Optical flow evaluation results: R5.0 angle error

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

R5.0 angle error	avg.	Army (Hidden texture) GT im0 im1			Mequon (Hidden texture) GT im0 im1			Schefflera (Hidden texture) GT im0 im1			Wooden (Hidden texture) GT im0 im1			Grove (Synthetic) GT im0 im1			Urban (Synthetic) GT im0 im1			Yosemite (Synthetic) GT im0 im1			Teddy (Stereo) GT im0 im1
	rank	all	disc	untext	all	disc	untext	all	disc	untext	all	disc	untext	all	disc	untext	all	disc	untext	all	disc	untext	all	disc	untext
NNF-Local [87]	10.8	7.69 2	26.2 5	3.54 2	7.19 14	30.7 27	6.11 20	5.88 5	19.3 8	4.53 14	4.01 10	23.9 16	2.00 12	11.7 1	16.4 3	5.52 3	10.4 15	29.7 9	9.30 10	5.25 17	20.0 24	2.61 7	1.88 7	6.47 27	0.19 1
MDP-Flow2 [68]	12.0	10.3 20	30.4 19	6.57 20	5.28 2	23.9 3	4.13 3	5.46 3	17.6 3	3.58 7	4.49 18	25.3 21	2.11 15	15.8 21	21.4 21	10.2 26	10.6 16	29.9 10	9.87 16	4.44 8	19.3 17	2.81 8	1.39 2	4.82 5	1.11 3
OFLAF [77]	12.8	8.96 9	27.9 9	4.57 8	7.36 17	26.4 8	6.68 22	4.80 2	14.9 2	3.37 6	4.43 17	21.2 8	2.81 26	13.4 7	19.0 10	7.08 7	11.4 25	28.0 3	9.28 9	5.35 19	19.1 16	3.15 10	2.47 16	5.73 18	5.50 32
NN-field [71]	13.9	8.65 5	28.3 10	4.00 4	8.38 26	33.1 41	7.44 28	5.86 4	19.0 7	4.53 14	3.15 2	21.4 10	1.25 3	12.0 4	16.9 5	5.41 2	6.58 2	20.2 1	3.45 1	8.64 47	23.6 52	2.88 9	2.47 16	8.48 38	0.20 2
PMMST [114]	19.5	12.9 46	32.6 31	8.45 42	10.2 42	30.5 24	10.7 58	7.39 15	22.2 12	6.31 31	3.87 9	13.5 1	2.73 23	14.0 10	18.7 9	7.52 8	10.9 22	28.9 6	9.95 17	4.99 14	20.8 29	3.18 11	1.52 3	3.87 2	1.12 4
ComponentFusion [96]	22.0	8.86 8	28.7 13	5.91 14	6.30 5	24.2 4	5.98 15	6.79 10	21.6 10	4.99 20	4.11 11	24.4 17	2.04 14	16.2 29	22.0 26	11.3 37	13.4 41	40.4 57	12.4 56	7.66 39	21.3 35	5.22 33	2.05 8	5.21 9	3.61 16
ALD-Flow [66]	22.4	8.44 4	27.6 8	4.09 5	6.49 7	27.2 12	5.04 9	7.66 20	24.1 22	3.72 9	4.58 20	27.1 32	2.01 13	16.0 24	22.7 34	8.55 12	9.39 8	33.3 21	8.46 8	7.21 36	18.5 10	17.3 85	4.06 47	11.1 52	5.94 40
HAST [109]	22.5	7.13 1	21.8 1	3.37 1	7.28 15	26.1 7	6.10 19	3.86 1	12.2 1	0.97 1	3.85 8	21.3 9	1.50 4	11.7 1	16.5 4	4.64 1	15.1 63	35.5 32	14.0 67	19.4 87	36.3 95	39.0 115	1.24 1	3.55 1	1.32 5
NNF-EAC [103]	23.5	10.9 28	31.8 26	6.97 21	6.64 9	26.4 8	5.65 11	6.61 9	20.7 9	4.44 13	5.48 39	27.1 32	2.97 31	16.5 32	22.3 30	11.1 34	12.1 29	30.7 11	9.97 18	6.42 27	21.4 37	3.94 22	2.95 29	7.51 33	4.63 25
nLayers [57]	23.5	8.66 6	25.4 3	4.54 6	13.0 80	32.1 34	13.7 87	7.74 22	21.8 11	8.85 60	3.29 4	18.2 2	1.89 9	11.8 3	16.2 2	6.65 6	12.2 30	28.4 4	10.3 20	8.59 46	21.5 39	4.98 29	2.36 14	5.74 19	5.08 28
LME [70]	23.6	9.71 15	29.0 15	6.46 19	5.49 4	22.8 2	4.79 7	8.62 36	22.4 13	11.2 71	4.73 21	28.3 46	2.35 18	16.5 32	21.9 24	12.6 50	10.7 17	34.0 25	9.81 15	5.57 20	21.3 35	3.87 20	2.40 15	6.32 24	4.52 23
TC/T-Flow [76]	24.5	9.15 10	32.1 28	3.69 3	6.89 12	31.2 30	4.32 4	7.32 14	23.1 17	4.08 10	5.14 30	27.2 35	2.80 25	15.6 20	21.9 24	9.71 20	8.63 6	29.2 7	8.40 6	6.72 29	20.2 26	19.7 99	3.86 45	9.43 42	6.28 46
WLIF-Flow [93]	25.6	9.40 14	27.1 6	6.06 16	10.0 39	33.0 39	9.71 42	7.26 13	22.4 13	5.90 30	4.53 19	23.7 14	2.56 22	16.1 25	22.3 30	11.3 37	12.8 33	33.2 20	10.4 23	6.85 31	18.8 14	7.36 46	2.80 26	6.48 28	5.62 35
FC-2Layers-FF [74]	26.6	9.97 16	28.7 13	7.96 31	10.4 44	35.3 49	9.95 44	6.11 7	18.1 6	6.82 35	4.12 12	20.9 6	2.48 21	13.3 6	17.7 6	9.47 17	13.5 42	32.6 15	11.3 35	14.0 71	26.0 65	12.5 66	1.84 5	4.18 3	4.53 24
RNLOD-Flow [121]	26.9	7.93 3	24.8 2	5.41 12	8.33 25	31.7 31	6.84 24	7.47 16	23.3 18	4.60 16	3.62 6	21.1 7	1.66 7	14.4 12	21.0 15	7.80 9	12.7 32	32.9 16	12.2 55	17.4 83	34.4 90	20.7 101	2.55 19	5.57 16	5.30 30
