Optical flow evaluation results: R0.5 endpoint error

Optical flow evaluation results	Statistics: Average SD R0.5 R1.0 R2.0 A50 A75 A95 Error type: endpoint angle interpolation normalized interpolation
Show images: below table above table

R0.5 endpoint 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
OFLAF [77]	10.8	1.67 4	9.54 5	0.81 11	4.12 7	23.1 5	2.26 15	3.43 1	11.8 1	1.47 7	2.31 8	15.3 5	0.67 8	18.1 2	27.2 3	8.68 5	12.6 35	27.0 11	6.93 29	0.78 18	1.08 29	3.37 24	11.6 8	25.9 3	16.2 15
MDP-Flow2 [68]	12.5	1.76 8	9.93 9	0.86 13	3.26 2	20.6 2	1.44 3	4.04 3	13.6 3	1.26 5	3.09 26	21.8 30	0.88 19	22.4 15	32.2 12	14.3 14	9.15 12	23.6 3	5.83 10	2.18 40	0.32 17	4.78 37	10.8 5	26.3 4	15.0 9
NNF-Local [87]	13.1	1.75 7	9.66 6	0.87 14	4.56 14	27.6 16	2.46 19	4.52 4	15.6 7	2.18 16	2.57 15	18.4 15	0.86 16	18.8 5	29.2 5	8.22 3	9.37 15	24.2 6	5.22 7	0.95 20	2.02 62	1.90 13	10.6 4	31.8 24	8.21 2
NN-field [71]	15.4	2.01 19	11.0 20	1.10 27	5.74 26	30.7 31	3.21 27	4.57 5	15.7 8	2.22 18	1.81 2	15.7 7	0.54 5	19.0 6	29.4 6	8.42 4	10.5 21	20.3 1	3.83 1	1.43 26	2.03 63	3.72 25	10.1 2	31.1 19	6.81 1
PMMST [114]	17.3	1.43 2	8.08 2	0.38 1	6.05 32	28.6 20	4.40 40	6.13 17	19.1 16	4.02 39	2.21 5	11.7 1	1.09 27	21.2 9	30.5 9	13.2 11	9.59 17	23.8 4	5.71 9	2.28 43	1.95 57	4.36 31	11.8 9	29.1 8	13.1 6
WLIF-Flow [93]	20.9	1.80 10	9.97 10	0.94 16	6.24 35	28.7 22	4.52 42	5.74 12	18.5 11	3.13 25	3.03 25	19.9 22	1.01 24	21.9 10	32.0 11	14.1 12	12.4 32	27.2 12	6.44 13	3.40 64	0.07 7	8.69 62	11.4 7	26.8 5	15.7 12
ComponentFusion [96]	21.1	1.73 6	9.92 8	0.77 8	3.75 5	22.3 4	2.27 16	5.02 9	17.1 10	2.21 17	2.87 19	19.3 20	0.93 20	24.1 19	35.1 22	18.6 34	12.4 32	39.0 71	8.29 55	2.41 44	0.13 15	4.73 36	11.9 11	30.0 15	15.5 11
Correlation Flow [75]	22.0	1.96 17	10.9 18	0.66 5	4.21 9	25.5 12	1.35 2	6.69 29	20.7 24	0.94 2	1.68 1	13.3 2	0.48 4	25.4 27	36.9 29	16.3 24	13.5 52	32.5 35	7.88 49	2.82 53	1.63 44	10.8 70	11.3 6	29.7 11	9.89 4
NNF-EAC [103]	23.5	1.95 16	10.5 15	1.10 27	4.29 11	25.1 10	2.20 12	5.06 10	16.6 9	1.81 11	3.94 52	23.4 39	1.63 52	22.5 16	32.5 15	14.7 16	11.3 25	25.8 8	6.81 23	2.71 50	2.13 65	4.63 35	12.4 14	30.2 17	16.5 16
Layers++ [37]	24.5	1.85 13	10.1 11	1.03 23	6.26 36	27.9 19	4.58 44	4.88 8	15.2 5	3.65 33	2.26 7	14.4 3	0.68 9	17.8 1	25.4 1	12.2 8	13.3 46	28.3 16	6.81 23	4.53 73	2.71 82	7.34 53	13.2 22	29.7 11	19.2 40
LME [70]	24.5	1.89 15	10.6 16	0.75 6	3.53 3	21.3 3	1.83 8	7.04 40	18.6 14	8.24 71	3.10 28	23.0 37	0.86 16	24.8 25	35.5 24	17.9 31	10.1 19	30.1 26	6.46 14	2.67 49	1.51 41	5.54 41	12.8 16	30.9 18	17.7 27
RNLOD-Flow [121]	26.6	1.68 5	9.49 4	0.75 6	5.76 27	30.6 30	3.18 25	6.58 26	21.1 28	2.92 22	2.61 16	17.5 12	0.74 11	22.0 11	32.8 16	14.9 19	11.5 27	28.0 15	7.37 37	5.53 87	4.17 95	15.2 93	11.8 9	27.7 7	15.1 10
nLayers [57]	26.8	1.40 1	7.64 1	0.64 3	8.47 67	30.5 29	7.07 79	6.80 35	20.0 20	5.79 63	2.13 4	14.7 4	0.78 14	18.3 3	26.0 2	12.2 8	12.9 41	25.6 7	6.84 25	1.93 36	2.24 69	3.35 23	14.5 34	32.0 27	20.9 49
HAST [109]	28.1	1.57 3	8.65 3	0.60 2	5.82 30	24.9 9	3.84 32	3.85 2	12.5 2	0.41 1	2.82 18	18.8 17	0.58 6	18.7 4	27.9 4	8.00 1	15.9 87	32.6 36	9.69 78	9.89 112	4.87 104	36.9 119	8.55 1	22.0 1	9.33 3
FC-2Layers-FF [74]	28.8	1.81 11	9.71 7	1.07 26	6.99 44	33.9 45	4.71 45	4.71 6	15.0 4	3.63 32	2.67 17	18.3 14	0.87 18	20.6 8	29.7 7	14.5 15	13.3 46	28.4 17	7.26 34	5.67 91	1.82 52	14.9 89	12.9 19	29.4 9	18.4 34
SVFilterOh [111]	29.7	2.18 29	11.4 24	0.78 9	5.94 31	29.2 24	3.13 23	4.85 7	15.2 5	2.01 13	2.35 9	17.4 10	0.63 7	20.4 7	30.1 8	8.12 2	14.7 79	29.5 22	8.51 62	10.3 113	3.98 93	31.3 111	12.0 12	26.9 6	13.4 7
