Optical flow evaluation results	Statistics:     Average   SD   R0.5   R1.0   R2.0   A50   A75   A95   Error type:   endpoint   angle   interpolation   normalized interpolation
Show images: below table   above table

R2.0 endpoint 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]	5.0	0.19 25	1.11 28	0.00 1	0.73 3	5.01 3	0.12 2	1.08 3	3.84 2	0.00 1	0.54 4	4.91 4	0.02 3	4.35 2	7.43 2	0.89 2	1.53 1	8.39 2	1.54 3	0.00 1	0.00 1	0.00 1	3.65 7	12.4 17	1.32 2
PMMST [114]	8.7	0.20 31	1.20 38	0.03 53	0.57 1	4.20 1	0.21 10	1.12 4	3.98 4	0.06 5	0.16 2	1.79 2	0.00 1	6.17 4	10.4 4	2.09 5	2.24 3	10.3 5	3.42 20	0.00 1	0.00 1	0.00 1	3.14 3	10.1 5	3.34 4
NN-field [71]	10.8	0.23 58	1.37 60	0.00 1	0.64 2	4.87 2	0.07 1	1.23 7	4.31 5	0.03 3	0.60 5	5.03 5	0.04 4	4.24 1	7.24 1	0.70 1	5.93 51	6.73 1	2.33 9	0.00 1	0.00 1	0.00 1	3.87 10	13.1 29	1.27 1
OFLAF [77]	11.8	0.20 31	1.21 39	0.00 1	0.92 9	5.66 7	0.25 15	1.22 5	4.32 6	0.12 10	1.03 16	8.42 19	0.22 26	7.31 9	12.4 9	2.79 9	3.20 13	11.6 7	3.15 17	0.00 1	0.00 1	0.00 1	3.66 8	9.73 4	7.15 20
Layers++ [37]	16.9	0.15 6	0.90 8	0.00 1	0.88 7	6.28 10	0.29 17	1.61 12	5.50 12	0.95 67	0.92 13	5.94 7	0.24 34	6.07 3	9.99 3	3.95 13	6.14 55	15.3 22	5.23 68	0.00 1	0.00 1	0.00 1	4.11 13	10.5 6	7.50 26
ComponentFusion [96]	16.9	0.17 14	1.02 18	0.03 53	0.93 10	6.31 11	0.23 14	1.48 10	5.26 10	0.22 16	0.68 6	6.90 11	0.05 5	10.5 39	17.2 41	7.34 40	3.34 15	15.8 26	3.80 29	0.00 1	0.00 1	0.00 1	3.81 9	11.2 11	6.45 15
TC/T-Flow [76]	17.1	0.11 2	0.67 2	0.00 1	1.63 46	8.48 32	0.45 24	2.21 18	7.45 19	0.16 13	1.20 39	10.2 47	0.16 7	9.34 31	14.9 28	6.04 29	1.76 2	9.86 4	1.36 1	0.00 1	0.00 1	0.00 1	4.64 21	12.6 20	7.19 21
MDP-Flow2 [68]	17.5	0.18 20	1.07 23	0.03 53	0.82 4	5.18 4	0.20 9	1.31 8	4.69 8	0.09 8	1.24 45	11.0 52	0.24 34	9.23 28	15.2 31	5.96 28	2.65 6	11.8 9	3.56 23	0.00 1	0.00 1	0.00 1	3.61 6	11.1 9	5.40 10
CombBMOF [113]	21.3	0.20 31	1.18 33	0.03 53	1.05 14	6.42 12	0.16 4	1.66 13	5.67 13	0.01 2	0.79 9	6.82 10	0.16 7	7.66 12	12.5 11	4.37 16	8.16 85	15.3 22	7.67 109	0.00 1	0.00 1	0.00 1	4.31 15	11.0 8	7.79 30
NNF-EAC [103]	21.8	0.17 14	1.03 19	0.01 31	1.01 11	5.78 9	0.38 23	1.81 15	6.09 15	0.13 11	1.27 49	11.4 54	0.24 34	8.50 21	14.2 24	5.00 22	4.85 34	12.2 11	4.55 46	0.00 1	0.00 1	0.00 1	4.77 25	13.1 29	7.37 23
WLIF-Flow [93]	22.0	0.20 31	1.21 39	0.01 31	0.91 8	5.77 8	0.26 16	2.30 21	7.50 20	0.38 30	1.10 25	8.71 21	0.25 45	8.40 20	14.0 20	4.94 21	4.91 36	13.0 13	3.79 28	0.00 1	0.00 1	0.00 1	4.98 30	12.6 20	8.37 42
MLDP_OF [89]	22.9	0.17 14	1.00 16	0.00 1	0.82 4	5.37 5	0.13 3	2.62 37	8.28 30	0.15 12	1.01 15	8.41 18	0.17 14	8.84 25	14.4 25	5.24 24	2.41 4	11.1 6	1.54 3	0.29 106	0.00 1	1.28 109	5.41 40	12.9 26	5.66 11
FC-2Layers-FF [74]	23.4	0.19 25	1.10 26	0.00 1	1.53 39	10.0 51	0.68 46	1.47 9	5.05 9	0.37 29	1.07 21	8.29 17	0.22 26	6.46 5	10.5 5	3.24 10	6.93 67	15.2 20	5.43 75	0.00 1	0.00 1	0.00 1	4.89 26	12.6 20	7.93 32
nLayers [57]	23.5	0.19 25	1.13 30	0.00 1	1.04 13	7.08 20	0.31 19	2.42 28	8.37 33	0.50 36	1.10 25	8.82 23	0.38 59	6.91 8	11.4 8	3.98 14	6.52 61	12.6 12	5.28 70	0.00 1	0.00 1	0.00 1	4.63 20	12.5 19	8.25 38
FlowFields+ [130]	24.1	0.15 6	0.88 6	0.01 31	1.38 31	8.90 38	0.68 46	2.23 20	7.99 23	0.44 34	0.70 7	6.60 8	0.20 20	12.0 48	19.4 51	7.65 43	2.62 5	16.5 40	1.82 7	0.00 1	0.00 1	0.00 1	5.64 45	18.0 55	5.92 12
Correlation Flow [75]	24.8	0.25 69	1.46 70	0.00 1	1.10 18	7.16 21	0.22 12	4.18 65	12.3 61	0.35 26	0.74 8	5.14 6	0.22 26	11.5 43	17.7 43	9.04 53	4.12 26	13.1 14	2.69 12	0.00 1	0.00 1	0.00 1	3.48 4	10.9 7	3.71 6
PH-Flow [101]	26.0	0.20 31	1.16 31	0.00 1	1.36 28	7.94 26	0.53 34	1.69 14	5.76 14	0.64 50	1.10 25	8.60 20	0.24 34	6.59 6	11.1 6	3.26 11	3.52 19	11.6 7	3.39 18	0.13 96	0.00 1	0.44 91	4.21 14	11.4 13	7.94 34
PMF [73]	26.6	0.20 31	1.19 35	0.03 53	1.06 17	6.51 13	0.18 6	1.50 11	5.33 11	0.09 8	1.26 48	9.04 29	0.23 29	7.32 10	12.4 9	1.91 3	5.47 41	16.3 32	4.67 50	0.09 90	0.00 1	0.25 86	3.51 5	9.50 2	6.99 18
IROF++ [58]	27.1	0.23 58	1.37 60	0.00 1	1.37 30	8.26 29	0.45 24	2.40 26	7.86 21	0.51 39	1.16 35	9.50 39	0.24 34	8.06 16	13.2 15	4.86 20	5.64 47	16.4 35	4.51 44	0.00 1	0.00 1	0.00 1	4.62 18	12.7 25	7.93 32
AGIF+OF [85]	27.2	0.21 43	1.25 49	0.00 1	1.48 37	8.75 35	0.37 21	2.50 33	8.15 27	0.38 30	1.14 30	8.88 24	0.23 29	7.56 11	12.5 11	4.30 15	6.71 64	15.2 20	4.99 61	0.00 1	0.00 1	0.00 1	5.07 33	13.0 28	8.68 49
HAST [109]	28.2	0.21 43	1.27 51	0.03 53	1.55 42	6.58 14	0.85 59	1.07 2	3.84 2	0.06 5	1.18 38	9.57 41	0.19 19	6.70 7	11.3 7	2.10 6	5.68 48	14.2 17	5.14 66	0.01 75	0.00 1	0.05 76	2.21 1	7.87 1	2.21 3
ProbFlowFields [128]	29.2	0.20 31	1.18 33	0.03 53	1.25 24	7.90 25	0.64 43	2.55 35	8.95 38	1.08 70	0.25 3	2.68 3	0.05 5	12.5 57	19.9 56	8.91 51	2.82 9	15.8 26	2.70 13	0.00 1	0.00 1	0.00 1	5.90 49	18.1 56	6.95 17
SVFilterOh [111]	30.5	0.22 51	1.31 53	0.05 71	1.14 20	6.84 17	0.30 18	2.13 17	7.39 17	0.69 54	0.86 10	7.24 12	0.16 7	8.17 17	13.8 18	2.18 7	6.69 62	15.3 22	4.47 43	0.27 105	0.00 1	0.74 96	2.89 2	9.59 3	3.97 8
CostFilter [40]	30.8	0.22 51	1.32 56	0.03 53	1.16 21	6.61 15	0.22 12	1.22 5	4.37 7	0.21 15	1.29 50	10.2 47	0.21 24	7.77 14	13.2 15	2.07 4	5.43 39	15.9 29	3.96 34	0.07 87	0.00 1	0.12 81	4.75 24	13.5 35	7.19 21
EPPM w/o HM [88]	30.9	0.21 43	1.25 49	0.03 53	1.05 14	6.95 19	0.19 7	2.42 28	8.24 29	0.08 7	1.00 14	7.81 15	0.21 24	7.69 13	13.0 13	2.55 8	6.45 60	18.5 59	4.04 35	0.43 113	0.00 1	0.76 97	3.98 12	11.1 9	7.10 19
TC-Flow [46]	30.9	0.13 3	0.77 3	0.00 1	1.38 31	8.10 28	0.47 27	2.97 49	10.0 49	0.34 24	1.36 56	10.5 50	0.25 45	11.2 42	18.1 45	7.49 41	3.36 16	17.1 47	1.78 6	0.00 1	0.00 1	0.00 1	6.35 55	17.8 54	10.0 67
