Optical flow evaluation results: R2.5 angle error

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

R2.5 angle error	avg.	Army (Hidden texture) GT im0 im1			Mequon (Hidden texture) GT im0 im1			Schefflera (Hidden texture) GT im0 im1			Wooden (Hidden texture) GT im0 im1			Grove (Synthetic) GT im0 im1			Urban (Synthetic) GT im0 im1			Yosemite (Synthetic) GT im0 im1			Teddy (Stereo) GT im0 im1
	rank	all	disc	untext	all	disc	untext	all	disc	untext	all	disc	untext	all	disc	untext	all	disc	untext	all	disc	untext	all	disc	untext
NNF-Local [87]	13.6	20.0 3	43.3 3	13.0 3	15.9 10	45.3 25	16.1 14	12.5 4	34.1 9	12.8 9	9.68 19	32.0 11	7.01 23	23.3 4	30.0 4	16.2 4	22.0 32	45.4 9	23.8 61	25.8 20	44.8 6	17.6 17	4.37 9	14.4 27	0.48 1
NN-field [71]	14.3	22.1 7	44.0 5	14.6 4	18.4 23	47.3 37	19.2 27	12.5 4	32.9 6	14.0 14	6.57 2	28.2 4	3.57 2	23.4 5	30.1 5	15.9 3	17.9 15	36.8 1	15.8 4	35.9 50	51.5 33	27.4 48	4.58 12	15.3 30	0.55 2
TC/T-Flow [76]	19.0	19.9 2	46.9 10	10.2 1	16.0 11	47.8 39	13.9 5	13.0 8	36.6 20	11.1 4	8.90 12	35.0 34	6.10 12	27.2 17	35.7 22	21.0 14	15.7 4	47.2 17	15.8 4	21.0 8	39.2 2	42.1 75	7.86 40	19.5 43	10.9 53
ALD-Flow [66]	19.4	21.6 6	46.0 7	15.9 6	15.5 8	41.3 9	15.6 10	13.1 9	35.2 12	12.2 5	8.17 10	33.4 20	5.35 9	28.1 24	37.3 33	20.3 10	16.4 6	47.4 19	16.0 6	26.5 23	45.4 8	41.8 73	8.39 44	22.3 53	11.3 55
ComponentFusion [96]	19.7	20.0 3	46.3 8	14.8 5	16.6 14	40.3 6	18.5 24	11.7 3	33.6 7	10.9 3	7.09 5	35.2 36	4.45 5	27.6 20	36.0 25	21.4 17	21.5 28	54.1 51	20.2 30	31.8 43	56.8 60	16.2 10	5.46 22	12.8 20	7.47 27
nLayers [57]	20.8	22.7 9	40.3 2	18.4 9	27.2 88	45.9 29	30.4 88	15.7 22	35.4 14	21.4 59	8.12 9	26.6 2	6.21 13	22.5 2	29.0 3	15.5 2	19.9 20	40.8 4	17.8 9	31.3 39	52.6 42	16.9 15	4.26 6	11.2 7	5.84 6
OFLAF [77]	21.3	29.6 34	47.4 12	24.8 18	17.8 19	40.9 7	18.3 22	11.6 2	26.9 2	13.2 11	11.5 39	29.3 8	8.97 59	23.7 6	30.9 6	16.5 5	22.2 36	41.5 5	19.3 20	30.2 34	50.6 27	32.9 54	5.94 27	13.8 22	8.44 36
WLIF-Flow [93]	21.6	27.0 19	47.0 11	23.1 16	21.7 43	46.6 35	23.3 41	14.8 13	36.2 16	16.3 22	9.33 16	32.2 13	6.64 17	27.7 21	35.1 20	23.4 29	21.6 29	47.7 21	19.2 17	28.7 30	45.3 7	32.0 53	4.50 11	11.2 7	6.43 11
RNLOD-Flow [121]	21.7	20.7 5	43.8 4	19.4 10	18.1 20	45.9 29	17.0 19	12.7 6	34.1 9	12.3 6	7.38 6	28.7 6	4.87 6	26.5 13	34.9 18	20.9 12	20.3 21	46.2 12	20.2 30	43.1 74	60.3 70	47.5 92	4.98 19	12.7 19	6.55 14
HAST [109]	22.0	19.5 1	40.1 1	11.6 2	16.1 12	39.7 4	14.8 8	8.49 1	21.2 1	7.09 1	6.79 3	29.0 7	3.66 3	21.6 1	28.3 2	13.8 1	24.3 56	48.5 25	24.3 63	41.7 71	59.8 69	63.0 116	6.05 29	11.5 13	8.72 39
MDP-Flow2 [68]	23.9	35.3 47	55.4 41	30.2 43	14.2 3	39.7 4	14.2 7	13.6 10	31.4 3	12.9 10	12.2 45	34.1 28	8.19 46	27.7 21	35.0 19	22.1 20	22.4 40	45.4 9	21.5 43	27.1 24	54.1 47	16.5 11	5.87 26	14.0 24	4.25 3
OAR-Flow [125]	24.5	25.6 12	54.9 38	22.4 14	18.7 24	44.8 22	19.1 25	17.2 35	43.7 44	18.0 28	8.53 11	31.6 10	5.65 10	29.7 34	39.3 38	20.9 12	14.5 2	47.3 18	13.4 2	14.1 2	38.0 1	20.7 30	9.84 58	21.1 49	16.1 70
Layers++ [37]	25.1	28.0 31	48.8 15	30.9 46	23.3 58	45.6 28	25.5 66	13.7 11	31.4 3	18.1 29	8.08 8	24.9 1	5.87 11	22.9 3	28.1 1	19.6 8	22.2 36	46.3 14	20.7 34	39.6 66	55.9 56	35.0 56	4.16 3	9.78 2	6.81 16
TC-Flow [46]	25.2	24.4 11	52.2 31	22.6 15	11.8 2	38.8 3	11.2 1	12.7 6	35.7 15	9.84 2	9.88 21	34.9 32	7.49 31	28.9 31	38.6 37	21.2 16	20.6 23	52.1 36	21.6 44	22.6 15	47.1 12	36.3 59	9.05 50	21.5 51	12.4 60
LME [70]	27.4	31.5 36	51.4 26	21.0 13	14.7 5	36.9 2	15.7 11	16.0 27	35.2 12	19.8 49	11.8 42	36.6 40	8.08 41	28.6 28	36.0 25	25.5 40	21.1 26	49.0 27	19.8 27	29.8 33	50.0 24	21.5 32	6.61 31	15.1 28	7.95 32
