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]	4.8	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 2	9.14 5	1.06 4	2.28 2	2.94 1	1.57 2	2.39 5	6.78 2	2.15 9	2.00 18	3.36 16	1.62 13	0.99 1	2.16 2	0.57 2
NN-field [71]	9.5	2.89 8	8.13 16	2.11 5	2.10 6	7.15 9	1.77 14	2.27 4	5.59 5	1.61 8	1.58 1	8.52 4	0.79 1	2.35 4	3.05 5	1.60 3	1.89 1	5.20 1	1.37 1	2.43 44	3.70 47	1.95 34	1.01 2	2.25 3	0.53 1
OFLAF [77]	12.2	3.04 15	7.80 10	2.40 13	2.14 7	7.02 5	1.72 9	2.25 3	5.32 2	1.56 5	2.62 17	13.7 22	1.37 19	2.35 4	3.13 6	1.62 4	2.98 21	7.73 7	2.57 19	2.08 23	3.27 11	2.05 37	1.33 12	2.43 6	1.40 15
PMMST [114]	13.4	3.42 41	7.60 5	2.65 27	2.32 11	6.39 1	2.20 31	2.63 11	6.08 8	2.03 25	2.06 4	6.07 1	1.44 26	2.60 10	3.27 8	1.91 10	2.56 7	6.78 2	2.09 5	2.06 20	3.53 35	1.63 14	1.27 9	2.29 4	1.02 6
nLayers [57]	15.7	2.80 6	7.42 3	2.20 8	2.71 29	7.24 10	2.55 58	2.61 9	6.24 9	2.45 49	2.30 10	12.7 11	1.16 7	2.30 3	3.02 3	1.70 5	2.62 10	6.95 4	2.09 5	2.29 38	3.46 25	1.89 31	1.38 14	3.06 17	1.29 13
MDP-Flow2 [68]	17.8	3.23 30	7.93 13	2.60 19	1.92 2	6.64 2	1.52 1	2.46 7	5.91 7	1.56 5	3.05 40	15.8 49	1.51 36	2.77 23	3.50 17	2.16 25	2.86 17	8.58 18	2.70 30	2.00 18	3.50 32	1.59 11	1.28 10	2.67 11	0.89 4
ComponentFusion [96]	18.2	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 9	13.1 13	1.01 3	2.71 20	3.56 19	2.10 21	3.55 49	12.4 53	3.22 55	2.19 33	3.60 41	1.54 10	1.32 11	2.91 13	1.13 8
TC/T-Flow [76]	20.6	2.69 3	7.75 9	1.87 2	2.76 32	10.2 44	1.73 10	3.33 24	9.01 30	1.49 2	2.86 32	16.7 59	1.21 9	2.60 10	3.49 16	1.90 9	2.21 2	7.65 5	2.04 4	1.84 9	3.23 8	3.14 82	2.03 37	4.53 37	1.49 19
FC-2Layers-FF [74]	23.6	3.02 14	7.87 12	2.61 20	2.72 30	9.35 35	2.29 39	2.36 5	5.47 4	2.15 32	2.48 11	12.6 10	1.28 11	2.49 7	3.19 7	2.03 16	3.39 38	8.92 20	2.83 40	2.83 67	3.92 60	2.80 61	1.25 7	2.57 10	1.20 10
WLIF-Flow [93]	24.7	2.96 10	7.67 6	2.40 13	2.41 16	7.70 15	2.10 25	2.98 17	7.63 18	1.97 24	2.71 24	13.5 18	1.33 14	3.01 40	4.00 46	2.40 42	3.03 24	8.32 12	2.44 15	2.09 25	3.36 16	2.04 36	2.26 44	4.97 44	2.59 49
Layers++ [37]	25.1	3.11 17	8.22 20	2.79 39	2.43 19	7.02 5	2.24 34	2.43 6	5.77 6	2.18 35	2.13 6	9.71 7	1.15 6	2.35 4	3.02 3	1.96 11	3.81 55	11.4 41	3.22 55	2.74 62	4.01 65	2.35 47	1.45 15	3.05 16	1.79 28
HAST [109]	25.2	2.58 1	7.12 1	1.81 1	2.41 16	7.05 7	2.10 25	1.83 1	4.19 1	1.17 1	2.84 31	15.5 44	1.08 5	2.23 1	2.97 2	1.40 1	3.72 54	10.0 31	3.92 76	3.40 86	4.90 91	5.66 111	1.20 6	2.09 1	1.24 11
FESL [72]	26.8	2.96 10	7.70 7	2.54 16	3.26 65	10.4 45	2.56 59	3.25 22	8.39 22	2.17 33	2.56 13	13.2 14	1.31 13	2.57 9	3.40 11	2.12 24	2.60 9	7.65 5	2.30 10	2.64 58	4.22 73	2.47 49	1.75 26	3.49 25	1.71 24
AGIF+OF [85]	26.9	3.06 16	8.20 17	2.55 18	3.17 55	10.6 48	2.46 52	3.46 29	8.97 29	2.24 38	2.61 15	13.7 22	1.33 14	2.63 14	3.46 14	2.11 22	2.88 19	8.34 14	2.35 12	2.10 27	3.56 37	2.09 39	1.80 28	3.68 28	2.24 38
Efficient-NL [60]	27.0	2.99 13	8.23 21	2.28 9	2.72 30	8.95 31	2.25 37	3.81 38	9.87 36	2.07 29	2.77 28	14.3 29	1.46 31	2.61 12	3.48 15	1.96 11	3.31 34	8.33 13	2.59 21	2.60 53	3.75 48	2.54 52	1.60 21	3.02 14	1.66 21
LME [70]	27.0	3.15 22	8.04 15	2.31 11	1.95 3	6.65 3	1.59 4	4.03 44	9.31 31	4.57 88	2.69 22	13.6 20	1.42 23	2.85 30	3.61 22	2.42 43	3.47 45	12.8 58	3.17 51	2.12 29	3.53 35	1.73 16	1.34 13	2.75 12	1.18 9
ALD-Flow [66]	27.3	2.82 7	7.86 11	2.16 6	2.84 38	10.1 42	1.86 16	3.73 36	10.4 39	1.67 11	3.10 42	16.8 60	1.28 11	2.69 19	3.60 21	1.85 8	2.79 13	11.3 40	2.32 11	2.07 22	3.25 10	3.10 79	2.03 37	5.11 45	1.94 31
