Optical flow evaluation results: Average 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

Average 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]	5.7	2.69 3	7.56 4	1.98 3	1.97 4	7.01 4	1.59 4	2.18 2	5.36 3	1.53 4	1.87 4	9.14 8	1.06 4	2.28 2	2.94 1	1.57 2	2.39 5	6.78 2	2.15 9	2.00 25	3.36 18	1.62 20	0.99 1	2.16 3	0.57 2
NN-field [71]	10.8	2.89 8	8.13 16	2.11 5	2.10 6	7.15 9	1.77 15	2.27 4	5.59 5	1.61 8	1.58 1	8.52 7	0.79 1	2.35 4	3.05 5	1.60 3	1.89 1	5.20 1	1.37 1	2.43 56	3.70 54	1.95 43	1.01 2	2.25 4	0.53 1
OFLAF [77]	14.0	3.04 15	7.80 10	2.40 14	2.14 7	7.02 5	1.72 9	2.25 3	5.32 2	1.56 5	2.62 21	13.7 28	1.37 23	2.35 4	3.13 6	1.62 4	2.98 23	7.73 7	2.57 22	2.08 31	3.27 12	2.05 48	1.33 13	2.43 7	1.40 17
PMMST [114]	15.2	3.42 43	7.60 5	2.65 32	2.32 11	6.39 1	2.20 34	2.63 11	6.08 8	2.03 28	2.06 7	6.07 3	1.44 31	2.60 10	3.27 8	1.91 10	2.56 7	6.78 2	2.09 5	2.06 27	3.53 38	1.63 21	1.27 10	2.29 5	1.02 7
nLayers [57]	17.5	2.80 6	7.42 3	2.20 8	2.71 31	7.24 10	2.55 61	2.61 9	6.24 9	2.45 54	2.30 13	12.7 15	1.16 8	2.30 3	3.02 3	1.70 5	2.62 11	6.95 4	2.09 5	2.29 48	3.46 27	1.89 40	1.38 15	3.06 18	1.29 15
MDP-Flow2 [68]	20.2	3.23 31	7.93 13	2.60 24	1.92 2	6.64 2	1.52 1	2.46 7	5.91 7	1.56 5	3.05 47	15.8 60	1.51 41	2.77 24	3.50 18	2.16 28	2.86 19	8.58 18	2.70 33	2.00 25	3.50 35	1.59 18	1.28 11	2.67 12	0.89 4
ComponentFusion [96]	20.5	2.78 5	8.20 17	2.05 4	2.04 5	7.31 11	1.66 8	2.55 8	6.78 13	1.61 8	2.24 12	13.1 18	1.01 3	2.71 21	3.56 20	2.10 23	3.55 53	12.4 61	3.22 60	2.19 42	3.60 47	1.54 17	1.32 12	2.91 14	1.13 9
TC/T-Flow [76]	22.9	2.69 3	7.75 9	1.87 2	2.76 34	10.2 48	1.73 10	3.33 25	9.01 31	1.49 2	2.86 37	16.7 70	1.21 10	2.60 10	3.49 17	1.90 9	2.21 2	7.65 5	2.04 4	1.84 13	3.23 9	3.14 100	2.03 39	4.53 40	1.49 21
FC-2Layers-FF [74]	26.8	3.02 14	7.87 12	2.61 25	2.72 32	9.35 36	2.29 42	2.36 5	5.47 4	2.15 35	2.48 14	12.6 14	1.28 14	2.49 7	3.19 7	2.03 17	3.39 42	8.92 20	2.83 44	2.83 80	3.92 70	2.80 79	1.25 8	2.57 11	1.20 12
WLIF-Flow [93]	27.9	2.96 10	7.67 6	2.40 14	2.41 16	7.70 15	2.10 28	2.98 17	7.63 19	1.97 27	2.71 29	13.5 24	1.33 17	3.01 45	4.00 51	2.40 47	3.03 26	8.32 12	2.44 17	2.09 33	3.36 18	2.04 47	2.26 47	4.97 49	2.59 56
Layers++ [37]	28.3	3.11 18	8.22 20	2.79 44	2.43 19	7.02 5	2.24 37	2.43 6	5.77 6	2.18 38	2.13 9	9.71 10	1.15 7	2.35 4	3.02 3	1.96 11	3.81 61	11.4 46	3.22 60	2.74 75	4.01 75	2.35 62	1.45 16	3.05 17	1.79 31
HAST [109]	29.0	2.58 1	7.12 1	1.81 1	2.41 16	7.05 7	2.10 28	1.83 1	4.19 1	1.17 1	2.84 36	15.5 54	1.08 5	2.23 1	2.97 2	1.40 1	3.72 58	10.0 33	3.92 85	3.40 103	4.90 109	5.66 132	1.20 7	2.09 1	1.24 13
AGIF+OF [85]	30.0	3.06 16	8.20 17	2.55 22	3.17 60	10.6 55	2.46 55	3.46 30	8.97 30	2.24 41	2.61 19	13.7 28	1.33 17	2.63 15	3.46 15	2.11 24	2.88 21	8.34 14	2.35 13	2.10 35	3.56 42	2.09 50	1.80 30	3.68 30	2.24 42
Efficient-NL [60]	30.2	2.99 13	8.23 21	2.28 9	2.72 32	8.95 32	2.25 40	3.81 40	9.87 37	2.07 32	2.77 33	14.3 37	1.46 36	2.61 12	3.48 16	1.96 11	3.31 38	8.33 13	2.59 24	2.60 66	3.75 55	2.54 69	1.60 22	3.02 15	1.66 23
LME [70]	30.3	3.15 23	8.04 15	2.31 11	1.95 3	6.65 3	1.59 4	4.03 46	9.31 32	4.57 98	2.69 27	13.6 26	1.42 28	2.85 31	3.61 23	2.42 49	3.47 49	12.8 68	3.17 56	2.12 37	3.53 38	1.73 23	1.34 14	2.75 13	1.18 11
FESL [72]	30.3	2.96 10	7.70 7	2.54 20	3.26 71	10.4 52	2.56 62	3.25 23	8.39 23	2.17 36	2.56 16	13.2 19	1.31 16	2.57 9	3.40 11	2.12 26	2.60 9	7.65 5	2.30 11	2.64 71	4.22 84	2.47 66	1.75 28	3.49 27	1.71 26
ALD-Flow [66]	30.6	2.82 7	7.86 11	2.16 6	2.84 41	10.1 45	1.86 18	3.73 38	10.4 41	1.67 12	3.10 49	16.8 71	1.28 14	2.69 20	3.60 22	1.85 8	2.79 15	11.3 45	2.32 12	2.07 29	3.25 11	3.10 97	2.03 39	5.11 50	1.94 34
RNLOD-Flow [121]	31.7	2.66 2	7.33 2	2.17 7	2.53 27	9.46 38	1.86 18	3.94 44	10.7 47	1.95 24	2.50 15	13.5 24	1.21 10	2.68 18	3.62 25	2.05 19	2.99 24	8.59 19	2.75 37	3.00 90	4.54 96	3.25 105	1.48 18	3.24 21	1.76 30
IROF++ [58]	31.9	3.17 25	8.69 30	2.61 25	2.79 36	9.61 39	2.33 43	3.43 27	8.86 27	2.38 48	2.87 39	14.8 42	1.52 43	2.74 22	3.57 21	2.19 30	3.20 34	9.70 30	2.71 34	1.96 23	3.45 26	1.22 8	1.80 30	4.06 32	2.50 52
NNF-EAC [103]	32.4	3.31 35	8.21 19	2.68 34	2.19 9	7.49 13	1.76 13	2.73 13	6.62 12	1.70 14	3.18 54	15.8 60	1.64 53	2.87 34	3.66 28	2.24 32	3.02 25	8.07 10	2.59 24	2.19 42	3.48 31	1.74 24	2.85 64	6.52 66	3.12 68
ProFlow_ROB [147]	32.4	3.29 32	9.91 56	2.35 13	2.50 25	10.0 44	1.83 16	4.04 47	11.6 55	1.96 26	2.86 37	15.0 45	1.22 12	2.87 34	3.89 42	1.97 14	2.60 9	10.5 36	2.20 10	1.53 4	3.54 40	1.53 16	2.50 53	6.37 65	2.33 47
PH-Flow [101]	32.8	3.19 28	8.87 35	2.71 35	2.84 41	9.33 35	2.37 45	2.85 14	7.20 15	2.36 45	2.92 42	15.4 51	1.51 41	2.63 15	3.42 12	2.04 18	3.03 26	8.52 17	2.49 19	2.69 73	3.60 47	3.13 99	1.25 8	2.53 9	1.34 16