Layers++ [37]	27.7	10.2 18	29.1 16	8.58 45	10.8 49	30.6 25	11.0 64	5.90 6	17.7 4	6.34 32	3.40 5	18.2 2	1.66 7	12.2 5	16.0 1	9.69 19	13.9 47	33.6 22	11.9 49	13.9 70	27.9 70	8.74 51	2.33 13	4.94 6	5.70 38
FESL [72]	28.8	8.67 7	25.6 4	4.73 9	13.6 85	39.4 84	12.9 82	8.22 30	24.3 26	7.10 39	3.76 7	20.3 5	2.12 16	14.1 11	20.1 12	9.08 14	11.2 24	31.0 12	9.80 14	11.3 56	30.4 78	7.66 48	2.10 9	5.40 12	2.40 8
SVFilterOh [111]	29.1	12.7 44	28.4 11	8.58 45	9.41 34	29.1 19	8.31 33	6.39 8	17.7 4	5.05 22	3.23 3	18.8 4	0.95 2	14.9 15	20.9 14	6.51 5	13.2 37	32.1 14	11.6 42	22.0 93	46.5 118	30.5 110	1.61 4	4.97 8	2.45 10
TC-Flow [46]	30.2	9.24 11	30.9 22	5.24 11	5.48 3	25.7 6	4.01 2	7.25 12	23.3 18	2.66 3	5.52 40	28.4 47	3.26 37	16.7 35	23.9 40	9.52 18	11.7 26	36.8 37	11.5 41	6.69 28	21.4 37	19.0 96	4.21 49	10.6 49	6.91 58
OAR-Flow [125]	32.0	10.8 26	34.0 40	6.23 17	8.94 29	31.9 32	7.53 29	10.4 51	30.3 53	7.58 48	5.60 44	25.9 27	3.04 33	17.0 36	24.0 41	9.35 15	8.62 5	33.0 17	7.87 4	3.37 4	14.6 3	5.54 37	4.80 56	10.4 48	9.44 73
Efficient-NL [60]	32.9	9.31 12	27.5 7	5.65 13	12.1 72	38.1 69	11.2 66	8.07 29	24.1 22	6.69 34	5.39 36	25.9 27	3.51 47	14.9 15	21.1 17	9.39 16	14.0 50	35.2 30	11.1 32	11.5 57	25.7 63	6.90 43	2.19 11	5.45 14	1.94 7
AGIF+OF [85]	33.0	10.4 21	29.4 17	7.42 24	12.4 73	37.5 62	12.4 77	7.91 27	24.3 26	7.24 41	4.84 24	23.8 15	2.91 28	14.8 14	20.6 13	9.72 21	13.2 37	36.5 35	10.5 25	7.06 33	20.8 29	7.54 47	3.02 34	6.27 22	6.37 48
IROF++ [58]	33.4	10.2 18	30.9 22	7.02 22	11.1 56	38.1 69	10.7 58	8.32 34	25.1 31	7.61 49	5.83 47	28.0 43	4.08 56	15.4 19	21.3 19	9.83 22	13.6 43	38.0 46	11.3 35	5.83 21	20.8 29	1.97 6	2.32 12	5.71 17	4.87 27
PMF [73]	34.0	11.6 37	29.9 18	4.55 7	7.81 20	30.2 21	6.00 16	7.17 11	23.3 18	3.21 5	4.88 26	23.1 12	2.42 19	13.6 8	18.5 7	6.49 4	16.1 71	42.7 67	15.4 77	27.2 107	43.5 115	28.9 107	2.15 10	4.96 7	4.81 26
PH-Flow [101]	34.5	10.9 28	32.6 31	7.94 30	10.9 51	37.4 60	10.5 52	7.56 19	22.8 16	7.74 52	5.75 46	27.2 35	4.04 54	14.4 12	19.8 11	8.81 13	13.0 35	33.6 22	11.1 32	12.7 64	23.1 48	17.6 87	1.84 5	4.19 4	4.50 22
Classic+CPF [83]	35.7	10.9 28	31.7 25	7.88 29	11.5 62	37.9 66	10.9 62	8.27 32	25.1 31	7.51 47	5.05 28	25.8 26	3.16 35	15.1 17	21.0 15	10.6 27	13.1 36	34.6 28	10.3 20	9.87 51	22.0 44	13.1 72	2.68 22	5.85 20	5.53 33
COFM [59]	36.1	10.1 17	32.0 27	7.63 25	8.06 22	30.4 23	7.17 25	8.93 40	25.9 37	8.04 56	4.17 13	24.9 19	1.63 6	18.8 48	24.0 41	18.6 89	14.4 55	33.0 17	11.7 44	8.15 42	20.4 27	14.7 79	3.16 36	5.36 11	8.09 68
Ramp [62]	37.0	10.9 28	32.7 35	7.96 31	10.9 51	37.1 57	10.6 54	7.85 23	24.2 24	7.41 45	5.29 33	27.0 31	3.44 42	16.1 25	22.3 30	10.8 28	13.8 46	35.4 31	11.0 31	11.6 58	21.1 33	18.2 91	2.52 18	5.44 13	5.23 29
Sparse-NonSparse [56]	37.5	10.7 24	32.5 30	8.38 40	10.9 51	36.8 55	10.7 58	7.95 28	24.5 30	7.30 43	5.42 38	27.6 38	3.49 46	16.1 25	22.1 27	11.0 32	13.3 39	36.0 34	10.6 27	10.6 54	21.1 33	10.9 57	2.91 27	5.93 21	6.18 44
Correlation Flow [75]	37.6	11.9 41	35.3 41	6.03 15	6.85 11	28.0 14	4.77 6	8.29 33	25.8 35	2.17 2	4.84 24	27.2 35	2.77 24	18.5 46	25.9 53	11.7 39	16.9 78	39.5 52	16.7 83	12.1 61	24.6 59	17.8 88	2.59 20	7.33 32	3.08 11
LSM [39]	38.0	10.4 21	32.6 31	8.24 35	10.8 49	37.4 60	10.4 51	7.85 23	24.3 26	7.05 37	5.32 34	27.6 38	3.41 41	15.8 21	21.5 22	11.1 34	13.7 44	35.6 33	10.9 30	13.0 65	23.2 49	12.5 66	2.99 33	6.43 26	6.14 43
ProbFlowFields [128]	38.8	16.2 62	47.8 74	11.7 73	8.96 30	31.0 28	8.86 36	9.73 46	28.4 45	10.1 65	6.09 54	25.5 23	4.53 65	18.2 44	25.5 47	10.9 30	9.76 12	34.2 26	11.7 44	4.63 9	18.8 14	3.79 19	2.95 29	8.94 41	3.52 14
Classic+NL [31]	40.5	10.5 23	31.4 24	8.38 40	11.1 56	37.9 66	10.6 54	7.87 25	24.0 21	7.48 46	5.57 42	27.6 38	3.62 49	15.8 21	21.5 22	10.8 28	14.1 51	37.4 41	11.4 38	14.8 74	25.9 64	13.4 75	2.61 21	5.29 10	6.10 42