RNLOD-Flow [121]	28.1	2.66 2	7.33 2	2.17 7	2.53 25	9.46 36	1.86 16	3.94 42	10.7 44	1.95 22	2.50 12	13.5 18	1.21 9	2.68 17	3.62 24	2.05 18	2.99 22	8.59 19	2.75 34	3.00 75	4.54 80	3.25 87	1.48 17	3.24 20	1.76 27
NNF-EAC [103]	28.6	3.31 33	8.21 19	2.68 29	2.19 9	7.49 13	1.76 12	2.73 13	6.62 12	1.70 12	3.18 47	15.8 49	1.64 47	2.87 32	3.66 27	2.24 28	3.02 23	8.07 10	2.59 21	2.19 33	3.48 28	1.74 17	2.85 56	6.52 56	3.12 60
IROF++ [58]	28.7	3.17 24	8.69 28	2.61 20	2.79 34	9.61 37	2.33 40	3.43 26	8.86 26	2.38 44	2.87 33	14.8 34	1.52 38	2.74 21	3.57 20	2.19 26	3.20 31	9.70 28	2.71 31	1.96 16	3.45 24	1.22 5	1.80 28	4.06 30	2.50 45
PH-Flow [101]	29.1	3.19 27	8.87 33	2.71 30	2.84 38	9.33 34	2.37 42	2.85 14	7.20 15	2.36 41	2.92 35	15.4 41	1.51 36	2.63 14	3.42 12	2.04 17	3.03 24	8.52 17	2.49 17	2.69 60	3.60 41	3.13 81	1.25 7	2.53 8	1.34 14
Classic+CPF [83]	29.6	3.14 20	8.60 26	2.63 24	3.03 53	10.6 48	2.33 40	3.66 33	9.58 32	2.20 36	2.61 15	14.1 27	1.34 17	2.68 17	3.53 18	2.21 27	2.85 16	7.95 9	2.38 13	2.44 46	3.49 30	2.90 72	1.67 24	3.40 23	2.43 44
Sparse-NonSparse [56]	31.1	3.14 20	8.75 30	2.76 37	3.02 51	10.6 48	2.43 47	3.45 28	8.96 27	2.36 41	2.66 19	13.7 22	1.42 23	2.85 30	3.75 33	2.33 33	3.28 33	9.40 25	2.73 32	2.42 43	3.31 13	2.69 56	1.47 16	3.07 18	1.66 21
TC-Flow [46]	32.1	2.91 9	8.00 14	2.34 12	2.18 8	8.77 26	1.52 1	3.84 40	10.7 44	1.49 2	3.13 43	16.6 58	1.46 31	2.78 24	3.73 32	1.96 11	3.08 27	11.4 41	2.66 25	1.94 14	3.43 21	3.20 86	3.06 60	7.04 58	4.08 83
LSM [39]	33.4	3.12 18	8.62 27	2.75 36	3.00 49	10.5 47	2.44 49	3.43 26	8.85 25	2.35 40	2.66 19	13.6 20	1.44 26	2.82 26	3.68 28	2.36 35	3.38 37	9.41 26	2.81 38	2.69 60	3.52 33	2.84 65	1.59 20	3.38 22	1.80 29
SVFilterOh [111]	33.8	3.63 47	8.82 31	2.86 41	2.60 27	8.06 18	2.05 24	2.95 15	7.09 14	2.03 25	2.80 30	13.8 25	1.41 22	2.63 14	3.42 12	1.75 7	3.49 46	10.3 33	3.23 57	3.63 93	5.75 111	4.47 104	1.09 4	2.45 7	0.92 5
Correlation Flow [75]	33.9	3.38 39	8.40 23	2.64 25	2.23 10	7.54 14	1.56 3	5.14 63	13.1 62	1.60 7	2.09 5	8.15 3	1.35 18	3.12 47	4.09 52	2.34 34	4.01 65	11.5 44	4.00 77	2.59 52	3.61 43	3.00 77	1.49 18	3.04 15	1.42 17
Ramp [62]	34.4	3.18 26	8.83 32	2.73 33	2.89 43	10.1 42	2.44 49	3.27 23	8.43 23	2.38 44	2.74 26	14.2 28	1.46 31	2.82 26	3.69 30	2.29 31	3.37 36	9.31 23	2.93 44	2.62 56	3.38 19	3.19 85	1.54 19	3.21 19	2.24 38
PMF [73]	34.6	3.61 45	9.07 36	2.62 22	2.40 14	8.05 17	1.83 15	2.61 9	6.27 10	1.65 10	3.35 56	15.4 41	1.58 42	2.54 8	3.27 8	1.71 6	3.59 50	11.1 39	3.46 63	4.07 102	6.18 116	4.02 102	1.06 3	2.38 5	1.25 12
ProbFlowFields [127]	35.0	4.18 65	12.4 78	3.40 70	2.43 19	8.16 20	2.19 30	3.65 32	9.72 34	2.86 64	2.22 7	9.42 6	1.42 23	3.01 40	3.96 43	2.36 35	2.73 12	10.9 34	2.51 18	1.89 13	3.39 20	1.82 21	2.59 50	6.21 54	2.75 52
COFM [59]	35.2	3.17 24	9.90 51	2.46 15	2.41 16	8.34 23	1.92 19	3.77 37	10.5 40	2.54 52	2.71 24	14.9 36	1.19 8	3.08 45	3.92 41	3.25 81	3.83 57	10.9 34	3.15 50	2.20 36	3.35 14	2.91 74	1.62 23	2.56 9	2.09 35
FMOF [94]	36.2	3.12 18	8.23 21	2.73 33	3.25 62	10.7 55	2.52 56	3.01 18	7.61 17	2.20 36	2.56 13	13.4 16	1.33 14	2.75 22	3.61 22	2.24 28	3.66 52	8.50 16	2.78 36	2.62 56	3.84 55	3.27 89	2.66 53	5.69 48	1.95 33
OAR-Flow [125]	37.3	3.37 37	9.87 50	2.67 28	4.22 83	12.8 78	2.87 75	4.95 59	13.4 65	2.66 56	3.23 49	16.4 57	1.37 19	2.83 28	3.82 36	1.97 14	2.49 6	10.9 34	1.87 3	1.52 2	2.82 1	1.86 26	1.85 31	4.35 35	1.68 23
Classic+NL [31]	37.5	3.20 29	8.72 29	2.81 40	3.02 51	10.6 48	2.44 49	3.46 29	8.84 24	2.38 44	2.78 29	14.3 29	1.46 31	2.83 28	3.68 28	2.31 32	3.40 39	9.09 22	2.76 35	2.87 69	3.82 54	2.86 69	1.67 24	3.53 26	2.26 41
