Optical flow evaluation results: R10.0 angle error

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

R10.0 angle error	avg.	Army (Hidden texture) GT im0 im1			Mequon (Hidden texture) GT im0 im1			Schefflera (Hidden texture) GT im0 im1			Wooden (Hidden texture) GT im0 im1			Grove (Synthetic) GT im0 im1			Urban (Synthetic) GT im0 im1			Yosemite (Synthetic) GT im0 im1			Teddy (Stereo) GT im0 im1
	rank	all	disc	untext	all	disc	untext	all	disc	untext	all	disc	untext	all	disc	untext	all	disc	untext	all	disc	untext	all	disc	untext
NNF-Local [87]	8.6	3.83 4	16.9 9	1.87 7	2.64 7	16.1 11	1.33 7	3.02 3	10.7 3	1.33 13	2.79 15	18.9 21	1.11 16	4.82 1	6.85 1	1.73 2	4.13 4	14.6 3	2.27 2	0.52 20	5.08 48	0.00 1	0.28 3	1.04 3	0.04 2
OFLAF [77]	9.1	3.84 5	17.2 11	1.99 12	2.48 5	14.1 5	1.41 8	2.96 2	10.1 2	1.17 9	2.36 8	14.7 7	0.82 9	5.15 3	7.90 7	2.15 4	6.43 32	18.2 12	4.98 18	0.27 2	2.42 4	0.07 16	0.79 9	1.88 7	1.81 22
MDP-Flow2 [68]	10.8	3.86 7	17.2 11	1.90 9	2.06 1	12.6 1	1.04 2	3.22 5	11.0 4	1.16 8	3.27 26	21.7 31	1.19 20	6.35 16	8.86 13	3.12 10	5.40 14	15.7 4	5.11 20	0.38 12	3.75 25	0.02 3	0.49 5	1.80 6	0.13 6
NN-field [71]	12.4	4.31 17	18.6 22	2.22 21	3.13 15	18.3 25	1.79 18	3.16 4	11.1 5	1.40 14	2.08 5	16.7 10	0.78 7	5.28 4	7.44 3	2.25 5	2.53 1	8.92 1	0.92 1	0.89 39	6.39 72	0.02 3	0.27 2	0.98 2	0.04 2
PMMST [114]	13.6	4.02 10	16.5 7	1.46 1	3.86 31	16.9 17	3.33 45	3.91 10	12.6 9	2.82 38	2.18 7	9.47 1	1.30 26	5.68 9	7.88 6	2.78 7	5.25 12	13.7 2	4.29 10	0.53 22	5.11 49	0.02 3	0.26 1	0.94 1	0.04 2
nLayers [57]	22.3	4.08 13	16.2 5	2.80 37	4.71 54	19.3 32	3.82 73	4.64 18	15.2 18	3.96 58	1.99 3	13.2 2	0.80 8	5.34 6	7.57 5	3.22 12	5.85 26	16.8 7	4.64 13	0.87 37	3.68 23	0.96 50	0.84 10	2.94 17	0.75 8
ComponentFusion [96]	23.2	4.03 11	17.7 15	2.19 20	2.17 4	13.1 3	1.26 5	3.86 9	13.2 11	1.58 16	2.85 18	18.0 14	1.03 14	6.68 23	9.59 22	4.14 32	8.35 69	27.3 68	8.34 80	0.60 24	4.03 30	0.52 39	0.76 8	2.22 11	1.09 10
LME [70]	23.5	3.70 3	16.1 4	1.69 2	2.13 2	13.0 2	1.19 4	5.91 44	15.4 19	7.43 85	3.23 22	22.4 35	1.19 20	6.60 22	9.12 16	4.39 41	6.11 28	20.8 21	6.60 50	0.52 20	4.96 46	0.07 16	1.09 14	3.28 24	1.86 24
SVFilterOh [111]	24.1	4.36 19	15.9 3	2.01 15	3.04 13	16.3 13	1.78 17	3.31 6	11.3 6	1.20 10	2.02 4	13.6 4	0.57 2	5.94 12	8.69 12	2.12 3	6.61 34	19.3 14	6.32 46	5.10 97	12.9 110	10.0 109	0.75 7	2.20 10	1.32 12
FC-2Layers-FF [74]	24.7	4.03 11	16.3 6	2.39 28	4.23 40	20.9 41	3.21 42	3.40 8	11.4 7	2.64 32	2.74 13	17.1 11	1.03 14	5.73 10	8.29 10	3.31 14	7.49 46	20.5 20	6.66 54	1.30 54	6.84 76	0.34 33	0.64 6	1.78 5	1.20 11
NNF-EAC [103]	25.3	4.32 18	18.6 22	2.18 19	2.69 8	15.1 7	1.64 13	3.93 11	13.1 10	1.27 11	4.17 58	23.0 44	1.95 55	7.09 31	9.97 29	3.89 28	6.33 30	17.4 9	5.53 23	0.55 23	5.18 50	0.02 3	1.60 39	4.32 40	1.93 27
HAST [109]	26.8	2.98 1	12.9 1	1.71 3	3.63 28	15.0 6	2.78 34	2.46 1	8.38 1	0.25 1	2.84 17	18.0 14	0.67 4	5.02 2	7.35 2	1.66 1	8.83 77	22.4 35	8.37 81	6.13 103	12.0 105	18.0 116	0.31 4	1.13 4	0.03 1
WLIF-Flow [93]	28.0	3.97 9	17.0 10	2.12 17	3.53 22	18.5 27	2.37 28	4.60 17	15.1 16	2.34 28	3.40 29	20.3 27	1.50 31	7.69 54	11.4 63	4.79 54	6.67 35	17.8 11	5.53 23	0.40 15	3.68 23	0.07 16	1.59 38	3.82 34	2.63 44
RNLOD-Flow [121]	28.9	3.58 2	15.7 2	1.83 6	3.60 26	19.9 35	2.06 22	5.53 38	18.1 40	2.42 30	2.75 14	17.7 12	1.01 13	6.01 13	9.09 15	3.98 29	7.21 40	20.0 16	6.87 63	2.59 75	9.67 88	1.95 67	1.06 13	2.66 14	1.79 21
FESL [72]	29.3	3.91 8	16.6 8	2.13 18	5.68 77	23.5 58	4.23 78	5.17 29	16.8 25	2.99 40	2.41 10	15.8 8	0.89 12	5.76 11	8.62 11	4.05 30	5.81 23	17.6 10	5.32 22	1.09 47	5.68 63	1.21 57	1.35 23	2.89 16	1.72 19