ALD-Flow [66]	33.4	0.14 5	0.85 5	0.01 31	1.70 48	8.34 30	0.50 30	2.94 47	9.96 48	0.38 30	1.68 66	13.0 64	0.32 54	11.8 47	18.8 48	8.42 48	2.93 10	16.4 35	1.70 5	0.00 1	0.00 1	0.00 1	5.91 51	17.4 52	8.45 45
FlowFields [110]	33.4	0.16 10	0.97 12	0.02 48	1.54 41	9.90 50	0.72 49	2.38 24	8.48 35	0.58 48	1.03 16	9.05 30	0.31 52	12.5 57	20.3 61	8.76 50	3.16 12	18.0 58	3.08 16	0.00 1	0.00 1	0.00 1	6.22 53	19.0 61	6.76 16
RNLOD-Flow [121]	34.2	0.17 14	1.03 19	0.00 1	1.50 38	9.63 45	0.56 37	3.15 52	10.1 51	0.56 47	1.14 30	9.02 28	0.20 20	9.73 35	15.7 35	6.54 34	5.43 39	14.7 19	4.56 48	0.06 85	0.00 1	0.34 87	4.41 16	11.3 12	7.56 27
COFM [59]	34.2	0.28 77	1.64 77	0.06 76	1.31 27	7.81 23	0.57 38	3.57 57	12.0 59	1.10 72	0.91 12	7.78 14	0.16 7	11.7 46	18.5 47	10.3 68	4.05 24	13.7 16	4.28 39	0.00 1	0.00 1	0.00 1	3.96 11	11.5 14	6.40 14
Sparse-NonSparse [56]	35.3	0.22 51	1.31 53	0.00 1	1.87 58	11.4 60	0.80 55	2.47 32	8.05 25	0.52 41	1.15 34	8.89 25	0.24 34	9.37 32	15.3 32	5.94 27	7.18 69	16.3 32	5.47 77	0.00 1	0.00 1	0.00 1	5.08 34	13.1 29	8.42 43
S2F-IF [123]	35.3	0.18 20	1.07 23	0.02 48	1.53 39	10.0 51	0.72 49	2.37 23	8.45 34	0.54 44	1.21 40	9.59 42	0.35 55	12.7 61	20.3 61	9.20 57	3.41 17	17.7 55	3.70 26	0.00 1	0.00 1	0.00 1	5.36 39	16.5 48	6.13 13
LSM [39]	35.5	0.21 43	1.23 44	0.00 1	1.88 60	11.5 61	0.82 57	2.45 30	8.04 24	0.52 41	1.12 28	9.06 31	0.23 29	9.27 30	15.1 30	6.05 30	7.21 71	16.5 40	5.47 77	0.00 1	0.00 1	0.00 1	5.29 38	13.8 37	8.49 47
LME [70]	35.7	0.24 62	1.40 64	0.04 68	0.84 6	5.51 6	0.21 10	3.70 58	8.78 36	5.39 96	1.38 57	11.0 52	0.37 57	9.52 34	15.3 32	7.58 42	3.73 21	16.9 45	4.43 41	0.00 1	0.00 1	0.00 1	4.62 18	12.6 20	7.77 29
FMOF [94]	35.8	0.20 31	1.19 35	0.00 1	1.61 45	9.42 43	0.53 34	2.03 16	6.86 16	0.22 16	1.04 18	8.71 21	0.16 7	8.59 22	14.0 20	4.44 17	7.80 79	16.2 30	5.73 84	0.09 90	0.00 1	0.81 99	5.75 47	14.6 42	8.44 44
HBM-GC [105]	36.2	0.29 79	1.72 82	0.03 53	1.36 28	8.81 36	0.71 48	2.92 45	10.0 49	0.79 55	1.21 40	8.97 27	0.37 57	8.90 26	14.6 26	5.72 26	5.58 45	9.50 3	3.51 22	0.00 1	0.00 1	0.00 1	5.23 37	15.1 43	8.28 39
MDP-Flow [26]	36.3	0.13 3	0.78 4	0.00 1	1.05 14	6.71 16	0.64 43	2.31 22	8.09 26	1.26 78	1.35 55	12.5 63	0.28 48	10.4 38	16.8 40	7.29 38	5.39 38	16.9 45	4.89 57	0.00 1	0.00 1	0.00 1	8.69 82	21.5 77	12.1 80
FESL [72]	36.4	0.23 58	1.35 59	0.00 1	1.71 50	9.38 42	0.54 36	2.22 19	7.40 18	0.31 19	1.08 22	9.18 33	0.16 7	7.97 15	13.0 13	4.61 18	7.68 76	16.5 40	5.87 87	0.09 90	0.00 1	0.17 84	4.96 29	12.4 17	8.31 40
NL-TV-NCC [25]	37.2	0.24 62	1.43 68	0.01 31	1.43 34	9.86 49	0.16 4	3.10 51	10.1 51	0.20 14	1.13 29	9.56 40	0.16 7	11.5 43	18.3 46	7.31 39	8.51 88	20.7 84	4.68 51	0.00 1	0.00 1	0.00 1	5.59 44	16.1 46	5.10 9
DPOF [18]	37.4	0.17 14	0.99 14	0.00 1	2.06 69	10.3 54	0.92 65	0.99 1	3.51 1	0.05 4	1.08 22	9.87 45	0.17 14	8.25 19	13.8 18	3.72 12	9.58 104	18.7 61	5.78 86	1.06 119	0.00 1	2.93 117	4.41 16	13.4 34	3.94 7
Classic+NL [31]	38.0	0.23 58	1.34 58	0.01 31	1.93 64	11.7 62	0.80 55	2.57 36	8.35 31	0.58 48	1.22 42	9.29 36	0.24 34	8.66 24	14.1 22	5.48 25	7.52 74	16.3 32	5.42 73	0.00 1	0.00 1	0.00 1	5.06 32	12.9 26	8.47 46
Classic+CPF [83]	39.6	0.21 43	1.23 44	0.01 31	1.47 36	8.95 39	0.37 21	2.73 39	8.84 37	0.35 26	1.14 30	9.32 37	0.23 29	8.60 23	14.1 22	5.23 23	8.01 83	16.4 35	5.26 69	0.20 102	0.00 1	0.86 101	4.71 22	12.0 16	8.34 41
Efficient-NL [60]	40.0	0.22 51	1.29 52	0.00 1	1.25 24	7.99 27	0.48 29	2.92 45	9.31 40	0.31 19	1.23 44	9.67 44	0.31 52	8.23 18	13.5 17	4.72 19	8.45 87	17.1 47	6.06 89	0.12 95	0.00 1	0.54 93	4.71 22	11.6 15	7.75 28
Aniso-Texture [82]	41.1	0.16 10	0.94 10	0.02 48	1.16 21	8.46 31	0.50 30	5.29 79	14.6 80	1.10 72	0.87 11	6.69 9	0.17 14	14.2 79	20.8 66	14.7 85	5.50 43	18.6 60	4.62 49	0.00 1	0.00 1	0.00 1	6.99 61	18.1 56	10.3 70
Complementary OF [21]	41.2	0.15 6	0.89 7	0.00 1	1.43 34	8.69 34	0.35 20	2.54 34	8.95 38	0.28 18	1.45 58	12.4 60	0.28 48	14.9 86	21.6 83	15.4 90	7.75 77	17.6 54	3.64 24	0.00 1	0.00 1	0.00 1	7.27 67	22.2 84	9.59 62
SRR-TVOF-NL [91]	41.6	0.19 25	1.05 21	0.03 53	3.08 91	13.8 87	1.68 89	3.97 62	12.4 62	0.84 56	1.22 42	9.35 38	0.20 20	11.5 43	16.7 39	12.3 77	2.79 7	13.5 15	3.68 25	0.00 1	0.00 1	0.00 1	5.69 46	13.1 29	10.1 68
Ramp [62]	43.0	0.21 43	1.24 47	0.00 1	1.77 51	11.1 58	0.79 53	2.39 25	7.95 22	0.55 46	1.17 37	9.16 32	0.24 34	9.25 29	14.9 28	6.31 31	7.18 69	15.7 25	5.42 73	0.19 101	0.00 1	0.96 103	5.18 36	13.3 33	8.87 55
IROF-TV [53]	43.5	0.22 51	1.24 47	0.01 31	1.83 56	11.9 65	0.87 61	2.96 48	9.37 41	0.50 36	1.70 67	14.6 71	0.46 66	9.51 33	15.4 34	6.49 33	4.78 32	22.9 95	4.55 46	0.00 1	0.00 1	0.00 1	5.17 35	14.4 41	8.73 51
OFH [38]	43.8	0.17 14	1.00 16	0.00 1	1.80 53	9.80 47	0.66 45	4.49 71	13.2 73	0.47 35	1.62 64	13.6 66	0.35 55	13.2 65	20.8 66	10.2 66	3.85 22	20.4 80	2.41 10	0.00 1	0.00 1	0.00 1	7.06 63	21.6 79	9.31 58
ROF-ND [107]	45.1	0.29 79	1.73 84	0.01 31	2.75 86	11.0 56	0.73 51	3.45 55	10.7 54	0.51 39	0.13 1	1.44 1	0.00 1	12.2 50	18.8 48	10.5 69	6.28 58	17.1 47	4.80 55	0.00 1	0.00 1	0.00 1	8.30 79	21.5 77	9.38 59
OAR-Flow [125]	45.2	0.19 25	1.12 29	0.06 76	2.86 87	12.0 66	1.41 84	4.36 68	13.9 76	1.43 84	1.51 61	11.7 56	0.23 29	12.6 60	20.0 59	8.47 49	2.80 8	16.4 35	1.37 2	0.00 1	0.00 1	0.00 1	5.56 43	16.7 49	8.15 37
TCOF [69]	46.6	0.18 20	1.06 22	0.00 1	1.56 43	9.24 41	0.60 40	4.63 73	12.8 69	0.89 59	1.34 54	12.4 60	0.20 20	12.4 53	19.6 54	9.52 60	6.02 52	14.3 18	5.03 62	0.34 111	0.00 1	1.23 108	4.94 27	13.6 36	8.00 35
