Optical flow evaluation results: R1.0 normalized interpolation error

Optical flow evaluation results	Statistics: Average SD R0.5 R1.0 R2.0 A90 A95 A99 Error type: endpoint angle interpolation normalized interpolation
Show images: below table above table

R1.0 normalized interpolation error	avg.	Mequon (Hidden texture) im0 GT im1			Schefflera (Hidden texture) im0 GT im1			Urban (Synthetic) im0 GT im1			Teddy (Stereo) im0 GT im1			Backyard (High-speed camera) im0 GT im1			Basketball (High-speed camera) im0 GT im1			Dumptruck (High-speed camera) im0 GT im1			Evergreen (High-speed camera) im0 GT 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
MDP-Flow2 [68]	11.5	6.71 2	9.78 2	8.39 8	6.36 9	11.5 11	6.23 12	7.12 4	9.73 7	5.42 2	21.2 5	18.8 8	41.2 11	30.1 5	26.5 3	45.6 48	27.7 18	19.5 26	36.0 18	6.48 2	13.8 4	10.3 6	8.19 23	16.4 17	7.11 25
PMMST [114]	13.0	6.84 9	9.80 3	8.50 16	6.74 26	11.7 13	6.36 29	7.10 3	9.45 3	5.41 1	21.1 1	18.6 2	41.1 3	30.2 9	26.5 3	45.7 60	27.3 2	18.1 4	36.0 18	6.51 4	13.9 9	10.3 6	8.22 26	16.5 24	7.15 37
PH-Flow [101]	14.5	7.05 22	10.9 17	8.50 16	6.10 3	10.6 5	6.14 3	7.18 5	9.83 8	5.55 6	21.1 1	18.5 1	41.1 3	30.1 5	26.6 8	45.2 14	27.9 35	21.7 86	35.7 9	6.60 15	14.3 19	10.3 6	8.11 14	16.3 14	7.14 33
NNF-Local [87]	14.5	6.74 4	10.1 6	8.30 2	5.97 1	10.3 2	6.14 3	7.09 2	9.63 6	5.44 3	21.7 31	20.5 61	41.2 11	30.3 19	26.6 8	45.4 23	27.9 35	20.6 62	36.0 18	6.51 4	13.8 4	10.3 6	8.04 9	16.1 6	7.10 23
CombBMOF [113]	15.8	6.93 13	9.87 5	8.43 10	6.33 8	11.4 9	6.22 11	7.60 44	10.3 14	6.25 75	21.5 20	19.4 22	41.2 11	30.2 9	26.6 8	45.3 19	27.7 18	19.2 18	36.1 31	6.57 11	14.1 12	10.3 6	7.67 1	15.4 3	6.82 1
NN-field [71]	18.9	6.88 10	10.8 13	8.48 13	5.99 2	10.3 2	6.13 2	7.65 53	9.54 4	5.81 32	21.9 46	21.0 82	41.4 24	30.2 9	26.6 8	45.4 23	27.8 28	20.0 43	36.0 18	6.48 2	13.7 3	10.3 6	8.02 8	16.1 6	7.04 16
IROF++ [58]	24.1	7.07 25	11.3 27	8.50 16	6.64 21	12.0 18	6.19 7	7.54 36	10.6 27	5.84 37	21.2 5	18.6 2	41.5 28	30.2 9	26.9 20	45.0 9	27.6 15	18.8 10	36.2 38	6.69 32	14.7 30	10.5 57	8.17 20	16.4 17	7.33 72
Layers++ [37]	24.5	7.17 36	11.1 22	8.79 57	6.14 6	10.3 2	6.41 33	7.34 16	10.3 14	5.69 19	21.3 10	19.0 14	41.3 17	30.5 35	27.1 39	45.4 23	28.1 53	21.1 74	36.2 38	6.51 4	13.8 4	10.2 1	8.20 24	16.4 17	7.13 31
nLayers [57]	26.4	7.15 34	10.4 10	8.81 62	6.44 12	11.4 9	6.42 34	7.23 8	9.30 1	5.65 14	21.4 15	19.1 17	41.5 28	30.7 71	27.4 80	45.6 48	28.0 44	20.8 66	36.2 38	6.54 9	13.6 1	10.3 6	8.07 10	16.3 14	6.91 3
Sparse-NonSparse [56]	27.9	7.09 26	11.3 27	8.57 24	6.53 16	11.8 16	6.21 9	7.40 24	10.5 24	5.64 10	21.5 20	19.0 14	41.7 45	30.4 26	27.0 29	45.4 23	28.3 65	21.5 83	36.4 61	6.66 24	14.4 21	10.3 6	8.23 27	16.6 29	7.09 21
ProbFlowFields [128]	28.5	7.03 19	11.8 49	8.66 38	6.41 10	11.7 13	6.31 22	7.18 5	10.3 14	5.58 7	21.7 31	19.6 29	41.8 53	30.7 71	27.1 39	46.0 104	27.9 35	20.5 54	36.2 38	6.51 4	13.8 4	10.3 6	7.80 3	15.6 4	7.14 33
2DHMM-SAS [92]	28.6	7.27 48	12.0 56	8.59 28	7.82 63	14.2 53	6.36 29	7.25 9	10.5 24	5.74 25	21.4 15	18.7 5	41.4 24	30.3 19	26.9 20	45.2 14	27.9 35	20.3 47	36.0 18	6.64 22	14.4 21	10.3 6	8.41 45	17.0 43	7.08 18
NNF-EAC [103]	29.6	7.35 56	11.1 22	8.79 57	6.92 36	12.5 30	6.29 20	7.52 35	10.1 11	5.76 28	21.8 38	19.5 27	42.8 101	30.2 9	26.6 8	45.4 23	27.5 7	18.9 12	36.0 18	6.58 13	14.2 15	10.4 32	8.32 34	16.8 33	7.19 46
AGIF+OF [85]	29.7	7.11 28	11.3 27	8.46 11	6.68 25	12.2 22	6.27 16	7.38 21	10.1 11	5.71 23	21.2 5	18.6 2	41.1 3	30.8 83	27.6 100	45.4 23	28.3 65	22.2 104	36.0 18	6.67 25	14.0 11	10.3 6	8.34 37	17.0 43	6.91 3
FlowFields [110]	29.9	7.02 18	11.5 35	8.54 22	6.66 24	12.5 30	6.44 38	7.45 28	11.5 52	5.64 10	21.9 46	20.5 61	41.8 53	30.6 51	27.0 29	45.5 38	27.7 18	20.2 45	36.0 18	6.59 14	14.3 19	10.4 32	8.00 7	16.2 11	7.08 18