Layers++ [37]	29.4	4.39 20	17.8 16	3.14 45	3.70 29	18.0 22	2.84 35	3.37 7	11.5 8	2.65 33	2.38 9	14.1 6	0.82 9	5.33 5	7.52 4	3.78 26	7.58 49	22.0 31	6.13 40	1.81 70	7.08 78	0.54 40	1.45 30	2.46 13	4.56 80
ALD-Flow [66]	30.5	4.22 14	18.2 19	1.93 11	3.20 18	16.8 16	1.59 12	5.21 30	17.4 32	1.13 7	3.70 37	22.9 42	1.26 22	6.54 21	9.31 18	3.14 11	5.25 12	21.5 26	4.98 18	0.88 38	4.67 38	4.48 92	2.69 59	6.66 58	4.79 81
TC/T-Flow [76]	31.3	4.57 24	20.6 30	2.00 13	3.45 21	18.7 28	1.52 10	4.30 13	14.3 12	0.67 3	3.97 53	23.1 45	1.80 46	6.35 16	9.50 21	3.36 17	4.48 6	15.7 4	4.78 15	1.30 54	6.94 77	5.07 95	2.08 51	5.10 48	2.83 49
PMF [73]	31.8	4.65 26	18.3 21	2.34 24	3.37 19	18.2 24	1.92 20	4.20 12	14.6 14	1.01 6	3.24 24	18.6 18	1.11 16	5.50 7	8.09 8	2.43 6	6.99 38	24.8 51	6.27 44	6.87 106	17.3 121	8.77 108	0.91 11	2.06 8	2.06 30
Correlation Flow [75]	32.1	4.57 24	20.5 29	1.87 7	2.71 9	16.2 12	1.16 3	5.74 42	17.9 38	0.66 2	1.91 1	13.5 3	0.85 11	8.00 63	12.0 73	4.57 44	8.69 72	23.8 44	8.93 86	0.84 34	4.74 39	0.96 50	1.38 26	3.81 33	1.91 26
Efficient-NL [60]	33.1	4.24 15	17.4 13	2.24 22	4.30 41	21.8 44	2.75 32	5.26 33	16.9 27	2.67 34	3.43 31	21.0 29	1.74 41	6.02 14	9.16 17	3.31 14	7.84 58	21.7 28	6.26 43	1.36 57	6.79 75	1.03 53	1.48 33	3.08 20	1.77 20
AGIF+OF [85]	33.4	4.39 20	18.0 18	2.80 37	5.10 65	23.7 61	3.70 66	5.06 25	16.8 25	3.11 41	3.29 27	20.1 25	1.45 30	6.45 19	9.31 18	4.62 46	6.77 37	19.7 15	5.86 32	0.37 11	3.60 21	0.25 29	1.79 45	3.68 30	3.12 58
TC-Flow [46]	34.7	4.27 16	19.0 25	1.91 10	2.85 10	16.6 15	1.45 9	5.05 24	16.9 27	0.80 5	4.05 54	23.9 53	1.74 41	6.73 24	9.68 24	2.93 9	5.83 24	22.9 39	5.68 29	1.39 59	4.87 44	7.32 104	2.46 56	6.13 55	4.49 77
OAR-Flow [125]	36.2	5.41 45	21.6 41	2.61 31	4.96 61	22.3 47	2.88 36	7.90 59	23.8 58	4.19 62	4.45 65	22.7 41	1.90 50	7.03 29	10.1 32	3.39 18	5.10 10	22.3 33	4.56 12	0.29 4	2.64 8	0.17 25	1.58 37	4.89 47	1.68 17
Classic+CPF [83]	36.3	4.77 30	19.7 27	2.99 40	4.59 46	23.3 56	3.08 40	5.24 31	17.2 30	2.81 37	3.32 28	21.3 30	1.57 35	6.51 20	9.49 20	4.24 37	7.39 43	21.5 26	6.27 44	1.02 42	5.33 54	1.40 59	1.47 32	3.19 22	2.47 42
COFM [59]	39.1	4.75 29	20.2 28	2.63 33	3.40 20	18.3 25	2.14 24	6.19 48	19.3 46	4.00 59	3.04 20	18.8 19	1.11 16	7.45 44	10.1 32	7.01 85	8.80 75	20.9 22	6.68 55	1.41 60	3.66 22	2.76 79	1.22 17	2.28 12	3.72 68
PH-Flow [101]	39.2	5.11 37	21.0 32	3.62 59	4.59 46	22.4 48	3.37 49	4.37 14	14.5 13	3.45 46	3.93 50	22.5 37	2.07 62	6.34 15	9.00 14	3.74 24	7.28 42	21.7 28	6.39 48	1.61 65	5.58 61	1.58 62	1.15 16	2.12 9	3.39 63
IROF++ [58]	39.3	4.68 27	19.4 26	2.70 34	4.66 50	23.1 52	3.42 54	5.25 32	17.2 30	3.79 54	3.95 51	23.2 46	2.05 59	6.97 27	9.84 27	4.64 47	7.99 62	24.6 48	7.05 67	0.44 17	4.30 35	0.00 1	1.37 25	3.26 23	2.83 49
HBM-GC [105]	39.4	5.82 52	18.2 19	2.00 13	4.47 45	18.7 28	3.80 72	4.39 15	15.1 16	1.73 20	2.42 11	13.8 5	0.72 6	6.77 25	9.60 23	4.08 31	7.61 51	19.0 13	5.76 30	4.61 93	12.6 107	2.83 81	2.39 54	5.72 51	5.01 85
ProbFlowFields [128]	39.8	8.29 81	31.1 80	5.73 95	3.54 23	18.0 22	2.75 32	6.07 47	18.9 45	5.22 65	3.54 34	17.7 12	1.91 51	7.66 53	10.8 49	4.59 45	5.06 9	20.0 16	5.58 27	0.38 12	3.08 14	0.07 16	1.70 42	4.56 43	2.41 41
Sparse-NonSparse [56]	41.0	4.98 33	20.8 31	4.09 68	4.63 49	22.9 49	3.41 53	5.02 23	16.7 24	3.47 49	3.89 46	22.6 40	1.91 51	7.17 34	10.2 35	4.30 39	7.66 53	22.3 33	6.80 59	0.69 29	3.53 20	0.89 47	1.52 35	3.56 29	2.97 54
CostFilter [40]	41.0	5.29 43	22.0 43	2.85 39	3.54 23	17.7 20	2.16 25	4.64 18	16.0 20	1.75 21	3.68 36	22.5 37	1.27 24	5.67 8	8.14 9	2.85 8	7.76 55	25.9 58	6.80 59	6.98 109	24.2 126	12.9 111	1.43 27	4.11 37	2.02 29
