Optical flow evaluation results: A75 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

A75 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]	14.9	2.17 4	5.35 5	1.92 4	1.56 4	6.39 19	1.67 9	1.51 7	3.67 8	1.61 7	1.20 34	4.36 16	1.02 36	2.30 4	3.18 3	1.73 6	2.16 37	6.35 8	2.39 61	2.54 20	4.18 12	2.17 25	0.81 9	1.50 16	0.67 4
NN-field [71]	15.2	2.31 8	5.94 13	1.98 5	1.83 21	7.21 35	1.97 27	1.54 8	3.55 7	1.68 11	0.96 9	3.04 4	0.75 5	2.30 4	3.25 4	1.69 5	1.72 17	3.81 1	1.65 6	3.12 47	4.76 49	2.60 46	0.79 6	1.58 24	0.59 2
TC/T-Flow [76]	18.0	2.06 1	7.42 29	1.55 1	1.61 10	6.77 28	1.52 3	1.46 5	4.33 16	1.56 4	0.88 2	6.96 39	0.69 1	2.91 19	4.35 25	1.88 10	1.47 4	6.12 7	1.61 5	2.22 5	3.98 3	4.00 86	1.06 38	1.99 42	1.19 48
ComponentFusion [96]	18.4	2.12 3	6.18 15	1.80 3	1.67 13	4.82 4	1.91 22	1.34 2	4.01 10	1.47 2	0.88 2	4.74 17	0.72 3	2.99 23	4.35 25	1.96 13	2.03 29	11.2 68	1.96 26	2.90 40	4.62 44	1.90 14	0.93 25	1.56 23	0.89 15
ALD-Flow [66]	20.9	2.26 6	5.81 10	2.07 6	1.56 4	5.71 13	1.66 7	1.46 5	4.64 22	1.66 10	0.95 8	7.15 43	0.80 10	3.18 32	4.57 34	1.76 7	1.51 5	7.63 20	1.60 4	2.61 23	4.27 17	3.90 82	1.04 36	2.22 53	1.18 45
WLIF-Flow [93]	21.3	2.63 18	5.51 6	2.41 16	2.14 42	7.08 32	2.32 41	1.63 11	4.19 13	1.84 17	1.05 14	4.21 13	0.88 22	2.92 21	4.37 27	2.30 28	2.02 28	7.21 16	1.89 22	2.73 30	4.27 17	2.78 52	0.84 10	1.37 5	0.83 10
nLayers [57]	22.7	2.33 9	5.14 3	2.17 9	2.75 87	7.22 37	3.07 88	1.69 19	4.04 11	2.21 60	0.88 2	2.83 2	0.70 2	2.08 2	3.25 4	1.30 1	1.88 20	6.06 6	1.85 13	2.89 39	4.59 39	2.28 30	0.92 22	1.48 14	0.94 25
OFLAF [77]	23.1	2.77 34	5.70 8	2.49 18	1.76 16	5.35 9	1.84 20	1.54 8	2.69 2	1.72 13	1.30 40	3.55 9	1.12 49	2.30 4	3.62 7	1.64 4	2.23 42	5.93 3	2.06 36	2.81 34	4.29 20	2.99 57	1.15 47	1.69 29	1.18 45
RNLOD-Flow [121]	23.7	2.19 5	4.96 2	2.19 10	1.79 18	7.20 34	1.76 15	1.42 3	4.31 15	1.56 4	0.97 10	3.42 8	0.84 15	2.75 14	4.16 16	2.01 15	1.86 19	7.24 17	1.95 25	4.02 81	6.41 96	4.48 101	0.90 17	1.51 17	0.85 11
MDP-Flow2 [68]	23.8	3.08 39	6.23 16	2.73 42	1.55 3	4.80 3	1.64 6	1.63 11	3.27 4	1.61 7	1.37 51	5.15 21	1.15 54	2.91 19	4.19 19	2.20 20	2.24 44	6.43 9	2.17 45	2.62 24	4.35 24	1.88 13	1.08 40	1.66 28	0.95 28
OAR-Flow [125]	24.8	2.55 12	7.57 30	2.36 14	1.81 19	7.94 43	1.94 26	1.72 26	8.40 57	1.95 24	0.94 7	5.90 31	0.79 8	3.49 40	4.83 42	1.84 9	1.16 2	7.84 25	1.22 2	1.93 2	3.63 1	2.26 28	1.10 43	2.16 49	1.34 56
AGIF+OF [85]	26.1	2.60 13	6.24 17	2.45 17	2.49 68	9.30 62	2.63 68	1.68 17	4.79 28	2.08 45	1.05 14	4.34 15	0.81 11	2.77 16	4.05 13	2.10 18	1.92 23	7.48 19	1.74 11	2.69 27	4.48 31	2.69 49	0.87 11	1.40 6	0.95 28
Layers++ [37]	26.4	2.70 29	6.40 19	2.83 49	2.33 59	6.62 25	2.54 65	1.65 14	3.24 3	2.02 34	0.92 5	2.48 1	0.75 5	2.12 3	3.11 1	1.50 3	2.06 32	8.25 27	1.94 24	3.59 69	5.41 71	3.21 60	0.89 14	1.32 4	0.90 18
LME [70]	27.6	2.90 36	5.83 11	2.30 13	1.60 7	4.45 2	1.74 14	1.71 23	4.14 12	2.00 30	1.35 46	6.61 34	1.13 50	3.07 28	4.32 24	2.57 40	2.08 34	8.09 26	2.00 32	2.78 33	4.53 36	2.29 31	1.06 38	1.74 31	0.96 32
HAST [109]	27.9	2.09 2	4.28 1	1.69 2	1.60 7	5.31 8	1.55 4	1.28 1	2.09 1	1.40 1	0.92 5	3.39 7	0.78 7	2.01 1	3.14 2	1.48 2	2.40 56	8.62 34	2.41 63	4.08 83	7.33 115	7.69 117	1.23 54	1.59 26	1.73 71
TC-Flow [46]	28.8	2.45 11	6.60 21	2.39 15	1.25 1	5.24 6	1.32 1	1.45 4	4.40 19	1.50 3	1.15 31	8.13 50	1.04 38	3.22 33	4.77 40	2.06 17	1.94 24	8.65 35	2.09 40	2.33 15	4.51 33	3.82 80	1.24 55	2.18 51	1.50 68
PH-Flow [101]	29.9	2.62 15	7.58 32	2.53 23	2.13 39	8.78 52	2.37 44	1.70 20	4.39 18	2.06 41	1.08 17	7.06 42	0.85 16	2.72 11	3.91 11	2.04 16	2.06 32	8.33 28	1.96 26	3.48 65	4.62 44	4.03 87	0.90 17	1.41 9	0.88 13
