Optical flow evaluation results: SD 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

SD 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
NNF-Local [87]	7.3	6.84 15	15.1 23	4.48 9	6.28 5	15.5 7	3.00 4	7.42 3	13.5 6	2.39 3	7.71 4	22.7 7	2.48 2	4.26 1	5.09 1	2.83 1	6.52 2	14.9 2	4.55 4	1.74 16	3.32 45	1.23 5	5.63 3	10.7 3	2.48 5
PMMST [114]	8.6	6.46 4	14.1 4	3.23 1	5.42 1	12.7 3	3.51 12	8.20 9	14.7 11	3.66 13	7.46 2	17.8 2	4.34 4	4.79 13	5.67 8	3.91 21	6.77 3	15.2 4	3.61 2	1.74 16	3.36 48	1.34 9	5.95 7	11.3 7	2.25 2
NN-field [71]	10.8	7.29 34	16.0 46	4.74 21	6.15 2	15.2 6	3.02 5	7.77 7	14.1 10	2.71 6	7.56 3	22.8 8	1.96 1	4.49 2	5.36 2	3.09 3	5.72 1	14.1 1	1.96 1	1.96 32	3.34 46	1.32 8	5.70 4	10.8 4	2.49 6
OFLAF [77]	13.6	6.75 12	14.9 16	4.44 7	7.07 11	17.5 17	3.10 6	8.45 10	15.5 13	2.50 5	13.0 39	35.0 72	6.38 16	4.57 3	5.57 5	3.12 4	7.63 7	15.9 7	5.90 13	1.68 13	2.86 14	1.43 17	5.83 6	11.0 6	2.79 7
MDP-Flow2 [68]	20.1	6.66 7	14.7 13	4.53 11	6.79 8	17.0 11	3.23 7	8.68 11	15.8 14	2.90 7	13.4 49	33.7 58	6.89 41	4.95 25	5.84 23	4.15 41	8.49 17	18.7 27	7.72 39	1.64 10	3.08 22	1.27 7	6.77 10	12.8 12	3.33 12
FC-2Layers-FF [74]	22.3	7.07 23	15.5 32	4.91 27	8.30 31	19.7 34	4.30 20	7.61 5	13.6 7	4.29 23	11.8 16	30.2 23	6.20 14	4.60 4	5.54 3	3.52 7	9.20 27	18.3 23	6.27 20	2.20 50	3.42 57	1.85 39	7.54 16	14.3 17	4.12 17
nLayers [57]	22.6	7.20 29	16.0 46	4.66 18	6.25 4	14.7 4	3.70 17	7.72 6	13.7 8	4.81 31	13.1 43	34.8 70	6.69 27	4.76 10	5.75 14	4.12 38	7.19 5	14.9 2	4.40 3	1.99 33	3.10 27	1.80 38	8.22 21	15.6 22	6.10 26
ComponentFusion [96]	26.6	7.22 30	15.9 43	4.61 16	7.60 17	19.2 29	3.30 8	9.70 16	17.6 19	3.77 15	11.1 11	31.0 31	4.45 5	4.96 26	5.88 26	4.25 45	10.9 49	23.7 52	9.40 60	1.90 29	3.08 22	1.69 32	8.00 19	15.1 20	4.57 19
FESL [72]	29.3	6.97 18	15.3 29	4.47 8	9.74 52	21.4 49	5.49 51	11.4 28	20.2 29	4.25 22	12.5 23	31.5 37	6.81 32	4.72 7	5.71 11	3.72 15	7.03 4	16.0 8	4.81 5	2.21 52	3.49 64	1.94 42	11.1 42	17.6 34	10.1 42
Correlation Flow [75]	30.0	6.66 7	14.5 9	3.81 2	7.78 22	17.5 17	2.85 2	18.0 75	29.0 83	4.31 25	9.28 6	22.1 6	5.57 9	5.12 45	6.13 59	3.98 25	11.0 51	23.3 47	10.5 71	2.07 40	3.08 22	2.32 56	6.79 11	12.5 10	4.83 20
AGIF+OF [85]	30.2	7.17 27	15.6 34	4.93 28	10.2 62	22.1 56	5.16 38	12.5 40	21.2 40	4.88 33	12.5 23	31.7 42	6.76 29	4.78 12	5.73 12	3.91 21	7.85 10	16.3 10	5.20 7	1.83 24	3.10 27	1.71 33	10.8 39	17.6 34	11.0 44
Layers++ [37]	30.8	7.19 28	15.7 36	5.08 34	6.15 2	14.8 5	3.42 11	7.83 8	14.0 9	4.84 32	10.9 10	26.9 13	6.19 13	4.83 17	5.84 23	4.36 49	12.4 67	25.2 62	10.5 71	2.43 58	3.56 69	1.92 41	8.66 24	16.1 24	7.77 33
NNF-EAC [103]	31.3	6.83 14	14.9 16	4.80 22	7.59 16	18.0 19	4.31 21	9.03 13	16.1 15	3.09 9	13.2 46	32.2 46	7.10 48	5.06 41	5.96 35	4.02 28	8.19 14	17.1 11	6.04 17	1.79 21	3.30 44	1.38 15	17.0 73	27.6 72	18.0 95
LME [70]	32.2	7.04 21	15.6 34	4.53 11	6.68 6	16.9 10	2.85 2	13.6 49	22.6 45	12.0 95	11.5 14	27.8 14	6.39 17	5.03 36	5.93 32	4.52 56	12.4 67	27.0 74	10.9 77	1.76 18	3.38 51	1.43 17	6.62 9	12.5 10	2.86 8
Efficient-NL [60]	32.3	7.43 46	16.2 54	4.85 23	7.73 20	18.1 20	4.58 23	14.0 52	23.6 53	4.47 27	13.1 43	32.9 53	7.50 60	4.77 11	5.77 18	3.65 11	8.08 12	16.1 9	5.25 8	2.48 62	3.37 50	3.12 76	6.91 12	12.1 8	5.78 24
IROF++ [58]	33.5	7.44 47	16.1 50	5.11 36	8.61 36	19.4 30	5.12 36	12.3 37	21.0 38	5.12 35	12.8 33	32.1 44	7.13 50	4.88 19	5.76 16	3.89 20	9.01 21	18.9 31	6.76 26	1.78 20	3.22 40	1.23 5	10.4 36	18.5 40	13.3 57
NL-TV-NCC [25]	33.5	6.92 17	14.6 12	3.96 3	8.32 32	19.8 37	2.84 1	15.4 61	26.0 64	3.92 18	10.8 9	26.6 11	5.58 10	5.09 42	6.00 41	4.07 31	11.1 53	23.5 48	10.5 71	2.09 41	3.06 21	2.27 54	11.6 45	20.4 45	9.14 38
PH-Flow [101]	34.4	7.38 41	15.9 43	5.22 41	9.30 44	19.5 31	5.71 53	9.62 15	17.2 17	5.09 34	13.4 49	34.5 64	7.10 48	4.82 14	5.73 12	3.82 18	8.36 16	17.1 11	5.35 10	2.66 67	3.43 59	3.42 85	7.13 15	13.3 15	5.33 23
TC/T-Flow [76]	34.5	6.31 2	13.3 2	4.85 23	11.5 79	23.6 72	6.67 68	13.4 48	23.2 51	3.00 8	14.2 60	36.9 88	6.33 15	4.72 7	5.64 6	3.64 10	7.70 8	17.1 11	5.61 12	2.00 34	3.45 62	2.86 70	10.9 41	18.2 38	3.59 13