TV-L1-MCT [64]	46.9	0.22 51	1.33 57	0.00 1	1.64 47	9.85 48	0.52 32	2.86 44	9.38 42	0.32 22	1.14 30	8.89 25	0.24 34	10.6 40	16.4 38	9.05 54	8.81 94	17.2 51	5.12 65	0.08 89	0.00 1	0.84 100	5.93 52	14.3 40	10.1 68
ACK-Prior [27]	47.7	0.15 6	0.91 9	0.00 1	1.21 23	7.63 22	0.19 7	2.41 27	8.36 32	0.35 26	1.25 47	10.5 50	0.18 17	12.4 53	18.0 44	11.2 70	9.00 99	19.4 71	6.39 94	0.17 99	0.00 1	1.01 105	9.72 89	20.1 67	13.3 86
2DHMM-SAS [92]	48.1	0.20 31	1.21 39	0.00 1	1.80 53	10.4 55	0.63 42	4.15 63	11.3 56	0.86 57	1.24 45	9.61 43	0.25 45	9.18 27	14.8 27	6.44 32	8.98 98	17.3 52	4.98 59	0.13 96	0.00 1	0.69 95	5.55 42	14.2 39	9.22 56
CRTflow [80]	49.0	0.18 20	0.99 14	0.03 53	1.70 48	9.09 40	0.59 39	4.56 72	12.8 69	0.68 51	2.03 82	15.1 76	0.64 74	12.4 53	20.0 59	8.26 46	4.42 27	24.0 100	3.47 21	0.00 1	0.00 1	0.00 1	7.88 73	22.0 83	10.4 72
PGM-C [120]	49.0	0.20 31	1.19 35	0.07 85	1.87 58	11.8 64	0.85 59	2.76 40	9.82 47	0.92 63	1.99 79	15.1 76	0.74 83	13.1 63	21.2 69	9.19 56	3.52 19	19.2 68	2.47 11	0.00 1	0.00 1	0.00 1	6.38 56	19.5 64	8.65 48
Sparse Occlusion [54]	50.2	0.24 62	1.38 62	0.06 76	1.27 26	7.83 24	0.45 24	3.42 53	11.1 55	0.33 23	1.52 62	11.4 54	0.28 48	10.9 41	17.6 42	6.76 36	4.10 25	16.2 30	4.27 38	0.03 77	0.17 121	0.15 82	5.90 49	15.7 44	8.69 50
SimpleFlow [49]	50.3	0.22 51	1.31 53	0.00 1	1.78 52	11.0 56	0.82 57	4.30 67	12.5 65	1.22 77	1.16 35	9.20 34	0.24 34	9.84 37	15.8 36	6.94 37	8.51 88	17.3 52	6.11 91	0.09 90	0.00 1	0.39 89	5.01 31	14.1 38	8.00 35
CPM-Flow [116]	51.2	0.21 43	1.23 44	0.07 85	1.92 62	12.1 67	0.89 62	2.64 38	9.39 43	0.92 63	1.96 75	14.8 72	0.72 81	13.1 63	21.2 69	8.92 52	4.84 33	19.1 67	3.40 19	0.00 1	0.00 1	0.00 1	6.92 60	20.5 69	9.43 60
ComplOF-FED-GPU [35]	52.5	0.19 25	1.10 26	0.03 53	2.32 76	12.5 75	0.92 65	2.76 40	9.65 46	0.31 19	1.65 65	13.1 65	0.38 59	13.4 69	21.2 69	10.2 66	8.60 92	22.8 93	4.22 37	0.00 1	0.00 1	0.00 1	7.44 68	22.4 85	9.81 63
Kuang [131]	54.1	0.21 43	1.22 43	0.03 53	1.92 62	12.1 67	0.91 64	3.01 50	10.6 53	0.52 41	1.47 59	11.8 58	0.44 65	13.5 71	21.5 77	9.33 58	6.38 59	20.4 80	5.73 84	0.00 1	0.00 1	0.00 1	7.20 65	18.6 59	13.2 85
EpicFlow [102]	54.9	0.20 31	1.17 32	0.07 85	1.91 61	12.1 67	0.90 63	3.70 58	12.7 67	0.89 59	1.96 75	14.8 72	0.74 83	13.4 69	21.3 73	9.97 62	6.71 64	19.3 69	3.90 31	0.00 1	0.00 1	0.00 1	7.03 62	20.1 67	9.91 64
S2D-Matching [84]	55.7	0.33 89	1.92 92	0.06 76	2.04 67	12.4 72	0.79 53	4.15 63	13.0 71	1.09 71	1.09 24	8.04 16	0.24 34	9.82 36	15.9 37	6.68 35	7.85 80	16.4 35	5.58 80	0.20 102	0.00 1	0.96 103	4.94 27	12.6 20	8.81 53
AggregFlow [97]	56.2	0.53 101	2.62 107	0.12 102	2.72 84	13.2 80	1.45 85	3.90 61	13.0 71	1.85 88	1.06 20	9.26 35	0.18 17	12.1 49	19.4 51	7.83 44	3.05 11	12.1 10	2.29 8	0.04 83	0.00 1	0.44 91	5.82 48	15.9 45	9.26 57
TF+OM [100]	56.3	0.16 10	0.98 13	0.01 31	1.85 57	10.0 51	1.15 77	4.71 76	12.6 66	5.84 97	1.79 70	14.0 69	0.69 78	15.4 91	22.1 86	16.5 94	5.62 46	19.8 75	3.95 33	0.00 1	0.00 1	0.00 1	8.18 78	20.6 71	12.0 79
DeepFlow2 [108]	56.7	0.26 72	1.53 73	0.08 92	2.56 81	11.7 62	1.30 82	3.73 60	11.5 58	0.90 61	1.99 79	15.1 76	0.65 76	12.3 51	19.5 53	9.07 55	4.57 29	18.7 61	2.81 14	0.00 1	0.00 1	0.00 1	8.01 77	21.0 72	10.8 74
Adaptive [20]	57.9	0.29 79	1.72 82	0.06 76	2.04 67	12.5 75	1.13 75	5.51 83	14.7 81	0.68 51	1.83 71	13.9 68	0.59 73	12.7 61	19.9 56	10.1 63	7.15 68	18.9 65	4.08 36	0.00 1	0.00 1	0.00 1	6.38 56	16.4 47	8.82 54
Steered-L1 [118]	62.0	0.10 1	0.59 1	0.01 31	1.01 11	6.84 17	0.52 32	2.76 40	9.44 44	0.91 62	1.78 69	14.9 74	0.51 70	14.0 78	20.5 63	15.2 88	9.02 100	19.9 76	6.57 98	1.07 120	0.00 1	7.19 121	12.1 99	22.9 88	20.6 105
RFlow [90]	62.3	0.20 31	1.21 39	0.01 31	1.58 44	9.77 46	0.77 52	4.69 75	13.5 74	0.34 24	2.30 93	17.7 93	0.80 88	14.3 80	21.4 75	15.1 87	5.47 41	20.9 86	4.98 59	0.01 75	0.00 1	0.15 82	7.91 75	21.0 72	10.6 73
TriangleFlow [30]	63.4	0.24 62	1.39 63	0.00 1	2.50 80	14.3 88	0.98 68	4.46 70	12.7 67	0.41 33	1.49 60	12.2 59	0.42 63	15.8 95	23.1 95	16.4 93	8.57 90	17.7 55	4.86 56	0.03 77	0.00 1	0.05 76	6.59 58	17.3 51	9.55 61
Aniso. Huber-L1 [22]	63.6	0.29 79	1.66 78	0.06 76	2.43 79	13.1 79	1.12 74	5.68 84	14.3 79	1.27 81	1.56 63	12.4 60	0.30 51	12.4 53	19.2 50	10.1 63	4.70 31	17.1 47	4.45 42	0.17 99	0.00 1	0.89 102	6.28 54	16.7 49	8.80 52
DeepFlow [86]	64.1	0.34 92	1.74 86	0.09 94	2.87 88	12.4 72	1.56 86	4.42 69	12.4 62	2.69 92	2.20 87	16.1 82	0.81 89	12.3 51	19.8 55	8.36 47	4.85 34	20.1 77	2.95 15	0.00 1	0.00 1	0.00 1	9.35 85	23.3 91	12.7 82
Occlusion-TV-L1 [63]	64.2	0.27 75	1.55 75	0.06 76	1.99 66	12.1 67	1.14 76	5.42 81	14.9 83	0.93 65	1.83 71	14.0 69	0.49 68	13.6 72	21.2 69	11.5 72	6.13 54	19.6 74	5.37 72	0.00 1	0.00 1	0.00 1	9.19 84	23.4 92	11.5 77
CBF [12]	65.6	0.18 20	1.09 25	0.01 31	2.37 77	12.9 77	1.94 92	4.28 66	12.0 59	1.13 75	1.97 78	16.1 82	0.66 77	13.3 68	20.5 63	12.7 80	5.89 50	18.8 64	4.73 52	0.45 115	0.00 1	1.33 111	7.67 70	18.9 60	12.7 82
LocallyOriented [52]	66.7	0.49 100	2.66 108	0.06 76	3.28 92	15.4 91	1.91 91	6.59 93	16.9 94	1.20 76	1.29 50	10.1 46	0.52 72	14.6 82	21.5 77	12.6 78	7.79 78	16.7 43	4.33 40	0.00 1	0.00 1	0.00 1	7.87 72	19.1 62	11.1 76
FlowNet2 [122]	68.5	0.74 110	3.32 113	0.12 102	5.00 106	17.2 97	2.46 99	6.13 89	15.0 84	5.85 98	1.29 50	10.3 49	0.40 62	13.2 65	21.6 83	8.11 45	7.27 72	17.7 55	6.06 89	0.00 1	0.00 1	0.05 76	5.52 41	17.5 53	3.70 5
DF-Auto [115]	69.0	0.61 106	2.33 100	0.10 98	4.04 99	15.3 90	2.49 100	6.59 93	14.8 82	6.66 100	1.84 73	15.0 75	0.51 70	14.9 86	21.5 77	16.8 96	3.47 18	16.7 43	3.82 30	0.00 1	0.00 1	0.00 1	7.99 76	19.5 64	11.6 78