AGIF+OF [85]	27.4	26.3 13	48.8 15	24.1 17	24.9 68	52.0 67	26.2 68	16.0 27	39.4 30	19.7 46	8.96 13	32.0 11	6.64 17	26.7 15	33.7 12	21.5 19	21.4 27	49.4 28	18.5 13	28.2 27	49.6 21	30.5 52	4.63 15	11.3 12	7.30 24
PH-Flow [101]	28.3	26.7 15	51.6 27	25.6 24	21.7 43	49.4 49	23.8 47	15.5 17	37.6 23	19.5 42	10.2 26	33.8 24	7.44 30	26.4 11	33.7 12	21.0 14	22.0 32	50.3 30	20.4 32	38.8 60	48.4 17	45.4 82	4.26 6	11.2 7	6.38 10
FC-2Layers-FF [74]	28.5	26.9 18	48.6 14	28.3 37	22.1 47	48.8 44	23.8 47	14.1 12	32.4 5	19.6 44	9.10 14	28.3 5	6.47 16	25.5 7	31.7 7	23.0 27	23.3 45	47.7 21	21.8 45	44.1 77	56.3 59	46.0 87	3.54 1	9.08 1	5.44 5
Classic+CPF [83]	29.5	27.1 23	51.2 25	25.5 23	23.9 63	51.4 63	25.1 61	15.9 24	39.3 29	19.4 40	9.17 15	32.8 16	6.69 19	27.3 18	34.5 16	23.9 30	21.0 25	48.7 26	18.0 11	35.6 49	49.9 23	45.7 84	4.25 5	10.7 4	6.61 15
NNF-EAC [103]	29.8	34.9 42	54.8 36	29.9 42	15.6 9	41.7 11	15.9 12	15.1 16	34.8 11	15.7 16	12.4 49	35.1 35	8.33 49	28.1 24	35.7 22	22.9 25	24.5 59	46.2 12	22.7 55	31.6 42	49.1 19	20.1 28	7.54 38	17.1 38	7.44 26
COFM [59]	29.9	22.1 7	49.1 17	16.6 7	18.2 21	43.5 16	19.2 27	15.9 24	38.3 26	21.5 60	7.02 4	32.6 14	4.40 4	31.4 42	37.9 34	35.0 78	22.1 34	46.4 15	18.3 12	28.7 30	45.9 10	45.5 83	9.25 51	15.5 32	15.7 69
IROF++ [58]	30.3	27.4 25	50.7 21	26.7 30	22.1 47	50.1 58	24.1 51	16.3 30	40.0 34	19.6 44	10.6 33	34.3 29	7.72 36	27.9 23	35.7 22	22.5 21	22.3 38	54.1 51	19.9 28	25.5 19	49.6 21	11.5 6	5.49 23	14.1 25	6.54 13
Sparse-NonSparse [56]	30.4	26.8 17	51.9 30	26.8 31	22.2 52	49.0 47	24.6 57	15.6 19	39.4 30	19.0 35	9.46 18	33.6 22	7.07 25	29.0 32	37.2 32	24.3 35	21.7 31	50.4 31	19.5 25	34.0 45	45.8 9	41.6 71	4.44 10	10.9 5	6.96 20
FESL [72]	31.3	27.0 19	46.3 8	31.2 48	26.0 79	51.8 66	26.9 72	15.8 23	37.0 21	19.5 42	7.89 7	30.7 9	5.17 8	26.6 14	33.8 14	22.6 22	20.3 21	45.8 11	19.3 20	39.9 67	61.2 74	35.6 57	5.04 20	12.5 18	6.48 12
Efficient-NL [60]	31.9	23.3 10	44.7 6	17.6 8	24.6 67	51.6 65	25.4 65	15.0 15	36.2 16	17.5 26	9.92 22	33.1 18	6.94 21	26.4 11	34.0 15	20.3 10	27.2 68	49.4 28	22.6 50	37.6 55	50.5 26	37.1 60	6.98 36	16.2 34	8.16 33
LSM [39]	33.4	26.5 14	50.8 22	27.0 32	22.1 47	49.4 49	24.4 55	15.6 19	38.2 25	19.4 40	10.1 23	33.0 17	7.43 29	28.6 28	36.3 27	24.7 36	22.8 43	50.5 32	20.9 38	40.3 69	49.3 20	45.8 86	4.76 17	12.0 15	6.93 19
PMMST [114]	34.0	42.4 71	58.9 45	40.2 67	22.1 47	45.3 25	24.7 60	17.9 41	38.4 27	18.8 32	12.2 45	28.1 3	8.26 47	25.7 8	32.7 10	18.5 7	22.3 38	45.3 8	20.8 36	28.5 29	50.7 28	18.3 19	8.51 47	16.8 35	8.73 40
Classic+NL [31]	34.8	27.2 24	47.8 13	28.4 38	22.0 46	49.4 49	24.1 51	15.5 17	37.4 22	19.8 49	10.4 30	33.8 24	7.40 28	28.5 26	36.4 28	24.2 34	23.3 45	52.2 37	21.1 39	43.9 75	52.5 39	44.6 79	4.60 14	11.2 7	7.04 22
Ramp [62]	34.8	27.0 19	52.3 32	26.1 28	22.3 54	49.1 48	24.6 57	15.6 19	37.8 24	19.8 49	10.5 32	33.6 22	7.61 33	28.6 28	36.6 29	24.0 31	23.6 48	51.2 33	21.8 45	38.2 56	44.5 4	46.1 88	4.86 18	12.1 16	7.13 23
2DHMM-SAS [92]	37.6	26.7 15	51.8 29	25.6 24	22.2 52	51.3 61	23.8 47	17.8 39	42.3 39	20.0 54	10.4 30	34.6 31	7.52 32	28.5 26	36.6 29	24.0 31	22.7 42	54.5 54	20.6 33	39.0 62	47.8 15	45.1 80	5.51 24	13.9 23	7.73 30
FMOF [94]	38.0	27.8 30	50.4 20	27.1 34	26.2 81	52.5 71	27.4 75	15.9 24	36.4 19	21.7 61	9.71 20	32.7 15	6.98 22	27.3 18	34.6 17	24.1 33	23.7 50	47.6 20	19.7 26	38.4 57	55.4 53	48.9 95	5.80 25	14.1 25	7.00 21
SVFilterOh [111]	39.2	39.6 61	54.6 35	39.8 66	23.8 62	44.7 21	24.0 50	17.3 36	33.8 8	18.8 32	10.2 26	36.4 39	4.93 7	25.9 10	32.1 8	19.6 8	24.7 60	46.7 16	22.9 58	52.0 99	76.3 117	59.2 110	4.23 4	10.9 5	5.01 4
S2D-Matching [84]	40.0	27.7 27	50.2 19	28.4 38	22.1 47	48.8 44	24.3 53	16.5 32	40.0 34	19.3 38	10.6 33	33.8 24	7.78 37	29.1 33	36.7 31	25.0 37	24.4 58	53.0 47	22.6 50	47.0 88	53.6 44	50.8 100	4.58 12	11.2 7	7.54 28
