.8 17	8.85 72	3.29 8	18.2 7	1.89 15	11.8 4	16.2 2	6.65 10	12.2 41	28.4 9	10.3 29	8.59 70	21.5 52	4.98 51	2.36 23	5.74 27	5.08 45
LME [70]	32.8	9.71 17	29.0 17	6.46 27	5.49 6	22.8 4	4.79 9	8.62 46	22.4 21	11.2 85	4.73 30	28.3 62	2.35 26	16.5 41	21.9 33	12.6 64	10.7 25	34.0 34	9.81 23	5.57 36	21.3 45	3.87 40	2.40 24	6.32 32	4.52 39
NNF-EAC [101]	33.3	10.9 34	31.8 30	6.97 32	6.64 14	26.4 13	5.65 16	6.61 13	20.7 13	4.44 21	5.48 51	27.1 45	2.97 42	16.5 41	22.3 39	11.1 45	12.1 40	30.7 17	9.97 27	6.42 48	21.4 48	3.94 42	2.95 41	7.51 45	4.63 42
TC/T-Flow [77]	34.0	9.15 11	32.1 32	3.69 5	6.89 17	31.2 37	4.32 4	7.32 23	23.1 26	4.08 16	5.14 42	27.2 48	2.80 36	15.6 26	21.9 33	9.71 29	8.63 10	29.2 12	8.40 9	6.72 51	20.2 33	19.7 129	3.86 62	9.43 58	6.28 66
ProFlow_ROB [142]	34.2	10.7 29	32.8 41	5.26 17	7.26 20	30.8 34	5.92 19	9.43 54	29.1 58	5.30 33	4.35 22	25.3 31	1.73 14	17.4 51	24.7 57	9.39 24	9.20 11	33.5 29	9.28 13	3.74 11	21.4 48	0.91 8	4.20 67	11.6 70	6.06 61
WLIF-Flow [91]	35.3	9.40 15	27.1 8	6.06 23	10.0 47	33.0 48	9.71 52	7.26 22	22.4 21	5.90 41	4.53 25	23.7 23	2.56 32	16.1 33	22.3 39	11.3 48	12.8 43	33.2 27	10.4 32	6.85 53	18.8 17	7.36 72	2.80 37	6.48 36	5.62 53
FC-2Layers-FF [74]	36.4	9.97 19	28.7 15	7.96 43	10.4 52	35.3 60	9.95 54	6.11 11	18.1 9	6.82 46	4.12 17	20.9 13	2.48 30	13.3 8	17.7 8	9.47 26	13.5 55	32.6 22	11.3 45	14.0 98	26.0 88	12.5 96	1.84 11	4.18 8	4.53 40
RNLOD-Flow [119]	36.8	7.93 4	24.8 2	5.41 19	8.33 34	31.7 39	6.84 32	7.47 25	23.3 28	4.60 25	3.62 11	21.1 14	1.66 12	14.4 15	21.0 19	7.80 13	12.7 42	32.9 23	12.2 66	17.4 112	34.4 118	20.7 131	2.55 28	5.57 24	5.30 47
PRAFlow_RVC [177]	36.9	18.4 91	41.9 72	7.63 36	13.9 105	36.5 64	13.3 100	11.1 68	30.2 63	10.6 80	2.23 5	15.0 6	0.54 5	15.3 23	21.3 23	8.44 16	9.59 17	24.7 6	9.82 24	2.25 5	20.1 31	0.44 4	1.96 14	4.67 11	1.90 16
Layers++ [37]	37.2	10.2 22	29.1 18	8.58 57	10.8 58	30.6 31	11.0 73	5.90 9	17.7 7	6.34 43	3.40 9	18.2 7	1.66 12	12.2 7	16.0 1	9.69 28	13.9 62	33.6 30	11.9 59	13.9 97	27.9 94	8.74 78	2.33 22	4.94 13	5.70 56
UnDAF [187]	37.2	10.6 28	32.9 42	6.71 29	6.11 8	28.4 23	4.73 7	6.87 18	22.3 20	4.21 18	4.82 32	28.2 60	2.25 23	16.2 37	21.8 32	11.5 50	11.7 35	38.7 61	9.79 21	5.77 40	21.1 41	3.69 36	5.19 80	16.5 111	4.58 41
FESL [72]	38.8	8.67 8	25.6 4	4.73 13	13.6 100	39.4 101	12.9 97	8.22 40	24.3 36	7.10 50	3.76 12	20.3 12	2.12 22	14.1 14	20.1 15	9.08 20	11.2 33	31.0 18	9.80 22	11.3 81	30.4 104	7.66 74	2.10 17	5.40 20	2.40 18
SVFilterOh [109]	38.9	12.7 52	28.4 14	8.58 57	9.41 42	29.1 25	8.31 43	6.39 12	17.7 7	5.05 32	3.23 7	18.8 10	0.95 6	14.9 18	20.9 18	6.51 9	13.2 49	32.1 21	11.6 51	22.0 123	46.5 150	30.5 142	1.61 10	4.97 15	2.45 20
TC-Flow [46]	41.0	9.24 13	30.9 25	5.24 16	5.48 5	25.7 10	4.01 2	7.25 20	23.3 28	2.66 8	5.52 52	28.4 63	3.26 50	16.7 45	23.9 52	9.52 27	11.7 35	36.8 46	11.5 50	6.69 50	21.4 48	19.0 126	4.21 68	10.6 65	6.91 80
OAR-Flow [123]	42.6	10.8 32	34.0 46	6.23 24	8.94 38	31.9 41	7.53 38	10.4 62	30.3 65	7.58 58	5.60 56	25.9 40	3.04 45	17.0 46	24.0 53	9.35 23	8.62 9	33.0 24	7.87 7	3.37 8	14.6 5	5.54 61	4.80 75	10.4 64	9.44 103
IROF++ [58]	43.8	10.2 22	30.9 25	7.02 33	11.1 66	38.1 84	10.7 67	8.32 44	25.1 41	7.61 59	5.83 59	28.0 57	4.08 71	15.4 25	21.3 23	9.83 31	13.6 56	38.0 56	11.3 45	5.83 41	20.8 37	1.97 19	2.32 21	5.71 25	4.87 44
AGIF+OF [84]	43.9	10.4 25	29.4 20	7.42 35	12.4 85	37.5 74	12.4 90	7.91 36	24.3 36	7.24 52	4.84 35	23.8 24	2.91 39	14.8 17	20.6 17	9.72 30	13.2 49	36.5 44	10.5 34	7.06 55	20.8 37	7.54 73	3.02 47	6.27 30	6.37 69
Efficient-NL [60]	44.0	9.31 14	27.5 9	5.65 20	12.1 83	38.1 84	11.2 75	8.07 39	24.1 32	6.69 45	5.39 48	25.9 40	3.51 61	14.9 18	21.1 21	9.39 24	14.0 65	35.2 39	11.1 42	11.5 83	25.7 86	6.90 69	2.19 20	5.45 22	1.94 17
PMF [73]	45.3	11.6 44	29.9 21	4.55 11	7.81 26	30.2 27	6.00 23	7.17 19	23.3 28	3.21 10	4.88 38	23.1 21	2.42 27	13.6 11	18.5 9	6.49 8	16.1 87	42.7 85	15.4 91	27.2 140	43.5 147	28.9 139	2.15 19	4.96 14	4.81 43
PH-Flow [99]	45.4	10.9 34	32.6 35	7.94 42	10.9 60	37.4 72	10.5 61	7.56 28	22.8 25	7.74 63	5.75 58	27.2 48	4.04 69	14.4 15	19.8 14	8.81 18	13.0 45	33.6 30	11.1 42	12.7 91	23.1 64	17.6 118	1.84 11	4.19 9	4.50 38
Classic+CPF [82]	46.8	10.9 34	31.7 29	7.88 41	11.5 72	37.9 80	10.9 71	8.27 42	25.1 41	7.51 57	5.05 40	25.8 37	3.16 47	15.1 21	21.0 19	10.6 37	13.1 46	34.6 37	10.3 29	9.87 75	22.0 58	13.1 102	2.68 31	5.85 28	5.53 50
3DFlow [133]	47.8	12.6 51	34.9 47	5.05 15	8.12 30	31.5 38	5.95 20	6.67 15	21.9 18	2.51 5	4.59 28	18.4 9	3.30 53	16.6 43	23.0 45	10.9 40	19.7 108	46.8 108	19.8 118	18.3 115	24.2 79	33.3 144	1.20 5	3.86 5	0.68 8
COFM [59]	47.9	10.1 20	32.0 31	7.63 36	8.06 29	30.4 29	7.17 33	8.93 51	25.9 47	8.04 68	4.17 18	24.9 29	1.63 11	18.8 63	24.0 53	18.6 112	14.4 69	33.0 24	11.7 53	8.15 66	20.4 34	14.7 109	3.16 49	5.36 19	8.09 96
Ramp [62]	48.6	10.9 34	32.7 39	7.96 43	10.9 60	37.1 69	10.6 63	7.85 32	24.2 34	7.41 55	5.29 45	27.0 44	3.44 56	16.1 33	22.3 39	10.8 38	13.8 60	35.4 40	11.0 41	11.6 84	21.1 41	18.2 122	2.52 27	5.44 21	5.23 46
Sparse-NonSparse [56]	49.2	10.7 29	32.5 34	8.38 52	10.9 60	36.8 67	10.7 67	7.95 38	24.5 40	7.30 53	5.42 50	27.6 51	3.49 60	16.1 33	22.1 36	11.0 43	13.3 51	36.0 43	10.6 36	10.6 79	21.1 41	10.9 87	2.91 38	5.93 29	6.18 64
Correlation Flow [76]	49.5	11.9 48	35.3 48	6.03 22	6.85 16	28.0 18	4.77 8	8.29 43	25.8 45	2.17 4	4.84 35	27.2 48	2.77 35	18.5 59	25.9 70	11.7 52	16.9 95	39.5 64	16.7 99	12.1 87	24.6 81	17.8 119	2.59 29	7.33 43	3.08 21
JOF [136]	49.9	9.77 18	29.1 18	7.11 34	12.1 83	37.9 80	12.0 86	7.25 20	21.2 15	7.73 61	4.82 32	25.8 37	3.02 44	14.9 18	20.2 16	10.0 35	13.1 46	33.7 32	10.6 36	17.8 113	29.5 99	28.8 138	2.94 40	6.80 39	5.71 57
LSM [39]	50.2	10.4 25	32.6 35	8.24 47	10.8 58	37.4 72	10.4 60	7.85 32	24.3 36	7.05 48	5.32 46	27.6 51	3.41 55	15.8 28	21.5 29	11.1 45	13.7 58	35.6 42	10.9 40	13.0 92	23.2 66	12.5 96	2.99 46	6.43 34	6.14 63
ProbFlowFields [126]	50.3	16.2 73	47.8 88	11.7 87	8.96 39	31.0 35	8.86 46	9.73 58	28.4 56	10.1 77	6.09 68	25.5 34	4.53 81	18.2 57	25.5 63	10.9 40	9.76 19	34.2 35	11.7 53	4.63 19	18.8 17	3.79 39	2.95 41	8.94 57	3.52 26
PBOFVI [189]	50.5	19.4 100	38.3 59	13.1 94	7.96 28	31.8 40	6.15 28	6.77 16	21.0 14	2.12 3	3.57 10	19.8 11	1.24 7	18.5 59	25.4 60	11.5 50	14.7 73	38.6 60	16.2 97	7.61 61	21.6 53	17.3 115	3.38 54	8.26 52	6.29 68
Classic+NL [31]	52.8	10.5 27	31.4 28	8.38 52	11.1 66	37.9 80	10.6 63	7.87 34	24.0 31	7.48 56	5.57 54	27.6 51	3.62 63	15.8 28	21.5 29	10.8 38	14.1 66	37.4 51	11.4 47	14.8 101	25.9 87	13.4 105	2.61 30	5.29 17	6.10 62
S2D-Matching [83]	54.6	10.7 29	32.2 33	8.71 59	10.7 56	36.6 66	10.2 57	8.94 52	27.2 52	6.96 47	5.17 43	26.0 42	3.36 54	16.3 39	22.1 36	10.9 40	14.4 69	37.0 47	11.7 53	16.4 106	26.0 88	16.5 113	2.79 36	5.49 23	6.49 71
FMOF [92]	54.6	11.0 42	30.4 22	8.33 51	13.0 94	38.5 91	12.6 92	7.51 26	22.6 23	7.34 54	5.06 41	25.2 30	3.44 56	15.3 23	21.3 23	9.87 33	14.9 77	33.1 26	11.4 47	11.7 86	24.3 80	15.0 111	3.92 63	8.59 54	6.28 66
HCFN [157]	55.9	10.9 34	36.7 56	6.43 26	5.32 3	24.0 6	4.44 5	6.63 14	22.6 23	2.60 6	4.85 37	27.9 56	2.54 31	15.6 26	21.5 29	9.30 22	15.0 78	40.7 74	13.9 81	35.1 154	46.6 151	40.3 152	5.63 90	12.3 75	11.0 112
IROF-TV [53]	56.2	11.6 44	35.3 48	9.03 62	11.2 69	38.2 87	10.9 71	8.85 50	26.5 48	7.73 61	6.04 66	33.0 86	3.62 63	17.1 49	23.1 47	13.5 80	16.3 88	44.8 93	13.5 79	3.41 9	16.9 7	1.13 14	2.71 33	6.80 39	5.67 55
IIOF-NLDP [129]	56.6	14.6 60	41.6 71	6.71 29	11.0 64	37.5 74	8.25 42	8.77 49	26.9 50	4.19 17	6.07 67	28.0 57	3.76 65	19.7 78	27.1 85	12.6 64	16.7 92	40.7 74	15.4 91	4.68 23	23.0 62	4.41 46	2.78 35	7.26 42	3.16 22
TV-L1-MCT [64]	58.0	10.9 34	30.5 24	8.56 56	13.8 103	40.9 112	13.2 99	8.68 47	25.8 45	7.98 66	4.83 34	25.7 35	3.26 50	17.4 51	23.5 50	13.7 84	14.8 76	36.7 45	12.7 69	5.84 42	19.4 23	10.1 84	3.53 57	6.42 33	6.63 73
SimpleFlow [49]	59.2	11.6 44	33.7 43	8.98 61	12.5 90	38.9 94	12.6 92	10.4 62	29.3 60	9.20 73	5.99 63	27.6 51	4.08 71	16.3 39	22.2 38	11.1 45	16.7 92	37.4 51	12.7 69	8.29 67	19.9 28	6.11 65	2.74 34	6.28 31	5.86 58
2DHMM-SAS [90]	59.2	10.9 34	32.6 35	8.06 45	11.5 72	39.5 102	10.6 63	10.0 61	28.3 55	7.91 65	5.93 62	28.2 60	4.07 70	16.1 33	22.3 39	11.0 43	13.7 58	38.3 58	11.1 42	12.3 89	23.2 66	18.0 121	3.08 48	6.48 36	6.24 65
CostFilter [40]	59.8	14.1 59	36.2 55	8.48 55	8.61 37	30.6 31	7.43 35	8.26 41	26.9 50	4.40 20	5.72 57	28.1 59	3.24 49	13.7 12	18.5 9	7.81 14	16.6 91	45.0 98	16.0 95	26.8 138	48.6 154	32.7 143	2.93 39	7.59 46	5.38 48