LSM [39]	34.9	7.06 22	15.2 27	5.21 40	9.65 50	21.2 48	5.48 50	11.9 33	20.2 29	5.33 40	12.0 17	30.0 21	6.84 36	5.14 49	6.13 59	4.62 62	9.12 24	18.1 20	6.53 22	2.13 46	3.11 29	2.11 46	8.09 20	14.3 17	6.76 30
Classic+CPF [83]	35.1	7.31 36	15.8 39	5.09 35	9.93 57	22.1 56	5.05 34	13.3 46	22.4 44	4.64 30	12.5 23	32.0 43	6.79 31	4.87 18	5.83 22	3.99 26	7.43 6	15.6 6	5.32 9	2.26 54	3.21 39	2.78 68	9.89 33	16.5 26	13.8 61
CombBMOF [113]	35.2	7.30 35	15.1 23	4.61 16	8.08 27	18.2 22	3.69 16	10.2 19	18.1 22	2.47 4	11.2 12	28.1 15	6.67 25	4.82 14	5.76 16	4.13 39	13.3 79	22.1 39	14.1 101	2.90 76	4.33 93	2.14 47	11.8 47	21.0 48	3.23 11
Sparse-NonSparse [56]	35.5	7.26 33	15.7 36	5.22 41	9.83 54	21.6 51	5.45 49	12.2 35	20.8 34	5.35 42	12.1 19	30.0 21	6.86 38	5.14 49	6.13 59	4.58 59	9.16 25	18.5 26	6.58 23	2.05 38	3.03 18	2.06 45	7.62 17	13.8 16	5.98 25
MLDP_OF [89]	35.7	7.02 19	14.5 9	4.89 26	7.00 10	17.0 11	3.34 9	14.3 54	24.0 54	3.73 14	12.9 36	34.8 70	5.88 11	4.89 21	5.69 9	3.92 24	8.18 13	17.4 15	7.22 37	3.64 95	3.68 71	5.99 109	13.2 55	20.5 46	9.16 39
HAST [109]	36.5	7.11 25	16.0 46	4.27 4	8.90 38	17.4 16	7.54 77	6.79 1	12.4 3	1.56 1	14.8 67	37.2 89	6.63 21	4.68 6	5.70 10	2.91 2	10.5 45	20.7 35	11.1 78	3.74 98	4.39 96	5.40 108	5.74 5	10.9 5	2.09 1
WLIF-Flow [93]	36.7	6.80 13	14.9 16	4.60 15	6.93 9	16.7 9	4.11 19	10.3 20	18.2 23	4.11 21	12.7 32	30.9 30	6.67 25	6.60 115	7.87 119	5.60 89	8.54 18	17.4 15	6.03 16	1.85 25	3.18 36	1.67 30	14.4 59	23.2 57	15.3 69
Ramp [62]	37.6	7.32 37	15.8 39	5.20 39	8.74 37	19.8 37	5.27 44	11.4 28	19.7 28	5.40 44	12.5 23	31.6 40	6.86 38	4.97 27	5.88 26	4.08 33	9.04 22	18.3 23	7.00 33	2.65 66	3.36 48	4.00 97	8.65 23	15.3 21	11.7 49
PMF [73]	37.8	7.59 52	16.5 64	4.94 29	7.64 18	18.3 25	3.66 14	7.48 4	13.2 5	2.31 2	14.7 66	36.3 86	6.84 36	4.66 5	5.64 6	3.18 6	9.85 33	21.3 37	9.22 57	3.62 94	5.25 116	3.70 93	7.12 14	13.2 14	6.82 31
Classic+NL [31]	38.9	7.40 44	16.1 50	5.36 50	9.49 49	20.9 44	5.44 48	12.3 37	20.8 34	5.20 38	12.5 23	31.3 36	6.82 33	5.03 36	5.98 39	4.18 43	8.94 20	17.5 17	5.91 14	2.29 56	3.44 61	2.17 48	9.88 32	17.0 31	11.9 51
FlowFields+ [130]	39.1	8.95 85	17.2 77	7.26 93	7.52 15	17.3 14	5.15 37	10.4 21	17.6 19	6.16 50	10.1 7	24.5 9	6.49 19	5.11 44	6.00 41	4.57 58	9.28 28	22.2 40	6.70 25	1.77 19	3.20 37	1.51 24	13.1 54	21.8 50	15.6 72
CostFilter [40]	39.8	7.36 39	15.7 36	4.96 31	7.76 21	18.1 20	3.83 18	7.07 2	12.4 3	3.12 10	14.6 65	36.3 86	6.57 20	4.82 14	5.81 20	3.54 8	12.4 67	20.5 34	10.3 69	3.86 103	5.96 119	4.36 100	8.86 27	16.7 29	3.72 15
FMOF [94]	40.7	7.14 26	15.5 32	5.28 45	10.4 66	22.7 63	5.42 45	10.8 24	19.0 27	3.90 17	12.2 21	31.0 31	6.63 21	4.98 29	5.97 36	4.10 34	10.0 40	17.2 14	6.98 31	2.46 60	3.40 54	4.60 101	14.7 61	23.2 57	10.1 42
TV-L1-MCT [64]	41.9	7.42 45	16.1 50	5.22 41	9.84 55	21.6 51	5.20 40	14.3 54	24.7 57	5.27 39	12.5 23	31.2 35	7.21 53	5.01 32	5.90 28	4.41 52	9.10 23	18.8 30	7.14 35	2.17 49	2.78 5	4.69 102	9.81 31	16.8 30	11.5 46
ProbFlowFields [128]	42.0	8.84 83	17.9 90	6.90 87	7.20 13	17.3 14	4.75 30	11.6 31	20.5 32	6.31 53	8.48 5	22.0 5	5.26 7	5.25 64	6.24 74	4.67 71	9.96 38	23.9 56	6.99 32	1.64 10	2.80 7	1.41 16	14.7 61	25.5 64	14.7 65
RNLOD-Flow [121]	42.3	6.72 10	14.9 16	4.39 6	9.09 40	21.0 45	5.06 35	15.2 60	25.8 61	5.42 45	12.6 30	32.2 46	6.64 23	5.46 79	6.48 89	4.34 48	8.35 15	17.8 19	6.16 19	2.82 73	3.90 82	3.36 83	9.02 28	16.1 24	9.83 40
SVFilterOh [111]	42.4	7.94 58	17.5 83	4.94 29	8.27 30	19.8 37	3.66 14	9.83 18	17.7 21	4.59 28	13.4 49	34.5 64	6.82 33	4.89 21	5.93 32	3.15 5	10.4 44	22.7 42	9.35 59	3.51 91	4.72 105	4.17 98	7.68 18	14.3 17	4.90 21
Complementary OF [21]	42.9	7.37 40	15.1 23	5.30 48	9.46 46	22.5 60	4.63 24	13.0 41	22.8 47	4.04 20	14.8 67	37.9 95	6.87 40	4.97 27	5.86 25	4.39 51	11.0 51	24.4 57	8.06 44	1.79 21	2.79 6	2.22 51	12.2 48	22.0 52	11.2 45
ALD-Flow [66]	43.4	6.69 9	14.4 8	4.54 13	12.5 88	25.7 84	6.93 71	14.3 54	24.9 58	4.29 23	14.8 67	35.3 77	7.04 46	4.98 29	5.92 30	3.66 12	10.1 41	23.6 50	6.92 29	2.02 36	3.23 41	2.86 70	10.2 35	18.9 41	6.45 29
EPPM w/o HM [88]	44.0	7.33 38	14.5 9	5.00 33	7.20 13	17.2 13	3.41 10	11.8 32	20.8 34	3.20 11	12.6 30	31.1 34	7.00 45	5.14 49	6.08 53	4.67 71	12.0 61	22.7 42	9.97 66	4.48 114	3.69 73	6.02 110	10.4 36	17.6 34	11.5 46