ProbFlowFields [128]	40.5	33.9 38	68.5 68	30.9 46	20.3 33	45.3 25	22.1 37	19.2 48	44.8 47	22.9 65	11.7 40	39.3 48	8.91 58	31.0 40	40.1 42	23.3 28	16.8 7	47.8 23	19.2 17	23.7 18	55.9 56	23.9 42	8.39 44	22.4 54	9.82 49
SimpleFlow [49]	40.9	28.6 33	51.6 27	29.5 41	25.0 69	51.3 61	28.2 80	18.6 46	43.0 42	23.3 66	10.1 23	33.5 21	7.04 24	29.9 35	37.9 34	26.2 42	27.8 73	52.2 37	24.0 62	35.1 48	47.9 16	29.7 51	4.66 16	12.4 17	6.92 18
PMF [73]	42.0	37.7 56	58.3 43	27.3 36	19.4 26	45.0 24	18.3 22	16.2 29	39.9 33	14.0 14	13.6 61	35.7 37	8.03 40	25.8 9	32.5 9	17.1 6	30.2 80	57.0 64	31.6 82	58.9 107	74.7 110	55.9 107	3.95 2	10.3 3	6.15 9
Adaptive [20]	42.6	27.0 19	52.6 33	19.8 11	21.9 45	47.8 39	22.6 39	20.5 53	47.8 55	19.7 46	10.3 28	39.9 51	6.31 15	45.6 112	52.6 110	51.3 111	17.4 8	48.2 24	13.9 3	34.8 47	56.9 62	19.8 26	6.04 28	15.2 29	7.54 28
TV-L1-MCT [64]	43.4	27.5 26	49.4 18	27.0 32	26.5 82	52.8 76	27.8 79	16.8 33	39.1 28	21.8 62	10.6 33	33.8 24	7.82 38	30.3 37	38.1 36	28.7 58	24.7 60	53.4 49	23.4 59	27.4 25	52.5 39	19.4 23	7.28 37	15.3 30	11.4 57
Correlation Flow [75]	43.5	36.6 53	55.3 40	34.4 56	16.6 14	44.4 20	14.8 8	18.3 45	42.9 41	12.7 8	12.4 49	39.7 50	8.46 53	32.2 45	40.6 45	25.2 38	29.0 76	54.2 53	29.7 79	39.1 63	52.1 37	47.4 91	6.53 30	16.1 33	6.88 17
IROF-TV [53]	44.0	30.9 35	54.8 36	31.6 50	22.8 57	50.5 59	25.1 61	16.9 34	40.8 36	20.8 56	14.0 63	43.7 65	10.1 63	31.2 41	39.5 39	28.6 55	26.8 67	58.7 73	25.6 67	18.8 4	48.4 17	8.08 5	5.28 21	13.6 21	7.77 31
AggregFlow [97]	44.3	36.3 52	52.7 34	35.7 58	26.6 83	52.6 74	26.7 70	23.3 66	48.2 57	28.9 84	12.1 43	34.9 32	8.63 55	30.2 36	40.3 44	21.4 17	15.9 5	38.3 2	16.8 7	26.1 22	47.3 13	16.8 13	12.6 71	20.3 45	20.0 80
Occlusion-TV-L1 [63]	44.8	34.3 41	58.5 44	25.1 19	20.0 29	46.5 34	20.8 33	22.3 63	49.9 58	20.6 55	12.6 55	41.7 57	8.41 52	35.2 64	44.9 73	32.3 69	17.7 11	52.6 42	21.1 39	28.2 27	52.5 39	13.0 7	9.66 54	23.7 58	10.6 51
Aniso-Texture [82]	48.0	28.5 32	50.9 23	32.7 53	21.2 37	41.7 11	25.3 64	17.6 38	41.2 37	21.1 58	5.72 1	33.2 19	2.54 1	35.1 63	43.7 60	31.3 67	23.6 48	53.6 50	22.6 50	62.2 114	75.8 115	53.5 104	6.89 35	17.4 39	8.20 34
Classic++ [32]	48.1	27.7 27	51.0 24	28.7 40	21.5 42	45.9 29	24.3 53	18.1 44	44.3 45	19.9 52	10.3 28	37.7 42	7.14 26	33.4 48	44.1 63	27.9 51	24.0 54	57.9 69	21.4 42	46.3 84	55.6 54	49.7 97	8.45 46	20.7 46	9.69 48
MDP-Flow [26]	49.3	35.5 48	65.0 54	32.4 51	20.6 35	43.8 18	24.4 55	18.0 43	43.4 43	19.9 52	14.9 71	41.8 59	11.5 72	30.8 39	39.6 40	25.3 39	23.8 52	57.4 67	22.2 47	31.0 37	59.3 67	16.8 13	10.8 66	26.5 63	10.9 53
DeepFlow2 [108]	49.7	39.1 59	66.4 58	44.4 75	20.1 31	47.9 41	20.9 34	23.8 69	52.8 65	26.3 76	12.2 45	43.2 64	7.64 34	31.4 42	41.7 52	22.9 25	18.2 17	52.4 39	17.9 10	29.6 32	44.7 5	39.0 64	16.2 85	31.2 85	22.7 86
OFH [38]	50.1	41.9 70	61.6 49	48.5 81	14.7 5	42.7 14	14.1 6	17.4 37	47.6 53	12.6 7	10.6 33	38.5 44	8.39 51	34.8 61	43.7 60	34.5 76	27.2 68	61.9 87	29.5 78	21.3 11	57.6 63	21.4 31	12.2 70	29.8 77	16.2 71
BriefMatch [124]	51.3	34.1 40	59.4 46	30.2 43	17.1 16	43.6 17	16.1 14	14.8 13	36.3 18	13.4 12	9.41 17	34.3 29	6.26 14	33.4 48	41.2 49	31.8 68	40.3 107	63.5 89	42.7 110	47.2 89	61.1 72	59.1 109	12.7 73	23.4 55	21.6 84
CPM-Flow [116]	52.1	35.2 43	67.0 60	25.3 20	25.8 76	53.6 80	28.2 80	25.5 73	58.5 85	27.5 77	12.4 49	48.0 78	8.11 44	33.9 53	44.2 64	27.1 45	17.6 9	51.8 34	19.1 15	21.3 11	51.7 34	22.6 38	9.92 59	27.2 68	11.3 55
PGM-C [120]	52.8	35.2 43	67.1 62	25.4 22	25.8 76	53.6 80	28.2 80	25.8 76	59.3 88	27.5 77	12.4 49	48.1 79	8.16 45	33.9 53	44.3 67	27.1 45	17.7 11	52.8 46	19.1 15	20.8 7	50.3 25	22.4 36	9.82 57	27.2 68	11.5 59
