Optical flow evaluation results: A95 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

A95 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]	8.8	7.47 3	40.1 11	3.98 3	6.49 13	30.3 3	5.60 16	5.82 4	26.1 3	4.68 14	3.86 10	53.5 4	2.99 18	9.77 1	12.4 1	5.36 3	8.67 5	31.8 3	7.03 3	5.10 18	10.1 48	3.70 7	2.31 8	5.34 10	1.21 2
NN-field [71]	12.1	8.38 14	43.1 26	4.19 4	7.34 22	28.7 2	6.26 26	5.82 4	28.9 6	4.68 14	2.94 2	54.1 5	2.16 3	10.4 4	13.2 4	5.24 2	6.12 1	17.5 1	4.46 1	6.24 46	10.6 63	4.10 8	2.35 12	6.44 19	1.14 1
MDP-Flow2 [68]	14.0	8.02 8	38.6 6	5.75 18	5.17 2	31.1 4	4.55 3	5.48 3	30.8 9	4.22 8	4.49 19	99.9 42	3.27 26	11.3 13	13.4 6	8.04 18	10.8 14	54.4 32	10.5 21	4.84 8	9.33 32	4.31 11	2.69 26	4.85 5	2.20 3
OFLAF [77]	15.6	7.70 5	39.8 8	4.74 8	6.40 12	32.5 7	5.82 21	4.73 2	25.3 2	3.96 4	4.47 18	99.9 42	3.55 42	10.2 3	13.0 2	6.29 7	13.3 32	42.1 19	9.90 18	5.10 18	8.01 10	4.66 21	2.75 27	5.59 14	6.33 32
PMMST [114]	16.7	8.63 17	31.3 1	6.03 23	8.51 36	26.8 1	8.18 60	7.50 11	28.0 5	6.07 33	4.26 16	34.8 2	3.29 27	10.9 8	13.2 4	6.26 6	10.4 12	29.9 2	9.42 13	5.00 14	10.1 48	4.37 12	3.25 35	4.40 4	3.36 10
nLayers [57]	23.7	8.19 11	45.3 51	4.62 6	9.65 58	31.7 6	8.88 77	8.87 18	33.6 11	8.22 60	3.62 6	99.9 42	2.93 15	10.5 5	13.6 8	6.52 8	11.3 20	33.4 4	9.45 15	6.02 42	8.56 17	4.99 28	2.31 8	6.80 24	5.53 29
NNF-EAC [103]	23.8	8.80 18	40.8 14	6.14 25	6.13 7	39.3 25	5.36 11	6.97 9	35.1 12	4.73 16	5.83 38	87.9 28	3.49 38	12.1 26	14.6 20	8.87 29	12.5 29	41.2 15	11.8 34	5.35 24	10.1 48	4.61 19	3.19 33	7.62 34	3.99 20
ComponentFusion [96]	24.8	8.30 13	49.1 70	5.87 20	5.69 5	35.4 14	5.40 12	7.24 10	35.3 13	4.99 20	3.69 7	99.9 42	2.32 5	11.7 19	14.1 13	8.75 27	15.8 53	66.6 52	15.0 67	5.71 32	8.88 22	5.09 33	2.64 23	5.25 9	3.68 15
FC-2Layers-FF [74]	27.0	8.26 12	41.4 18	6.27 26	8.85 40	37.9 21	7.81 43	6.02 7	31.8 10	6.25 36	3.91 11	88.8 31	2.86 13	11.0 11	13.7 10	7.20 10	16.5 62	40.4 11	16.3 82	7.14 65	10.7 67	6.61 54	2.15 3	3.87 1	2.77 5
FESL [72]	27.5	7.69 4	40.2 12	4.90 9	11.0 77	48.5 49	9.07 79	10.5 27	42.1 25	6.42 38	3.60 5	99.9 42	2.55 8	10.9 8	13.6 8	8.86 28	11.1 18	36.4 6	10.6 22	6.73 59	10.2 52	5.95 44	2.51 20	5.37 12	3.35 9
LME [70]	28.2	7.85 7	42.7 24	6.01 22	5.33 3	34.6 10	4.91 7	14.6 48	54.5 47	40.7 98	4.66 21	73.0 11	3.25 23	11.5 16	13.8 11	9.65 51	11.6 22	70.4 61	12.1 39	5.14 20	9.97 46	4.51 18	2.86 28	6.45 20	4.29 25
TC/T-Flow [76]	28.3	9.01 21	38.1 4	3.81 1	6.64 15	55.1 67	4.62 4	8.13 14	46.4 38	4.20 7	5.32 31	99.9 42	2.88 14	11.5 16	14.1 13	7.28 12	8.85 6	38.5 9	9.44 14	5.85 38	10.7 67	10.0 94	3.61 43	10.0 48	8.53 62
HAST [109]	28.7	6.42 1	43.9 34	3.97 2	7.16 18	33.1 8	5.92 23	3.76 1	23.5 1	2.83 1	3.36 3	99.9 42	2.08 2	10.0 2	13.0 2	4.83 1	16.7 67	59.3 39	19.4 96	11.3 105	12.9 92	17.6 115	2.67 25	4.13 2	2.93 7
WLIF-Flow [93]	30.5	8.04 9	40.7 13	5.53 15	7.98 31	34.6 10	7.20 37	8.75 17	40.5 20	5.74 30	4.27 17	96.8 40	2.94 16	13.4 75	16.1 83	9.77 54	13.5 36	41.3 17	11.8 34	5.65 30	9.12 28	5.96 45	2.29 7	7.30 27	6.96 41
PMF [73]	31.0	9.38 28	48.9 68	4.67 7	7.10 17	37.5 18	5.58 15	7.91 12	30.0 8	4.06 5	4.89 27	99.9 42	3.29 27	10.6 7	14.0 12	5.76 5	12.4 28	54.8 33	11.5 31	11.2 104	17.9 123	11.1 98	2.06 1	4.95 8	4.00 21
ALD-Flow [66]	31.2	8.18 10	41.6 22	4.51 5	6.32 10	54.8 65	5.03 9	10.7 31	61.8 54	4.24 9	4.24 15	99.9 42	2.61 9	11.7 19	14.3 15	7.21 11	10.8 14	61.4 41	9.93 19	5.96 41	9.55 36	9.77 93	3.87 47	15.7 57	9.38 74
Layers++ [37]	31.8	9.07 22	44.4 37	8.41 61	8.47 35	31.5 5	8.03 53	5.85 6	37.9 15	6.02 32	3.76 9	62.6 6	2.79 11	10.5 5	13.5 7	8.22 23	17.6 75	55.0 34	14.5 65	7.44 70	10.9 71	5.70 43	2.27 6	4.86 6	9.14 67