FlowFields+ [130]	30.6	6.99 15	11.4 33	8.47 12	6.61 20	12.3 24	6.48 41	7.42 26	11.5 52	5.67 17	21.7 31	20.3 49	41.6 35	30.7 71	27.2 54	45.6 48	27.8 28	20.3 47	36.1 31	6.61 16	14.4 21	10.4 32	7.99 6	16.2 11	7.03 14
FMOF [94]	30.7	7.36 58	12.0 56	8.73 48	6.42 11	11.3 8	6.30 21	7.63 49	10.4 19	6.02 65	21.8 38	19.9 36	41.2 11	30.5 35	27.0 29	45.4 23	28.0 44	20.5 54	36.1 31	6.51 4	13.8 4	10.2 1	8.30 32	16.7 32	7.12 27
S2F-IF [123]	31.4	7.01 17	11.5 35	8.48 13	6.57 17	12.2 22	6.42 34	7.40 24	11.2 48	5.64 10	21.6 24	20.1 42	41.3 17	30.7 71	27.3 68	45.7 60	27.8 28	20.4 51	36.1 31	6.71 37	14.9 45	10.4 32	7.98 5	16.1 6	7.04 16
LSM [39]	32.8	7.17 36	11.8 49	8.58 26	6.64 21	12.1 21	6.17 6	7.49 31	10.9 39	5.69 19	21.6 24	19.6 29	41.6 35	30.5 35	27.1 39	45.4 23	28.3 65	21.6 84	36.3 52	6.68 30	14.6 26	10.2 1	8.35 40	16.9 41	7.03 14
LME [70]	33.4	6.72 3	9.86 4	8.36 5	6.97 38	12.4 27	7.40 85	7.51 33	11.8 60	5.70 22	21.3 10	19.2 20	41.3 17	31.0 104	27.6 100	46.6 116	27.8 28	20.5 54	36.0 18	6.45 1	13.6 1	10.2 1	8.08 13	16.3 14	7.12 27
TV-L1-MCT [64]	34.5	7.50 77	12.5 84	8.79 57	7.19 44	13.4 42	6.37 31	7.28 11	10.6 27	5.80 31	21.4 15	18.8 8	41.3 17	30.5 35	27.1 39	45.1 12	27.9 35	18.6 7	36.6 74	6.72 41	15.0 52	10.4 32	7.92 4	15.9 5	7.20 49
WLIF-Flow [93]	34.6	6.99 15	11.0 19	8.48 13	6.76 27	12.4 27	6.39 32	7.38 21	10.3 14	5.68 18	21.4 15	18.8 8	41.9 66	30.4 26	26.9 20	45.9 94	28.8 98	21.9 93	36.9 93	6.56 10	13.9 9	10.3 6	8.34 37	16.8 33	7.15 37
ComponentFusion [96]	34.7	6.91 11	10.8 13	8.55 23	6.49 15	12.0 18	6.10 1	7.49 31	11.2 48	5.72 24	21.3 10	19.4 22	41.2 11	30.6 51	27.2 54	45.8 76	27.8 28	19.6 29	36.2 38	6.92 74	16.4 92	10.4 32	8.43 49	17.0 43	7.16 40
COFM [59]	35.8	7.04 20	10.7 12	8.70 42	6.60 18	11.9 17	6.35 26	7.26 10	9.93 9	5.63 9	21.2 5	18.8 8	41.0 1	30.4 26	27.3 68	44.9 8	27.7 18	22.6 107	35.1 2	6.86 67	14.7 30	11.2 111	8.67 76	17.2 60	7.78 108
MDP-Flow [26]	36.5	6.83 7	10.8 13	8.50 16	6.65 23	12.4 27	6.51 46	7.46 29	10.6 27	5.88 45	22.1 65	20.6 66	41.7 45	30.4 26	26.8 15	45.6 48	28.2 62	21.9 93	36.3 52	6.69 32	14.8 39	10.4 32	8.15 16	16.6 29	7.10 23
HAST [109]	37.7	6.97 14	10.2 7	8.69 40	6.46 13	11.6 12	6.26 15	7.72 61	11.1 44	5.97 58	21.1 1	18.7 5	41.1 3	30.5 35	27.5 87	44.8 6	28.2 62	22.8 113	35.5 5	6.76 50	15.2 60	10.4 32	8.81 82	18.0 91	6.96 8
RNLOD-Flow [121]	37.8	7.12 30	11.5 35	8.64 35	7.38 50	14.0 50	6.35 26	7.55 37	11.2 48	5.83 36	21.3 10	19.0 14	41.1 3	30.5 35	27.2 54	45.4 23	28.3 65	21.6 84	36.2 38	6.62 17	14.2 15	10.4 32	8.70 79	17.7 80	7.02 12
OFLAF [77]	38.3	6.81 6	10.2 7	8.40 9	6.10 3	10.7 6	6.21 9	7.36 18	10.6 27	5.54 5	21.1 1	18.8 8	41.0 1	30.8 83	27.4 80	45.7 60	28.1 53	21.9 93	36.0 18	7.02 85	16.1 89	10.4 32	8.90 89	18.1 98	7.16 40
Ramp [62]	40.5	7.31 54	12.1 60	8.78 54	6.60 18	12.0 18	6.27 16	7.36 18	10.4 19	5.65 14	21.3 10	18.9 13	41.4 24	30.5 35	27.1 39	45.4 23	28.7 93	22.3 105	36.6 74	6.73 46	14.9 45	10.3 6	8.55 63	17.3 65	7.26 60
Second-order prior [8]	41.2	7.30 53	11.3 27	8.90 71	8.52 81	15.6 80	6.74 60	8.32 94	13.6 103	6.42 88	21.8 38	20.0 39	41.5 28	30.1 5	26.5 3	45.5 38	27.5 7	19.0 13	36.0 18	6.67 25	14.6 26	10.3 6	8.25 28	16.8 33	7.11 25
PGM-C [120]	42.6	7.19 41	12.1 60	8.72 45	6.82 29	12.9 34	6.62 52	7.67 57	12.2 72	5.78 30	21.9 46	20.9 76	41.8 53	30.6 51	27.1 39	45.8 76	27.7 18	19.5 26	36.2 38	6.65 23	14.7 30	10.3 6	8.20 24	16.6 29	7.31 67
Kuang [131]	43.0	7.12 30	12.1 60	8.58 26	7.14 41	13.5 44	6.50 44	7.77 67	12.1 68	5.94 53	21.9 46	20.7 69	41.7 45	30.6 51	27.1 39	45.6 48	27.5 7	19.6 29	35.9 13	6.84 60	15.6 74	10.5 57	8.07 10	16.4 17	7.14 33