Classic+CPF [83]	33.3	3.14 21	8.60 27	2.63 29	3.03 57	10.6 55	2.33 43	3.66 34	9.58 33	2.20 39	2.61 19	14.1 33	1.34 20	2.68 18	3.53 19	2.21 31	2.85 18	7.95 9	2.38 14	2.44 58	3.49 33	2.90 90	1.67 26	3.40 24	2.43 50
Sparse-NonSparse [56]	35.0	3.14 21	8.75 32	2.76 42	3.02 55	10.6 55	2.43 50	3.45 29	8.96 28	2.36 45	2.66 24	13.7 28	1.42 28	2.85 31	3.75 36	2.33 37	3.28 37	9.40 26	2.73 35	2.42 55	3.31 14	2.69 74	1.47 17	3.07 19	1.66 23
TC-Flow [46]	36.3	2.91 9	8.00 14	2.34 12	2.18 8	8.77 27	1.52 1	3.84 42	10.7 47	1.49 2	3.13 50	16.6 69	1.46 36	2.78 25	3.73 35	1.96 11	3.08 29	11.4 46	2.66 28	1.94 21	3.43 23	3.20 104	3.06 69	7.04 71	4.08 93
3DFlow [135]	36.7	3.44 44	8.63 29	2.46 16	2.43 19	8.59 26	1.75 12	3.71 36	9.93 39	1.64 10	1.61 3	4.58 1	1.23 13	2.86 33	3.72 33	2.16 28	4.52 85	11.6 52	4.20 92	3.16 98	4.02 76	4.44 124	1.13 5	2.14 2	0.89 4
LSM [39]	37.5	3.12 19	8.62 28	2.75 41	3.00 53	10.5 54	2.44 52	3.43 27	8.85 26	2.35 44	2.66 24	13.6 26	1.44 31	2.82 27	3.68 29	2.36 40	3.38 41	9.41 27	2.81 42	2.69 73	3.52 36	2.84 83	1.59 21	3.38 23	1.80 32
Ramp [62]	38.3	3.18 27	8.83 34	2.73 38	2.89 46	10.1 45	2.44 52	3.27 24	8.43 24	2.38 48	2.74 31	14.2 34	1.46 36	2.82 27	3.69 32	2.29 35	3.37 40	9.31 24	2.93 48	2.62 69	3.38 21	3.19 103	1.54 20	3.21 20	2.24 42
SVFilterOh [111]	38.4	3.63 50	8.82 33	2.86 46	2.60 29	8.06 18	2.05 27	2.95 15	7.09 14	2.03 28	2.80 35	13.8 31	1.41 27	2.63 15	3.42 12	1.75 7	3.49 50	10.3 35	3.23 63	3.63 112	5.75 131	4.47 125	1.09 4	2.45 8	0.92 6
Correlation Flow [75]	38.4	3.38 41	8.40 23	2.64 30	2.23 10	7.54 14	1.56 3	5.14 69	13.1 68	1.60 7	2.09 8	8.15 6	1.35 22	3.12 53	4.09 58	2.34 38	4.01 72	11.5 50	4.00 87	2.59 65	3.61 49	3.00 95	1.49 19	3.04 16	1.42 19
ProbFlowFields [128]	39.2	4.18 70	12.4 85	3.40 76	2.43 19	8.16 20	2.19 33	3.65 33	9.72 35	2.86 69	2.22 10	9.42 9	1.42 28	3.01 45	3.96 48	2.36 40	2.73 14	10.9 38	2.51 20	1.89 19	3.39 22	1.82 29	2.59 56	6.21 63	2.75 59
PMF [73]	39.3	3.61 48	9.07 38	2.62 27	2.40 14	8.05 17	1.83 16	2.61 9	6.27 10	1.65 11	3.35 64	15.4 51	1.58 48	2.54 8	3.27 8	1.71 6	3.59 54	11.1 43	3.46 69	4.07 122	6.18 137	4.02 120	1.06 3	2.38 6	1.25 14
COFM [59]	39.5	3.17 25	9.90 55	2.46 16	2.41 16	8.34 23	1.92 21	3.77 39	10.5 42	2.54 57	2.71 29	14.9 44	1.19 9	3.08 50	3.92 46	3.25 92	3.83 64	10.9 38	3.15 55	2.20 45	3.35 16	2.91 92	1.62 25	2.56 10	2.09 38
FMOF [94]	40.7	3.12 19	8.23 21	2.73 38	3.25 68	10.7 62	2.52 59	3.01 18	7.61 18	2.20 39	2.56 16	13.4 22	1.33 17	2.75 23	3.61 23	2.24 32	3.66 56	8.50 16	2.78 40	2.62 69	3.84 63	3.27 107	2.66 61	5.69 54	1.95 36
JOF [141]	40.9	3.08 17	8.56 26	2.51 19	3.27 72	10.2 48	2.81 82	3.02 19	7.55 17	2.42 52	2.64 22	14.2 34	1.34 20	2.62 13	3.42 12	2.08 20	3.26 35	8.96 21	2.56 21	3.12 97	4.26 85	4.09 122	2.11 45	4.58 42	2.18 40
OAR-Flow [125]	41.6	3.37 39	9.87 54	2.67 33	4.22 93	12.8 89	2.87 84	4.95 65	13.4 71	2.66 61	3.23 56	16.4 68	1.37 23	2.83 29	3.82 39	1.97 14	2.49 6	10.9 38	1.87 3	1.52 3	2.82 1	1.86 34	1.85 33	4.35 37	1.68 25
Classic+NL [31]	42.0	3.20 30	8.72 31	2.81 45	3.02 55	10.6 55	2.44 52	3.46 30	8.84 25	2.38 48	2.78 34	14.3 37	1.46 36	2.83 29	3.68 29	2.31 36	3.40 43	9.09 23	2.76 39	2.87 82	3.82 62	2.86 87	1.67 26	3.53 28	2.26 46
TV-L1-MCT [64]	43.1	3.16 24	8.48 25	2.71 35	3.28 73	10.8 66	2.60 70	3.95 45	10.5 42	2.38 48	2.69 27	13.9 32	1.45 35	2.94 41	3.79 37	2.63 69	3.50 51	9.75 31	3.06 52	2.08 31	3.35 16	2.29 59	1.95 36	3.89 31	2.71 58
PWC-Net_ROB [148]	45.8	4.86 102	12.4 85	3.56 85	3.14 59	10.3 51	2.60 70	4.38 55	11.6 55	3.18 76	2.56 16	10.6 12	1.52 43	3.25 69	4.18 62	2.46 51	3.10 31	10.6 37	2.75 37	1.44 2	3.56 42	1.01 2	1.60 22	3.41 25	1.14 10
IIOF-NLDP [131]	46.5	3.65 51	9.81 53	2.56 23	2.79 36	9.36 37	2.00 23	4.28 53	11.3 53	1.69 13	2.02 6	7.52 5	1.38 26	3.36 76	4.52 91	2.40 47	3.82 62	11.2 44	3.67 78	2.07 29	3.79 59	1.88 38	2.91 66	5.30 53	4.17 94
SimpleFlow [49]	47.4	3.35 36	9.20 41	2.98 53	3.18 63	10.7 62	2.71 76	5.06 67	12.6 66	2.70 63	2.95 44	15.1 47	1.58 48	2.91 39	3.79 37	2.47 52	3.59 54	9.49 28	2.99 50	2.39 53	3.46 27	2.24 58	1.60 22	3.56 29	1.57 22
CostFilter [40]	47.6	3.84 55	9.64 49	3.06 55	2.55 28	8.09 19	2.03 25	2.69 12	6.47 11	1.88 20	3.66 75	16.8 71	1.88 65	2.62 13	3.34 10	1.99 16	4.05 73	11.0 42	3.65 77	4.16 124	7.18 144	4.66 127	1.16 6	3.36 22	0.87 3
2DHMM-SAS [92]	49.8	3.19 28	8.89 36	2.71 35	3.20 66	11.5 76	2.38 46	5.19 70	12.2 62	2.73 65	2.92 42	15.2 48	1.53 45	2.79 26	3.65 27	2.27 34	3.45 47	9.34 25	2.78 40	2.66 72	3.56 42	3.07 96	2.34 50	5.12 51	2.97 66
S2D-Matching [84]	50.8	3.36 37	9.66 50	2.86 46	3.19 65	11.1 70	2.46 55	4.86 64	12.9 67	2.47 55	2.67 26	13.2 19	1.44 31	2.87 34	3.72 33	2.38 43	3.45 47	9.76 32	2.95 49	3.05 91	3.79 59	3.30 109	1.95 36	4.16 35	3.00 67