TC/T-Flow [76]	29.8	2.19 31	11.8 31	1.21 36	5.00 17	29.4 25	1.89 9	5.57 11	18.5 11	1.28 6	3.63 41	23.7 42	1.24 34	24.1 19	35.4 23	15.2 21	7.02 2	25.8 8	5.43 8	3.79 68	2.13 65	19.3 102	14.6 37	36.2 40	17.9 29
AGIF+OF [85]	30.5	1.99 18	10.9 18	1.16 32	8.96 72	37.8 65	6.95 74	6.31 20	20.4 21	3.86 37	2.89 20	19.1 18	0.93 20	22.1 12	32.4 13	14.8 17	12.9 41	28.6 19	6.79 22	3.48 67	0.08 13	8.67 61	12.8 16	29.9 14	17.5 23
TC-Flow [46]	30.8	2.04 20	11.0 20	0.94 16	3.69 4	23.4 6	1.68 5	5.96 16	19.9 19	1.06 3	3.73 44	23.6 41	1.24 34	25.7 31	38.0 34	14.8 17	8.99 11	34.6 45	5.03 6	2.82 53	2.47 75	15.9 97	16.0 44	38.1 46	22.0 53
HBM-GC [105]	31.5	2.10 24	10.8 17	0.94 16	7.69 57	31.9 38	6.10 66	6.21 19	18.5 11	3.82 35	2.22 6	15.5 6	0.78 14	22.1 12	31.8 10	14.2 13	13.3 46	24.1 5	7.49 41	8.91 109	1.87 53	21.4 106	12.7 15	31.1 19	16.7 19
ALD-Flow [66]	33.1	2.22 33	11.8 31	1.02 22	4.33 12	24.7 7	2.04 11	6.35 22	20.9 25	1.56 8	3.67 42	24.0 46	1.10 28	25.8 32	37.5 33	15.8 23	8.24 5	32.7 38	4.68 3	3.06 58	2.62 81	16.6 99	15.6 43	39.3 48	19.8 44
ProbFlowFields [128]	35.2	3.29 69	17.1 72	1.88 83	6.54 38	29.8 26	5.50 56	7.69 47	23.8 45	6.90 66	3.09 26	17.1 9	1.28 36	27.8 39	39.5 40	19.2 40	6.56 1	27.2 12	5.00 5	0.09 3	0.03 3	0.86 7	17.1 51	39.4 49	17.5 23
IROF++ [58]	35.5	2.18 29	11.5 27	1.33 43	7.91 58	36.5 60	5.96 63	6.71 31	21.7 34	4.85 56	3.62 40	22.8 33	1.63 52	24.2 22	34.9 21	17.1 28	13.3 46	33.4 41	7.82 47	0.55 15	1.09 30	1.01 9	12.8 16	32.9 31	16.7 19
FESL [72]	36.2	1.86 14	10.2 12	0.99 19	10.4 88	39.3 76	7.94 87	6.79 34	21.2 30	4.35 46	2.39 10	16.2 8	0.76 13	24.2 22	34.7 19	19.2 40	12.6 35	27.7 14	7.10 33	3.35 62	2.20 68	8.03 57	14.5 34	31.2 21	17.6 26
FMOF [94]	36.5	2.09 22	11.2 23	1.44 56	9.20 74	37.9 66	6.96 75	6.14 18	19.5 17	3.82 35	2.52 13	17.4 10	0.73 10	24.1 19	34.8 20	17.7 30	13.5 52	28.4 17	6.99 30	4.64 76	1.63 44	14.5 85	14.5 34	32.7 29	17.2 22
Classic+CPF [83]	37.1	2.16 27	11.6 28	1.42 54	8.04 60	36.6 61	5.76 60	6.70 30	21.8 36	3.86 37	3.02 24	21.2 28	1.14 30	23.6 18	34.2 18	17.0 27	13.4 51	29.1 20	7.03 32	4.58 75	1.50 40	13.6 82	12.9 19	29.7 11	17.5 23
MLDP_OF [89]	37.4	2.46 45	13.6 48	1.14 31	4.43 13	27.8 18	1.99 10	6.80 35	21.7 34	1.89 12	2.52 13	19.6 21	0.75 12	28.2 40	40.4 43	19.2 40	11.8 29	29.6 23	9.40 74	8.30 107	2.15 67	31.7 113	13.3 23	33.4 34	15.9 13
PH-Flow [101]	38.0	2.30 38	12.2 35	1.45 58	7.61 55	35.2 50	5.81 61	5.92 15	19.0 15	4.67 54	3.52 36	22.1 31	1.49 46	23.4 17	33.9 17	16.3 24	12.4 32	29.4 21	6.87 26	4.88 80	2.25 71	14.0 83	12.2 13	30.1 16	16.6 17
Efficient-NL [60]	39.3	2.41 43	11.8 31	1.55 69	8.28 63	35.4 53	5.90 62	6.72 32	20.9 25	3.78 34	2.95 23	19.1 18	1.20 32	22.1 12	32.4 13	15.3 22	14.6 77	31.0 30	8.05 53	3.32 60	2.40 74	7.02 49	13.6 27	29.6 10	18.2 31
PMF [73]	39.7	2.30 38	12.8 41	1.01 20	5.64 25	31.4 35	2.51 20	6.83 37	22.5 41	1.72 10	3.26 29	21.1 26	0.94 22	24.3 24	36.7 28	9.56 6	14.4 73	40.0 77	8.40 59	7.45 104	8.62 121	24.8 107	10.4 3	25.6 2	12.7 5
Aniso-Texture [82]	40.2	1.84 12	10.4 14	0.81 11	4.68 15	24.7 7	3.74 30	8.07 51	23.8 45	3.23 28	2.10 3	18.4 15	0.46 2	29.0 51	41.0 48	22.9 55	12.7 37	32.7 38	8.37 58	6.63 99	5.87 112	14.8 88	16.4 45	35.2 38	24.2 63
OAR-Flow [125]	40.4	2.96 63	15.1 63	1.58 71	6.72 41	31.3 34	4.10 37	9.12 59	27.1 56	4.72 55	3.73 44	23.9 44	1.17 31	28.3 42	40.8 45	17.6 29	8.30 6	33.5 43	4.69 4	0.25 7	0.17 16	2.56 18	17.5 55	40.5 51	22.6 56
Sparse-NonSparse [56]	41.4	2.11 25	11.7 29	1.39 49	7.47 54	35.1 49	5.75 59	6.48 24	21.2 30	4.34 44	3.52 36	22.9 34	1.38 41	26.1 35	37.3 32	19.6 47	13.5 52	31.2 32	7.42 38	5.02 84	1.18 34	13.5 81	13.5 25	31.7 22	18.8 38