Classic+CPF [83]	30.2	2.65 24	7.22 28	2.53 23	2.37 61	9.14 59	2.51 61	1.67 16	5.05 32	2.03 36	1.01 11	5.38 24	0.79 8	2.90 18	4.17 17	2.33 30	1.88 20	8.44 30	1.70 8	3.19 50	4.60 41	3.72 73	0.92 22	1.40 6	0.95 28
Sparse-NonSparse [56]	30.4	2.62 15	7.58 32	2.60 31	2.18 48	8.74 50	2.46 57	1.68 17	4.86 29	2.00 30	1.04 12	7.97 48	0.81 11	3.13 30	4.45 31	2.42 35	1.98 27	8.53 32	1.87 17	3.13 48	4.32 23	3.51 67	0.88 12	1.41 9	0.91 19
NNF-EAC [103]	30.5	3.07 38	6.73 23	2.70 41	1.62 12	5.30 7	1.72 12	1.71 23	3.89 9	1.79 15	1.36 49	6.36 33	1.15 54	3.02 24	4.37 27	2.28 26	2.44 59	6.90 12	2.28 56	2.90 40	4.51 33	2.19 26	1.12 44	1.83 36	0.98 34
FC-2Layers-FF [74]	30.7	2.63 18	5.87 12	2.68 39	2.19 52	8.07 45	2.39 49	1.65 14	3.42 5	2.05 38	1.10 23	3.11 5	0.88 22	2.57 7	3.49 6	2.30 28	2.26 46	7.68 22	2.17 45	3.70 71	5.18 66	3.73 74	0.90 17	1.41 9	0.93 23
IROF++ [58]	31.2	2.66 25	6.82 24	2.58 29	2.17 46	9.07 57	2.41 50	1.75 32	5.03 31	2.06 41	1.11 25	7.65 45	0.90 28	2.92 21	4.18 18	2.25 24	2.13 35	9.85 50	1.98 29	2.53 19	4.53 36	1.43 3	0.97 33	1.58 24	0.94 25
COFM [59]	33.2	2.28 7	7.19 26	2.08 8	1.78 17	6.57 22	1.93 24	1.56 10	5.33 37	2.19 58	0.86 1	4.90 18	0.73 4	3.76 49	4.80 41	3.87 86	2.03 29	7.67 21	1.72 9	2.76 31	4.21 13	4.04 89	1.62 77	1.87 38	2.04 82
FESL [72]	33.4	2.63 18	5.15 4	2.77 46	2.62 80	9.27 61	2.73 74	1.72 26	4.77 26	2.07 44	1.15 31	3.36 6	0.98 34	2.75 14	3.93 12	2.20 20	1.95 25	7.12 14	1.98 29	3.40 60	5.71 80	2.89 54	0.88 12	1.55 20	0.87 12
Efficient-NL [60]	33.9	2.38 10	5.67 7	2.07 6	2.45 67	8.54 47	2.55 66	1.64 13	4.63 21	1.95 24	1.04 12	5.76 29	0.81 11	2.74 12	4.14 15	1.94 12	2.87 72	8.58 33	2.23 49	3.30 55	5.12 63	3.05 58	1.15 47	1.83 36	1.19 48
LSM [39]	35.6	2.60 13	7.70 36	2.61 33	2.19 52	8.77 51	2.44 55	1.70 20	4.78 27	2.06 41	1.08 17	8.13 50	0.86 18	3.05 26	4.30 22	2.47 36	2.18 39	8.66 38	2.08 38	3.56 68	4.68 48	3.74 75	0.90 17	1.43 12	0.93 23
PMMST [114]	35.7	3.55 60	6.48 20	3.33 69	2.18 48	6.49 21	2.47 60	1.93 44	4.28 14	2.09 46	1.60 68	2.84 3	1.43 72	2.60 8	3.72 9	1.91 11	2.22 41	6.05 5	2.13 43	2.70 29	4.48 31	2.08 20	1.18 53	1.91 40	1.09 42
Classic+NL [31]	36.5	2.63 18	7.57 30	2.64 37	2.18 48	9.04 55	2.41 50	1.71 23	4.70 23	2.09 46	1.08 17	7.69 46	0.88 22	3.04 25	4.31 23	2.41 34	2.27 47	8.65 35	2.09 40	3.79 74	5.12 63	3.81 78	0.89 14	1.44 13	0.89 15
FMOF [94]	37.1	2.71 31	6.71 22	2.62 35	2.64 81	9.19 60	2.77 75	1.73 29	4.37 17	2.22 61	1.06 16	5.18 22	0.81 11	2.89 17	4.25 21	2.40 33	2.31 50	7.76 24	1.96 26	3.35 58	4.88 58	3.81 78	0.93 25	1.55 20	0.92 21
Ramp [62]	37.5	2.64 23	7.64 35	2.56 27	2.20 55	8.90 54	2.46 57	1.73 29	4.74 25	2.09 46	1.11 25	6.81 38	0.88 22	3.07 28	4.46 32	2.38 32	2.28 48	8.52 31	2.15 44	3.40 60	4.25 14	4.16 96	0.94 27	1.53 19	0.97 33
ProbFlowFields [128]	38.4	3.31 48	13.8 82	2.85 51	2.03 31	6.67 27	2.23 36	1.89 42	6.06 42	2.29 65	1.23 37	5.14 20	0.95 31	3.70 46	5.08 45	2.29 27	1.60 9	7.44 18	1.85 13	2.43 18	4.30 21	2.45 41	1.29 57	2.32 55	1.38 60
2DHMM-SAS [92]	40.0	2.62 15	7.61 34	2.53 23	2.17 46	10.1 70	2.38 47	1.83 37	6.16 45	2.10 50	1.09 21	8.07 49	0.86 18	3.06 27	4.42 30	2.37 31	2.16 37	9.30 44	2.01 33	3.50 66	4.64 46	4.14 95	0.96 31	1.64 27	0.99 37
Adaptive [20]	40.0	2.63 18	8.08 40	2.23 11	2.18 48	8.66 49	2.27 39	2.04 51	9.57 64	2.09 46	1.12 28	10.6 68	0.87 20	4.47 91	5.30 60	4.44 99	1.55 6	8.65 35	1.43 3	3.32 56	5.51 73	2.14 24	0.77 5	1.52 18	0.75 8
PMF [73]	41.0	3.22 45	6.34 18	2.60 31	1.95 27	6.66 26	1.92 23	1.85 38	4.58 20	1.83 16	1.62 69	4.27 14	1.37 67	2.61 9	3.74 10	1.83 8	3.18 80	9.94 53	3.34 84	5.29 107	8.26 121	5.58 108	0.68 3	1.21 3	0.67 4