AggregFlow [95]	60.0	13.9 58	33.8 44	11.2 80	13.7 101	39.6 104	12.6 92	12.0 76	31.3 66	13.7 98	5.40 49	23.5 22	3.44 56	17.5 53	25.4 60	7.98 15	8.57 8	25.9 7	8.42 10	7.00 54	24.1 78	4.53 47	5.53 87	9.80 60	11.7 115
Adaptive [20]	60.9	10.9 34	33.8 44	4.92 14	10.5 53	35.0 59	9.53 51	12.2 77	33.7 71	7.68 60	5.57 54	30.3 72	2.95 41	21.7 105	26.7 79	20.6 119	10.8 27	34.9 38	7.26 6	14.0 98	28.8 98	4.88 48	4.50 72	10.2 63	6.84 78
MDP-Flow [26]	61.5	12.2 50	40.6 65	8.88 60	9.32 41	28.3 21	10.5 61	9.09 53	28.1 54	9.37 75	6.03 65	30.6 74	3.99 67	17.2 50	23.1 47	12.4 59	13.9 62	42.7 85	12.5 68	7.10 57	23.6 71	4.09 44	5.35 83	13.2 82	7.09 83
RFlow [88]	62.8	14.8 61	43.9 76	11.2 80	6.64 14	26.6 15	5.76 17	11.7 72	35.9 81	5.04 31	4.31 20	27.1 45	1.94 16	19.4 69	26.8 82	13.0 75	14.7 73	42.2 82	11.8 58	13.1 93	22.2 59	13.1 102	5.87 95	14.1 91	8.71 100
Occlusion-TV-L1 [63]	63.1	12.9 54	36.1 54	8.26 50	9.51 45	32.7 45	8.99 47	12.3 78	34.4 76	8.27 69	5.53 53	29.8 70	3.04 45	20.5 94	28.5 105	13.8 86	9.95 20	37.9 53	11.6 51	7.64 62	21.8 57	3.47 31	5.69 91	13.9 89	7.59 90
WRT [146]	63.3	15.4 68	39.7 61	5.30 18	15.2 109	40.5 109	13.7 103	12.4 79	33.7 71	4.05 15	4.55 26	22.8 20	2.34 25	17.0 46	22.7 43	13.0 75	24.7 136	45.1 99	18.9 113	6.51 49	23.5 69	6.55 67	3.24 51	7.37 44	3.24 23
OFH [38]	64.6	15.0 63	40.9 67	14.4 100	7.06 18	29.9 26	5.37 14	10.8 65	33.1 70	4.86 29	5.84 60	30.6 74	3.46 59	19.5 73	26.1 72	15.3 94	15.6 83	46.5 106	16.6 98	4.19 14	21.7 55	3.74 38	5.39 85	15.4 102	7.23 85
PWC-Net_RVC [143]	66.5	23.5 113	52.0 110	13.4 96	13.0 94	37.8 78	12.4 90	14.0 90	39.3 90	14.4 101	7.08 79	24.7 27	2.76 34	19.9 82	25.7 69	12.7 68	13.8 60	43.4 88	13.4 78	3.79 13	22.4 60	1.03 11	2.14 18	6.78 38	1.08 10
MLDP_OF [87]	67.5	18.8 97	51.3 105	16.0 103	8.16 31	32.0 42	6.76 31	10.7 64	31.9 68	5.45 36	4.81 31	26.1 43	2.44 29	18.7 61	24.3 55	13.7 84	15.6 83	37.9 53	18.6 111	19.2 116	28.5 96	38.7 147	3.53 57	7.25 41	4.27 35
DeepFlow2 [106]	69.8	15.0 63	43.6 75	11.0 76	10.1 48	34.2 56	9.29 49	12.9 81	36.8 82	11.1 84	7.47 86	32.1 81	4.75 87	17.8 54	25.4 60	9.97 34	10.7 25	40.2 69	10.3 29	6.78 52	18.7 16	13.3 104	9.05 122	17.3 116	15.3 126
SegFlow [156]	70.2	17.6 83	50.4 98	10.9 72	11.9 76	39.1 96	11.7 82	13.7 86	39.9 98	13.3 95	7.50 89	35.7 104	4.56 83	19.7 78	27.4 89	13.0 75	9.37 13	37.2 49	9.48 18	5.08 31	19.8 26	5.25 56	4.01 64	11.9 72	5.55 51
DMF_ROB [135]	70.7	16.8 79	47.5 87	11.2 80	10.6 55	34.1 55	9.86 53	14.7 93	41.2 102	11.9 88	7.41 85	33.9 92	4.25 74	18.8 63	25.9 70	12.7 68	11.9 38	41.3 80	11.7 53	4.39 15	18.8 17	5.30 58	6.00 97	14.9 98	8.11 97
S2F-IF [121]	71.0	18.0 89	51.9 108	10.9 72	11.1 66	38.6 92	10.6 63	13.9 88	40.6 100	13.4 97	7.68 95	32.6 83	5.18 94	19.7 78	27.2 86	13.3 79	10.8 27	39.5 64	11.9 59	4.99 28	19.9 28	6.26 66	3.26 52	10.1 62	3.57 27
PGM-C [118]	71.1	17.7 84	50.5 99	11.0 76	11.9 76	39.1 96	11.6 80	13.9 88	40.4 99	13.3 95	7.52 90	35.8 106	4.62 86	19.6 76	27.5 90	12.4 59	9.48 16	37.9 53	9.36 16	4.63 19	16.9 7	5.02 52	4.83 76	14.2 94	6.69 74
Steered-L1 [116]	71.4	11.4 43	37.9 58	7.71 40	4.42 1	21.7 2	3.76 1	7.71 30	25.7 43	4.29 19	4.91 39	29.8 70	2.26 24	20.2 88	26.7 79	16.6 102	18.1 101	46.1 105	14.6 84	32.4 147	37.9 131	51.5 158	8.58 119	15.5 104	15.2 125
Sparse Occlusion [54]	72.0	12.7 52	35.8 53	8.24 47	12.4 85	33.4 51	13.4 101	9.67 56	29.1 58	6.55 44	5.99 63	28.5 64	3.56 62	19.4 69	26.4 77	12.4 59	14.7 73	39.4 63	11.7 53	37.7 155	48.6 154	17.8 119	3.66 59	9.43 58	5.64 54
CPM-Flow [114]	72.2	17.7 84	50.5 99	11.0 76	11.9 76	39.0 95	11.7 82	13.7 86	39.8 96	13.2 93	7.49 88	35.5 101	4.58 85	19.5 73	27.2 86	12.3 58	9.44 15	37.3 50	9.46 17	5.05 30	19.5 24	5.17 54	5.21 81	14.8 96	7.36 88
Classic++ [32]	72.4	10.8 32	32.7 39	8.25 49	10.5 53	32.9 46	10.7 67	10.8 65	31.6 67	8.46 70	5.25 44	29.7 69	2.99 43	20.0 84	28.0 98	13.9 88	15.2 81	44.1 91	11.9 59	17.3 111	26.2 90	18.3 123	5.82 93	12.7 78	8.14 98
FlowFields+ [128]	72.5	18.4 91	52.2 112	11.4 84	11.9 76	39.9 105	11.5 77	14.9 97	43.4 109	14.4 101	7.97 98	33.1 87	5.58 99	19.4 69	26.9 84	12.7 68	10.2 21	39.9 67	10.5 34	4.74 24	20.1 31	4.29 45	3.80 60	12.4 77	3.48 25
MCPFlow_RVC [197]	73.1	36.5 128	52.9 115	17.9 105	27.7 129	50.9 133	28.4 125	31.7 132	57.9 128	38.4 132	5.84 60	21.5 18	3.17 48	19.3 68	26.7 79	9.11 21	13.1 46	30.1 16	12.9 72	3.64 10	21.2 44	1.06 13	3.29 53	7.79 50	3.64 29
EpicFlow [100]	73.8	17.7 84	50.6 101	10.9 72	12.0 81	39.3 100	11.7 82	14.5 92	42.2 104	13.2 93	7.47 86	35.5 101	4.57 84	19.8 81	27.6 91	12.8 73	9.73 18	38.1 57	10.1 28	4.63 19	17.2 9	4.88 48	5.31 82	14.3 95	7.47 89
VCN_RVC [178]	74.1	24.5 114	58.3 120	19.8 110	13.8 103	38.2 87	14.0 106	13.4 84	39.2 89	10.1 77	7.66 94	35.8 106	4.85 90	18.7 61	24.6 56	12.6 64	13.6 56	41.1 79	13.2 77	3.76 12	21.4 48	1.03 11	2.97 44	8.89 56	4.24 34
NL-TV-NCC [25]	74.5	16.5 75	40.4 63	9.10 63	10.7 56	37.0 68	8.07 40	8.59 45	26.8 49	3.17 9	6.24 70	33.4 88	3.26 50	21.4 102	29.7 119	12.7 68	21.2 116	48.2 110	17.3 104	13.4 96	35.6 121	13.0 101	4.73 74	12.8 79	3.24 23
ACK-Prior [27]	75.0	19.5 103	41.5 69	14.3 99	6.57 13	27.6 17	4.53 6	7.87 34	25.7 43	3.70 13	4.33 21	25.7 35	1.53 10	20.5 94	25.6 66	18.3 109	23.1 131	44.0 90	18.5 110	29.9 143	33.1 112	45.6 156	7.91 115	14.8 96	11.7 115
BriefMatch [122]	75.1	11.8 47	35.7 52	6.41 25	7.52 24	30.3 28	5.97 21	7.54 27	24.2 34	4.62 26	4.28 19	25.4 33	1.98 17	20.6 96	26.2 75	20.9 121	26.8 142	49.2 112	28.2 144	22.8 128	35.9 122	39.6 151	9.81 125	15.1 100	18.3 133
CombBMOF [111]	75.5	15.2 65	48.2 90	7.67 38	11.3 70	34.5 57	9.95 54	8.75 48	27.2 52	5.37 35	7.60 93	32.1 81	5.65 102	18.0 56	23.0 45	13.9 88	21.7 121	44.9 95	24.3 135	22.6 125	37.2 126	14.5 108	2.97 44	7.73 49	4.35 36
FlowFields [108]	75.7	18.3 90	51.9 108	11.1 79	11.9 76	39.5 102	11.5 77	14.8 94	43.3 108	14.2 99	7.96 97	33.5 91	5.52 98	19.9 82	27.6 91	13.6 82	11.0 31	40.5 71	12.1 63	4.93 27	19.7 25	5.34 59	3.85 61	12.3 75	3.89 31
Complementary OF [21]	77.2	20.9 106	51.7 106	21.5 116	6.41 11	28.3 21	4.86 10	9.56 55	30.2 63	5.62 37	8.21 100	31.4 77	6.20 105	19.2 66	25.6 66	15.5 95	21.5 119	49.3 113	17.4 105	6.34 46	19.8 26	11.5 90	6.44 105	16.1 109	10.2 107
ROF-ND [105]	78.2	18.4 91	45.8 83	11.5 85	7.31 22	25.4 9	6.02 24	9.70 57	29.4 61	4.66 28	9.09 107	28.7 65	5.98 104	21.6 104	29.5 116	14.5 91	19.9 109	44.8 93	15.3 90	33.3 151	41.0 137	30.1 141	2.95 41	7.63 48	2.41 19
TF+OM [98]	78.3	14.8 61	35.4 50	7.68 39	9.06 40	28.4 23	9.32 50	11.6 71	28.4 56	16.0 107	6.43 71	29.0 67	4.29 75	20.2 88	25.6 66	18.4 110	17.9 99	38.5 59	16.9 102	16.6 107	33.8 114	14.7 109	6.87 108	15.5 104	9.68 104
DeepFlow [85]	80.2	17.5 82	46.9 85	16.5 104	11.8 74	35.8 61	11.2 75	15.1 98	39.6 94	15.2 105	7.81 96	32.6 83	5.12 93	17.8 54	25.5 63	9.86 32	12.0 39	44.9 95	11.4 47	6.11 45	18.0 12	12.8 99	10.8 130	18.7 125	18.8 135
GMFlow_RVC [196]	80.2	46.3 141	57.0 118	42.4 139	15.4 110	33.8 52	17.3 110	16.6 108	32.3 69	12.8 92	8.66 104	22.6 19	5.32 95	19.4 69	24.7 57	13.1 78	21.1 115	38.7 61	19.0 114	17.1 109	41.9 140	1.75 18	0.93 3	3.40 3	0.05 2
ComplOF-FED-GPU [35]	80.4	17.9 87	52.0 110	15.4 102	7.90 27	33.9 54	5.82 18	10.8 65	34.2 74	5.67 38	6.99 78	31.5 78	4.51 80	19.2 66	26.3 76	12.9 74	18.2 102	50.5 121	18.6 111	15.1 103	23.6 71	22.3 133	5.37 84	15.4 102	6.76 75
TCOF [69]	80.4	17.2 81	45.4 82	15.3 101	12.6 91	37.6 76	12.3 88	15.7 100	39.5 92	16.6 109	6.72 74	27.7 55	4.48 79	22.5 114	30.9 132	11.9 53	9.21 12	28.4 9	10.8 39	22.9 129	35.0 120	9.29 80	4.22 69	11.3 69	6.79 76
CVENG22+RIC [199]	81.1	16.7 78	49.7 94	10.8 70	12.4 85	41.2 113	11.6 80	14.8 94	43.2 107	12.2 89	7.23 82	35.7 104	4.53 81	22.0 110	29.9 121	16.1 100	11.0 31	40.5 71	12.1 63	4.63 19	17.2 9	4.90 50	5.84 94	16.8 114	7.32 87
TV-L1-improved [17]	81.2	11.9 48	36.8 57	8.23 46	8.49 36	31.0 35	7.83 39	11.9 74	33.7 71	7.19 51	5.35 47	28.9 66	2.91 39	20.3 92	28.0 98	12.0 55	27.2 144	55.4 135	30.4 146	23.1 131	38.0 132	22.9 134	5.61 89	14.0 90	7.74 93
EPPM w/o HM [86]	81.8	19.4 100	53.2 116	11.2 80	8.23 32	34.8 58	6.07 25	11.1 68	35.1 79	5.89 40	7.31 83	33.4 88	4.76 88	18.9 65	23.2 49	17.1 104	21.3 117	50.3 120	20.1 119	20.7 121	30.3 103	40.9 154	3.20 50	8.13 51	5.59 52
HBM-GC [103]	81.9	31.9 120	41.2 68	25.6 123	13.2 97	32.9 46	14.2 107	9.93 60	24.4 39	8.75 71	10.1 115	24.7 27	6.95 113	16.6 43	21.1 21	13.6 82	18.5 103	33.7 32	15.5 93	33.9 153	47.5 153	20.1 130	3.38 54	8.62 55	5.97 60