DeepFlow2 [108]	43.3	7.28 49	11.3 27	8.88 68	7.68 59	14.4 60	6.94 73	7.58 42	12.3 76	5.88 45	21.9 46	20.2 46	41.7 45	30.5 35	26.8 15	45.9 94	27.5 7	18.0 3	36.4 61	6.67 25	14.6 26	10.4 32	8.18 21	16.4 17	7.31 67
Aniso. Huber-L1 [22]	43.5	7.61 84	12.2 69	9.19 83	8.99 91	15.7 83	7.12 79	7.73 63	11.0 42	5.86 42	21.8 38	20.0 39	41.6 35	30.2 9	26.6 8	45.5 38	27.4 4	19.6 29	35.7 9	6.68 30	14.6 26	10.3 6	8.34 37	16.8 33	7.29 66
Classic+NL [31]	43.5	7.44 71	12.3 72	8.86 65	6.78 28	12.3 24	6.28 18	7.32 15	10.4 19	5.69 19	21.6 24	19.4 22	41.8 53	30.5 35	27.1 39	45.5 38	28.6 88	21.8 89	36.6 74	6.72 41	14.7 30	10.3 6	8.50 57	17.2 60	7.24 57
FC-2Layers-FF [74]	43.7	7.22 44	11.9 54	8.70 42	6.10 3	10.2 1	6.47 40	7.31 14	10.5 24	5.64 10	21.4 15	19.1 17	41.6 35	30.7 71	27.5 87	45.6 48	28.6 88	22.7 110	36.4 61	6.77 52	15.0 52	10.3 6	8.57 65	17.2 60	7.20 49
SRR-TVOF-NL [91]	44.5	7.42 69	11.5 35	8.86 65	7.79 61	14.8 68	7.08 77	7.62 46	11.5 52	5.85 40	21.7 31	19.6 29	41.1 3	30.3 19	27.1 39	45.2 14	27.5 7	20.5 54	35.3 4	6.72 41	14.8 39	10.4 32	8.97 96	18.3 104	7.17 43
DF-Auto [115]	45.7	7.54 79	11.1 22	9.32 91	8.42 77	14.5 63	8.82 96	7.35 17	10.3 14	5.65 14	22.0 57	20.2 46	41.5 28	30.4 26	26.7 14	45.8 76	27.5 7	18.7 9	36.1 31	6.82 59	15.3 64	10.5 57	8.43 49	17.1 51	7.20 49
FESL [72]	46.0	7.36 58	11.7 44	8.65 37	6.82 29	12.6 32	6.33 24	7.51 33	10.7 34	5.89 48	21.6 24	19.6 29	41.3 17	30.9 100	27.5 87	45.7 60	28.4 77	22.1 102	36.2 38	6.70 35	14.8 39	10.2 1	8.59 66	17.4 71	7.08 18
CPM-Flow [116]	46.2	7.21 42	12.2 69	8.71 44	6.83 32	12.9 34	6.65 54	7.61 45	11.7 57	5.88 45	22.2 71	21.4 95	41.8 53	30.6 51	27.1 39	45.8 76	27.9 35	19.1 14	36.6 74	6.67 25	14.7 30	10.3 6	8.16 18	16.5 24	7.34 75
Classic+CPF [83]	46.5	7.22 44	11.6 40	8.52 21	6.90 35	12.6 32	6.28 18	7.37 20	10.6 27	5.76 28	21.2 5	18.7 5	41.1 3	31.1 108	27.9 108	45.5 38	28.7 93	23.1 117	36.3 52	6.92 74	15.3 64	10.3 6	8.75 81	17.9 87	6.99 9
S2D-Matching [84]	48.2	7.37 61	12.3 72	8.80 60	7.62 58	14.2 53	6.43 37	7.28 11	10.4 19	5.74 25	21.6 24	19.1 17	42.2 80	30.6 51	27.3 68	45.4 23	28.6 88	22.5 106	36.4 61	6.76 50	14.5 24	10.3 6	8.46 52	17.0 43	7.32 69
DeepFlow [86]	48.4	7.21 42	11.0 19	8.88 68	7.79 61	14.3 56	7.33 84	7.64 51	12.6 84	5.95 55	22.1 65	20.1 42	42.0 71	30.6 51	26.8 15	46.1 107	28.0 44	17.9 2	37.2 100	6.57 11	14.1 12	10.4 32	8.07 10	16.2 11	7.32 69
IROF-TV [53]	48.5	7.33 55	12.3 72	8.82 63	6.83 32	12.3 24	6.23 12	7.70 60	12.9 92	5.93 52	21.5 20	19.5 27	42.0 71	30.8 83	27.3 68	45.9 94	27.5 7	20.2 45	35.6 6	6.75 48	15.1 58	10.5 57	8.18 21	16.4 17	7.37 79
EPPM w/o HM [88]	48.5	6.77 5	10.4 10	8.32 3	7.00 39	13.4 42	6.16 5	8.19 87	13.6 103	6.26 76	21.7 31	20.3 49	41.5 28	30.5 35	27.2 54	45.5 38	28.5 83	21.8 89	36.5 69	6.84 60	15.6 74	10.6 82	8.41 45	17.1 51	6.95 7
p-harmonic [29]	48.9	7.04 20	11.3 27	8.62 33	8.81 85	15.8 85	6.98 74	7.76 66	13.1 94	6.18 73	22.4 81	20.7 69	41.9 66	30.5 35	27.0 29	45.5 38	27.8 28	19.2 18	36.4 61	6.71 37	15.1 58	10.3 6	8.29 31	16.8 33	7.12 27
Brox et al. [5]	49.1	7.28 49	11.4 33	8.76 50	7.86 64	14.6 65	6.92 72	8.03 81	13.1 94	6.34 82	21.9 46	19.9 36	41.4 24	30.6 51	27.0 29	45.8 76	27.7 18	19.5 26	36.2 38	6.80 55	15.4 69	10.4 32	8.16 18	16.5 24	7.19 46
Efficient-NL [60]	49.5	7.28 49	11.6 40	8.61 32	7.24 47	13.3 41	6.35 26	8.21 89	10.8 36	6.39 87	21.7 31	19.6 29	41.2 11	30.4 26	27.0 29	45.3 19	28.3 65	22.8 113	35.6 6	6.86 67	15.6 74	10.4 32	9.10 102	18.3 104	7.14 33
SepConv-v1 [127]	50.2	4.07 1	8.88 1	4.61 1	6.87 34	13.0 38	7.47 86	6.42 1	9.58 5	9.25 124	23.4 105	20.0 39	44.0 109	30.2 9	26.3 2	45.7 60	27.9 35	16.5 1	37.4 106	7.61 113	15.6 74	12.9 129	7.71 2	13.8 1	9.78 128