FlowFields+ [130]	51.0	4.57 88	13.7 99	3.35 68	2.94 51	10.1 45	2.58 66	4.05 48	10.6 44	3.26 79	2.90 41	13.2 19	1.81 63	3.18 57	4.20 66	2.54 58	2.68 13	11.4 46	2.40 16	1.84 13	3.62 50	1.77 25	2.48 52	5.86 56	2.77 60
MLDP_OF [89]	51.0	4.13 66	10.3 63	3.60 86	2.34 12	7.70 15	1.88 20	4.23 52	10.9 50	1.87 19	2.74 31	14.6 41	1.37 23	3.10 51	3.91 45	2.48 56	3.40 43	9.00 22	3.79 82	3.46 105	4.20 82	5.55 131	2.31 48	4.64 44	1.98 37
MDP-Flow [26]	51.6	3.48 46	9.46 46	3.10 57	2.45 22	7.36 12	2.41 47	3.21 22	8.31 22	2.78 67	3.18 54	17.8 78	1.70 58	3.03 47	3.87 40	2.60 65	3.43 45	12.6 65	2.81 42	2.19 42	3.88 67	1.60 19	4.13 90	9.96 97	3.86 88
AggregFlow [97]	51.7	4.25 76	11.9 82	3.26 60	4.46 99	13.7 100	3.43 95	4.76 62	12.4 63	3.93 95	3.28 59	15.6 56	1.68 55	2.89 37	3.89 42	2.08 20	2.32 3	7.75 8	2.14 7	2.06 27	3.77 57	1.48 14	2.07 43	4.11 33	2.36 48
IROF-TV [53]	52.0	3.40 42	9.29 43	2.95 52	2.99 52	11.1 70	2.53 60	3.81 40	9.81 36	2.44 53	3.25 58	16.9 73	1.78 62	3.27 73	4.10 59	2.93 83	4.47 82	16.0 100	3.53 71	1.70 6	3.21 7	1.12 5	1.91 35	4.75 46	2.19 41
CombBMOF [113]	53.5	3.94 59	10.6 67	2.74 40	2.80 38	8.55 25	2.16 31	3.10 21	7.99 21	1.76 15	2.99 45	13.4 22	1.95 69	3.04 48	3.89 42	2.49 57	5.64 110	12.3 59	6.74 125	3.54 108	5.16 117	2.81 80	1.85 33	4.60 43	1.10 8
S2F-IF [123]	53.5	4.51 86	13.6 98	3.31 64	2.90 47	10.4 52	2.48 58	4.07 50	10.8 49	3.15 74	3.31 60	15.7 59	1.90 66	3.17 55	4.19 64	2.55 61	2.81 17	11.6 52	2.60 26	1.86 16	3.67 52	1.87 35	2.11 45	4.64 44	2.54 55
WRT [151]	53.7	3.74 53	9.34 44	2.48 18	3.37 78	10.2 48	2.58 66	6.80 94	15.3 86	2.24 41	1.58 1	5.01 2	1.09 6	2.89 37	3.68 29	2.35 39	5.52 108	12.0 56	4.21 94	2.30 49	3.85 64	2.34 61	3.20 72	4.91 47	4.21 95
FlowFields [110]	56.2	4.57 88	13.7 99	3.38 71	3.01 54	10.6 55	2.59 68	4.19 51	11.1 51	3.30 80	3.17 53	15.0 45	1.96 70	3.21 64	4.24 73	2.61 68	2.91 22	12.4 61	2.66 28	1.84 13	3.46 27	1.84 32	2.50 53	6.15 61	2.79 61
Sparse Occlusion [54]	57.5	3.62 49	9.12 39	2.90 48	2.92 49	9.08 33	2.56 62	4.49 59	11.8 60	2.11 34	3.14 51	15.8 60	1.57 47	3.26 70	4.22 69	2.36 40	3.52 52	10.9 38	2.66 28	5.10 140	6.32 138	3.15 101	2.02 38	4.92 48	1.71 26
NL-TV-NCC [25]	57.7	3.89 57	9.16 40	2.98 53	2.87 45	9.69 40	1.99 22	4.44 58	11.6 55	1.76 15	2.64 22	11.8 13	1.48 40	3.49 88	4.60 98	2.47 52	4.67 92	13.5 73	4.26 98	2.83 80	4.57 98	2.84 83	2.62 59	6.00 60	2.25 44
EPPM w/o HM [88]	58.2	4.25 76	11.1 71	3.13 58	2.36 13	8.35 24	1.76 13	3.72 37	10.2 40	1.81 17	3.24 57	14.5 40	1.94 68	3.16 54	3.94 47	2.82 78	4.78 96	12.9 69	4.32 99	3.64 114	4.54 96	5.73 133	1.76 29	4.11 33	1.94 34
OFH [38]	58.3	3.90 58	9.77 52	3.62 89	2.84 41	11.0 69	2.04 26	5.52 77	14.4 79	1.89 21	3.52 67	20.5 99	1.60 51	3.18 57	4.06 56	2.82 78	3.86 65	14.1 82	3.59 73	1.77 9	3.62 50	1.81 28	2.64 60	7.08 74	2.15 39
PGM-C [120]	58.6	4.62 93	14.0 104	3.39 73	3.29 75	12.3 82	2.70 75	4.39 57	11.7 58	3.43 83	4.00 83	19.8 88	2.15 75	3.19 59	4.23 70	2.54 58	2.79 15	11.9 55	2.45 18	1.83 11	3.21 7	1.83 30	2.31 48	5.87 57	1.82 33
Occlusion-TV-L1 [63]	60.5	3.59 47	9.61 47	2.64 30	2.93 50	10.6 55	2.41 47	6.16 85	15.2 84	2.70 63	3.32 62	17.0 74	1.68 55	3.38 78	4.44 84	2.82 78	3.10 31	13.2 72	2.68 31	2.17 38	3.52 36	1.46 12	4.63 103	11.1 112	3.53 77
Complementary OF [21]	61.1	4.44 82	11.2 74	4.04 98	2.51 26	9.77 42	1.74 11	3.93 43	10.6 44	2.04 30	3.87 79	18.8 80	2.19 79	3.17 55	4.00 51	2.92 82	4.64 90	13.8 79	3.64 76	2.17 38	3.36 18	2.51 67	3.08 70	7.04 71	3.65 81
Adaptive [20]	62.8	3.29 32	9.43 45	2.28 9	3.10 58	11.4 73	2.46 55	6.58 90	15.7 91	2.52 56	3.14 51	15.6 56	1.56 46	3.67 99	4.46 86	3.48 102	3.32 39	13.0 71	2.38 14	2.76 78	4.39 90	1.93 42	3.58 78	8.18 83	2.88 63
Aniso-Texture [82]	63.8	2.96 10	7.72 8	2.54 20	2.48 24	8.26 22	2.24 37	6.48 88	15.9 96	2.63 59	1.96 5	10.1 11	0.98 2	3.26 70	4.21 67	2.60 65	5.74 113	16.9 109	5.61 115	4.47 132	5.88 134	3.33 110	3.51 77	7.12 75	3.68 83
ACK-Prior [27]	64.1	4.19 72	9.27 42	3.60 86	2.40 14	8.21 21	1.65 7	3.40 26	8.96 28	1.84 18	2.87 39	14.4 39	1.44 31	3.36 76	4.15 60	3.07 87	6.35 121	16.1 102	4.90 109	4.21 127	4.80 103	6.03 135	3.29 74	5.99 59	2.82 62
CPM-Flow [116]	64.9	4.63 94	14.1 107	3.39 73	3.33 76	12.5 86	2.73 77	4.37 54	11.7 58	3.43 83	4.00 83	19.9 91	2.14 74	3.19 59	4.23 70	2.54 58	3.08 29	12.0 56	2.88 46	1.87 17	3.44 24	1.84 32	2.91 66	7.48 80	2.91 65
DPOF [18]	65.2	4.67 97	12.6 91	3.30 62	3.57 83	10.6 55	3.12 91	3.09 20	7.50 16	2.32 43	3.06 48	14.8 42	1.82 64	3.21 64	4.18 62	2.79 77	4.47 82	12.5 63	3.33 64	4.09 123	3.92 70	6.96 137	2.09 44	4.39 38	1.74 28
EpicFlow [102]	65.4	4.61 92	14.0 104	3.39 73	3.33 76	12.5 86	2.74 78	5.37 73	14.8 82	3.46 86	3.94 82	19.2 84	2.13 73	3.20 61	4.23 70	2.58 64	2.87 20	12.2 58	2.64 27	1.83 11	3.28 13	1.83 30	3.21 73	7.12 75	3.61 78