MLDP_OF [89]	41.5	6.35 59	26.0 59	3.41 50	2.97 12	16.4 14	1.76 16	5.47 36	17.8 36	1.30 12	2.79 15	19.7 23	1.12 19	7.13 33	10.0 30	3.75 25	7.49 46	21.4 25	9.75 92	5.04 95	6.22 69	17.0 115	1.79 45	4.28 39	2.14 32
FMOF [94]	42.3	4.42 22	17.8 16	3.06 43	5.03 62	23.1 52	3.63 62	4.45 16	14.8 15	2.80 36	2.94 19	18.8 19	1.26 22	7.00 28	10.2 35	4.71 50	8.92 78	20.9 22	7.13 69	1.06 45	6.34 71	1.85 66	2.58 58	5.80 53	3.06 56
Classic+NL [31]	42.3	5.07 36	21.0 32	4.22 72	4.70 53	23.4 57	3.27 43	4.98 22	16.5 22	3.48 50	3.75 40	22.5 37	1.68 38	7.21 37	10.2 35	4.32 40	7.82 56	22.4 35	6.71 56	1.47 61	6.39 72	1.18 56	1.12 15	2.87 15	2.27 36
LSM [39]	42.5	5.00 35	21.2 38	3.93 65	4.62 48	22.9 49	3.37 49	5.13 27	17.1 29	3.26 44	3.80 41	22.9 42	1.87 48	6.92 26	9.78 25	4.41 43	7.71 54	22.4 35	6.74 57	1.00 41	4.76 41	1.16 55	1.68 41	3.94 35	2.90 53
Ramp [62]	42.9	5.12 38	21.1 36	3.82 64	4.68 52	23.2 54	3.47 57	4.89 21	16.3 21	3.46 47	3.83 43	22.3 34	1.93 54	7.23 38	10.2 35	4.80 55	7.61 51	22.1 32	6.80 59	1.20 50	5.04 47	1.43 60	1.36 24	2.98 18	2.31 40
Aniso-Texture [82]	44.4	3.84 5	17.5 14	1.76 4	2.88 11	15.9 9	2.11 23	7.10 56	20.9 51	2.30 24	1.97 2	16.5 9	0.57 2	8.24 69	11.9 70	5.22 68	8.82 76	26.6 65	6.77 58	8.34 115	16.2 120	1.43 60	2.42 55	5.55 49	2.84 51
NL-TV-NCC [25]	44.7	5.44 46	21.7 42	2.24 22	4.00 35	21.9 45	1.69 14	5.27 34	17.8 36	0.67 3	2.52 12	19.1 22	0.67 4	8.37 72	12.5 82	5.12 67	11.5 96	32.0 92	9.19 89	0.86 35	4.93 45	1.35 58	2.16 52	6.46 56	1.63 14
S2D-Matching [84]	44.9	4.97 32	21.3 40	3.55 56	4.74 55	23.6 59	3.35 47	6.50 50	20.9 51	3.46 47	3.49 32	20.4 28	1.60 36	7.07 30	10.0 30	4.22 35	7.82 56	23.1 40	6.87 63	1.78 69	5.90 66	2.12 69	1.30 20	3.14 21	2.74 46
TV-L1-MCT [64]	46.4	4.69 28	18.9 24	3.60 58	5.64 76	25.6 73	4.21 76	5.53 38	18.1 40	3.23 42	3.04 20	19.9 24	1.35 27	7.49 45	10.6 43	4.91 61	8.34 67	22.8 38	7.50 76	0.79 33	2.61 6	3.57 86	1.73 44	3.45 27	3.26 61
MDP-Flow [26]	46.5	5.65 50	24.7 55	4.93 86	3.70 29	17.6 19	3.40 52	5.47 36	18.7 44	4.66 63	3.87 44	24.3 56	1.88 49	7.12 32	9.89 28	5.00 65	6.17 29	25.9 58	4.66 14	0.61 25	5.65 62	0.05 13	3.28 71	8.39 71	3.45 66
AggregFlow [97]	46.8	6.17 56	23.3 50	2.58 30	7.01 86	28.0 91	5.29 87	8.46 64	24.2 60	7.66 87	3.73 38	20.2 26	1.73 40	7.25 39	10.6 43	3.52 19	4.43 5	16.4 6	4.80 17	0.75 32	5.43 57	0.25 29	1.92 48	4.46 42	4.12 72
IROF-TV [53]	47.8	5.22 41	22.6 48	3.59 57	4.80 57	24.2 65	3.73 71	5.71 41	18.4 43	3.64 52	4.19 59	25.7 70	1.92 53	7.63 52	10.7 46	5.26 69	9.22 82	30.2 82	6.60 50	0.30 6	2.86 10	0.02 3	1.32 22	3.76 32	2.27 36
CombBMOF [113]	49.6	6.51 61	28.6 69	2.61 31	3.98 34	18.7 28	2.29 27	5.29 35	17.4 32	2.33 27	5.12 76	26.1 76	3.28 81	6.35 16	9.81 26	3.34 16	12.0 98	28.4 73	15.1 110	3.73 86	12.8 109	0.76 44	0.98 12	3.00 19	0.09 5
OFH [38]	50.2	6.38 60	25.7 58	4.69 82	3.90 32	20.6 39	2.24 26	7.85 58	24.2 60	2.27 23	4.11 57	25.1 60	1.72 39	7.44 43	10.4 40	4.69 48	8.13 64	28.9 76	8.44 84	0.44 17	4.25 32	0.12 21	2.80 61	8.82 79	2.74 46
Adaptive [20]	50.5	5.12 38	22.0 43	2.34 24	4.82 59	23.2 54	3.50 58	8.67 69	24.5 65	3.56 51	4.19 59	25.3 66	1.83 47	7.40 42	10.6 43	3.63 21	5.84 25	23.2 42	3.75 8	3.25 83	8.86 85	0.89 47	2.87 63	6.69 59	3.14 60
Sparse Occlusion [54]	50.5	4.99 34	21.1 36	2.79 36	4.13 38	20.1 38	3.00 39	5.94 46	19.4 47	2.15 22	3.41 30	21.8 32	1.35 27	8.17 68	12.1 76	4.74 51	7.87 60	25.6 57	6.34 47	11.4 120	17.7 122	2.71 78	1.64 40	4.70 46	1.81 22
Occlusion-TV-L1 [63]	50.8	5.23 42	22.2 45	2.36 27	4.40 43	21.2 42	3.39 51	8.46 64	24.8 66	3.83 56	3.92 48	24.8 58	1.74 41	9.11 84	13.1 94	5.75 75	4.65 7	23.9 45	3.52 6	1.27 53	3.13 16	0.44 36	3.56 77	8.92 80	3.28 62