FlowFields [110]	45.0	9.03 89	17.5 83	7.31 94	8.02 26	18.6 27	5.24 43	11.1 27	18.9 26	6.32 54	11.6 15	28.7 16	7.40 58	5.14 49	6.03 46	4.64 66	10.1 41	23.8 55	7.62 38	1.69 14	2.86 14	1.51 24	12.9 50	22.8 56	14.9 68
TC-Flow [46]	45.2	6.56 6	14.1 4	4.48 9	9.25 43	21.4 49	5.03 33	14.9 58	25.8 61	3.93 19	14.5 64	35.9 81	6.97 44	5.01 32	5.97 36	3.63 9	9.98 39	22.8 45	6.83 28	2.11 43	3.25 42	3.61 92	16.7 71	26.8 70	20.0 106
MDP-Flow [26]	45.2	6.73 11	14.1 4	5.59 58	6.70 7	16.0 8	4.65 26	9.78 17	17.2 17	6.61 58	13.0 39	34.7 69	6.48 18	5.54 83	6.17 68	5.84 93	10.5 45	23.6 50	7.81 40	1.89 27	3.42 57	1.37 12	20.0 86	32.5 92	19.1 100
S2F-IF [123]	45.8	8.86 84	17.2 77	7.05 90	7.84 25	18.3 25	5.20 40	10.8 24	18.5 25	6.21 51	13.0 39	30.7 28	8.15 68	5.09 42	5.98 39	4.58 59	9.62 31	22.7 42	7.20 36	1.92 30	3.77 78	1.73 34	10.7 38	18.4 39	12.8 56
SRR-TVOF-NL [91]	47.8	7.83 55	15.4 30	5.99 71	13.2 94	26.1 87	8.61 84	13.2 44	22.0 42	6.78 60	13.6 54	30.3 24	7.34 57	4.72 7	5.56 4	4.04 30	9.87 35	19.8 33	8.30 48	3.25 86	3.73 76	3.18 79	6.93 13	12.9 13	4.92 22
SimpleFlow [49]	47.9	7.56 51	16.2 54	5.59 58	9.48 47	21.0 45	5.68 52	17.1 71	27.0 73	6.29 52	13.0 39	32.8 51	7.09 47	5.18 58	6.16 64	4.62 62	8.75 19	17.6 18	6.81 27	2.13 46	3.50 65	2.30 55	9.78 30	17.4 33	7.10 32
IROF-TV [53]	48.0	7.55 50	16.1 50	5.46 55	9.23 42	21.8 53	5.88 55	13.3 46	22.2 43	5.50 47	12.8 33	31.6 40	7.28 54	5.12 45	6.09 54	4.67 71	13.3 79	29.6 91	9.97 66	1.60 5	3.12 32	1.06 3	11.5 44	21.3 49	11.5 46
ACK-Prior [27]	49.0	6.39 3	13.4 3	4.38 5	8.58 35	19.7 34	3.63 13	11.4 28	20.3 31	3.82 16	12.5 23	33.7 58	5.09 6	5.53 81	6.42 86	4.76 74	15.0 93	28.4 80	11.4 80	3.54 92	4.36 95	4.84 104	13.2 55	20.2 44	8.94 36
HBM-GC [105]	49.6	8.35 76	18.4 95	4.98 32	7.66 19	18.2 22	5.20 40	13.8 50	24.2 56	5.34 41	12.0 17	30.5 26	6.83 35	5.04 38	6.02 45	4.43 53	9.34 29	15.5 5	7.92 41	3.03 81	4.57 101	2.51 63	16.1 66	26.0 66	17.9 93
2DHMM-SAS [92]	50.8	7.39 43	15.9 43	5.25 44	10.7 70	23.4 69	5.43 46	18.0 75	27.0 73	7.95 79	13.1 43	32.7 49	7.28 54	4.89 21	5.78 19	4.01 27	9.88 36	18.2 21	6.37 21	2.50 65	3.39 52	3.37 84	13.8 58	22.4 55	15.5 71
Sparse Occlusion [54]	51.5	7.38 41	15.8 39	5.28 45	8.25 29	19.9 40	4.71 28	15.5 63	26.3 68	4.63 29	13.5 53	33.3 57	6.92 42	5.25 64	6.26 75	4.11 36	9.70 32	21.1 36	5.94 15	4.85 115	5.95 118	3.71 94	10.8 39	20.0 43	8.01 35
DPOF [18]	51.9	8.43 78	16.8 67	6.36 79	10.7 70	20.7 42	9.52 90	8.72 12	15.4 12	3.63 12	11.3 13	29.1 18	6.09 12	5.34 71	6.18 69	5.38 86	12.2 63	22.4 41	8.06 44	5.27 116	3.55 66	6.79 115	8.85 26	16.5 26	4.24 18
COFM [59]	52.5	8.49 80	18.5 97	5.95 67	8.44 33	18.7 28	4.71 28	13.0 41	22.9 48	5.84 49	13.8 56	35.2 75	6.78 30	5.63 88	6.58 92	5.94 94	11.7 58	23.5 48	9.80 63	2.29 56	3.20 37	2.72 66	6.42 8	12.1 8	3.07 10
OFH [38]	52.6	7.25 32	15.0 22	5.29 47	11.1 74	25.0 80	7.19 74	18.6 80	29.1 85	5.56 48	16.0 79	40.8 112	7.44 59	5.05 39	5.92 30	4.28 46	11.4 55	26.0 67	8.73 52	1.64 10	3.04 19	1.36 11	12.5 49	23.3 59	7.79 34
ROF-ND [107]	53.4	7.02 19	14.7 13	4.66 18	8.18 28	19.5 31	4.66 27	15.5 63	25.9 63	5.47 46	4.68 1	12.6 1	2.76 3	5.93 96	7.12 111	5.27 83	12.2 63	25.4 64	9.09 56	4.23 111	4.20 89	3.32 82	14.7 61	22.1 54	18.8 98
PGM-C [120]	54.0	9.43 93	18.5 97	7.51 98	9.84 55	23.4 69	6.05 57	12.0 34	20.6 33	7.17 66	15.8 76	35.3 77	9.67 80	5.20 61	6.11 56	4.66 68	9.86 34	23.7 52	7.06 34	1.63 8	2.84 10	1.49 22	11.1 42	20.8 47	6.23 28
S2D-Matching [84]	54.2	8.25 72	18.0 92	5.76 64	10.9 72	22.8 64	5.71 53	17.3 72	28.5 79	6.38 56	12.1 19	30.3 24	6.65 24	5.15 53	6.13 59	4.63 64	9.18 26	19.5 32	6.69 24	2.66 67	3.35 47	3.50 86	11.6 45	19.9 42	14.7 65
OAR-Flow [125]	54.4	8.08 66	16.8 67	6.19 76	17.4 107	28.9 105	12.3 104	16.3 69	26.7 72	7.01 63	15.1 71	35.2 75	7.31 56	5.17 55	6.16 64	4.24 44	9.51 30	22.9 46	5.52 11	1.54 4	2.98 17	1.59 29	9.10 29	17.1 32	3.69 14
TCOF [69]	54.9	7.46 48	15.1 23	5.70 61	9.13 41	21.1 47	5.43 46	19.6 85	29.8 90	8.31 82	12.9 36	31.0 31	7.17 52	6.02 101	7.00 106	4.59 61	7.92 11	18.4 25	6.11 18	3.78 100	4.09 86	5.18 106	8.51 22	15.9 23	3.80 16