Ramp [62]	42.2	2.21 32	12.1 34	1.45 58	7.45 53	35.5 55	5.63 58	6.33 21	20.6 23	4.23 41	3.43 33	22.5 32	1.38 41	25.8 32	37.0 30	19.3 44	13.5 52	30.3 27	7.45 39	4.89 81	1.97 59	15.1 92	13.1 21	31.9 25	18.0 30
NL-TV-NCC [25]	42.4	2.25 35	11.7 29	0.78 9	6.94 43	35.5 55	2.54 21	6.48 24	21.1 28	1.08 4	2.48 12	21.5 29	0.46 2	31.5 64	46.7 81	16.7 26	17.3 91	41.0 86	10.2 81	4.41 72	0.10 14	10.1 67	18.4 56	43.2 57	18.2 31
LSM [39]	42.6	2.24 34	12.4 37	1.39 49	7.37 51	35.4 53	5.47 55	6.61 27	21.6 33	4.24 42	3.46 35	23.8 43	1.30 38	25.8 32	37.0 30	19.3 44	13.7 58	32.1 34	7.36 36	5.34 86	1.11 32	14.5 85	13.7 30	32.3 28	18.2 31
OFH [38]	42.7	2.82 58	13.9 51	2.01 84	4.91 16	28.6 20	2.33 17	8.97 58	28.0 61	2.88 21	4.00 55	27.0 56	1.43 44	31.0 61	44.5 68	22.7 53	10.5 21	41.8 88	6.88 27	0.03 1	0.02 1	0.27 3	17.1 51	46.4 70	19.2 40
Sparse Occlusion [54]	43.4	2.14 26	11.4 24	1.03 23	7.32 49	31.0 32	6.11 67	7.29 44	22.9 43	2.48 20	3.29 30	22.9 34	1.03 25	26.9 37	39.4 38	14.9 19	13.0 43	33.3 40	7.63 44	7.80 105	8.76 124	12.2 77	14.8 39	34.8 37	16.9 21
Classic+NL [31]	43.5	2.08 21	11.4 24	1.35 45	7.33 50	35.9 58	5.30 52	6.47 23	21.0 27	4.53 51	3.59 38	22.9 34	1.49 46	25.4 27	36.3 26	19.4 46	13.8 61	31.1 31	7.57 42	5.78 93	2.32 73	15.0 90	13.5 25	31.9 25	18.7 37
IROF-TV [53]	45.9	2.51 48	13.5 46	1.41 52	8.08 61	38.7 73	6.19 69	6.97 39	22.3 38	4.43 48	4.23 58	28.8 64	1.72 57	28.3 42	39.9 41	22.6 52	13.8 61	40.0 77	8.01 51	0.23 6	0.39 21	0.67 5	13.7 30	33.6 35	17.8 28
RFlow [90]	46.2	2.42 44	13.5 46	1.16 32	3.98 6	25.2 11	1.81 7	8.89 57	27.5 58	3.13 25	3.45 34	26.8 54	1.60 50	30.5 59	43.6 59	24.5 63	14.2 68	38.1 65	7.94 50	3.36 63	1.65 47	8.47 60	16.9 50	42.3 53	20.5 48
TV-L1-MCT [64]	46.4	2.09 22	11.1 22	1.39 49	9.67 77	39.0 75	7.35 80	7.11 42	22.3 38	4.27 43	2.94 22	20.7 25	1.13 29	28.4 44	39.4 38	25.8 70	16.0 89	35.0 48	9.27 71	1.27 24	0.57 23	7.24 51	14.8 39	33.2 33	23.3 60
COFM [59]	47.0	2.51 48	13.9 51	1.42 54	5.54 21	28.9 23	3.40 28	7.79 49	23.6 44	4.53 51	2.90 21	18.2 13	0.95 23	29.1 52	40.8 45	25.4 67	15.5 85	30.7 29	9.39 73	4.69 77	1.23 35	15.4 96	16.8 49	37.1 41	21.8 52
S2D-Matching [84]	47.1	2.39 41	13.0 42	1.52 66	7.22 47	35.6 57	5.13 50	7.63 46	24.4 48	4.38 47	3.30 31	20.5 24	1.35 40	25.3 26	36.0 25	19.2 40	14.1 65	31.5 33	7.77 46	6.21 96	2.24 69	16.9 100	13.6 27	31.7 22	19.3 42
Occlusion-TV-L1 [63]	48.9	2.47 46	13.1 43	1.12 29	6.32 37	32.2 40	4.45 41	9.86 64	28.3 62	4.44 49	3.86 50	26.7 53	1.43 44	31.9 70	44.3 65	27.1 76	11.3 25	35.3 52	10.4 83	0.47 13	1.16 33	0.67 5	19.8 64	46.6 72	22.9 58
Complementary OF [21]	49.0	2.69 55	15.0 61	1.33 43	4.19 8	26.9 14	1.70 6	7.15 43	24.4 48	3.09 24	3.86 50	26.1 50	1.41 43	33.2 76	44.6 70	29.7 87	13.3 46	40.7 85	7.00 31	0.74 17	0.03 3	7.12 50	23.9 85	53.1 91	33.9 91
ACK-Prior [27]	49.1	2.16 27	12.4 37	0.64 3	4.26 10	25.9 13	1.33 1	5.91 14	20.4 21	1.67 9	2.39 10	20.4 23	0.33 1	30.0 56	41.1 49	24.4 62	19.2 102	40.3 82	12.2 94	14.4 122	6.30 115	40.9 122	21.0 69	43.6 59	27.5 77
CostFilter [40]	49.2	2.65 52	14.9 59	1.01 20	5.51 20	31.6 36	2.23 14	7.39 45	24.1 47	3.06 23	3.82 48	26.1 50	1.08 26	25.4 27	38.9 37	10.2 7	15.1 82	42.7 91	8.52 63	8.99 110	10.3 126	29.5 110	14.4 33	37.2 42	16.1 14
MDP-Flow [26]	49.4	2.33 40	13.4 45	1.20 34	5.61 24	26.9 14	4.88 47	6.76 33	22.6 42	5.72 62	4.13 56	29.5 69	1.92 61	28.7 47	40.8 45	23.2 58	12.8 39	36.7 57	8.04 52	2.54 46	2.96 84	4.43 33	19.4 63	44.6 63	26.0 72
2DHMM-SAS [92]	51.1	2.28 37	12.3 36	1.46 60	8.45 66	38.1 68	6.02 65	8.34 53	24.4 48	5.21 58	3.73 44	23.4 39	1.62 51	25.5 30	36.6 27	18.8 35	14.5 74	33.4 41	8.46 61	5.07 85	2.05 64	15.3 94	13.3 23	32.9 31	18.5 36