TV-L1-improved [17]	69.1	0.25 69	1.46 70	0.07 85	1.82 55	11.1 58	1.02 69	5.46 82	15.0 84	1.32 82	2.26 90	16.4 85	0.79 87	13.8 74	21.5 77	11.8 73	9.40 101	23.6 99	6.48 95	0.00 1	0.00 1	0.00 1	7.89 74	21.3 76	10.3 70
TriFlow [95]	69.4	0.27 75	1.62 76	0.01 31	2.27 75	13.2 80	1.24 79	7.10 98	16.9 94	7.37 103	1.31 53	11.7 56	0.43 64	17.3 103	23.4 101	20.6 105	3.24 14	15.8 26	3.91 32	4.67 127	0.00 1	18.1 127	6.86 59	18.1 56	7.89 31
CLG-TV [48]	70.8	0.31 86	1.67 80	0.06 76	2.10 71	13.0 78	0.92 65	5.33 80	14.2 78	1.04 69	1.71 68	13.7 67	0.38 59	13.9 76	21.4 75	12.0 74	5.20 37	22.7 92	5.07 63	0.20 102	0.00 1	1.01 105	7.85 71	19.4 63	9.91 64
Bartels [41]	72.5	0.24 62	1.40 64	0.01 31	1.42 33	8.88 37	0.62 41	3.51 56	12.4 62	1.00 68	2.26 90	15.8 80	0.95 93	15.5 92	23.3 99	15.8 92	7.99 82	23.0 96	5.20 67	0.33 108	0.00 1	1.92 114	9.95 91	24.0 93	13.7 89
Brox et al. [5]	72.9	0.25 69	1.45 69	0.03 53	2.22 74	13.7 86	1.07 71	3.44 54	11.4 57	0.68 51	2.20 87	16.7 89	0.72 81	17.8 107	23.4 101	24.6 113	8.90 97	24.6 102	6.63 99	0.00 1	0.00 1	0.00 1	10.6 94	25.9 100	14.8 93
Fusion [6]	73.2	0.24 62	1.41 66	0.05 71	1.13 19	8.57 33	0.47 27	2.45 30	8.15 27	0.93 65	2.12 86	18.3 99	1.21 97	16.2 99	23.1 95	19.6 103	6.72 66	18.7 61	5.67 82	0.07 87	0.15 119	0.10 80	10.4 93	24.9 98	14.6 92
SegOF [10]	73.7	0.24 62	1.41 66	0.05 71	4.72 104	21.4 106	3.68 106	9.28 105	19.5 104	4.83 94	1.05 19	7.42 13	0.78 86	20.7 117	27.1 115	28.6 118	10.4 107	26.0 105	7.80 110	0.00 1	0.00 1	0.00 1	7.25 66	20.0 66	7.44 25
Classic++ [32]	73.8	0.26 72	1.50 72	0.07 85	2.08 70	12.2 71	1.03 70	4.84 78	14.0 77	1.26 78	2.07 84	16.1 82	0.64 74	13.9 76	22.5 89	10.1 63	6.11 53	23.1 97	5.28 70	0.06 85	0.00 1	0.34 87	8.66 81	21.7 80	11.0 75
Rannacher [23]	75.6	0.33 89	1.95 93	0.07 85	2.21 73	13.4 83	1.29 81	5.78 85	15.6 89	1.48 85	2.51 97	17.8 94	0.95 93	14.5 81	22.5 89	12.2 76	9.72 105	24.8 103	6.66 100	0.00 1	0.00 1	0.00 1	7.50 69	21.1 75	9.97 66
SuperFlow [81]	76.7	0.45 96	1.75 87	0.10 98	3.01 90	13.4 83	2.04 93	6.82 97	15.2 87	8.24 106	1.96 75	17.0 91	0.50 69	15.6 94	22.2 87	19.2 102	5.87 49	20.6 82	5.46 76	0.00 1	0.00 1	0.00 1	10.1 92	24.6 96	13.4 87
Local-TV-L1 [65]	77.5	0.53 101	2.10 94	0.12 102	4.96 105	18.0 101	3.44 105	8.54 103	16.7 93	6.16 99	2.47 96	18.5 101	1.01 96	12.5 57	19.9 56	9.65 61	5.53 44	19.5 72	4.95 58	0.00 1	0.00 1	0.00 1	13.4 104	24.2 94	27.1 114
BriefMatch [124]	77.5	0.16 10	0.94 10	0.01 31	1.97 65	9.60 44	1.10 73	2.79 43	9.56 45	0.54 44	2.11 85	15.6 79	0.70 79	14.6 82	21.5 77	15.2 88	10.4 107	22.0 90	8.36 113	2.52 126	0.62 129	13.7 126	13.6 107	25.5 99	22.0 109
SIOF [67]	78.0	0.42 95	2.28 96	0.08 92	3.55 95	17.7 100	2.05 94	8.15 102	17.9 101	7.78 105	2.41 95	17.9 95	1.00 95	15.5 92	22.7 92	17.8 100	4.67 30	19.5 72	4.77 54	0.00 1	0.00 1	0.00 1	9.35 85	21.8 81	17.7 99
p-harmonic [29]	80.6	0.29 79	1.73 84	0.02 48	2.16 72	13.2 80	1.33 83	5.87 86	15.8 90	1.59 86	2.55 98	17.9 95	1.49 100	17.0 101	22.7 92	23.3 110	4.53 28	21.5 89	4.53 45	0.03 77	0.02 118	0.00 1	9.65 88	23.2 90	15.0 94
Second-order prior [8]	80.7	0.26 72	1.53 73	0.05 71	2.88 89	15.5 92	1.60 87	5.87 86	15.3 88	1.11 74	2.21 89	17.2 92	0.94 92	13.8 74	21.3 73	12.6 78	7.46 73	27.8 111	5.71 83	0.16 98	0.00 1	0.76 97	8.65 80	21.0 72	13.9 91
Dynamic MRF [7]	82.6	0.30 85	1.79 89	0.04 68	2.37 77	14.9 89	1.09 72	4.81 77	15.0 84	0.86 57	2.66 99	18.2 98	1.25 98	17.6 105	25.7 113	18.1 101	10.9 112	30.4 116	7.45 106	0.00 1	0.00 1	0.00 1	15.1 110	29.9 115	21.9 108
F-TV-L1 [15]	82.9	0.46 97	2.58 104	0.07 85	4.05 100	16.2 94	2.21 95	6.59 93	15.9 91	1.39 83	2.35 94	17.9 95	0.88 91	13.7 73	21.5 77	11.4 71	7.53 75	21.1 88	4.75 53	0.03 77	0.17 121	0.05 76	7.15 64	20.5 69	7.39 24
Shiralkar [42]	83.0	0.28 77	1.66 78	0.02 48	3.80 97	19.8 105	1.78 90	6.50 91	16.1 92	1.26 78	3.17 103	20.8 105	1.56 102	16.3 100	25.1 111	14.5 84	12.4 116	29.4 114	6.20 92	0.00 1	0.00 1	0.00 1	12.6 100	30.1 116	13.8 90
CNN-flow-warp+ref [117]	84.4	0.33 89	1.91 91	0.09 94	2.72 84	12.4 72	2.32 97	6.77 96	18.9 103	2.09 89	2.28 92	16.4 85	0.82 90	17.7 106	23.9 105	22.8 109	9.40 101	24.3 101	6.73 101	0.00 1	0.00 1	0.00 1	14.0 108	26.8 106	20.2 104
GraphCuts [14]	86.8	0.29 79	1.67 80	0.16 109	6.77 112	22.4 110	3.81 107	7.73 101	17.2 97	9.04 107	1.86 74	16.8 90	0.46 66	15.8 95	24.0 106	14.1 83	20.2 125	22.8 93	12.5 122	0.00 1	0.00 1	0.00 1	13.4 104	27.0 109	23.4 111
StereoOF-V1MT [119]	88.1	0.41 93	2.35 101	0.04 68	4.27 102	21.6 107	1.66 88	6.25 90	17.7 99	0.50 36	3.13 102	23.2 109	1.41 99	19.4 114	27.9 119	20.7 106	11.6 114	32.5 118	7.60 108	0.00 1	0.00 1	0.00 1	16.7 114	32.8 118	21.3 106
Ad-TV-NDC [36]	89.2	0.79 111	2.68 109	0.12 102	13.0 120	26.5 114	12.9 120	12.9 113	22.0 108	9.24 108	5.02 108	20.3 104	4.82 108	13.2 65	20.5 63	9.43 59	6.17 56	20.3 79	5.07 63	0.03 77	0.00 1	0.00 1	20.5 119	26.9 107	40.8 125
HBpMotionGpu [43]	89.8	0.80 112	2.79 110	0.18 110	5.57 107	23.8 113	4.00 108	13.1 114	27.8 119	11.6 114	2.05 83	16.4 85	0.74 83	17.9 108	25.1 111	22.3 108	6.69 62	21.0 87	6.04 88	0.00 1	0.00 1	0.00 1	14.1 109	27.7 110	25.1 112
Filter Flow [19]	91.0	0.58 105	2.59 105	0.11 101	4.48 103	19.7 103	2.66 103	12.1 108	23.7 111	13.5 118	14.5 118	30.4 115	15.0 118	18.7 113	23.7 104	27.5 117	8.11 84	20.7 84	6.48 95	0.00 1	0.00 1	0.00 1	11.0 96	21.9 82	17.2 97
StereoFlow [44]	91.2	2.82 128	6.92 127	1.29 127	21.5 126	42.6 129	13.8 121	20.5 127	33.3 128	20.4 123	20.6 125	51.2 127	18.6 123	14.9 86	22.6 91	13.7 82	3.89 23	18.9 65	3.74 27	0.00 1	0.00 1	0.00 1	11.3 98	25.9 100	18.7 102