Efficient-NL [60]	32.8	8.40 15	43.6 31	5.38 13	9.12 44	37.4 17	7.83 45	11.1 35	56.6 49	6.16 34	5.71 35	99.9 42	3.32 30	11.3 13	14.7 23	7.66 13	16.6 66	37.6 7	12.7 47	6.93 63	10.8 69	5.98 47	2.89 30	5.47 13	2.90 6
RNLOD-Flow [121]	32.9	7.26 2	38.7 7	5.25 12	7.65 27	43.0 32	6.20 25	12.7 41	75.2 60	4.59 12	3.52 4	99.9 42	2.46 6	11.4 15	14.9 31	7.74 15	16.2 56	40.2 10	16.2 79	8.20 78	12.2 84	7.63 71	2.49 19	5.98 17	7.10 44
SVFilterOh [111]	33.8	8.96 20	55.2 97	5.63 16	7.20 19	37.8 20	6.39 29	6.18 8	43.0 28	5.02 21	3.71 8	99.9 42	2.49 7	11.1 12	14.3 15	5.54 4	13.5 36	50.6 31	14.1 60	10.1 96	16.8 120	12.4 107	2.20 4	4.91 7	2.50 4
TC-Flow [46]	34.1	8.59 16	41.0 16	5.12 11	5.47 4	45.6 40	4.31 2	10.2 24	94.7 71	3.49 2	6.08 44	99.9 42	3.50 39	11.9 24	14.5 18	7.90 17	11.9 26	61.7 43	11.5 31	5.72 33	9.85 42	11.5 102	4.02 50	15.0 55	9.14 67
AGIF+OF [85]	34.2	8.89 19	44.1 35	6.77 30	10.2 64	44.4 36	8.51 73	10.2 24	43.5 30	6.63 42	4.80 24	99.9 42	3.25 23	11.7 19	14.7 23	9.52 47	13.7 39	40.9 12	12.6 44	5.73 35	8.99 23	5.96 45	2.36 14	7.50 30	7.55 47
IROF++ [58]	36.3	9.29 26	45.0 49	6.30 27	9.55 55	43.0 32	8.20 61	10.8 32	43.1 29	7.57 52	6.28 47	99.9 42	3.90 49	12.2 29	14.8 27	9.41 45	15.2 51	44.1 26	14.5 65	5.27 21	9.48 35	3.69 6	2.62 22	6.48 21	4.14 22
Correlation Flow [75]	36.6	9.27 25	38.3 5	5.40 14	6.33 11	36.7 15	4.85 6	18.4 52	99.9 80	3.58 3	4.87 26	35.8 3	3.47 35	12.9 55	15.8 68	9.17 39	16.0 54	68.6 55	16.5 87	6.59 55	9.85 42	7.93 78	2.88 29	7.57 33	3.06 8
PH-Flow [101]	36.6	10.2 37	44.6 41	7.86 43	9.41 50	42.0 31	8.11 56	8.41 16	38.9 17	7.40 51	6.39 49	99.9 42	3.93 51	11.8 22	14.5 18	7.72 14	13.8 40	42.9 23	13.1 50	7.21 66	10.3 55	7.61 70	2.34 11	4.20 3	4.20 23
Classic+CPF [83]	37.0	9.64 31	43.4 28	7.93 46	9.43 51	46.1 43	7.82 44	10.6 30	51.0 42	6.68 43	5.09 29	99.9 42	3.22 22	12.0 25	15.2 42	9.15 37	14.7 46	34.4 5	13.4 53	6.42 50	10.1 48	6.89 59	2.26 5	6.77 23	7.08 43
CostFilter [40]	39.7	10.5 43	46.8 60	6.98 34	7.51 24	38.1 22	6.31 28	9.22 19	29.7 7	4.74 17	5.86 40	99.9 42	3.97 53	10.9 8	14.3 15	6.77 9	13.4 33	56.1 35	12.3 41	11.6 108	20.5 127	14.2 109	2.12 2	8.52 39	6.71 38
OAR-Flow [125]	41.1	11.1 49	48.8 67	5.85 19	9.88 60	82.9 102	6.47 30	27.3 70	99.9 80	8.06 56	6.75 55	99.9 42	2.80 12	12.4 35	15.1 40	8.20 22	10.3 10	58.1 37	8.37 9	4.07 2	8.06 12	5.45 40	4.81 58	9.74 47	6.36 33
ProbFlowFields [128]	41.9	16.3 82	53.8 89	10.9 95	7.72 28	40.6 28	7.12 33	14.1 46	45.2 36	10.5 65	6.67 54	62.6 6	4.39 63	12.9 55	15.4 52	9.64 50	10.1 9	63.2 48	10.8 23	4.86 10	8.16 14	4.69 23	3.36 37	9.23 44	3.72 16
Sparse-NonSparse [56]	42.1	9.96 32	44.2 36	8.85 69	9.39 49	50.6 54	8.08 55	10.1 23	43.7 32	7.21 47	6.10 45	88.2 29	3.41 33	12.5 40	15.5 61	8.96 31	16.3 58	41.9 18	16.2 79	6.48 52	9.05 25	6.27 50	2.33 10	7.33 28	7.76 55
MLDP_OF [89]	42.4	11.8 55	41.7 23	8.40 60	6.97 16	35.2 13	5.88 22	11.3 38	65.3 56	5.23 24	4.76 23	99.9 42	3.09 19	12.2 29	14.8 27	8.87 29	13.4 33	48.1 28	17.2 92	10.0 95	10.9 71	18.1 116	3.58 42	8.07 38	4.53 27
LSM [39]	42.8	10.0 35	42.9 25	8.48 62	9.36 48	49.6 52	7.99 50	10.5 27	43.6 31	6.80 44	5.80 36	88.6 30	3.38 32	12.5 40	15.4 52	9.03 33	16.5 62	42.3 21	16.3 82	6.94 64	9.84 39	6.71 55	2.42 17	7.96 37	7.72 52
Ramp [62]	42.9	10.2 37	44.4 37	8.09 55	9.47 52	46.1 43	8.17 59	9.51 21	42.4 27	6.88 46	5.40 32	99.9 42	3.53 40	12.5 40	15.2 42	9.71 52	16.7 67	42.1 19	16.5 87	6.76 60	10.0 47	7.07 62	2.46 18	5.84 16	5.24 28
MDP-Flow [26]	45.3	11.2 50	43.1 26	9.86 87	8.14 34	35.1 12	8.21 62	11.2 36	42.1 25	9.44 63	6.41 50	99.9 42	4.20 60	12.2 29	14.6 20	10.0 65	11.7 24	63.6 49	9.60 16	5.56 28	10.9 71	4.39 13	5.78 67	99.9 94	8.99 64