SimpleFlow [49]	51.6	2.39 41	12.6 40	1.60 73	9.03 73	38.3 69	7.38 81	8.52 55	25.7 52	5.41 59	4.36 63	25.5 49	2.48 70	26.8 36	38.0 34	21.6 49	14.0 64	29.7 24	7.65 45	2.77 52	1.97 59	6.48 46	14.3 32	32.8 30	19.7 43
Steered-L1 [118]	52.7	1.78 9	10.2 12	0.92 15	2.76 1	19.0 1	1.44 3	5.78 13	19.7 18	2.10 14	3.99 54	27.5 59	1.53 49	31.3 63	43.3 57	27.6 78	14.6 77	39.2 73	9.88 79	14.6 123	5.70 110	47.1 123	22.4 76	48.1 76	29.3 82
AggregFlow [97]	54.1	3.49 77	17.6 76	1.74 75	10.4 88	42.0 87	6.99 76	11.4 74	30.6 67	9.73 80	3.75 47	21.1 26	1.52 48	28.9 50	41.8 53	18.2 32	7.46 3	22.8 2	4.30 2	1.95 37	2.54 77	4.24 29	20.5 66	42.7 56	25.8 70
TF+OM [100]	54.5	2.89 62	14.7 57	1.35 45	5.44 19	27.6 16	3.94 33	10.2 65	26.3 53	12.8 90	3.61 39	24.6 48	1.28 36	29.8 55	40.5 44	25.7 68	12.7 37	34.9 47	6.11 11	5.62 90	4.89 105	14.0 83	21.5 71	46.5 71	23.8 62
EPPM w/o HM [88]	56.5	3.53 79	16.6 68	1.44 56	5.80 28	35.3 52	2.22 13	8.01 50	26.3 53	2.45 19	4.38 64	28.0 60	1.77 59	27.0 38	40.0 42	13.0 10	18.6 97	44.4 100	10.8 86	10.5 115	2.30 72	37.7 120	13.6 27	35.6 39	13.9 8
DeepFlow2 [108]	56.9	3.18 67	16.9 71	1.48 62	6.68 40	33.7 44	4.15 38	10.7 69	31.1 68	7.69 69	5.96 82	31.1 77	3.33 81	28.5 45	41.3 50	19.1 37	9.45 16	34.7 46	6.60 17	1.60 29	1.66 49	10.4 68	23.5 80	47.9 75	30.2 85
CombBMOF [113]	57.2	2.67 53	14.4 54	1.05 25	7.17 45	33.9 45	4.00 34	6.89 38	21.4 32	4.18 40	5.20 75	28.8 64	3.04 79	28.2 40	41.7 52	18.5 33	21.8 106	39.5 74	20.1 110	4.02 69	4.61 102	6.08 43	17.1 51	37.5 43	24.5 66
Adaptive [20]	57.4	2.49 47	13.2 44	1.13 30	7.17 45	34.5 48	4.97 48	10.2 65	28.7 64	4.34 44	4.31 62	28.2 61	1.66 54	34.5 83	48.1 87	28.2 80	14.3 71	36.1 54	8.24 54	4.04 70	4.49 101	7.32 52	14.7 38	34.7 36	18.9 39
ComplOF-FED-GPU [35]	59.9	2.87 61	15.6 65	1.35 45	6.14 33	33.6 43	3.15 24	8.26 52	27.4 57	3.30 29	4.29 59	28.2 61	1.71 56	33.1 75	47.9 86	24.1 61	14.7 79	45.7 103	9.19 69	3.33 61	1.48 39	15.0 90	18.8 59	48.4 78	22.0 53
TCOF [69]	60.4	3.05 65	15.4 64	1.75 76	8.12 62	38.6 71	5.20 51	13.8 86	34.5 81	13.2 92	8.75 98	29.0 66	8.90 103	33.8 79	47.3 83	23.1 57	9.33 14	25.9 10	6.64 19	2.59 47	1.95 57	6.38 45	15.3 42	39.1 47	18.4 34
ROF-ND [107]	61.3	3.29 69	14.7 57	1.29 40	7.42 52	31.8 37	2.42 18	7.10 41	22.1 37	2.13 15	3.68 43	23.2 38	2.87 77	31.1 62	43.7 60	23.6 60	19.3 104	38.5 67	10.7 85	8.90 108	2.99 85	24.8 107	21.9 73	48.6 79	22.7 57
BriefMatch [124]	61.4	2.27 36	12.5 39	1.23 38	6.23 34	32.1 39	3.54 29	6.68 28	22.3 38	3.16 27	3.32 32	23.9 44	1.23 33	30.8 60	43.3 57	26.8 73	23.9 109	43.6 95	21.0 112	11.0 118	4.44 100	33.1 116	21.5 71	44.8 64	29.2 81
DeepFlow [86]	62.0	3.36 72	17.3 73	1.54 68	7.91 58	35.2 50	5.35 53	12.1 78	33.0 76	10.5 86	6.24 84	32.1 79	3.55 83	28.8 48	42.3 54	18.8 35	9.77 18	37.4 61	6.90 28	1.30 25	0.37 18	9.70 66	27.4 95	52.7 87	35.8 93
Classic++ [32]	62.0	2.59 51	13.9 51	1.51 65	6.79 42	32.3 41	5.37 54	9.15 60	27.8 60	5.54 60	4.29 59	29.0 66	1.77 59	30.2 58	43.9 61	22.8 54	14.8 81	40.0 77	8.36 57	6.82 100	4.17 95	16.5 98	16.5 46	39.5 50	19.8 44
TV-L1-improved [17]	63.1	2.56 50	13.6 48	1.20 34	5.80 28	30.0 28	4.04 35	9.84 63	28.4 63	4.60 53	4.16 57	27.0 56	1.66 54	31.5 64	45.4 75	23.0 56	17.5 92	45.5 102	13.7 99	7.01 102	4.32 99	20.5 104	17.1 51	42.5 55	20.4 47
Bartels [41]	64.3	3.26 68	16.7 70	1.37 48	5.33 18	29.8 26	3.18 25	8.40 54	26.9 55	4.45 50	4.40 65	26.9 55	2.16 63	32.7 72	45.4 75	28.4 83	14.3 71	38.1 65	12.9 95	8.20 106	3.82 91	31.3 111	18.4 56	43.7 61	23.3 60
S2F-IF [123]	64.8	4.50 93	23.8 98	2.14 90	8.86 69	42.8 91	6.52 70	11.0 70	34.0 80	9.17 75	4.86 68	29.2 68	2.39 68	35.6 89	51.0 100	26.9 74	8.49 8	36.6 56	6.30 12	0.60 16	0.03 3	2.49 17	23.6 81	52.7 87	25.9 71