Rannacher [23]	85.1	15.5 69	43.5 74	10.7 68	11.4 71	35.8 61	11.5 77	14.2 91	39.0 88	10.8 81	6.59 73	30.8 76	4.20 73	21.0 98	29.6 118	12.6 64	19.1 106	50.8 122	15.2 88	14.7 100	26.8 91	16.7 114	4.86 77	12.9 80	7.03 82
Aniso. Huber-L1 [22]	85.2	13.6 57	40.4 63	9.77 64	19.4 114	40.1 107	22.0 116	16.4 104	38.4 84	18.3 112	7.56 91	33.4 88	5.00 92	20.1 87	27.7 96	12.5 62	14.5 71	39.7 66	10.4 32	20.8 122	32.0 108	12.9 100	4.35 70	10.8 66	6.56 72
SIOF [67]	85.8	16.5 75	40.1 62	10.8 70	10.3 51	37.1 69	9.10 48	16.4 104	38.3 83	18.4 113	8.56 101	35.1 99	5.87 103	21.3 101	28.5 105	16.5 101	17.6 97	43.6 89	19.7 117	7.08 56	21.6 53	3.65 35	6.65 106	16.1 109	10.9 111
FF++_ROB [141]	85.9	19.0 98	52.4 114	12.2 91	12.4 85	40.0 106	11.9 85	16.2 102	45.2 112	16.3 108	9.16 109	35.6 103	7.36 115	20.2 88	28.0 98	13.8 86	13.3 51	40.5 71	12.8 71	5.68 38	19.2 21	8.50 76	4.50 72	11.8 71	7.66 91
F-TV-L1 [15]	86.1	31.8 119	60.6 123	43.6 143	13.7 101	38.4 90	13.1 98	15.6 99	39.4 91	10.1 77	10.9 118	37.3 113	8.78 119	20.0 84	26.5 78	16.0 99	12.9 44	40.7 74	10.7 38	9.68 74	23.7 74	3.52 33	4.49 71	12.0 73	4.19 33
Brox et al. [5]	89.2	18.5 94	51.2 103	20.8 115	14.0 106	37.8 78	15.1 109	13.6 85	38.8 86	11.7 86	7.20 81	36.8 110	4.02 68	23.0 118	28.5 105	24.3 134	10.8 27	45.3 101	9.57 19	7.81 64	22.7 61	1.58 16	9.61 124	19.2 128	15.0 124
SRR-TVOF-NL [89]	89.3	22.3 112	44.7 78	12.5 92	12.0 81	38.1 84	10.2 57	14.8 94	40.6 100	10.9 83	6.13 69	34.1 94	2.81 37	19.6 76	25.5 63	13.5 80	16.4 89	42.4 83	13.0 74	30.5 144	42.5 143	18.3 123	6.41 104	11.0 67	12.0 117
LocallyOriented [52]	89.6	15.8 72	41.5 69	10.9 72	15.0 108	44.5 120	13.7 103	17.6 109	43.4 109	14.2 99	7.16 80	31.5 78	4.82 89	21.0 98	29.0 111	12.5 62	11.7 35	34.5 36	12.9 72	11.6 84	29.6 100	12.0 93	7.94 116	18.4 122	11.1 113
DPOF [18]	90.1	20.5 105	50.2 97	10.5 65	12.6 91	41.8 115	11.0 73	11.8 73	34.3 75	10.8 81	8.61 103	38.9 121	5.43 96	19.5 73	26.1 72	15.1 92	16.8 94	41.5 81	15.2 88	23.3 132	23.9 77	50.1 157	5.05 78	14.1 91	4.13 32
CRTflow [81]	91.2	16.5 75	49.5 93	10.6 67	9.63 46	33.8 52	8.65 45	13.1 82	38.8 86	7.80 64	6.86 76	34.3 96	4.44 78	20.0 84	27.8 97	12.2 56	31.4 150	59.0 146	36.7 150	10.3 76	30.4 104	12.0 93	8.56 118	20.4 134	12.9 122
Bartels [41]	93.2	19.3 99	39.6 60	22.4 119	9.47 44	28.2 20	10.0 56	9.91 59	29.7 62	7.09 49	9.18 110	29.3 68	7.40 116	21.7 105	27.6 91	21.1 123	19.1 106	44.4 92	24.2 134	23.0 130	36.3 123	36.2 145	7.46 114	14.9 98	11.5 114
Dynamic MRF [7]	94.2	22.0 110	52.3 113	25.2 121	7.67 25	33.0 48	6.18 29	12.4 79	39.8 96	5.34 34	6.49 72	35.4 100	3.86 66	22.9 116	29.2 114	20.7 120	22.2 125	57.8 143	22.9 129	7.42 59	18.1 13	25.1 135	13.2 137	21.3 139	20.5 139
CBF [12]	96.3	15.2 65	44.8 79	12.1 89	23.7 121	37.7 77	30.9 128	13.2 83	34.6 77	14.5 103	6.86 76	32.8 85	4.32 76	22.6 115	28.4 104	20.2 118	15.6 83	41.0 78	12.1 63	32.9 149	39.7 135	29.8 140	5.49 86	13.2 82	8.30 99
Local-TV-L1 [65]	97.1	24.6 115	51.2 103	30.0 125	22.5 120	40.6 110	25.2 119	23.5 120	46.1 114	28.3 123	9.73 113	37.4 114	6.92 112	18.3 58	25.2 59	12.7 68	13.9 62	43.2 87	12.0 62	5.25 32	20.6 35	5.15 53	15.8 142	21.0 137	32.1 147
DF-Auto [113]	97.1	19.4 100	46.0 84	10.5 65	26.6 127	46.1 124	31.1 129	23.7 121	46.1 114	37.0 128	9.05 106	36.8 110	5.59 100	21.7 105	29.1 113	17.4 106	7.80 6	31.8 20	7.93 8	19.5 118	37.4 128	3.25 29	10.9 131	19.6 130	16.4 128
TriangleFlow [30]	97.9	18.7 96	43.9 76	18.0 106	10.1 48	37.2 71	8.18 41	11.9 74	35.5 80	5.81 39	6.72 74	34.6 98	4.37 77	26.7 143	34.7 146	23.4 129	23.1 131	49.6 114	23.5 130	16.7 108	37.2 126	16.3 112	6.85 107	17.3 116	10.3 108
LDOF [28]	98.7	17.1 80	48.0 89	12.9 93	13.3 98	40.6 110	12.2 87	15.8 101	42.4 105	12.7 91	9.70 112	44.0 130	6.27 107	20.7 97	28.0 98	16.8 103	14.3 68	45.9 104	13.8 80	8.36 68	23.3 68	7.98 75	11.2 134	21.2 138	18.3 133
CNN-flow-warp+ref [115]	100.0	18.5 94	50.0 95	13.9 98	17.8 112	37.9 80	21.1 114	21.3 117	47.3 118	29.7 124	9.13 108	38.8 118	6.72 110	21.8 108	28.2 102	19.6 114	14.2 67	45.7 103	13.1 75	5.94 43	18.5 14	10.9 87	12.4 136	20.6 136	16.4 128
CLG-TV [48]	100.5	15.7 71	42.2 73	11.7 87	20.9 115	39.2 99	24.8 118	16.4 104	39.5 92	18.0 111	9.23 111	37.9 115	6.54 108	22.9 116	30.0 124	17.9 108	16.5 90	47.2 109	14.2 83	19.9 119	30.4 104	11.5 90	5.79 92	14.1 91	6.98 81
TriFlow [93]	100.5	21.3 108	44.9 80	13.5 97	16.0 111	36.5 64	18.7 112	18.2 111	38.6 85	27.8 120	7.35 84	30.3 72	5.59 100	21.2 100	27.3 88	18.5 111	15.1 79	37.1 48	15.0 87	49.5 160	41.7 139	95.6 163	6.38 103	13.3 84	9.77 105
p-harmonic [29]	101.4	21.2 107	63.8 131	20.6 114	12.4 85	35.9 63	12.7 95	17.7 110	47.5 119	14.9 104	10.9 118	42.1 126	8.85 120	20.4 93	26.1 72	17.1 104	17.9 99	52.5 126	18.4 108	15.6 105	28.6 97	5.86 63	5.89 96	13.5 86	7.67 92
OFRF [132]	102.0	20.1 104	40.7 66	18.8 108	25.4 125	43.7 117	27.8 124	20.4 115	39.7 95	25.4 116	12.5 121	34.3 96	11.1 126	17.0 46	23.7 51	8.99 19	17.6 97	40.0 68	16.7 99	15.0 102	28.4 95	28.3 137	16.1 143	19.4 129	34.6 148
FlowNetS+ft+v [110]	102.2	15.2 65	44.9 80	10.7 68	13.4 99	38.2 87	13.4 101	18.8 113	42.8 106	24.4 115	9.01 105	38.8 118	6.24 106	23.2 121	31.5 137	15.9 97	13.3 51	42.6 84	13.1 75	18.2 114	32.6 111	21.9 132	8.73 120	19.1 127	12.8 121
Second-order prior [8]	104.8	15.6 70	48.2 90	12.1 89	12.6 91	39.1 96	12.3 88	16.2 102	44.6 111	12.2 89	7.57 92	31.6 80	5.45 97	22.2 111	30.6 126	14.3 90	20.8 114	56.8 139	17.7 107	28.0 141	33.8 114	27.1 136	7.43 113	17.4 118	10.4 110
ContinualFlow_ROB [148]	107.8	38.4 129	64.1 133	31.4 127	31.9 134	52.5 138	35.2 135	32.8 133	61.5 135	38.0 131	15.4 126	42.4 127	9.42 123	27.2 147	32.9 145	23.6 131	30.9 147	52.8 129	37.9 151	3.27 7	17.7 11	1.31 15	3.48 56	10.0 61	1.76 15
Fusion [6]	109.2	17.9 87	57.7 119	18.6 107	9.42 43	32.3 44	10.2 57	11.4 70	34.8 78	11.7 86	8.57 102	40.2 122	6.89 111	25.0 133	30.8 131	24.9 140	23.9 134	52.3 125	25.0 138	33.3 151	43.4 145	19.3 128	9.01 121	18.8 126	13.4 123
C-RAFT_RVC [181]	109.4	46.7 142	69.9 142	38.2 133	33.2 137	55.6 143	34.0 133	33.8 136	63.4 138	37.9 129	14.2 125	34.0 93	9.21 121	26.2 139	32.8 144	21.9 126	20.0 110	46.5 106	21.2 125	10.3 76	31.1 107	3.57 34	2.70 32	7.59 46	1.02 9
Learning Flow [11]	109.7	16.4 74	47.3 86	11.5 85	14.0 106	40.3 108	14.4 108	16.4 104	41.7 103	15.6 106	8.05 99	40.7 124	4.87 91	27.1 145	35.0 148	22.5 127	17.2 96	50.0 119	16.0 95	15.5 104	34.1 117	13.9 107	10.1 127	20.2 133	12.5 120
LiteFlowNet [138]	109.8	32.9 122	71.7 147	19.9 111	18.2 113	45.2 122	17.8 111	21.8 118	55.4 125	17.5 110	10.7 117	34.2 95	7.20 114	24.3 129	30.7 129	20.9 121	21.5 119	52.5 126	18.4 108	11.3 81	33.3 113	3.15 26	6.04 98	13.5 86	7.98 95
StereoFlow [44]	109.9	85.4 163	89.0 163	87.9 162	73.1 163	88.5 163	68.8 158	66.8 162	87.5 161	52.4 154	81.5 162	91.1 162	78.5 161	25.9 138	27.6 91	29.7 149	6.38 4	29.4 13	6.60 5	1.39 2	10.9 2	0.20 2	6.34 102	13.8 88	10.3 108
SegOF [10]	113.1	28.8 118	51.1 102	13.2 95	37.3 141	51.8 136	44.6 144	30.0 129	53.0 123	43.3 141	27.0 143	49.6 137	22.4 139	24.0 128	27.6 91	28.4 147	24.9 138	58.5 144	24.4 136	2.04 4	16.2 6	0.47 5	10.0 126	16.5 111	16.7 130
EAI-Flow [147]	114.2	40.7 131	62.9 128	39.5 134	21.2 117	43.8 118	21.4 115	25.8 122	57.4 126	28.2 121	11.7 120	38.0 116	9.37 122	21.9 109	29.0 111	15.1 92	22.2 125	50.8 122	20.5 120	29.0 142	36.6 125	10.6 86	5.07 79	13.3 84	6.83 77
Shiralkar [42]	115.6	22.0 110	69.5 140	19.6 109	10.9 60	42.6 116	8.48 44	18.4 112	54.0 124	9.43 76	10.1 115	45.4 131	7.72 117	21.5 103	28.9 110	15.9 97	26.8 142	60.7 147	25.4 140	24.3 136	29.9 102	39.4 149	11.0 132	23.8 144	12.2 118
Ad-TV-NDC [36]	116.2	44.8 140	63.0 129	69.1 154	40.3 145	48.4 129	48.3 147	34.8 138	58.5 129	39.9 134	26.5 142	47.8 135	27.7 143	20.2 88	28.5 105	11.9 53	15.2 81	40.9 77	14.7 85	8.46 69	21.0 40	5.69 62	23.9 153	28.3 153	41.9 159
AugFNG_ROB [139]	116.6	44.1 138	62.1 126	25.2 121	42.2 147	56.7 144	50.8 149	37.7 142	66.7 141	42.9 139	19.0 132	41.2 125	13.4 128	26.5 141	32.3 141	23.4 129	22.6 128	57.7 142	21.1 123	6.07 44	27.3 92	0.91 8	5.54 88	15.1 100	3.80 30
StereoOF-V1MT [117]	116.7	21.7 109	68.0 138	20.3 113	11.8 74	50.4 132	7.18 34	20.7 116	62.8 136	9.21 74	9.80 114	50.8 138	6.56 109	27.9 149	35.8 150	23.9 132	25.0 139	67.3 151	24.0 132	8.00 65	27.7 93	12.2 95	13.4 138	23.5 143	15.8 127
CompactFlow_ROB [155]	117.0	53.0 151	62.6 127	27.7 124	31.6 133	53.2 140	35.5 136	39.0 144	69.8 146	49.1 149	16.7 127	40.3 123	12.3 127	26.3 140	32.1 139	23.1 128	22.1 124	53.3 131	24.0 132	4.89 25	23.7 74	0.94 10	6.25 101	15.9 107	6.48 70