S2F-IF [123]	52.8	35.7 49	69.0 71	26.6 29	24.1 65	54.0 87	26.0 67	25.6 75	60.9 90	25.9 72	12.4 49	46.6 75	8.38 50	34.3 57	44.2 64	27.7 50	18.0 16	53.2 48	19.2 17	21.2 10	51.7 34	22.9 39	8.97 49	24.9 61	9.11 44
CostFilter [40]	54.4	43.6 77	65.7 56	40.8 68	20.8 36	45.9 29	21.0 35	18.8 47	44.9 48	18.3 31	17.3 80	39.5 49	13.9 82	26.7 15	32.8 11	22.7 24	30.9 83	60.0 81	31.6 82	59.7 108	81.5 124	59.3 111	4.31 8	11.9 14	5.93 7
EpicFlow [102]	54.7	35.2 43	67.2 63	25.3 20	25.8 76	53.8 83	28.2 80	26.1 78	60.1 89	27.5 77	12.4 49	48.1 79	8.10 43	34.2 56	44.5 69	28.0 52	17.8 13	52.7 44	19.4 22	21.0 8	52.1 37	22.5 37	10.4 62	27.3 70	12.7 62
RFlow [90]	54.7	44.4 79	75.2 92	50.5 85	16.5 13	42.1 13	17.2 20	21.4 57	52.2 61	16.0 17	11.3 38	36.2 38	7.25 27	35.2 64	44.3 67	32.3 69	24.3 56	55.6 59	22.6 50	38.6 58	55.3 51	41.6 71	13.4 75	29.9 78	16.9 75
FlowFields+ [130]	55.0	35.8 51	69.2 73	26.0 27	25.6 74	55.3 93	27.7 77	26.7 79	62.9 95	27.7 81	12.7 56	45.9 73	8.84 57	34.0 55	44.2 64	26.6 43	17.6 9	54.9 56	19.0 14	20.6 6	53.9 46	22.3 35	9.31 53	26.5 63	8.79 41
TV-L1-improved [17]	56.1	27.7 27	57.9 42	20.5 12	18.2 21	44.9 23	19.1 25	19.4 49	47.7 54	17.0 25	10.1 23	38.5 44	6.75 20	35.9 72	46.0 79	27.3 47	43.7 113	70.2 110	47.5 114	51.4 96	60.5 71	50.3 99	10.3 61	26.8 67	10.8 52
FlowFields [110]	56.9	35.7 49	68.7 69	25.8 26	25.6 74	55.1 92	27.7 77	26.8 82	62.8 94	27.6 80	13.0 57	46.8 76	9.13 60	34.6 60	44.9 73	28.1 53	17.8 13	54.9 56	19.4 22	21.3 11	54.8 49	24.2 43	9.25 51	26.4 62	8.47 37
Kuang [131]	57.6	33.2 37	67.7 64	27.1 34	23.4 60	56.2 96	23.7 45	24.5 71	62.6 93	24.2 69	11.7 40	44.2 67	7.99 39	35.7 70	46.3 83	28.6 55	23.7 50	59.1 75	25.0 66	20.2 5	50.9 30	21.7 33	10.6 63	28.3 71	14.5 66
Steered-L1 [118]	59.0	38.7 58	67.9 65	43.1 73	11.6 1	34.7 1	12.3 2	16.3 30	41.3 38	13.9 13	12.1 43	38.6 46	8.30 48	34.5 59	43.4 57	32.5 71	29.4 78	61.1 86	25.9 68	60.6 111	67.0 92	70.1 122	15.9 84	30.6 81	24.0 88
Sparse Occlusion [54]	59.3	38.6 57	61.8 50	32.5 52	26.0 79	48.8 44	29.5 87	20.0 51	45.2 49	19.2 37	14.3 67	38.4 43	9.67 62	34.4 58	42.6 54	26.7 44	25.4 63	52.4 39	22.4 49	67.3 119	75.9 116	48.3 93	8.07 42	19.9 44	7.36 25
DeepFlow [86]	60.8	47.3 85	71.9 82	64.0 99	21.4 40	48.2 42	22.7 40	27.9 86	58.2 82	31.6 88	15.1 72	42.7 60	10.6 68	31.5 44	42.1 53	22.6 22	19.6 19	56.7 63	19.4 22	27.6 26	46.2 11	39.7 65	20.6 93	35.3 100	28.0 96
MLDP_OF [89]	60.9	48.8 88	77.3 95	52.2 87	20.1 31	49.6 54	19.3 29	23.5 68	54.6 69	18.9 34	12.3 48	38.6 46	7.65 35	33.5 51	40.9 46	29.1 62	28.4 74	55.9 60	31.6 82	49.1 93	62.2 78	60.5 113	7.91 41	16.8 35	8.95 42
TF+OM [100]	62.1	39.8 62	55.2 39	30.4 45	20.4 34	41.0 8	23.5 43	19.5 50	39.4 30	28.0 82	18.4 83	37.0 41	18.0 88	35.0 62	41.1 47	39.6 90	29.6 79	52.6 42	29.1 77	49.0 92	66.8 91	43.6 78	14.4 77	29.3 74	18.0 77
CombBMOF [113]	64.0	42.4 71	71.5 80	31.3 49	25.2 70	52.9 77	25.1 61	17.9 41	45.8 50	16.1 18	14.2 65	41.7 57	11.4 71	33.5 51	40.2 43	29.6 63	34.5 90	59.9 80	37.3 96	55.2 105	69.9 101	46.5 90	6.78 33	16.8 35	8.62 38
EPPM w/o HM [88]	64.5	43.1 75	72.4 84	38.1 64	18.7 24	52.5 71	16.9 18	21.3 56	56.2 74	17.5 26	16.2 76	45.3 72	12.7 79	33.4 48	39.6 40	30.0 64	33.5 86	65.4 95	33.8 88	45.5 79	66.1 89	65.5 119	6.88 34	18.1 40	9.16 46
Complementary OF [21]	65.8	51.9 95	74.9 91	59.3 95	14.2 3	41.6 10	13.7 3	20.4 52	46.6 51	19.7 46	22.2 90	40.8 53	21.0 93	36.0 74	43.4 57	38.5 88	33.9 89	63.8 90	31.6 82	31.1 38	51.9 36	36.2 58	18.9 90	34.4 98	29.4 98
Aniso. Huber-L1 [22]	66.6	33.9 38	65.1 55	32.8 54	34.0 94	54.0 87	40.0 94	27.9 86	55.0 71	38.4 93	15.2 73	49.9 83	12.0 74	35.3 66	44.6 70	28.5 54	23.9 53	55.9 60	20.7 34	50.6 94	62.1 77	39.7 65	8.15 43	20.7 46	8.39 35