EpicFlow [102]	52.9	7.18 38	12.0 56	8.72 45	7.42 52	14.4 60	6.72 59	7.68 58	12.1 68	5.92 51	22.1 65	21.1 86	42.0 71	30.7 71	27.1 39	45.8 76	27.5 7	19.9 41	35.9 13	6.79 54	15.2 60	10.4 32	8.40 44	17.1 51	7.33 72
DPOF [18]	53.3	7.58 83	13.2 102	9.07 74	6.27 7	11.0 7	6.54 48	8.10 85	10.6 27	6.27 78	22.0 57	20.5 61	41.9 66	30.2 9	26.8 15	45.4 23	28.0 44	21.2 76	35.8 12	6.84 60	15.0 52	10.7 92	8.62 70	17.4 71	7.26 60
PMF [73]	53.6	6.83 7	10.3 9	8.37 6	6.96 37	13.1 39	6.19 7	7.86 72	13.1 94	6.03 66	21.5 20	19.4 22	41.3 17	31.0 104	27.7 104	45.8 76	28.7 93	20.5 54	37.2 100	6.80 55	15.0 52	10.5 57	8.87 85	18.2 101	7.00 10
ComplOF-FED-GPU [35]	54.4	7.23 46	11.8 49	8.72 45	7.20 45	13.9 47	6.62 52	8.43 97	12.6 84	6.45 89	21.9 46	20.8 75	42.3 82	30.4 26	26.9 20	45.4 23	27.7 18	20.1 44	36.1 31	6.86 67	15.4 69	10.5 57	8.55 63	17.3 65	7.28 65
Sparse Occlusion [54]	55.5	7.37 61	12.3 72	8.87 67	8.04 68	15.3 74	6.48 41	7.58 42	10.8 36	5.87 43	22.0 57	20.4 55	41.5 28	30.6 51	27.2 54	45.5 38	28.3 65	21.8 89	36.4 61	6.80 55	15.3 64	10.3 6	8.74 80	17.7 80	7.18 45
TC/T-Flow [76]	55.9	7.37 61	11.8 49	8.59 28	7.31 48	14.0 50	6.42 34	7.47 30	11.1 44	5.81 32	21.8 38	20.5 61	41.7 45	30.8 83	27.5 87	45.7 60	28.1 53	20.9 67	36.2 38	7.03 87	16.0 87	10.6 82	8.62 70	17.6 77	7.13 31
CLG-TV [48]	57.5	7.52 78	12.3 72	9.14 78	8.67 83	15.8 85	7.11 78	7.97 78	12.7 88	6.26 76	22.1 65	20.3 49	42.0 71	30.5 35	26.9 20	45.7 60	27.6 15	19.1 14	36.2 38	6.71 37	14.9 45	10.4 32	8.53 62	17.3 65	7.24 57
AggregFlow [97]	57.5	7.71 90	12.6 86	9.11 76	7.50 55	13.9 47	7.06 75	7.19 7	9.98 10	5.53 4	21.9 46	20.4 55	41.6 35	30.8 83	27.3 68	46.1 107	29.0 102	19.7 37	37.9 113	6.75 48	14.7 30	10.5 57	8.32 34	16.8 33	7.40 83
SuperFlow [81]	57.7	7.43 70	11.5 35	9.30 88	8.55 82	14.8 68	9.15 99	7.91 76	12.0 65	6.31 79	22.1 65	19.9 36	42.0 71	30.7 71	27.2 54	45.9 94	27.3 2	18.4 6	35.9 13	6.86 67	15.8 81	10.6 82	8.15 16	16.5 24	7.16 40
RFlow [90]	58.0	7.24 47	12.1 60	8.90 71	8.42 77	15.6 80	6.49 43	7.72 61	12.2 72	6.01 64	22.0 57	20.6 66	41.7 45	30.4 26	27.1 39	45.7 60	27.4 4	19.8 40	35.6 6	6.84 60	15.9 85	10.5 57	8.91 92	18.0 91	7.47 88
SIOF [67]	58.5	7.66 87	12.6 86	9.09 75	9.45 99	16.6 98	8.48 93	7.65 53	11.9 63	5.98 59	21.9 46	20.1 42	41.8 53	30.0 3	26.5 3	45.3 19	28.1 53	19.7 37	36.6 74	6.63 20	14.7 30	10.5 57	8.82 83	17.9 87	7.46 85
TCOF [69]	58.5	7.36 58	12.1 60	8.68 39	9.41 98	16.6 98	7.17 81	7.38 21	10.7 34	5.61 8	21.8 38	20.4 55	41.8 53	30.4 26	27.0 29	45.6 48	28.1 53	21.8 89	35.9 13	6.85 64	15.7 80	10.4 32	9.30 111	19.0 115	7.61 102
IAOF [50]	59.0	8.70 111	12.9 95	10.3 108	12.4 121	19.2 125	9.77 110	7.74 64	12.0 65	6.21 74	22.8 91	20.2 46	42.0 71	30.2 9	26.5 3	45.5 38	27.7 18	19.6 29	36.1 31	6.67 25	15.0 52	10.3 6	8.41 45	17.1 51	7.12 27
TC-Flow [46]	59.3	7.18 38	11.8 49	8.78 54	7.46 54	14.6 65	6.77 64	7.86 72	12.6 84	5.89 48	21.8 38	20.3 49	41.9 66	30.7 71	27.4 80	45.7 60	28.3 65	21.0 70	36.6 74	6.73 46	14.8 39	10.5 57	8.51 58	17.3 65	7.24 57
OAR-Flow [125]	60.8	7.45 73	11.7 44	8.98 73	7.57 57	14.4 60	6.91 71	7.62 46	12.4 79	5.82 34	21.6 24	20.3 49	41.6 35	30.9 100	27.5 87	45.8 76	28.0 44	20.5 54	36.4 61	6.97 82	15.6 74	10.5 57	8.46 52	17.1 51	7.34 75
ALD-Flow [66]	62.6	7.54 79	12.1 60	9.14 78	7.43 53	14.3 56	6.85 68	7.66 56	12.5 82	5.87 43	21.8 38	20.4 55	42.3 82	30.8 83	27.4 80	45.9 94	28.1 53	19.9 41	36.6 74	6.62 17	14.2 15	10.5 57	8.68 77	17.5 76	7.46 85
OFH [38]	63.2	7.39 66	12.1 60	8.88 68	8.07 69	15.0 72	6.66 56	8.03 81	13.8 106	5.96 57	21.9 46	21.1 86	42.1 77	30.5 35	27.3 68	45.4 23	27.8 28	20.4 51	36.2 38	7.11 89	16.4 92	10.5 57	8.61 69	17.6 77	7.19 46