WOLF_ROB [144]	117.2	26.7 117	70.6 143	21.9 117	21.8 119	51.3 135	19.4 113	28.0 126	61.1 134	26.7 119	12.6 122	42.5 128	10.5 125	22.2 111	28.7 109	19.4 113	21.3 117	55.6 136	19.6 116	7.42 59	23.5 69	8.87 79	11.1 133	20.5 135	20.0 138
FlowNet2 [120]	121.0	47.2 144	61.0 124	42.4 139	44.5 149	57.5 145	51.3 150	37.6 141	64.7 139	43.1 140	21.0 135	35.8 106	17.9 135	25.8 134	30.6 126	24.6 136	20.4 111	49.7 117	21.0 121	32.5 148	53.3 159	4.06 43	4.13 66	13.0 81	1.49 14
LSM_FLOW_RVC [182]	121.5	52.5 150	83.5 158	49.8 145	28.0 130	54.9 142	28.5 126	38.4 143	77.4 155	34.8 127	16.8 129	53.2 141	13.4 128	25.8 134	32.7 143	19.9 117	21.7 121	57.2 140	23.7 131	5.76 39	24.9 83	2.24 21	7.05 109	18.1 120	7.10 84
HBpMotionGpu [43]	122.3	32.0 121	50.0 95	22.9 120	36.1 140	47.0 126	43.9 142	29.2 128	51.9 122	38.6 133	13.0 123	37.1 112	10.2 124	23.5 123	29.5 116	24.2 133	18.9 105	44.9 95	15.9 94	33.2 150	41.2 138	12.6 98	11.8 135	18.5 123	22.7 140
IAOF2 [51]	123.4	25.3 116	49.2 92	22.2 118	24.6 122	44.3 119	28.6 127	20.0 114	45.4 113	25.5 117	49.8 152	57.5 147	60.5 155	23.2 121	31.0 133	15.7 96	23.2 133	49.6 114	19.3 115	30.5 144	39.0 134	19.0 126	9.25 123	18.6 124	9.82 106
Modified CLG [34]	124.5	34.8 126	61.1 125	35.3 130	33.3 138	46.5 125	41.7 141	36.8 140	63.0 137	45.1 145	22.1 137	55.4 143	18.7 137	23.9 126	31.2 134	21.7 125	15.8 86	51.5 124	14.8 86	9.01 72	24.6 81	11.1 89	17.6 147	25.7 149	29.6 145
LFNet_ROB [145]	125.5	42.0 133	80.9 156	30.7 126	25.2 124	54.8 141	25.3 120	33.7 135	74.4 149	26.0 118	17.2 130	48.0 136	14.4 131	26.5 141	32.5 142	24.8 139	23.0 130	56.4 137	22.7 127	12.5 90	38.3 133	6.87 68	6.05 99	15.7 106	9.05 102
Filter Flow [19]	125.8	33.3 123	51.7 106	20.1 112	25.0 123	47.2 128	27.7 123	27.7 125	50.0 120	37.9 129	31.7 145	54.1 142	29.9 144	25.8 134	31.2 134	28.3 146	26.4 141	52.9 130	24.7 137	42.3 157	61.5 161	13.6 106	6.09 100	12.1 74	6.88 79
2D-CLG [1]	126.0	44.0 137	63.3 130	36.1 131	44.3 148	52.3 137	55.1 152	49.1 153	75.4 150	50.5 151	64.3 158	76.4 157	67.8 158	24.8 131	29.7 119	27.4 144	20.5 113	53.6 132	22.4 126	2.52 6	13.0 4	3.50 32	22.8 152	27.9 152	36.9 151
EPMNet [131]	126.8	47.1 143	71.6 146	41.3 137	41.8 146	61.0 150	47.5 146	34.2 137	60.0 132	40.1 135	22.9 140	38.8 118	20.2 138	25.8 134	30.6 126	24.6 136	20.4 111	49.7 117	21.0 121	23.9 135	44.7 148	3.33 30	7.39 112	18.0 119	7.28 86
ResPWCR_ROB [140]	126.8	47.7 145	78.6 150	41.4 138	20.9 115	45.6 123	22.0 116	26.3 124	59.1 131	28.2 121	16.7 127	47.4 133	13.7 130	23.7 124	28.3 103	27.2 143	25.0 139	57.6 141	25.4 140	22.7 127	42.0 141	10.0 82	8.31 117	16.9 115	12.2 118
TVL1_RVC [175]	126.8	66.5 157	79.2 153	85.9 159	52.8 155	52.7 139	65.9 156	51.6 154	78.1 156	53.5 157	55.5 156	75.9 156	58.3 154	23.0 118	31.4 136	17.6 107	14.5 71	49.6 114	17.1 103	4.61 18	20.7 36	2.07 20	26.8 157	31.4 154	40.9 158
BlockOverlap [61]	126.9	41.4 132	54.1 117	36.2 132	27.3 128	41.4 114	32.6 131	26.2 123	46.5 116	31.8 125	20.0 133	36.6 109	18.1 136	22.4 113	26.8 82	25.7 141	24.5 135	45.6 102	21.1 123	39.3 156	47.0 152	43.5 155	13.8 139	16.0 108	28.7 144
SPSA-learn [13]	127.3	35.8 127	71.2 144	43.1 142	28.4 131	47.0 126	32.8 132	31.4 131	57.7 127	42.2 138	22.2 138	51.0 139	22.9 141	23.9 126	29.4 115	24.6 136	24.8 137	56.5 138	25.1 139	10.7 80	25.1 84	3.72 37	21.7 150	24.9 148	35.5 150
IRR-PWC_RVC [180]	127.4	50.0 149	66.8 136	32.5 129	39.5 143	57.5 145	44.5 143	42.2 147	69.5 145	50.4 150	21.9 136	43.8 129	17.4 133	27.1 145	30.7 129	29.0 148	18.7 104	49.0 111	17.4 105	23.6 133	53.1 158	2.93 25	7.06 110	16.5 111	7.87 94
IAOF [50]	128.4	33.8 124	58.3 120	40.6 135	33.0 136	44.5 120	39.5 139	30.6 130	58.7 130	33.8 126	34.1 148	52.4 140	40.8 148	23.1 120	29.9 121	19.7 116	22.5 127	53.7 133	16.8 101	22.3 124	34.0 116	10.0 82	19.5 148	23.9 145	37.1 153
GraphCuts [14]	129.4	34.5 125	59.0 122	32.1 128	26.2 126	51.1 134	26.4 122	28.1 127	51.7 121	40.4 137	13.0 123	47.5 134	7.98 118	23.7 124	30.0 124	24.3 134	33.4 151	45.2 100	25.7 142	31.2 146	37.7 130	36.8 146	10.7 129	19.7 131	17.7 132
Black & Anandan [4]	131.3	38.5 130	69.5 140	53.4 146	28.5 132	49.6 131	32.0 130	33.4 134	60.5 133	40.2 136	22.6 139	55.9 144	22.8 140	24.5 130	32.2 140	19.6 114	21.7 121	58.9 145	22.7 127	22.6 125	37.6 129	5.27 57	16.7 145	22.6 142	25.4 142
GroupFlow [9]	132.2	42.7 134	67.1 137	53.4 146	44.8 150	63.8 155	50.2 148	36.7 139	69.4 144	43.9 142	17.2 130	46.0 132	16.7 132	27.8 148	34.9 147	21.2 124	36.7 154	67.0 150	43.6 155	6.40 47	21.7 55	7.17 70	16.6 144	25.9 150	25.0 141
2bit-BM-tele [96]	133.7	55.1 152	64.1 133	69.1 154	21.4 118	38.7 93	25.3 120	23.3 119	46.7 117	21.2 114	26.2 141	38.7 117	25.2 142	24.9 132	29.9 121	27.7 145	31.3 149	52.6 128	34.6 148	43.3 158	51.7 157	54.5 159	10.3 128	20.1 132	16.8 131
Nguyen [33]	137.2	43.9 136	66.0 135	42.8 141	54.0 156	49.4 130	70.1 159	42.9 148	67.4 142	47.3 147	55.4 155	65.7 151	64.4 156	27.0 144	31.8 138	31.0 150	22.8 129	54.8 134	27.3 143	13.1 93	25.2 85	6.08 64	22.2 151	26.9 151	38.9 155
SILK [80]	140.4	49.5 148	69.2 139	69.3 156	39.9 144	60.6 149	47.0 145	40.4 146	70.7 147	45.6 146	32.0 146	56.5 145	31.2 146	31.4 151	36.9 153	33.3 152	31.1 148	63.2 148	32.3 147	10.3 76	23.0 62	17.3 115	25.0 154	31.9 155	36.9 151
UnFlow [127]	141.5	70.9 159	78.9 151	58.5 150	51.3 154	67.4 158	56.9 153	54.4 156	83.6 159	52.8 155	33.4 147	60.2 148	30.1 145	36.7 159	38.4 156	46.2 160	38.2 155	69.6 154	42.8 154	26.2 137	40.3 136	1.60 17	7.20 111	18.3 121	8.75 101
Periodicity [79]	142.7	48.9 147	63.9 132	41.0 136	34.5 139	60.0 148	37.5 138	55.4 157	67.4 142	56.6 158	20.4 134	56.9 146	17.5 134	53.2 162	66.7 163	46.5 161	48.3 160	76.0 161	46.4 157	9.14 73	34.4 118	9.98 81	28.3 158	48.2 162	40.6 157
Horn & Schunck [3]	143.3	43.3 135	80.7 155	58.6 151	32.5 135	59.7 147	35.1 134	40.2 145	76.3 153	44.7 144	31.5 144	64.8 149	32.6 147	29.3 150	36.4 152	27.0 142	27.5 145	68.7 152	29.7 145	27.0 139	43.3 144	7.32 71	25.9 155	36.5 158	34.6 148
H+S_RVC [176]	145.7	57.6 153	76.9 148	55.7 149	56.7 157	76.2 161	62.8 155	60.6 159	89.2 163	51.5 153	78.6 161	78.7 158	82.0 162	34.1 155	35.7 149	43.6 159	46.6 159	75.5 160	53.1 160	5.52 35	32.5 110	5.42 60	34.3 159	35.1 157	37.9 154
Heeger++ [102]	146.9	61.9 156	80.2 154	47.4 144	44.8 150	77.8 162	40.9 140	68.0 163	84.7 160	62.1 161	43.6 151	69.1 152	41.9 149	32.6 153	37.9 154	32.0 151	51.1 161	78.4 162	54.2 161	13.3 95	43.4 145	10.4 85	15.0 140	21.4 140	19.6 136
SLK [47]	147.6	44.7 139	78.9 151	59.1 152	58.2 159	71.2 159	70.9 160	47.5 151	83.5 158	50.6 152	65.0 159	69.5 153	73.4 159	34.7 156	38.9 158	42.9 158	34.8 152	70.9 157	39.4 152	12.1 87	29.8 101	11.5 90	34.4 160	40.1 159	48.8 160
FFV1MT [104]	149.9	59.9 155	77.8 149	53.6 148	37.7 142	72.5 160	37.0 137	63.6 161	82.1 157	62.4 162	42.8 150	73.9 155	42.6 150	41.9 161	45.8 161	52.3 162	52.5 162	81.8 163	56.1 162	20.2 120	42.4 142	18.3 123	15.0 140	21.4 140	19.6 136
TI-DOFE [24]	150.2	73.1 160	84.6 161	89.6 163	61.2 161	64.7 157	74.8 162	58.6 158	88.7 162	58.0 159	70.9 160	81.6 160	76.1 160	31.7 152	38.0 155	35.4 153	29.7 146	68.7 152	36.3 149	17.1 109	32.0 108	8.67 77	35.5 161	42.8 160	49.8 161
FOLKI [16]	152.3	48.0 146	71.5 145	68.8 153	48.6 153	63.2 152	59.5 154	43.0 149	75.6 151	44.0 143	40.4 149	65.6 150	45.8 151	35.3 157	40.6 159	41.6 156	36.3 153	71.6 159	44.4 156	23.6 133	44.7 148	40.4 153	36.9 162	43.4 161	54.5 162
PGAM+LK [55]	154.8	58.6 154	80.9 156	69.8 157	45.1 152	63.7 154	51.9 151	43.2 150	76.2 152	47.5 148	50.3 153	82.2 161	51.4 152	32.7 154	36.0 151	42.3 157	41.4 156	70.1 155	41.2 153	56.3 161	58.0 160	55.0 160	25.9 155	32.4 156	40.3 156
Adaptive flow [45]	155.3	76.7 162	83.7 160	86.4 160	57.9 158	63.6 153	67.3 157	48.7 152	73.2 148	52.9 156	52.7 154	69.9 154	56.0 153	35.4 158	38.4 156	39.4 155	46.1 157	70.6 156	47.6 158	73.1 162	75.2 162	88.1 161	17.2 146	24.6 147	25.6 143
HCIC-L [97]	157.4	76.5 161	86.4 162	73.3 158	70.1 162	62.5 151	85.3 163	63.5 160	66.1 140	79.5 163	83.3 163	91.8 163	86.5 163	39.0 160	42.8 160	38.7 154	46.4 158	66.6 149	52.3 159	89.6 163	85.9 163	94.0 162	19.8 149	24.2 146	29.6 145
Pyramid LK [2]	159.2	68.1 158	83.5 158	86.8 161	59.4 160	64.5 156	73.1 161	52.8 155	76.3 153	61.4 160	60.2 157	79.0 159	65.9 157	53.8 163	61.8 162	64.5 163	59.4 163	71.1 158	63.0 163	43.9 159	49.4 156	39.5 150	50.2 163	60.2 163	70.8 163