DeepFlow2 [108]	66.8	4.04 63	11.2 74	3.38 71	3.80 86	12.4 85	2.86 83	5.12 68	13.4 71	3.00 70	4.17 89	20.1 93	2.18 78	2.96 42	3.97 49	2.08 20	3.06 28	12.6 65	2.69 32	2.17 38	3.24 10	2.71 75	4.74 105	10.4 105	4.38 102
ROF-ND [107]	67.3	4.12 64	10.0 57	3.37 70	2.78 35	8.82 29	2.12 30	4.61 61	11.9 61	2.09 33	2.23 11	6.56 4	1.69 57	3.60 95	4.75 108	2.85 81	4.92 99	13.6 76	3.75 80	4.59 134	5.18 118	4.10 123	2.67 62	5.19 52	3.46 76
TCOF [69]	67.6	4.17 69	10.4 65	3.71 92	3.17 60	10.7 62	2.59 68	6.58 90	15.7 91	3.82 93	3.69 77	16.1 65	2.37 88	3.78 103	4.95 120	2.47 52	2.59 8	8.47 15	2.58 23	3.66 116	4.83 104	2.67 73	1.83 32	4.20 36	1.46 20
HBM-GC [105]	68.8	5.25 105	10.5 66	4.34 105	3.17 60	8.78 28	2.94 87	4.38 55	10.6 44	2.68 62	3.59 71	12.8 16	2.47 91	2.96 42	3.64 26	2.64 70	3.96 71	8.26 11	3.56 72	4.40 130	5.92 135	3.62 114	2.55 55	6.34 64	3.29 71
RFlow [90]	68.9	3.82 54	10.0 57	3.44 79	2.61 30	9.73 41	2.02 24	5.66 79	14.5 80	2.05 31	3.93 81	23.1 113	1.90 66	3.24 66	4.19 64	2.66 72	4.12 76	15.2 96	3.34 66	2.61 67	3.56 42	2.65 72	4.48 98	10.5 108	3.93 92
Steered-L1 [118]	70.3	3.30 34	8.44 24	2.91 49	1.89 1	7.14 8	1.60 6	3.61 32	9.91 38	1.89 21	3.45 65	19.4 87	1.64 53	3.42 81	4.30 76	3.39 95	5.18 103	14.5 85	4.37 102	5.09 139	5.05 113	10.1 141	5.56 113	10.2 103	6.24 119
DMF_ROB [140]	71.2	4.37 79	12.3 84	3.62 89	3.46 81	12.9 91	2.60 70	5.98 82	15.8 93	3.23 78	4.05 85	19.8 88	2.15 75	3.10 51	4.06 56	2.57 63	3.79 60	14.3 83	3.13 54	1.88 18	3.12 5	1.99 46	4.34 92	10.0 98	3.87 89
SRR-TVOF-NL [91]	71.3	4.47 84	10.9 69	3.32 66	4.04 90	13.2 95	2.90 85	4.81 63	12.5 64	3.15 74	3.33 63	15.3 49	1.61 52	3.24 66	4.03 55	2.70 74	3.94 69	11.8 54	3.33 64	4.16 124	5.21 121	3.44 113	2.06 42	3.48 26	2.42 49
ComplOF-FED-GPU [35]	71.6	4.28 78	11.3 76	3.70 91	3.25 68	13.0 92	2.16 31	4.06 49	11.2 52	1.95 24	3.91 80	19.2 84	2.01 71	3.20 61	4.15 60	2.64 70	4.61 88	16.1 102	3.90 84	2.98 88	3.77 57	3.69 115	2.85 64	7.44 79	2.53 54
FF++_ROB [146]	72.9	4.84 101	14.8 114	3.46 80	3.18 63	11.4 73	2.69 74	5.30 72	14.1 76	3.73 92	3.31 60	14.2 34	2.20 80	3.26 70	4.29 75	2.72 75	4.58 87	12.7 67	3.70 79	1.91 20	3.46 27	2.19 57	3.65 80	7.31 77	5.97 116
TF+OM [100]	74.8	3.97 60	10.2 60	2.94 51	2.91 48	9.12 34	2.57 65	5.22 71	11.5 54	6.92 107	3.59 71	16.1 65	2.28 85	3.20 61	3.97 49	3.11 88	4.70 94	14.5 85	4.32 99	3.06 93	4.84 106	2.71 75	3.93 85	8.79 88	4.32 100
Aniso. Huber-L1 [22]	75.3	3.71 52	10.1 59	3.08 56	4.36 98	13.0 92	3.77 99	6.92 95	15.3 86	3.60 89	3.54 68	15.9 63	2.04 72	3.38 78	4.45 85	2.47 52	3.88 66	12.9 69	2.74 36	3.37 102	4.36 88	2.85 86	3.16 71	7.52 81	2.90 64
DeepFlow [86]	76.5	4.49 85	11.7 79	4.14 100	4.26 94	12.8 89	3.36 93	5.96 81	14.2 78	5.10 99	4.89 103	23.1 113	2.67 94	2.98 44	4.00 51	2.11 24	3.26 35	13.5 73	2.84 45	2.09 33	3.10 3	2.77 77	5.83 115	11.4 114	5.45 114
Classic++ [32]	77.3	3.37 39	9.67 51	2.91 49	3.28 73	12.1 79	2.61 73	5.46 76	14.1 76	3.00 70	3.63 73	20.2 96	1.70 58	3.24 66	4.34 78	2.60 65	4.65 91	16.0 100	3.60 74	3.09 94	3.94 73	3.28 108	4.64 104	10.4 105	3.71 84
TV-L1-improved [17]	78.3	3.36 37	9.63 48	2.62 27	2.82 39	10.7 62	2.23 35	6.50 89	15.8 93	2.73 65	3.80 78	21.3 104	1.76 61	3.34 75	4.38 82	2.39 44	5.97 115	18.1 117	5.67 116	3.57 110	4.92 111	3.43 112	4.01 88	9.84 96	3.44 75
LocallyOriented [52]	80.4	4.54 87	12.8 93	3.27 61	4.73 104	14.8 107	3.73 98	7.77 102	18.3 110	3.44 85	3.56 69	15.6 56	2.22 81	3.46 85	4.47 87	2.69 73	3.15 33	10.2 34	3.19 58	2.61 67	4.20 82	2.52 68	4.39 95	8.52 85	5.23 110
SIOF [67]	80.7	4.23 74	10.2 60	3.31 64	3.97 88	14.5 105	2.97 88	7.81 104	16.4 99	7.48 109	4.82 99	20.1 93	2.96 97	3.54 91	4.49 88	3.12 89	4.31 78	13.5 73	4.13 90	2.36 52	3.59 46	1.68 22	3.46 76	7.39 78	3.37 73
LiteFlowNet [143]	82.1	6.29 112	16.5 120	4.45 107	3.68 84	10.8 66	3.13 92	5.43 74	13.7 74	3.60 89	3.57 70	12.8 16	2.25 84	3.85 110	4.78 110	3.61 105	4.37 80	12.5 63	3.63 75	2.55 62	4.51 95	1.52 15	4.05 89	7.05 73	5.16 106
Brox et al. [5]	83.4	4.44 82	12.4 85	4.22 103	3.72 85	13.5 99	3.06 89	4.97 66	13.3 70	3.11 72	4.58 95	22.0 107	2.37 88	3.79 105	4.60 98	4.33 126	3.91 68	17.0 110	3.45 68	2.22 46	3.79 59	1.19 6	4.62 102	10.0 98	3.38 74
TriangleFlow [30]	83.7	4.12 64	10.6 67	3.47 81	3.47 82	13.1 94	2.41 47	6.00 83	15.2 84	2.17 36	2.99 45	16.0 64	1.58 48	4.46 130	5.79 135	4.15 122	5.42 107	13.9 81	5.24 110	3.10 96	5.47 127	2.90 90	3.02 68	6.82 68	3.64 80
CRTflow [80]	84.1	4.18 70	11.8 81	3.20 59	3.22 67	10.8 66	2.43 50	6.20 86	15.5 89	2.63 59	4.21 90	22.0 107	2.24 82	3.32 74	4.34 78	2.44 50	7.43 128	19.3 123	8.15 131	2.55 62	4.09 78	2.59 71	4.60 101	11.2 113	4.45 103