SVFilterOh [111]	63.6	7.18 38	10.9 17	8.76 50	6.48 14	11.7 13	6.45 39	7.62 46	10.2 13	5.99 61	21.7 31	19.4 22	42.5 92	31.3 112	28.0 112	46.6 116	28.6 88	22.0 98	36.5 69	6.92 74	14.1 12	11.4 115	8.97 96	17.8 85	8.09 113
MLDP_OF [89]	64.9	7.10 27	11.2 26	8.64 35	7.33 49	13.7 45	6.31 22	7.44 27	10.9 39	5.75 27	22.0 57	19.8 35	42.3 82	30.6 51	27.3 68	46.2 113	31.0 125	22.6 107	40.0 125	6.93 77	15.2 60	11.0 105	8.65 74	17.4 71	7.79 110
CostFilter [40]	65.1	6.91 11	11.1 22	8.37 6	6.82 29	12.9 34	6.25 14	7.99 79	13.9 107	6.10 68	21.9 46	20.6 66	41.7 45	31.1 108	27.9 108	45.9 94	29.8 113	20.3 47	39.1 121	6.94 79	15.8 81	10.6 82	8.82 83	18.1 98	7.09 21
Modified CLG [34]	65.7	7.63 85	11.6 40	9.65 94	10.7 109	17.2 106	10.7 114	8.25 91	14.3 112	6.60 95	22.4 81	21.1 86	41.8 53	30.6 51	26.9 20	45.8 76	27.7 18	19.2 18	36.3 52	6.69 32	14.9 45	10.4 32	8.41 45	17.0 43	7.35 78
Fusion [6]	66.0	7.13 33	12.3 72	8.60 31	7.18 43	13.1 39	6.56 49	7.63 49	10.9 39	6.13 71	22.5 87	21.1 86	41.5 28	30.7 71	28.2 115	44.3 2	28.1 53	23.8 120	35.2 3	7.22 98	17.9 105	10.6 82	9.64 118	19.9 122	7.32 69
F-TV-L1 [15]	67.0	8.24 101	13.1 99	9.92 101	9.28 94	16.3 93	7.48 87	8.00 80	13.2 99	6.35 84	22.3 77	20.9 76	42.3 82	29.9 2	26.9 20	44.8 6	27.9 35	19.4 23	36.5 69	6.87 71	15.4 69	10.5 57	8.46 52	16.8 33	7.58 98
FlowNet2 [122]	67.9	9.30 114	14.6 115	10.5 111	8.42 77	14.6 65	9.24 103	8.03 81	12.5 82	6.14 72	22.2 71	21.9 104	41.8 53	30.9 100	27.5 87	45.8 76	28.0 44	20.5 54	36.0 18	6.72 41	14.9 45	10.4 32	8.31 33	16.9 41	7.01 11
Complementary OF [21]	68.8	7.11 28	12.1 60	8.50 16	7.17 42	14.0 50	6.58 51	8.76 106	12.0 65	6.55 92	22.3 77	21.4 95	42.6 98	30.6 51	27.5 87	45.2 14	28.1 53	20.9 67	36.4 61	7.15 92	16.7 96	10.5 57	9.09 101	18.7 111	7.38 80
SimpleFlow [49]	69.0	7.37 61	12.4 81	8.74 49	7.88 65	14.3 56	6.50 44	8.59 102	11.5 52	6.51 90	21.6 24	19.3 21	41.8 53	30.6 51	27.3 68	45.5 38	28.5 83	22.9 115	36.2 38	7.66 116	20.5 124	10.8 100	8.89 88	18.2 101	7.15 37
LDOF [28]	69.5	8.08 96	12.3 72	9.79 99	8.94 89	14.9 70	9.18 101	8.23 90	13.5 102	6.52 91	22.3 77	21.1 86	42.4 90	30.6 51	27.0 29	45.8 76	27.9 35	18.8 10	36.6 74	6.77 52	15.3 64	10.4 32	8.44 51	17.1 51	7.38 80
ROF-ND [107]	70.4	7.46 74	11.0 19	8.77 52	7.96 66	15.4 75	6.76 63	7.55 37	11.0 42	5.85 40	23.3 102	23.5 121	41.6 35	30.6 51	27.1 39	45.8 76	28.3 65	22.7 110	35.9 13	7.52 110	17.5 102	11.4 115	9.25 109	18.7 111	7.27 62
Local-TV-L1 [65]	70.5	8.46 107	12.6 86	10.4 109	9.68 100	16.0 91	8.93 98	7.56 41	11.2 48	5.84 37	23.1 98	20.4 55	46.0 121	30.6 51	27.1 39	45.9 94	30.1 118	19.1 14	39.9 124	6.72 41	14.9 45	10.5 57	8.13 15	16.1 6	7.58 98
TF+OM [100]	70.8	7.41 67	12.1 60	9.19 83	7.21 46	12.9 34	7.83 88	7.55 37	12.3 76	5.82 34	22.2 71	21.0 82	41.9 66	30.8 83	27.5 87	46.0 104	28.3 65	20.5 54	36.8 88	6.97 82	16.3 90	10.5 57	8.65 74	17.3 65	7.75 106
TriFlow [95]	71.6	7.77 92	13.7 110	9.28 86	8.98 90	15.7 83	9.30 105	7.65 53	12.4 79	5.84 37	22.0 57	20.9 76	41.1 3	30.9 100	27.7 104	45.7 60	28.4 77	21.3 80	36.3 52	6.85 64	15.5 73	10.4 32	8.69 78	17.4 71	7.23 56
Classic++ [32]	72.2	7.49 76	12.5 84	9.11 76	8.07 69	15.2 73	6.67 57	7.89 75	12.6 84	6.04 67	22.3 77	20.7 69	42.2 80	30.6 51	27.2 54	45.7 60	29.0 102	21.0 70	37.6 108	6.81 58	15.2 60	10.5 57	8.62 70	17.4 71	7.46 85
Occlusion-TV-L1 [63]	72.2	7.44 71	12.3 72	9.14 78	8.91 88	16.5 96	6.85 68	7.83 70	12.8 89	6.32 80	22.6 90	21.5 99	42.5 92	30.5 35	26.9 20	45.8 76	28.4 77	19.6 29	37.1 97	7.15 92	14.8 39	10.7 92	8.51 58	17.1 51	7.34 75
Nguyen [33]	72.6	9.74 117	12.6 86	12.4 121	12.3 119	18.6 121	11.1 115	8.27 93	14.8 114	6.69 97	23.4 105	21.7 101	41.8 53	30.3 19	26.8 15	45.3 19	27.4 4	19.6 29	35.7 9	7.24 100	18.3 108	10.5 57	8.37 42	17.0 43	7.22 55