AdaConv-v1 [124]	164.6	100.0 165	99.9 165	100.0 164	100.0 164	100.0 164	100.0 164	99.9 164	99.9 164	99.9 164	100.0 165	100.0 165	99.9 165	99.9 165	99.9 165	99.9 165	99.9 165	99.8 165	100.0 165	99.7 165	97.0 165	99.9 165	99.9 164	99.9 164	99.9 164
SepConv-v1 [125]	164.6	100.0 165	99.9 165	100.0 164	100.0 164	100.0 164	100.0 164	99.9 164	99.9 164	99.9 164	100.0 165	100.0 165	99.9 165	99.9 165	99.9 165	99.9 165	99.9 165	99.8 165	100.0 165	99.7 165	97.0 165	99.9 165	99.9 164	99.9 164	99.9 164
SuperSlomo [130]	164.6	100.0 165	99.9 165	100.0 164	100.0 164	100.0 164	100.0 164	99.9 164	99.9 164	99.9 164	100.0 165	100.0 165	99.9 165	99.9 165	99.9 165	99.9 165	99.9 165	99.8 165	100.0 165	99.7 165	97.0 165	99.9 165	99.9 164	99.9 164	99.9 164
CtxSyn [134]	164.6	100.0 165	99.9 165	100.0 164	100.0 164	100.0 164	100.0 164	99.9 164	99.9 164	99.9 164	100.0 165	100.0 165	99.9 165	99.9 165	99.9 165	99.9 165	99.9 165	99.8 165	100.0 165	99.7 165	97.0 165	99.9 165	99.9 164	99.9 164	99.9 164
CyclicGen [149]	164.6	100.0 165	99.9 165	100.0 164	100.0 164	100.0 164	100.0 164	99.9 164	99.9 164	99.9 164	100.0 165	100.0 165	99.9 165	99.9 165	99.9 165	99.9 165	99.9 165	99.8 165	100.0 165	99.7 165	97.0 165	99.9 165	99.9 164	99.9 164	99.9 164
TOF-M [150]	164.6	100.0 165	99.9 165	100.0 164	100.0 164	100.0 164	100.0 164	99.9 164	99.9 164	99.9 164	100.0 165	100.0 165	99.9 165	99.9 165	99.9 165	99.9 165	99.9 165	99.8 165	100.0 165	99.7 165	97.0 165	99.9 165	99.9 164	99.9 164	99.9 164
MPRN [151]	164.6	100.0 165	99.9 165	100.0 164	100.0 164	100.0 164	100.0 164	99.9 164	99.9 164	99.9 164	100.0 165	100.0 165	99.9 165	99.9 165	99.9 165	99.9 165	99.9 165	99.8 165	100.0 165	99.7 165	97.0 165	99.9 165	99.9 164	99.9 164	99.9 164
DAIN [152]	164.6	100.0 165	99.9 165	100.0 164	100.0 164	100.0 164	100.0 164	99.9 164	99.9 164	99.9 164	100.0 165	100.0 165	99.9 165	99.9 165	99.9 165	99.9 165	99.9 165	99.8 165	100.0 165	99.7 165	97.0 165	99.9 165	99.9 164	99.9 164	99.9 164
FRUCnet [153]	164.6	100.0 165	99.9 165	100.0 164	100.0 164	100.0 164	100.0 164	99.9 164	99.9 164	99.9 164	100.0 165	100.0 165	99.9 165	99.9 165	99.9 165	99.9 165	99.9 165	99.8 165	100.0 165	99.7 165	97.0 165	99.9 165	99.9 164	99.9 164	99.9 164
OFRI [154]	164.6	100.0 165	99.9 165	100.0 164	100.0 164	100.0 164	100.0 164	99.9 164	99.9 164	99.9 164	100.0 165	100.0 165	99.9 165	99.9 165	99.9 165	99.9 165	99.9 165	99.8 165	100.0 165	99.7 165	97.0 165	99.9 165	99.9 164	99.9 164	99.9 164
FGME [158]	164.6	100.0 165	99.9 165	100.0 164	100.0 164	100.0 164	100.0 164	99.9 164	99.9 164	99.9 164	100.0 165	100.0 165	99.9 165	99.9 165	99.9 165	99.9 165	99.9 165	99.8 165	100.0 165	99.7 165	97.0 165	99.9 165	99.9 164	99.9 164	99.9 164
MS-PFT [159]	164.6	100.0 165	99.9 165	100.0 164	100.0 164	100.0 164	100.0 164	99.9 164	99.9 164	99.9 164	100.0 165	100.0 165	99.9 165	99.9 165	99.9 165	99.9 165	99.9 165	99.8 165	100.0 165	99.7 165	97.0 165	99.9 165	99.9 164	99.9 164	99.9 164
MEMC-Net+ [160]	164.6	100.0 165	99.9 165	100.0 164	100.0 164	100.0 164	100.0 164	99.9 164	99.9 164	99.9 164	100.0 165	100.0 165	99.9 165	99.9 165	99.9 165	99.9 165	99.9 165	99.8 165	100.0 165	99.7 165	97.0 165	99.9 165	99.9 164	99.9 164	99.9 164
ADC [161]	164.6	100.0 165	99.9 165	100.0 164	100.0 164	100.0 164	100.0 164	99.9 164	99.9 164	99.9 164	100.0 165	100.0 165	99.9 165	99.9 165	99.9 165	99.9 165	99.9 165	99.8 165	100.0 165	99.7 165	97.0 165	99.9 165	99.9 164	99.9 164	99.9 164
DSepConv [162]	164.6	100.0 165	99.9 165	100.0 164	100.0 164	100.0 164	100.0 164	99.9 164	99.9 164	99.9 164	100.0 165	100.0 165	99.9 165	99.9 165	99.9 165	99.9 165	99.9 165	99.8 165	100.0 165	99.7 165	97.0 165	99.9 165	99.9 164	99.9 164	99.9 164
MAF-net [163]	164.6	100.0 165	99.9 165	100.0 164	100.0 164	100.0 164	100.0 164	99.9 164	99.9 164	99.9 164	100.0 165	100.0 165	99.9 165	99.9 165	99.9 165	99.9 165	99.9 165	99.8 165	100.0 165	99.7 165	97.0 165	99.9 165	99.9 164	99.9 164	99.9 164
STAR-Net [164]	164.6	100.0 165	99.9 165	100.0 164	100.0 164	100.0 164	100.0 164	99.9 164	99.9 164	99.9 164	100.0 165	100.0 165	99.9 165	99.9 165	99.9 165	99.9 165	99.9 165	99.8 165	100.0 165	99.7 165	97.0 165	99.9 165	99.9 164	99.9 164	99.9 164
AdaCoF [165]	164.6	100.0 165	99.9 165	100.0 164	100.0 164	100.0 164	100.0 164	99.9 164	99.9 164	99.9 164	100.0 165	100.0 165	99.9 165	99.9 165	99.9 165	99.9 165	99.9 165	99.8 165	100.0 165	99.7 165	97.0 165	99.9 165	99.9 164	99.9 164	99.9 164
TC-GAN [166]	164.6	100.0 165	99.9 165	100.0 164	100.0 164	100.0 164	100.0 164	99.9 164	99.9 164	99.9 164	100.0 165	100.0 165	99.9 165	99.9 165	99.9 165	99.9 165	99.9 165	99.8 165	100.0 165	99.7 165	97.0 165	99.9 165	99.9 164	99.9 164	99.9 164
FeFlow [167]	164.6	100.0 165	99.9 165	100.0 164	100.0 164	100.0 164	100.0 164	99.9 164	99.9 164	99.9 164	100.0 165	100.0 165	99.9 165	99.9 165	99.9 165	99.9 165	99.9 165	99.8 165	100.0 165	99.7 165	97.0 165	99.9 165	99.9 164	99.9 164	99.9 164
DAI [168]	164.6	100.0 165	99.9 165	100.0 164	100.0 164	100.0 164	100.0 164	99.9 164	99.9 164	99.9 164	100.0 165	100.0 165	99.9 165	99.9 165	99.9 165	99.9 165	99.9 165	99.8 165	100.0 165	99.7 165	97.0 165	99.9 165	99.9 164	99.9 164	99.9 164
SoftSplat [169]	164.6	100.0 165	99.9 165	100.0 164	100.0 164	100.0 164	100.0 164	99.9 164	99.9 164	99.9 164	100.0 165	100.0 165	99.9 165	99.9 165	99.9 165	99.9 165	99.9 165	99.8 165	100.0 165	99.7 165	97.0 165	99.9 165	99.9 164	99.9 164	99.9 164
STSR [170]	164.6	100.0 165	99.9 165	100.0 164	100.0 164	100.0 164	100.0 164	99.9 164	99.9 164	99.9 164	100.0 165	100.0 165	99.9 165	99.9 165	99.9 165	99.9 165	99.9 165	99.8 165	100.0 165	99.7 165	97.0 165	99.9 165	99.9 164	99.9 164	99.9 164
BMBC [171]	164.6	100.0 165	99.9 165	100.0 164	100.0 164	100.0 164	100.0 164	99.9 164	99.9 164	99.9 164	100.0 165	100.0 165	99.9 165	99.9 165	99.9 165	99.9 165	99.9 165	99.8 165	100.0 165	99.7 165	97.0 165	99.9 165	99.9 164	99.9 164	99.9 164
GDCN [172]	164.6	100.0 165	99.9 165	100.0 164	100.0 164	100.0 164	100.0 164	99.9 164	99.9 164	99.9 164	100.0 165	100.0 165	99.9 165	99.9 165	99.9 165	99.9 165	99.9 165	99.8 165	100.0 165	99.7 165	97.0 165	99.9 165	99.9 164	99.9 164	99.9 164
EDSC [173]	164.6	100.0 165	99.9 165	100.0 164	100.0 164	100.0 164	100.0 164	99.9 164	99.9 164	99.9 164	100.0 165	100.0 165	99.9 165	99.9 165	99.9 165	99.9 165	99.9 165	99.8 165	100.0 165	99.7 165	97.0 165	99.9 165	99.9 164	99.9 164	99.9 164
MV_VFI [183]	164.6	100.0 165	99.9 165	100.0 164	100.0 164	100.0 164	100.0 164	99.9 164	99.9 164	99.9 164	100.0 165	100.0 165	99.9 165	99.9 165	99.9 165	99.9 165	99.9 165	99.8 165	100.0 165	99.7 165	97.0 165	99.9 165	99.9 164	99.9 164	99.9 164
DistillNet [184]	164.6	100.0 165	99.9 165	100.0 164	100.0 164	100.0 164	100.0 164	99.9 164	99.9 164	99.9 164	100.0 165	100.0 165	99.9 165	99.9 165	99.9 165	99.9 165	99.9 165	99.8 165	100.0 165	99.7 165	97.0 165	99.9 165	99.9 164	99.9 164	99.9 164
SepConv++ [185]	164.6	100.0 165	99.9 165	100.0 164	100.0 164	100.0 164	100.0 164	99.9 164	99.9 164	99.9 164	100.0 165	100.0 165	99.9 165	99.9 165	99.9 165	99.9 165	99.9 165	99.8 165	100.0 165	99.7 165	97.0 165	99.9 165	99.9 164	99.9 164	99.9 164
EAFI [186]	164.6	100.0 165	99.9 165	100.0 164	100.0 164	100.0 164	100.0 164	99.9 164	99.9 164	99.9 164	100.0 165	100.0 165	99.9 165	99.9 165	99.9 165	99.9 165	99.9 165	99.8 165	100.0 165	99.7 165	97.0 165	99.9 165	99.9 164	99.9 164	99.9 164
FLAVR [188]	164.6	100.0 165	99.9 165	100.0 164	100.0 164	100.0 164	100.0 164	99.9 164	99.9 164	99.9 164	100.0 165	100.0 165	99.9 165	99.9 165	99.9 165	99.9 165	99.9 165	99.8 165	100.0 165	99.7 165	97.0 165	99.9 165	99.9 164	99.9 164	99.9 164
SoftsplatAug [190]	164.6	100.0 165	99.9 165	100.0 164	100.0 164	100.0 164	100.0 164	99.9 164	99.9 164	99.9 164	100.0 165	100.0 165	99.9 165	99.9 165	99.9 165	99.9 165	99.9 165	99.8 165	100.0 165	99.7 165	97.0 165	99.9 165	99.9 164	99.9 164	99.9 164
ProBoost-Net [191]	164.6	100.0 165	99.9 165	100.0 164	100.0 164	100.0 164	100.0 164	99.9 164	99.9 164	99.9 164	100.0 165	100.0 165	99.9 165	99.9 165	99.9 165	99.9 165	99.9 165	99.8 165	100.0 165	99.7 165	97.0 165	99.9 165	99.9 164	99.9 164	99.9 164
IDIAL [192]	164.6	100.0 165	99.9 165	100.0 164	100.0 164	100.0 164	100.0 164	99.9 164	99.9 164	99.9 164	100.0 165	100.0 165	99.9 165	99.9 165	99.9 165	99.9 165	99.9 165	99.8 165	100.0 165	99.7 165	97.0 165	99.9 165	99.9 164	99.9 164	99.9 164
IFRNet [193]	164.6	100.0 165	99.9 165	100.0 164	100.0 164	100.0 164	100.0 164	99.9 164	99.9 164	99.9 164	100.0 165	100.0 165	99.9 165	99.9 165	99.9 165	99.9 165	99.9 165	99.8 165	100.0 165	99.7 165	97.0 165	99.9 165	99.9 164	99.9 164	99.9 164