TV-L1-MCT [64]	38.5	3.16 23	8.48 25	2.71 30	3.28 66	10.8 59	2.60 65	3.95 43	10.5 40	2.38 44	2.69 22	13.9 26	1.45 30	2.94 36	3.79 34	2.63 60	3.50 47	9.75 29	3.06 48	2.08 23	3.35 14	2.29 45	1.95 34	3.89 29	2.71 51
CostFilter [40]	42.2	3.84 50	9.64 46	3.06 50	2.55 26	8.09 19	2.03 22	2.69 12	6.47 11	1.88 18	3.66 66	16.8 60	1.88 58	2.62 13	3.34 10	1.99 15	4.05 66	11.0 38	3.65 70	4.16 104	7.18 123	4.66 106	1.16 5	3.36 21	0.87 3
SimpleFlow [49]	42.2	3.35 34	9.20 39	2.98 48	3.18 58	10.7 55	2.71 68	5.06 61	12.6 60	2.70 58	2.95 37	15.1 38	1.58 42	2.91 35	3.79 34	2.47 45	3.59 50	9.49 27	2.99 46	2.39 41	3.46 25	2.24 44	1.60 21	3.56 27	1.57 20
2DHMM-SAS [92]	44.2	3.19 27	8.89 34	2.71 30	3.20 60	11.5 66	2.38 43	5.19 64	12.2 56	2.73 60	2.92 35	15.2 39	1.53 39	2.79 25	3.65 26	2.27 30	3.45 43	9.34 24	2.78 36	2.66 59	3.56 37	3.07 78	2.34 47	5.12 46	2.97 58
MLDP_OF [89]	45.3	4.13 61	10.3 58	3.60 78	2.34 12	7.70 15	1.88 18	4.23 48	10.9 47	1.87 17	2.74 26	14.6 33	1.37 19	3.10 46	3.91 40	2.48 49	3.40 39	9.00 21	3.79 73	3.46 88	4.20 71	5.55 110	2.31 45	4.64 40	1.98 34
S2D-Matching [84]	45.4	3.36 35	9.66 47	2.86 41	3.19 59	11.1 62	2.46 52	4.86 58	12.9 61	2.47 50	2.67 21	13.2 14	1.44 26	2.87 32	3.72 31	2.38 38	3.45 43	9.76 30	2.95 45	3.05 76	3.79 52	3.30 91	1.95 34	4.16 33	3.00 59
MDP-Flow [26]	45.5	3.48 43	9.46 43	3.10 52	2.45 21	7.36 12	2.41 44	3.21 21	8.31 21	2.78 62	3.18 47	17.8 65	1.70 52	3.03 42	3.87 37	2.60 56	3.43 41	12.6 56	2.81 38	2.19 33	3.88 58	1.60 12	4.13 76	9.96 81	3.86 79
AggregFlow [97]	46.2	4.25 71	11.9 76	3.26 55	4.46 88	13.7 87	3.43 85	4.76 56	12.4 57	3.93 85	3.28 52	15.6 45	1.68 49	2.89 34	3.89 38	2.08 19	2.32 3	7.75 8	2.14 7	2.06 20	3.77 50	1.48 9	2.07 41	4.11 31	2.36 42
CombBMOF [113]	46.4	3.94 54	10.6 62	2.74 35	2.80 35	8.55 25	2.16 28	3.10 20	7.99 20	1.76 13	2.99 38	13.4 16	1.95 62	3.04 43	3.89 38	2.49 50	5.64 92	12.3 51	6.74 106	3.54 89	5.16 98	2.81 62	1.85 31	4.60 39	1.10 7
IROF-TV [53]	46.7	3.40 40	9.29 41	2.95 47	2.99 48	11.1 62	2.53 57	3.81 38	9.81 35	2.44 48	3.25 51	16.9 62	1.78 56	3.27 64	4.10 53	2.93 73	4.47 72	16.0 84	3.53 65	1.70 4	3.21 6	1.12 3	1.91 33	4.75 42	2.19 37
S2F-IF [123]	48.0	4.51 80	13.6 90	3.31 59	2.90 44	10.4 45	2.48 55	4.07 46	10.8 46	3.15 69	3.31 53	15.7 48	1.90 59	3.17 49	4.19 57	2.55 53	2.81 15	11.6 46	2.60 23	1.86 11	3.67 45	1.87 27	2.11 43	4.64 40	2.54 48
FlowFields [110]	50.1	4.57 82	13.7 91	3.38 65	3.01 50	10.6 48	2.59 63	4.19 47	11.1 48	3.30 72	3.17 46	15.0 37	1.96 63	3.21 57	4.24 65	2.61 59	2.91 20	12.4 53	2.66 25	1.84 9	3.46 25	1.84 24	2.50 48	6.15 53	2.79 53
NL-TV-NCC [25]	50.3	3.89 52	9.16 38	2.98 48	2.87 42	9.69 38	1.99 20	4.44 52	11.6 51	1.76 13	2.64 18	11.8 9	1.48 35	3.49 78	4.60 85	2.47 45	4.67 79	13.5 63	4.26 84	2.83 67	4.57 82	2.84 65	2.62 51	6.00 52	2.25 40
Sparse Occlusion [54]	50.9	3.62 46	9.12 37	2.90 43	2.92 46	9.08 32	2.56 59	4.49 53	11.8 54	2.11 31	3.14 44	15.8 49	1.57 41	3.26 62	4.22 61	2.36 35	3.52 48	10.9 34	2.66 25	5.10 119	6.32 117	3.15 83	2.02 36	4.92 43	1.71 24
EPPM w/o HM [88]	51.2	4.25 71	11.1 66	3.13 53	2.36 13	8.35 24	1.76 12	3.72 35	10.2 38	1.81 15	3.24 50	14.5 32	1.94 61	3.16 48	3.94 42	2.82 68	4.78 82	12.9 59	4.32 85	3.64 95	4.54 80	5.73 112	1.76 27	4.11 31	1.94 31
OFH [38]	51.3	3.90 53	9.77 49	3.62 81	2.84 38	11.0 61	2.04 23	5.52 69	14.4 70	1.89 19	3.52 59	20.5 81	1.60 45	3.18 51	4.06 51	2.82 68	3.86 58	14.1 71	3.59 67	1.77 6	3.62 44	1.81 20	2.64 52	7.08 60	2.15 36