2D-CLG [1]	72.7	8.44 105	12.3 72	10.6 113	11.9 117	18.0 116	12.3 121	8.94 109	13.9 107	7.33 112	23.1 98	21.2 93	41.3 17	30.5 35	26.9 20	45.8 76	27.6 15	19.2 18	36.2 38	7.14 90	17.2 100	10.5 57	8.37 42	16.5 24	7.20 49
FlowNetS+ft+v [112]	73.7	7.81 94	11.7 44	9.63 93	9.77 102	16.8 100	9.16 100	8.06 84	13.4 101	6.36 85	22.1 65	20.7 69	42.1 77	30.8 83	27.4 80	45.8 76	27.7 18	19.4 23	36.3 52	7.01 84	16.4 92	10.5 57	8.51 58	17.2 60	7.33 72
Aniso-Texture [82]	74.2	7.16 35	11.6 40	8.78 54	8.84 86	16.5 96	6.86 70	8.38 96	11.8 60	5.99 61	22.4 81	21.4 95	42.5 92	31.0 104	27.5 87	46.0 104	29.0 102	24.2 123	36.7 83	6.70 35	14.7 30	10.3 6	8.90 89	18.0 91	7.27 62
Shiralkar [42]	74.5	7.48 75	12.8 93	8.80 60	9.00 92	15.8 85	6.65 54	8.52 100	16.1 118	6.84 101	23.4 105	22.3 108	41.6 35	30.0 3	27.0 29	44.5 3	28.7 93	21.1 74	37.1 97	7.49 108	18.7 116	10.6 82	8.64 73	17.7 80	6.93 5
Adaptive [20]	75.6	7.71 90	13.2 102	9.21 85	9.40 97	16.8 100	7.07 76	7.87 74	12.4 79	6.12 69	22.0 57	20.3 49	41.8 53	30.7 71	27.3 68	45.6 48	28.4 77	20.7 65	36.8 88	6.95 81	16.0 87	10.4 32	8.87 85	17.9 87	7.55 95
CNN-flow-warp+ref [117]	77.7	7.35 56	10.8 13	9.30 88	8.87 87	16.2 92	8.14 92	8.60 103	14.1 110	6.62 96	23.7 109	21.9 104	42.7 99	30.8 83	27.3 68	45.9 94	28.0 44	19.1 14	36.7 83	7.37 103	18.5 113	10.6 82	8.33 36	16.8 33	7.27 62
CRTflow [80]	78.1	7.69 88	12.6 86	9.28 86	8.45 80	15.5 77	6.81 66	8.55 101	14.0 109	7.29 110	22.4 81	20.7 69	43.8 108	30.7 71	27.2 54	45.7 60	28.1 53	19.6 29	36.7 83	6.87 71	15.8 81	10.6 82	8.59 66	17.2 60	7.65 103
Black & Anandan [4]	78.2	8.54 108	12.8 93	10.2 107	10.9 111	17.3 109	9.40 106	9.06 111	13.6 103	6.99 105	22.9 95	21.3 94	41.7 45	30.7 71	27.2 54	45.9 94	28.0 44	18.6 7	36.7 83	6.93 77	15.9 85	10.4 32	8.46 52	17.0 43	7.20 49
HBpMotionGpu [43]	78.9	9.39 115	14.6 115	11.3 117	11.7 116	18.9 123	11.5 118	7.55 37	11.1 44	6.00 63	23.3 102	22.3 108	43.5 107	30.3 19	27.2 54	45.2 14	28.7 93	20.9 67	37.1 97	6.62 17	14.2 15	10.5 57	8.99 98	17.8 85	8.04 112
StereoOF-V1MT [119]	79.3	7.65 86	13.5 107	8.77 52	8.69 84	15.9 90	6.52 47	9.43 116	15.4 116	7.23 108	24.4 115	22.3 108	43.2 105	30.5 35	27.2 54	45.0 9	28.9 100	21.2 76	37.2 100	7.77 119	19.4 119	11.0 105	8.26 30	16.4 17	6.93 5
GraphCuts [14]	79.4	8.65 110	14.1 114	9.83 100	8.28 73	14.2 53	9.28 104	9.89 120	10.6 27	7.38 113	23.0 96	21.1 86	42.5 92	30.3 19	27.3 68	44.7 5	27.2 1	21.4 81	34.7 1	7.42 106	17.8 103	11.0 105	9.32 112	18.9 113	7.66 104
HBM-GC [105]	80.8	7.91 95	12.6 86	9.75 97	7.51 56	13.9 47	6.80 65	7.29 13	9.43 2	5.94 53	22.0 57	19.7 34	42.3 82	32.1 122	28.6 119	48.0 123	30.0 115	24.6 126	37.8 110	7.14 90	14.8 39	11.6 118	8.95 95	17.7 80	8.28 115
Steered-L1 [118]	81.0	7.06 24	12.2 69	8.59 28	7.40 51	14.3 56	6.83 67	8.48 99	11.7 57	6.69 97	22.8 91	20.9 76	42.7 99	31.2 111	28.1 113	45.8 76	28.3 65	21.2 76	36.7 83	7.25 101	17.8 103	10.9 101	9.00 99	18.3 104	7.58 98
CBF [12]	81.0	7.41 67	11.9 54	9.31 90	8.07 69	14.9 70	7.14 80	7.69 59	11.1 44	5.95 55	22.8 91	20.7 69	45.1 117	30.8 83	27.3 68	47.0 120	28.2 62	20.6 62	36.5 69	7.17 95	16.6 95	11.2 111	9.16 106	17.9 87	8.83 122
TriangleFlow [30]	81.1	7.79 93	13.0 98	9.16 82	8.36 76	15.5 77	6.69 58	8.20 88	11.9 63	6.59 93	22.5 87	21.0 82	42.5 92	30.1 5	27.0 29	45.0 9	28.9 100	22.6 107	36.5 69	7.42 106	18.3 108	11.0 105	9.49 115	19.3 117	7.47 88
Correlation Flow [75]	81.6	7.05 22	11.7 44	8.32 3	8.29 74	15.6 80	6.56 49	7.64 51	10.8 36	5.89 48	22.2 71	20.1 42	42.9 103	31.7 116	27.7 104	49.9 128	29.6 110	23.8 120	37.2 100	7.62 114	19.0 118	11.3 113	9.22 108	18.6 110	7.51 94
IAOF2 [51]	83.4	8.43 104	13.6 109	9.76 98	9.86 104	17.4 110	8.67 94	7.74 64	12.2 72	6.33 81	23.1 98	21.7 101	42.3 82	31.0 104	27.9 108	45.6 48	28.5 83	21.0 70	36.6 74	6.71 37	15.0 52	10.3 6	9.14 104	18.4 107	7.49 92