Modified CLG [34]	93.0	0.62 107	2.54 103	0.12 102	3.52 94	18.7 102	2.59 102	12.2 110	23.5 110	12.5 116	3.25 104	20.1 103	2.03 104	18.6 112	25.0 110	25.0 114	8.86 96	26.9 110	7.14 104	0.00 1	0.00 1	0.00 1	13.4 104	29.8 114	21.8 107
2bit-BM-tele [98]	93.3	0.54 103	2.59 105	0.20 111	2.58 82	16.3 95	1.26 80	6.04 88	17.8 100	2.26 90	2.77 101	19.7 102	1.50 101	15.8 95	23.3 99	16.5 94	8.58 91	22.4 91	5.62 81	1.37 122	0.00 1	5.84 120	10.7 95	24.8 97	16.5 96
Learning Flow [11]	94.2	0.32 88	1.89 90	0.01 31	2.61 83	16.0 93	1.21 78	6.52 92	17.9 101	1.65 87	4.69 107	24.9 112	3.14 107	20.8 118	27.4 118	26.9 116	10.9 112	28.6 113	7.90 111	0.10 94	0.00 1	0.64 94	13.3 103	28.5 113	18.1 100
FlowNetS+ft+v [112]	95.5	0.31 86	1.76 88	0.09 94	3.39 93	13.5 85	2.49 100	7.24 99	16.9 94	5.12 95	3.32 105	18.3 99	2.07 105	17.1 102	23.2 98	20.3 104	6.23 57	23.2 98	5.50 79	0.35 112	0.52 126	1.50 113	8.79 83	23.1 89	13.6 88
SPSA-learn [13]	95.9	0.86 114	3.30 112	0.28 116	6.02 109	22.0 108	4.09 109	10.6 106	21.3 106	9.82 112	5.83 111	22.9 107	5.66 112	17.9 108	23.4 101	23.4 111	10.2 106	25.0 104	8.09 112	0.00 1	0.00 1	0.00 1	15.9 113	28.1 111	23.3 110
IAOF2 [51]	96.5	0.47 99	2.28 96	0.33 117	3.77 96	16.3 95	2.22 96	7.40 100	17.2 97	7.06 102	14.7 119	29.4 114	16.6 120	15.2 90	23.0 94	14.7 85	10.5 109	20.6 82	7.03 103	0.32 107	0.00 1	2.00 116	11.2 97	22.6 87	15.4 95
UnFlow [129]	97.1	1.88 123	6.59 125	0.87 124	6.75 111	27.2 115	4.57 111	12.5 112	27.9 120	7.37 103	5.65 110	21.0 106	5.26 110	22.5 121	30.2 122	26.4 115	9.48 103	30.5 117	7.32 105	0.00 1	0.00 1	0.00 1	9.58 87	26.9 107	12.3 81
LDOF [28]	97.7	0.41 93	2.31 99	0.09 94	3.85 98	17.3 98	2.32 97	4.68 74	13.5 74	2.59 91	3.97 106	24.8 111	2.14 106	16.1 98	23.1 95	17.6 98	8.24 86	26.0 105	6.50 97	0.33 108	0.34 123	1.95 115	9.77 90	26.7 104	12.9 84
IAOF [50]	99.1	0.46 97	2.11 95	0.10 98	6.63 110	19.7 103	4.61 112	13.8 116	23.3 109	9.33 109	9.91 113	23.2 109	11.3 115	14.8 85	22.2 87	15.5 91	10.6 110	26.8 109	6.99 102	0.05 84	0.00 1	0.42 90	18.0 117	24.4 95	35.4 122
Nguyen [33]	99.9	0.83 113	3.37 114	0.22 112	7.27 114	22.1 109	6.46 115	15.4 119	26.8 117	12.4 115	17.6 122	30.4 115	20.2 125	18.5 111	24.7 108	24.5 112	8.82 95	28.5 112	8.81 115	0.00 1	0.00 1	0.00 1	18.9 118	31.7 117	29.4 116
BlockOverlap [61]	100.0	0.62 107	2.30 98	0.14 108	4.11 101	17.6 99	3.02 104	9.12 104	19.6 105	6.97 101	2.74 100	16.5 88	1.72 103	14.7 84	20.9 68	16.8 96	7.89 81	19.3 69	6.25 93	2.12 123	0.52 126	10.9 125	15.2 111	22.4 85	32.3 120
GroupFlow [9]	100.6	0.56 104	3.20 111	0.05 71	9.79 117	32.3 121	7.11 118	11.4 107	24.6 112	9.68 111	2.01 81	16.0 81	0.70 79	19.8 115	29.9 121	12.0 74	15.2 122	32.5 118	15.4 124	0.33 108	0.00 1	1.11 107	13.2 102	28.4 112	17.2 97
2D-CLG [1]	103.0	1.77 122	6.22 123	0.51 122	5.91 108	22.4 110	4.54 110	16.4 120	28.6 122	18.1 122	17.9 123	35.8 119	19.9 124	20.4 116	25.8 114	29.3 119	12.0 115	29.6 115	11.4 119	0.00 1	0.00 1	0.00 1	17.7 116	32.8 118	26.2 113
Heeger++ [104]	104.1	0.95 115	4.26 117	0.26 115	11.8 119	39.5 127	6.54 116	12.3 111	24.7 114	3.51 93	10.7 114	37.1 121	8.97 113	30.7 126	36.7 126	39.2 125	21.4 127	46.3 128	18.3 127	0.00 1	0.00 1	0.00 1	24.4 122	37.0 121	31.9 118
FFV1MT [106]	107.2	1.42 118	6.80 126	0.25 113	10.3 118	35.9 124	6.76 117	18.0 121	30.0 124	16.5 120	17.2 121	51.3 128	16.2 119	31.5 127	37.1 127	43.2 128	20.9 126	44.0 127	17.2 125	0.00 1	0.00 1	0.00 1	24.4 122	37.0 121	31.9 118
TI-DOFE [24]	107.3	1.58 120	5.19 120	0.36 119	16.8 122	34.3 122	17.7 123	19.3 126	30.2 126	21.6 125	23.3 126	39.1 123	27.6 126	21.1 119	27.1 115	29.5 120	14.4 120	35.1 122	12.1 121	0.00 1	0.00 1	0.00 1	27.2 126	40.8 125	41.2 126
Black & Anandan [4]	108.3	0.68 109	2.46 102	0.13 107	7.01 113	23.6 112	4.65 113	12.1 108	21.6 107	9.40 110	5.45 109	23.1 108	4.84 109	17.5 104	24.3 107	21.6 107	10.6 110	26.6 108	7.49 107	0.43 113	0.15 119	1.31 110	12.7 101	26.7 104	18.8 103
Horn & Schunck [3]	109.5	1.05 116	4.22 116	0.25 113	7.74 115	29.1 118	5.17 114	13.6 115	24.6 112	10.8 113	12.7 116	36.1 120	12.8 116	21.4 120	27.3 117	30.9 122	13.9 119	35.2 123	11.6 120	0.03 77	0.00 1	0.17 84	22.3 121	37.9 123	31.7 117
SILK [79]	113.0	1.05 116	4.27 118	0.44 120	9.69 116	27.9 116	8.93 119	15.2 118	26.9 118	13.1 117	6.14 112	25.9 113	5.57 111	23.0 122	29.2 120	34.1 123	12.6 117	33.9 120	9.78 117	0.81 118	0.00 1	3.50 118	21.5 120	33.3 120	34.5 121
HCIC-L [99]	114.1	1.95 124	6.24 124	0.99 126	28.7 128	32.2 120	35.8 128	18.6 124	26.2 115	25.7 128	25.4 129	46.6 126	27.7 127	15.1 89	21.9 85	12.7 80	8.77 93	20.2 78	9.24 116	6.40 129	0.49 125	23.0 129	15.3 112	26.4 103	18.4 101
Adaptive flow [45]	117.5	1.75 121	5.07 119	0.34 118	18.4 124	28.3 117	18.5 125	18.5 122	28.6 122	22.9 126	13.3 117	37.4 122	13.9 117	17.9 108	24.7 108	17.7 99	12.9 118	26.2 107	8.68 114	5.20 128	0.61 128	22.8 128	16.7 114	26.0 102	28.2 115
PGAM+LK [55]	117.5	2.98 129	6.17 122	6.36 131	16.8 122	36.2 125	17.8 124	14.7 117	26.5 116	14.5 119	19.1 124	53.9 129	18.3 122	23.1 123	30.6 123	29.9 121	14.4 120	36.8 124	11.1 118	1.07 120	0.00 1	4.16 119	25.9 124	40.1 124	40.3 124
SLK [47]	117.8	1.44 119	5.58 121	0.49 121	14.4 121	35.8 123	14.8 122	18.5 122	30.1 125	21.4 124	24.6 127	35.7 118	27.7 127	26.3 125	31.9 124	39.4 126	15.6 123	38.8 125	13.6 123	0.55 117	0.00 1	1.35 112	31.7 127	41.0 126	49.0 127
FOLKI [16]	119.5	1.98 125	7.18 128	0.87 124	24.5 127	36.3 126	30.3 127	18.7 125	32.4 127	16.5 120	15.2 120	33.2 117	18.1 121	26.0 124	32.3 125	36.0 124	17.7 124	40.6 126	17.9 126	2.33 125	0.00 1	10.6 123	33.9 128	43.6 127	52.7 128