SVFilterOh [111]	41.0	3.48 57	5.72 9	3.45 73	2.38 63	6.06 16	2.41 50	1.93 44	3.46 6	2.05 38	1.53 64	3.71 10	1.27 63	2.65 10	4.10 14	2.00 14	2.47 61	7.08 13	2.28 56	4.60 93	7.10 108	5.92 109	0.73 4	1.16 2	0.70 7
S2D-Matching [84]	42.1	2.69 28	7.86 38	2.68 39	2.19 52	9.11 58	2.44 55	1.77 33	6.11 44	2.04 37	1.13 29	5.86 30	0.91 29	3.17 31	4.41 29	2.50 37	2.41 57	9.09 41	2.25 53	4.00 80	5.16 65	4.07 90	0.91 21	1.40 6	0.95 28
TV-L1-MCT [64]	42.3	2.68 27	6.83 25	2.61 33	2.68 82	10.3 73	2.80 78	1.78 34	5.24 34	2.24 62	1.13 29	5.44 26	0.87 20	3.39 38	4.69 39	2.96 57	2.45 60	9.14 42	2.31 59	2.64 25	4.37 26	2.04 18	1.08 40	1.74 31	1.36 58
SimpleFlow [49]	43.9	2.74 32	8.28 44	2.73 42	2.50 69	9.73 68	2.83 83	1.89 42	6.81 50	2.35 67	1.11 25	10.4 65	0.89 27	3.27 34	4.47 33	2.63 42	3.03 75	8.91 40	2.39 61	3.10 46	4.25 14	2.76 51	0.89 14	1.49 15	0.89 15
Occlusion-TV-L1 [63]	44.5	3.15 41	8.42 46	2.50 19	2.03 31	7.42 39	2.14 33	2.24 62	9.79 66	2.16 56	1.35 46	9.59 58	1.11 45	4.10 71	5.77 88	3.22 67	1.68 16	9.21 43	2.08 38	2.69 27	4.59 39	1.70 9	1.05 37	2.36 58	0.98 34
IROF-TV [53]	44.8	2.89 35	8.67 49	2.81 48	2.25 56	9.54 66	2.51 61	1.79 36	5.50 39	2.15 55	1.53 64	11.5 73	1.27 63	3.33 35	4.62 35	2.85 50	2.78 67	13.5 88	2.57 67	2.15 4	4.14 9	1.37 2	0.94 27	1.55 20	0.94 25
MDP-Flow [26]	45.0	3.14 40	9.81 55	2.83 49	2.06 34	6.10 17	2.43 53	1.87 40	6.10 43	2.10 50	1.44 55	8.90 54	1.15 54	3.37 36	4.62 35	2.54 39	2.35 53	10.4 58	2.23 49	2.88 38	4.83 53	1.94 16	1.27 56	2.62 62	1.09 42
Correlation Flow [75]	45.2	3.18 43	7.85 37	2.85 51	1.74 14	5.77 14	1.69 11	1.94 46	5.25 35	1.70 12	1.47 59	5.67 28	1.26 62	3.66 43	5.20 54	2.53 38	3.06 76	9.57 46	3.10 80	3.42 63	4.94 59	4.03 87	1.17 51	1.80 33	1.16 44
AggregFlow [97]	47.7	3.29 47	8.49 47	3.14 61	2.70 84	12.2 90	2.68 71	2.32 66	9.03 59	2.88 83	1.44 55	4.19 12	1.25 61	3.48 39	5.09 46	2.19 19	1.55 6	5.36 2	1.68 7	2.56 22	4.78 51	1.77 11	1.54 73	2.15 47	2.16 85
Aniso-Texture [82]	49.1	2.75 33	6.00 14	3.09 58	2.13 39	5.64 11	2.52 64	1.78 34	6.80 49	2.20 59	1.08 17	4.01 11	0.92 30	4.08 68	5.44 70	3.26 71	2.31 50	11.8 73	2.26 54	5.87 114	8.09 120	4.24 97	0.80 8	1.70 30	0.67 4
OFH [38]	49.6	3.60 68	10.3 58	3.80 83	1.58 6	7.05 30	1.66 7	1.70 20	9.23 60	1.58 6	1.19 33	10.1 60	1.08 42	3.98 54	5.22 56	3.57 76	2.80 68	12.6 80	3.12 81	2.30 13	4.60 41	2.35 33	1.41 69	2.90 74	1.75 72
CostFilter [40]	50.5	3.59 65	8.35 45	3.26 65	2.12 37	6.60 23	2.16 34	2.00 50	5.56 40	2.01 32	2.04 84	7.05 41	1.89 85	2.74 12	3.70 8	2.27 25	3.29 81	10.3 57	3.33 83	5.22 105	9.79 126	6.16 111	0.38 1	1.08 1	0.35 1
Classic++ [32]	50.9	2.66 25	8.18 43	2.65 38	2.13 39	7.96 44	2.43 53	1.85 38	9.39 63	2.10 50	1.09 21	10.4 65	0.88 22	3.97 52	5.60 81	2.89 53	2.36 54	13.6 91	2.10 42	4.03 82	5.20 67	4.33 98	0.99 35	2.05 44	0.92 21
DeepFlow2 [108]	51.1	3.44 53	12.6 72	3.36 71	1.98 28	8.60 48	2.10 31	2.38 69	11.1 70	2.60 75	1.34 43	14.6 83	1.11 45	3.53 41	5.09 46	2.23 23	1.66 15	9.90 52	1.77 12	2.76 31	4.06 6	3.40 64	1.72 81	3.21 84	2.13 83
S2F-IF [123]	52.3	3.54 59	19.2 108	2.58 29	2.41 65	10.4 74	2.59 67	2.57 75	10.6 69	2.59 73	1.29 39	10.5 67	0.98 34	4.07 66	5.38 65	2.90 54	1.65 14	9.94 53	1.85 13	2.27 8	4.26 16	2.35 33	1.33 61	2.49 61	1.32 53
FlowFields+ [130]	53.5	3.58 63	19.0 104	2.56 27	2.57 74	10.8 77	2.78 76	2.72 81	11.9 76	2.78 81	1.32 41	10.9 70	1.03 37	3.97 52	5.34 62	2.74 43	1.64 13	9.79 49	1.87 17	2.26 7	4.30 21	2.35 33	1.35 62	2.62 62	1.34 56
RFlow [90]	53.8	3.62 70	9.91 56	3.53 76	1.83 21	5.50 10	1.93 24	2.14 57	9.57 64	1.86 19	1.32 41	6.75 35	1.14 52	3.98 54	5.35 63	3.24 68	2.39 55	11.7 72	2.24 51	3.45 64	4.60 41	3.63 68	1.64 79	2.90 74	1.91 78