FMOF [94]	45.8	9.17 23	43.6 31	8.04 52	10.0 62	48.1 46	8.48 71	8.35 15	38.3 16	6.49 40	5.08 28	99.9 42	3.45 34	12.6 46	15.4 52	9.19 40	18.1 81	41.2 15	15.5 73	6.67 58	10.6 63	7.47 68	3.00 31	16.1 58	7.74 53
Classic+NL [31]	46.0	10.1 36	44.9 47	8.90 70	9.49 53	51.6 57	7.87 46	9.93 22	43.9 34	7.31 50	6.07 43	99.9 42	3.78 47	12.5 40	15.3 48	9.06 34	17.1 71	41.0 13	15.8 76	7.32 69	10.8 69	6.80 58	2.35 12	5.62 15	7.69 51
OFH [38]	46.3	12.6 61	43.4 28	9.45 79	7.30 21	64.4 78	5.27 10	27.6 71	99.9 80	4.87 19	6.60 53	99.9 42	3.74 45	12.4 35	14.7 23	9.62 49	15.5 52	74.1 66	15.6 75	4.60 6	9.39 33	4.64 20	5.39 62	26.0 66	6.68 37
IROF-TV [53]	46.7	10.4 41	44.5 40	8.16 56	9.69 59	51.1 56	8.44 66	12.6 40	46.8 39	7.27 49	6.80 56	87.5 27	3.93 51	13.0 62	15.7 65	10.4 70	18.3 83	86.9 88	13.7 57	4.44 5	7.40 5	3.05 4	2.60 21	7.55 32	7.56 48
TV-L1-MCT [64]	46.7	9.57 29	44.7 43	8.66 65	10.9 76	48.1 46	9.11 80	11.8 39	58.1 51	6.61 41	4.74 22	99.9 42	3.34 31	12.9 55	15.2 42	9.89 59	17.8 79	47.8 27	16.0 78	5.28 22	8.09 13	7.71 72	3.33 36	7.26 26	7.53 46
CombBMOF [113]	46.8	12.5 60	41.3 17	6.50 29	8.58 38	36.9 16	6.88 32	10.9 33	35.9 14	5.21 23	10.4 76	85.0 25	5.90 85	11.6 18	15.2 42	8.15 20	27.3 102	60.9 40	35.6 116	9.20 87	14.7 108	6.75 57	3.17 32	7.53 31	4.20 23
NL-TV-NCC [25]	46.9	10.7 45	40.8 14	6.45 28	8.52 37	41.1 29	6.30 27	11.2 36	93.6 70	4.18 6	5.99 42	75.9 14	4.02 54	13.2 68	16.2 89	10.1 67	16.7 67	70.9 62	16.3 82	6.56 54	9.91 45	7.05 61	4.76 56	16.9 59	3.56 13
COFM [59]	47.1	9.37 27	55.5 98	6.86 32	7.28 20	44.2 35	6.17 24	14.3 47	47.6 41	8.22 60	4.15 14	99.9 42	2.23 4	13.2 68	16.2 89	12.2 91	17.6 75	75.4 68	15.5 73	6.20 45	8.77 21	7.35 66	3.62 44	5.35 11	6.43 35
Adaptive [20]	47.5	10.2 37	46.1 54	4.95 10	9.63 56	55.4 68	7.80 42	36.7 84	99.9 80	7.64 53	6.15 46	78.7 16	2.96 17	12.1 26	14.8 27	9.09 35	12.3 27	85.8 86	6.06 2	8.72 82	12.5 88	4.97 27	3.55 41	34.8 70	9.13 66
AggregFlow [97]	49.3	13.2 62	62.1 116	6.79 31	14.9 87	73.1 87	10.6 87	26.8 69	55.1 48	20.5 89	5.48 33	99.9 42	3.67 43	12.5 40	15.0 34	7.76 16	8.55 4	38.2 8	8.90 11	5.78 36	10.6 63	4.74 25	5.43 63	8.53 40	7.58 49
S2F-IF [123]	50.2	20.0 95	51.4 77	9.91 88	9.64 57	48.3 48	7.93 48	19.7 54	41.7 23	13.6 77	9.98 73	84.3 22	5.40 78	12.8 53	15.3 48	9.92 62	10.9 17	62.4 46	10.9 25	5.00 14	10.4 58	5.30 38	3.71 45	8.55 41	3.87 18
RFlow [90]	50.4	11.5 53	45.3 51	8.80 68	6.22 9	49.2 51	5.41 13	26.2 67	99.9 80	5.04 22	4.14 13	99.9 42	3.11 20	12.6 46	15.0 34	9.87 57	16.5 62	83.2 81	13.8 59	6.62 56	8.58 18	6.16 48	6.33 72	99.9 94	12.0 94
Sparse Occlusion [54]	50.8	9.98 34	41.5 21	7.82 42	9.00 43	40.5 27	8.28 64	13.5 44	85.5 65	5.96 31	5.82 37	99.9 42	3.90 49	13.0 62	15.9 69	9.77 54	13.8 40	49.9 30	12.3 41	13.6 119	15.7 116	7.81 75	3.51 38	9.05 42	6.42 34
Complementary OF [21]	50.8	13.6 66	46.2 55	9.35 76	6.20 8	50.4 53	4.92 8	12.8 42	58.8 53	5.45 26	7.89 63	99.9 42	5.59 80	12.3 33	14.6 20	9.99 64	18.9 85	69.9 59	14.3 63	5.44 25	7.80 7	7.78 74	6.13 70	26.9 67	9.66 81
S2D-Matching [84]	51.0	9.96 32	53.0 86	8.51 63	9.53 54	53.0 59	7.94 49	20.5 56	99.9 80	6.80 44	5.30 30	83.0 18	3.53 40	12.4 35	15.2 42	9.16 38	17.3 72	41.1 14	16.8 90	7.75 74	10.5 61	7.90 77	2.36 14	6.34 18	9.58 79
FlowFields+ [130]	51.1	20.3 96	52.0 79	10.3 91	10.3 66	44.8 38	8.44 66	19.5 53	40.2 19	14.1 79	10.2 75	66.6 9	6.28 90	12.8 53	15.4 52	9.97 63	10.3 10	61.8 44	10.2 20	4.85 9	10.9 71	4.79 26	3.97 49	12.8 52	3.85 17