FMOF [94]	41.8	11.0 35	30.4 19	8.33 39	13.0 80	38.5 75	12.6 78	7.51 17	22.6 15	7.34 44	5.06 29	25.2 20	3.44 42	15.3 18	21.3 19	9.87 24	14.9 62	33.1 19	11.4 38	11.7 60	24.3 58	15.0 81	3.92 46	8.59 39	6.28 46
S2D-Matching [84]	41.9	10.7 24	32.2 29	8.71 47	10.7 47	36.6 54	10.2 47	8.94 41	27.2 41	6.96 36	5.17 31	26.0 29	3.36 40	16.3 30	22.1 27	10.9 30	14.4 55	37.0 38	11.7 44	16.4 78	26.0 65	16.5 83	2.79 25	5.49 15	6.49 49
IROF-TV [53]	44.5	11.6 37	35.3 41	9.03 50	11.2 59	38.2 72	10.9 62	8.85 39	26.5 38	7.73 51	6.04 53	33.0 70	3.62 49	17.1 37	23.1 36	13.5 60	16.3 72	44.8 75	13.5 65	3.41 5	16.9 5	1.13 3	2.71 23	6.80 30	5.67 37
TV-L1-MCT [64]	45.0	10.9 28	30.5 21	8.56 44	13.8 88	40.9 95	13.2 84	8.68 37	25.8 35	7.98 55	4.83 23	25.7 24	3.26 37	17.4 39	23.5 39	13.7 64	14.8 61	36.7 36	12.7 59	5.84 22	19.4 18	10.1 55	3.53 40	6.42 25	6.63 52
Aniso-Texture [82]	46.2	9.33 13	28.5 12	7.26 23	9.17 32	26.8 11	10.2 47	10.1 50	29.2 48	7.28 42	2.77 1	22.6 11	0.94 1	19.9 62	27.1 66	13.3 58	14.6 58	38.5 49	12.4 56	31.5 113	46.3 117	18.2 91	4.45 52	10.1 45	6.60 51
2DHMM-SAS [92]	46.2	10.9 28	32.6 31	8.06 33	11.5 62	39.5 85	10.6 54	10.0 49	28.3 44	7.91 54	5.93 49	28.2 45	4.07 55	16.1 25	22.3 30	11.0 32	13.7 44	38.3 48	11.1 32	12.3 63	23.2 49	18.0 90	3.08 35	6.48 28	6.24 45
AggregFlow [97]	46.5	13.9 49	33.8 38	11.2 67	13.7 86	39.6 87	12.6 78	12.0 64	31.3 54	13.7 81	5.40 37	23.5 13	3.44 42	17.5 40	25.4 45	7.98 11	8.57 4	25.9 2	8.42 7	7.00 32	24.1 57	4.53 26	5.53 67	9.80 44	11.7 85
CostFilter [40]	46.6	14.1 50	36.2 47	8.48 43	8.61 28	30.6 25	7.43 27	8.26 31	26.9 40	4.40 12	5.72 45	28.1 44	3.24 36	13.7 9	18.5 7	7.81 10	16.6 75	45.0 80	16.0 80	26.8 105	48.6 121	32.7 111	2.93 28	7.59 34	5.38 31
SimpleFlow [49]	46.7	11.6 37	33.7 37	8.98 49	12.5 76	38.9 78	12.6 78	10.4 51	29.3 49	9.20 61	5.99 50	27.6 38	4.08 56	16.3 30	22.2 29	11.1 34	16.7 76	37.4 41	12.7 59	8.29 43	19.9 22	6.11 41	2.74 24	6.28 23	5.86 39
Adaptive [20]	47.2	10.9 28	33.8 38	4.92 10	10.5 45	35.0 48	9.53 41	12.2 65	33.7 58	7.68 50	5.57 42	30.3 56	2.95 30	21.7 86	26.7 61	20.6 95	10.8 19	34.9 29	7.26 3	14.0 71	28.8 73	4.88 27	4.50 54	10.2 47	6.84 56
MDP-Flow [26]	47.6	12.2 43	40.6 54	8.88 48	9.32 33	28.3 16	10.5 52	9.09 42	28.1 43	9.37 63	6.03 52	30.6 58	3.99 52	17.2 38	23.1 36	12.4 45	13.9 47	42.7 67	12.5 58	7.10 35	23.6 52	4.09 24	5.35 62	13.2 62	7.09 61
RFlow [90]	48.1	14.8 51	43.9 63	11.2 67	6.64 9	26.6 10	5.76 12	11.7 60	35.9 67	5.04 21	4.31 15	27.1 32	1.94 10	19.4 52	26.8 63	13.0 57	14.7 59	42.2 64	11.8 48	13.1 66	22.2 45	13.1 72	5.87 72	14.1 69	8.71 71
Occlusion-TV-L1 [63]	48.7	12.9 46	36.1 46	8.26 38	9.51 37	32.7 36	8.99 37	12.3 66	34.4 62	8.27 57	5.53 41	29.8 54	3.04 33	20.5 73	28.5 84	13.8 66	9.95 13	37.9 43	11.6 42	7.64 38	21.8 43	3.47 13	5.69 69	13.9 67	7.59 65
OFH [38]	50.2	15.0 53	40.9 55	14.4 84	7.06 13	29.9 20	5.37 10	10.8 54	33.1 57	4.86 19	5.84 48	30.6 58	3.46 45	19.5 55	26.1 54	15.3 73	15.6 67	46.5 87	16.6 82	4.19 6	21.7 41	3.74 18	5.39 64	15.4 79	7.23 62
MLDP_OF [89]	52.6	18.8 84	51.3 89	16.0 87	8.16 23	32.0 33	6.76 23	10.7 53	31.9 56	5.45 25	4.81 22	26.1 30	2.44 20	18.7 47	24.3 43	13.7 64	15.6 67	37.9 43	18.6 91	19.2 86	28.5 71	38.7 114	3.53 40	7.25 31	4.27 20
DeepFlow2 [108]	54.4	15.0 53	43.6 62	11.0 63	10.1 40	34.2 45	9.29 39	12.9 68	36.8 68	11.1 70	7.47 70	32.1 65	4.75 69	17.8 41	25.4 45	9.97 25	10.7 17	40.2 56	10.3 20	6.78 30	18.7 13	13.3 74	9.05 91	17.3 87	15.3 95
Steered-L1 [118]	55.4	11.4 36	37.9 49	7.71 28	4.42 1	21.7 1	3.76 1	7.71 21	25.7 33	4.29 11	4.91 27	29.8 54	2.26 17	20.2 68	26.7 61	16.6 80	18.1 83	46.1 86	14.6 69	32.4 114	37.9 102	51.5 124	8.58 88	15.5 81	15.2 94