SIOF [67]	65.4	2.67 53	13.6 48	1.23 38	7.65 56	37.9 66	4.78 46	14.2 89	32.9 75	15.4 94	6.36 85	34.7 85	3.84 85	34.0 80	45.8 78	33.2 91	13.5 52	35.7 53	10.5 84	1.99 38	0.99 27	4.21 28	20.5 66	45.6 66	30.7 87
F-TV-L1 [15]	66.5	3.34 71	17.3 73	1.80 80	9.99 85	38.6 71	7.03 77	13.2 84	32.6 74	7.82 70	5.85 81	32.9 80	2.91 78	31.5 64	45.0 72	25.2 65	15.1 82	38.7 68	8.99 67	2.19 41	3.29 88	3.03 21	15.1 41	38.0 45	16.6 17
CRTflow [80]	67.0	3.53 79	18.6 80	1.79 78	6.54 38	34.0 47	4.08 36	10.5 67	31.6 71	5.02 57	4.95 73	30.6 73	2.31 64	30.1 57	44.2 64	19.1 37	24.2 110	50.1 109	26.0 116	1.80 34	0.92 26	6.63 47	22.7 77	52.1 85	30.0 84
PGM-C [120]	67.8	4.80 99	24.9 105	2.34 98	9.79 78	42.4 88	7.86 85	11.3 73	34.5 81	9.49 76	5.28 78	34.6 84	2.54 73	34.5 83	49.1 93	26.5 71	9.26 13	37.2 58	6.72 21	0.42 11	0.07 7	1.85 12	23.7 82	53.0 90	25.6 69
FlowFields [110]	68.8	4.67 95	24.4 101	2.22 92	9.80 79	44.3 95	7.67 83	11.8 76	36.4 88	10.1 83	4.90 70	30.5 72	2.63 75	36.4 92	51.6 103	28.9 85	8.76 10	38.7 68	6.48 15	0.85 19	0.03 3	2.71 19	23.3 79	53.7 95	22.3 55
TriangleFlow [30]	69.0	2.81 57	14.9 59	1.22 37	7.27 48	37.1 63	3.76 31	9.83 62	30.2 66	3.34 30	3.84 49	27.1 58	1.72 57	39.4 106	53.7 108	34.8 96	21.8 106	43.5 94	16.0 102	4.72 79	7.40 118	8.30 59	18.5 58	44.3 62	21.5 51
FlowFields+ [130]	69.1	4.68 96	24.5 102	2.22 92	9.86 82	44.7 97	7.63 82	12.1 78	37.3 91	10.3 85	4.92 72	30.3 71	2.61 74	36.3 91	51.6 103	28.3 82	8.62 9	38.7 68	6.57 16	0.41 9	0.02 1	1.92 14	23.7 82	53.6 94	25.5 68
Rannacher [23]	69.6	3.03 64	16.1 66	1.59 72	8.35 64	36.9 62	6.87 73	11.1 71	31.8 72	6.71 65	4.88 69	29.7 70	2.34 65	31.7 69	45.9 79	23.3 59	16.8 90	44.0 97	10.3 82	4.89 81	2.57 79	12.1 76	16.7 48	41.9 52	19.9 46
SRR-TVOF-NL [91]	69.7	3.16 66	16.2 67	1.49 63	8.87 70	38.5 70	5.57 57	12.3 80	33.4 77	8.53 72	3.96 53	26.6 52	1.34 39	32.8 73	44.6 70	27.2 77	13.8 61	39.0 71	8.34 56	5.55 89	5.38 109	17.8 101	22.0 74	43.3 58	25.2 67
CPM-Flow [116]	69.9	4.79 98	24.9 105	2.32 96	9.83 80	42.4 88	7.89 86	11.2 72	33.9 79	9.50 77	5.25 76	34.3 82	2.50 72	34.7 86	49.3 96	26.7 72	10.3 20	37.5 63	7.62 43	0.42 11	0.07 7	1.82 11	24.5 87	54.2 96	26.8 75
LocallyOriented [52]	70.1	4.06 87	20.2 88	1.87 82	12.1 92	47.6 100	8.49 90	15.9 93	39.1 98	11.1 88	5.10 74	28.6 63	2.84 76	34.0 80	47.4 84	25.7 68	11.8 29	32.6 36	7.84 48	1.10 21	1.51 41	6.95 48	20.3 65	46.8 73	23.1 59
EpicFlow [102]	70.4	4.80 99	24.9 105	2.33 97	9.90 83	42.9 92	7.95 88	11.8 76	35.7 86	9.56 78	5.26 77	34.4 83	2.49 71	34.6 85	49.2 95	27.0 75	10.8 24	37.5 63	7.33 35	0.41 9	0.07 7	1.80 10	24.1 86	53.5 93	26.4 73
Aniso. Huber-L1 [22]	70.6	2.84 60	14.5 55	1.46 60	14.0 94	42.6 90	12.9 93	13.4 85	31.3 69	13.0 91	6.50 86	35.2 87	4.19 88	29.7 54	42.4 55	21.8 51	14.5 74	35.0 48	8.43 60	5.54 88	3.18 87	12.8 79	16.6 47	37.7 44	20.9 49
Kuang [131]	71.6	4.54 94	24.0 99	2.07 86	9.42 75	45.2 98	6.53 71	12.5 81	38.7 96	8.97 74	4.91 71	31.0 76	2.36 67	37.7 98	53.5 107	29.5 86	12.8 39	42.9 93	9.24 70	0.53 14	0.07 7	2.71 19	18.9 60	47.6 74	24.3 64
Dynamic MRF [7]	71.9	3.39 73	18.9 83	1.30 41	5.60 23	33.5 42	2.81 22	9.67 61	31.3 69	3.54 31	4.64 67	33.7 81	2.39 68	38.0 99	51.2 101	34.9 97	19.2 102	51.8 112	15.2 101	3.41 65	0.37 18	20.9 105	25.1 89	52.2 86	31.7 89
DPOF [18]	74.1	4.03 85	21.8 92	2.11 87	9.50 76	40.4 79	5.97 64	8.88 56	27.5 58	6.05 64	4.29 59	30.8 74	2.08 62	31.5 64	45.1 73	21.6 49	15.9 87	37.3 60	9.53 76	15.3 124	1.61 43	47.3 124	22.1 75	46.3 69	28.5 78
CBF [12]	76.5	2.82 58	15.0 61	1.32 42	18.0 99	40.4 79	21.6 101	10.6 68	29.2 65	9.72 79	6.57 87	34.8 86	4.55 90	31.5 64	44.0 63	24.5 63	14.5 74	35.0 48	8.92 66	10.9 117	6.02 113	26.2 109	20.7 68	43.6 59	27.2 76