Occlusion-TV-L1 [63]	52.1	10.4 41	44.9 47	6.90 33	8.77 39	53.3 61	7.54 39	33.8 80	99.9 80	7.96 54	5.88 41	99.9 42	3.48 37	13.6 83	16.3 91	10.6 74	9.50 7	80.1 75	8.60 10	6.12 44	8.69 20	4.39 13	6.52 75	99.9 94	9.37 71
2DHMM-SAS [92]	52.2	10.3 40	44.8 45	8.03 51	10.5 71	52.4 58	8.21 62	21.6 57	97.4 75	8.20 59	6.88 57	99.9 42	3.86 48	12.4 35	15.0 34	9.87 57	17.7 77	43.3 25	15.9 77	6.81 61	10.2 52	7.15 65	2.65 24	7.68 35	7.29 45
FlowFields [110]	52.3	20.3 96	52.3 82	10.2 89	10.2 64	49.0 50	8.46 69	20.3 55	40.5 20	14.7 80	10.8 80	76.6 15	6.16 88	12.9 55	15.4 52	10.0 65	11.1 18	69.9 59	11.0 27	4.97 13	8.63 19	5.12 35	4.04 52	14.0 53	3.98 19
HBM-GC [105]	52.4	10.9 47	57.5 106	7.03 36	9.35 47	40.2 26	8.80 75	8.09 13	52.3 44	6.42 38	6.91 58	84.3 22	6.17 89	11.8 22	14.7 23	8.40 24	14.7 46	43.2 24	12.6 44	9.81 94	17.8 122	8.50 84	3.54 40	10.1 49	10.1 85
Aniso-Texture [82]	52.8	7.75 6	40.0 10	5.87 20	7.36 23	41.5 30	7.19 36	41.3 92	99.9 80	6.22 35	2.73 1	65.4 8	1.92 1	12.9 55	15.3 48	10.1 67	29.2 104	99.9 101	16.5 87	11.8 111	14.9 112	8.22 81	3.80 46	12.6 51	8.58 63
SimpleFlow [49]	53.4	11.3 52	46.6 57	9.79 86	10.7 73	45.0 39	9.15 81	23.1 61	99.9 80	8.38 62	8.00 65	99.9 42	3.72 44	12.7 50	15.5 61	9.36 44	16.3 58	42.6 22	15.3 72	5.91 40	9.61 37	5.39 39	2.39 16	7.08 25	9.41 75
ACK-Prior [27]	53.6	10.7 45	37.9 3	7.90 45	6.01 6	38.5 23	4.80 5	10.2 24	41.5 22	4.35 10	4.56 20	99.9 42	3.75 46	13.2 68	15.9 69	11.3 83	27.3 102	82.2 78	23.1 103	11.6 108	14.9 112	16.2 113	6.43 73	15.5 56	6.11 31
EPPM w/o HM [88]	54.5	15.3 78	41.4 18	8.08 54	7.60 25	33.9 9	5.66 18	13.0 43	47.0 40	5.57 27	8.73 70	99.9 42	4.81 72	12.6 46	15.7 65	10.8 77	18.6 84	62.9 47	16.4 85	11.9 114	12.5 88	17.2 114	3.20 34	7.49 29	6.00 30
PGM-C [120]	55.4	20.6 99	54.2 92	9.59 83	10.1 63	60.8 73	8.47 70	22.3 60	44.3 35	14.8 82	10.7 79	99.9 42	4.15 58	13.1 66	15.4 52	9.90 60	11.8 25	64.6 51	12.0 38	4.86 10	7.96 8	5.01 30	4.78 57	14.4 54	7.01 42
ROF-ND [107]	56.3	12.0 56	39.9 9	8.22 59	6.49 13	45.6 40	5.49 14	13.5 44	92.7 68	4.83 18	8.05 66	23.7 1	5.54 79	14.2 95	17.5 102	11.2 82	20.1 91	72.0 64	15.0 67	13.0 117	13.3 98	10.5 97	3.52 39	6.67 22	3.36 10
ComplOF-FED-GPU [35]	56.7	13.2 62	44.7 43	7.95 47	9.18 45	82.6 101	5.63 17	15.3 49	58.5 52	5.67 28	7.59 61	99.9 42	4.68 68	12.3 33	14.8 27	9.20 41	18.2 82	83.8 82	16.4 85	7.54 72	9.84 39	11.1 98	5.44 64	31.6 69	7.74 53
Steered-L1 [118]	58.8	9.19 24	36.3 2	6.06 24	4.59 1	39.2 24	4.30 1	9.30 20	52.3 44	4.57 11	4.86 25	99.9 42	3.30 29	13.6 83	15.9 69	12.3 92	24.7 101	77.0 71	20.2 97	15.1 122	13.7 103	40.0 123	14.7 104	91.5 92	20.9 106
CPM-Flow [116]	59.4	20.6 99	54.3 93	9.58 81	10.3 66	62.5 76	8.50 72	21.9 58	43.8 33	14.7 80	10.6 78	99.9 42	4.06 55	13.2 68	15.4 52	9.85 56	12.6 30	68.9 56	13.3 51	5.02 16	9.16 30	5.04 32	5.27 60	19.2 61	9.63 80
EpicFlow [102]	59.9	20.6 99	54.1 91	9.59 83	10.3 66	62.9 77	8.55 74	26.3 68	99.4 79	15.1 84	10.4 76	99.9 42	4.07 56	13.1 66	15.4 52	9.90 60	11.6 22	67.3 53	11.9 37	4.86 10	7.97 9	4.99 28	5.34 61	19.2 61	9.74 83
DeepFlow2 [108]	59.9	14.3 71	47.4 63	7.03 36	10.4 69	77.1 92	8.01 51	23.3 62	99.9 80	11.8 71	16.1 88	99.9 42	4.41 64	12.4 35	15.0 34	8.16 21	13.4 33	68.4 54	14.2 61	5.65 30	9.07 27	8.50 84	8.52 88	92.9 93	10.6 88
Kuang [131]	59.9	18.9 93	52.2 81	9.08 71	10.8 74	60.0 72	7.89 47	22.1 59	46.0 37	13.9 78	8.60 69	99.9 42	4.98 73	12.9 55	15.4 52	10.4 70	16.0 54	76.0 69	15.1 70	4.68 7	8.35 16	5.11 34	5.65 66	17.5 60	11.2 89
SRR-TVOF-NL [91]	60.0	14.4 74	46.7 58	8.18 58	13.1 82	74.0 89	8.44 66	24.1 64	63.2 55	11.9 72	6.51 52	85.1 26	3.25 23	12.1 26	15.0 34	10.3 69	17.5 73	61.6 42	13.4 53	10.4 100	12.3 86	8.92 87	5.52 65	7.83 36	7.58 49