CPM-Flow [116]	54.2	3.47 55	19.0 104	2.52 20	2.59 76	11.0 82	2.82 80	2.56 73	11.3 72	2.75 77	1.34 43	15.7 87	1.06 39	4.02 59	5.42 67	2.78 45	1.59 8	9.39 45	1.87 17	2.30 13	4.17 11	2.36 36	1.35 62	2.69 68	1.39 62
PGM-C [120]	54.7	3.48 57	19.0 104	2.52 20	2.59 76	10.8 77	2.82 80	2.59 76	11.8 75	2.75 77	1.34 43	16.5 91	1.06 39	4.03 61	5.46 72	2.78 45	1.60 9	9.63 47	1.88 20	2.28 9	3.98 3	2.36 36	1.37 64	2.69 68	1.45 63
EpicFlow [102]	56.4	3.47 55	18.9 102	2.52 20	2.59 76	10.9 80	2.82 80	2.64 78	14.2 85	2.75 77	1.35 46	15.5 85	1.06 39	4.04 63	5.48 73	2.88 52	1.62 12	9.70 48	1.91 23	2.28 9	4.08 8	2.36 36	1.39 68	2.71 70	1.51 69
MLDP_OF [89]	56.9	4.16 86	10.4 59	4.04 85	2.04 33	6.61 24	2.04 29	2.36 68	6.60 48	2.05 38	1.43 53	5.65 27	1.18 58	3.75 48	4.86 43	2.96 57	2.96 73	8.71 39	3.47 86	4.20 86	5.51 73	7.24 114	1.16 50	1.87 38	1.21 50
FlowFields [110]	56.9	3.56 62	19.0 104	2.54 26	2.57 74	10.6 76	2.79 77	2.72 81	11.7 74	2.76 80	1.42 52	10.9 70	1.13 50	4.08 68	5.43 69	2.92 55	1.60 9	10.5 60	1.86 16	2.28 9	4.35 24	2.46 43	1.38 67	2.62 62	1.36 58
TV-L1-improved [17]	57.2	2.70 29	9.05 51	2.29 12	1.85 23	7.06 31	1.97 27	1.94 46	9.28 62	1.90 23	1.10 23	8.96 55	0.85 16	4.07 66	5.60 81	2.75 44	5.44 110	17.3 102	6.29 113	4.75 100	6.82 101	4.73 104	1.13 45	2.68 67	1.06 41
BriefMatch [124]	57.6	3.03 37	7.96 39	2.73 42	1.75 15	6.88 29	1.73 13	1.72 26	4.70 23	1.73 14	1.51 63	5.38 24	1.39 70	4.01 57	5.27 58	3.72 81	5.57 111	15.9 96	6.02 112	4.65 94	6.85 102	8.98 121	0.95 29	2.30 54	1.77 73
Kuang [131]	57.8	3.27 46	18.0 97	2.62 35	2.32 57	12.0 88	2.37 44	2.45 72	13.3 84	2.42 68	1.21 36	10.3 63	0.97 33	4.22 77	5.64 83	3.06 62	2.30 49	12.5 78	2.50 66	2.23 6	4.15 10	2.36 36	1.30 60	2.79 71	1.47 65
Steered-L1 [118]	58.2	3.21 44	8.15 42	3.11 60	1.39 2	4.13 1	1.51 2	1.73 29	5.20 33	1.65 9	1.28 38	10.3 63	1.11 45	4.05 65	5.35 63	3.55 74	3.10 77	12.6 80	2.59 68	6.15 119	6.90 103	13.1 126	1.73 82	3.04 82	2.39 89
CombBMOF [113]	59.0	3.55 60	11.6 67	2.79 47	2.52 70	7.21 35	2.51 61	1.88 41	5.63 41	1.84 17	1.67 74	11.2 72	1.51 77	3.68 44	4.62 35	3.07 63	4.08 93	11.4 70	4.79 103	4.72 98	6.58 99	3.82 80	0.92 22	1.82 35	0.88 13
Sparse Occlusion [54]	60.5	3.36 49	8.08 40	2.90 53	2.61 79	7.68 40	3.01 87	2.10 53	6.40 47	2.13 53	1.45 57	6.80 36	1.14 52	4.01 57	5.31 61	2.81 48	2.55 63	10.4 58	2.21 48	6.70 122	8.26 121	4.34 99	1.15 47	2.08 45	0.99 37
DeepFlow [86]	61.0	3.94 78	12.7 73	4.14 89	2.12 37	9.06 56	2.28 40	2.84 85	12.5 80	3.16 88	1.68 75	15.6 86	1.44 74	3.58 42	5.10 48	2.20 20	1.78 18	11.1 66	1.88 20	2.65 26	4.07 7	3.40 64	2.08 96	3.59 97	3.09 99
EPPM w/o HM [88]	61.8	4.03 83	13.7 81	3.25 64	1.91 25	7.71 41	1.83 18	2.14 57	7.85 55	1.96 26	1.80 77	10.2 61	1.63 81	3.72 47	4.62 35	3.24 68	3.93 91	13.2 86	3.79 94	4.35 90	5.68 79	7.45 115	0.97 33	1.93 41	0.98 34
Complementary OF [21]	64.0	4.47 93	12.4 71	4.63 96	1.60 7	6.16 18	1.67 9	2.10 53	6.85 51	2.16 56	2.27 89	9.76 59	2.19 93	4.00 56	5.10 48	3.71 79	3.96 92	12.9 83	3.32 82	2.83 35	4.46 30	3.08 59	2.04 94	3.33 88	2.86 94
HBM-GC [105]	64.5	5.52 99	7.21 27	5.03 101	2.96 90	7.15 33	3.23 90	2.79 84	4.90 30	2.88 83	3.12 100	4.92 19	2.97 100	3.37 36	4.23 20	3.46 72	3.80 90	6.63 10	3.52 87	5.86 113	7.23 113	4.53 102	0.64 2	2.02 43	0.64 3
TF+OM [100]	65.6	3.58 63	9.07 52	2.75 45	2.07 35	6.43 20	2.37 44	1.99 49	7.56 54	2.78 81	2.07 85	7.02 40	2.07 91	4.19 76	5.12 51	4.32 95	3.15 79	10.1 56	3.00 76	4.10 84	6.00 85	3.92 83	1.54 73	2.98 79	1.94 79