PGM-C [120]	52.0	4.62 86	14.0 95	3.39 67	3.29 68	12.3 71	2.70 67	4.39 51	11.7 52	3.43 75	4.00 74	19.8 72	2.15 68	3.19 52	4.23 62	2.54 51	2.79 13	11.9 48	2.45 16	1.83 7	3.21 6	1.83 22	2.31 45	5.87 49	1.82 30
Occlusion-TV-L1 [63]	53.0	3.59 44	9.61 44	2.64 25	2.93 47	10.6 48	2.41 44	6.16 75	15.2 73	2.70 58	3.32 54	17.0 63	1.68 49	3.38 68	4.44 75	2.82 68	3.10 29	13.2 62	2.68 28	2.17 30	3.52 33	1.46 7	4.63 85	11.1 95	3.53 68
Complementary OF [21]	53.9	4.44 76	11.2 69	4.04 88	2.51 24	9.77 40	1.74 11	3.93 41	10.6 42	2.04 27	3.87 70	18.8 67	2.19 71	3.17 49	4.00 46	2.92 72	4.64 77	13.8 68	3.64 69	2.17 30	3.36 16	2.51 50	3.08 61	7.04 58	3.65 72
Adaptive [20]	55.0	3.29 31	9.43 42	2.28 9	3.10 54	11.4 65	2.46 52	6.58 79	15.7 79	2.52 51	3.14 44	15.6 45	1.56 40	3.67 87	4.46 77	3.48 90	3.32 35	13.0 61	2.38 13	2.76 65	4.39 77	1.93 33	3.58 68	8.18 67	2.88 55
Aniso-Texture [82]	55.0	2.96 10	7.72 8	2.54 16	2.48 23	8.26 22	2.24 34	6.48 77	15.9 83	2.63 54	1.96 3	10.1 8	0.98 2	3.26 62	4.21 59	2.60 56	5.74 94	16.9 92	5.61 97	4.47 111	5.88 114	3.33 92	3.51 67	7.12 61	3.68 74
ACK-Prior [27]	55.7	4.19 67	9.27 40	3.60 78	2.40 14	8.21 21	1.65 7	3.40 25	8.96 27	1.84 16	2.87 33	14.4 31	1.44 26	3.36 67	4.15 54	3.07 76	6.35 102	16.1 86	4.90 91	4.21 106	4.80 86	6.03 114	3.29 64	5.99 51	2.82 54
CPM-Flow [116]	57.4	4.63 87	14.1 98	3.39 67	3.33 69	12.5 75	2.73 69	4.37 49	11.7 52	3.43 75	4.00 74	19.9 74	2.14 67	3.19 52	4.23 62	2.54 51	3.08 27	12.0 49	2.88 42	1.87 12	3.44 22	1.84 24	2.91 58	7.48 65	2.91 57
EpicFlow [102]	57.6	4.61 85	14.0 95	3.39 67	3.33 69	12.5 75	2.74 70	5.37 66	14.8 72	3.46 78	3.94 73	19.2 69	2.13 66	3.20 54	4.23 62	2.58 55	2.87 18	12.2 50	2.64 24	1.83 7	3.28 12	1.83 22	3.21 63	7.12 61	3.61 69
DPOF [18]	57.8	4.67 90	12.6 83	3.30 57	3.57 74	10.6 48	3.12 82	3.09 19	7.50 16	2.32 39	3.06 41	14.8 34	1.82 57	3.21 57	4.18 56	2.79 67	4.47 72	12.5 55	3.33 58	4.09 103	3.92 60	6.96 116	2.09 42	4.39 36	1.74 26
DeepFlow2 [108]	58.5	4.04 58	11.2 69	3.38 65	3.80 76	12.4 74	2.86 74	5.12 62	13.4 65	3.00 65	4.17 79	20.1 76	2.18 70	2.96 37	3.97 44	2.08 19	3.06 26	12.6 56	2.69 29	2.17 30	3.24 9	2.71 57	4.74 87	10.4 88	4.38 88
ROF-ND [107]	59.1	4.12 59	10.0 52	3.37 64	2.78 33	8.82 28	2.12 27	4.61 55	11.9 55	2.09 30	2.23 8	6.56 2	1.69 51	3.60 84	4.75 95	2.85 71	4.92 85	13.6 66	3.75 71	4.59 113	5.18 99	4.10 103	2.67 54	5.19 47	3.46 67
TCOF [69]	59.3	4.17 64	10.4 60	3.71 83	3.17 55	10.7 55	2.59 63	6.58 79	15.7 79	3.82 83	3.69 68	16.1 54	2.37 78	3.78 90	4.95 104	2.47 45	2.59 8	8.47 15	2.58 20	3.66 96	4.83 87	2.67 55	1.83 30	4.20 34	1.46 18
RFlow [90]	60.2	3.82 49	10.0 52	3.44 73	2.61 28	9.73 39	2.02 21	5.66 71	14.5 71	2.05 28	3.93 72	23.1 95	1.90 59	3.24 59	4.19 57	2.66 63	4.12 69	15.2 80	3.34 60	2.61 54	3.56 37	2.65 54	4.48 81	10.5 91	3.93 82
Steered-L1 [118]	60.8	3.30 32	8.44 24	2.91 44	1.89 1	7.14 8	1.60 6	3.61 31	9.91 37	1.89 19	3.45 57	19.4 71	1.64 47	3.42 71	4.30 67	3.39 84	5.18 87	14.5 73	4.37 87	5.09 118	5.05 94	10.1 120	5.56 94	10.2 86	6.24 101
HBM-GC [105]	61.1	5.25 95	10.5 61	4.34 95	3.17 55	8.78 27	2.94 78	4.38 50	10.6 42	2.68 57	3.59 62	12.8 12	2.47 81	2.96 37	3.64 25	2.64 61	3.96 64	8.26 11	3.56 66	4.40 109	5.92 115	3.62 96	2.55 49	6.34 55	3.29 63
ComplOF-FED-GPU [35]	62.9	4.28 73	11.3 71	3.70 82	3.25 62	13.0 80	2.16 28	4.06 45	11.2 49	1.95 22	3.91 71	19.2 69	2.01 64	3.20 54	4.15 54	2.64 61	4.61 76	16.1 86	3.90 75	2.98 74	3.77 50	3.69 97	2.85 56	7.44 64	2.53 47
SRR-TVOF-NL [91]	63.2	4.47 78	10.9 64	3.32 61	4.04 80	13.2 83	2.90 76	4.81 57	12.5 58	3.15 69	3.33 55	15.3 40	1.61 46	3.24 59	4.03 50	2.70 65	3.94 62	11.8 47	3.33 58	4.16 104	5.21 102	3.44 95	2.06 40	3.48 24	2.42 43