ComplOF-FED-GPU [35]	68.8	49.5 90	75.7 94	55.3 90	15.3 7	47.0 36	13.8 4	21.1 55	52.7 64	16.1 18	17.1 79	40.6 52	14.2 83	35.7 70	45.1 76	32.5 71	35.3 93	67.5 103	34.4 90	46.5 86	59.0 66	50.8 100	12.8 74	29.6 75	16.6 74
ACK-Prior [27]	68.9	55.9 97	72.7 86	59.3 95	17.5 18	43.3 15	16.0 13	17.8 39	42.3 39	16.3 22	17.6 81	41.3 55	12.0 74	35.3 66	41.1 47	35.9 79	37.4 103	59.6 78	34.3 89	59.8 110	61.1 72	74.7 123	17.7 89	29.2 73	27.4 91
TCOF [69]	68.9	45.0 80	70.0 74	51.5 86	25.5 73	53.7 82	26.7 70	26.7 79	56.2 74	32.0 89	21.9 89	43.1 63	22.2 95	37.8 84	48.9 96	25.7 41	18.8 18	44.7 7	20.0 29	52.1 100	67.5 94	25.8 44	10.7 65	26.7 65	11.4 57
Rannacher [23]	69.0	43.1 75	71.0 78	45.2 77	24.1 65	49.4 49	26.4 69	26.0 77	56.9 78	26.0 74	14.2 65	42.7 60	10.5 67	37.1 81	47.9 91	30.7 66	32.3 84	65.2 94	27.0 72	44.0 76	56.0 58	39.7 65	7.83 39	21.2 50	9.19 47
F-TV-L1 [15]	71.2	66.8 105	84.2 104	77.3 107	27.1 86	52.0 67	29.1 86	27.2 83	57.3 80	24.2 69	24.1 94	52.0 87	19.5 91	39.3 91	47.7 90	39.3 89	24.2 55	56.5 62	24.9 64	33.2 44	53.7 45	20.1 28	6.69 32	18.6 41	5.94 8
ROF-ND [107]	71.2	49.2 89	71.5 80	49.4 83	22.5 56	46.2 33	20.6 31	21.4 57	50.0 59	18.1 29	23.1 92	53.7 91	16.0 85	35.9 72	46.1 81	28.6 55	33.5 86	58.5 71	30.3 80	60.7 112	70.5 102	60.4 112	9.67 55	20.9 48	10.2 50
LDOF [28]	71.5	41.7 69	70.7 75	47.2 80	24.0 64	53.9 86	24.6 57	26.7 79	58.4 84	25.5 71	15.8 74	57.4 97	10.2 65	36.0 74	45.3 77	34.8 77	22.1 34	58.1 70	21.3 41	30.6 35	56.8 60	23.4 40	22.5 102	38.6 107	30.2 99
SIOF [67]	72.4	50.3 93	66.0 57	47.1 79	19.8 28	48.4 43	20.7 32	29.8 93	55.1 72	32.6 91	25.9 96	48.3 81	25.0 97	37.7 82	46.4 84	36.8 82	32.9 85	58.6 72	35.6 94	37.2 54	53.1 43	18.6 20	16.8 87	33.0 90	21.3 83
LocallyOriented [52]	73.2	39.4 60	60.5 47	35.7 58	27.9 89	57.8 100	28.5 84	28.1 89	58.3 83	30.6 87	13.9 62	41.3 55	10.1 63	37.8 84	47.3 88	33.0 75	24.8 62	52.5 41	28.1 74	39.4 65	62.4 79	37.5 61	15.7 82	33.0 90	18.2 79
Second-order prior [8]	73.5	37.6 55	70.9 77	37.2 62	22.4 55	51.2 60	23.7 45	24.5 71	59.1 87	22.6 63	11.2 37	41.2 54	8.48 54	37.9 86	49.6 103	28.8 59	29.0 76	68.8 107	26.0 69	55.5 106	64.7 84	52.7 103	14.7 79	34.1 96	17.3 76
CRTflow [80]	73.6	40.9 64	72.5 85	36.2 60	21.3 39	49.4 49	21.8 36	22.9 64	57.5 81	19.0 35	14.0 63	44.7 69	10.3 66	35.4 69	45.0 75	30.4 65	46.6 117	73.4 112	53.8 118	38.8 60	65.5 86	38.2 62	19.5 92	38.5 106	27.6 94
NL-TV-NCC [25]	73.8	46.1 82	68.3 67	43.4 74	25.2 70	55.3 93	23.5 43	21.8 62	47.2 52	16.9 24	16.9 78	44.1 66	11.9 73	38.5 88	49.1 100	27.3 47	36.5 99	65.7 96	34.9 92	46.2 81	75.7 114	45.7 84	12.6 71	28.8 72	9.10 43
Brox et al. [5]	73.9	43.7 78	74.4 89	56.4 92	27.0 85	52.5 71	30.5 89	23.4 67	54.5 68	23.3 66	13.4 58	50.0 84	8.66 56	39.8 94	46.5 85	47.6 106	21.6 29	59.8 79	22.8 57	30.9 36	59.3 67	7.61 3	23.1 105	37.0 104	33.4 106
FlowNetS+ft+v [112]	74.0	36.8 54	67.0 60	39.4 65	25.3 72	52.1 69	27.6 76	27.8 85	57.2 79	35.5 92	13.5 59	50.9 85	9.44 61	40.1 95	49.5 102	36.8 82	20.9 24	57.0 64	20.8 36	46.3 84	66.3 90	41.0 69	17.4 88	34.3 97	24.1 89
DF-Auto [115]	74.0	41.6 68	64.2 53	32.8 54	42.0 99	58.8 103	49.6 100	34.8 99	62.2 92	47.2 100	20.7 87	53.4 90	14.5 84	36.2 77	44.7 71	37.2 85	15.1 3	42.9 6	17.4 8	45.3 78	69.0 98	13.0 7	24.2 108	37.7 105	31.9 102
TriangleFlow [30]	74.7	41.5 67	63.2 51	42.6 72	21.4 40	52.4 70	20.2 30	21.7 61	53.6 67	16.2 20	14.8 69	44.4 68	10.9 69	43.2 105	52.9 112	43.4 97	36.8 102	65.9 97	38.8 99	42.2 72	65.4 85	41.8 73	15.8 83	35.2 99	22.5 85
SRR-TVOF-NL [91]	74.9	47.6 86	69.1 72	41.9 71	23.3 58	52.7 75	23.3 41	25.5 73	56.8 77	25.9 72	13.5 59	47.9 77	8.09 42	36.9 80	43.8 62	32.9 74	25.8 64	57.7 68	22.6 50	62.8 116	75.2 112	49.1 96	21.0 95	29.7 76	31.6 101