Periodicity [78]	120.1	2.36 127	9.12 129	0.79 123	19.1 125	40.7 128	20.6 126	28.2 129	35.2 129	26.8 129	11.2 115	40.6 124	10.3 114	42.3 129	55.4 129	41.1 127	31.7 128	56.3 129	27.7 128	0.54 116	0.00 1	7.78 122	26.1 125	51.0 128	36.2 123
Pyramid LK [2]	126.2	2.31 126	4.20 115	3.47 130	31.6 129	32.0 119	40.4 129	21.0 128	28.5 121	24.0 127	24.6 127	43.7 125	28.6 129	37.5 128	46.6 128	43.7 129	33.1 129	34.2 121	31.3 129	2.17 124	0.47 124	10.7 124	46.5 129	57.3 129	67.2 129
AdaConv-v1 [126]	129.9	6.16 130	11.8 130	2.11 128	91.0 130	93.3 130	87.2 130	83.4 130	79.4 130	87.3 130	47.3 130	64.4 130	46.2 130	89.6 130	93.2 130	73.3 130	69.7 130	60.5 130	67.1 130	41.7 130	14.2 130	92.5 130	99.6 130	98.7 130	100.0 130
SepConv-v1 [127]	129.9	6.16 130	11.8 130	2.11 128	91.0 130	93.3 130	87.2 130	83.4 130	79.4 130	87.3 130	47.3 130	64.4 130	46.2 130	89.6 130	93.2 130	73.3 130	69.7 130	60.5 130	67.1 130	41.7 130	14.2 130	92.5 130	99.6 130	98.7 130	100.0 130

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

References

Method	time*	frames	color		Reference and notes
[1] 2D-CLG	844	2	gray		The 2D-CLG method by Bruhn et al. as implemented by Stefan Roth. [A. Bruhn, J. Weickert, and C. Schnörr. Lucas/Kanade meets Horn/Schunck: combining local and global optic flow methods. IJCV 63(3), 2005.] Parameters were set to match the published performance on Yosemite sequence, which may not be optimal for other sequences.
[2] Pyramid LK	12	2	color		A modification of Bouguet's pyramidal implementation of Lucas-Kanade.
[3] Horn & Schunck	49	2	gray		A modern Matlab implementation of the Horn & Schunck method by Deqing Sun. Parameters set to optimize AAE on all training data.
[4] Black & Anandan	328	2	gray		A modern Matlab implementation of the Black & Anandan method by Deqing Sun.
[5] Brox et al.	18	2	color		T. Brox, A. Bruhn, N. Papenberg, and J. Weickert. High accuracy optical flow estimation based on a theory for warping. ECCV 2004. (Improved using separate robust functions as proposed in A. Bruhn and J. Weickert, Towards ultimate motion estimation, ICCV 2005; improved by training on the training set.)
[6] Fusion	2,666	2	color		V. Lempitsky, S. Roth, and C. Rother. Discrete-continuous optimization for optical flow estimation. CVPR 2008.
[7] Dynamic MRF	366	2	gray		B. Glocker, N. Paragios, N. Komodakis, G. Tziritas, and N. Navab. Optical flow estimation with uncertainties through dynamic MRFs. CVPR 2008. (Method improved since publication.)
[8] Second-order prior	14	2	gray		W. Trobin, T. Pock, D. Cremers, and H. Bischof. An unbiased second-order prior for high-accuracy motion estimation. DAGM 2008. (Method improved since publication; for details see W. Trobin, Ph.D. thesis, 2009.)
[9] GroupFlow	600	2	gray		X. Ren. Local Grouping for Optical Flow. CVPR 2008.
[10] SegOF	60	2	color		L. Xu, J. Chen, and J. Jia. Segmentation based variational model for accurate optical flow estimation. ECCV 2008. Code available.
[11] Learning Flow	825	2	gray		D. Sun, S. Roth, J.P. Lewis, and M. Black. Learning optical flow (SRF-LFC). ECCV 2008.
[12] CBF	69	2	color		W. Trobin, T. Pock, D. Cremers, and H. Bischof. Continuous energy minimization via repeated binary fusion. ECCV 2008. (Method improved since publication; for details see W. Trobin, Ph.D. thesis, 2009.)
[13] SPSA-learn	200	2	color		Y. Li and D. Huttenlocher. Learning for optical flow using stochastic optimization. ECCV 2008.
[14] GraphCuts	1,200	2	color		T. Cooke. Two applications of graph-cuts to image processing. DICTA 2008.
[15] F-TV-L1	8	2	gray		A. Wedel, T. Pock, J. Braun, U. Franke, and D. Cremers. Duality TV-L1 flow with fundamental matrix prior. IVCNZ 2008.
[16] FOLKI	1.4	2	gray		G. Le Besnerais and F. Champagnat. Dense optical flow by iterative local window registration. ICIP 2005.
[17] TV-L1-improved	2.9	2	gray		A. Wedel, T. Pock, C. Zach, H. Bischof, and D. Cremers. An improved algorithm for TV-L1 optical flow computation. Proceedings of the Dagstuhl Visual Motion Analysis Workshop 2008. Code at GPU4Vision.
[18] DPOF	287	2	color		C. Lei and Y.-H. Yang. Optical flow estimation on coarse-to-fine region-trees using discrete optimization. ICCV 2009. (Method improved since publication.)
[19] Filter Flow	34,000	2	color		S. Seitz and S. Baker. Filter flow. ICCV 2009.
[20] Adaptive	9.2	2	gray		A. Wedel, D. Cremers, T. Pock, and H. Bischof. Structure- and motion-adaptive regularization for high accuracy optic flow. ICCV 2009.
[21] Complementary OF	44	2	color		H. Zimmer, A. Bruhn, J. Weickert, L. Valgaerts, A. Salgado, B. Rosenhahn, and H.-P. Seidel. Complementary optic flow. EMMCVPR 2009.
[22] Aniso. Huber-L1	2	2	gray		M. Werlberger, W. Trobin, T. Pock, A. Wedel, D. Cremers, and H. Bischof. Anisotropic Huber-L1 optical flow. BMVC 2009. Code at GPU4Vision.
[23] Rannacher	0.12	2	gray		J. Rannacher. Realtime 3D motion estimation on graphics hardware. Bachelor thesis, Heidelberg University, 2009.
[24] TI-DOFE	260	2	gray		C. Cassisa, S. Simoens, and V. Prinet. Two-frame optical flow formulation in an unwarped multiresolution scheme. CIARP 2009.
[25] NL-TV-NCC	20	2	color		M. Werlberger, T. Pock, and H. Bischof. Motion estimation with non-local total variation regularization. CVPR 2010.
[26] MDP-Flow	188	2	color		L. Xu, J. Jia, and Y. Matsushita. Motion detail preserving optical flow estimation. CVPR 2010.
[27] ACK-Prior	5872	2	color		K. Lee, D. Kwon, I. Yun, and S. Lee. Optical flow estimation with adaptive convolution kernel prior on discrete framework. CVPR 2010.