BriefMatch [124]	85.4	7.38 65	12.0 56	8.85 64	7.71 60	14.5 63	7.86 89	8.77 107	11.7 57	7.25 109	24.2 113	22.0 106	46.2 122	30.8 83	27.4 80	46.1 107	31.8 128	21.7 86	41.3 128	6.85 64	15.3 64	10.7 92	8.51 58	17.1 51	7.56 97
SegOF [10]	87.1	8.16 99	12.4 81	10.1 106	9.10 93	15.5 77	8.83 97	9.48 117	14.1 110	7.46 115	22.8 91	23.0 119	41.6 35	30.8 83	27.4 80	45.8 76	28.3 65	22.0 98	36.3 52	7.83 120	21.5 126	11.0 105	8.46 52	17.1 51	7.17 43
BlockOverlap [61]	87.9	8.81 112	12.4 81	11.1 116	10.0 106	15.8 85	10.6 113	7.84 71	10.4 19	6.59 93	23.3 102	20.4 55	46.3 123	31.9 119	27.9 108	48.8 126	30.3 121	19.7 37	39.8 123	7.08 88	14.5 24	11.7 121	8.35 40	16.1 6	8.60 120
TV-L1-improved [17]	88.4	7.55 81	12.9 95	9.15 81	9.36 95	16.9 102	7.19 82	8.63 104	12.2 72	6.92 103	22.2 71	21.0 82	42.3 82	30.8 83	27.5 87	45.6 48	28.5 83	21.4 81	36.8 88	7.38 104	18.6 115	10.7 92	8.94 93	18.0 91	7.75 106
Dynamic MRF [7]	88.8	7.29 52	13.1 99	8.69 40	8.20 72	16.3 93	6.74 60	9.18 113	16.4 121	7.22 107	24.5 117	23.1 120	44.4 111	30.3 19	27.2 54	45.1 12	29.2 107	23.4 118	37.2 100	7.64 115	19.8 122	10.7 92	9.14 104	18.0 91	7.48 91
AdaConv-v1 [126]	89.3	9.81 119	13.9 112	11.6 119	12.1 118	17.6 112	16.0 127	11.4 127	16.1 118	13.1 129	26.5 126	24.4 126	45.3 118	28.4 1	24.4 1	44.6 4	28.4 77	18.1 4	37.7 109	7.74 118	16.3 90	13.1 130	8.25 28	15.1 2	10.1 129
LocallyOriented [52]	89.6	8.08 96	13.1 99	9.72 95	9.73 101	17.0 104	7.88 90	8.34 95	12.8 89	6.34 82	23.0 96	22.1 107	43.0 104	30.6 51	27.2 54	45.6 48	30.0 115	21.9 93	38.5 119	7.02 85	15.8 81	10.5 57	9.05 100	18.4 107	7.39 82
SPSA-learn [13]	90.4	8.28 102	12.9 95	9.95 103	9.92 105	16.3 93	9.49 107	9.15 112	12.8 89	7.30 111	23.1 98	20.5 61	41.6 35	30.8 83	27.5 87	45.7 60	28.0 44	20.4 51	36.3 52	8.81 130	27.1 131	11.8 122	10.0 123	21.0 126	7.20 49
Rannacher [23]	91.1	7.69 88	13.2 102	9.32 91	9.37 96	16.9 102	7.28 83	8.67 105	13.0 93	6.91 102	22.2 71	21.1 86	42.4 90	30.8 83	27.5 87	45.7 60	28.5 83	21.2 76	36.9 93	7.35 102	18.5 113	10.7 92	8.90 89	18.0 91	7.78 108
Ad-TV-NDC [36]	91.5	10.8 122	13.9 112	13.4 122	11.6 115	17.6 112	11.2 116	7.77 67	12.3 76	6.12 69	24.0 111	21.6 100	44.4 111	31.1 108	27.6 100	46.1 107	29.0 102	19.3 22	38.0 114	6.87 71	15.4 69	10.5 57	8.59 66	17.0 43	7.71 105
ACK-Prior [27]	92.5	7.12 30	11.7 44	8.57 24	7.08 40	13.8 46	6.34 25	8.81 108	11.8 60	6.69 97	22.5 87	21.4 95	42.3 82	32.6 126	29.3 125	48.2 124	30.7 124	25.6 128	38.1 116	7.95 123	18.8 117	12.0 123	10.8 128	21.8 128	8.53 119
Horn & Schunck [3]	93.2	8.45 106	13.3 106	10.0 104	11.4 114	18.1 118	9.84 111	9.65 118	16.1 118	7.89 118	24.6 118	22.8 113	42.8 101	30.6 51	27.2 54	45.6 48	28.3 65	19.4 23	36.8 88	7.41 105	18.0 107	10.6 82	8.94 93	17.7 80	7.55 95
UnFlow [129]	95.4	9.13 113	15.0 119	10.7 114	10.9 111	18.1 118	9.23 102	9.21 114	16.9 124	7.18 106	22.4 81	21.8 103	41.8 53	30.8 83	27.6 100	45.9 94	28.6 88	22.7 110	36.0 18	6.94 79	15.6 74	10.5 57	10.0 123	19.3 117	7.47 88
TI-DOFE [24]	95.8	11.8 124	14.7 117	14.8 125	13.9 126	20.3 127	13.5 125	9.26 115	16.5 123	7.69 117	25.1 120	22.8 113	43.3 106	30.2 9	27.1 39	45.4 23	28.4 77	19.6 29	36.8 88	7.22 98	17.2 100	10.7 92	9.21 107	18.1 98	7.59 101
StereoFlow [44]	96.0	13.8 127	20.2 130	14.0 123	14.1 127	21.3 130	12.0 120	7.79 69	13.3 100	5.98 59	22.4 81	20.9 76	42.1 77	33.7 130	32.3 130	46.1 107	30.5 122	31.8 131	36.3 52	6.63 20	14.7 30	10.4 32	9.98 122	21.0 126	7.42 84
Filter Flow [19]	101.1	8.30 103	13.2 102	10.0 104	10.8 110	17.1 105	11.7 119	7.96 77	12.1 68	6.38 86	23.7 109	20.9 76	44.5 115	31.5 115	28.2 115	46.7 118	28.8 98	21.0 70	37.3 105	7.15 92	17.0 99	10.7 92	9.54 117	18.9 113	8.47 118