S2F-IF [123]	56.3	18.0 77	51.9 92	10.9 60	11.1 56	38.6 76	10.6 54	13.9 73	40.6 82	13.4 80	7.68 77	32.6 67	5.18 76	19.7 60	27.2 68	13.3 58	10.8 19	39.5 52	11.9 49	4.99 14	19.9 22	6.26 42	3.26 38	10.1 45	3.57 15
Sparse Occlusion [54]	56.4	12.7 44	35.8 45	8.24 35	12.4 73	33.4 42	13.4 85	9.67 44	29.1 47	6.55 33	5.99 50	28.5 48	3.56 48	19.4 52	26.4 59	12.4 45	14.7 59	39.4 51	11.7 44	37.7 121	48.6 121	17.8 88	3.66 42	9.43 42	5.64 36
PGM-C [120]	56.5	17.7 72	50.5 83	11.0 63	11.9 66	39.1 80	11.6 71	13.9 73	40.4 81	13.3 79	7.52 73	35.8 84	4.62 68	19.6 58	27.5 71	12.4 45	9.48 10	37.9 43	9.36 11	4.63 9	16.9 5	5.02 30	4.83 57	14.2 72	6.69 53
Classic++ [32]	56.6	10.8 26	32.7 35	8.25 37	10.5 45	32.9 37	10.7 58	10.8 54	31.6 55	8.46 58	5.25 32	29.7 53	2.99 32	20.0 64	28.0 79	13.9 67	15.2 65	44.1 72	11.9 49	17.3 82	26.2 67	18.3 93	5.82 71	12.7 58	8.14 69
CPM-Flow [116]	57.4	17.7 72	50.5 83	11.0 63	11.9 66	39.0 79	11.7 72	13.7 72	39.8 79	13.2 77	7.49 72	35.5 82	4.58 67	19.5 55	27.2 68	12.3 44	9.44 9	37.3 40	9.46 12	5.05 16	19.5 19	5.17 32	5.21 60	14.8 75	7.36 63
BriefMatch [124]	57.8	11.8 40	35.7 44	6.41 18	7.52 18	30.3 22	5.97 14	7.54 18	24.2 24	4.62 17	4.28 14	25.4 22	1.98 11	20.6 75	26.2 57	20.9 97	26.8 110	49.2 90	28.2 112	22.8 97	35.9 94	39.6 118	9.81 94	15.1 78	18.3 103
FlowFields+ [130]	58.0	18.4 79	52.2 96	11.4 70	11.9 66	39.9 89	11.5 68	14.9 80	43.4 90	14.4 84	7.97 80	33.1 71	5.58 80	19.4 52	26.9 65	12.7 52	10.2 14	39.9 55	10.5 25	4.74 12	20.1 25	4.29 25	3.80 43	12.4 57	3.48 13
ACK-Prior [27]	58.2	19.5 88	41.5 57	14.3 83	6.57 8	27.6 13	4.53 5	7.87 25	25.7 33	3.70 8	4.33 16	25.7 24	1.53 5	20.5 73	25.6 50	18.3 86	23.1 101	44.0 71	18.5 90	29.9 109	33.1 85	45.6 122	7.91 85	14.8 75	11.7 85
NL-TV-NCC [25]	58.5	16.5 65	40.4 52	9.10 51	10.7 47	37.0 56	8.07 31	8.59 35	26.8 39	3.17 4	6.24 56	33.4 73	3.26 37	21.4 83	29.7 97	12.7 52	21.2 93	48.2 89	17.3 86	13.4 69	35.6 93	13.0 71	4.73 55	12.8 59	3.24 12
EpicFlow [102]	58.6	17.7 72	50.6 85	10.9 60	12.0 70	39.3 83	11.7 72	14.5 76	42.2 86	13.2 77	7.47 70	35.5 82	4.57 66	19.8 61	27.6 72	12.8 55	9.73 11	38.1 47	10.1 19	4.63 9	17.2 7	4.88 27	5.31 61	14.3 74	7.47 64
CombBMOF [113]	59.0	15.2 55	48.2 76	7.67 26	11.3 60	34.5 46	9.95 44	8.75 38	27.2 41	5.37 24	7.60 76	32.1 65	5.65 83	18.0 43	23.0 35	13.9 67	21.7 96	44.9 77	24.3 104	22.6 95	37.2 97	14.5 78	2.97 32	7.73 36	4.35 21
Complementary OF [21]	60.4	20.9 90	51.7 90	21.5 96	6.41 6	28.3 16	4.86 8	9.56 43	30.2 52	5.62 26	8.21 82	31.4 61	6.20 86	19.2 50	25.6 50	15.5 74	21.5 95	49.3 91	17.4 87	6.34 25	19.8 21	11.5 60	6.44 78	16.1 84	10.2 78
FlowFields [110]	60.6	18.3 78	51.9 92	11.1 66	11.9 66	39.5 85	11.5 68	14.8 78	43.3 89	14.2 82	7.96 79	33.5 76	5.52 79	19.9 62	27.6 72	13.6 62	11.0 23	40.5 58	12.1 53	4.93 13	19.7 20	5.34 36	3.85 44	12.3 56	3.89 17
TF+OM [100]	61.3	14.8 51	35.4 43	7.68 27	9.06 31	28.4 18	9.32 40	11.6 59	28.4 45	16.0 89	6.43 57	29.0 51	4.29 59	20.2 68	25.6 50	18.4 87	17.9 81	38.5 49	16.9 85	16.6 79	33.8 86	14.7 79	6.87 81	15.5 81	9.68 75
ROF-ND [107]	62.0	18.4 79	45.8 70	11.5 71	7.31 16	25.4 5	6.02 17	9.70 45	29.4 50	4.66 18	9.09 88	28.7 49	5.98 85	21.6 85	29.5 94	14.5 70	19.9 89	44.8 75	15.3 75	33.3 118	41.0 107	30.1 109	2.95 29	7.63 35	2.41 9
TV-L1-improved [17]	63.2	11.9 41	36.8 48	8.23 34	8.49 27	31.0 28	7.83 30	11.9 62	33.7 58	7.19 40	5.35 35	28.9 50	2.91 28	20.3 71	28.0 79	12.0 42	27.2 112	55.4 107	30.4 114	23.1 100	38.0 103	22.9 104	5.61 68	14.0 68	7.74 67
ComplOF-FED-GPU [35]	63.6	17.9 75	52.0 94	15.4 86	7.90 21	33.9 44	5.82 13	10.8 54	34.2 60	5.67 27	6.99 64	31.5 62	4.51 64	19.2 50	26.3 58	12.9 56	18.2 84	50.5 97	18.6 91	15.1 75	23.6 52	22.3 103	5.37 63	15.4 79	6.76 54