Rannacher [23]	66.3	3.60 68	11.3 64	3.27 66	2.41 65	9.53 65	2.63 68	2.60 77	11.9 76	2.58 72	1.36 49	12.1 75	1.09 43	4.22 77	5.90 94	3.14 65	3.63 86	16.1 98	2.75 72	3.72 72	5.24 68	3.70 72	0.96 31	2.16 49	0.91 19
Aniso. Huber-L1 [22]	66.6	3.17 42	9.57 54	3.05 56	3.72 94	11.5 86	4.38 94	2.86 87	10.5 68	3.80 92	1.70 76	11.6 74	1.42 71	4.04 63	5.58 78	2.98 59	2.34 52	9.88 51	2.05 35	4.49 92	5.91 84	3.42 66	1.08 40	2.10 46	1.02 39
ComplOF-FED-GPU [35]	67.1	4.09 85	12.8 74	4.09 88	1.61 10	9.86 69	1.62 5	2.12 55	8.39 56	1.87 21	1.85 79	12.4 77	1.70 84	3.95 51	5.25 57	3.25 70	3.54 84	15.1 95	3.60 91	3.93 77	4.83 53	4.60 103	1.55 75	2.93 78	1.80 74
F-TV-L1 [15]	67.7	5.69 101	13.3 76	6.62 105	2.71 85	12.0 88	2.86 85	2.76 83	12.6 81	2.43 70	2.41 93	16.3 90	2.02 90	4.17 74	5.27 58	3.74 82	2.41 57	10.8 64	2.49 65	3.04 44	4.84 57	2.26 28	0.79 6	1.81 34	0.76 9
TCOF [69]	67.8	3.95 79	11.0 62	4.20 90	2.56 73	9.31 63	2.68 71	2.71 80	12.8 83	3.34 90	2.30 90	6.80 36	2.33 95	4.50 93	6.28 107	2.58 41	1.89 22	6.02 4	2.06 36	4.72 98	6.30 90	2.58 44	1.37 64	2.63 65	1.22 52
NL-TV-NCC [25]	67.9	3.89 76	8.49 47	3.34 70	2.52 70	8.44 46	2.38 47	2.25 63	5.49 38	1.99 28	1.87 80	7.61 44	1.53 78	4.36 85	5.91 95	2.78 45	4.12 96	13.0 84	3.58 90	3.85 75	5.74 81	3.79 77	1.63 78	2.81 72	1.46 64
ACK-Prior [27]	68.2	4.28 88	9.53 53	3.85 84	1.87 24	5.68 12	1.83 18	1.97 48	5.25 35	1.96 26	1.98 83	5.26 23	1.65 82	4.08 68	5.12 51	3.79 85	4.53 102	13.0 84	3.61 92	5.63 109	6.40 94	8.50 119	1.92 91	2.90 74	2.64 91
ROF-ND [107]	68.7	4.03 83	11.1 63	3.48 75	2.33 59	5.09 5	2.22 35	2.19 60	6.22 46	2.02 34	2.30 90	5.92 32	1.68 83	4.23 79	6.03 101	2.94 56	3.70 88	12.2 75	3.05 78	6.21 120	6.93 105	5.53 107	1.43 70	2.21 52	1.32 53
CRTflow [80]	72.1	3.65 71	14.0 84	3.10 59	2.16 44	7.88 42	2.23 36	2.25 63	11.2 71	1.99 28	1.56 66	12.7 78	1.35 65	4.02 59	5.53 77	3.01 60	6.86 116	19.6 112	8.64 118	3.29 53	5.53 75	3.23 61	2.05 95	3.95 106	2.71 92
LDOF [28]	73.1	3.72 72	14.9 88	3.59 79	2.38 63	14.0 97	2.46 57	2.69 79	14.4 86	2.55 71	1.48 61	33.9 111	1.10 44	4.24 81	5.59 80	3.75 83	2.04 31	16.4 100	1.99 31	2.83 35	4.83 53	2.43 40	2.28 103	4.02 107	3.38 102
SRR-TVOF-NL [91]	73.7	4.62 96	12.2 70	3.55 77	2.32 57	10.8 77	2.34 42	2.56 73	12.4 79	2.59 73	1.49 62	8.56 53	1.17 57	4.12 72	5.10 48	3.51 73	2.63 64	10.9 65	2.26 54	5.64 110	6.92 104	4.13 94	2.19 101	2.87 73	2.86 94
DPOF [18]	73.9	4.32 89	16.2 91	3.30 67	2.69 83	10.2 71	2.69 73	2.44 71	7.17 52	2.61 76	1.95 82	10.2 61	1.55 79	3.85 50	5.20 54	3.03 61	2.84 70	11.1 66	2.67 69	4.71 97	4.83 53	8.84 120	1.73 82	3.03 81	1.86 75
LocallyOriented [52]	74.2	3.46 54	14.6 86	3.01 55	2.84 89	13.3 93	2.85 84	2.92 89	17.6 92	3.05 87	1.63 71	10.6 68	1.43 72	4.23 79	5.79 89	3.19 66	2.48 62	7.69 23	2.87 74	3.41 62	5.63 77	3.25 62	1.65 80	3.48 92	1.86 75
SIOF [67]	74.2	4.00 81	8.74 50	3.46 74	2.00 30	13.6 94	2.13 32	3.02 93	15.7 88	3.38 91	2.55 96	13.5 82	2.50 97	4.27 82	5.70 84	3.70 78	3.55 85	11.5 71	4.01 95	3.17 49	4.64 46	2.12 23	1.85 90	3.29 87	2.15 84
Second-order prior [8]	74.9	3.40 50	13.6 78	3.19 62	2.16 44	13.8 96	2.34 42	2.43 70	17.1 90	2.26 63	1.20 34	15.7 87	0.96 32	4.44 89	6.10 103	3.08 64	3.41 83	19.7 113	2.67 69	5.42 108	6.02 87	5.40 106	1.44 71	3.44 91	1.48 66
Brox et al. [5]	75.5	4.01 82	14.7 87	4.49 94	2.75 87	11.5 86	3.21 89	2.33 67	12.2 78	2.34 66	1.46 58	19.9 95	1.19 59	4.62 97	5.71 85	4.89 105	2.13 35	13.3 87	2.28 56	2.87 37	4.78 51	1.55 6	2.30 105	3.68 100	3.31 100
Bartels [41]	76.0	4.23 87	10.7 61	4.70 98	2.37 61	5.83 15	2.66 70	2.21 61	7.42 53	2.42 68	2.59 97	8.46 52	2.53 98	4.33 84	5.50 75	4.37 97	3.69 87	14.6 94	4.80 104	4.75 100	6.30 90	7.59 116	1.13 45	2.33 57	1.32 53