ComplOF-FED-GPU [35]	55.2	7.49 49	15.4 30	5.37 52	12.1 85	26.5 93	7.51 76	13.1 43	22.7 46	4.33 26	15.6 74	37.7 94	7.55 62	4.94 24	5.82 21	4.13 39	12.3 65	27.7 77	8.48 50	2.49 64	3.04 19	3.56 89	12.9 50	23.9 60	9.01 37
Kuang [131]	57.4	9.02 88	17.7 85	7.11 91	9.74 52	22.6 61	6.03 56	12.4 39	21.1 39	6.61 58	14.3 62	34.5 64	8.42 71	5.17 55	6.05 51	4.81 76	12.7 74	25.1 60	11.4 80	1.62 6	2.84 10	1.54 27	13.2 55	23.9 60	13.5 58
AggregFlow [97]	58.2	10.3 102	21.3 120	6.91 88	15.1 100	27.6 100	10.7 95	14.0 52	24.0 54	8.70 84	14.1 58	35.0 72	7.15 51	5.05 39	6.03 46	4.02 28	7.73 9	18.2 21	5.09 6	2.13 46	4.40 97	1.67 30	9.91 34	18.1 37	6.13 27
CPM-Flow [116]	60.8	9.46 95	18.6 100	7.51 98	10.0 59	23.7 73	6.20 59	12.2 35	20.9 37	7.13 64	15.8 76	35.6 80	9.71 81	5.20 61	6.12 58	4.65 67	10.9 49	23.7 52	8.90 55	1.73 15	3.15 34	1.51 24	14.5 60	26.0 66	13.6 60
EpicFlow [102]	62.0	9.39 92	18.4 95	7.50 97	10.0 59	23.8 75	6.29 61	15.8 66	26.6 71	7.35 72	15.5 73	34.4 63	9.65 78	5.20 61	6.11 56	4.66 68	10.2 43	24.5 58	8.18 47	1.63 8	2.81 9	1.48 19	15.8 65	24.8 62	17.1 89
Aniso-Texture [82]	62.6	6.49 5	14.2 7	4.54 13	7.83 24	19.5 31	4.37 22	22.0 109	33.6 120	8.47 83	10.3 8	25.4 10	5.55 8	5.19 60	6.10 55	4.45 54	18.7 114	33.0 114	20.1 123	4.07 109	4.62 103	2.47 62	20.1 87	28.5 74	20.7 108
Occlusion-TV-L1 [63]	63.3	7.73 54	16.4 61	5.36 50	9.48 47	22.4 59	6.18 58	19.2 81	30.0 93	7.14 65	14.4 63	33.8 60	7.91 66	5.31 70	6.27 77	4.47 55	11.9 59	27.9 78	8.04 42	2.09 41	3.08 22	1.50 23	21.9 98	35.3 108	17.5 90
RFlow [90]	64.0	7.10 24	14.9 16	5.40 53	8.98 39	21.9 54	5.19 39	18.5 79	29.3 87	5.35 42	18.1 98	44.9 130	9.86 84	5.12 45	6.01 44	4.38 50	13.0 76	29.3 89	9.93 65	2.28 55	2.85 12	3.52 87	20.3 89	33.3 98	16.1 80
DeepFlow2 [108]	64.2	8.14 68	16.6 65	5.96 69	14.1 95	26.5 93	10.2 91	15.8 66	26.2 67	6.46 57	16.5 87	37.4 92	9.54 76	5.01 32	5.94 34	3.72 15	10.7 47	25.1 60	8.08 46	1.92 30	3.12 32	2.45 60	20.3 89	32.1 89	16.3 82
Adaptive [20]	65.2	7.94 58	17.0 72	5.33 49	10.2 62	23.9 76	6.28 60	21.2 97	31.7 108	7.69 76	13.6 54	29.8 20	7.87 65	4.88 19	5.75 14	3.71 14	13.2 77	28.9 86	9.72 62	3.21 85	4.71 104	2.91 72	19.3 83	30.5 81	15.6 72
TF+OM [100]	66.7	7.97 62	16.8 67	5.98 70	9.40 45	20.7 42	6.33 62	15.4 61	22.9 48	17.5 104	13.4 49	31.5 37	8.10 67	5.13 48	6.06 52	4.66 68	13.9 84	29.3 89	14.0 99	2.47 61	4.09 86	2.00 43	18.3 76	29.4 78	19.3 102
Steered-L1 [118]	67.5	5.97 1	12.7 1	4.67 20	7.14 12	18.2 22	4.63 24	13.2 44	23.3 52	5.12 35	15.2 72	38.1 96	7.54 61	5.85 95	6.73 96	6.98 113	13.7 83	26.5 69	11.7 86	6.39 121	4.25 91	13.3 124	22.3 101	32.7 94	20.3 107
Aniso. Huber-L1 [22]	68.1	7.96 61	16.3 59	6.10 74	11.4 76	24.7 78	6.77 69	20.6 92	29.6 89	7.26 68	13.2 46	29.2 19	7.77 64	5.52 80	6.58 92	4.29 47	12.4 67	26.7 72	8.83 54	2.93 79	3.68 71	3.10 75	16.5 70	27.4 71	13.9 62
LocallyOriented [52]	70.1	9.89 98	19.9 111	6.55 81	14.7 99	27.7 101	11.0 97	21.7 103	31.9 110	7.32 71	13.3 48	30.5 26	8.32 69	5.16 54	6.04 48	4.11 36	9.90 37	21.6 38	8.75 53	2.20 50	3.43 59	2.17 48	18.3 76	26.0 66	19.6 103
SIOF [67]	70.7	8.21 71	17.0 72	5.49 56	13.0 93	27.4 99	8.63 85	20.1 90	29.0 83	16.3 103	16.8 89	37.3 91	10.0 88	5.40 77	6.34 82	4.88 77	11.6 56	25.4 64	10.1 68	1.81 23	3.27 43	1.34 9	15.1 64	25.1 63	11.9 51
DeepFlow [86]	72.4	8.73 81	17.1 76	6.26 77	15.3 103	26.7 96	11.9 103	17.3 72	26.5 70	14.1 101	18.6 104	42.8 124	11.0 94	5.00 31	5.91 29	3.75 17	11.2 54	26.2 68	8.35 49	1.88 26	2.80 7	2.60 64	22.5 103	33.6 99	17.5 90
SegOF [10]	73.0	9.43 93	17.7 85	8.06 105	12.0 84	22.9 67	10.4 94	17.0 70	26.1 66	12.6 97	13.8 56	26.8 12	11.2 96	5.53 81	6.26 75	6.06 97	19.0 115	35.6 126	18.8 117	1.37 2	2.60 2	0.83 2	16.4 68	30.0 79	14.0 63
CRTflow [80]	73.4	7.92 56	16.0 46	5.91 66	11.4 76	23.7 73	6.55 66	19.8 87	30.1 94	7.77 77	17.2 94	41.3 115	9.76 82	5.34 71	6.30 78	3.69 13	15.8 98	31.0 102	14.3 102	2.12 45	2.92 16	2.35 57	19.2 82	32.9 96	15.3 69
TriangleFlow [30]	73.5	8.08 66	17.0 72	5.12 37	11.7 82	26.1 87	6.98 73	19.5 83	30.3 95	6.34 55	12.9 36	33.0 54	6.71 28	7.00 118	8.16 122	6.63 110	12.8 75	24.5 58	10.5 71	3.59 93	5.17 114	3.27 81	12.9 50	21.9 51	12.1 53
AdaConv-v1 [126]	73.8	13.0 117	16.4 61	8.62 112	10.2 62	9.17 1	11.5 100	10.7 22	11.1 1	7.66 74	16.3 84	17.8 2	14.4 108	10.8 127	8.97 124	16.5 127	19.7 118	18.7 27	19.4 119	19.7 130	17.1 130	8.18 119	3.51 1	4.92 1	2.42 3