RFlow [90]	51.6	5.85 53	24.8 57	4.44 78	3.18 16	17.9 21	1.88 19	7.81 57	24.4 64	2.32 26	3.25 25	23.4 49	1.55 32	7.94 57	11.6 66	4.86 57	8.23 65	28.0 72	6.64 53	1.16 49	2.13 2	1.13 54	4.10 87	9.22 86	6.81 93
2DHMM-SAS [92]	52.5	5.14 40	21.0 32	3.79 63	5.26 70	25.2 70	3.45 55	6.97 55	20.2 48	4.18 61	4.06 55	23.3 48	2.10 63	7.18 35	10.2 35	4.92 62	8.29 66	23.7 43	7.16 70	1.26 51	5.41 56	1.63 63	1.71 43	3.75 31	2.74 46
ACK-Prior [27]	52.7	5.49 48	24.0 53	1.81 5	2.55 6	15.7 8	0.83 1	5.07 26	17.7 35	1.52 15	2.14 6	18.1 16	0.50 1	8.64 75	11.6 66	7.10 88	14.6 109	30.7 84	11.7 99	8.46 116	11.5 101	19.5 118	3.68 81	7.25 62	2.64 45
SimpleFlow [49]	53.4	5.65 50	22.4 47	4.93 86	5.47 75	24.5 68	4.28 79	6.88 54	21.0 53	3.95 57	4.74 69	25.2 62	3.02 75	7.19 36	10.1 32	4.70 49	8.34 67	23.1 40	7.16 70	1.02 42	4.61 37	0.89 47	1.29 19	3.44 25	2.47 42
S2F-IF [123]	55.7	9.49 94	37.6 100	4.93 86	4.81 58	25.6 73	3.34 46	8.25 62	26.1 69	6.40 73	4.99 73	25.6 69	2.93 73	7.80 55	11.0 54	4.90 60	5.61 17	24.9 54	5.83 31	0.62 27	5.35 55	0.22 27	1.43 27	4.11 37	1.67 16
Complementary OF [21]	55.8	7.27 70	30.0 74	4.31 73	3.18 16	18.9 31	1.52 10	5.91 44	20.2 48	2.31 25	4.22 62	24.8 58	2.05 59	7.50 47	10.4 40	4.99 64	12.3 101	31.7 91	8.87 85	0.61 25	2.69 9	1.72 65	3.33 72	9.22 86	4.88 84
ROF-ND [107]	56.6	6.70 62	27.6 65	3.53 54	3.08 14	16.0 10	1.73 15	5.81 43	18.3 42	1.58 16	3.81 42	18.4 17	2.20 64	9.45 89	14.0 106	6.31 82	11.3 95	29.6 80	7.27 73	9.92 118	10.8 93	7.29 103	1.53 36	3.44 25	1.64 15
TCOF [69]	57.3	7.04 68	26.9 62	3.54 55	4.93 60	23.7 61	3.45 55	9.94 83	27.8 77	7.40 84	3.74 39	23.7 52	1.55 32	10.0 98	14.3 107	4.40 42	4.91 8	17.0 8	5.53 23	5.08 96	9.68 89	4.19 90	1.43 27	4.44 41	1.69 18
PGM-C [120]	58.6	9.47 92	37.1 97	4.81 85	5.08 63	26.1 78	3.63 62	8.75 71	27.6 75	7.02 78	5.65 84	28.1 91	3.63 89	7.99 60	11.3 60	4.88 58	5.71 21	24.5 47	5.97 34	0.31 7	3.01 13	0.02 3	2.07 50	6.50 57	2.14 32
TF+OM [100]	59.0	6.03 55	23.7 52	2.78 35	4.39 42	19.9 35	3.57 59	8.73 70	23.0 57	11.2 93	3.57 35	23.2 46	1.36 29	7.98 59	11.1 57	5.89 77	8.95 79	25.3 56	7.06 68	1.68 67	11.2 98	0.20 26	3.56 77	8.35 70	4.18 73
Steered-L1 [118]	59.1	4.54 23	21.2 38	2.09 16	2.13 2	13.9 4	1.31 6	4.80 20	16.5 22	1.64 18	3.87 44	25.1 60	1.60 36	8.62 74	11.5 65	7.01 85	11.1 92	28.7 74	10.4 95	12.0 124	12.3 106	34.9 126	5.90 98	9.03 84	11.6 106
FlowFields [110]	60.5	9.65 96	37.6 100	5.13 89	5.09 64	25.9 76	3.72 68	8.92 73	28.3 81	7.07 80	5.45 80	26.0 74	3.82 90	7.95 58	11.2 59	5.01 66	5.75 22	26.1 61	6.01 35	0.40 15	3.29 18	0.12 21	1.92 48	5.99 54	1.89 25
DeepFlow2 [108]	61.0	6.80 64	28.5 68	2.99 40	5.20 69	22.9 49	3.60 61	8.88 72	26.2 70	5.75 70	5.76 86	26.8 81	3.41 86	7.34 41	10.7 46	3.58 20	5.86 27	24.8 51	6.22 42	1.02 42	3.78 27	3.08 84	4.35 90	9.84 90	5.80 88
EPPM w/o HM [88]	61.2	8.62 85	33.5 88	3.62 59	3.58 25	19.7 33	1.93 21	6.19 48	20.5 50	1.64 18	4.64 67	25.2 62	2.54 67	7.60 51	10.4 40	5.81 76	11.2 93	31.6 89	9.82 93	6.91 108	8.93 86	15.9 114	1.48 33	4.06 36	2.01 28
FlowFields+ [130]	61.2	9.76 98	38.1 104	5.31 91	5.14 66	26.2 80	3.72 68	8.99 74	28.6 83	7.15 83	5.09 75	25.9 73	3.29 82	7.82 56	11.0 54	4.94 63	5.22 11	24.7 50	5.18 21	0.70 31	5.85 65	0.30 32	1.79 45	5.77 52	1.45 13
CPM-Flow [116]	61.9	9.47 92	37.1 97	4.79 83	5.15 67	26.3 81	3.67 64	8.59 68	27.1 73	7.00 77	5.59 82	27.8 88	3.57 87	7.99 60	11.3 60	4.74 51	5.70 20	24.1 46	6.05 37	0.48 19	4.29 34	0.02 3	2.76 60	7.63 66	4.11 71
EpicFlow [102]	63.0	9.44 91	37.1 97	4.80 84	5.15 67	26.4 82	3.70 66	9.58 79	30.0 85	7.07 80	5.38 79	27.8 88	3.29 82	8.01 65	11.3 60	4.88 58	5.67 19	24.6 48	6.12 39	0.32 10	3.13 16	0.02 3	3.10 69	7.52 64	4.79 81
ComplOF-FED-GPU [35]	63.2	6.96 66	30.7 77	3.33 48	4.74 55	24.9 69	2.66 30	6.71 52	22.4 54	2.45 31	4.44 64	26.2 77	2.05 59	7.50 47	10.7 46	4.20 34	9.78 83	34.0 97	9.47 91	2.42 74	4.74 39	6.63 101	3.09 67	9.17 85	3.91 70