Dynamic MRF [7]	76.1	4.55 95	13.6 78	5.02 100	1.81 19	8.86 53	1.82 17	2.13 56	12.6 81	1.87 21	1.62 69	13.2 80	1.45 75	4.61 95	5.80 92	4.32 95	4.14 97	21.3 115	4.42 98	3.22 52	4.41 29	5.01 105	2.11 97	3.92 105	3.51 103
TriangleFlow [30]	76.9	3.96 80	11.5 65	4.08 87	2.14 42	10.2 71	2.07 30	2.16 59	9.80 67	1.86 19	1.47 59	9.22 56	1.11 45	5.37 112	7.25 118	4.72 102	4.49 101	13.7 92	4.62 101	3.78 73	7.33 115	4.11 92	1.73 82	3.48 92	2.30 86
CLG-TV [48]	77.5	3.59 65	9.91 56	3.24 63	4.16 96	11.1 83	4.96 95	3.12 94	11.5 73	3.97 93	2.31 92	13.0 79	1.99 89	4.56 94	6.11 104	3.95 88	2.85 71	12.2 75	2.78 73	4.23 89	5.87 83	2.86 53	1.17 51	2.45 60	1.04 40
Local-TV-L1 [65]	78.0	4.95 97	13.2 75	5.40 102	4.37 97	14.6 99	5.04 97	4.59 100	17.8 94	5.96 97	2.42 94	16.9 93	2.25 94	3.68 44	5.03 44	2.82 49	2.25 45	10.6 61	2.24 51	2.55 21	4.37 26	2.91 56	2.73 111	4.10 109	7.77 117
p-harmonic [29]	78.4	4.47 93	14.4 85	4.52 95	2.71 85	9.33 64	2.89 86	3.40 95	15.0 87	3.02 86	1.93 81	24.1 102	1.59 80	4.15 73	5.18 53	3.66 77	3.37 82	16.0 97	3.54 88	3.90 76	5.36 70	2.71 50	1.29 57	2.41 59	1.38 60
FlowNetS+ft+v [112]	78.6	3.42 52	13.4 77	3.39 72	2.54 72	11.2 84	2.80 78	2.94 90	18.6 97	4.76 95	1.43 53	27.4 105	1.20 60	4.67 99	6.35 109	3.71 79	1.96 26	12.3 77	2.01 33	4.10 84	6.00 85	4.11 92	1.76 85	3.49 95	2.37 88
DF-Auto [115]	78.7	3.88 75	17.6 95	2.93 54	5.44 103	14.7 100	6.44 103	4.54 99	16.8 89	9.38 103	2.22 88	15.0 84	1.95 87	4.32 83	6.00 99	3.88 87	1.44 3	7.14 15	1.73 10	4.20 86	6.78 100	1.70 9	2.42 107	4.04 108	3.34 101
CNN-flow-warp+ref [117]	79.2	3.90 77	19.8 109	3.73 82	3.40 93	10.9 80	4.21 93	3.85 98	23.8 105	6.07 98	1.65 73	22.4 100	1.37 67	4.38 88	5.58 78	4.08 90	2.23 42	13.8 93	2.33 60	2.41 17	4.27 17	2.24 27	2.43 108	3.66 99	3.64 107
CBF [12]	79.5	3.59 65	10.5 60	3.68 81	4.72 99	10.4 74	6.02 102	2.28 65	9.24 61	2.96 85	1.63 71	12.2 76	1.36 66	4.48 92	5.75 87	3.99 89	2.70 66	10.7 63	2.48 64	6.13 118	7.02 106	5.92 109	1.45 72	2.65 66	1.66 70
SuperFlow [81]	81.0	3.40 50	11.5 65	3.31 68	3.97 95	12.2 90	5.00 96	3.01 92	17.9 95	7.70 101	2.66 98	18.1 94	2.64 99	4.17 74	5.42 67	4.16 93	2.19 40	10.6 61	2.20 47	4.22 88	6.11 89	2.45 41	2.12 99	3.64 98	3.59 105
StereoFlow [44]	83.5	21.8 128	37.8 124	27.4 128	24.3 126	37.6 128	22.4 124	28.3 128	39.2 124	28.8 124	24.0 127	47.7 121	21.6 126	5.15 110	5.49 74	6.01 117	0.95 1	6.87 11	1.06 1	1.68 1	3.70 2	0.92 1	1.29 57	2.32 55	1.49 67
Fusion [6]	85.8	3.76 73	16.9 92	4.07 86	1.99 29	7.37 38	2.26 38	2.07 52	8.51 58	2.28 64	1.59 67	24.8 103	1.37 67	5.00 108	6.36 110	4.98 109	4.70 104	16.2 99	5.01 107	6.00 117	7.50 117	4.38 100	2.97 114	3.74 103	3.55 104
Learning Flow [11]	86.1	3.80 74	11.9 68	3.58 78	3.02 91	13.0 92	3.34 91	2.84 85	17.9 95	3.18 89	1.82 78	34.6 114	1.50 76	5.44 114	7.32 119	4.61 101	3.10 77	18.8 108	3.04 77	3.94 78	6.38 93	3.65 70	1.37 64	3.38 89	1.18 45
TriFlow [95]	86.4	4.44 90	13.8 82	3.62 80	3.16 92	9.65 67	3.81 92	2.89 88	19.6 99	6.36 99	2.48 95	7.88 47	2.33 95	4.37 87	5.44 70	4.28 94	2.98 74	8.42 29	3.07 79	11.7 127	7.70 118	21.5 127	1.76 85	2.98 79	1.94 79
Shiralkar [42]	86.8	4.46 91	18.3 99	4.36 91	1.93 26	16.4 101	1.87 21	2.99 91	17.6 92	2.01 32	2.10 86	21.0 97	1.96 88	4.36 85	5.72 86	3.55 74	5.65 112	19.4 110	5.11 109	4.90 103	5.57 76	7.14 113	2.11 97	4.71 114	2.53 90
StereoOF-V1MT [119]	88.8	4.46 91	18.0 97	4.46 93	2.09 36	18.6 105	1.79 16	3.70 97	20.6 102	2.13 53	2.18 87	25.0 104	1.91 86	5.52 116	6.98 116	4.82 103	5.01 108	25.8 120	4.73 102	3.21 51	5.27 69	3.64 69	2.32 106	4.64 112	2.83 93