SepConv-v1 [127]	73.8	13.0 117	16.4 61	8.62 112	10.2 62	9.17 1	11.5 100	10.7 22	11.1 1	7.66 74	16.3 84	17.8 2	14.4 108	10.8 127	8.97 124	16.5 127	19.7 118	18.7 27	19.4 119	19.7 130	17.1 130	8.18 119	3.51 1	4.92 1	2.42 3
TriFlow [95]	75.2	9.01 87	18.5 97	6.60 83	11.4 76	26.4 92	7.40 75	20.9 94	29.9 92	20.9 112	12.2 21	30.8 29	6.95 43	5.26 68	6.16 64	4.88 77	12.0 61	26.5 69	11.6 84	6.97 122	4.54 99	6.57 114	13.0 53	22.0 52	9.86 41
Brox et al. [5]	75.7	8.46 79	16.7 66	6.56 82	11.3 75	26.2 89	6.94 72	15.0 59	25.4 60	6.89 62	17.4 95	38.8 100	9.80 83	5.99 99	6.88 103	6.26 102	14.1 86	31.1 105	12.0 88	2.03 37	3.41 56	1.14 4	19.1 81	30.3 80	12.1 53
p-harmonic [29]	77.1	8.15 70	16.2 54	6.50 80	9.66 51	22.6 61	6.57 67	21.2 97	31.2 101	9.65 87	15.7 75	33.9 61	10.0 88	5.18 58	6.04 48	5.31 84	14.3 88	30.3 97	12.2 91	3.10 82	3.55 66	1.91 40	23.1 106	34.8 106	17.8 92
Fusion [6]	77.9	8.76 82	17.7 85	7.01 89	7.82 23	19.7 34	4.78 31	10.9 26	18.4 24	7.23 67	12.8 33	32.6 48	8.32 69	7.04 119	8.11 120	6.57 108	14.9 91	28.3 79	13.2 94	4.37 113	5.18 115	2.77 67	26.2 116	38.6 118	26.4 118
CBF [12]	78.3	7.23 31	14.9 16	5.16 38	9.95 58	21.9 54	7.69 79	17.6 74	27.0 73	7.28 70	16.4 86	39.3 104	9.18 74	6.34 111	7.35 116	6.11 100	13.4 81	28.4 80	8.52 51	5.54 118	5.11 111	6.41 112	18.7 78	30.8 82	16.4 83
FlowNet2 [122]	78.6	12.8 116	23.3 125	8.09 106	16.8 106	28.5 102	12.5 105	13.9 51	21.6 41	12.0 95	15.0 70	34.0 62	9.98 87	5.36 75	6.23 73	4.94 79	14.0 85	29.9 94	12.2 91	3.36 87	6.60 123	2.24 52	8.83 25	16.6 28	3.02 9
TV-L1-improved [17]	79.2	7.64 53	16.2 54	5.67 60	10.1 61	23.4 69	6.38 63	21.3 99	32.0 113	9.27 86	17.5 96	42.1 119	9.54 76	5.25 64	6.13 59	4.07 31	14.5 89	30.4 99	11.4 80	3.38 88	5.02 109	2.99 74	19.6 85	32.1 89	16.6 85
Local-TV-L1 [65]	80.0	9.74 97	17.9 90	6.89 86	18.4 111	29.4 107	14.9 111	24.4 114	30.8 99	20.2 110	19.4 109	42.4 122	12.7 104	5.35 74	6.00 41	4.10 34	13.6 82	28.9 86	9.26 58	1.62 6	2.58 1	1.48 19	20.3 89	32.0 88	16.4 83
SuperFlow [81]	80.0	9.27 91	17.3 80	6.63 84	11.7 82	22.8 64	8.84 87	19.3 82	28.0 78	18.4 108	16.9 91	38.2 97	9.89 85	5.44 78	6.32 79	5.61 90	11.9 59	26.6 71	9.56 61	2.92 77	4.08 85	1.79 36	20.1 87	32.4 91	15.8 78
CLG-TV [48]	80.2	7.94 58	16.2 54	5.80 65	10.5 67	24.0 77	6.44 64	19.9 88	29.8 90	6.83 61	14.1 58	31.5 37	7.73 63	5.98 97	7.01 107	5.15 82	14.8 90	31.0 102	12.2 91	4.20 110	4.80 106	5.22 107	19.3 83	32.6 93	15.7 76
Classic++ [32]	80.9	8.05 64	17.4 82	6.09 73	11.5 79	26.3 91	6.91 70	18.1 77	28.7 81	8.18 80	16.2 80	39.0 102	8.57 72	5.36 75	6.33 80	4.54 57	15.0 93	30.4 99	11.6 84	2.70 70	3.55 66	2.94 73	21.9 98	34.0 102	17.9 93
Rannacher [23]	82.0	8.07 65	16.8 67	6.15 75	10.6 68	24.8 79	6.51 65	21.9 107	32.6 119	10.9 90	18.4 101	43.3 126	10.5 90	5.27 69	6.18 69	4.17 42	15.5 96	32.3 111	12.0 88	2.69 69	3.57 70	2.68 65	17.8 75	31.0 83	16.1 80
F-TV-L1 [15]	82.4	8.41 77	16.8 67	6.31 78	18.0 110	29.7 108	12.8 106	21.6 101	30.5 96	10.1 89	18.2 99	42.3 121	9.65 78	5.02 35	5.97 36	3.91 21	14.1 86	30.8 101	10.6 75	2.79 71	4.90 107	2.35 57	20.5 92	32.7 94	15.6 72
DF-Auto [115]	85.3	10.7 107	19.8 110	7.42 96	16.3 105	25.3 81	13.1 108	19.5 83	28.5 79	17.8 106	16.9 91	37.2 89	10.8 92	6.76 117	8.12 121	5.56 88	10.8 48	25.2 62	6.95 30	3.69 97	4.92 108	1.48 19	17.7 74	27.9 73	14.6 64
BriefMatch [124]	85.4	6.91 16	14.8 15	4.85 23	11.0 73	22.8 64	8.24 82	9.42 14	16.6 16	5.13 37	16.7 88	40.7 111	8.61 73	9.76 125	10.6 128	14.1 125	18.2 111	31.0 102	17.4 113	9.26 126	6.60 123	20.9 130	28.0 120	36.5 110	34.9 124
StereoOF-V1MT [119]	85.5	8.14 68	15.8 39	5.42 54	17.6 108	34.3 126	10.3 92	21.6 101	31.5 105	7.27 69	15.8 76	32.7 49	10.7 91	5.62 87	6.40 84	6.00 96	16.6 101	30.0 95	15.5 104	2.01 35	3.08 22	3.15 77	33.6 125	44.0 126	32.8 122
Dynamic MRF [7]	87.2	8.32 75	17.3 80	5.95 67	12.3 86	28.5 102	7.75 80	19.6 85	31.8 109	7.56 73	18.4 101	42.2 120	11.6 100	5.25 64	6.16 64	4.80 75	17.7 105	34.4 122	16.6 109	1.89 27	2.63 3	3.21 80	30.3 122	43.6 125	29.0 120
Bartels [41]	87.5	8.31 74	17.7 85	5.51 57	8.46 34	20.6 41	4.89 32	14.8 57	26.0 64	7.89 78	18.5 103	43.6 127	11.1 95	6.18 104	6.51 91	8.63 121	15.6 97	32.6 113	13.9 98	3.86 103	4.56 100	7.09 116	22.5 103	36.5 110	18.4 96