AVG_FLOW_ROB [137]	165.5	99.9 164	99.7 164	100.0 164	100.0 164	100.0 164	100.0 164	99.9 164	99.9 164	99.9 164	99.6 164	98.8 164	99.5 164	99.6 164	99.7 164	99.0 164	97.0 164	96.3 164	95.6 164	99.1 164	92.9 164	99.6 164	100.0 199	99.9 164	99.9 164

Move the mouse over the numbers in the table to see the corresponding images. Click to compare with the ground truth.

References

Method	time*	frames	color	Reference and notes
[1] 2D-CLG	844	2	gray	The 2D-CLG method by Bruhn et al. as implemented by Stefan Roth. [A. Bruhn, J. Weickert, and C. Schnörr. Lucas/Kanade meets Horn/Schunck: combining local and global optic flow methods. IJCV 63(3), 2005.] Parameters were set to match the published performance on Yosemite sequence, which may not be optimal for other sequences.
[2] Pyramid LK	12	2	color	A modification of Bouguet's pyramidal implementation of Lucas-Kanade.
[3] Horn & Schunck	49	2	gray	A modern Matlab implementation of the Horn & Schunck method by Deqing Sun. Parameters set to optimize AAE on all training data.
[4] Black & Anandan	328	2	gray	A modern Matlab implementation of the Black & Anandan method by Deqing Sun.
[5] Brox et al.	18	2	color	T. Brox, A. Bruhn, N. Papenberg, and J. Weickert. High accuracy optical flow estimation based on a theory for warping. ECCV 2004. (Improved using separate robust functions as proposed in A. Bruhn and J. Weickert, Towards ultimate motion estimation, ICCV 2005; improved by training on the training set.)
[6] Fusion	2,666	2	color	V. Lempitsky, S. Roth, and C. Rother. Discrete-continuous optimization for optical flow estimation. CVPR 2008.
[7] Dynamic MRF	366	2	gray	B. Glocker, N. Paragios, N. Komodakis, G. Tziritas, and N. Navab. Optical flow estimation with uncertainties through dynamic MRFs. CVPR 2008. (Method improved since publication.)
[8] Second-order prior	14	2	gray	W. Trobin, T. Pock, D. Cremers, and H. Bischof. An unbiased second-order prior for high-accuracy motion estimation. DAGM 2008. (Method improved since publication; for details see W. Trobin, Ph.D. thesis, 2009.)
[9] GroupFlow	600	2	gray	X. Ren. Local Grouping for Optical Flow. CVPR 2008.
[10] SegOF	60	2	color	L. Xu, J. Chen, and J. Jia. Segmentation based variational model for accurate optical flow estimation. ECCV 2008. Code available.
[11] Learning Flow	825	2	gray	D. Sun, S. Roth, J.P. Lewis, and M. Black. Learning optical flow (SRF-LFC). ECCV 2008.
[12] CBF	69	2	color	W. Trobin, T. Pock, D. Cremers, and H. Bischof. Continuous energy minimization via repeated binary fusion. ECCV 2008. (Method improved since publication; for details see W. Trobin, Ph.D. thesis, 2009.)
[13] SPSA-learn	200	2	color	Y. Li and D. Huttenlocher. Learning for optical flow using stochastic optimization. ECCV 2008.
[14] GraphCuts	1,200	2	color	T. Cooke. Two applications of graph-cuts to image processing. DICTA 2008.
[15] F-TV-L1	8	2	gray	A. Wedel, T. Pock, J. Braun, U. Franke, and D. Cremers. Duality TV-L1 flow with fundamental matrix prior. IVCNZ 2008.
[16] FOLKI	1.4	2	gray	G. Le Besnerais and F. Champagnat. Dense optical flow by iterative local window registration. ICIP 2005.
[17] TV-L1-improved	2.9	2	gray	A. Wedel, T. Pock, C. Zach, H. Bischof, and D. Cremers. An improved algorithm for TV-L1 optical flow computation. Proceedings of the Dagstuhl Visual Motion Analysis Workshop 2008. Code at GPU4Vision.
[18] DPOF	287	2	color	C. Lei and Y.-H. Yang. Optical flow estimation on coarse-to-fine region-trees using discrete optimization. ICCV 2009. (Method improved since publication.)
[19] Filter Flow	34,000	2	color	S. Seitz and S. Baker. Filter flow. ICCV 2009.
[20] Adaptive	9.2	2	gray	A. Wedel, D. Cremers, T. Pock, and H. Bischof. Structure- and motion-adaptive regularization for high accuracy optic flow. ICCV 2009.
[21] Complementary OF	44	2	color	H. Zimmer, A. Bruhn, J. Weickert, L. Valgaerts, A. Salgado, B. Rosenhahn, and H.-P. Seidel. Complementary optic flow. EMMCVPR 2009.
[22] Aniso. Huber-L1	2	2	gray	M. Werlberger, W. Trobin, T. Pock, A. Wedel, D. Cremers, and H. Bischof. Anisotropic Huber-L1 optical flow. BMVC 2009. Code at GPU4Vision.
[23] Rannacher	0.12	2	gray	J. Rannacher. Realtime 3D motion estimation on graphics hardware. Bachelor thesis, Heidelberg University, 2009.
[24] TI-DOFE	260	2	gray	C. Cassisa, S. Simoens, and V. Prinet. Two-frame optical flow formulation in an unwarped multiresolution scheme. CIARP 2009.
[25] NL-TV-NCC	20	2	color	M. Werlberger, T. Pock, and H. Bischof. Motion estimation with non-local total variation regularization. CVPR 2010.
[26] MDP-Flow	188	2	color	L. Xu, J. Jia, and Y. Matsushita. Motion detail preserving optical flow estimation. CVPR 2010.
[27] ACK-Prior	5872	2	color	K. Lee, D. Kwon, I. Yun, and S. Lee. Optical flow estimation with adaptive convolution kernel prior on discrete framework. CVPR 2010.
[28] LDOF	122	2	color	T. Brox and J. Malik. Large displacement optical flow: descriptor matching in variational motion estimation. PAMI 33(3):500-513, 2011.
[29] p-harmonic	565	2	gray	J. Gai and R. Stevenson. Optical flow estimation with p-harmonic regularization. ICIP 2010.
[30] TriangleFlow	4200	2	gray	B. Glocker, H. Heibel, N. Navab, P. Kohli, and C. Rother. TriangleFlow: Optical flow with triangulation-based higher-order likelihoods. ECCV 2010.
[31] Classic+NL	972	2	color	D. Sun, S. Roth, and M. Black. Secrets of optical flow estimation and their principles. CVPR 2010. Matlab code.
[32] Classic++	486	2	gray	A modern implementation of the classical formulation descended from Horn & Schunck and Black & Anandan; see D. Sun, S. Roth, and M. Black, Secrets of optical flow estimation and their principles, CVPR 2010.
[33] Nguyen	33	2	gray	D. Nguyen. Tuning optical flow estimation with image-driven functions. ICRA 2011.
[34] Modified CLG	133	2	gray	R. Fezzani, F. Champagnat, and G. Le Besnerais. Combined local global method for optic flow computation. EUSIPCO 2010.
[35] ComplOF-FED-GPU	0.97	2	color	P. Gwosdek, H. Zimmer, S. Grewenig, A. Bruhn, and J. Weickert. A highly efficient GPU implementation for variational optic flow based on the Euler-Lagrange framework. CVGPU Workshop 2010.
[36] Ad-TV-NDC	35	2	gray	M. Nawaz. Motion estimation with adaptive regularization and neighborhood dependent constraint. DICTA 2010.
[37] Layers++	18206	2	color	D. Sun, E. Sudderth, and M. Black. Layered image motion with explicit occlusions, temporal consistency, and depth ordering. NIPS 2010.
[38] OFH	620	3	color	H. Zimmer, A. Bruhn, J. Weickert. Optic flow in harmony. IJCV 93(3) 2011.
[39] LSM	1615	2	color	K. Jia, X. Wang, and X. Tang. Optical flow estimation using learned sparse model. ICCV 2011.
[40] CostFilter	55	2	color	C. Rhemann, A. Hosni, M. Bleyer, C. Rother, and M. Gelautz. Fast cost-volume filtering for visual correspondence and beyond. CVPR 2011.
[41] Bartels	0.15	2	gray	C. Bartels and G. de Haan. Smoothness constraints in recursive search motion estimation for picture rate conversion. IEEE TCSVT 2010. Version improved since publication: mapped on GPU.
[42] Shiralkar	600	2	gray	M. Shiralkar and R. Schalkoff. A self organization-based optical flow estimator with GPU implementation. MVA 23(6):1229-1242.
[43] HBpMotionGpu	1000	5	gray	S. Grauer-Gray and C. Kambhamettu. Hierarchical belief propagation to reduce search space using CUDA for stereo and motion estimation. WACV 2009. (Method improved since publication.)
[44] StereoFlow	7200	2	color	G. Rosman, S. Shem-Tov, D. Bitton, T. Nir, G. Adiv, R. Kimmel, A. Feuer, and A. Bruckstein. Over-parameterized optical flow using a stereoscopic constraint. SSVM 2011:761-772.
[45] Adaptive flow	121	2	gray	Tarik Arici and Vural Aksakalli. Energy minimization based motion estimation using adaptive smoothness priors. VISAPP 2012.
[46] TC-Flow	2500	5	color	S. Volz, A. Bruhn, L. Valgaerts, and H. Zimmer. Modeling temporal coherence for optical flow. ICCV 2011.
[47] SLK	300	2	gray	T. Corpetti and E. Mémin. Stochastic uncertainty models for the luminance consistency assumption. IEEE TIP 2011.
[48] CLG-TV	29	2	gray	M. Drulea. Total variation regularization of local-global optical flow. ITSC 2011. Matlab code.
[49] SimpleFlow	1.7	2	color	M. Tao, J. Bai, P. Kohli, S. Paris. SimpleFlow: a non-iterative, sublinear optical flow algorithm. EUROGRAPHICS 2012.