[28] LDOF	122	2	color		T. Brox and J. Malik. Large displacement optical flow: descriptor matching in variational motion estimation. PAMI 33(3):500-513, 2011.
[29] p-harmonic	565	2	gray		J. Gai and R. Stevenson. Optical flow estimation with p-harmonic regularization. ICIP 2010.
[30] TriangleFlow	4200	2	gray		B. Glocker, H. Heibel, N. Navab, P. Kohli, and C. Rother. TriangleFlow: Optical flow with triangulation-based higher-order likelihoods. ECCV 2010.
[31] Classic+NL	972	2	color		D. Sun, S. Roth, and M. Black. Secrets of optical flow estimation and their principles. CVPR 2010. Matlab code.
[32] Classic++	486	2	gray		A modern implementation of the classical formulation descended from Horn & Schunck and Black & Anandan; see D. Sun, S. Roth, and M. Black, Secrets of optical flow estimation and their principles, CVPR 2010.
[33] Nguyen	33	2	gray		D. Nguyen. Tuning optical flow estimation with image-driven functions. ICRA 2011.
[34] Modified CLG	133	2	gray		R. Fezzani, F. Champagnat, and G. Le Besnerais. Combined local global method for optic flow computation. EUSIPCO 2010.
[35] ComplOF-FED-GPU	0.97	2	color		P. Gwosdek, H. Zimmer, S. Grewenig, A. Bruhn, and J. Weickert. A highly efficient GPU implementation for variational optic flow based on the Euler-Lagrange framework. CVGPU Workshop 2010.
[36] Ad-TV-NDC	35	2	gray		M. Nawaz. Motion estimation with adaptive regularization and neighborhood dependent constraint. DICTA 2010.
[37] Layers++	18206	2	color		D. Sun, E. Sudderth, and M. Black. Layered image motion with explicit occlusions, temporal consistency, and depth ordering. NIPS 2010.
[38] OFH	620	3	color		H. Zimmer, A. Bruhn, J. Weickert. Optic flow in harmony. IJCV 93(3) 2011.
[39] LSM	1615	2	color		K. Jia, X. Wang, and X. Tang. Optical flow estimation using learned sparse model. ICCV 2011.
[40] CostFilter	55	2	color		C. Rhemann, A. Hosni, M. Bleyer, C. Rother, and M. Gelautz. Fast cost-volume filtering for visual correspondence and beyond. CVPR 2011.
[41] Bartels	0.15	2	gray		C. Bartels and G. de Haan. Smoothness constraints in recursive search motion estimation for picture rate conversion. IEEE TCSVT 2010. Version improved since publication: mapped on GPU.
[42] Shiralkar	600	2	gray		M. Shiralkar and R. Schalkoff. A self organization-based optical flow estimator with GPU implementation. MVA 23(6):1229-1242.
[43] HBpMotionGpu	1000	5	gray		S. Grauer-Gray and C. Kambhamettu. Hierarchical belief propagation to reduce search space using CUDA for stereo and motion estimation. WACV 2009. (Method improved since publication.)
[44] StereoFlow	7200	2	color		G. Rosman, S. Shem-Tov, D. Bitton, T. Nir, G. Adiv, R. Kimmel, A. Feuer, and A. Bruckstein. Over-parameterized optical flow using a stereoscopic constraint. SSVM 2011:761-772.
[45] Adaptive flow	121	2	gray		T. Arici. Energy minimization based motion estimation using adaptive smoothness priors. Submitted to IEEE TIP 2011.
[46] TC-Flow	2500	5	color		S. Volz, A. Bruhn, L. Valgaerts, and H. Zimmer. Modeling temporal coherence for optical flow. ICCV 2011.
[47] SLK	300	2	gray		T. Corpetti and E. Mémin. Stochastic uncertainty models for the luminance consistency assumption. IEEE TIP 2011.
[48] CLG-TV	29	2	gray		M. Drulea. Total variation regularization of local-global optical flow. ITSC 2011. Matlab code.
[49] SimpleFlow	1.7	2	color		M. Tao, J. Bai, P. Kohli, S. Paris. SimpleFlow: a non-iterative, sublinear optical flow algorithm. EUROGRAPHICS 2012.
[50] IAOF	57	2	gray		D. Nguyen. Improving motion estimation using image-driven functions and hybrid scheme. PSIVT 2011.
[51] IAOF2	56	2	gray		D. Nguyen. Enhancing the sharpness of flow field using image-driven functions with occlusion-aware filter. Submitted to TIP 2011.
[52] LocallyOriented	9541	2	gray		Y.Niu, A. Dick, and M. Brooks. Locally oriented optical flow computation. To appear in TIP 2012.
[53] IROF-TV	261	2	color		H. Rashwan, D. Puig, and M. Garcia. On improving the robustness of differential optical flow. ICCV 2011 Artemis workshop.
[54] Sparse Occlusion	2312	2	color		A. Ayvaci, M. Raptis, and S. Soatto. Sparse occlusion detection with optical flow. Submitted to IJCV 2011.
[55] PGAM+LK	0.37	2	gray		A. Alba, E. Arce-Santana, and M. Rivera. Optical flow estimation with prior models obtained from phase correlation. ISVC 2010.
[56] Sparse-NonSparse	713	2	color		L. Chen, J. Wang, and Y. Wu. Decomposing and regularizing sparse/non-sparse components for motion field estimation. Submitted to PAMI 2013.
[57] nLayers	36150	4	color		D. Sun, E. Sudderth, and M. Black. Layered segmentation and optical flow estimation over time. CVPR 2012.
[58] IROF++	187	2	color		H. Rashwan, D. Puig, and M. Garcia. Variational optical flow estimation based on stick tensor voting. IEEE TIP 2013.
[59] COFM	600	3	color		M. Mozerov. Constrained optical flow estimation as a matching problem. IEEE TIP 2013.
[60] Efficient-NL	400	2	color		P. Krähenbühl and V. Koltun. Efficient nonlocal regularization for optical flow. ECCV 2012.
[61] BlockOverlap	2	2	gray		M. Santoro, G. AlRegib, and Y. Altunbasak. Motion estimation using block overlap minimization. Submitted to MMSP 2012.
[62] Ramp	1200	2	color		A. Singh and N. Ahuja. Exploiting ramp structures for improving optical flow estimation. ICPR 2012.
[63] Occlusion-TV-L1	538	3	gray		C. Ballester, L. Garrido, V. Lazcano, and V. Caselles. A TV-L1 optical flow method with occlusion detection. DAGM-OAGM 2012.
[64] TV-L1-MCT	90	2	color		M. Mohamed and B. Mertsching. TV-L1 optical flow estimation with image details recovering based on modified census transform. ISVC 2012.
[65] Local-TV-L1	500	2	gray		L. Raket. Local smoothness for global optical flow. ICIP 2012.
[66] ALD-Flow	61	2	color		M. Stoll, A. Bruhn, and S. Volz. Adaptive integration of feature matches into variational optic flow methods. ACCV 2012.
[67] SIOF	234	2	color		L. Xu, Z. Dai, and J. Jia. Scale invariant optical flow. ECCV 2012.
[68] MDP-Flow2	342	2	color		L. Xu, J. Jia, and Y. Matsushita. Motion detail preserving optical flow estimation. PAMI 34(9):1744-1757, 2012. Code available.
[69] TCOF	1421	all	gray		J. Sanchez, A. Salgado, and N. Monzon. Optical flow estimation with consistent spatio-temporal coherence models. VISAPP 2013.
[70] LME	476	2	color		W. Li, D. Cosker, M. Brown, and R. Tang. Optical flow estimation using Laplacian mesh energy. CVPR 2013.
[71] NN-field	362	2	color		L. Chen, H. Jin, Z. Lin, S. Cohen, and Y. Wu. Large displacement optical flow from nearest neighbor fields. CVPR 2013.
[72] FESL	3310	2	color		W. Dong, G. Shi, X. Hu, and Y. Ma. Nonlocal sparse and low-rank regularization for optical flow estimation. Submitted to IEEE TIP 2013.
[73] PMF	35	2	color		J. Lu, H. Yang, D. Min, and M. Do. PatchMatch filter: efficient edge-aware filtering meets randomized search for fast correspondence field estimation. CVPR 2013.
[74] FC-2Layers-FF	2662	4	color		D. Sun, J. Wulff, E. Sudderth, H. Pfister, and M. Black. A fully-connected layered model of foreground and background flow. CVPR 2013.
[75] Correlation Flow	290	2	color		M. Drulea and S. Nedevschi. Motion estimation using the correlation transform. TIP 2013. Matlab code.
[76] TC/T-Flow	341	5	color		M. Stoll, S. Volz, and A. Bruhn. Joint trilateral filtering for multiframe optical flow. ICIP 2013.
[77] OFLAF	1530	2	color		T. Kim, H. Lee, and K. Lee. Optical flow via locally adaptive fusion of complementary data costs. ICCV 2013.
[78] Periodicity	8000	4	color		G. Khachaturov, S. Gonzalez-Brambila, and J. Gonzalez-Trejo. Periodicity-based computation of optical flow. Submitted to Computacion y Sistemas (CyS) 2013.
[79] SILK	572	2	gray		P. Zille, C. Xu, T. Corpetti, L. Shao. Observation models based on scale interactions for optical flow estimation. Submitted to IEEE TIP.
[80] CRTflow	13	3	color		O. Demetz, D. Hafner, and J. Weickert. The complete rank transform: a tool for accurate and morphologically invariant matching of structures. BMVC 2013.