GraphCuts [14]	88.2	9.24 90	17.2 77	7.53 100	22.1 120	33.0 121	16.8 118	15.9 68	23.1 50	15.6 102	14.2 60	28.9 17	9.34 75	5.83 94	6.82 99	6.25 101	18.5 113	29.7 92	12.0 88	2.85 74	3.39 52	3.52 87	23.8 110	36.1 109	19.1 100
Shiralkar [42]	88.5	7.92 56	15.2 27	5.73 62	14.6 98	30.3 109	9.04 88	21.7 103	31.2 101	9.77 88	19.6 111	41.5 116	13.2 106	5.17 55	6.04 48	4.63 64	18.0 110	31.1 105	14.7 103	3.67 96	3.40 54	4.74 103	23.9 111	36.6 112	18.9 99
StereoFlow [44]	90.1	16.2 127	22.6 123	13.9 128	22.1 120	31.6 113	18.8 122	24.7 115	30.6 98	21.2 114	23.3 122	39.9 105	19.6 122	5.98 97	6.22 72	7.11 115	11.6 56	27.6 75	8.05 43	1.36 1	2.85 12	0.65 1	20.7 93	33.7 100	17.0 88
Filter Flow [19]	90.2	10.5 105	19.4 106	8.33 108	12.5 88	25.8 85	8.69 86	19.9 88	27.0 73	21.7 115	19.0 106	33.1 56	15.9 112	5.34 71	6.21 71	5.37 85	16.2 100	26.7 72	15.5 104	3.46 90	4.48 98	2.26 53	23.3 109	31.5 86	18.5 97
Second-order prior [8]	90.8	8.04 63	16.3 59	6.01 72	12.5 88	26.5 93	9.10 89	21.0 95	31.1 100	9.23 85	16.2 80	36.2 85	9.93 86	5.70 91	6.67 95	5.09 81	20.7 122	34.1 120	21.0 124	3.76 99	3.89 81	4.25 99	18.9 79	31.8 87	19.9 105
CNN-flow-warp+ref [117]	91.4	9.91 99	19.6 108	7.85 102	10.6 68	22.9 67	8.55 83	21.3 99	32.1 115	11.9 94	18.7 105	42.0 118	11.3 97	5.56 84	6.34 82	6.08 99	12.3 65	27.6 75	10.8 76	2.06 39	3.69 73	3.16 78	33.3 123	40.4 121	34.7 123
IAOF2 [51]	92.5	10.0 100	19.9 111	7.91 104	14.4 97	26.7 96	10.9 96	22.0 109	32.2 116	17.6 105	19.1 107	33.0 54	17.1 115	5.81 93	6.86 101	4.94 79	12.6 72	25.9 66	11.9 87	4.26 112	4.11 88	7.75 118	16.2 67	26.5 69	13.5 58
FlowNetS+ft+v [112]	92.8	8.96 86	17.8 89	6.83 85	14.2 96	26.2 89	11.1 98	22.3 111	32.2 116	12.7 98	16.8 89	36.1 84	10.9 93	6.31 110	7.29 114	6.26 102	12.5 71	28.8 84	9.88 64	3.84 102	6.75 125	6.30 111	16.4 68	29.0 76	14.7 65
Ad-TV-NDC [36]	94.7	12.1 114	18.6 100	10.7 121	25.5 125	32.0 117	22.2 125	29.3 126	34.3 124	22.8 119	16.9 91	32.8 51	12.2 102	5.99 99	7.20 113	3.83 19	12.6 72	28.5 82	10.3 69	2.79 71	4.07 84	1.78 35	24.1 112	31.4 85	25.1 116
2D-CLG [1]	96.3	15.2 125	24.5 128	11.2 123	15.1 100	25.9 86	13.5 109	27.5 120	33.9 121	24.7 126	22.2 119	38.3 98	19.0 121	5.67 89	6.44 87	6.29 105	17.5 103	34.3 121	16.4 108	1.47 3	2.68 4	1.54 27	21.7 97	34.6 105	16.9 87
LDOF [28]	97.5	9.66 96	18.9 103	7.13 92	15.1 100	29.1 106	11.3 99	15.6 65	25.1 59	10.9 90	20.9 115	43.1 125	14.9 110	6.12 102	7.01 107	6.26 102	16.0 99	31.2 107	13.2 94	3.89 105	5.61 117	8.96 121	19.0 80	33.0 97	11.7 49
UnFlow [129]	97.6	16.0 126	25.1 129	11.3 124	12.7 92	22.2 58	11.7 102	20.4 91	27.6 77	13.6 99	20.8 113	36.0 82	17.9 118	6.34 111	6.89 104	7.74 119	17.7 105	33.5 117	17.1 112	2.92 77	4.33 93	1.37 12	20.7 93	37.3 116	15.6 72
Nguyen [33]	98.3	11.4 109	19.6 108	8.44 110	21.0 117	31.7 115	17.7 120	29.8 128	36.3 128	24.1 125	18.2 99	34.6 68	13.9 107	6.28 106	6.85 100	7.51 118	15.4 95	33.0 114	14.0 99	2.24 53	3.11 29	1.79 36	21.6 96	33.9 101	15.8 78
IAOF [50]	99.1	10.1 101	18.6 100	7.89 103	22.2 122	33.7 124	15.9 114	33.0 130	39.2 131	23.7 122	16.2 80	32.1 44	11.6 100	5.80 92	6.87 102	5.39 87	17.8 107	30.1 96	11.4 80	3.13 84	3.69 73	3.88 96	22.4 102	29.3 77	21.6 111
SPSA-learn [13]	99.5	11.3 108	19.4 106	8.52 111	20.9 115	34.2 125	16.2 117	26.5 118	33.9 121	22.4 118	22.5 121	39.9 105	18.9 120	5.59 85	6.41 85	5.94 94	17.8 107	31.4 109	18.3 116	2.11 43	3.11 29	1.37 12	24.3 113	35.1 107	19.7 104
Learning Flow [11]	100.1	8.28 73	17.0 72	5.75 63	12.3 86	28.5 102	7.79 81	18.4 78	28.7 81	8.29 81	22.2 119	40.6 110	17.3 116	8.52 120	10.3 127	7.04 114	19.9 120	35.1 124	16.8 111	3.11 83	4.59 102	3.59 91	26.9 118	40.0 120	20.9 110
HBpMotionGpu [43]	100.8	12.2 115	22.1 121	8.34 109	17.9 109	31.4 112	14.7 110	29.2 125	37.8 130	20.6 111	19.1 107	44.6 129	11.5 99	5.60 86	6.45 88	6.06 97	13.2 77	28.8 84	11.1 78	3.45 89	3.97 83	2.04 44	23.1 106	34.1 103	20.7 108
Modified CLG [34]	103.5	11.6 111	20.5 116	9.14 117	12.6 91	25.4 83	10.3 92	27.4 119	34.4 125	23.8 123	21.8 117	42.7 123	16.7 114	6.18 104	7.06 110	6.57 108	14.9 91	33.0 114	13.4 96	2.45 59	3.80 79	3.81 95	22.0 100	36.6 112	16.8 86