TF+OM [100]	65.1	3.97 55	10.2 55	2.94 46	2.91 45	9.12 33	2.57 62	5.22 65	11.5 50	6.92 93	3.59 62	16.1 54	2.28 75	3.20 54	3.97 44	3.11 77	4.70 80	14.5 73	4.32 85	3.06 78	4.84 88	2.71 57	3.93 72	8.79 72	4.32 87
Aniso. Huber-L1 [22]	65.8	3.71 48	10.1 54	3.08 51	4.36 87	13.0 80	3.77 89	6.92 83	15.3 75	3.60 81	3.54 60	15.9 52	2.04 65	3.38 68	4.45 76	2.47 45	3.88 59	12.9 59	2.74 33	3.37 85	4.36 76	2.85 68	3.16 62	7.52 66	2.90 56
DeepFlow [86]	67.0	4.49 79	11.7 73	4.14 90	4.26 84	12.8 78	3.36 83	5.96 72	14.2 69	5.10 89	4.89 90	23.1 95	2.67 84	2.98 39	4.00 46	2.11 22	3.26 32	13.5 63	2.84 41	2.09 25	3.10 3	2.77 59	5.83 96	11.4 97	5.45 98
Classic++ [32]	67.5	3.37 37	9.67 48	2.91 44	3.28 66	12.1 69	2.61 66	5.46 68	14.1 68	3.00 65	3.63 64	20.2 79	1.70 52	3.24 59	4.34 69	2.60 56	4.65 78	16.0 84	3.60 68	3.09 79	3.94 63	3.28 90	4.64 86	10.4 88	3.71 75
TV-L1-improved [17]	67.6	3.36 35	9.63 45	2.62 22	2.82 36	10.7 55	2.23 32	6.50 78	15.8 81	2.73 60	3.80 69	21.3 86	1.76 55	3.34 66	4.38 73	2.39 39	5.97 96	18.1 98	5.67 98	3.57 91	4.92 92	3.43 94	4.01 75	9.84 80	3.44 66
LocallyOriented [52]	70.2	4.54 81	12.8 85	3.27 56	4.73 92	14.8 94	3.73 88	7.77 89	18.3 96	3.44 77	3.56 61	15.6 45	2.22 72	3.46 75	4.47 78	2.69 64	3.15 30	10.2 32	3.19 53	2.61 54	4.20 71	2.52 51	4.39 78	8.52 69	5.23 94
SIOF [67]	70.5	4.23 69	10.2 55	3.31 59	3.97 78	14.5 92	2.97 79	7.81 91	16.4 86	7.48 94	4.82 86	20.1 76	2.96 87	3.54 81	4.49 79	3.12 78	4.31 70	13.5 63	4.13 80	2.36 40	3.59 40	1.68 15	3.46 66	7.39 63	3.37 64
TriangleFlow [30]	72.3	4.12 59	10.6 62	3.47 74	3.47 73	13.1 82	2.41 44	6.00 73	15.2 73	2.17 33	2.99 38	16.0 53	1.58 42	4.46 111	5.79 116	4.15 103	5.42 90	13.9 70	5.24 92	3.10 81	5.47 107	2.90 72	3.02 59	6.82 57	3.64 71
CRTflow [80]	73.0	4.18 65	11.8 75	3.20 54	3.22 61	10.8 59	2.43 47	6.20 76	15.5 77	2.63 54	4.21 80	22.0 89	2.24 73	3.32 65	4.34 69	2.44 44	7.43 109	19.3 104	8.15 112	2.55 50	4.09 67	2.59 53	4.60 83	11.2 96	4.45 89
Brox et al. [5]	73.1	4.44 76	12.4 78	4.22 93	3.72 75	13.5 86	3.06 80	4.97 60	13.3 64	3.11 67	4.58 84	22.0 89	2.37 78	3.79 92	4.60 85	4.33 107	3.91 61	17.0 93	3.45 62	2.22 37	3.79 52	1.19 4	4.62 84	10.0 82	3.38 65
BriefMatch [124]	74.5	3.44 42	9.01 35	2.77 38	2.85 41	9.93 41	2.23 32	2.97 16	7.65 19	1.94 21	3.64 65	20.1 76	1.75 54	4.10 105	4.90 102	5.82 117	7.95 111	17.8 96	8.08 111	4.73 115	5.20 100	12.2 122	7.88 112	12.0 100	13.7 118
Rannacher [23]	75.0	4.13 61	11.0 65	3.61 80	3.39 71	12.3 71	2.80 73	7.26 85	17.4 92	3.59 80	4.40 82	23.1 95	2.24 73	3.43 73	4.54 82	2.56 54	5.41 89	18.5 99	4.23 82	2.92 71	3.91 59	2.82 63	3.45 65	9.14 73	3.27 62
F-TV-L1 [15]	76.1	5.44 98	12.5 82	5.69 102	5.46 96	15.0 97	4.03 91	7.48 86	16.3 85	3.42 74	5.08 92	23.3 98	2.81 86	3.42 71	4.34 69	3.03 74	4.05 66	15.1 79	3.18 52	2.43 44	3.92 60	1.87 27	3.90 71	9.35 77	2.61 50
Local-TV-L1 [65]	76.8	5.33 96	12.6 83	5.19 100	6.90 102	15.7 100	6.22 100	10.0 102	18.2 95	8.89 95	5.81 98	24.7 103	3.70 97	3.05 44	4.00 46	2.39 39	4.05 66	14.6 75	3.09 49	1.95 15	3.11 4	2.15 41	5.85 97	10.8 93	7.34 104
SuperFlow [81]	76.8	4.16 63	11.1 66	3.32 61	4.80 93	12.2 70	4.68 95	7.80 90	16.0 84	10.6 103	5.16 94	22.4 93	3.24 94	3.39 70	4.24 65	3.71 94	3.44 42	13.7 67	2.91 43	3.19 82	4.62 84	1.87 27	4.74 87	10.6 92	4.24 85
DF-Auto [115]	77.0	5.04 94	13.7 91	3.30 57	6.51 99	14.1 91	6.09 99	8.14 95	16.5 87	10.2 101	5.06 91	21.3 86	3.10 93	3.74 88	4.91 103	3.25 81	2.67 11	11.4 41	2.14 7	3.36 84	5.23 103	1.45 6	4.45 80	9.18 74	4.28 86