SRR-TVOF-NL [91]	64.4	7.45 73	28.6 69	3.09 44	6.20 80	26.1 78	3.90 74	9.82 81	28.4 82	5.78 71	3.96 52	23.5 50	1.55 32	7.55 49	10.8 49	5.27 70	9.21 81	26.7 66	7.25 72	5.74 100	11.5 101	4.01 88	1.30 20	3.49 28	2.19 35
TV-L1-improved [17]	64.8	5.52 49	23.4 51	3.42 51	4.13 38	20.8 40	2.96 37	8.29 63	24.2 60	3.64 52	4.06 55	24.4 57	1.77 44	8.34 71	12.1 76	4.15 33	13.7 105	38.4 106	14.9 107	4.40 92	10.1 91	2.14 70	3.33 72	8.42 72	3.40 64
F-TV-L1 [15]	65.0	8.70 86	31.4 82	8.47 103	7.61 90	27.3 87	5.86 88	11.0 86	28.0 78	5.73 69	5.75 85	28.7 93	3.32 85	7.28 40	10.8 49	3.72 22	6.59 33	26.4 62	4.38 11	1.26 51	5.30 53	0.44 36	3.04 66	7.76 67	2.29 39
SIOF [67]	66.2	5.37 44	22.6 48	2.34 24	6.11 78	28.4 92	4.30 80	12.6 92	29.2 84	14.4 94	5.52 81	27.4 85	3.00 74	8.96 83	12.6 84	6.02 78	8.72 73	27.9 70	7.93 77	0.38 12	3.48 19	0.02 3	3.09 67	7.58 65	4.85 83
DPOF [18]	66.3	9.01 88	34.7 90	3.68 62	6.16 79	25.4 72	4.32 81	5.55 40	17.9 38	3.36 45	3.92 48	25.3 66	2.00 57	8.14 67	11.0 54	6.05 79	10.5 88	27.9 70	8.16 79	9.33 117	6.19 68	21.0 120	1.46 31	4.57 44	0.80 9
Aniso. Huber-L1 [22]	66.5	5.98 54	24.2 54	3.23 47	8.53 94	27.3 87	7.91 93	9.64 80	25.6 68	5.52 67	5.00 74	25.7 70	2.75 71	8.66 76	12.8 89	4.74 51	7.60 50	24.8 51	3.51 5	3.65 85	7.24 79	3.00 83	2.57 57	6.69 59	2.86 52
Kuang [131]	66.8	9.07 89	37.0 96	4.51 80	5.33 73	27.5 89	3.72 68	9.47 77	30.4 87	6.52 75	4.70 68	25.3 66	2.62 70	8.00 63	11.4 63	5.58 72	8.05 63	28.7 74	8.99 87	0.31 7	3.11 15	0.02 3	3.00 65	7.43 63	5.99 90
BriefMatch [124]	67.9	4.78 31	21.0 32	2.40 29	4.00 35	19.8 34	2.68 31	5.13 27	17.5 34	2.41 29	3.23 22	22.1 33	1.28 25	9.81 96	12.0 73	13.1 116	17.2 111	33.8 96	17.8 113	7.84 111	12.7 108	22.3 121	8.01 110	10.5 96	16.1 117
Classic++ [32]	68.2	5.46 47	22.2 45	4.35 75	4.66 50	22.1 46	3.57 59	8.00 60	24.3 63	5.06 64	4.21 61	25.2 62	2.01 58	8.77 79	12.7 87	5.47 71	9.03 80	30.2 82	7.29 74	2.92 81	7.73 81	3.10 85	3.83 84	8.53 73	3.87 69
LocallyOriented [52]	68.6	8.05 79	30.6 76	3.63 61	8.09 92	30.8 98	6.17 90	12.3 91	32.3 92	7.04 79	4.88 72	25.2 62	2.88 72	8.80 81	12.7 87	4.27 38	5.41 15	20.4 18	6.07 38	1.35 56	6.03 67	0.99 52	3.73 83	8.62 75	4.18 73
CRTflow [80]	70.8	7.63 76	31.8 85	3.42 51	4.40 43	21.2 42	2.97 38	8.99 74	26.6 71	4.11 60	4.86 71	26.5 78	2.57 68	7.99 60	11.7 68	3.26 13	18.0 115	40.2 109	22.2 118	1.47 61	4.45 36	2.51 76	4.73 92	11.4 101	7.30 94
DeepFlow [86]	71.2	7.55 74	29.3 72	4.67 81	6.29 82	23.7 61	4.86 83	10.0 84	28.0 78	8.76 92	6.15 93	27.3 83	3.83 91	7.49 45	10.8 49	3.72 22	6.40 31	26.8 67	6.85 62	1.12 48	2.92 12	3.94 87	7.07 103	11.2 100	12.7 108
TriFlow [95]	71.2	7.87 78	30.1 75	3.19 46	7.12 87	24.4 67	7.15 92	13.9 95	31.4 89	20.0 99	3.50 33	22.4 35	1.77 44	8.70 77	11.7 68	7.03 87	7.51 48	21.9 30	6.63 52	28.6 127	14.7 117	78.3 129	2.16 52	5.57 50	2.14 32
Rannacher [23]	73.0	6.99 67	27.1 63	5.36 93	5.27 71	24.3 66	4.22 77	9.51 78	27.1 73	5.54 68	4.76 70	25.7 70	2.58 69	8.80 81	12.9 91	4.82 56	11.0 90	35.7 100	9.36 90	2.33 73	4.76 41	2.39 75	2.82 62	8.01 68	3.13 59
Brox et al. [5]	73.4	8.32 82	32.6 86	6.95 98	6.23 81	26.9 86	5.23 85	9.13 76	27.6 75	6.55 76	5.85 88	28.2 92	3.26 79	10.2 100	12.9 91	11.0 111	5.43 16	29.3 79	4.79 16	0.86 35	4.00 29	0.12 21	4.32 88	10.2 93	4.54 79
Bartels [41]	73.6	6.83 65	26.2 60	5.19 90	3.93 33	17.4 18	3.30 44	6.63 51	22.6 55	3.25 43	4.45 65	23.9 53	2.48 66	9.12 85	12.1 76	8.25 96	10.6 89	31.1 85	12.3 102	5.74 100	10.4 92	18.9 117	5.34 94	9.52 88	8.47 100