TCOF [69]	63.8	17.2 70	45.4 69	15.3 85	12.6 77	37.6 63	12.3 75	15.7 83	39.5 76	16.6 90	6.72 60	27.7 42	4.48 63	22.5 92	30.9 106	11.9 40	9.21 7	28.4 4	10.8 29	22.9 98	35.0 92	9.29 52	4.22 50	11.3 53	6.79 55
DeepFlow [86]	63.8	17.5 71	46.9 72	16.5 88	11.8 64	35.8 50	11.2 66	15.1 81	39.6 78	15.2 87	7.81 78	32.6 67	5.12 75	17.8 41	25.5 47	9.86 23	12.0 28	44.9 77	11.4 38	6.11 24	18.0 8	12.8 69	10.8 100	18.7 94	18.8 105
EPPM w/o HM [88]	64.7	19.4 86	53.2 98	11.2 67	8.23 24	34.8 47	6.07 18	11.1 57	35.1 65	5.89 29	7.31 68	33.4 73	4.76 70	18.9 49	23.2 38	17.1 82	21.3 94	50.3 96	20.1 95	20.7 91	30.3 77	40.9 120	3.20 37	8.13 37	5.59 34
Kuang [131]	65.3	17.1 68	52.1 95	10.8 58	11.0 55	40.8 94	9.84 43	14.7 77	44.8 93	11.8 74	7.30 67	33.1 71	5.00 73	20.8 77	28.6 88	14.8 71	14.1 51	44.7 74	15.3 75	4.37 7	18.5 10	5.30 35	5.49 65	14.2 72	9.48 74
HBM-GC [105]	65.4	31.9 101	41.2 56	25.6 101	13.2 82	32.9 37	14.2 89	9.93 48	24.4 29	8.75 59	10.1 96	24.7 18	6.95 94	16.6 34	21.1 17	13.6 62	18.5 85	33.7 24	15.5 78	33.9 120	47.5 120	20.1 100	3.38 39	8.62 40	5.97 41
Aniso. Huber-L1 [22]	67.4	13.6 48	40.4 52	9.77 52	19.4 94	40.1 90	22.0 94	16.4 86	38.4 70	18.3 92	7.56 74	33.4 73	5.00 73	20.1 67	27.7 77	12.5 48	14.5 57	39.7 54	10.4 23	20.8 92	32.0 81	12.9 70	4.35 51	10.8 50	6.56 50
SIOF [67]	68.0	16.5 65	40.1 51	10.8 58	10.3 43	37.1 57	9.10 38	16.4 86	38.3 69	18.4 93	8.56 83	35.1 80	5.87 84	21.3 82	28.5 84	16.5 79	17.6 80	43.6 70	19.7 94	7.08 34	21.6 40	3.65 16	6.65 79	16.1 84	10.9 82
Rannacher [23]	68.0	15.5 58	43.5 61	10.7 56	11.4 61	35.8 50	11.5 68	14.2 75	39.0 74	10.8 67	6.59 59	30.8 60	4.20 58	21.0 79	29.6 96	12.6 50	19.1 87	50.8 98	15.2 73	14.7 73	26.8 68	16.7 84	4.86 58	12.9 60	7.03 60
F-TV-L1 [15]	68.2	31.8 100	60.6 103	43.6 112	13.7 86	38.4 74	13.1 83	15.6 82	39.4 75	10.1 65	10.9 98	37.3 90	8.78 99	20.0 64	26.5 60	16.0 78	12.9 34	40.7 59	10.7 28	9.68 50	23.7 55	3.52 15	4.49 53	12.0 54	4.19 19
Brox et al. [5]	70.9	18.5 81	51.2 87	20.8 95	14.0 89	37.8 65	15.1 91	13.6 71	38.8 72	11.7 72	7.20 66	36.8 87	4.02 53	23.0 96	28.5 84	24.3 105	10.8 19	45.3 82	9.57 13	7.81 40	22.7 46	1.58 4	9.61 93	19.2 97	15.0 93
LocallyOriented [52]	70.9	15.8 61	41.5 57	10.9 60	15.0 91	44.5 100	13.7 87	17.6 90	43.4 90	14.2 82	7.16 65	31.5 62	4.82 71	21.0 79	29.0 90	12.5 48	11.7 26	34.5 27	12.9 61	11.6 58	29.6 74	12.0 63	7.94 86	18.4 91	11.1 83
SRR-TVOF-NL [91]	71.0	22.3 96	44.7 65	12.5 77	12.0 70	38.1 69	10.2 47	14.8 78	40.6 82	10.9 69	6.13 55	34.1 77	2.81 26	19.6 58	25.5 47	13.5 60	16.4 73	42.4 65	13.0 62	30.5 110	42.5 111	18.3 93	6.41 77	11.0 51	12.0 87
CRTflow [80]	71.6	16.5 65	49.5 79	10.6 55	9.63 38	33.8 43	8.65 35	13.1 69	38.8 72	7.80 53	6.86 62	34.3 78	4.44 62	20.0 64	27.8 78	12.2 43	31.4 117	59.0 113	36.7 118	10.3 52	30.4 78	12.0 63	8.56 87	20.4 103	12.9 91
DPOF [18]	72.0	20.5 89	50.2 82	10.5 53	12.6 77	41.8 97	11.0 64	11.8 61	34.3 61	10.8 67	8.61 85	38.9 96	5.43 77	19.5 55	26.1 54	15.1 72	16.8 77	41.5 63	15.2 73	23.3 101	23.9 56	50.1 123	5.05 59	14.1 69	4.13 18
Bartels [41]	73.9	19.3 85	39.6 50	22.4 98	9.47 36	28.2 15	10.0 46	9.91 47	29.7 51	7.09 38	9.18 90	29.3 52	7.40 96	21.7 86	27.6 72	21.1 98	19.1 87	44.4 73	24.2 103	23.0 99	36.3 95	36.2 112	7.46 84	14.9 77	11.5 84
Dynamic MRF [7]	74.6	22.0 94	52.3 97	25.2 100	7.67 19	33.0 39	6.18 21	12.4 67	39.8 79	5.34 23	6.49 58	35.4 81	3.86 51	22.9 94	29.2 92	20.7 96	22.2 98	57.8 110	22.9 100	7.42 37	18.1 9	25.1 105	13.2 106	21.3 107	20.5 108
CBF [12]	76.9	15.2 55	44.8 66	12.1 75	23.7 99	37.7 64	30.9 102	13.2 70	34.6 63	14.5 85	6.86 62	32.8 69	4.32 60	22.6 93	28.4 83	20.2 93	15.6 67	41.0 61	12.1 53	32.9 116	39.7 105	29.8 108	5.49 65	13.2 62	8.30 70