Black & Anandan [4]	105.1	10.5 105	18.0 92	8.14 107	21.4 119	32.8 120	16.1 116	26.1 117	32.3 118	21.8 116	20.8 113	38.9 101	16.1 113	6.29 108	7.41 117	5.62 91	17.1 102	31.2 107	13.7 97	4.06 108	5.11 111	2.37 59	23.1 106	34.1 103	15.7 76
GroupFlow [9]	105.9	11.5 110	21.2 119	8.71 114	20.5 113	33.0 121	15.7 113	23.6 112	31.6 106	20.1 109	16.2 80	34.5 64	11.3 97	8.86 121	9.84 126	6.50 107	18.2 111	30.3 97	17.4 113	3.82 101	5.05 110	6.55 113	21.1 95	28.8 75	22.4 114
Heeger++ [104]	106.5	11.7 112	18.3 94	9.04 116	23.3 124	37.0 130	17.4 119	21.0 95	29.1 85	11.1 92	27.0 126	40.5 109	24.4 125	5.69 90	6.33 80	5.80 92	25.9 129	37.5 129	26.3 129	2.48 62	3.88 80	2.20 50	37.6 128	46.0 129	41.6 130
HCIC-L [99]	106.7	14.6 122	21.1 117	9.67 118	42.7 131	36.7 129	46.6 131	21.8 105	30.5 96	17.9 107	21.1 116	35.1 74	18.6 119	6.42 113	6.58 92	7.35 117	17.8 107	28.6 83	16.6 109	12.8 128	14.3 129	15.3 126	16.8 72	25.7 65	12.7 55
2bit-BM-tele [98]	106.8	10.3 102	20.1 113	7.40 95	11.5 79	26.7 96	7.57 78	20.6 92	32.0 113	11.4 93	17.9 97	40.3 108	12.3 103	6.29 108	6.80 98	6.93 112	21.0 123	32.0 110	18.0 115	7.61 123	6.57 122	11.1 123	27.5 119	39.4 119	29.2 121
TI-DOFE [24]	109.1	14.6 122	21.1 117	11.7 125	22.4 123	31.6 113	19.6 123	28.6 123	31.2 101	26.3 127	26.3 125	36.0 82	25.1 126	6.43 114	7.34 115	6.67 111	19.1 117	33.8 118	18.9 118	2.88 75	3.15 34	2.82 69	25.7 115	36.6 112	22.3 113
BlockOverlap [61]	110.1	10.3 102	19.1 104	7.74 101	15.4 104	25.3 81	13.0 107	24.3 113	31.9 110	21.0 113	19.5 110	43.8 128	13.1 105	9.14 122	7.60 118	13.9 124	19.0 115	29.0 88	15.7 106	11.0 127	8.77 127	24.8 131	23.0 105	31.3 84	25.9 117
Horn & Schunck [3]	110.1	11.8 113	19.3 105	8.85 115	20.1 112	33.5 123	15.3 112	25.5 116	31.2 101	24.0 124	26.1 124	38.6 99	23.5 123	6.12 102	6.99 105	6.34 106	19.9 120	33.9 119	19.6 121	3.95 107	4.28 92	2.46 61	25.3 114	37.5 117	22.0 112
FFV1MT [106]	115.5	13.9 121	23.4 126	10.8 122	20.9 115	34.5 127	16.0 115	21.8 105	29.5 88	13.8 100	31.0 130	41.7 117	29.4 129	9.97 126	6.78 97	17.9 129	23.8 126	35.3 125	25.0 128	2.99 80	4.24 90	3.57 90	37.6 128	46.0 129	41.6 130
SILK [79]	116.5	13.2 119	22.4 122	10.2 120	20.5 113	32.2 119	17.7 120	29.0 124	34.1 123	23.4 121	22.0 118	40.8 112	17.3 116	6.28 106	7.17 112	7.12 116	21.7 124	35.8 127	19.6 121	5.44 117	3.45 62	10.8 122	29.3 121	40.6 123	26.7 119
Adaptive flow [45]	117.1	13.2 119	20.2 114	9.78 119	27.3 127	31.9 116	25.4 126	28.3 122	31.6 106	30.5 130	19.7 112	37.6 93	15.3 111	11.2 129	12.6 129	8.93 122	17.6 104	29.8 93	15.8 107	13.1 129	11.0 128	18.3 127	26.4 117	36.8 115	22.7 115
PGAM+LK [55]	118.0	14.9 124	22.8 124	14.1 129	25.5 125	31.3 111	26.6 127	21.9 107	26.4 69	22.2 117	26.0 123	39.1 103	24.0 124	9.61 124	6.49 90	14.1 125	24.3 127	35.0 123	23.0 126	6.19 120	6.24 121	7.26 117	34.3 126	40.4 121	40.8 129
SLK [47]	118.3	17.2 130	24.0 127	18.2 130	21.3 118	30.4 110	20.1 124	27.9 121	31.9 110	23.3 120	31.4 131	39.9 105	30.0 131	6.60 115	7.04 109	8.37 120	22.8 125	36.4 128	21.6 125	3.90 106	3.75 77	5.02 105	33.3 123	41.7 124	35.2 125
Pyramid LK [2]	124.7	17.0 129	20.4 115	19.4 131	31.7 128	32.1 118	32.5 128	32.9 129	34.8 126	29.9 129	29.4 128	35.3 77	29.9 130	31.7 130	35.5 130	29.8 130	29.9 130	32.3 111	28.0 130	9.01 125	7.93 126	18.9 128	39.9 130	45.4 128	39.0 127
FOLKI [16]	125.7	16.2 127	25.9 130	12.7 127	32.5 129	35.6 128	34.7 129	29.4 127	35.1 127	26.9 128	29.1 127	41.0 114	27.9 128	9.58 123	8.90 123	13.5 123	25.7 128	38.3 130	24.5 127	7.71 124	5.13 113	14.5 125	36.5 127	44.4 127	36.1 126
Periodicity [78]	129.2	18.0 131	30.5 131	12.3 126	34.0 130	41.4 131	35.6 130	36.5 131	36.5 129	35.2 131	29.7 129	46.6 131	26.8 127	51.8 131	56.5 131	45.5 131	36.7 131	42.4 131	37.1 131	5.99 119	6.16 120	19.3 129	40.1 131	51.1 131	40.4 128

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	T. Arici. Energy minimization based motion estimation using adaptive smoothness priors. Submitted to IEEE TIP 2011.
[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	D. Nguyen. Enhancing the sharpness of flow field using image-driven functions with occlusion-aware filter. Submitted to TIP 2011.