Brox et al. [5]	76.7	3.55 82	18.8 82	1.64 74	10.1 87	39.6 77	8.97 91	11.7 75	33.4 77	8.96 73	6.57 87	36.4 89	3.41 82	38.2 101	47.6 85	45.3 113	13.5 52	42.4 90	9.63 77	0.27 8	0.99 27	0.47 4	31.0 101	56.4 102	43.3 105
Fusion [6]	76.7	3.40 74	19.1 84	2.16 91	5.57 22	31.1 33	4.53 43	7.70 48	25.2 51	7.53 68	5.78 80	35.6 88	4.10 87	36.6 93	47.1 82	38.8 102	14.2 68	41.9 89	13.2 96	6.84 101	5.31 107	11.7 74	24.8 88	51.1 84	31.6 88
Local-TV-L1 [65]	77.7	4.05 86	19.4 86	2.51 100	17.1 98	43.6 94	15.9 96	19.8 100	37.3 91	23.3 97	9.20 101	43.3 97	6.89 100	28.6 46	41.3 50	20.2 48	14.1 65	35.1 51	8.67 64	1.24 22	0.62 24	3.94 26	33.5 108	57.2 103	49.6 112
CLG-TV [48]	78.3	2.80 56	14.6 56	1.41 52	14.0 94	40.7 82	14.1 95	12.7 82	32.0 73	11.0 87	8.13 94	47.7 105	5.99 96	32.0 71	45.2 74	25.2 65	14.1 65	40.1 81	10.9 87	6.45 97	5.82 111	10.4 68	19.0 61	42.4 54	26.6 74
LDOF [28]	80.1	4.09 89	20.0 87	2.31 95	9.96 84	41.8 85	7.06 78	14.1 87	37.0 89	10.1 83	8.41 95	43.3 97	4.97 91	34.4 82	46.2 80	32.5 89	12.2 31	41.0 86	8.88 65	1.63 31	2.00 61	5.79 42	29.9 96	56.0 100	38.8 100
p-harmonic [29]	80.1	3.47 76	19.1 84	2.29 94	8.40 65	35.9 58	6.80 72	12.8 83	34.6 83	9.84 82	9.04 99	47.6 104	6.72 98	37.1 95	48.7 91	39.6 103	13.1 44	44.0 97	11.2 88	3.43 66	2.50 76	6.33 44	21.2 70	45.2 65	30.5 86
DF-Auto [115]	80.5	4.71 97	22.2 93	2.11 87	21.1 102	49.4 102	21.6 101	20.3 101	39.8 100	31.0 103	7.62 91	37.5 91	5.08 93	33.6 77	43.9 61	33.3 92	8.36 7	29.8 25	7.48 40	2.60 48	5.21 106	2.22 15	32.4 104	53.1 91	43.2 104
TriFlow [95]	81.9	3.53 79	17.9 77	1.77 77	11.0 91	37.3 64	10.6 92	16.7 96	35.8 87	25.3 99	4.44 66	30.9 75	2.34 65	35.0 88	44.3 65	35.7 98	10.7 23	30.4 28	6.68 20	33.4 127	9.63 125	90.0 129	30.3 99	55.2 99	36.8 96
FlowNetS+ft+v [112]	84.3	3.75 84	18.5 79	2.13 89	10.0 86	38.8 74	8.25 89	16.3 94	37.5 94	20.0 95	7.89 92	37.0 90	5.17 94	36.9 94	48.2 88	35.7 98	11.6 28	40.0 77	9.13 68	4.56 74	4.02 94	14.6 87	23.8 84	51.0 82	31.9 90
SuperFlow [81]	84.5	3.40 74	16.6 68	1.81 81	15.3 96	41.5 83	15.9 96	17.0 97	35.2 85	27.6 100	10.0 103	43.1 96	8.60 102	36.1 90	44.3 65	46.7 114	13.1 44	39.8 76	11.3 89	2.49 45	4.24 98	4.26 30	30.0 97	54.7 97	40.8 102
Second-order prior [8]	85.8	3.49 77	18.6 80	1.79 78	9.83 80	40.6 81	7.83 84	14.1 87	39.0 97	9.82 81	6.20 83	31.4 78	3.83 84	34.7 86	49.4 97	27.6 78	18.7 98	52.1 113	11.4 90	9.18 111	3.60 90	20.1 103	19.1 62	48.2 77	24.4 65
Learning Flow [11]	85.9	3.56 83	18.2 78	1.56 70	8.71 68	41.5 83	6.17 68	14.5 90	37.8 95	11.8 89	7.92 93	41.1 95	5.02 92	40.9 109	51.7 105	42.4 105	15.4 84	47.2 105	11.4 90	2.73 51	6.19 114	7.64 56	23.0 78	49.9 80	28.9 80
Ad-TV-NDC [36]	87.8	10.3 115	20.2 88	18.1 124	38.1 114	53.0 108	43.3 116	28.0 110	45.6 106	35.7 107	20.6 110	48.9 108	23.8 111	29.1 52	42.5 56	19.1 37	13.7 58	36.1 54	9.46 75	2.03 39	1.43 38	4.38 32	41.4 116	65.3 114	57.0 118
CNN-flow-warp+ref [117]	88.4	4.85 101	24.6 103	2.62 102	13.2 93	41.8 85	12.9 93	17.7 99	39.6 99	25.0 98	8.67 97	44.8 100	5.78 95	38.1 100	48.2 88	43.5 108	13.7 58	40.4 83	9.32 72	1.80 34	1.29 37	9.16 63	33.2 105	57.7 105	42.9 103
StereoOF-V1MT [119]	89.8	4.08 88	23.0 94	1.49 63	10.4 88	53.3 109	4.35 39	16.3 94	49.5 111	5.71 61	7.37 90	48.5 106	4.03 86	46.7 113	62.9 116	42.9 107	21.9 108	64.7 119	17.0 103	1.58 28	1.87 53	9.43 65	33.2 105	66.2 115	36.5 95
BlockOverlap [61]	92.0	4.14 92	17.3 73	3.50 105	23.3 103	43.1 93	25.5 105	21.0 102	37.2 90	27.8 101	13.1 104	39.0 93	13.7 106	28.8 48	38.6 36	28.2 80	18.7 98	37.2 58	13.3 97	12.6 120	6.40 117	40.8 121	26.8 93	45.8 67	43.3 105