TriFlow [95]	77.5	4.73 92	12.4 78	3.49 76	4.03 79	12.5 75	3.70 87	8.18 97	17.2 90	10.4 102	3.50 58	15.4 41	2.32 77	3.43 73	4.21 59	3.42 85	3.90 60	12.3 51	3.76 72	7.86 124	5.72 110	16.2 124	2.80 55	5.89 50	2.50 45
CLG-TV [48]	77.8	4.00 56	10.3 58	3.40 70	4.33 86	12.3 71	4.08 92	6.78 81	15.5 77	3.64 82	4.07 76	17.7 64	2.39 80	3.79 92	4.86 98	3.23 80	4.48 74	16.5 90	3.80 74	3.55 90	4.65 85	2.89 71	4.00 74	10.1 84	3.18 61
CBF [12]	79.1	3.88 51	10.2 55	3.50 77	4.60 90	11.3 64	5.06 96	5.43 67	13.1 62	3.39 73	4.09 77	21.2 85	2.16 69	3.80 95	4.72 94	3.52 91	4.33 71	14.4 72	3.01 47	4.97 116	5.51 108	4.93 108	3.99 73	9.27 76	3.91 81
Bartels [41]	80.5	4.43 74	11.1 66	4.17 92	2.83 37	8.84 29	2.56 59	4.54 54	12.5 58	2.80 63	4.87 87	22.1 91	3.05 91	3.58 83	4.35 72	4.15 103	5.55 91	17.5 94	5.78 99	3.74 97	5.02 93	5.98 113	5.21 93	11.9 99	5.20 93
Fusion [6]	80.8	4.43 74	13.7 91	4.08 89	2.47 22	8.91 30	2.24 34	3.70 34	9.68 33	3.12 68	3.68 67	19.8 72	2.54 83	4.26 108	5.16 109	4.31 106	6.32 99	16.8 91	6.15 103	4.55 112	5.78 112	3.10 79	7.12 107	13.6 108	7.86 108
p-harmonic [29]	81.2	4.64 88	13.0 86	4.43 96	3.41 72	11.9 67	2.93 77	7.60 87	18.1 94	3.96 86	4.65 85	21.0 83	2.97 89	3.46 75	4.33 68	3.34 83	4.75 81	17.5 94	4.60 90	3.05 76	4.17 69	2.15 41	5.09 92	10.9 94	3.77 77
CNN-flow-warp+ref [117]	82.2	4.93 93	14.5 102	4.29 94	4.18 82	11.9 67	4.24 93	8.23 98	19.7 103	6.35 92	5.13 93	24.4 102	2.96 87	3.55 82	4.40 74	3.85 96	3.82 56	15.0 77	3.39 61	1.96 16	3.44 22	2.14 40	10.0 116	14.8 113	10.8 114
Dynamic MRF [7]	82.9	4.58 83	12.4 78	4.14 90	3.25 62	13.9 88	2.27 38	6.02 74	16.8 88	2.36 41	4.39 81	22.6 94	2.51 82	3.61 85	4.55 83	3.46 87	6.81 104	22.2 114	6.78 108	2.41 42	3.48 28	3.69 97	9.26 114	17.8 117	10.2 111
SegOF [10]	83.5	5.85 99	13.5 89	3.98 87	7.40 103	14.9 95	8.13 108	8.55 100	17.3 91	9.01 96	6.50 102	18.1 66	5.14 104	3.90 99	4.53 81	4.81 111	6.57 103	21.7 112	6.81 109	1.65 3	3.49 30	1.08 2	3.71 69	9.23 75	3.63 70
FlowNetS+ft+v [112]	84.2	4.22 68	12.1 77	3.48 75	4.50 89	13.4 84	3.85 90	8.29 99	18.4 97	6.20 91	4.87 87	21.6 88	3.01 90	3.93 100	5.04 106	3.47 89	3.71 53	15.3 81	3.21 54	3.32 83	5.12 96	3.87 99	3.76 70	9.44 78	3.74 76
LDOF [28]	84.7	4.60 84	13.0 86	3.77 84	4.67 91	15.5 99	3.67 86	5.63 70	14.0 67	4.21 87	5.80 97	27.1 111	3.43 95	3.52 80	4.50 80	3.46 87	4.84 84	17.8 96	4.04 78	2.46 48	4.14 68	3.25 87	4.85 90	12.0 100	3.78 78
Second-order prior [8]	84.9	4.03 57	11.6 72	3.35 63	3.88 77	14.0 90	3.08 81	7.21 84	17.6 93	3.57 79	4.14 78	19.9 74	2.31 76	3.66 86	4.86 98	2.73 66	7.32 107	21.2 110	6.76 107	4.02 100	4.58 83	4.01 101	4.27 77	10.4 88	5.12 90
StereoFlow [44]	88.2	17.1 126	28.1 126	17.9 125	18.7 123	29.7 124	16.5 118	20.1 123	30.9 123	17.5 120	21.2 124	38.3 125	17.9 122	4.60 112	5.05 107	5.52 113	2.38 4	11.5 44	1.77 2	1.25 1	2.92 2	0.71 1	4.49 82	10.3 87	4.23 84
FlowNet2 [122]	88.4	8.58 113	18.6 111	6.31 104	9.39 110	17.6 104	9.09 111	8.06 94	15.8 81	9.81 99	5.61 96	16.2 56	4.12 99	4.04 103	4.88 100	3.79 95	4.92 85	16.2 88	4.50 88	4.28 107	6.73 119	2.84 65	2.05 39	4.54 38	1.41 16
Ad-TV-NDC [36]	89.9	8.36 112	14.0 95	11.1 119	12.9 116	19.9 110	12.8 116	14.4 112	23.1 106	12.1 106	7.40 105	20.6 82	6.33 105	3.47 77	4.66 90	2.39 39	3.95 63	13.8 68	3.51 64	2.48 49	3.75 48	2.05 37	9.75 115	12.1 102	16.7 121
Learning Flow [11]	92.9	4.23 69	11.7 73	3.41 72	4.16 81	15.3 98	3.42 84	6.78 81	16.9 89	3.83 84	6.41 101	25.3 106	4.25 100	4.66 114	6.01 120	4.00 99	6.33 101	20.7 109	5.30 93	3.09 79	4.84 88	2.91 74	7.08 106	15.0 114	5.27 95