Local-TV-L1 [65]	73.9	9.60 95	30.8 78	7.89 102	12.7 100	30.2 97	13.3 100	15.9 101	32.3 92	17.3 96	6.19 94	28.0 90	3.84 92	7.55 49	10.9 53	4.22 35	7.48 45	26.4 62	6.02 36	0.28 3	1.87 1	0.15 24	9.10 112	10.8 98	20.5 118
Dynamic MRF [7]	74.4	7.74 77	31.6 84	4.44 78	4.12 37	23.6 59	2.47 29	8.49 66	28.0 78	2.83 39	4.25 63	27.4 85	2.41 65	8.61 73	12.0 73	6.08 80	14.5 108	43.2 113	14.9 107	0.64 28	2.35 3	4.51 93	9.85 115	15.6 117	15.3 115
SuperFlow [81]	74.8	7.15 69	27.4 64	3.52 53	10.4 97	27.8 90	11.2 97	14.5 99	31.5 91	22.4 101	5.93 89	31.6 99	3.23 78	8.77 79	11.9 70	8.59 100	5.61 17	25.9 58	3.72 7	3.76 88	11.1 97	0.37 35	3.59 79	8.96 82	3.01 55
CBF [12]	78.7	6.32 57	26.2 60	3.35 49	11.1 98	25.6 73	13.7 101	8.51 67	24.1 59	7.12 82	5.12 76	26.0 74	3.04 77	10.3 101	13.6 101	9.59 107	7.85 59	26.4 62	4.25 9	11.8 121	13.8 112	14.2 113	3.54 76	8.06 69	5.32 86
CLG-TV [48]	79.8	6.33 58	24.7 55	4.13 69	9.08 96	26.6 83	9.31 96	9.85 82	26.8 72	5.82 72	5.30 78	26.5 78	3.03 76	10.4 103	14.6 111	7.57 91	7.95 61	31.1 85	6.51 49	5.92 102	11.4 99	4.36 91	3.41 74	8.81 78	3.06 56
TriangleFlow [30]	80.0	7.35 71	28.2 67	4.31 73	5.35 74	25.2 70	3.36 48	8.00 60	24.8 66	2.70 35	3.90 47	24.1 55	1.97 56	12.9 117	17.8 123	10.7 109	13.1 103	32.3 94	13.9 105	4.71 94	16.1 119	4.04 89	3.65 80	8.73 77	5.69 87
DF-Auto [115]	81.5	9.74 97	34.1 89	4.36 76	14.1 103	31.9 101	15.4 103	15.6 100	33.1 98	23.6 103	5.94 90	27.3 83	3.59 88	10.4 103	14.8 114	6.97 84	3.80 3	21.1 24	2.46 3	5.25 99	11.4 99	0.49 38	4.33 89	10.4 94	4.33 75
p-harmonic [29]	82.0	8.47 83	36.3 94	7.17 99	5.27 71	24.1 64	4.39 82	11.2 88	31.4 89	8.13 91	7.18 99	32.4 100	5.24 100	8.04 66	11.1 57	6.89 83	9.82 84	36.4 102	10.6 96	2.61 76	5.51 59	0.54 40	4.07 86	9.01 83	4.34 76
CNN-flow-warp+ref [117]	82.8	9.81 100	35.7 92	7.67 101	8.14 93	26.0 77	8.55 94	14.3 97	35.8 101	15.7 95	6.69 96	30.3 96	4.31 96	9.17 87	12.2 79	8.71 102	7.03 39	29.6 80	5.55 26	0.69 29	3.77 26	2.00 68	7.79 108	12.1 105	8.13 99
FlowNet2 [122]	84.2	21.7 114	43.8 108	13.5 107	24.6 115	42.3 114	27.3 116	19.8 104	40.5 104	29.9 109	8.21 103	23.6 51	5.39 102	9.85 97	12.6 84	8.54 98	8.76 74	28.9 76	5.89 33	2.77 78	15.5 118	0.81 46	1.28 18	4.68 45	0.26 7
FlowNetS+ft+v [112]	85.4	7.57 75	29.4 73	3.96 66	7.50 89	26.6 83	6.48 91	14.3 97	32.7 96	17.5 97	7.55 100	31.3 97	5.28 101	10.5 105	14.7 112	7.49 90	6.75 36	27.8 69	6.97 66	4.01 90	8.84 84	6.77 102	3.52 75	9.71 89	3.61 67
SegOF [10]	86.2	12.6 103	34.9 91	7.20 100	21.3 111	36.9 109	25.3 114	21.6 107	40.5 104	31.8 113	14.1 111	37.7 108	10.8 106	10.3 101	12.5 82	12.6 115	10.2 86	40.2 109	11.2 98	0.29 4	2.91 11	0.07 16	2.90 64	8.68 76	2.07 31
Fusion [6]	86.9	8.51 84	37.6 100	6.69 97	3.62 27	20.0 37	3.08 40	6.82 53	22.6 55	6.47 74	5.78 87	31.3 97	4.29 95	11.2 113	14.7 112	10.6 108	14.0 106	35.2 98	15.0 109	7.88 112	14.3 115	2.22 71	5.35 95	11.0 99	8.56 101
LDOF [28]	86.9	8.22 80	31.4 82	4.08 67	7.64 91	29.4 94	5.87 89	10.7 85	30.3 86	7.99 90	7.80 101	36.8 106	4.86 99	9.14 86	12.4 81	8.24 95	8.58 70	32.0 92	8.38 82	1.75 68	5.26 52	5.02 94	5.52 96	12.9 109	6.04 91
Learning Flow [11]	89.4	6.74 63	28.1 66	3.03 42	6.37 83	28.7 93	5.02 84	11.8 89	32.6 95	7.93 89	6.87 98	33.2 103	4.32 97	12.5 116	17.4 121	7.78 92	9.98 85	35.2 98	8.41 83	2.66 77	10.9 95	2.24 72	6.76 102	13.7 111	6.41 92
StereoFlow [44]	90.2	58.0 129	76.4 129	63.7 126	51.8 128	66.9 129	48.3 124	51.0 129	73.0 129	41.6 123	63.5 129	83.4 129	56.7 127	13.3 119	13.7 102	19.1 122	3.63 2	20.4 18	2.73 4	0.26 1	2.49 5	0.05 13	4.06 85	8.57 74	5.81 89
Ad-TV-NDC [36]	91.2	21.2 113	36.8 95	34.1 121	25.9 117	38.5 111	29.9 117	23.5 111	41.0 106	27.1 104	13.3 108	32.4 100	13.3 111	8.75 78	13.2 95	3.82 27	7.43 44	25.1 55	6.92 65	1.50 63	4.84 43	0.34 33	17.1 124	15.9 121	37.2 127