NL-TV-NCC [25]	103.2	7.56 82	12.7 92	8.62 33	8.00 67	15.4 75	6.74 60	8.46 98	13.1 94	6.70 100	24.2 113	24.0 124	45.0 116	32.8 127	28.3 118	52.0 131	29.4 108	24.1 122	36.9 93	7.89 122	17.9 105	12.4 127	10.1 125	19.9 122	8.92 123
Bartels [41]	104.5	8.10 98	13.8 111	9.94 102	8.35 75	15.8 85	8.75 95	8.11 86	12.1 68	6.97 104	24.1 112	22.7 111	47.6 125	32.4 123	27.8 107	51.1 130	35.4 130	23.0 116	46.5 130	7.18 96	14.9 45	12.3 126	9.36 114	18.0 91	9.76 127
SILK [79]	104.5	9.77 118	15.1 120	11.8 120	12.3 119	18.7 122	11.2 116	10.3 121	16.4 121	8.14 120	25.2 121	22.8 113	45.9 120	30.8 83	27.5 87	45.7 60	30.6 123	20.3 47	40.1 126	7.19 97	16.8 97	10.9 101	8.87 85	17.6 77	7.50 93
SLK [47]	109.7	11.4 123	15.4 121	14.4 124	12.4 121	18.0 116	12.6 122	10.9 125	17.6 126	8.85 122	27.8 127	25.2 127	46.6 124	30.6 51	28.1 113	43.6 1	29.0 102	21.9 93	37.0 96	8.25 126	22.4 127	11.3 113	9.33 113	18.5 109	7.91 111
GroupFlow [9]	110.2	10.1 121	16.9 125	11.3 117	10.4 108	17.8 115	10.0 112	10.8 124	17.5 125	9.21 123	23.6 108	23.9 122	42.5 92	31.9 119	29.3 125	46.2 113	30.1 118	24.5 125	37.8 110	7.55 111	18.4 111	10.6 82	9.52 116	19.8 121	6.89 2
Heeger++ [104]	110.4	9.81 119	17.3 126	10.4 109	11.3 113	17.2 106	9.67 108	13.6 129	23.8 130	10.2 127	26.3 124	22.8 113	44.4 111	31.8 118	28.9 124	46.3 115	29.6 110	22.0 98	37.5 107	8.17 125	19.8 122	10.9 101	9.10 102	18.2 101	7.02 12
Learning Flow [11]	112.3	8.21 100	14.8 118	9.74 96	9.78 103	17.6 112	8.11 91	9.68 119	15.5 117	7.56 116	25.0 119	24.3 125	45.4 119	31.9 119	28.7 122	47.3 121	29.4 108	22.0 98	37.8 110	7.49 108	18.3 108	10.9 101	10.2 126	20.2 124	8.31 116
2bit-BM-tele [98]	113.1	8.61 109	13.5 107	10.5 111	10.0 106	17.5 111	9.73 109	8.26 92	11.5 52	7.40 114	24.4 115	22.7 111	48.1 126	32.5 125	28.6 119	50.2 129	34.7 129	24.3 124	44.9 129	9.35 131	26.2 130	13.8 131	9.25 109	17.3 65	10.2 130
FFV1MT [106]	117.0	9.53 116	16.7 124	10.7 114	12.6 124	18.2 120	12.8 123	13.3 128	23.5 129	10.5 128	26.3 124	22.8 113	44.4 111	31.4 114	28.2 115	46.1 107	29.8 113	20.6 62	38.1 116	8.32 128	20.5 124	11.0 105	10.5 127	20.4 125	8.45 117
Adaptive flow [45]	119.6	13.2 126	15.9 122	16.2 127	14.2 128	19.9 126	16.4 128	9.02 110	13.1 94	8.03 119	26.0 123	22.8 113	48.6 127	32.4 123	29.4 127	47.9 122	30.1 118	24.6 126	38.0 114	7.55 111	16.9 98	12.2 124	9.85 119	19.5 119	8.97 126
FOLKI [16]	119.8	15.0 129	17.4 127	19.4 129	14.3 129	20.9 129	14.4 126	10.7 123	19.2 128	9.99 126	29.8 129	26.8 128	53.1 130	31.3 112	28.6 119	45.8 76	30.0 115	21.7 86	38.9 120	7.85 121	19.4 119	11.6 118	9.85 119	19.2 116	8.80 121
Pyramid LK [2]	122.1	16.3 130	16.1 123	21.6 130	16.0 130	20.3 127	18.2 130	16.7 130	15.3 115	14.3 130	35.7 131	36.7 131	56.5 131	32.8 127	31.2 129	45.7 60	29.7 112	22.1 102	38.2 118	8.31 127	23.1 128	11.6 118	11.8 129	25.0 129	8.22 114
PGAM+LK [55]	123.1	12.7 125	18.1 128	15.3 126	12.4 121	19.1 124	13.0 124	11.1 126	18.6 127	9.33 125	29.2 128	27.5 129	51.6 129	31.7 116	28.8 123	46.7 118	31.3 126	23.7 119	40.1 126	7.67 117	19.4 119	11.4 115	9.89 121	19.5 119	8.95 124
HCIC-L [99]	123.8	18.0 131	18.7 129	23.1 131	12.7 125	17.2 106	17.0 129	10.5 122	14.4 113	8.49 121	25.7 122	23.9 122	44.3 110	33.2 129	29.8 128	49.0 127	31.7 127	26.4 130	39.2 122	7.98 124	18.4 111	12.4 127	12.4 130	25.3 131	8.96 125
Periodicity [78]	129.7	14.9 128	20.8 131	18.2 128	20.1 131	22.0 131	21.5 131	17.7 131	26.4 131	16.1 131	29.8 129	34.8 130	49.7 128	35.4 131	34.2 131	48.7 125	37.1 131	25.8 129	47.4 131	8.68 129	23.6 129	12.2 124	13.3 131	25.1 130	11.6 131

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.