SegOF [10]	89.8	5.62 100	17.1 93	3.08 57	8.33 112	20.9 107	10.1 114	7.44 106	21.7 103	13.3 110	5.42 111	21.0 97	4.47 107	4.81 104	5.51 76	5.74 116	4.97 107	17.1 101	4.83 105	2.12 3	4.38 28	1.46 4	2.17 100	3.23 85	3.74 108
Ad-TV-NDC [36]	92.1	8.75 112	15.3 90	12.3 120	10.5 117	24.2 115	12.3 117	8.96 111	28.2 108	11.5 105	5.31 110	22.8 101	5.55 111	4.03 61	5.79 89	2.86 51	2.80 68	10.0 55	2.87 74	3.04 44	4.52 35	2.66 48	4.62 121	5.79 121	30.9 127
Modified CLG [34]	96.5	6.79 106	24.7 112	6.63 106	7.09 108	17.4 102	9.40 112	10.1 112	29.2 110	16.6 115	4.48 108	27.5 106	3.86 103	4.80 102	6.31 108	4.48 100	2.65 65	17.6 104	2.69 71	2.92 42	4.94 59	2.07 19	3.19 116	5.17 117	5.78 113
Filter Flow [19]	96.7	6.76 105	17.6 95	4.37 92	5.01 101	17.6 103	5.49 100	5.98 103	26.3 106	18.4 118	7.23 113	29.9 109	6.91 114	5.12 109	6.23 106	5.36 111	5.23 109	11.9 74	4.95 106	6.64 121	8.75 124	3.75 76	0.95 29	2.15 47	1.21 50
IAOF2 [51]	98.5	5.05 98	13.6 78	4.64 97	4.90 100	14.5 98	5.78 101	3.68 96	18.6 97	5.15 96	12.3 121	34.1 113	13.8 121	4.65 98	6.21 105	3.78 84	4.47 100	13.5 88	3.70 93	5.73 111	7.13 110	3.98 85	1.96 92	3.53 96	2.35 87
FlowNet2 [122]	98.8	8.99 113	25.8 113	7.01 107	9.84 115	19.0 106	10.7 115	7.98 109	20.1 100	13.5 111	4.47 107	9.41 57	4.21 106	5.17 111	6.08 102	4.92 107	4.10 95	11.2 68	4.43 99	5.98 116	7.71 119	2.90 55	1.78 87	2.92 77	1.89 77
BlockOverlap [61]	99.5	6.80 107	12.1 69	5.94 104	5.51 104	13.6 94	6.58 104	5.32 102	22.2 104	7.30 100	4.20 103	16.7 92	4.06 105	4.45 90	5.39 66	5.11 110	4.91 105	12.5 78	4.34 97	6.77 123	7.13 110	9.52 122	2.02 93	3.24 86	9.49 120
2D-CLG [1]	100.5	9.69 116	37.7 123	7.18 109	11.1 118	21.9 111	13.9 120	19.0 124	34.8 116	28.7 123	13.0 122	46.7 119	12.8 120	4.97 106	5.79 89	5.47 114	4.08 93	21.2 114	4.32 96	2.29 12	4.00 5	1.64 7	4.47 120	5.51 120	6.55 115
HBpMotionGpu [43]	100.5	5.92 102	15.0 89	4.79 99	7.78 110	22.4 112	9.04 111	7.17 105	39.2 124	17.3 117	3.31 101	13.4 81	3.14 101	4.71 100	5.88 93	4.84 104	3.74 89	13.5 88	3.54 88	5.96 115	7.17 112	3.68 71	2.24 102	3.43 90	4.65 109
SPSA-learn [13]	100.7	6.87 108	21.3 110	7.92 112	6.02 106	21.1 109	6.96 106	7.55 107	27.5 107	12.7 109	4.44 105	29.2 107	4.59 109	4.80 102	5.92 96	4.93 108	4.94 106	17.3 102	5.02 108	3.37 59	5.01 61	2.29 31	4.14 119	4.97 116	6.49 114
GroupFlow [9]	101.0	9.15 115	25.8 113	10.5 118	11.6 120	30.0 121	12.3 117	10.2 113	35.4 118	11.9 106	3.50 102	15.8 89	3.39 102	5.48 115	6.56 112	4.42 98	9.25 121	24.8 117	10.8 123	2.35 16	4.58 38	1.67 8	2.93 113	5.22 118	4.99 110
GraphCuts [14]	101.9	6.34 103	17.1 93	5.55 103	5.30 102	20.9 107	5.26 99	6.05 104	20.4 101	12.4 108	2.85 99	20.9 96	2.15 92	4.74 101	5.95 97	4.90 106	8.69 120	12.6 80	5.19 110	5.79 112	6.40 94	6.80 112	2.45 109	3.48 92	3.59 105
Black & Anandan [4]	102.2	7.19 109	18.9 102	8.40 113	5.96 105	22.6 113	6.69 105	8.73 110	28.7 109	12.1 107	4.46 106	29.4 108	4.52 108	4.91 105	6.59 113	4.09 91	4.18 98	19.4 110	4.44 100	4.69 95	6.36 92	2.01 17	3.13 115	4.46 110	5.06 111
IAOF [50]	103.3	6.54 104	18.3 99	7.13 108	6.99 107	18.4 104	7.90 109	7.71 108	32.3 113	8.44 102	8.21 116	31.8 110	9.78 118	4.61 95	6.01 100	4.14 92	4.35 99	18.9 109	3.43 85	4.69 95	6.08 88	3.31 63	3.23 117	4.69 113	15.9 125
2bit-BM-tele [98]	106.1	8.99 113	18.6 101	10.2 116	4.45 98	11.3 85	5.04 97	4.66 101	17.3 91	4.41 94	5.23 109	21.7 99	5.04 110	4.99 107	5.96 98	5.46 113	6.47 115	18.3 105	7.49 116	7.77 125	8.57 123	12.5 125	2.29 104	3.89 104	2.99 98
Nguyen [33]	107.3	8.16 110	23.0 111	7.57 111	16.5 122	22.7 114	19.3 122	16.8 119	36.0 119	20.7 121	13.8 123	39.5 115	14.7 123	5.40 113	6.44 111	6.70 118	4.54 103	18.5 107	5.42 111	3.50 66	5.02 62	2.08 20	4.00 118	5.50 119	8.53 118