Second-order prior [8]	91.7	7.35 71	31.2 81	4.16 70	6.80 85	29.5 95	5.27 86	11.8 89	33.3 99	7.78 88	6.05 91	27.2 82	3.90 93	9.67 94	13.8 104	5.74 74	14.0 106	41.8 112	11.7 99	6.86 105	9.72 90	7.61 106	4.72 91	10.1 92	7.78 97
HBpMotionGpu [43]	92.8	11.7 101	32.8 87	6.34 96	18.9 108	35.4 107	22.0 111	22.3 108	42.7 109	31.1 112	5.62 83	26.7 80	3.31 84	9.47 90	13.0 93	8.55 99	8.68 71	31.2 87	5.58 27	6.88 107	11.9 103	0.64 42	7.67 107	11.4 101	15.2 113
StereoOF-V1MT [119]	94.5	9.29 90	44.8 110	4.17 71	7.22 88	34.5 104	3.68 65	13.7 94	42.6 108	3.80 55	6.06 92	38.5 110	3.27 80	11.0 110	15.1 116	9.55 106	15.1 110	49.9 119	14.0 106	1.08 46	5.51 59	5.44 96	9.33 114	15.5 116	9.73 104
Shiralkar [42]	94.8	9.76 98	46.6 112	4.40 77	6.53 84	31.3 100	4.04 75	12.7 93	37.5 102	5.34 66	6.47 95	32.9 102	4.34 98	8.33 70	11.9 70	5.58 72	17.4 114	43.3 115	15.5 111	6.82 104	8.77 83	14.0 112	7.36 105	15.7 120	7.83 98
SPSA-learn [13]	95.0	15.7 108	48.8 114	16.5 109	16.6 105	35.0 106	17.5 106	21.4 106	42.3 107	29.7 108	12.6 106	37.4 107	12.3 109	9.64 92	12.8 89	9.16 103	11.0 90	37.9 105	12.2 101	0.98 40	3.88 28	0.05 13	8.38 111	11.6 103	15.2 113
Filter Flow [19]	97.0	14.6 105	38.2 105	8.96 105	12.4 99	34.6 105	11.3 98	20.2 105	38.3 103	30.1 110	19.2 113	43.4 114	18.6 114	10.0 98	13.4 97	9.43 105	10.3 87	31.4 88	9.08 88	8.21 114	19.6 123	0.79 45	3.72 82	6.85 61	3.41 65
Modified CLG [34]	97.2	15.7 108	43.7 107	12.2 106	19.1 109	33.3 103	23.7 112	25.1 112	47.4 112	35.6 118	13.2 107	35.5 105	11.1 107	10.7 107	14.4 109	9.36 104	7.26 41	35.8 101	6.19 41	1.66 66	5.21 51	6.43 99	5.94 99	13.8 113	7.73 96
2D-CLG [1]	98.5	24.4 116	51.8 117	19.4 113	27.4 118	38.7 112	33.8 120	34.6 119	57.7 118	42.2 125	33.4 123	57.1 124	32.9 122	9.64 92	12.2 79	11.0 111	11.2 93	40.2 109	12.8 103	0.31 7	2.62 7	0.25 29	6.33 100	13.7 111	7.33 95
GraphCuts [14]	98.5	12.6 103	36.1 93	5.46 94	14.7 104	39.4 113	12.5 99	17.8 103	35.6 100	29.1 107	6.86 97	33.7 104	4.15 94	9.33 88	12.6 84	8.69 101	23.0 120	31.6 89	15.5 111	3.52 84	7.38 80	11.7 110	5.33 93	9.87 91	8.75 102
IAOF2 [51]	98.5	8.72 87	30.9 79	5.32 92	13.9 102	31.1 99	15.4 103	14.1 96	33.0 97	18.2 98	30.8 121	42.2 113	36.4 123	9.74 95	13.8 104	6.09 81	12.0 98	33.4 95	7.96 78	7.92 113	13.9 113	7.49 105	5.69 97	10.6 97	4.51 78
BlockOverlap [61]	101.5	12.3 102	29.2 71	8.49 104	13.7 101	29.6 96	15.3 102	16.2 102	32.5 94	20.0 99	8.87 104	27.4 85	7.62 104	10.9 109	13.4 97	12.5 114	13.3 104	29.1 78	10.3 94	11.8 121	14.4 116	23.8 122	10.6 116	8.92 80	24.8 120
IAOF [50]	103.0	14.7 106	37.8 103	14.8 108	17.3 107	33.2 102	18.7 107	22.7 109	44.3 110	23.3 102	20.9 115	38.7 111	24.5 118	9.60 91	13.3 96	8.28 97	13.0 102	38.8 107	7.37 75	4.20 91	7.90 82	2.59 77	14.5 121	13.4 110	32.0 125
Black & Anandan [4]	104.1	15.1 107	45.4 111	18.1 112	16.6 105	36.3 108	16.9 105	23.3 110	44.9 111	27.8 105	13.5 109	38.1 109	13.1 110	11.1 112	15.7 117	7.97 93	11.6 97	39.6 108	11.0 97	5.17 98	9.06 87	2.27 73	7.28 104	12.3 106	10.2 105
2bit-BM-tele [98]	104.9	20.3 112	39.1 106	26.1 117	8.84 95	26.8 85	9.29 95	11.1 87	30.7 88	7.60 86	8.06 102	29.9 95	5.91 103	11.0 110	13.7 102	11.8 113	18.0 115	37.1 103	19.8 117	17.1 125	20.4 125	30.8 125	6.54 101	11.7 104	11.8 107
UnFlow [129]	105.0	45.7 126	58.8 118	25.7 116	28.2 119	44.0 115	31.2 119	38.6 125	68.3 127	37.0 120	19.4 114	46.0 115	16.6 113	13.8 121	14.9 115	18.2 121	20.9 118	49.2 118	23.5 119	2.86 79	6.76 74	0.22 27	3.12 70	10.4 94	2.27 36
GroupFlow [9]	105.8	22.9 115	47.1 113	26.7 119	28.4 120	50.0 121	30.8 118	25.4 113	52.4 114	30.6 111	9.32 105	29.6 94	8.14 105	10.7 107	13.4 97	7.16 89	23.0 120	46.3 116	27.8 123	1.56 64	5.72 64	2.76 79	8.00 109	12.5 107	15.3 115