TCOF [69]	61.1	13.6 66	44.8 45	8.02 50	9.90 61	54.6 64	8.02 52	31.3 76	99.9 80	15.4 86	6.49 51	82.4 17	4.76 70	14.9 103	18.0 110	9.50 46	9.71 8	48.3 29	12.6 44	10.1 96	12.7 90	8.96 88	4.29 53	9.21 43	6.80 39
DPOF [18]	62.5	17.4 90	49.1 70	7.77 41	12.3 78	45.6 40	8.91 78	10.9 33	26.6 4	8.09 57	7.81 62	99.3 41	5.31 75	13.5 79	16.0 79	11.0 80	17.5 73	61.8 44	12.2 40	13.1 118	10.9 71	18.1 116	5.04 59	9.50 46	4.49 26
F-TV-L1 [15]	62.5	15.6 79	47.4 63	13.4 100	18.8 96	99.1 110	11.6 88	43.1 96	99.9 80	11.3 70	14.7 86	99.9 42	7.03 93	12.2 29	14.9 31	9.00 32	13.5 36	99.9 101	7.56 6	6.41 49	10.5 61	4.23 9	3.91 48	80.3 84	3.38 12
TF+OM [100]	62.7	12.1 57	51.3 76	7.13 38	8.92 42	44.4 36	8.13 58	33.8 80	54.4 46	45.8 101	6.28 47	90.1 33	4.63 67	12.7 50	15.2 42	10.6 74	18.9 85	99.9 101	11.3 29	7.81 75	14.2 105	6.33 51	7.09 82	43.5 72	8.22 58
Aniso. Huber-L1 [22]	63.3	11.7 54	43.8 33	8.16 56	13.6 84	66.3 80	12.0 90	35.9 82	99.9 80	10.5 65	10.0 74	72.9 10	5.00 74	13.4 75	16.3 91	9.61 48	15.1 50	63.7 50	7.96 7	8.96 85	11.6 79	7.95 79	4.02 50	26.9 67	7.97 57
TV-L1-improved [17]	65.0	10.9 47	45.2 50	7.42 39	8.12 32	54.0 62	6.79 31	36.5 83	99.9 80	7.26 48	5.84 39	99.9 42	3.15 21	13.2 68	15.9 69	9.11 36	22.1 95	99.9 101	20.8 98	9.59 92	13.3 98	9.04 89	6.19 71	88.8 88	9.71 82
SIOF [67]	66.5	10.6 44	49.7 74	7.01 35	14.8 86	85.9 104	8.40 65	49.7 104	98.3 76	49.2 104	12.0 83	99.9 42	5.88 83	13.5 79	15.9 69	10.8 77	16.3 58	74.2 67	13.6 56	5.51 27	9.02 24	4.42 16	6.52 75	19.5 63	9.85 84
CRTflow [80]	67.6	15.0 76	46.3 56	7.89 44	8.87 41	54.9 66	7.15 34	30.1 73	99.9 80	8.03 55	9.30 72	99.9 42	4.50 66	13.0 62	15.7 65	8.05 19	32.5 108	99.9 101	34.3 115	6.62 56	9.72 38	7.52 69	9.30 92	99.9 94	14.7 99
DeepFlow [86]	68.4	14.7 75	49.0 69	9.78 85	12.9 80	79.3 96	9.80 83	30.1 73	96.1 74	24.4 91	21.4 95	99.9 42	5.36 77	12.5 40	15.1 40	8.59 25	14.0 43	71.9 63	15.1 70	5.46 26	8.01 10	8.73 86	14.2 102	99.9 94	15.7 103
Brox et al. [5]	68.5	16.0 80	49.2 72	12.0 97	12.3 78	80.4 97	10.3 85	23.7 63	73.1 59	13.2 74	24.2 96	99.9 42	4.23 61	14.7 100	16.8 96	15.4 113	10.7 13	96.7 94	9.71 17	5.88 39	9.05 25	3.01 3	8.78 89	67.7 80	9.37 71
LocallyOriented [52]	69.5	17.0 89	55.7 99	8.00 49	17.0 93	82.3 100	12.1 91	42.4 95	99.9 80	14.8 82	9.13 71	89.2 32	4.79 71	13.4 75	16.1 83	9.20 41	10.8 14	58.1 37	11.8 34	6.89 62	10.6 63	7.14 64	7.78 86	74.9 81	9.42 76
BriefMatch [124]	69.5	9.63 30	44.4 37	5.74 17	7.64 26	51.0 55	5.70 19	10.5 27	39.2 18	4.63 13	3.95 12	99.9 42	2.72 10	16.2 114	17.6 106	33.0 125	41.4 120	99.4 100	43.4 122	12.7 116	13.2 96	67.6 127	79.5 120	99.9 94	99.9 122
Dynamic MRF [7]	70.9	14.0 70	50.7 75	9.58 81	7.75 29	85.7 103	5.76 20	31.5 77	99.9 80	5.23 24	7.97 64	99.9 42	4.10 57	13.0 62	15.6 64	10.7 76	30.4 107	99.9 101	29.5 112	5.64 29	7.52 6	9.61 92	67.3 118	99.9 94	66.7 118
Classic++ [32]	71.4	11.2 50	49.4 73	9.13 72	9.34 46	68.4 82	8.11 56	30.7 75	95.1 73	10.2 64	5.59 34	99.9 42	3.47 35	13.5 79	16.1 83	10.4 70	19.7 89	99.9 101	17.6 93	8.38 79	11.5 78	8.30 83	7.20 84	99.9 94	9.54 78
Rannacher [23]	73.5	13.8 69	47.5 65	10.8 94	10.5 71	62.0 75	8.84 76	41.1 91	99.9 80	11.0 68	8.49 67	99.9 42	4.28 62	13.5 79	16.1 83	9.72 53	22.5 97	99.9 101	17.0 91	7.66 73	9.88 44	7.82 76	4.72 55	75.1 82	9.37 71
SuperFlow [81]	73.5	14.3 71	47.0 62	9.26 74	19.5 98	58.7 71	17.9 98	45.9 99	99.9 80	56.1 107	19.0 93	99.9 42	5.88 83	13.3 74	16.1 83	12.6 96	11.5 21	74.0 65	8.27 8	9.24 88	12.9 92	4.70 24	8.27 87	89.2 90	8.28 59
CBF [12]	74.5	12.2 59	41.4 18	8.65 64	16.5 90	47.1 45	16.6 97	24.7 65	88.1 67	12.9 73	11.0 82	99.9 42	4.18 59	14.9 103	17.5 102	14.0 108	15.0 49	79.8 74	8.97 12	14.9 121	15.1 114	15.9 112	5.78 67	63.0 79	10.1 85