UnFlow [129]	107.3	19.4 127	44.0 128	10.2 116	11.3 119	21.2 110	12.4 119	18.1 121	36.0 119	15.5 113	7.47 115	34.0 112	6.40 113	7.10 123	7.11 117	8.76 123	8.31 119	24.8 117	9.26 119	5.13 104	6.50 97	1.52 5	1.57 76	3.14 83	2.01 81
Heeger++ [104]	109.5	18.3 126	32.1 120	10.5 118	9.98 116	34.3 127	7.90 109	16.0 117	32.7 114	11.0 104	9.23 117	47.6 120	7.86 116	5.95 118	6.74 114	5.71 115	23.4 127	49.5 128	24.5 127	3.61 70	6.53 98	2.58 44	1.78 87	3.68 100	2.96 96
SILK [79]	110.2	10.7 117	31.4 119	13.1 122	8.77 113	26.6 117	9.80 113	13.6 115	34.9 117	16.7 116	6.53 112	45.4 117	6.08 112	6.11 119	7.36 121	6.71 119	6.96 117	29.4 123	7.39 115	2.97 43	4.77 50	3.96 84	5.00 122	6.99 122	10.7 121
Horn & Schunck [3]	111.0	8.40 111	27.2 115	9.62 114	7.28 109	28.3 119	7.55 108	13.3 114	31.9 112	15.8 114	7.35 114	48.5 122	7.69 115	5.84 117	7.34 120	5.45 112	5.80 113	25.8 120	6.79 114	5.25 106	7.11 109	2.11 22	5.21 123	8.30 124	6.66 116
Periodicity [78]	114.1	12.3 119	51.4 131	7.39 110	9.23 114	38.3 129	11.1 116	34.7 129	48.1 129	36.0 128	4.27 104	57.7 128	3.99 104	24.4 129	73.2 129	16.2 128	29.6 129	74.3 129	29.4 129	3.29 53	5.67 78	1.90 14	6.64 125	44.8 129	21.5 126
FFV1MT [106]	114.8	17.0 125	35.1 122	10.1 115	8.30 111	33.0 125	7.40 107	17.2 120	40.0 126	15.3 112	9.57 118	55.8 127	8.75 117	7.99 127	8.46 127	10.1 126	21.8 126	36.1 126	23.3 126	4.43 91	7.02 106	4.09 91	1.78 87	3.68 100	2.96 96
TI-DOFE [24]	114.8	16.4 123	34.0 121	21.2 127	21.5 125	31.9 122	25.3 126	24.4 127	41.0 127	33.0 127	22.8 126	46.3 118	25.2 127	6.25 120	7.67 123	6.82 120	6.37 114	25.6 119	7.87 117	3.94 78	5.78 82	1.78 12	8.49 127	9.86 126	12.5 122
SLK [47]	115.7	12.3 119	43.0 127	16.5 124	19.8 124	32.9 124	22.4 124	21.4 125	38.4 122	29.3 126	41.6 129	51.6 124	44.5 129	6.87 122	7.63 122	8.94 124	8.09 118	31.2 125	9.56 120	3.34 57	5.41 71	2.60 46	8.13 126	9.23 125	13.9 124
Adaptive flow [45]	121.2	16.4 123	28.7 117	16.7 125	17.2 123	25.5 116	19.6 123	18.8 123	37.6 121	36.5 129	11.2 120	43.6 116	11.9 119	7.11 124	7.79 124	7.88 121	10.1 124	24.1 116	10.1 122	16.1 128	14.2 128	22.1 128	2.92 112	4.89 115	5.22 112
HCIC-L [99]	121.9	24.1 129	31.3 118	12.9 121	27.6 128	28.7 120	69.9 129	16.0 117	30.6 111	23.1 122	18.1 125	55.4 126	17.8 125	7.39 125	8.35 126	8.32 122	12.6 125	18.3 105	14.4 125	25.6 129	23.8 129	23.6 129	2.62 110	4.51 111	9.36 119
PGAM+LK [55]	122.6	14.0 121	40.6 125	18.8 126	14.9 121	33.1 126	17.8 121	14.4 116	32.9 115	19.3 119	15.7 124	63.6 129	14.9 124	6.36 121	6.81 115	9.14 125	9.83 123	30.7 124	9.83 121	10.4 126	12.2 127	10.3 123	5.30 124	7.26 123	13.0 123
FOLKI [16]	123.0	11.0 118	41.0 126	14.5 123	24.9 127	32.3 123	36.7 127	18.7 122	43.8 128	20.5 120	10.9 119	50.5 123	13.8 121	7.42 126	8.28 125	10.6 127	9.75 122	36.9 127	12.1 124	4.77 102	7.29 114	11.0 124	12.2 128	11.4 127	36.4 128
Pyramid LK [2]	125.5	15.8 122	28.2 116	30.4 129	35.8 129	28.0 118	49.6 128	22.3 126	38.6 123	29.1 125	31.8 128	51.7 125	39.0 128	18.3 128	24.8 128	24.1 129	26.7 128	28.6 122	26.7 128	7.19 124	8.98 125	7.70 118	32.7 129	40.6 128	57.0 129
AdaConv-v1 [126]	130.0	45.2 130	50.1 129	46.0 130	78.3 130	79.6 130	76.9 130	78.3 130	73.0 130	77.6 130	79.2 130	80.8 130	79.6 130	83.7 130	84.3 130	83.6 130	82.1 130	80.6 130	81.5 130	69.6 130	58.5 130	75.3 130	84.4 130	84.7 130	83.9 130
SepConv-v1 [127]	130.0	45.2 130	50.1 129	46.0 130	78.3 130	79.6 130	76.9 130	78.3 130	73.0 130	77.6 130	79.2 130	80.8 130	79.6 130	83.7 130	84.3 130	83.6 130	82.1 130	80.6 130	81.5 130	69.6 130	58.5 130	75.3 130	84.4 130	84.7 130	83.9 130

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.