Shiralkar [42]	93.7	4.11 90	23.3 95	1.52 66	8.88 71	44.5 96	5.07 49	14.5 90	41.9 102	6.96 67	7.32 89	44.7 99	4.37 89	38.8 105	55.2 111	33.1 90	26.7 115	60.7 114	18.5 108	10.4 114	3.38 89	32.9 115	26.7 92	61.6 109	29.5 83
SegOF [10]	94.1	6.07 109	25.2 108	3.62 107	36.7 112	55.3 110	41.7 114	26.1 106	43.8 104	39.2 110	15.2 106	45.4 101	12.3 104	46.5 112	56.0 112	57.5 118	18.2 96	49.9 108	14.9 100	0.19 4	0.71 25	0.86 7	31.1 102	52.9 89	35.9 94
StereoFlow [44]	94.8	28.4 128	55.1 129	37.7 128	81.1 129	92.6 129	77.8 128	65.0 127	82.9 129	51.1 123	69.6 129	90.7 129	65.5 126	52.7 120	67.5 120	44.9 112	8.13 4	33.5 43	6.60 17	0.05 2	0.37 18	0.17 2	32.2 103	54.8 98	40.4 101
HBpMotionGpu [43]	95.4	5.00 102	21.6 91	2.81 104	31.1 109	49.5 103	35.0 109	26.3 108	45.3 105	37.1 109	9.18 100	39.3 94	7.65 101	33.7 78	45.6 77	32.2 88	15.7 86	37.4 61	9.89 80	5.71 92	4.19 97	12.5 78	33.4 107	56.3 101	47.8 110
SPSA-learn [13]	96.4	5.52 106	25.3 110	4.12 109	25.2 106	50.0 104	26.8 106	25.1 105	45.7 107	36.7 108	19.0 107	54.2 110	20.8 108	38.6 102	48.4 90	44.4 110	17.9 94	45.3 101	17.6 105	1.60 29	0.54 22	5.27 40	39.6 113	57.9 106	53.3 115
2bit-BM-tele [98]	96.8	5.17 104	23.4 96	3.54 106	16.3 97	40.3 78	16.7 98	15.2 92	34.6 83	14.2 93	8.49 96	37.6 92	6.75 99	32.8 73	44.5 68	28.6 84	24.3 111	43.9 96	22.5 114	15.6 125	8.72 123	50.0 126	26.2 90	50.8 81	37.5 98
FlowNet2 [122]	97.6	7.84 111	30.7 111	2.58 101	41.4 116	65.2 116	44.4 117	29.6 112	48.0 109	46.8 118	5.31 79	24.0 46	3.06 80	47.8 114	64.8 119	36.3 101	17.8 93	44.3 99	13.4 98	2.93 56	8.71 122	5.22 38	30.2 98	61.7 110	28.8 79
IAOF2 [51]	98.2	4.13 91	20.4 90	2.02 85	18.0 99	45.9 99	18.0 99	17.1 98	37.3 91	21.4 96	46.4 120	57.8 112	56.1 123	37.4 97	48.8 92	35.9 100	25.5 112	42.7 91	20.4 111	6.62 98	3.04 86	15.3 94	26.8 93	51.0 82	37.9 99
Black & Anandan [4]	98.9	5.52 106	25.2 108	4.71 111	24.4 105	52.8 106	24.4 104	26.8 109	48.3 110	34.4 105	20.9 111	60.4 113	22.4 110	38.7 104	49.7 98	42.8 106	18.9 100	49.4 106	17.1 104	1.78 33	2.57 79	3.30 22	36.0 109	57.3 104	49.5 111
Filter Flow [19]	99.1	5.13 103	23.5 97	2.40 99	20.5 101	51.3 105	19.6 100	23.3 104	42.6 103	35.3 106	27.2 113	48.8 107	28.3 113	39.4 106	49.1 93	44.6 111	17.9 94	40.5 84	11.8 93	7.39 103	7.67 120	11.5 73	26.6 91	46.0 68	33.9 91
Modified CLG [34]	101.1	7.42 110	31.9 113	5.50 112	31.7 110	52.9 107	37.6 112	28.4 111	50.8 112	40.4 112	20.3 109	60.5 114	21.3 109	39.5 108	51.2 101	42.2 104	14.2 68	45.8 104	11.5 92	3.24 59	1.70 51	9.31 64	40.3 114	65.1 113	54.7 117
IAOF [50]	102.1	5.70 108	24.0 99	3.65 108	30.0 108	48.7 101	33.9 108	26.2 107	47.8 108	29.5 102	28.0 114	51.3 109	32.9 114	37.3 96	50.2 99	34.4 95	26.2 113	50.9 111	18.0 107	5.85 94	1.63 44	11.7 74	36.3 110	59.4 108	51.9 113
GraphCuts [14]	104.0	5.45 105	24.7 104	2.64 103	24.3 104	55.8 112	21.9 103	21.4 103	40.8 101	32.4 104	9.25 102	46.4 102	6.31 97	38.6 102	51.7 105	33.8 93	28.8 118	39.7 75	18.7 109	12.1 119	2.87 83	35.1 118	38.5 111	58.4 107	53.9 116
2D-CLG [1]	104.5	14.0 119	40.7 120	8.09 117	45.8 118	59.5 113	54.5 121	36.8 118	60.4 119	47.3 119	48.9 122	75.1 123	54.2 122	44.9 110	54.8 109	52.5 115	19.0 101	50.3 110	17.8 106	1.26 23	0.07 7	4.43 33	47.4 121	71.2 121	59.9 122
GroupFlow [9]	104.9	8.95 114	33.2 114	7.07 114	43.6 117	70.7 121	45.5 118	32.7 114	59.8 118	42.4 115	13.2 105	46.6 103	12.4 105	51.1 117	70.0 122	34.2 94	30.8 120	62.8 116	33.8 121	1.54 27	2.56 78	4.14 27	39.0 112	67.0 117	47.4 109
UnFlow [129]	106.0	22.1 126	43.6 123	7.44 116	52.6 122	73.8 125	54.3 120	47.7 124	74.9 127	47.5 120	26.4 112	68.7 118	24.7 112	64.5 125	75.5 126	65.6 125	28.5 117	67.0 123	27.1 118	0.19 4	1.87 53	0.05 1	30.4 100	62.4 111	36.8 96