OFRF [134]	85.2	4.77 100	11.6 77	4.03 97	8.72 123	15.3 112	8.51 126	8.49 115	16.7 101	7.32 108	4.55 94	15.3 49	3.16 104	2.92 40	3.87 40	2.13 27	3.76 59	9.69 29	3.22 60	2.98 88	4.50 94	4.04 121	4.59 100	5.76 55	8.61 127
BriefMatch [124]	86.1	3.44 44	9.01 37	2.77 43	2.85 44	9.93 43	2.23 35	2.97 16	7.65 20	1.94 23	3.64 74	20.1 93	1.75 60	4.10 121	4.90 118	5.82 136	7.95 130	17.8 113	8.08 130	4.73 136	5.20 119	12.2 143	7.88 132	12.0 118	13.7 138
Rannacher [23]	86.3	4.13 66	11.0 70	3.61 88	3.39 79	12.3 82	2.80 81	7.26 97	17.4 106	3.59 88	4.40 92	23.1 113	2.24 82	3.43 83	4.54 94	2.56 62	5.41 106	18.5 118	4.23 95	2.92 85	3.91 69	2.82 81	3.45 75	9.14 89	3.27 70
F-TV-L1 [15]	87.2	5.44 108	12.5 90	5.69 116	5.46 109	15.0 110	4.03 102	7.48 99	16.3 98	3.42 82	5.08 106	23.3 116	2.81 96	3.42 81	4.34 78	3.03 85	4.05 73	15.1 93	3.18 57	2.43 56	3.92 70	1.87 35	3.90 84	9.35 93	2.61 57
SuperFlow [81]	88.5	4.16 68	11.1 71	3.32 66	4.80 105	12.2 80	4.68 108	7.80 103	16.0 97	10.6 120	5.16 110	22.4 111	3.24 106	3.39 80	4.24 73	3.71 109	3.44 46	13.7 78	2.91 47	3.19 99	4.62 100	1.87 35	4.74 105	10.6 109	4.24 97
TriFlow [95]	88.5	4.73 99	12.4 85	3.49 83	4.03 89	12.5 86	3.70 97	8.18 112	17.2 104	10.4 119	3.50 66	15.4 51	2.32 87	3.43 83	4.21 67	3.42 96	3.90 67	12.3 59	3.76 81	7.86 145	5.72 130	16.2 145	2.80 63	5.89 58	2.50 52
Local-TV-L1 [65]	88.7	5.33 106	12.6 91	5.19 112	6.90 116	15.7 115	6.22 115	10.0 119	18.2 109	8.89 112	5.81 115	24.7 121	3.70 113	3.05 49	4.00 51	2.39 44	4.05 73	14.6 87	3.09 53	1.95 22	3.11 4	2.15 53	5.85 116	10.8 110	7.34 122
ContinualFlow_ROB [153]	88.8	7.36 121	17.7 125	5.46 113	5.94 111	12.2 80	5.98 113	8.16 111	18.3 110	7.89 110	5.11 107	19.3 86	3.18 105	4.15 123	5.04 123	3.68 107	5.65 111	15.1 93	6.17 122	1.72 7	3.34 15	1.11 4	2.34 50	4.48 39	2.25 44
DF-Auto [115]	88.8	5.04 104	13.7 99	3.30 62	6.51 113	14.1 104	6.09 114	8.14 108	16.5 100	10.2 118	5.06 105	21.3 104	3.10 103	3.74 101	4.91 119	3.25 92	2.67 12	11.4 46	2.14 7	3.36 101	5.23 123	1.45 11	4.45 97	9.18 90	4.28 99
CLG-TV [48]	89.4	4.00 61	10.3 63	3.40 76	4.33 97	12.3 82	4.08 103	6.78 92	15.5 89	3.64 91	4.07 86	17.7 77	2.39 90	3.79 105	4.86 113	3.23 91	4.48 84	16.5 107	3.80 83	3.55 109	4.65 101	2.89 89	4.00 87	10.1 101	3.18 69
CBF [12]	90.3	3.88 56	10.2 60	3.50 84	4.60 101	11.3 72	5.06 109	5.43 74	13.1 68	3.39 81	4.09 87	21.2 103	2.16 77	3.80 108	4.72 107	3.52 103	4.33 79	14.4 84	3.01 51	4.97 137	5.51 128	4.93 129	3.99 86	9.27 92	3.91 91
Bartels [41]	92.6	4.43 80	11.1 71	4.17 102	2.83 40	8.84 30	2.56 62	4.54 60	12.5 64	2.80 68	4.87 100	22.1 109	3.05 101	3.58 94	4.35 81	4.15 122	5.55 109	17.5 111	5.78 117	3.74 117	5.02 112	5.98 134	5.21 112	11.9 117	5.20 109
Fusion [6]	93.2	4.43 80	13.7 99	4.08 99	2.47 23	8.91 31	2.24 37	3.70 35	9.68 34	3.12 73	3.68 76	19.8 88	2.54 93	4.26 127	5.16 128	4.31 125	6.32 118	16.8 108	6.15 121	4.55 133	5.78 132	3.10 97	7.12 126	13.6 127	7.86 126
p-harmonic [29]	93.2	4.64 95	13.0 94	4.43 106	3.41 80	11.9 77	2.93 86	7.60 100	18.1 108	3.96 96	4.65 96	21.0 101	2.97 99	3.46 85	4.33 77	3.34 94	4.75 95	17.5 111	4.60 106	3.05 91	4.17 80	2.15 53	5.09 111	10.9 111	3.77 86
CNN-flow-warp+ref [117]	94.2	4.93 103	14.5 111	4.29 104	4.18 92	11.9 77	4.24 105	8.23 113	19.7 118	6.35 106	5.13 108	24.4 120	2.96 97	3.55 92	4.40 83	3.85 112	3.82 62	15.0 90	3.39 67	1.96 23	3.44 24	2.14 52	10.0 136	14.8 133	10.8 134
EAI-Flow [152]	94.9	7.40 122	16.3 118	6.04 119	5.29 108	15.0 110	4.27 106	6.28 87	15.0 83	5.22 102	4.99 104	19.1 83	3.49 109	3.55 92	4.55 95	3.01 84	4.69 93	14.8 88	4.25 97	4.16 124	4.83 104	2.55 70	2.61 58	6.99 70	2.48 51
Dynamic MRF [7]	95.2	4.58 90	12.4 85	4.14 100	3.25 68	13.9 101	2.27 41	6.02 84	16.8 102	2.36 45	4.39 91	22.6 112	2.51 92	3.61 96	4.55 95	3.46 98	6.81 123	22.2 133	6.78 127	2.41 54	3.48 31	3.69 115	9.26 134	17.8 137	10.2 131
SegOF [10]	96.5	5.85 110	13.5 97	3.98 96	7.40 117	14.9 108	8.13 124	8.55 116	17.3 105	9.01 113	6.50 120	18.1 79	5.14 122	3.90 114	4.53 92	4.81 130	6.57 122	21.7 131	6.81 128	1.65 5	3.49 33	1.08 3	3.71 81	9.23 91	3.63 79
FlowNetS+ft+v [112]	96.5	4.22 73	12.1 83	3.48 82	4.50 100	13.4 97	3.85 100	8.29 114	18.4 112	6.20 105	4.87 100	21.6 106	3.01 100	3.93 115	5.04 123	3.47 101	3.71 57	15.3 97	3.21 59	3.32 100	5.12 115	3.87 117	3.76 82	9.44 94	3.74 85
ResPWCR_ROB [145]	96.8	7.29 120	16.3 118	6.15 120	4.28 95	11.4 73	3.95 101	5.85 80	13.6 73	5.20 101	4.75 98	17.5 76	3.50 110	3.80 108	4.53 92	4.12 121	4.96 102	15.0 90	4.81 108	3.52 107	5.22 122	2.40 63	3.61 79	6.77 67	4.27 98
LDOF [28]	96.9	4.60 91	13.0 94	3.77 93	4.67 102	15.5 114	3.67 96	5.63 78	14.0 75	4.21 97	5.80 114	27.1 130	3.43 108	3.52 90	4.50 90	3.46 98	4.84 98	17.8 113	4.04 88	2.46 60	4.14 79	3.25 105	4.85 108	12.0 118	3.78 87