[81] SuperFlow	178	2	color		Anonymous. Superpixel based optical flow estimation. ICCV 2013 submission 507.
[82] Aniso-Texture	300	2	color		Anonymous. Texture information-based optical flow estimation using an incremental multi-resolution approach. ITC-CSCC 2013 submission 267.
[83] Classic+CPF	640	2	gray		Z. Tu, R. Veltkamp, and N. van der Aa. A combined post-filtering method to improve accuracy of variational optical flow estimation. Submitted to Pattern Recognition 2013.
[84] S2D-Matching	1200	2	color		Anonymous. Locally affine sparse-to-dense matching for motion and occlusion estimation. ICCV 2013 submission 1479.
[85] AGIF+OF	438	2	gray		Z. Tu, R. Poppe, and R. Veltkamp. Adaptive guided image filter to warped interpolation image for variational optical flow computation. Submitted to Signal Processing 2015.
[86] DeepFlow	13	2	color		P. Weinzaepfel, J. Revaud, Z. Harchaoui, and C. Schmid. DeepFlow: large displacement optical flow with deep matching. ICCV 2013.
[87] NNF-Local	673	2	color		Z. Chen, H. Jin, Z. Lin, S. Cohen, and Y. Wu. Large displacement optical flow with nearest neighbor field. Submitted to PAMI 2014.
[88] EPPM w/o HM	2.5	2	color		L. Bao, Q. Yang, and H. Jin. Fast edge-preserving PatchMatch for large displacement optical flow. CVPR 2014.
[89] MLDP_OF	165	2	gray		M. Mohamed, H. Rashwan, B. Mertsching, M. Garcia, and D. Puig. Illumination-robust optical flow approach using local directional pattern. IEEE TCSVT 24(9):1499-1508, 2014.
[90] RFlow	20	2	gray		S. Ali, C. Daul, and W. Blondel. Robust and accurate optical flow estimation for weak texture and varying illumination condition: Application to cystoscopy. IPTA 2014.
[91] SRR-TVOF-NL	32	all	color		P. Pohl, M. Sirotenko, E. Tolstaya, and V. Bucha. Edge preserving motion estimation with occlusions correction for assisted 2D to 3D conversion. IS&T/SPIE Electronic Imaging 2014.
[92] 2DHMM-SAS	157	2	color		M.-C. Shih, R. Shenoy, and K. Rose. A two-dimensional hidden Markov model with spatially-adaptive states with application of optical flow. ICIP 2014 submission.
[93] WLIF-Flow	700	2	color		Z. Tu, R. Veltkamp, N. van der Aa, and C. Van Gemeren. Weighted local intensity fusion method for variational optical flow estimation. Submitted to TIP 2014.
[94] FMOF	215	2	color		N. Jith, A. Ramakanth, and V. Babu. Optical flow estimation using approximate nearest neighbor field fusion. ICASSP 2014.
[95] TriFlow	150	2	color		TriFlow. Optical flow with geometric occlusion estimation and fusion of multiple frames. ECCV 2014 submission 914.
[96] ComponentFusion	6.5	2	color		Anonymous. Fast optical flow by component fusion. ECCV 2014 submission 941.
[97] AggregFlow	1642	2	color		D. Fortun, P. Bouthemy, and C. Kervrann. Aggregation of local parametric candidates and exemplar-based occlusion handling for optical flow. Preprint arXiv:1407.5759.
[98] 2bit-BM-tele	124	2	gray		R. Xu and D. Taubman. Robust dense block-based motion estimation using a two-bit transform on a Laplacian pyramid. ICIP 2013.
[99] HCIC-L	330	2	color		Anonymous. Globally-optimal image correspondence using a hierarchical graphical model. NIPS 2014 submission 114.
[100] TF+OM	600	2	color		R. Kennedy and C. Taylor. Optical flow with geometric occlusion estimation and fusion of multiple frames. EMMCVPR 2015.
[101] PH-Flow	800	2	color		J. Yang and H. Li. Dense, accurate optical flow estimation with piecewise parametric model. CVPR 2015.
[102] EpicFlow	16	2	color		J. Revaud, P. Weinzaepfel, Z. Harchaoui, and C. Schmid. EpicFlow: edge-preserving interpolation of correspondences for optical flow. CVPR 2015.
[103] NNF-EAC	380	2	color		Anonymous. Variational method for joint optical flow estimation and edge-aware image restoration. CVPR 2015 submission 2336.
[104] Heeger++	6600	5	gray		Anonymous. A context aware biologically inspired algorithm for optical flow (updated results). CVPR 2015 submission 2238.
[105] HBM-GC	330	2	color		A. Zheng and Y. Yuan. Motion estimation via hierarchical block matching and graph cut. Submitted to ICIP 2015.
[106] FFV1MT	358	5	gray		F. Solari, M. Chessa, N. Medathati, and P. Kornprobst. What can we expect from a V1-MT feedforward architecture for optical flow estimation? Submitted to Signal Processing: Image Communication 2015.
[107] ROF-ND	4	2	color		S. Ali, C. Daul, E. Galbrun, and W. Blondel. Illumination invariant large displacement optical flow using robust neighbourhood descriptors. Submitted to CVIU 2015.
[108] DeepFlow2	16	2	color		J. Revaud, P. Weinzaepfel, Z. Harchaoui, and C. Schmid. Deep convolutional matching. Submitted to IJCV, 2015.
[109] HAST	2667	2	color		Anonymous. Highly accurate optical flow estimation on superpixel tree. ICCV 2015 submission 2221.
[110] FlowFields	15	2	color		C. Bailer, B. Taetz, and D. Stricker. Flow Fields: Dense unregularized correspondence fields for highly accurate large displacement optical flow estimation. ICCV 2015.
[111] SVFilterOh	1.56	2	color		Anonymous. Fast estimation of large displacement optical flow using PatchMatch and dominant motion patterns. CVPR 2016 submission 1788.
[112] FlowNetS+ft+v	0.5	2	color		Anonymous. Learning optical flow with convolutional neural networks. ICCV 2015 submission 235.
[113] CombBMOF	51	2	color		M. Brüggemann, R. Kays, P. Springer, and O. Erdler. Combined block-matching and adaptive differential motion estimation in a hierarchical multi-scale framework. ICGIP 2014. (Method improved since publication.)
[114] PMMST	182	2	color		F. Zhang, S. Xu, and X. Zhang. High accuracy correspondence field estimation via MST based patch matching. Submitted to TIP 2015.
[115] DF-Auto	70	2	color		N. Monzon, A. Salgado, and J. Sanchez. Regularization strategies for discontinuity-preserving optical flow methods. Submitted to TIP 2015.
[116] CPM-Flow	3	2	color		Anonymous. Efficient coarse-to-fine PatchMatch for large displacement optical flow. CVPR 2016 submission 241.
[117] CNN-flow-warp+ref	1.4	3	color		D. Teney and M. Hebert. Learning to extract motion from videos in convolutional neural networks. ArXiv 1601.07532, 2016.
[118] Steered-L1	804	2	color		Anonymous. Optical flow estimation via steered-L1 norm. Submitted to WSCG 2016.
[119] StereoOF-V1MT	343	2	gray		Anonymous. Visual features for action-oriented tasks: a cortical-like model for disparity and optic flow computation. BMVC 2016 submission 132.
[120] PGM-C	5	2	color		Y. Li. Pyramidal gradient matching for optical flow estimation. Submitted to PAMI 2016.
[121] RNLOD-Flow	1040	2	gray		C. Zhang, Z. Chen, M. Wang, M. Li, and S. Jiang. Robust non-local TV-L1 optical flow estimation with occlusion detection. Submitted to TIP 2016.
[122] FlowNet2	0.091	2	color		Anonymous. FlowNet 2.0: Evolution of optical flow estimation with deep networks. CVPR 2017 submission 900.
[123] S2F-IF	20	2	color		Anonymous. S2F-IF: Slow-to-fast interpolator flow. CVPR 2017 submission 765.
[124] BriefMatch	0.068	2	gray		G. Eilertsen, P.-E. Forssen, and J. Unger. Dense binary feature matching for real-time optical flow estimation. SCIA 2017 submission 62.
[125] OAR-Flow	60	2	color		Anonymous. Order-adaptive regularisation for variational optical flow: global, local and in between. SSVM 2017 submission 20.
[126] AdaConv-v1	2.8	2	color		S. Niklaus, L. Mai, and F. Liu. (Interpolation results only.) Video frame interpolation via adaptive convolution. CVPR 2017.
[127] SepConv-v1	0.2	2	color		S. Niklaus, L. Mai, and F. Liu. (Interpolation results only.) Video frame interpolation via adaptive separable convolution. ICCV 2017.
[128] ProbFlowFields	37	2	color		A. Wannenwetsch, M. Keuper, and S. Roth. ProbFlow: joint optical flow and uncertainty estimation. ICCV 2017.
[129] UnFlow	0.12	2	color		Anonymous. UnFlow: Unsupervised learning of optical flow with a bidirectional census loss. Submitted to AAAI 2018.
[130] FlowFields+	10.5	2	color		C. Bailer, B. Taetz, and D. Stricker. Flow fields: Dense correspondence fields for highly accurate large displacement optical flow estimation. Submitted to PAMI 2017.
[131] Kuang	9.9	2	gray		F. Kuang. PatchMatch algorithms for motion estimation and stereo reconstruction. Master thesis, University of Stuttgart, 2017.

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