[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	A. Ayvaci, M. Raptis, and S. Soatto. Sparse occlusion detection with optical flow. Submitted to IJCV 2011.
[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	L. Chen, J. Wang, and Y. Wu. Decomposing and regularizing sparse/non-sparse components for motion field estimation. Submitted to PAMI 2013.
[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	M. Santoro, G. AlRegib, and Y. Altunbasak. Motion estimation using block overlap minimization. Submitted to 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	W. Dong, G. Shi, X. Hu, and Y. Ma. Nonlocal sparse and low-rank regularization for optical flow estimation. Submitted to IEEE TIP 2013.
[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] Correlation Flow	290	2	color	M. Drulea and S. Nedevschi. Motion estimation using the correlation transform. TIP 2013. Matlab code.
[76] TC/T-Flow	341	5	color	M. Stoll, S. Volz, and A. Bruhn. Joint trilateral filtering for multiframe optical flow. ICIP 2013.
[77] OFLAF	1530	2	color	T. Kim, H. Lee, and K. Lee. Optical flow via locally adaptive fusion of complementary data costs. ICCV 2013.
[78] Periodicity	8000	4	color	G. Khachaturov, S. Gonzalez-Brambila, and J. Gonzalez-Trejo. Periodicity-based computation of optical flow. Submitted to Computacion y Sistemas (CyS) 2013.
[79] SILK	572	2	gray	P. Zille, C. Xu, T. Corpetti, L. Shao. Observation models based on scale interactions for optical flow estimation. Submitted to IEEE TIP.
[80] 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.
[81] SuperFlow	178	2	color	Anonymous. Superpixel based optical flow estimation. ICCV 2013 submission 507.
[82] Aniso-Texture	300	2	color	Anonymous. Texture information-based optical flow estimation using an incremental multi-resolution approach. ITC-CSCC 2013 submission 267.
[83] Classic+CPF	640	2	gray	Z. Tu, R. Veltkamp, and N. van der Aa. A combined post-filtering method to improve accuracy of variational optical flow estimation. Submitted to Pattern Recognition 2013.
[84] S2D-Matching	1200	2	color	Anonymous. Locally affine sparse-to-dense matching for motion and occlusion estimation. ICCV 2013 submission 1479.
[85] AGIF+OF	438	2	gray	Z. Tu, R. Poppe, and R. Veltkamp. Adaptive guided image filter to warped interpolation image for variational optical flow computation. Submitted to Signal Processing 2015.
[86] DeepFlow	13	2	color	P. Weinzaepfel, J. Revaud, Z. Harchaoui, and C. Schmid. DeepFlow: large displacement optical flow with deep matching. ICCV 2013.
[87] NNF-Local	673	2	color	Z. Chen, H. Jin, Z. Lin, S. Cohen, and Y. Wu. Large displacement optical flow with nearest neighbor field. Submitted to PAMI 2014.
[88] 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.
[89] 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.
[90] 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.
[91] 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.
[92] 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.
[93] 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.
[94] FMOF	215	2	color	N. Jith, A. Ramakanth, and V. Babu. Optical flow estimation using approximate nearest neighbor field fusion. ICASSP 2014.
[95] TriFlow	150	2	color	TriFlow. Optical flow with geometric occlusion estimation and fusion of multiple frames. ECCV 2014 submission 914.
[96] ComponentFusion	6.5	2	color	Anonymous. Fast optical flow by component fusion. ECCV 2014 submission 941.
[97] 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.
[98] 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.
[99] HCIC-L	330	2	color	Anonymous. Globally-optimal image correspondence using a hierarchical graphical model. NIPS 2014 submission 114.
[100] TF+OM	600	2	color	R. Kennedy and C. Taylor. Optical flow with geometric occlusion estimation and fusion of multiple frames. EMMCVPR 2015.
[101] PH-Flow	800	2	color	J. Yang and H. Li. Dense, accurate optical flow estimation with piecewise parametric model. CVPR 2015.
[102] EpicFlow	16	2	color	J. Revaud, P. Weinzaepfel, Z. Harchaoui, and C. Schmid. EpicFlow: edge-preserving interpolation of correspondences for optical flow. CVPR 2015.
[103] NNF-EAC	380	2	color	Anonymous. Variational method for joint optical flow estimation and edge-aware image restoration. CVPR 2015 submission 2336.
[104] Heeger++	6600	5	gray	Anonymous. A context aware biologically inspired algorithm for optical flow (updated results). CVPR 2015 submission 2238.
[105] HBM-GC	330	2	color	A. Zheng and Y. Yuan. Motion estimation via hierarchical block matching and graph cut. Submitted to ICIP 2015.
[106] 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.
[107] 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.
[108] DeepFlow2	16	2	color	J. Revaud, P. Weinzaepfel, Z. Harchaoui, and C. Schmid. Deep convolutional matching. Submitted to IJCV, 2015.
[109] HAST	2667	2	color	Anonymous. Highly accurate optical flow estimation on superpixel tree. ICCV 2015 submission 2221.
[110] 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.
[111] SVFilterOh	1.56	2	color	Anonymous. Fast estimation of large displacement optical flow using PatchMatch and dominant motion patterns. CVPR 2016 submission 1788.
[112] FlowNetS+ft+v	0.5	2	color	Anonymous. Learning optical flow with convolutional neural networks. ICCV 2015 submission 235.
[113] 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.)
[114] 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.
[115] DF-Auto	70	2	color	N. Monzon, A. Salgado, and J. Sanchez. Regularization strategies for discontinuity-preserving optical flow methods. Submitted to TIP 2015.
[116] CPM-Flow	3	2	color	Anonymous. Efficient coarse-to-fine PatchMatch for large displacement optical flow. CVPR 2016 submission 241.
[117] 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.
[118] Steered-L1	804	2	color	Anonymous. Optical flow estimation via steered-L1 norm. Submitted to WSCG 2016.
[119] 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.
[120] PGM-C	5	2	color	Y. Li. Pyramidal gradient matching for optical flow estimation. Submitted to PAMI 2016.
[121] 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. Submitted to TIP 2016.
[122] FlowNet2	0.091	2	color	Anonymous. FlowNet 2.0: Evolution of optical flow estimation with deep networks. CVPR 2017 submission 900.
[123] S2F-IF	20	2	color	Anonymous. S2F-IF: Slow-to-fast interpolator flow. CVPR 2017 submission 765.
[124] 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.
[125] OAR-Flow	60	2	color	Anonymous. Order-adaptive regularisation for variational optical flow: global, local and in between. SSVM 2017 submission 20.
[126] AdaConv-v1	2.8	2	color	S. Niklaus, L. Mai, and F. Liu. (Interpolation results only.) Video frame interpolation via adaptive convolution. CVPR 2017.
[127] SepConv-v1	0.2	2	color	S. Niklaus, L. Mai, and F. Liu. (Interpolation results only.) Video frame interpolation via adaptive separable convolution. ICCV 2017.
[128] ProbFlowFields	37	2	color	A. Wannenwetsch, M. Keuper, and S. Roth. ProbFlow: joint optical flow and uncertainty estimation. ICCV 2017.
[129] UnFlow	0.12	2	color	Anonymous. UnFlow: Unsupervised learning of optical flow with a bidirectional census loss. Submitted to AAAI 2018.
[130] 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.
[131] Kuang	9.9	2	gray	F. Kuang. PatchMatch algorithms for motion estimation and stereo reconstruction. Master thesis, University of Stuttgart, 2017.

* 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.