Second-order prior [8]	97.9	4.03 62	11.6 77	3.35 68	3.88 87	14.0 103	3.08 90	7.21 96	17.6 107	3.57 87	4.14 88	19.9 91	2.31 86	3.66 98	4.86 113	2.73 76	7.32 126	21.2 129	6.76 126	4.02 120	4.58 99	4.01 119	4.27 91	10.4 105	5.12 105
WOLF_ROB [149]	98.6	5.79 109	16.6 121	4.49 108	7.62 118	21.2 132	5.10 111	9.70 118	21.0 122	5.66 104	5.32 111	19.0 81	3.78 114	3.61 96	4.49 88	3.54 104	4.63 89	13.6 76	4.34 101	2.30 49	3.89 68	2.16 56	4.37 93	7.52 81	6.03 117
AugFNG_ROB [144]	99.5	8.29 128	19.2 129	5.66 115	7.67 119	16.0 117	8.01 123	10.1 120	20.5 120	11.0 122	5.13 108	15.5 54	3.64 112	4.11 122	4.97 121	3.93 113	4.45 81	15.1 93	4.20 92	2.27 47	4.37 89	1.23 9	3.80 83	6.87 69	4.34 101
FlowNet2 [122]	102.8	8.58 131	18.6 126	6.31 121	9.39 128	17.6 121	9.09 129	8.06 107	15.8 93	9.81 116	5.61 113	16.2 67	4.12 116	4.04 118	4.88 115	3.79 110	4.92 99	16.2 104	4.50 103	4.28 128	6.73 140	2.84 83	2.05 41	4.54 41	1.41 18
StereoFlow [44]	103.0	17.1 147	28.1 147	17.9 146	18.7 144	29.7 145	16.5 139	20.1 144	30.9 143	17.5 139	21.2 144	38.3 146	17.9 142	4.60 131	5.05 125	5.52 132	2.38 4	11.5 50	1.77 2	1.25 1	2.92 2	0.71 1	4.49 99	10.3 104	4.23 96
EPMNet [133]	103.9	8.37 130	18.8 128	6.44 123	9.35 127	18.4 123	8.78 128	7.42 98	14.7 81	8.61 111	5.98 117	20.4 98	4.27 118	4.04 118	4.88 115	3.79 110	4.92 99	16.2 104	4.50 103	3.65 115	6.14 136	2.42 65	2.60 57	6.15 61	1.74 28
Ad-TV-NDC [36]	104.6	8.36 129	14.0 104	11.1 139	12.9 135	19.9 129	12.8 135	14.4 131	23.1 124	12.1 125	7.40 123	20.6 100	6.33 123	3.47 87	4.66 103	2.39 44	3.95 70	13.8 79	3.51 70	2.48 61	3.75 55	2.05 48	9.75 135	12.1 120	16.7 142
LFNet_ROB [150]	105.6	7.69 123	19.8 130	5.72 117	4.70 103	13.3 96	4.13 104	8.15 110	20.0 119	5.42 103	4.73 97	17.1 75	3.42 107	4.15 123	5.10 127	4.05 117	5.28 105	18.0 115	4.64 107	2.87 82	4.74 102	1.98 45	4.92 109	11.4 114	5.01 104
Shiralkar [42]	107.3	4.64 95	14.1 107	3.94 94	4.29 96	16.9 119	2.77 79	7.75 101	18.8 114	3.19 77	5.54 112	25.0 123	3.56 111	3.51 89	4.55 95	3.04 86	7.41 127	20.1 127	6.41 123	3.76 118	4.35 87	5.28 130	6.56 122	14.4 132	5.30 112
Learning Flow [11]	107.8	4.23 74	11.7 79	3.41 78	4.16 91	15.3 112	3.42 94	6.78 92	16.9 103	3.83 94	6.41 119	25.3 124	4.25 117	4.66 133	6.01 140	4.00 116	6.33 120	20.7 128	5.30 111	3.09 94	4.84 106	2.91 92	7.08 125	15.0 134	5.27 111
StereoOF-V1MT [119]	108.3	4.71 98	14.1 107	3.95 95	5.10 107	20.3 131	2.78 80	7.98 106	20.7 121	2.57 58	4.48 93	21.1 102	2.79 95	4.20 126	5.29 130	4.10 119	6.85 125	22.3 134	6.42 124	2.45 59	4.17 80	3.15 101	10.5 137	18.4 140	10.5 132
IAOF2 [51]	109.5	5.38 107	13.7 99	4.50 109	5.95 112	14.6 106	5.61 112	8.80 117	18.8 114	9.40 114	12.2 133	23.8 119	13.1 137	3.86 111	4.89 117	3.12 89	5.21 104	14.9 89	4.54 105	4.33 129	5.15 116	3.93 118	4.39 95	8.57 86	3.87 89
TVL1_ROB [139]	110.7	11.3 136	19.8 130	13.0 141	12.9 135	19.6 128	13.7 137	17.4 138	27.8 137	18.0 140	12.6 135	28.9 132	11.8 135	3.71 100	4.78 110	3.46 98	4.21 77	18.0 115	3.99 86	1.79 10	3.54 40	1.21 7	7.58 130	13.9 130	8.92 129
Modified CLG [34]	111.5	7.17 119	17.1 124	6.47 124	6.85 115	14.9 108	7.48 119	14.0 127	24.8 128	15.7 135	8.35 126	27.3 131	6.36 124	3.96 116	4.99 122	4.08 118	4.54 86	19.3 123	4.15 91	2.33 51	3.86 66	2.40 63	6.00 117	13.8 129	5.40 113
GraphCuts [14]	112.2	6.25 111	14.3 110	5.53 114	8.60 122	20.1 130	6.61 117	7.91 105	15.4 88	10.9 121	4.88 102	19.0 81	3.05 101	3.78 103	4.71 105	3.94 114	8.74 135	16.4 106	5.39 113	4.04 121	4.87 108	4.85 128	6.35 120	12.2 121	6.05 118
2D-CLG [1]	112.5	10.1 133	22.6 138	7.59 129	9.84 130	16.9 119	11.1 134	16.9 137	28.2 138	18.8 143	14.1 137	31.1 136	13.1 137	3.86 111	4.62 101	4.53 127	5.98 116	21.2 129	5.97 119	1.76 8	3.14 6	1.46 12	6.29 119	12.9 126	5.81 115
Filter Flow [19]	112.6	6.48 113	14.6 112	4.96 110	5.73 110	15.7 115	5.07 110	10.1 120	18.6 113	14.3 131	9.04 128	23.3 116	7.80 128	3.98 117	4.71 105	4.21 124	5.86 114	15.0 90	5.41 114	4.98 138	6.87 141	2.78 78	4.82 107	8.66 87	3.65 81
SPSA-learn [13]	113.2	6.84 118	16.7 122	6.74 125	8.47 121	19.4 126	7.49 120	12.5 123	23.1 124	13.1 129	8.40 127	25.8 127	7.08 126	3.87 113	4.66 103	4.10 119	6.32 118	18.8 119	6.89 129	2.56 64	3.85 64	1.79 26	7.29 127	12.5 123	7.47 124
HBpMotionGpu [43]	114.6	6.57 115	15.0 115	5.17 111	8.29 120	18.0 122	8.29 125	14.1 128	26.5 131	13.2 130	6.12 118	25.3 124	3.94 115	3.79 105	4.62 101	3.97 115	4.80 97	15.7 98	4.11 89	4.40 130	5.20 119	2.87 88	6.28 118	11.7 116	7.31 121
IAOF [50]	115.5	6.49 114	14.6 112	6.42 122	9.22 126	18.5 124	7.94 122	16.4 136	27.4 135	13.0 128	8.22 124	22.2 110	7.73 127	3.77 102	4.76 109	3.42 96	6.84 124	18.8 119	4.23 95	3.59 111	4.46 92	2.83 82	7.51 129	10.1 101	10.6 133