Nguyen [33]	106.0	20.0 111	44.7 109	17.4 111	39.5 124	37.5 110	52.5 125	34.0 118	56.0 117	38.8 121	35.6 124	47.9 117	41.1 124	12.1 114	14.3 107	16.5 119	12.0 98	37.8 104	13.8 104	1.37 58	4.27 33	0.71 43	11.6 119	14.5 115	20.8 119
SILK [79]	110.8	26.9 117	51.7 116	36.6 123	22.3 113	45.5 117	24.5 113	28.8 114	54.7 116	34.2 114	18.4 112	41.6 112	15.8 112	13.1 118	16.5 118	16.1 118	19.1 117	47.8 117	19.3 116	2.87 80	4.22 31	6.53 100	15.9 122	19.1 122	25.8 121
Heeger++ [104]	111.8	42.8 124	66.4 128	26.2 118	25.0 116	60.7 128	19.8 110	38.4 124	66.9 124	28.0 106	23.5 117	49.3 118	19.7 115	10.6 106	13.4 97	8.12 94	40.8 127	67.2 129	45.1 127	2.04 72	10.9 95	1.70 64	11.2 117	15.6 117	12.8 109
Horn & Schunck [3]	113.2	19.9 110	61.0 122	23.3 114	19.4 110	44.3 116	19.1 108	29.5 115	58.8 120	34.9 116	21.0 116	49.9 119	21.2 116	12.3 115	16.5 118	10.8 110	17.3 113	50.6 121	18.0 114	7.23 110	11.9 103	2.34 74	13.4 120	22.2 124	14.9 112
Periodicity [78]	116.8	30.6 120	48.8 114	16.7 110	24.1 114	49.8 119	26.2 115	39.1 126	54.5 115	39.5 122	13.6 110	47.1 116	12.0 108	37.5 129	48.2 129	33.6 128	38.5 126	66.9 128	36.0 126	2.02 71	10.8 93	8.18 107	20.8 125	35.9 128	30.1 123
TI-DOFE [24]	117.5	44.7 125	66.3 127	66.5 128	44.2 126	50.5 122	54.8 127	43.5 128	72.0 128	44.7 127	48.6 126	63.3 125	54.0 126	13.6 120	17.7 122	15.1 117	17.2 111	50.3 120	19.0 115	3.07 82	5.50 58	2.93 82	21.5 126	24.7 126	33.9 126
FFV1MT [106]	117.5	39.5 122	59.3 119	25.4 115	21.8 112	56.3 127	19.1 108	38.0 123	67.0 125	34.9 116	24.3 118	55.7 123	22.2 117	17.7 125	18.9 125	25.5 126	41.8 128	66.3 127	45.5 128	3.73 86	12.9 110	6.35 98	11.2 117	15.6 117	12.8 109
SLK [47]	119.0	28.9 119	63.3 125	36.4 122	42.3 125	54.0 126	52.8 126	36.6 121	67.7 126	42.5 126	51.4 127	54.3 122	60.0 128	14.5 123	16.7 120	20.8 124	21.5 119	53.4 125	24.1 120	3.92 89	6.27 70	5.91 97	21.7 127	23.7 125	31.5 124
Adaptive flow [45]	121.8	49.6 127	62.1 123	66.8 129	37.4 122	46.5 118	43.1 122	34.9 120	58.6 119	41.7 124	27.3 120	53.5 121	28.7 119	16.1 124	18.2 124	17.6 120	25.3 124	52.9 123	25.1 122	45.4 128	38.1 128	74.4 127	9.25 113	14.4 114	13.9 111
PGAM+LK [55]	122.1	35.2 121	65.7 126	44.1 125	31.5 121	51.1 123	36.1 121	30.9 116	60.0 122	36.8 119	33.0 122	72.3 128	32.7 121	13.8 121	14.4 109	22.6 125	24.6 122	53.2 124	24.6 121	27.1 126	32.6 127	26.2 123	17.0 123	20.4 123	28.4 122
FOLKI [16]	122.3	27.4 118	59.9 120	40.6 124	37.4 122	51.5 124	46.6 123	32.4 117	61.6 123	34.2 114	26.3 119	50.6 120	30.2 120	18.2 127	19.7 126	26.3 127	24.6 122	56.6 126	28.8 124	10.3 119	13.9 113	26.7 124	27.1 128	26.9 127	45.3 128
HCIC-L [99]	122.8	51.6 128	60.9 121	30.9 120	58.4 129	53.4 125	73.0 129	39.8 127	50.8 113	52.8 129	63.4 128	71.0 127	69.9 129	18.1 126	20.6 127	20.5 123	29.9 125	43.2 113	34.1 125	73.0 129	62.0 129	76.4 128	7.45 106	12.5 107	8.87 103
Pyramid LK [2]	125.5	41.0 123	62.5 124	66.4 127	47.2 127	49.9 120	59.7 128	37.5 122	59.5 121	45.5 128	43.8 125	65.1 126	49.5 125	36.5 128	43.8 128	42.6 129	43.3 129	52.8 122	45.9 129	11.8 121	20.2 124	20.7 119	40.0 129	46.5 129	59.5 129
AdaConv-v1 [126]	130.0	99.3 130	97.8 130	99.8 130	99.9 130	100.0 130	99.8 130	99.9 130	99.9 130	99.9 130	99.5 130	99.9 130	99.9 130	99.9 130	99.9 130	99.9 130	99.1 130	98.9 130	99.7 130	98.5 130	93.0 130	100.0 130	99.9 130	99.9 130	99.9 130
SepConv-v1 [127]	130.0	99.3 130	97.8 130	99.8 130	99.9 130	100.0 130	99.8 130	99.9 130	99.9 130	99.9 130	99.5 130	99.9 130	99.9 130	99.9 130	99.9 130	99.9 130	99.1 130	98.9 130	99.7 130	98.5 130	93.0 130	100.0 130	99.9 130	99.9 130	99.9 130

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.