p-harmonic [29]	74.9	15.1 77	48.5 66	14.1 102	10.4 69	53.1 60	9.31 82	41.9 93	99.9 80	15.1 84	19.4 94	99.9 42	10.6 101	12.7 50	14.9 31	11.8 86	18.0 80	85.6 85	18.4 95	7.85 76	10.4 58	5.46 41	6.98 81	99.9 94	9.28 70
Local-TV-L1 [65]	75.3	16.5 84	52.3 82	11.7 96	27.7 104	96.9 107	22.5 103	68.8 111	99.9 80	47.5 102	34.6 105	99.9 42	7.04 94	12.6 46	15.0 34	9.25 43	17.7 77	84.7 83	13.7 57	5.09 17	7.36 4	5.03 31	20.7 107	88.8 88	29.4 111
CLG-TV [48]	76.2	12.1 57	43.5 30	9.42 78	14.1 85	60.9 74	13.2 93	33.2 79	99.9 80	11.2 69	10.8 80	84.7 24	5.82 82	14.8 102	17.9 108	12.1 89	13.8 40	99.9 101	11.3 29	10.9 102	14.2 105	9.22 90	6.69 78	99.9 94	8.52 61
FlowNet2 [122]	76.8	32.0 115	68.0 124	14.9 104	35.2 109	77.9 95	29.8 112	32.9 78	51.2 43	35.4 96	16.3 89	99.9 42	10.5 100	13.2 68	15.9 69	12.0 88	14.1 44	99.9 101	10.8 23	8.84 84	20.1 124	4.66 21	4.49 54	9.46 45	3.65 14
TriFlow [95]	77.2	16.1 81	57.1 104	7.97 48	13.3 83	65.2 79	12.3 92	48.8 102	99.9 80	61.8 111	7.03 59	91.4 35	5.33 76	13.4 75	15.3 48	11.4 84	14.3 45	76.1 70	13.3 51	22.7 127	14.3 107	26.7 120	5.93 69	11.8 50	7.86 56
SegOF [10]	77.6	22.8 108	54.8 94	15.4 106	27.9 105	56.0 69	27.4 108	39.3 86	87.9 66	33.2 95	37.5 106	75.4 13	22.3 106	14.4 98	16.3 91	14.7 111	21.7 93	99.9 101	24.5 105	4.09 3	7.28 2	2.18 2	6.79 79	48.3 75	6.93 40
DF-Auto [115]	77.6	20.5 98	55.9 101	9.21 73	22.7 100	74.8 90	19.3 101	44.7 98	93.5 69	57.8 109	27.1 99	99.9 42	5.70 81	15.0 106	18.9 119	11.8 86	7.13 2	57.9 36	7.09 4	10.2 99	13.9 104	4.23 9	8.94 90	54.0 77	9.21 69
TriangleFlow [30]	78.8	13.2 62	46.8 60	9.41 77	10.8 74	73.2 88	7.30 38	26.1 66	99.9 80	5.70 29	7.23 60	99.9 42	4.46 65	17.0 119	21.3 124	15.2 112	23.0 98	69.8 58	22.9 102	9.71 93	16.1 117	9.40 91	6.89 80	23.8 64	11.5 92
Bartels [41]	78.8	13.3 65	55.0 95	10.2 89	8.13 33	43.2 34	7.67 40	18.1 51	69.0 58	6.30 37	8.49 67	99.9 42	6.05 87	13.9 89	16.1 83	13.9 106	21.8 94	99.9 101	21.5 101	10.6 101	13.5 100	20.3 118	12.3 97	99.9 94	26.9 109
Fusion [6]	80.4	16.3 82	53.8 89	12.5 98	7.93 30	37.7 19	7.75 41	15.6 50	41.8 24	13.2 74	13.5 84	83.1 19	7.77 97	15.4 108	18.5 113	14.2 110	33.1 109	89.0 89	24.8 107	11.8 111	14.7 108	8.27 82	11.4 96	99.9 94	13.3 96
CNN-flow-warp+ref [117]	81.8	21.3 104	57.3 105	15.1 105	16.8 92	54.4 63	15.9 96	41.0 90	99.9 80	28.8 92	28.6 101	99.9 42	7.45 96	14.0 92	15.9 69	14.0 108	16.5 62	84.8 84	10.9 25	5.30 23	8.23 15	8.13 80	99.9 123	99.9 94	99.9 122
StereoFlow [44]	82.5	48.0 126	74.6 130	41.1 126	61.0 122	99.9 111	51.6 121	71.4 112	99.9 80	63.9 113	65.6 120	99.9 42	61.2 120	16.2 114	15.9 69	22.6 120	7.22 3	77.8 73	7.39 5	3.38 1	7.35 3	1.99 1	7.18 83	99.9 94	11.4 91
LDOF [28]	83.9	17.9 92	53.5 88	8.72 66	18.7 95	92.6 106	11.8 89	29.5 72	67.1 57	20.9 90	29.0 102	99.9 42	8.92 99	14.2 95	16.4 94	13.7 103	18.9 85	97.5 98	15.0 67	6.26 48	10.3 55	10.1 95	10.4 94	99.9 94	10.3 87
StereoOF-V1MT [119]	84.2	16.5 84	46.0 53	9.27 75	18.9 97	99.9 111	7.16 35	40.5 88	99.9 80	8.18 58	14.7 86	96.4 39	6.30 91	14.1 94	16.8 96	12.9 98	29.9 105	91.4 91	27.0 110	5.78 36	10.4 58	10.4 96	99.9 123	99.9 94	99.9 122
FlowNetS+ft+v [112]	84.7	16.5 84	52.1 80	8.06 53	17.4 94	76.9 91	13.4 94	46.3 100	99.9 80	30.4 94	29.8 103	99.9 42	13.8 103	15.5 109	18.5 113	13.8 105	12.7 31	89.5 90	11.7 33	9.24 88	13.5 100	12.1 105	7.39 85	57.9 78	9.52 77
Shiralkar [42]	85.1	16.8 87	44.6 41	9.46 80	16.5 90	98.8 109	8.05 54	42.0 94	99.9 80	10.8 67	18.4 92	99.9 42	8.02 98	12.9 55	15.5 61	10.4 70	30.2 106	99.9 101	25.1 108	11.4 107	11.8 82	15.8 111	22.2 109	99.9 94	17.5 105