GroupFlow [9]	116.0	8.00 125	18.6 126	8.09 131	11.1 133	23.7 137	10.3 132	12.6 124	25.6 129	12.8 127	5.84 116	20.3 97	4.39 119	4.69 134	5.81 136	3.67 106	9.29 136	22.4 135	10.1 138	2.11 36	3.99 74	2.29 59	5.75 114	10.0 98	7.39 123
Black & Anandan [4]	116.6	6.81 117	15.4 116	7.43 127	8.77 124	19.5 127	7.35 118	13.0 125	22.9 123	12.5 126	8.29 125	26.1 128	6.77 125	4.18 125	5.28 129	3.69 108	6.19 117	20.0 126	5.34 112	3.63 112	5.05 113	1.79 26	6.45 121	12.2 121	5.17 108
BlockOverlap [61]	119.8	6.67 116	13.1 96	5.87 118	6.62 114	13.9 101	6.53 116	10.6 122	19.5 117	10.1 117	6.97 122	24.9 122	5.13 121	4.38 128	4.61 100	6.37 139	7.47 129	15.7 98	6.05 120	6.23 141	6.41 139	13.0 144	6.92 124	9.60 95	12.2 136
Nguyen [33]	120.3	7.88 124	16.8 123	7.02 126	13.4 137	19.0 125	15.3 138	17.6 139	28.9 139	17.2 138	12.0 132	26.9 129	11.6 134	4.38 128	5.07 126	5.58 135	5.69 112	19.7 125	5.93 118	2.75 76	4.02 76	1.91 41	6.59 123	12.5 123	6.52 120
UnFlow [129]	121.2	14.6 145	25.8 143	9.09 135	9.40 129	16.8 118	9.89 131	14.2 129	26.9 132	11.2 123	10.0 129	25.4 126	8.67 130	5.43 140	5.90 137	6.72 140	8.64 133	24.0 137	9.41 136	3.51 106	4.90 109	1.37 10	4.37 93	12.6 125	3.33 72
2bit-BM-tele [98]	122.0	8.00 125	15.8 117	8.40 133	4.91 106	13.4 97	4.67 107	8.14 108	19.0 116	5.12 100	6.62 121	23.5 118	5.04 120	4.08 120	4.78 110	4.61 129	8.68 134	18.8 119	8.31 132	6.46 143	7.08 143	9.47 140	7.36 128	14.1 131	9.62 130
Horn & Schunck [3]	126.8	8.01 127	19.9 132	8.38 132	9.13 125	23.2 136	7.71 121	14.2 129	25.9 130	14.6 133	12.4 134	30.6 134	11.3 133	4.64 132	5.64 132	4.60 128	8.21 132	24.4 138	8.45 133	4.01 119	5.41 124	1.95 43	9.16 133	17.5 135	8.86 128
SILK [79]	128.2	9.34 132	20.4 133	10.5 138	10.4 131	21.9 133	10.3 132	16.0 135	27.5 136	14.5 132	10.3 130	29.0 133	8.54 129	4.81 135	5.65 133	5.56 134	9.41 137	25.4 140	8.74 134	2.79 79	3.68 53	4.62 126	10.9 138	17.8 137	12.3 137
Heeger++ [104]	129.8	11.9 139	21.8 136	8.08 130	12.5 134	29.7 145	9.42 130	14.8 132	27.1 133	9.68 115	14.3 138	31.0 135	12.7 136	4.98 137	5.74 134	4.97 131	17.5 145	34.1 146	18.4 145	2.75 76	5.44 125	2.15 53	12.3 140	18.8 141	14.8 140
TI-DOFE [24]	130.7	13.4 143	23.2 139	16.5 145	16.5 141	24.1 138	18.2 143	20.2 145	31.1 145	20.6 144	19.9 143	32.9 139	20.8 144	4.89 136	5.90 137	5.54 133	8.04 131	23.9 136	8.81 135	2.97 87	4.34 86	1.88 38	10.9 138	17.7 136	11.9 135
H+S_ROB [138]	132.1	13.0 141	27.1 146	9.66 136	13.4 137	24.8 140	13.4 136	18.7 143	30.9 143	18.3 142	25.8 147	35.7 143	26.4 146	7.08 145	8.13 145	9.10 144	14.6 143	31.3 145	16.3 143	2.17 38	4.44 91	2.11 51	15.1 144	19.9 143	14.2 139
HCIC-L [99]	134.9	15.7 146	22.0 137	10.1 137	31.5 147	26.6 143	41.0 147	14.8 132	23.1 124	16.8 137	18.4 142	34.4 141	18.2 143	5.94 141	6.35 141	6.35 138	10.6 140	19.2 122	11.4 140	18.7 147	17.8 147	19.2 146	4.93 110	8.34 84	5.16 106
SLK [47]	135.0	11.6 137	26.0 144	14.6 144	15.3 140	25.0 141	17.5 141	17.8 141	30.1 142	18.1 141	25.4 146	33.6 140	28.0 147	5.25 138	5.90 137	7.03 141	10.3 139	27.4 142	10.6 139	2.89 84	4.47 93	2.94 94	14.9 143	20.7 144	18.8 143
FFV1MT [106]	136.0	12.0 140	23.3 140	8.83 134	10.7 132	26.6 143	8.71 127	15.6 134	29.0 140	12.0 124	16.6 141	36.3 145	15.5 140	6.51 144	6.40 142	10.4 145	16.2 144	30.7 144	17.7 144	3.41 104	5.44 125	3.35 111	12.3 140	18.8 141	14.8 140
Adaptive flow [45]	137.9	13.2 142	20.8 134	14.0 143	17.1 143	22.0 134	17.9 142	18.1 142	27.1 133	22.8 146	11.8 131	31.1 136	10.5 131	6.35 143	7.13 144	6.25 137	9.87 138	21.8 132	9.44 137	12.6 146	11.4 146	20.0 147	7.75 131	13.6 127	7.73 125
PGAM+LK [55]	139.2	11.8 138	25.6 141	13.9 142	14.8 139	24.4 139	16.7 140	13.2 126	24.0 127	15.0 134	16.2 140	41.2 147	15.3 139	5.40 139	5.45 131	8.10 142	12.3 142	26.5 141	12.1 141	7.42 144	8.24 145	7.87 138	13.2 142	18.3 139	19.4 144
Periodicity [78]	140.1	11.2 135	27.0 145	7.46 128	16.6 142	29.8 147	18.2 143	25.3 147	31.2 147	24.9 147	12.7 136	35.7 143	11.1 132	31.7 147	41.4 147	25.1 147	23.8 147	41.5 147	23.8 147	2.92 85	5.62 129	6.90 136	18.6 146	33.1 147	22.3 145
FOLKI [16]	140.9	10.5 134	25.6 141	11.9 140	20.9 145	26.2 142	26.1 145	17.6 139	31.1 145	16.5 136	15.4 139	32.6 138	16.0 141	6.16 142	6.53 143	9.07 143	12.2 141	29.7 143	13.0 142	4.67 135	5.83 133	9.41 139	18.2 145	22.8 145	25.1 146
Pyramid LK [2]	143.8	13.9 144	20.9 135	21.4 147	24.1 146	23.1 135	30.2 146	20.9 146	29.5 141	21.9 145	22.2 145	34.6 142	25.0 145	18.7 146	23.1 146	20.2 146	21.2 146	24.5 139	21.0 146	6.41 142	7.02 142	10.8 142	25.6 147	31.5 146	34.5 147
AdaConv-v1 [126]	148.4	39.2 148	39.9 148	41.8 148	73.0 148	74.5 148	71.1 148	70.1 148	67.3 148	71.8 148	64.4 148	66.2 148	65.9 148	76.5 149	78.1 149	72.0 149	68.2 149	64.9 149	66.5 149	52.3 149	45.1 149	70.9 149	81.8 148	81.6 148	82.3 148