TriangleFlow [30]	77.0	18.7 83	43.9 63	18.0 89	10.1 40	37.2 59	8.18 32	11.9 62	35.5 66	5.81 28	6.72 60	34.6 79	4.37 61	26.7 112	34.7 113	23.4 102	23.1 101	49.6 92	23.5 101	16.7 80	37.2 97	16.3 82	6.85 80	17.3 87	10.3 79
DF-Auto [115]	77.1	19.4 86	46.0 71	10.5 53	26.6 103	46.1 102	31.1 103	23.7 101	46.1 95	37.0 103	9.05 87	36.8 87	5.59 81	21.7 86	29.1 91	17.4 84	7.80 3	31.8 13	7.93 5	19.5 88	37.4 99	3.25 12	10.9 101	19.6 99	16.4 97
Local-TV-L1 [65]	77.5	24.6 97	51.2 87	30.0 102	22.5 98	40.6 92	25.2 97	23.5 100	46.1 95	28.3 98	9.73 93	37.4 91	6.92 93	18.3 45	25.2 44	12.7 52	13.9 47	43.2 69	12.0 52	5.25 17	20.6 28	5.15 31	15.8 111	21.0 105	32.1 116
SuperFlow [81]	78.7	16.2 62	42.7 60	13.0 79	20.9 95	39.6 87	25.0 96	19.7 95	40.6 82	31.8 100	9.89 95	41.2 99	7.16 95	20.9 78	27.1 66	20.3 94	12.2 30	41.1 62	11.3 35	19.0 85	32.1 83	3.87 20	10.1 96	19.3 98	16.4 97
LDOF [28]	79.0	17.1 68	48.0 75	12.9 78	13.3 83	40.6 92	12.2 74	15.8 84	42.4 87	12.7 76	9.70 92	44.0 101	6.27 88	20.7 76	28.0 79	16.8 81	14.3 54	45.9 85	13.8 66	8.36 44	23.3 51	7.98 49	11.2 103	21.2 106	18.3 103
CNN-flow-warp+ref [117]	80.1	18.5 81	50.0 80	13.9 82	17.8 93	37.9 66	21.1 93	21.3 98	47.3 99	29.7 99	9.13 89	38.8 94	6.72 91	21.8 89	28.2 82	19.6 90	14.2 53	45.7 84	13.1 63	5.94 23	18.5 10	10.9 57	12.4 105	20.6 104	16.4 97
CLG-TV [48]	80.2	15.7 60	42.2 59	11.7 73	20.9 95	39.2 82	24.8 95	16.4 86	39.5 76	18.0 91	9.23 91	37.9 92	6.54 89	22.9 94	30.0 101	17.9 85	16.5 74	47.2 88	14.2 68	19.9 89	30.4 78	11.5 60	5.79 70	14.1 69	6.98 59
TriFlow [95]	80.7	21.3 92	44.9 67	13.5 81	16.0 92	36.5 53	18.7 92	18.2 92	38.6 71	27.8 97	7.35 69	30.3 56	5.59 81	21.2 81	27.3 70	18.5 88	15.1 63	37.1 39	15.0 72	49.5 126	41.7 109	95.6 129	6.38 76	13.3 64	9.77 76
p-harmonic [29]	81.0	21.2 91	63.8 108	20.6 94	12.4 73	35.9 52	12.7 81	17.7 91	47.5 100	14.9 86	10.9 98	42.1 100	8.85 100	20.4 72	26.1 54	17.1 82	17.9 81	52.5 101	18.4 89	15.6 77	28.6 72	5.86 39	5.89 73	13.5 65	7.67 66
FlowNetS+ft+v [112]	81.7	15.2 55	44.9 67	10.7 56	13.4 84	38.2 72	13.4 85	18.8 94	42.8 88	24.4 95	9.01 86	38.8 94	6.24 87	23.2 98	31.5 110	15.9 76	13.3 39	42.6 66	13.1 63	18.2 84	32.6 84	21.9 102	8.73 89	19.1 96	12.8 90
Second-order prior [8]	84.0	15.6 59	48.2 76	12.1 75	12.6 77	39.1 80	12.3 75	16.2 85	44.6 92	12.2 75	7.57 75	31.6 64	5.45 78	22.2 90	30.6 103	14.3 69	20.8 92	56.8 109	17.7 88	28.0 108	33.8 86	27.1 106	7.43 83	17.4 89	10.4 81
StereoFlow [44]	85.9	85.4 129	89.0 129	87.9 128	73.1 129	88.5 129	68.8 124	66.8 128	87.5 128	52.4 121	81.5 128	91.1 128	78.5 128	25.9 111	27.6 72	29.7 116	6.38 1	29.4 8	6.60 2	1.39 1	10.9 1	0.20 1	6.34 75	13.8 66	10.3 79
Fusion [6]	87.0	17.9 75	57.7 100	18.6 90	9.42 35	32.3 35	10.2 47	11.4 58	34.8 64	11.7 72	8.57 84	40.2 97	6.89 92	25.0 108	30.8 105	24.9 109	23.9 104	52.3 100	25.0 107	33.3 118	43.4 113	19.3 98	9.01 90	18.8 95	13.4 92
Learning Flow [11]	87.5	16.4 64	47.3 73	11.5 71	14.0 89	40.3 91	14.4 90	16.4 86	41.7 85	15.6 88	8.05 81	40.7 98	4.87 72	27.1 114	35.0 115	22.5 101	17.2 79	50.0 95	16.0 80	15.5 76	34.1 89	13.9 77	10.1 96	20.2 102	12.5 89
SegOF [10]	89.6	28.8 99	51.1 86	13.2 80	37.3 112	51.8 112	44.6 114	30.0 106	53.0 104	43.3 111	27.0 111	49.6 106	22.4 107	24.0 104	27.6 72	28.4 115	24.9 107	58.5 111	24.4 105	2.04 2	16.2 4	0.47 2	10.0 95	16.5 86	16.7 100
Ad-TV-NDC [36]	91.6	44.8 115	63.0 106	69.1 121	40.3 115	48.4 107	48.3 116	34.8 110	58.5 107	39.9 106	26.5 110	47.8 105	27.7 111	20.2 68	28.5 84	11.9 40	15.2 65	40.9 60	14.7 70	8.46 45	21.0 32	5.69 38	23.9 121	28.3 121	41.9 125