[50] IAOF	57	2	gray	D. Nguyen. Improving motion estimation using image-driven functions and hybrid scheme. PSIVT 2011.
[51] IAOF2	56	2	gray	Duc Dung Nguyen and Jae Wook Jeon. Enhancing accuracy and sharpness of motion field with adaptive scheme and occlusion-aware filter. IET Image Processing 7.2 (2013): 144-153.
[52] LocallyOriented	9541	2	gray	Y.Niu, A. Dick, and M. Brooks. Locally oriented optical flow computation. To appear in TIP 2012.
[53] IROF-TV	261	2	color	H. Rashwan, D. Puig, and M. Garcia. On improving the robustness of differential optical flow. ICCV 2011 Artemis workshop.
[54] Sparse Occlusion	2312	2	color	Alper Ayvaci, Michalis Raptis, and Stefano Soatto. Sparse occlusion detection with optical flow. IJCV 97(3):322-338, 2012.
[55] PGAM+LK	0.37	2	gray	A. Alba, E. Arce-Santana, and M. Rivera. Optical flow estimation with prior models obtained from phase correlation. ISVC 2010.
[56] Sparse-NonSparse	713	2	color	Zhuoyuan Chen, Jiang Wang, and Ying Wu. Decomposing and regularizing sparse/non-sparse components for motion field estimation. CVPR 2012.
[57] nLayers	36150	4	color	D. Sun, E. Sudderth, and M. Black. Layered segmentation and optical flow estimation over time. CVPR 2012.
[58] IROF++	187	2	color	H. Rashwan, D. Puig, and M. Garcia. Variational optical flow estimation based on stick tensor voting. IEEE TIP 2013.
[59] COFM	600	3	color	M. Mozerov. Constrained optical flow estimation as a matching problem. IEEE TIP 2013.
[60] Efficient-NL	400	2	color	P. Krähenbühl and V. Koltun. Efficient nonlocal regularization for optical flow. ECCV 2012.
[61] BlockOverlap	2	2	gray	Michael Santoro, Ghassan AlRegib, and Yucel Altunbasak. Motion estimation using block overlap minimization. MMSP 2012.
[62] Ramp	1200	2	color	A. Singh and N. Ahuja. Exploiting ramp structures for improving optical flow estimation. ICPR 2012.
[63] Occlusion-TV-L1	538	3	gray	C. Ballester, L. Garrido, V. Lazcano, and V. Caselles. A TV-L1 optical flow method with occlusion detection. DAGM-OAGM 2012.
[64] TV-L1-MCT	90	2	color	M. Mohamed and B. Mertsching. TV-L1 optical flow estimation with image details recovering based on modified census transform. ISVC 2012.
[65] Local-TV-L1	500	2	gray	L. Raket. Local smoothness for global optical flow. ICIP 2012.
[66] ALD-Flow	61	2	color	M. Stoll, A. Bruhn, and S. Volz. Adaptive integration of feature matches into variational optic flow methods. ACCV 2012.
[67] SIOF	234	2	color	L. Xu, Z. Dai, and J. Jia. Scale invariant optical flow. ECCV 2012.
[68] MDP-Flow2	342	2	color	L. Xu, J. Jia, and Y. Matsushita. Motion detail preserving optical flow estimation. PAMI 34(9):1744-1757, 2012. Code available.
[69] TCOF	1421	all	gray	J. Sanchez, A. Salgado, and N. Monzon. Optical flow estimation with consistent spatio-temporal coherence models. VISAPP 2013.
[70] LME	476	2	color	W. Li, D. Cosker, M. Brown, and R. Tang. Optical flow estimation using Laplacian mesh energy. CVPR 2013.
[71] NN-field	362	2	color	L. Chen, H. Jin, Z. Lin, S. Cohen, and Y. Wu. Large displacement optical flow from nearest neighbor fields. CVPR 2013.
[72] FESL	3310	2	color	Weisheng Dong, Guangming Shi, Xiaocheng Hu, and Yi Ma. Nonlocal sparse and low-rank regularization for optical flow estimation. IEEE TIP 23(10):4527-4538, 2014.
[73] PMF	35	2	color	J. Lu, H. Yang, D. Min, and M. Do. PatchMatch filter: efficient edge-aware filtering meets randomized search for fast correspondence field estimation. CVPR 2013.
[74] FC-2Layers-FF	2662	4	color	D. Sun, J. Wulff, E. Sudderth, H. Pfister, and M. Black. A fully-connected layered model of foreground and background flow. CVPR 2013.
[75] NNF-Local	673	2	color	Zhuoyuan Chen, Hailin Jin, Zhe Lin, Scott Cohen, and Ying Wu. Large displacement optical flow from nearest neighbor fields. CVPR 2013.
[76] Correlation Flow	290	2	color	M. Drulea and S. Nedevschi. Motion estimation using the correlation transform. TIP 2013. Matlab code.
[77] TC/T-Flow	341	5	color	M. Stoll, S. Volz, and A. Bruhn. Joint trilateral filtering for multiframe optical flow. ICIP 2013.
[78] OFLAF	1530	2	color	T. Kim, H. Lee, and K. Lee. Optical flow via locally adaptive fusion of complementary data costs. ICCV 2013.
[79] Periodicity	8000	4	color	Georgii Khachaturov, Silvia Gonzalez-Brambila, and Jesus Gonzalez-Trejo. Periodicity-based computation of optical flow. Computacion y Sistemas (CyS) 2014.
[80] SILK	572	2	gray	Pascal Zille, Thomas Corpetti, Liang Shao, and Xu Chen. Observation model based on scale interactions for optical flow estimation. IEEE TIP 23(8):3281-3293, 2014.
[81] CRTflow	13	3	color	O. Demetz, D. Hafner, and J. Weickert. The complete rank transform: a tool for accurate and morphologically invariant matching of structures. BMVC 2013.
[82] Classic+CPF	640	2	gray	Zhigang Tu, Nico van der Aa, Coert Van Gemeren, and Remco Veltkamp. A combined post-filtering method to improve accuracy of variational optical flow estimation. Pattern Recognition 47(5):1926-1940, 2014.
[83] S2D-Matching	1200	2	color	Marius Leordeanu, Andrei Zanfir, and Cristian Sminchisescu. Locally affine sparse-to-dense matching for motion and occlusion estimation. ICCV 2013.
[84] AGIF+OF	438	2	gray	Zhigang Tu, Ronald Poppe, and Remco Veltkamp. Adaptive guided image filter for warping in variational optical flow computation. Signal Processing 127:253-265, 2016.
[85] DeepFlow	13	2	color	P. Weinzaepfel, J. Revaud, Z. Harchaoui, and C. Schmid. DeepFlow: large displacement optical flow with deep matching. ICCV 2013.
[86] EPPM w/o HM	2.5	2	color	L. Bao, Q. Yang, and H. Jin. Fast edge-preserving PatchMatch for large displacement optical flow. CVPR 2014.
[87] MLDP_OF	165	2	gray	M. Mohamed, H. Rashwan, B. Mertsching, M. Garcia, and D. Puig. Illumination-robust optical flow approach using local directional pattern. IEEE TCSVT 24(9):1499-1508, 2014.
[88] RFlow	20	2	gray	S. Ali, C. Daul, and W. Blondel. Robust and accurate optical flow estimation for weak texture and varying illumination condition: Application to cystoscopy. IPTA 2014.
[89] SRR-TVOF-NL	32	all	color	P. Pohl, M. Sirotenko, E. Tolstaya, and V. Bucha. Edge preserving motion estimation with occlusions correction for assisted 2D to 3D conversion. IS&T/SPIE Electronic Imaging 2014.
[90] 2DHMM-SAS	157	2	color	M.-C. Shih, R. Shenoy, and K. Rose. A two-dimensional hidden Markov model with spatially-adaptive states with application of optical flow. ICIP 2014 submission.
[91] WLIF-Flow	700	2	color	Z. Tu, R. Veltkamp, N. van der Aa, and C. Van Gemeren. Weighted local intensity fusion method for variational optical flow estimation. Submitted to TIP 2014.
[92] FMOF	215	2	color	N. Jith, A. Ramakanth, and V. Babu. Optical flow estimation using approximate nearest neighbor field fusion. ICASSP 2014.
[93] TriFlow	150	2	color	TriFlow. Optical flow with geometric occlusion estimation and fusion of multiple frames. ECCV 2014 submission 914.
[94] ComponentFusion	6.5	2	color	Anonymous. Fast optical flow by component fusion. ECCV 2014 submission 941.
[95] AggregFlow	1642	2	color	D. Fortun, P. Bouthemy, and C. Kervrann. Aggregation of local parametric candidates and exemplar-based occlusion handling for optical flow. Preprint arXiv:1407.5759.
[96] 2bit-BM-tele	124	2	gray	R. Xu and D. Taubman. Robust dense block-based motion estimation using a two-bit transform on a Laplacian pyramid. ICIP 2013.
[97] HCIC-L	330	2	color	Anonymous. Globally-optimal image correspondence using a hierarchical graphical model. NIPS 2014 submission 114.
[98] TF+OM	600	2	color	R. Kennedy and C. Taylor. Optical flow with geometric occlusion estimation and fusion of multiple frames. EMMCVPR 2015.
[99] PH-Flow	800	2	color	J. Yang and H. Li. Dense, accurate optical flow estimation with piecewise parametric model. CVPR 2015.
[100] EpicFlow	16	2	color	J. Revaud, P. Weinzaepfel, Z. Harchaoui, and C. Schmid. EpicFlow: edge-preserving interpolation of correspondences for optical flow. CVPR 2015.
[101] NNF-EAC	380	2	color	Anonymous. Variational method for joint optical flow estimation and edge-aware image restoration. CVPR 2015 submission 2336.
[102] Heeger++	6600	5	gray	Anonymous. A context aware biologically inspired algorithm for optical flow (updated results). CVPR 2015 submission 2238.
[103] HBM-GC	330	2	color	A. Zheng and Y. Yuan. Motion estimation via hierarchical block matching and graph cut. Submitted to ICIP 2015.
[104] FFV1MT	358	5	gray	F. Solari, M. Chessa, N. Medathati, and P. Kornprobst. What can we expect from a V1-MT feedforward architecture for optical flow estimation? Submitted to Signal Processing: Image Communication 2015.
[105] ROF-ND	4	2	color	S. Ali, C. Daul, E. Galbrun, and W. Blondel. Illumination invariant large displacement optical flow using robust neighbourhood descriptors. Submitted to CVIU 2015.
[106] DeepFlow2	16	2	color	J. Revaud, P. Weinzaepfel, Z. Harchaoui, and C. Schmid. Deep convolutional matching. Submitted to IJCV, 2015.
[107] HAST	2667	2	color	Anonymous. Highly accurate optical flow estimation on superpixel tree. ICCV 2015 submission 2221.
[108] FlowFields	15	2	color	C. Bailer, B. Taetz, and D. Stricker. Flow Fields: Dense unregularized correspondence fields for highly accurate large displacement optical flow estimation. ICCV 2015.
[109] SVFilterOh	1.56	2	color	Anonymous. Fast estimation of large displacement optical flow using PatchMatch and dominant motion patterns. CVPR 2016 submission 1788.
[110] FlowNetS+ft+v	0.5	2	color	Anonymous. Learning optical flow with convolutional neural networks. ICCV 2015 submission 235.
[111] CombBMOF	51	2	color	M. Brüggemann, R. Kays, P. Springer, and O. Erdler. Combined block-matching and adaptive differential motion estimation in a hierarchical multi-scale framework. ICGIP 2014. (Method improved since publication.)
[112] PMMST	182	2	color	F. Zhang, S. Xu, and X. Zhang. High accuracy correspondence field estimation via MST based patch matching. Submitted to TIP 2015.
[113] DF-Auto	70	2	color	N. Monzon, A. Salgado, and J. Sanchez. Regularization strategies for discontinuity-preserving optical flow methods. Submitted to TIP 2015.
[114] CPM-Flow	3	2	color	Anonymous. Efficient coarse-to-fine PatchMatch for large displacement optical flow. CVPR 2016 submission 241.
[115] CNN-flow-warp+ref	1.4	3	color	D. Teney and M. Hebert. Learning to extract motion from videos in convolutional neural networks. ArXiv 1601.07532, 2016.
[116] Steered-L1	804	2	color	Anonymous. Optical flow estimation via steered-L1 norm. Submitted to WSCG 2016.
[117] StereoOF-V1MT	343	2	gray	Anonymous. Visual features for action-oriented tasks: a cortical-like model for disparity and optic flow computation. BMVC 2016 submission 132.
[118] PGM-C	5	2	color	Y. Li. Pyramidal gradient matching for optical flow estimation. Submitted to PAMI 2016.
[119] RNLOD-Flow	1040	2	gray	C. Zhang, Z. Chen, M. Wang, M. Li, and S. Jiang. Robust non-local TV-L1 optical flow estimation with occlusion detection. IEEE TIP 26(8):4055-4067, 2017.
[120] FlowNet2	0.091	2	color	Anonymous. FlowNet 2.0: Evolution of optical flow estimation with deep networks. CVPR 2017 submission 900.
[121] S2F-IF	20	2	color	Anonymous. S2F-IF: Slow-to-fast interpolator flow. CVPR 2017 submission 765.
[122] BriefMatch	0.068	2	gray	G. Eilertsen, P.-E. Forssen, and J. Unger. Dense binary feature matching for real-time optical flow estimation. SCIA 2017 submission 62.