SepConv-v1 [127]	148.4	39.2 148	39.9 148	41.8 148	73.0 148	74.5 148	71.1 148	70.1 148	67.3 148	71.8 148	64.4 148	66.2 148	65.9 148	76.5 149	78.1 149	72.0 149	68.2 149	64.9 149	66.5 149	52.3 149	45.1 149	70.9 149	81.8 148	81.6 148	82.3 148
SuperSlomo [132]	148.4	39.2 148	39.9 148	41.8 148	73.0 148	74.5 148	71.1 148	70.1 148	67.3 148	71.8 148	64.4 148	66.2 148	65.9 148	76.5 149	78.1 149	72.0 149	68.2 149	64.9 149	66.5 149	52.3 149	45.1 149	70.9 149	81.8 148	81.6 148	82.3 148
FGIK [136]	148.4	39.2 148	39.9 148	41.8 148	73.0 148	74.5 148	71.1 148	70.1 148	67.3 148	71.8 148	64.4 148	66.2 148	65.9 148	76.5 149	78.1 149	72.0 149	68.2 149	64.9 149	66.5 149	52.3 149	45.1 149	70.9 149	81.8 148	81.6 148	82.3 148
CtxSyn [137]	148.4	39.2 148	39.9 148	41.8 148	73.0 148	74.5 148	71.1 148	70.1 148	67.3 148	71.8 148	64.4 148	66.2 148	65.9 148	76.5 149	78.1 149	72.0 149	68.2 149	64.9 149	66.5 149	52.3 149	45.1 149	70.9 149	81.8 148	81.6 148	82.3 148
CyclicGen [154]	148.4	39.2 148	39.9 148	41.8 148	73.0 148	74.5 148	71.1 148	70.1 148	67.3 148	71.8 148	64.4 148	66.2 148	65.9 148	76.5 149	78.1 149	72.0 149	68.2 149	64.9 149	66.5 149	52.3 149	45.1 149	70.9 149	81.8 148	81.6 148	82.3 148
CFRF [155]	148.4	39.2 148	39.9 148	41.8 148	73.0 148	74.5 148	71.1 148	70.1 148	67.3 148	71.8 148	64.4 148	66.2 148	65.9 148	76.5 149	78.1 149	72.0 149	68.2 149	64.9 149	66.5 149	52.3 149	45.1 149	70.9 149	81.8 148	81.6 148	82.3 148
AVG_FLOW_ROB [142]	152.4	62.1 155	56.6 155	61.5 155	99.9 155	96.7 155	99.9 155	81.2 155	81.9 155	80.3 155	65.8 155	68.9 155	67.4 155	68.4 148	75.2 148	67.5 148	62.4 148	55.3 148	59.6 148	31.5 148	28.0 148	29.3 148	86.1 155	96.7 155	87.2 155

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. IEEE TIP 26(8):4055-4067, 2017.
[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] 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.
[132] 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.
[133] EPMNet	0.061	2	color	Anonymous. EPM-convolution multilayer-network for optical flow estimation. ICME 2018 submission 1119.
[134] OFRF	90	2	color	T. Mai, M. Gouiffes, and S. Bouchafa. Optical Flow refinement using iterative propagation under color, proximity and flow reliability constraints. Submitted to Signal, Image and Video Processing 2017.
[135] 3DFlow	328	2	color	J. Chen, Z. Cai, J. Lai, and X. Xie. A filtering based framework for optical flow estimation. IEEE TCSVT 2018.
[136] FGIK	0.18	2	color	Anonymous. (Interpolation results only.) Learning flow-guided interpolation kernels for video frame synthesis. ECCV 2018 submission 433.
[137] CtxSyn	0.07	2	color	S. Niklaus and F. Liu. (Interpolation results only.) Context-aware synthesis for video frame interpolation. CVPR 2018.
[138] H+S_ROB	5	2	color	ROB 2018 baseline submission, based on: E. Meinhardt-Llopis, J. Sanchez, and D. Kondermann. Horn-Schunck optical flow with a multi-scale strategy. Image Processing On Line 3:151–172, 2013.
[139] TVL1_ROB	1	2	color	ROB 2018 baseline submission, based on: J. Sanchez, E. Meinhardt-Llopis, and G. Facciolo. TV-L1 optical flow estimation. Image Processing On Line 3:137-150, 2013.
[140] 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.
[141] 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.
[142] AVG_FLOW_ROB	N/A	2	N/A	Average flow field of ROB 2018 training set.
[143] 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.
[144] AugFNG_ROB	0.10	all	color	Anonymous. FusionNet and AugmentedFlowNet: Selective proxy ground truth for training on unlabeled images. ECCV 2018 submission 2834.
[145] ResPWCR_ROB	0.2	2	color	Anonymous. Learning optical flow with residual connections. ROB 2018 submission.
[146] 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.
[147] ProFlow_ROB	76	3	color	Anonymous. ProFlow: Learning to predict optical flow. BMVC 2018 submission 277.
[148] PWC-Net_ROB	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.
[149] WOLF_ROB	0.02	2	color	Anonymous. Reversed deep neural network for optical flow. ROB 2018 submission.
[150] LFNet_ROB	0.068	2	color	Anonymous. Learning a flow network. ROB 2018 submission.
[151] 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.
[152] 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.
[153] ContinualFlow_ROB	0.5	all	color	Anonymous. Continual Flow. ACCV 2018 submission 273.
[154] CyclicGen	0.088	2	color	Anonymous. (Interpolation results only.) Deep video frame interpolation using cyclic frame generation. AAAI 2019 submission 323.
[155] CFRF	0.128	2	color	Anonymous. (Interpolation results only.) Coarse-to-fine refinement framework for video frame interpolation. AAAI 2019 submission 4489.

* 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.