StereoOF-V1MT [119]	92.1	21.7 93	68.0 113	20.3 93	11.8 64	50.4 110	7.18 26	20.7 97	62.8 110	9.21 62	9.80 94	50.8 107	6.56 90	27.9 116	35.8 116	23.9 103	25.0 108	67.3 118	24.0 102	8.00 41	27.7 69	12.2 65	13.4 107	23.5 111	15.8 96
Shiralkar [42]	92.5	22.0 94	69.5 115	19.6 91	10.9 51	42.6 98	8.48 34	18.4 93	54.0 105	9.43 64	10.1 96	45.4 102	7.72 97	21.5 84	28.9 89	15.9 76	26.8 110	60.7 114	25.4 109	24.3 103	29.9 76	39.4 116	11.0 102	23.8 112	12.2 88
FlowNet2 [122]	95.1	47.2 116	61.0 104	42.4 109	44.5 117	57.5 114	51.3 118	37.6 113	64.7 112	43.1 110	21.0 105	35.8 84	17.9 104	25.8 109	30.6 103	24.6 107	20.4 90	49.7 94	21.0 96	32.5 115	53.3 125	4.06 23	4.13 48	13.0 61	1.49 6
HBpMotionGpu [43]	97.9	32.0 102	50.0 80	22.9 99	36.1 111	47.0 104	43.9 113	29.2 105	51.9 103	38.6 105	13.0 100	37.1 89	10.2 101	23.5 100	29.5 94	24.2 104	18.9 86	44.9 77	15.9 79	33.2 117	41.2 108	12.6 68	11.8 104	18.5 92	22.7 109
IAOF2 [51]	98.2	25.3 98	49.2 78	22.2 97	24.6 100	44.3 99	28.6 101	20.0 96	45.4 94	25.5 96	49.8 120	57.5 115	60.5 122	23.2 98	31.0 107	15.7 75	23.2 103	49.6 92	19.3 93	30.5 110	39.0 104	19.0 96	9.25 92	18.6 93	9.82 77
Modified CLG [34]	98.4	34.8 106	61.1 105	35.3 104	33.3 109	46.5 103	41.7 112	36.8 112	63.0 111	45.1 115	22.1 106	55.4 111	18.7 106	23.9 102	31.2 108	21.7 100	15.8 70	51.5 99	14.8 71	9.01 48	24.6 59	11.1 59	17.6 115	25.7 117	29.6 114
2D-CLG [1]	99.7	44.0 113	63.3 107	36.1 105	44.3 116	52.3 113	55.1 120	49.1 121	75.4 119	50.5 119	64.3 125	76.4 124	67.8 125	24.8 106	29.7 97	27.4 112	20.5 91	53.6 104	22.4 98	2.52 3	13.0 2	3.50 14	22.8 120	27.9 120	36.9 119
Filter Flow [19]	99.8	33.3 103	51.7 90	20.1 92	25.0 101	47.2 106	27.7 100	27.7 103	50.0 101	37.9 104	31.7 113	54.1 110	29.9 112	25.8 109	31.2 108	28.3 114	26.4 109	52.9 103	24.7 106	42.3 123	61.5 127	13.6 76	6.09 74	12.1 55	6.88 57
SPSA-learn [13]	100.2	35.8 107	71.2 117	43.1 111	28.4 105	47.0 104	32.8 106	31.4 108	57.7 106	42.2 109	22.2 107	51.0 108	22.9 109	23.9 102	29.4 93	24.6 107	24.8 106	56.5 108	25.1 108	10.7 55	25.1 61	3.72 17	21.7 118	24.9 116	35.5 118
BlockOverlap [61]	101.1	41.4 109	54.1 99	36.2 106	27.3 104	41.4 96	32.6 105	26.2 102	46.5 97	31.8 100	20.0 103	36.6 86	18.1 105	22.4 91	26.8 63	25.7 110	24.5 105	45.6 83	21.1 97	39.3 122	47.0 119	43.5 121	13.8 108	16.0 83	28.7 113
IAOF [50]	102.1	33.8 104	58.3 101	40.6 107	33.0 108	44.5 100	39.5 110	30.6 107	58.7 108	33.8 102	34.1 116	52.4 109	40.8 116	23.1 97	29.9 99	19.7 92	22.5 99	53.7 105	16.8 84	22.3 94	34.0 88	10.0 54	19.5 116	23.9 113	37.1 121
GroupFlow [9]	103.2	42.7 110	67.1 112	53.4 114	44.8 118	63.8 122	50.2 117	36.7 111	69.4 116	43.9 112	17.2 102	46.0 103	16.7 102	27.8 115	34.9 114	21.2 99	36.7 121	67.0 117	43.6 122	6.40 26	21.7 41	7.17 44	16.6 112	25.9 118	25.0 110
GraphCuts [14]	103.4	34.5 105	59.0 102	32.1 103	26.2 102	51.1 111	26.4 99	28.1 104	51.7 102	40.4 108	13.0 100	47.5 104	7.98 98	23.7 101	30.0 101	24.3 105	33.4 118	45.2 81	25.7 110	31.2 112	37.7 101	36.8 113	10.7 99	19.7 100	17.7 102
Black & Anandan [4]	103.6	38.5 108	69.5 115	53.4 114	28.5 106	49.6 109	32.0 104	33.4 109	60.5 109	40.2 107	22.6 108	55.9 112	22.8 108	24.5 105	32.2 112	19.6 90	21.7 96	58.9 112	22.7 99	22.6 95	37.6 100	5.27 34	16.7 113	22.6 110	25.4 111
2bit-BM-tele [98]	106.3	55.1 120	64.1 110	69.1 121	21.4 97	38.7 77	25.3 98	23.3 99	46.7 98	21.2 94	26.2 109	38.7 93	25.2 110	24.9 107	29.9 99	27.7 113	31.3 116	52.6 102	34.6 116	43.3 124	51.7 124	54.5 125	10.3 98	20.1 101	16.8 101
Nguyen [33]	107.8	43.9 112	66.0 111	42.8 110	54.0 123	49.4 108	70.1 125	42.9 116	67.4 114	47.3 117	55.4 123	65.7 119	64.4 123	27.0 113	31.8 111	31.0 117	22.8 100	54.8 106	27.3 111	13.1 66	25.2 62	6.08 40	22.2 119	26.9 119	38.9 122