[123] OAR-Flow	60	2	color	Anonymous. Order-adaptive regularisation for variational optical flow: global, local and in between. SSVM 2017 submission 20.
[124] AdaConv-v1	2.8	2	color	Simon Niklaus, Long Mai, and Feng Liu. (Interpolation results only.) Video frame interpolation via adaptive convolution. CVPR 2017.
[125] SepConv-v1	0.2	2	color	Simon Niklaus, Long Mai, and Feng Liu. (Interpolation results only.) Video frame interpolation via adaptive separable convolution. ICCV 2017.
[126] ProbFlowFields	37	2	color	A. Wannenwetsch, M. Keuper, and S. Roth. ProbFlow: joint optical flow and uncertainty estimation. ICCV 2017.
[127] UnFlow	0.12	2	color	Anonymous. UnFlow: Unsupervised learning of optical flow with a bidirectional census loss. Submitted to AAAI 2018.
[128] FlowFields+	10.5	2	color	C. Bailer, B. Taetz, and D. Stricker. Flow fields: Dense correspondence fields for highly accurate large displacement optical flow estimation. Submitted to PAMI 2017.
[129] IIOF-NLDP	150	2	color	D.-H. Trinh, W. Blondel, and C. Daul. A general form of illumination-invariant descriptors in variational optical flow estimation. ICIP 2017.
[130] SuperSlomo	0.5	2	color	Anonymous. (Interpolation results only.) Super SloMo: High quality estimation of multiple intermediate frames for video interpolation. CVPR 2018 submission 325.
[131] EPMNet	0.061	2	color	Anonymous. EPM-convolution multilayer-network for optical flow estimation. ICME 2018 submission 1119.
[132] OFRF	90	2	color	Tan Khoa Mai, Michele Gouiffes, and Samia Bouchafa. Optical flow refinement using iterative propagation under colour, proximity and flow reliability constraints. IET Image Processing 2020.
[133] 3DFlow	328	2	color	J. Chen, Z. Cai, J. Lai, and X. Xie. A filtering based framework for optical flow estimation. IEEE TCSVT 2018.
[134] CtxSyn	0.07	2	color	Simon Niklaus and Feng Liu. (Interpolation results only.) Context-aware synthesis for video frame interpolation. CVPR 2018.
[135] DMF_ROB	10	2	color	ROB 2018 baseline submission, based on: P. Weinzaepfel, J. Revaud, Z. Harchaoui, and C. Schmid. DeepFlow: large displacement optical flow with deep matching. ICCV 2013.
[136] JOF	657	2	gray	C. Zhang, L. Ge, Z. Chen, M. Li, W. Liu, and H. Chen. Refined TV-L1 optical flow estimation using joint filtering. Submitted to IEEE TMM, 2018.
[137] AVG_FLOW_ROB	N/A	2	N/A	Average flow field of ROB 2018 training set.
[138] LiteFlowNet	0.06	2	color	T.-W. Hui, X. Tang, and C. C. Loy. LiteFlowNet: A lightweight convolutional neural network for optical flow estimation. CVPR 2018.
[139] AugFNG_ROB	0.10	all	color	Anonymous. FusionNet and AugmentedFlowNet: Selective proxy ground truth for training on unlabeled images. ECCV 2018 submission 2834.
[140] ResPWCR_ROB	0.2	2	color	Anonymous. Learning optical flow with residual connections. ROB 2018 submission.
[141] FF++_ROB	17.43	2	color	R. Schuster, C. Bailer, O. Wasenmueller, D. Stricker. FlowFields++: Accurate optical flow correspondences meet robust interpolation. ICIP 2018. Submitted to ROB 2018.
[142] ProFlow_ROB	76	3	color	Anonymous. ProFlow: Learning to predict optical flow. BMVC 2018 submission 277.
[143] PWC-Net_RVC	0.069	2	color	D. Sun, X. Yang, M.-Y. Liu, and J. Kautz. PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. CVPR 2018. Also RVC 2020 baseline submission.
[144] WOLF_ROB	0.02	2	color	Anonymous. Reversed deep neural network for optical flow. ROB 2018 submission.
[145] LFNet_ROB	0.068	2	color	Anonymous. Learning a flow network. ROB 2018 submission.
[146] WRT	9	2	color	L. Mei, J. Lai, X. Xie, J. Zhu, and J. Chen. Illumination-invariance optical flow estimation using weighted regularization transform. Submitted to IEEE TCSVT 2018.
[147] EAI-Flow	2.1	2	color	Anonymous. Hierarchical coherency sensitive hashing and interpolation with RANSAC for large displacement optical flow. CVIU 2018 submission 17-678.
[148] ContinualFlow_ROB	0.5	all	color	Michal Neoral, Jan Sochman, and Jiri Matas. Continual occlusions and optical flow estimation. ACCV 2018.
[149] CyclicGen	0.088	2	color	Anonymous. (Interpolation results only.) Deep video frame interpolation using cyclic frame generation. AAAI 2019 submission 323.
[150] TOF-M	0.393	2	color	Tianfan Xue, Baian Chen, Jiajun Wu, Donglai Wei, and William Freeman. Video enhancement with task-oriented flow. arXiv 1711.09078, 2017.
[151] MPRN	0.32	4	color	Anonymous. (Interpolation results only.) Multi-frame pyramid refinement network for video frame interpolation. CVPR 2019 submission 1361.
[152] DAIN	0.13	2	color	Wenbo Bao, Wei-Sheng Lai, Chao Ma, Xiaoyun Zhang, Zhiyong Gao, and Ming-Hsuan Yang. (Interpolation results only.) DAIN: Depth-aware video frame interpolation. CVPR 2019.
[153] FRUCnet	0.65	2	color	Van Thang Nguyen, Kyujoong Lee, and Hyuk-Jae Lee. (Interpolation results only.) A stacked deep MEMC network for frame rate up conversion and its application to HEVC. Submitted to IEEE TCSVT 2019.
[154] OFRI	0.31	2	color	Anonymous. (Interpolation results only.) Efficient video frame interpolation via optical flow refinement. CVPR 2019 submission 6743.
[155] CompactFlow_ROB	0.05	2	color	Anonymous. CompactFlow: spatially shiftable window revisited. CVPR 2019 submission 1387.
[156] SegFlow	3.2	2	color	Jun Chen, Zemin Cai, Jianhuang Lai, and Xiaohua Xie. Efficient segmentation-based PatchMatch for large displacement optical flow estimation. IEEE TCSVT 2018.
[157] HCFN	0.18	2	color	Anonymous. Practical coarse-to-fine optical flow with deep networks. ICCV 2019 submission 116.
[158] FGME	0.23	2	color	Bo Yan, Weimin Tan, Chuming Lin, and Liquan Shen. (Interpolation results only.) Fine-grained motion estimation for video frame interpolation. IEEE Transactions on Broadcasting, 2020.
[159] MS-PFT	0.44	2	color	Xianhang Cheng and Zhenzhong Chen. (Interpolation results only.) A multi-scale position feature transform network for video frame interpolation. IEEE TCSVT 2020.
[160] MEMC-Net+	0.12	2	color	Wenbo Bao, Wei-Sheng Lai, Xiaoyun Zhang, Zhiyong Gao, and Ming-Hsuan Yang. (Interpolation results only.) MEMC-Net: Motion estimation and motion compensation driven neural network for video interpolation and enhancement. Submitted to PAMI 2018.
[161] ADC	0.01	2	color	Anonymous. (Interpolation results only.) Learning spatial transform for video frame interpolation. ICCV 2019 submission 5424.
[162] DSepConv	0.3	2	color	Xianhang Cheng and Zhenzhong Chen. (Interpolation results only.) Video frame interpolation via deformable separable convolution. AAAI 2020.
[163] MAF-net	0.3	2	color	Mengshun Hu, Jing Xiao, Liang Liao, Zheng Wang, Chia-Wen Lin, Mi Wang, and Shinichi Satoh. Capturing small, fast-moving objects: Frame interpolation via recurrent motion enhancement. IEEE TCSVT 2021.
[164] STAR-Net	0.049	2	color	Anonymous. (Interpolation results only.) Space-time-aware multiple resolution for video enhancement. CPVR 2020 submission 430.
[165] AdaCoF	0.03	2	color	Hyeongmin Lee, Taeoh Kim, Tae-young Chung, Daehyun Pak, Yuseok Ban, and Sangyoun Lee. (Interpolation results only.) AdaCoF: Adaptive collaboration of flows for video frame interpolation. CVPR 2020. Code available.
[166] TC-GAN	0.13	2	color	Anonymous. (Interpolation results only.) A temporal and contextual generative adversarial network for video frame interpolation. CVPR 2020 submission 111.
[167] FeFlow	0.52	2	color	Shurui Gui, Chaoyue Wang, Qihua Chen, and Dacheng Tao. (Interpolation results only.) FeatureFlow: Robust video interpolation via structure-to-texture generation. CVPR 2020. Code available.
[168] DAI	0.23	2	color	Anonymous. (Interpolation results only.) Deep animation inbetweening. CVPR 2020 submission 6404.
[169] SoftSplat	0.1	2	color	Simon Niklaus and Feng Liu. (Interpolation results only.) Softmax splatting for video frame interpolation. CVPR 2020.
[170] STSR	5.35	2	color	Anonymous. (Interpolation results only.) Spatial and temporal video super-resolution with a frequency domain loss. ECCV 2020 submission 2340.
[171] BMBC	0.77	2	color	Anonymous. (Interpolation results only.) BMBC: Bilateral motion estimation with bilateral cost volume for video interpolation. ECCV 2020 submission 2095.
[172] GDCN	1.0	2	color	Anonymous. (Interpolation results only.) Video interpolation via generalized deformable convolution. ECCV 2020 submission 4347.
[173] EDSC	0.56	2	color	Xianhang Cheng and Zhenzhong Chen. (Interpolation results only.) Multiple video frame interpolation via enhanced deformable separable convolution. Submitted to PAMI 2020.
[174] CoT-AMFlow	0.04	2	color	Anonymous. CoT-AMFlow: Adaptive modulation network with co-teaching strategy for unsupervised optical flow estimation. CoRL 2020 submission 36.
[175] TVL1_RVC	11.6	2	color	RVC 2020 baseline submission by Toby Weed, based on: Javier Sanchez, Enric Meinhardt-Llopis, and Gabriele Facciolo. TV-L1 optical flow estimation. IPOL 3:137-150, 2013.
[176] H+S_RVC	44.7	2	color	RVC 2020 baseline submission by Toby Weed, based on: Enric Meinhardt-Llopis, Javier Sanchez, and Daniel Kondermann. Horn-Schunck optical flow with a multi-scale strategy. IPOL 3:151–172, 2013.
[177] PRAFlow_RVC	0.34	2	color	Zhexiong Wan, Yuxin Mao, and Yuchao Dai. Pyramid recurrent all-pairs flow. RVC 2020 submission.
[178] VCN_RVC	0.84	2	color	Gengshan Yang and Deva Ramanan. Volumetric correspondence networks for optical flow. NeurIPS 2019. RVC 2020 submission.
[179] RAFT-TF_RVC	1.51	2	color	Deqing Sun, Charles Herrmann, Varun Jampani, Mike Krainin, Forrester Cole, Austin Stone, Rico Jonschkowski, Ramin Zabih, William Freeman, and Ce Liu. A TensorFlow implementation of RAFT (Zachary Teed and Jia Deng. RAFT: Recurrent all-pairs field transforms for optical flow. ECCV 2020.) RVC 2020 submission.
[180] IRR-PWC_RVC	0.18	2	color	Junhwa Hur and Stefan Roth. Iterative residual refinement for joint optical flow and occlusion estimation. CVPR 2019. RVC 2020 submission.
[181] C-RAFT_RVC	0.60	2	color	Henrique Morimitsu and Xiangyang Ji. Classification RAFT. RVC 2020 submission.
[182] LSM_FLOW_RVC	0.2	2	color	Chengzhou Tang, Lu Yuan, and Ping Tan. LSM: Learning subspace minimization for low-level vision. CVPR 2020. RVC 2020 submission.
[183] MV_VFI	0.23	2	color	Zhenfang Wang, Yanjiang Wang, and Baodi Liu. (Interpolation results only.) Multi-view based video interpolation algorithm. ICASSP 2021 submission.
[184] DistillNet	0.12	2	color	Anonymous. (Interpolation results only.) A teacher-student optical-flow distillation framework for video frame interpolation. CVPR 2021 submission 7534.
[185] SepConv++	0.1	2	color	Simon Niklaus, Long Mai, and Oliver Wang. (Interpolation results only.) Revisiting adaptive convolutions for video frame interpolation. WACV 2021.
[186] EAFI	0.18	2	color	Anonymous. (Interpolation results only.) Error-aware spatial ensembles for video frame interpolation. ICCV 2021 submission 8020.
[187] UnDAF	0.04	2	color	Anonymous. UnDAF: A general unsupervised domain adaptation framework for disparity, optical flow or scene flow estimation. CVPR 2021 submission 236.
[188] FLAVR	0.029	all	color	Anonymous. (Interpolation results only.) FLAVR frame interpolation. NeurIPS 2021 submission 1300.
[189] PBOFVI	150	2	color	Zemin Cai, Jianhuang Lai, Xiaoxin Liao, and Xucong Chen. Physics-based optical flow under varying illumination. Submitted to IEEE TCSVT 2021.
[190] SoftsplatAug	0.17	2	color	Anonymous. (Interpolation results only.) Transformation data augmentation for sports video frame interpolation. ICCV 2021 submission 3245.

* The "time" column lists the reported runtime in seconds on the "Urban" sequence. Note that these runtimes are not normalized by processor speed or type.