Shiralkar [42]	93.0	4.64 88	14.1 98	3.94 85	4.29 85	16.9 102	2.77 71	7.75 88	18.8 99	3.19 71	5.54 95	25.0 105	3.56 96	3.51 79	4.55 83	3.04 75	7.41 108	20.1 108	6.41 104	3.76 98	4.35 75	5.28 109	6.56 103	14.4 112	5.30 96
StereoOF-V1MT [119]	93.7	4.71 91	14.1 98	3.95 86	5.10 95	20.3 112	2.78 72	7.98 93	20.7 104	2.57 53	4.48 83	21.1 84	2.79 85	4.20 107	5.29 111	4.10 101	6.85 106	22.3 115	6.42 105	2.45 47	4.17 69	3.15 83	10.5 117	18.4 120	10.5 112
IAOF2 [51]	94.3	5.38 97	13.7 91	4.50 97	5.95 98	14.6 93	5.61 98	8.80 101	18.8 99	9.40 97	12.2 114	23.8 101	13.1 117	3.86 96	4.89 101	3.12 78	5.21 88	14.9 76	4.54 89	4.33 108	5.15 97	3.93 100	4.39 78	8.57 70	3.87 80
Modified CLG [34]	95.8	7.17 107	17.1 110	6.47 106	6.85 101	14.9 95	7.48 104	14.0 109	24.8 110	15.7 116	8.35 108	27.3 112	6.36 106	3.96 101	4.99 105	4.08 100	4.54 75	19.3 104	4.15 81	2.33 39	3.86 57	2.40 48	6.00 98	13.8 110	5.40 97
2D-CLG [1]	96.0	10.1 115	22.6 119	7.59 111	9.84 111	16.9 102	11.1 115	16.9 118	28.2 118	18.8 122	14.1 117	31.1 116	13.1 117	3.86 96	4.62 88	4.53 108	5.98 97	21.2 110	5.97 101	1.76 5	3.14 5	1.46 7	6.29 100	12.9 107	5.81 99
GraphCuts [14]	96.7	6.25 100	14.3 101	5.53 101	8.60 106	20.1 111	6.61 102	7.91 92	15.4 76	10.9 104	4.88 89	19.0 68	3.05 91	3.78 90	4.71 92	3.94 97	8.74 115	16.4 89	5.39 95	4.04 101	4.87 90	4.85 107	6.35 101	12.2 103	6.05 100
Filter Flow [19]	96.8	6.48 101	14.6 103	4.96 98	5.73 97	15.7 100	5.07 97	10.1 103	18.6 98	14.3 112	9.04 110	23.3 98	7.80 110	3.98 102	4.71 92	4.21 105	5.86 95	15.0 77	5.41 96	4.98 117	6.87 120	2.78 60	4.82 89	8.66 71	3.65 72
SPSA-learn [13]	97.0	6.84 106	16.7 108	6.74 107	8.47 105	19.4 108	7.49 105	12.5 105	23.1 106	13.1 110	8.40 109	25.8 108	7.08 108	3.87 98	4.66 90	4.10 101	6.32 99	18.8 100	6.89 110	2.56 51	3.85 56	1.79 18	7.29 108	12.5 105	7.47 106
HBpMotionGpu [43]	98.5	6.57 103	15.0 105	5.17 99	8.29 104	18.0 105	8.29 109	14.1 110	26.5 113	13.2 111	6.12 100	25.3 106	3.94 98	3.79 92	4.62 88	3.97 98	4.80 83	15.7 82	4.11 79	4.40 109	5.20 100	2.87 70	6.28 99	11.7 98	7.31 103
GroupFlow [9]	99.0	8.00 109	18.6 111	8.09 113	11.1 114	23.7 117	10.3 113	12.6 106	25.6 111	12.8 108	5.84 99	20.3 80	4.39 101	4.69 115	5.81 117	3.67 92	9.29 116	22.4 116	10.1 118	2.11 28	3.99 64	2.29 45	5.75 95	10.0 82	7.39 105
IAOF [50]	99.1	6.49 102	14.6 103	6.42 105	9.22 109	18.5 106	7.94 107	16.4 117	27.4 116	13.0 109	8.22 106	22.2 92	7.73 109	3.77 89	4.76 96	3.42 85	6.84 105	18.8 100	4.23 82	3.59 92	4.46 78	2.83 64	7.51 110	10.1 84	10.6 113
Black & Anandan [4]	99.6	6.81 105	15.4 106	7.43 109	8.77 107	19.5 109	7.35 103	13.0 107	22.9 105	12.5 107	8.29 107	26.1 109	6.77 107	4.18 106	5.28 110	3.69 93	6.19 98	20.0 107	5.34 94	3.63 93	5.05 94	1.79 18	6.45 102	12.2 103	5.17 92
Nguyen [33]	102.7	7.88 108	16.8 109	7.02 108	13.4 117	19.0 107	15.3 117	17.6 119	28.9 119	17.2 119	12.0 113	26.9 110	11.6 115	4.38 109	5.07 108	5.58 116	5.69 93	19.7 106	5.93 100	2.75 63	4.02 66	1.91 32	6.59 104	12.5 105	6.52 102
BlockOverlap [61]	103.0	6.67 104	13.1 88	5.87 103	6.62 100	13.9 88	6.53 101	10.6 104	19.5 102	10.1 100	6.97 104	24.9 104	5.13 103	4.38 109	4.61 87	6.37 120	7.47 110	15.7 82	6.05 102	6.23 120	6.41 118	13.0 123	6.92 105	9.60 79	12.2 116
2bit-BM-tele [98]	105.2	8.00 109	15.8 107	8.40 115	4.91 94	13.4 84	4.67 94	8.14 95	19.0 101	5.12 90	6.62 103	23.5 100	5.04 102	4.08 104	4.78 97	4.61 110	8.68 114	18.8 100	8.31 113	6.46 122	7.08 122	9.47 119	7.36 109	14.1 111	9.62 110