DPOF [18]	75.8	45.3 81	68.9 70	37.6 63	26.7 84	56.9 97	26.9 72	24.3 70	54.6 69	26.2 75	18.6 84	54.6 94	13.6 81	33.3 47	43.1 55	28.9 60	27.6 72	60.8 83	26.3 70	47.2 89	55.3 51	76.0 125	14.3 76	31.0 84	15.3 68
Bartels [41]	76.6	48.6 87	63.2 51	61.4 98	23.4 60	44.0 19	27.1 74	21.4 57	44.6 46	23.8 68	26.2 97	43.0 62	25.4 98	36.8 78	44.7 71	41.7 94	33.7 88	60.0 81	41.7 107	52.6 101	67.2 93	61.1 114	11.2 67	23.4 55	16.3 72
Dynamic MRF [7]	77.0	49.5 90	78.0 96	55.8 91	17.2 17	47.4 38	16.5 17	21.6 60	56.3 76	16.2 20	14.8 69	46.4 74	12.5 77	41.2 99	49.0 98	45.1 101	35.3 93	70.7 111	38.5 98	37.1 53	57.7 64	55.1 105	21.3 97	36.7 103	31.2 100
SuperFlow [81]	77.2	35.2 43	61.1 48	34.4 56	35.2 95	53.5 79	42.5 95	27.7 84	52.5 63	43.5 99	27.5 98	60.5 101	27.6 99	36.0 74	43.3 56	42.2 95	22.6 41	59.2 77	22.2 47	46.2 81	62.0 76	23.4 40	21.8 100	36.0 101	32.5 104
CBF [12]	77.8	41.4 65	74.0 88	48.5 81	40.2 98	51.5 64	51.7 102	22.9 64	50.8 60	28.5 83	14.3 67	44.7 69	11.2 70	38.3 87	46.1 81	36.1 80	26.3 66	55.1 58	24.9 64	61.5 113	71.0 103	52.0 102	11.6 69	26.7 65	14.8 67
Local-TV-L1 [65]	78.0	56.8 99	79.1 97	74.5 103	39.5 97	53.8 83	46.1 97	38.1 100	66.2 98	43.1 98	23.9 93	52.9 89	21.1 94	32.3 46	41.4 51	27.5 49	23.2 44	54.8 55	22.7 55	25.8 20	47.5 14	33.4 55	26.9 111	40.5 109	40.5 115
CLG-TV [48]	78.1	41.4 65	68.0 66	40.8 68	37.0 96	53.1 78	45.3 96	30.9 94	58.7 86	40.2 95	22.8 91	62.0 102	19.3 90	39.0 90	47.6 89	38.2 87	27.2 68	61.0 85	27.1 73	46.2 81	57.8 65	29.3 50	9.74 56	24.6 60	9.11 44
CNN-flow-warp+ref [117]	78.4	42.6 73	71.2 79	49.8 84	31.6 93	53.8 83	37.3 92	32.7 96	63.8 96	42.7 97	16.0 75	55.1 95	12.1 76	38.6 89	46.0 79	43.8 99	23.3 45	59.1 75	23.6 60	23.5 17	50.9 30	21.9 34	24.2 108	36.2 102	32.9 105
HBM-GC [105]	80.0	73.5 113	79.7 98	79.4 109	30.0 90	49.7 56	33.6 90	29.3 92	47.8 55	30.4 85	35.4 101	45.2 71	33.3 101	30.5 38	35.1 20	32.6 73	34.6 91	52.0 35	35.4 93	70.9 122	80.1 120	62.6 115	8.83 48	19.1 42	13.0 63
TriFlow [95]	83.5	47.1 84	66.5 59	41.1 70	31.2 92	49.6 54	37.3 92	27.9 86	52.3 62	39.4 94	24.7 95	49.3 82	22.3 96	37.7 82	43.5 59	43.1 96	28.4 74	52.7 44	29.0 76	76.7 126	73.7 107	99.5 129	16.2 85	30.5 80	20.1 81
p-harmonic [29]	85.7	50.4 94	86.6 114	56.5 94	27.1 86	54.5 89	28.9 85	32.9 97	69.3 104	30.4 85	19.8 85	65.1 105	16.2 86	39.6 93	47.2 87	40.3 92	30.4 81	66.5 99	32.2 86	45.6 80	64.4 82	28.9 49	10.2 60	24.1 59	13.2 64
Learning Flow [11]	86.2	42.9 74	70.7 75	44.8 76	30.2 91	54.7 90	34.7 91	28.2 90	55.9 73	32.3 90	17.6 81	57.1 96	12.6 78	44.0 108	52.6 110	47.8 108	30.5 82	64.6 92	30.3 80	46.9 87	63.3 80	42.8 77	14.7 79	31.6 88	16.3 72
Fusion [6]	87.1	40.5 63	75.6 93	45.9 78	20.0 29	50.0 57	22.5 38	20.8 54	52.8 65	22.8 64	16.2 76	52.8 88	13.5 80	43.1 104	49.0 98	47.5 103	39.6 106	67.8 105	43.8 112	63.9 117	75.0 111	46.3 89	35.5 120	42.6 115	53.3 125
StereoFlow [44]	87.3	95.9 129	96.0 128	97.4 129	88.3 129	96.2 129	86.2 125	82.6 126	94.8 128	73.7 123	91.4 128	96.3 128	90.3 128	53.0 119	61.6 123	52.8 112	11.2 1	39.3 3	11.7 1	10.5 1	42.5 3	1.70 1	11.5 68	23.6 57	18.0 77
Shiralkar [42]	89.2	46.1 82	85.6 110	54.3 89	19.7 27	57.7 99	18.2 21	28.2 90	70.8 105	19.3 38	20.5 86	59.0 99	18.4 89	39.5 92	49.7 104	36.3 81	40.4 108	76.1 114	41.2 106	51.9 98	65.8 87	64.2 117	21.0 95	42.4 114	25.3 90
SegOF [10]	92.5	56.3 98	71.9 82	37.1 61	57.3 113	62.9 110	68.3 115	46.0 106	69.0 103	57.2 112	41.0 103	59.5 100	37.2 102	43.5 106	48.3 94	56.4 116	38.2 104	69.6 109	39.1 100	17.9 3	64.5 83	3.40 2	22.7 104	33.0 90	32.0 103
StereoOF-V1MT [119]	93.0	49.7 92	86.0 111	56.4 92	21.2 37	68.8 113	16.3 16	32.2 95	80.7 112	20.8 56	21.3 88	66.1 107	17.4 87	47.0 114	57.0 114	47.5 103	41.7 111	81.2 118	40.9 104	38.6 58	68.1 96	48.6 94	23.2 106	42.2 113	27.7 95