Learning Flow [11]	85.2	13.7 68	52.8 84	7.67 40	12.9 80	87.1 105	10.0 84	40.5 88	95.0 72	13.4 76	38.1 107	99.9 42	4.74 69	17.1 121	21.7 125	12.5 95	24.2 100	99.9 101	13.5 55	7.95 77	12.7 90	6.98 60	23.9 111	99.9 94	14.9 100
Ad-TV-NDC [36]	85.8	31.0 114	53.3 87	33.1 123	70.2 123	99.9 111	49.0 120	93.2 123	99.9 80	54.0 106	38.9 108	95.0 37	29.4 109	13.8 88	17.3 101	8.71 26	14.7 46	77.1 72	13.0 49	6.24 46	9.84 39	5.19 37	46.4 116	76.4 83	54.0 116
Second-order prior [8]	87.6	14.3 71	46.7 58	8.79 67	15.2 88	72.5 86	10.5 86	39.2 85	99.9 80	16.6 88	17.5 90	99.9 42	6.01 86	14.4 98	17.5 102	10.8 77	38.6 118	99.9 101	24.7 106	11.3 105	12.2 84	11.2 100	9.13 91	89.5 91	15.6 102
Filter Flow [19]	87.7	21.6 106	57.7 107	14.4 103	24.6 102	77.5 94	18.1 99	54.3 106	80.8 61	66.3 117	52.8 113	91.0 34	46.5 114	13.6 83	16.0 79	12.3 92	17.0 70	69.6 57	14.2 61	12.0 115	16.1 117	7.39 67	6.58 77	37.5 71	8.36 60
2D-CLG [1]	89.7	46.1 124	67.5 123	28.2 119	39.5 113	77.3 93	38.9 115	93.9 125	99.9 80	74.9 122	53.6 114	99.9 42	51.0 116	13.9 89	15.9 69	13.5 102	24.0 99	99.9 101	21.2 99	4.28 4	7.24 1	4.50 17	12.7 98	99.9 94	11.6 93
GraphCuts [14]	90.0	21.7 107	52.8 84	10.4 92	39.2 112	99.9 111	23.1 104	39.7 87	58.0 50	49.6 105	25.6 98	74.6 12	7.31 95	13.6 83	15.9 69	13.2 101	37.8 115	97.6 99	16.2 79	9.36 90	11.4 76	11.7 103	10.3 93	99.9 94	15.0 101
HBpMotionGpu [43]	90.6	19.5 94	63.6 117	13.5 101	31.0 106	99.9 111	27.4 108	99.9 128	99.9 80	59.3 110	18.2 91	99.9 42	6.69 92	13.6 83	16.0 79	12.4 94	16.2 56	91.5 92	11.1 28	11.1 103	13.0 95	6.73 56	21.3 108	99.9 94	21.6 107
SPSA-learn [13]	91.2	23.6 109	55.7 99	20.1 111	32.9 108	99.9 111	25.2 105	91.2 122	99.9 80	64.5 115	49.6 111	99.9 42	31.2 110	14.0 92	16.0 79	13.0 100	19.9 90	99.9 101	23.3 104	6.53 53	9.13 29	4.40 15	15.8 105	99.9 94	16.5 104
IAOF2 [51]	91.4	16.8 87	59.5 112	10.5 93	20.1 99	69.0 84	18.1 99	53.3 105	99.9 80	56.8 108	55.3 117	95.0 37	54.7 118	14.2 95	17.2 100	11.0 80	19.2 88	81.1 76	14.3 63	11.8 111	13.2 96	13.0 108	13.5 99	45.0 73	9.00 65
Modified CLG [34]	92.8	27.9 112	58.6 110	23.4 114	26.5 103	71.1 85	26.2 107	93.4 124	99.9 80	73.1 120	49.2 110	99.9 42	22.6 107	15.2 107	18.1 111	13.7 103	16.3 58	99.9 101	12.8 48	6.43 51	10.2 52	11.7 103	11.3 95	99.9 94	11.3 90
UnFlow [129]	93.8	49.8 127	69.9 127	23.3 113	32.8 107	57.8 70	33.0 113	49.4 103	98.6 77	39.8 97	53.6 114	99.9 42	52.1 117	16.2 114	18.5 113	20.6 117	35.3 112	99.9 101	38.4 118	8.81 83	11.4 76	3.08 5	6.46 74	99.9 94	6.47 36
IAOF [50]	94.2	20.6 99	55.0 95	17.4 108	36.6 110	99.9 111	27.6 110	99.9 128	99.9 80	75.5 123	32.7 104	93.3 36	25.5 108	13.9 89	16.6 95	12.1 89	36.5 113	92.3 93	12.3 41	9.40 91	11.7 80	7.13 63	26.2 112	47.4 74	28.8 110
GroupFlow [9]	96.4	27.3 111	66.8 121	21.6 112	41.1 114	99.9 111	35.1 114	71.4 112	99.9 80	61.8 111	25.1 97	99.9 42	14.4 104	14.7 100	17.9 108	11.7 85	40.6 119	97.2 97	40.6 120	5.72 33	11.7 80	6.48 52	16.4 106	53.8 76	23.0 108
Black & Anandan [4]	96.6	21.5 105	51.7 78	19.8 110	38.6 111	99.9 111	25.8 106	81.3 115	99.9 80	65.5 116	50.4 112	99.9 42	31.6 111	15.5 109	19.1 120	12.8 97	22.4 96	96.8 96	18.2 94	10.1 96	12.4 87	5.16 36	13.9 101	99.9 94	12.9 95
Nguyen [33]	97.1	27.2 110	56.2 102	18.7 109	46.7 117	99.9 111	44.1 119	97.7 127	99.9 80	74.1 121	45.7 109	99.9 42	38.0 112	16.4 117	17.7 107	21.8 119	20.8 92	99.9 101	21.2 99	7.21 66	9.42 34	5.61 42	14.6 103	99.9 94	14.2 98
2bit-BM-tele [98]	101.1	20.8 103	59.0 111	16.3 107	16.4 89	68.5 83	15.3 95	43.9 97	99.9 80	15.4 86	14.6 85	99.9 42	11.2 102	15.6 111	17.5 102	16.8 115	34.9 111	99.9 101	31.1 113	18.8 125	20.4 126	30.8 121	23.7 110	99.9 94	61.9 117