Horn & Schunck [3]	108.4	8.01 111	19.9 113	8.38 114	9.13 108	23.2 116	7.71 106	14.2 111	25.9 112	14.6 114	12.4 115	30.6 114	11.3 114	4.64 113	5.64 113	4.60 109	8.21 113	24.4 118	8.45 114	4.01 99	5.41 104	1.95 34	9.16 113	17.5 115	8.86 109
SILK [79]	109.6	9.34 114	20.4 114	10.5 118	10.4 112	21.9 113	10.3 113	16.0 116	27.5 117	14.5 113	10.3 111	29.0 113	8.54 111	4.81 116	5.65 114	5.56 115	9.41 117	25.4 120	8.74 115	2.79 66	3.68 46	4.62 105	10.9 118	17.8 117	12.3 117
Heeger++ [104]	110.9	11.9 120	21.8 117	8.08 112	12.5 115	29.7 124	9.42 112	14.8 113	27.1 114	9.68 98	14.3 118	31.0 115	12.7 116	4.98 118	5.74 115	4.97 112	17.5 124	34.1 125	18.4 124	2.75 63	5.44 105	2.15 41	12.3 120	18.8 121	14.8 119
TI-DOFE [24]	111.8	13.4 123	23.2 120	16.5 124	16.5 120	24.1 118	18.2 122	20.2 124	31.1 124	20.6 123	19.9 123	32.9 119	20.8 124	4.89 117	5.90 118	5.54 114	8.04 112	23.9 117	8.81 116	2.97 73	4.34 74	1.88 30	10.9 118	17.7 116	11.9 115
HCIC-L [99]	115.3	15.7 125	22.0 118	10.1 117	31.5 126	26.6 122	41.0 126	14.8 113	23.1 106	16.8 118	18.4 122	34.4 121	18.2 123	5.94 121	6.35 121	6.35 119	10.6 120	19.2 103	11.4 120	18.7 126	17.8 126	19.2 125	4.93 91	8.34 68	5.16 91
SLK [47]	115.5	11.6 118	26.0 124	14.6 123	15.3 119	25.0 120	17.5 120	17.8 121	30.1 122	18.1 121	25.4 126	33.6 120	28.0 126	5.25 119	5.90 118	7.03 121	10.3 119	27.4 122	10.6 119	2.89 70	4.47 79	2.94 76	14.9 123	20.7 123	18.8 122
FFV1MT [106]	116.4	12.0 121	23.3 121	8.83 116	10.7 113	26.6 122	8.71 110	15.6 115	29.0 120	12.0 105	16.6 121	36.3 124	15.5 120	6.51 124	6.40 122	10.4 124	16.2 123	30.7 124	17.7 123	3.41 87	5.44 105	3.35 93	12.3 120	18.8 121	14.8 119
Adaptive flow [45]	118.0	13.2 122	20.8 115	14.0 122	17.1 122	22.0 114	17.9 121	18.1 122	27.1 114	22.8 125	11.8 112	31.1 116	10.5 112	6.35 123	7.13 124	6.25 118	9.87 118	21.8 113	9.44 117	12.6 125	11.4 125	20.0 126	7.75 111	13.6 108	7.73 107
PGAM+LK [55]	119.2	11.8 119	25.6 122	13.9 121	14.8 118	24.4 119	16.7 119	13.2 108	24.0 109	15.0 115	16.2 120	41.2 126	15.3 119	5.40 120	5.45 112	8.10 122	12.3 122	26.5 121	12.1 121	7.42 123	8.24 124	7.87 117	13.2 122	18.3 119	19.4 123
Periodicity [78]	119.9	11.2 117	27.0 125	7.46 110	16.6 121	29.8 126	18.2 122	25.3 126	31.2 126	24.9 126	12.7 116	35.7 123	11.1 113	31.7 126	41.4 126	25.1 126	23.8 126	41.5 126	23.8 126	2.92 71	5.62 109	6.90 115	18.6 125	33.1 126	22.3 124
FOLKI [16]	120.7	10.5 116	25.6 122	11.9 120	20.9 124	26.2 121	26.1 124	17.6 119	31.1 124	16.5 117	15.4 119	32.6 118	16.0 121	6.16 122	6.53 123	9.07 123	12.2 121	29.7 123	13.0 122	4.67 114	5.83 113	9.41 118	18.2 124	22.8 124	25.1 125
Pyramid LK [2]	123.2	13.9 124	20.9 116	21.4 126	24.1 125	23.1 115	30.2 125	20.9 125	29.5 121	21.9 124	22.2 125	34.6 122	25.0 125	18.7 125	23.1 125	20.2 125	21.2 125	24.5 119	21.0 125	6.41 121	7.02 121	10.8 121	25.6 126	31.5 125	34.5 126
AdaConv-v1 [126]	127.0	39.2 127	39.9 127	41.8 127	73.0 127	74.5 127	71.1 127	70.1 127	67.3 127	71.8 127	64.4 127	66.2 127	65.9 127	76.5 127	78.1 127	72.0 127	68.2 127	64.9 127	66.5 127	52.3 127	45.1 127	70.9 127	81.8 127	81.6 127	82.3 127

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	Anonymous. Flow Fields: Dense unregularized correspondence fields for highly accurate large displacement optical flow estimation. ICCV 2015 submission 744.
[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] ProbFlowFields	37	2	color	A. Wannenwetsch, M. Keuper, and S. Roth. ProbFlow: joint optical flow and uncertainty estimation. ICCV 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.