Ad-TV-NDC [36]	93.5	73.7 114	85.4 108	89.5 124	56.9 112	60.4 106	67.5 114	51.0 110	75.9 108	57.6 113	45.7 106	65.7 106	47.9 109	35.3 66	45.3 77	28.9 60	27.3 71	57.2 66	28.2 75	34.6 46	55.0 50	27.3 47	34.0 117	48.7 121	47.5 119
Modified CLG [34]	95.8	68.7 108	80.5 99	76.1 105	52.0 109	61.0 107	63.6 111	51.9 111	79.4 110	55.6 110	47.4 109	72.1 112	46.7 108	41.2 99	49.7 104	46.0 102	26.0 65	64.7 93	26.7 71	31.4 41	55.6 54	19.9 27	29.0 114	43.8 117	39.9 114
IAOF2 [51]	97.2	54.9 96	73.7 87	53.9 88	42.6 100	58.3 101	50.7 101	33.9 98	61.9 91	42.1 96	64.4 118	75.7 115	74.3 120	41.5 101	49.9 107	37.1 84	36.4 98	64.0 91	34.4 90	59.7 108	69.6 100	41.3 70	19.4 91	33.4 94	23.0 87
Filter Flow [19]	99.2	62.9 100	74.4 89	60.8 97	42.8 101	60.1 105	49.4 99	42.5 102	66.0 97	51.2 103	52.1 114	69.5 109	50.2 110	44.8 110	49.7 104	54.4 114	41.9 112	66.7 100	43.6 111	74.3 125	88.9 127	42.6 76	10.6 63	21.6 52	12.6 61
GroupFlow [9]	99.9	66.4 103	85.2 107	80.8 112	61.6 116	75.4 120	69.0 116	51.9 111	83.6 115	57.0 111	33.5 100	63.9 103	32.5 100	49.6 115	61.0 118	39.9 91	51.3 122	81.7 119	59.4 122	22.8 16	51.1 32	16.5 11	28.0 113	41.9 112	37.9 112
SPSA-learn [13]	100.8	65.1 101	87.4 117	72.7 102	45.7 105	59.3 104	53.4 105	45.2 105	74.7 107	52.2 105	41.6 104	69.9 111	42.5 104	42.7 103	48.9 96	48.7 110	38.8 105	69.0 108	42.5 109	39.1 63	61.9 75	19.3 22	36.0 121	45.6 118	48.3 120
2D-CLG [1]	100.9	77.2 117	82.3 102	75.4 104	61.5 115	65.9 112	73.7 119	63.2 120	89.6 122	60.8 118	82.8 126	88.3 123	86.8 126	43.5 106	49.3 101	54.8 115	35.1 92	67.5 103	36.1 95	21.3 11	50.8 29	15.5 9	34.4 119	46.3 120	46.0 117
BlockOverlap [61]	101.5	77.2 117	86.0 111	82.8 114	48.2 106	55.8 95	57.6 108	46.9 108	66.9 99	51.9 104	49.1 110	54.1 92	51.2 111	36.8 78	41.3 50	47.5 103	40.5 109	59.0 74	39.6 102	68.9 121	80.2 121	65.1 118	20.8 94	30.8 82	34.9 108
IAOF [50]	101.5	66.3 102	81.3 101	77.8 108	50.1 108	58.4 102	59.7 109	45.0 104	74.1 106	49.6 102	50.8 111	68.2 108	58.0 116	40.2 96	48.7 95	37.8 86	36.7 100	66.9 101	33.5 87	54.9 103	63.5 81	40.8 68	30.1 116	41.3 111	43.5 116
HBpMotionGpu [43]	102.1	67.0 106	80.7 100	72.3 101	55.3 111	57.3 98	66.7 113	44.7 103	67.2 101	54.1 108	39.5 102	57.8 98	38.4 103	42.0 102	48.2 93	48.3 109	35.9 95	60.9 84	39.2 101	65.1 118	72.0 105	50.1 98	22.6 103	32.5 89	36.9 109
Black & Anandan [4]	103.7	70.3 110	88.0 118	84.1 115	45.5 104	61.4 108	52.0 103	47.4 109	77.3 109	52.9 106	42.3 105	77.5 116	42.8 105	44.0 108	51.8 108	45.0 100	35.9 95	75.9 113	38.3 97	50.8 95	71.3 104	17.8 18	29.8 115	42.7 116	37.6 111
GraphCuts [14]	104.0	66.6 104	87.0 115	80.0 111	43.1 102	63.0 111	46.1 97	41.8 101	67.0 100	53.4 107	28.5 99	64.0 104	20.8 92	40.2 96	48.1 92	43.5 98	46.5 115	63.4 88	40.5 103	62.7 115	75.4 113	69.5 121	23.8 107	33.3 93	38.5 113
FlowNet2 [122]	105.6	78.8 119	86.2 113	79.4 109	63.5 120	70.2 115	72.8 118	57.2 117	82.0 113	59.7 117	46.2 107	51.4 86	45.6 107	45.3 111	54.0 113	41.1 93	36.7 100	67.3 102	41.0 105	67.6 120	80.8 122	55.1 105	14.5 78	30.0 79	13.9 65
Nguyen [33]	107.8	75.6 116	85.4 108	85.8 117	67.0 121	61.6 109	83.0 123	57.1 116	80.2 111	64.1 120	70.8 119	80.2 118	77.4 122	45.9 113	52.2 109	56.4 116	36.3 97	68.1 106	42.0 108	41.4 70	66.0 88	19.7 25	34.1 118	45.6 118	46.1 118
SILK [79]	107.8	72.5 111	85.1 106	88.3 121	61.9 117	71.8 116	73.7 119	54.9 114	85.3 116	58.0 115	53.6 115	69.8 110	54.6 115	52.8 117	57.9 115	61.8 120	46.5 115	77.6 115	48.9 115	31.3 39	54.3 48	38.9 63	37.8 122	50.6 122	49.7 122