BlockOverlap [61]	102.4	17.6 91	56.5 103	13.1 99	24.0 101	66.9 81	21.5 102	67.6 110	99.9 80	49.0 103	28.2 100	99.9 42	15.2 105	17.6 123	18.3 112	32.7 124	38.1 116	81.6 77	26.6 109	14.5 120	15.5 115	67.6 127	39.8 114	82.5 85	84.7 119
Heeger++ [104]	102.9	33.0 116	58.3 109	23.6 115	60.7 120	99.9 111	39.4 116	59.1 107	99.9 80	30.1 93	86.5 129	99.9 42	70.5 122	14.9 103	17.1 98	12.9 98	76.5 127	99.9 101	79.8 127	7.49 71	12.9 92	6.59 53	99.9 123	99.9 94	99.9 122
SILK [79]	105.0	33.3 117	64.6 118	29.2 121	46.3 116	99.9 111	39.4 116	95.0 126	99.9 80	66.5 118	56.7 118	99.9 42	50.2 115	16.1 113	18.7 117	17.2 116	48.7 122	99.9 101	37.3 117	7.26 68	9.22 31	14.5 110	70.9 119	99.9 94	51.7 115
Horn & Schunck [3]	105.3	28.8 113	57.7 107	25.3 116	41.1 114	99.9 111	28.2 111	80.0 114	99.9 80	75.9 124	75.5 121	99.9 42	66.3 121	15.7 112	18.5 113	13.9 106	41.9 121	99.9 101	41.4 121	11.6 108	13.6 102	6.16 48	45.4 115	99.9 94	39.5 113
TI-DOFE [24]	106.7	40.2 120	61.2 115	38.6 125	60.2 119	99.9 111	53.7 122	90.4 121	99.9 80	78.2 126	83.9 127	99.9 42	82.8 128	16.6 118	19.4 122	16.4 114	38.1 116	99.9 101	38.7 119	8.51 81	10.3 55	7.75 73	56.3 117	99.9 94	49.7 114
HCIC-L [99]	109.0	44.4 123	66.7 120	29.1 120	99.9 129	99.9 111	99.9 129	47.8 101	99.9 80	44.6 100	58.5 119	99.9 42	55.7 119	20.3 125	20.6 123	24.0 122	33.9 110	86.5 87	33.6 114	38.0 129	49.5 129	36.0 122	13.7 100	25.8 65	13.7 97
FFV1MT [106]	110.7	36.4 119	71.0 128	25.8 117	50.3 118	99.9 111	39.7 118	65.6 109	99.1 78	41.6 99	99.9 130	99.9 42	97.4 131	20.2 124	19.3 121	33.1 126	62.5 124	99.9 101	71.1 126	9.04 86	14.8 110	11.2 100	99.9 123	99.9 94	99.9 122
SLK [47]	111.0	53.1 128	66.3 119	59.5 131	60.9 121	98.1 108	58.6 123	89.7 119	99.9 80	67.7 119	99.9 130	99.9 42	95.1 130	17.0 119	18.8 118	23.5 121	56.6 123	99.9 101	51.2 123	8.43 80	11.9 83	12.2 106	99.9 123	99.9 94	99.9 122
PGAM+LK [55]	112.6	43.6 121	67.1 122	43.7 127	73.8 124	99.9 111	77.5 124	61.9 108	82.9 64	63.9 113	76.6 122	99.9 42	72.5 123	17.1 121	17.1 98	31.6 123	66.2 125	99.9 101	64.8 125	18.9 126	20.3 125	22.1 119	99.9 123	99.9 94	99.9 122
Adaptive flow [45]	112.8	34.4 118	59.7 113	29.7 122	84.5 127	99.9 111	77.7 125	87.6 118	99.9 80	92.8 130	54.9 116	99.9 42	39.3 113	20.6 126	23.8 127	20.9 118	37.5 114	96.7 94	29.2 111	35.2 128	30.1 128	58.6 126	38.1 113	99.9 94	33.0 112
AdaConv-v1 [126]	114.3	66.7 130	69.5 125	54.6 128	80.0 125	82.0 98	79.5 126	81.7 116	82.0 62	80.9 127	82.3 124	83.1 19	82.6 126	85.8 128	85.9 128	85.2 128	88.2 129	83.0 79	82.9 129	75.7 130	67.1 130	77.8 130	85.1 121	86.4 86	84.8 120
SepConv-v1 [127]	114.3	66.7 130	69.5 125	54.6 128	80.0 125	82.0 98	79.5 126	81.7 116	82.0 62	80.9 127	82.3 124	83.1 19	82.6 126	85.8 128	85.9 128	85.2 128	88.2 129	83.0 79	82.9 129	75.7 130	67.1 130	77.8 130	85.1 121	86.4 86	84.8 120
Periodicity [78]	114.6	54.4 129	84.3 131	26.5 118	99.9 129	99.9 111	99.9 129	99.9 128	99.9 80	99.9 131	81.4 123	99.9 42	76.3 124	99.9 131	99.9 130	99.9 131	99.9 131	99.9 101	99.9 131	6.06 43	14.8 110	70.6 129	99.9 123	99.9 94	99.9 122
FOLKI [16]	116.7	43.8 122	74.4 129	38.0 124	99.9 129	99.9 111	99.9 129	89.7 119	99.9 80	76.2 125	85.0 128	99.9 42	81.0 125	23.2 127	22.5 126	38.8 127	66.2 125	99.9 101	62.7 124	17.0 124	17.6 121	42.6 124	99.9 123	99.9 94	99.9 122
Pyramid LK [2]	117.6	47.7 125	60.7 114	56.1 130	88.6 128	99.9 111	91.9 128	99.9 128	99.9 80	89.4 129	83.0 126	99.9 42	83.2 129	98.4 130	99.9 130	87.9 130	87.4 128	99.9 101	81.4 128	16.9 123	16.6 119	57.0 125	99.9 123	99.9 94	99.9 122

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.