SILK [79]	110.0	49.5 119	69.2 114	69.3 123	39.9 114	60.6 117	47.0 115	40.4 115	70.7 117	45.6 116	32.0 114	56.5 113	31.2 114	31.4 118	36.9 119	33.3 119	31.1 115	63.2 115	32.3 115	10.3 52	23.0 47	17.3 85	25.0 122	31.9 122	36.9 119
UnFlow [129]	110.0	70.9 125	78.9 120	58.5 117	51.3 122	67.4 125	56.9 121	54.4 123	83.6 126	52.8 122	33.4 115	60.2 116	30.1 113	36.7 125	38.4 122	46.2 126	38.2 122	69.6 121	42.8 121	26.2 104	40.3 106	1.60 5	7.20 82	18.3 90	8.75 72
Periodicity [78]	111.8	48.9 118	63.9 109	41.0 108	34.5 110	60.0 116	37.5 109	55.4 124	67.4 114	56.6 124	20.4 104	56.9 114	17.5 103	53.2 128	66.7 129	46.5 127	48.3 126	76.0 127	46.4 124	9.14 49	34.4 90	9.98 53	28.3 125	48.2 128	40.6 124
Horn & Schunck [3]	112.2	43.3 111	80.7 123	58.6 118	32.5 107	59.7 115	35.1 107	40.2 114	76.3 122	44.7 114	31.5 112	64.8 117	32.6 115	29.3 117	36.4 118	27.0 111	27.5 113	68.7 119	29.7 113	27.0 106	43.3 112	7.32 45	25.9 123	36.5 124	34.6 117
Heeger++ [104]	114.8	61.9 123	80.2 122	47.4 113	44.8 118	77.8 128	40.9 111	68.0 129	84.7 127	62.1 127	43.6 119	69.1 120	41.9 117	32.6 120	37.9 120	32.0 118	51.1 127	78.4 128	54.2 127	13.3 68	43.4 113	10.4 56	15.0 109	21.4 108	19.6 106
SLK [47]	115.5	44.7 114	78.9 120	59.1 119	58.2 125	71.2 126	70.9 126	47.5 119	83.5 125	50.6 120	65.0 126	69.5 121	73.4 126	34.7 122	38.9 124	42.9 125	34.8 119	70.9 124	39.4 119	12.1 61	29.8 75	11.5 60	34.4 126	40.1 125	48.8 126
TI-DOFE [24]	117.6	73.1 126	84.6 127	89.6 129	61.2 127	64.7 124	74.8 128	58.6 125	88.7 129	58.0 125	70.9 127	81.6 126	76.1 127	31.7 119	38.0 121	35.4 120	29.7 114	68.7 119	36.3 117	17.1 81	32.0 81	8.67 50	35.5 127	42.8 126	49.8 127
FFV1MT [106]	117.8	59.9 122	77.8 119	53.6 116	37.7 113	72.5 127	37.0 108	63.6 127	82.1 124	62.4 128	42.8 118	73.9 123	42.6 118	41.9 127	45.8 127	52.3 128	52.5 128	81.8 129	56.1 128	20.2 90	42.4 110	18.3 93	15.0 109	21.4 108	19.6 106
FOLKI [16]	120.0	48.0 117	71.5 118	68.8 120	48.6 121	63.2 119	59.5 122	43.0 117	75.6 120	44.0 113	40.4 117	65.6 118	45.8 119	35.3 123	40.6 125	41.6 123	36.3 120	71.6 126	44.4 123	23.6 102	44.7 116	40.4 119	36.9 128	43.4 127	54.5 128
PGAM+LK [55]	122.0	58.6 121	80.9 124	69.8 124	45.1 120	63.7 121	51.9 119	43.2 118	76.2 121	47.5 118	50.3 121	82.2 127	51.4 120	32.7 121	36.0 117	42.3 124	41.4 123	70.1 122	41.2 120	56.3 127	58.0 126	55.0 126	25.9 123	32.4 123	40.3 123
Adaptive flow [45]	122.4	76.7 128	83.7 126	86.4 126	57.9 124	63.6 120	67.3 123	48.7 120	73.2 118	52.9 123	52.7 122	69.9 122	56.0 121	35.4 124	38.4 122	39.4 122	46.1 124	70.6 123	47.6 125	73.1 128	75.2 128	88.1 127	17.2 114	24.6 115	25.6 112
HCIC-L [99]	124.2	76.5 127	86.4 128	73.3 125	70.1 128	62.5 118	85.3 129	63.5 126	66.1 113	79.5 129	83.3 129	91.8 129	86.5 129	39.0 126	42.8 126	38.7 121	46.4 125	66.6 116	52.3 126	89.6 129	85.9 129	94.0 128	19.8 117	24.2 114	29.6 114
Pyramid LK [2]	125.7	68.1 124	83.5 125	86.8 127	59.4 126	64.5 123	73.1 127	52.8 122	76.3 122	61.4 126	60.2 124	79.0 125	65.9 124	53.8 129	61.8 128	64.5 129	59.4 129	71.1 125	63.0 129	43.9 125	49.4 123	39.5 117	50.2 129	60.2 129	70.8 129
AdaConv-v1 [126]	130.0	100.0 130	99.9 130	100.0 130	100.0 130	100.0 130	100.0 130	99.9 130	99.9 130	99.9 130	100.0 130	100.0 130	99.9 130	99.9 130	99.9 130	99.9 130	99.9 130	99.8 130	100.0 130	99.7 130	97.0 130	99.9 130	99.9 130	99.9 130	99.9 130
SepConv-v1 [127]	130.0	100.0 130	99.9 130	100.0 130	100.0 130	100.0 130	100.0 130	99.9 130	99.9 130	99.9 130	100.0 130	100.0 130	99.9 130	99.9 130	99.9 130	99.9 130	99.9 130	99.8 130	100.0 130	99.7 130	97.0 130	99.9 130	99.9 130	99.9 130	99.9 130

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

References

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

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