Nguyen [33]	107.0	8.19 112	31.4 112	4.40 110	54.9 123	55.7 111	70.2 124	33.6 116	54.6 113	43.3 116	43.5 119	60.9 115	50.5 120	45.1 111	54.9 110	54.2 116	21.1 105	49.4 106	21.0 112	2.92 55	1.87 53	7.39 54	44.7 119	66.2 115	58.5 120
Horn & Schunck [3]	108.2	8.56 113	35.5 115	7.11 115	29.4 107	65.4 117	28.1 107	33.4 115	64.4 121	41.2 114	30.6 115	67.2 117	33.6 115	49.1 116	61.3 113	55.5 117	26.4 114	64.7 119	26.1 117	3.02 57	3.95 92	2.44 16	48.8 122	75.0 123	58.8 121
SILK [79]	110.2	10.7 116	35.7 116	14.6 121	37.7 113	64.2 114	42.5 115	32.3 113	59.3 116	40.6 113	19.9 108	56.8 111	20.4 107	51.6 119	62.6 115	59.6 121	27.2 116	63.0 117	23.2 115	4.92 83	1.68 50	13.1 80	46.9 120	71.6 122	61.7 124
TI-DOFE [24]	113.7	21.2 124	43.7 124	34.5 127	64.6 127	71.9 124	76.5 127	47.1 123	75.6 128	53.7 124	57.3 124	76.4 124	65.6 127	51.5 118	63.9 117	60.0 122	30.4 119	65.8 122	33.2 119	2.24 42	1.65 47	5.22 38	59.1 126	82.1 128	71.2 127
Periodicity [78]	114.0	11.0 117	41.5 121	5.85 113	35.3 111	64.5 115	36.8 111	51.0 125	55.5 114	61.7 126	49.6 123	81.4 126	48.4 119	66.0 127	83.6 129	59.0 119	46.1 125	76.4 127	43.0 125	1.67 32	5.33 108	8.13 58	48.8 122	78.9 125	57.5 119
Heeger++ [104]	115.0	24.4 127	45.8 125	9.72 119	47.8 120	85.9 128	37.8 113	66.2 128	69.8 124	75.2 127	59.7 126	88.6 128	56.9 124	66.6 128	80.3 128	62.4 124	65.6 129	87.3 129	67.1 129	4.14 71	1.26 36	7.39 54	41.9 117	68.8 118	43.4 107
SLK [47]	117.6	17.8 122	50.1 128	21.7 125	62.2 126	77.8 127	74.7 126	40.4 121	72.7 126	48.1 121	66.2 127	73.9 121	76.1 129	60.9 123	71.2 123	72.9 129	33.1 121	68.3 125	35.4 123	5.99 95	1.09 30	11.4 72	60.5 128	81.9 127	75.0 128
FFV1MT [106]	118.1	22.0 125	42.2 122	9.20 118	41.0 115	76.9 126	36.1 110	66.5 129	71.9 125	79.2 128	58.5 125	87.7 127	57.1 125	64.8 126	76.5 127	70.3 128	64.8 128	85.8 128	65.3 128	4.70 78	4.86 103	11.3 71	41.9 117	68.8 118	43.4 107
Adaptive flow [45]	119.1	16.0 120	36.3 117	17.5 123	57.9 124	67.3 118	64.2 123	38.7 120	59.2 115	48.8 122	39.9 117	69.2 119	44.2 118	49.0 115	62.0 114	43.6 109	39.1 124	62.2 115	34.3 122	34.2 128	23.4 128	82.8 127	40.6 115	64.9 112	51.9 113
FOLKI [16]	120.3	13.4 118	45.9 126	16.0 122	48.5 121	67.4 119	57.8 122	36.6 117	66.2 122	40.3 111	32.2 116	66.9 116	37.2 116	52.9 121	64.2 118	60.1 123	34.7 122	65.7 121	40.2 124	12.9 121	7.56 119	33.8 117	55.3 125	78.0 124	70.7 126
PGAM+LK [55]	122.3	17.6 121	48.4 127	26.7 126	45.8 118	71.8 123	49.9 119	38.3 119	67.6 123	44.6 117	42.8 118	79.5 125	42.6 117	56.5 122	69.9 121	59.0 119	37.8 123	70.5 126	33.6 120	23.5 126	15.0 127	48.8 125	54.6 124	80.9 126	61.1 123
Pyramid LK [2]	122.8	19.8 123	36.7 118	40.3 129	61.8 125	68.8 120	74.6 125	43.5 122	63.7 120	58.2 125	46.9 121	72.7 120	54.1 121	61.5 124	74.5 125	65.8 126	51.3 126	64.1 118	50.1 126	10.8 116	6.30 115	31.9 114	68.2 129	84.9 129	84.8 129
HCIC-L [99]	125.5	29.6 129	37.7 119	11.4 120	77.3 128	71.7 122	90.9 129	63.0 126	59.4 117	83.6 129	66.9 128	74.8 122	69.8 128	68.1 129	74.1 124	66.1 127	54.4 127	67.4 124	52.5 127	58.9 129	43.5 129	89.4 128	59.4 127	70.0 120	67.4 125
AdaConv-v1 [126]	130.0	82.5 130	78.4 130	94.0 130	98.6 130	99.3 130	97.9 130	99.9 130	99.9 130	99.9 130	97.4 130	96.9 130	99.7 130	100.0 130	99.9 130	99.8 130	93.4 130	95.5 130	93.4 130	86.5 130	85.7 130	99.4 130	99.9 130	99.9 130	99.9 130
SepConv-v1 [127]	130.0	82.5 130	78.4 130	94.0 130	98.6 130	99.3 130	97.9 130	99.9 130	99.9 130	99.9 130	97.4 130	96.9 130	99.7 130	100.0 130	99.9 130	99.8 130	93.4 130	95.5 130	93.4 130	86.5 130	85.7 130	99.4 130	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.