2bit-BM-tele [98]	107.9	82.4 121	87.2 116	91.8 125	44.4 103	54.7 90	52.9 104	46.0 106	68.3 102	47.4 101	51.5 113	54.4 93	53.4 114	40.5 98	47.1 86	47.7 107	47.9 118	66.4 98	53.1 116	71.7 123	83.6 125	75.1 124	21.9 101	39.3 108	29.2 97
UnFlow [129]	109.5	89.6 126	94.1 124	86.4 118	72.2 123	83.9 126	77.9 122	71.4 123	93.2 125	69.8 121	54.8 116	74.4 113	51.4 112	62.0 124	66.9 125	69.1 127	50.0 120	80.9 116	57.1 119	53.2 102	69.0 98	7.68 4	15.6 81	30.8 82	21.0 82
Periodicity [78]	110.8	68.9 109	83.5 103	65.5 100	52.2 110	69.8 114	57.0 107	78.4 125	82.5 114	87.2 127	47.2 108	74.7 114	45.5 106	69.7 129	81.9 129	65.6 124	59.5 124	84.9 126	60.7 123	36.3 52	79.8 119	19.2 21	40.7 124	66.5 128	53.1 124
Horn & Schunck [3]	111.2	74.1 115	93.2 123	86.9 119	49.1 107	73.8 117	53.9 106	53.1 113	89.0 121	54.6 109	50.9 112	81.4 119	52.4 113	51.3 116	58.8 116	54.3 113	41.2 110	82.3 120	44.6 113	55.0 104	74.3 109	19.6 24	40.7 124	56.8 124	48.8 121
Heeger++ [104]	113.4	86.1 123	91.0 120	77.1 106	67.6 122	91.6 128	65.1 112	85.6 128	94.6 127	83.6 126	71.0 120	88.2 122	68.8 118	62.9 125	69.8 127	67.3 125	69.4 128	91.0 128	70.8 126	40.2 68	80.9 123	26.3 46	21.7 98	31.2 85	27.4 91
SLK [47]	114.4	67.5 107	90.3 119	82.1 113	72.2 123	84.7 127	84.8 124	58.4 118	94.0 126	58.1 116	78.1 123	82.5 120	84.6 125	55.4 122	61.5 121	68.1 126	49.4 119	83.7 124	57.7 120	36.2 51	68.1 96	26.2 45	50.3 127	60.0 125	65.2 128
FFV1MT [106]	115.7	85.0 122	92.0 121	84.4 116	60.6 114	83.8 125	60.8 110	85.4 127	92.2 124	88.2 128	71.2 121	89.9 125	69.1 119	64.0 127	69.3 126	78.0 128	69.2 127	91.5 129	73.2 128	51.7 97	73.9 108	45.3 81	21.7 98	31.2 85	27.4 91
TI-DOFE [24]	116.4	90.7 127	94.6 126	97.1 128	76.9 127	79.5 124	89.4 128	73.1 124	96.1 129	74.4 124	84.6 127	93.6 127	88.3 127	52.9 118	59.9 117	63.9 122	44.5 114	83.6 123	53.5 117	42.9 73	68.0 95	17.0 16	50.5 128	65.5 127	62.4 126
FOLKI [16]	116.6	72.5 111	84.5 105	87.5 120	62.3 118	74.9 119	74.0 121	56.9 115	87.2 118	57.7 114	59.8 117	78.1 117	65.4 117	53.3 120	61.2 120	62.7 121	50.9 121	81.0 117	62.7 124	47.3 91	73.3 106	56.8 108	49.0 126	62.9 126	64.3 127
PGAM+LK [55]	121.9	79.4 120	92.7 122	88.7 122	62.9 119	76.8 123	71.9 117	59.9 119	88.3 120	62.8 119	74.9 122	90.6 126	75.6 121	54.4 121	61.0 118	64.6 123	58.1 123	82.7 121	58.6 121	77.6 127	85.6 126	78.1 126	38.8 123	52.7 123	50.7 123
Adaptive flow [45]	122.3	91.3 128	95.7 127	96.2 126	75.8 126	75.9 121	86.2 125	68.9 121	85.6 117	72.4 122	80.6 125	85.2 121	83.9 124	57.3 123	61.5 121	60.2 119	66.7 125	84.1 125	70.4 125	90.2 128	92.7 128	95.0 127	27.7 112	40.5 109	37.0 110
HCIC-L [99]	123.1	88.6 125	96.7 129	89.3 123	80.8 128	74.4 118	93.1 129	86.6 129	87.2 118	95.2 129	95.8 129	97.4 129	96.9 129	63.4 126	66.7 124	57.8 118	68.9 126	82.7 121	73.1 127	96.0 129	95.9 129	98.6 128	25.3 110	33.9 95	34.7 107
Pyramid LK [2]	125.3	86.7 124	94.5 125	96.2 126	73.1 125	76.5 122	86.4 127	70.8 122	89.6 122	78.5 125	78.1 123	88.6 124	82.7 123	68.8 128	76.4 128	80.4 129	75.7 129	85.1 127	78.1 129	73.1 124	79.6 118	69.3 120	60.8 129	74.5 129	79.9 129
AdaConv-v1 [126]	130.0	100.0 130	100.0 130	100.0 130	100.0 130	100.0 130	100.0 130	99.9 130	99.9 130	99.9 130	99.9 130	99.9 130	99.9 130	99.9 130	99.9 130	99.9 130	99.9 130	99.9 130	99.9 130	100.0 130	99.7 130	99.9 130	99.9 130	99.9 130	99.9 130
SepConv-v1 [127]	130.0	100.0 130	100.0 130	100.0 130	100.0 130	100.0 130	100.0 130	99.9 130	99.9 130	99.9 130	99.9 130	99.9 130	99.9 130	99.9 130	99.9 130	99.9 130	99.9 130	99.9 130	99.9 130	100.0 130	99.7 130	99.9 130	99.9 130	99.9 130	99.9 130

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

References

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

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