Optical flow evaluation results: R5.0 interpolation error

Optical flow evaluation results	Statistics: Average SD R2.5 R5.0 R10.0 A90 A95 A99 Error type: endpoint angle interpolation normalized interpolation
Show images: below table above table

R5.0 interpolation error	avg.	Mequon (Hidden texture) im0 GT im1			Schefflera (Hidden texture) im0 GT im1			Urban (Synthetic) im0 GT im1			Teddy (Stereo) im0 GT im1			Backyard (High-speed camera) im0 GT im1			Basketball (High-speed camera) im0 GT im1			Dumptruck (High-speed camera) im0 GT im1			Evergreen (High-speed camera) im0 GT im1
	rank	all	disc	untext	all	disc	untext	all	disc	untext	all	disc	untext	all	disc	untext	all	disc	untext	all	disc	untext	all	disc	untext
PMMST [114]	12.4	4.93 3	13.9 3	0.13 4	8.97 27	17.1 12	0.43 10	6.00 10	13.4 4	0.27 1	17.6 3	26.2 6	5.24 20	43.0 14	57.7 8	5.17 20	10.3 5	39.1 8	0.87 12	9.75 17	41.0 16	0.44 17	21.5 28	51.9 35	0.47 15
MDP-Flow2 [68]	13.4	4.89 2	14.4 5	0.12 3	8.58 11	16.9 10	0.39 3	5.95 7	13.6 6	0.28 3	17.7 4	26.7 14	5.32 34	42.9 6	57.6 5	5.13 15	10.6 19	40.1 25	0.92 22	9.75 17	41.0 16	0.43 10	21.6 40	51.9 35	0.46 9
SepConv-v1 [127]	14.9	3.41 1	11.0 1	0.08 1	8.39 7	16.7 9	1.04 99	2.81 1	7.63 1	0.74 99	18.0 17	25.2 1	5.82 98	42.9 6	57.4 2	4.74 2	9.03 1	34.1 1	0.60 1	9.34 2	38.6 1	0.42 4	20.1 1	48.6 1	0.35 1
NN-field [71]	17.9	5.14 19	16.1 48	0.13 4	8.21 3	15.7 3	0.38 2	6.39 43	13.6 6	0.30 7	18.4 35	28.7 70	5.33 38	42.9 6	57.6 5	5.08 11	10.7 26	40.2 30	0.94 29	9.62 7	40.5 7	0.44 17	21.2 4	51.2 7	0.45 3
NNF-Local [87]	18.0	5.11 15	15.7 26	0.11 2	8.18 2	15.8 4	0.39 3	6.01 13	13.5 5	0.27 1	18.3 33	28.3 54	5.29 27	43.0 14	57.6 5	5.11 13	10.8 42	40.9 56	1.01 46	9.67 9	40.7 9	0.46 33	21.2 4	51.2 7	0.46 9
Layers++ [37]	21.5	5.25 32	15.9 34	0.17 35	8.27 4	15.5 2	0.37 1	6.16 22	14.3 12	0.38 39	18.0 17	26.9 18	5.32 34	43.1 25	57.9 21	5.24 37	10.7 26	40.6 48	0.97 41	9.70 10	40.7 9	0.39 1	21.3 10	51.3 9	0.48 28
PH-Flow [101]	24.3	5.32 44	16.4 59	0.16 25	8.28 5	15.9 5	0.44 13	6.12 19	13.9 9	0.33 16	17.5 1	25.8 2	5.15 7	42.8 4	57.5 3	5.03 8	11.0 67	41.6 85	1.09 68	9.71 11	41.0 16	0.46 33	21.3 10	51.4 11	0.50 63
nLayers [57]	26.0	5.26 35	15.8 32	0.16 25	8.54 9	16.6 8	0.45 16	5.89 4	13.1 2	0.30 7	18.1 23	27.1 23	5.35 44	43.3 50	58.0 30	5.36 69	10.8 42	40.9 56	1.11 71	9.65 8	40.1 3	0.48 56	21.2 4	51.1 5	0.45 3
COFM [59]	26.1	5.08 13	15.1 13	0.19 53	8.86 19	17.4 17	0.48 25	6.37 39	14.2 11	0.40 47	17.7 4	26.2 6	5.11 2	42.9 6	57.8 12	5.02 7	10.9 53	41.6 85	1.11 71	9.24 1	38.8 2	0.50 69	21.5 28	51.9 35	0.46 9
Sparse-NonSparse [56]	26.8	5.31 43	16.3 57	0.17 35	8.74 14	17.2 16	0.48 25	6.19 23	14.7 24	0.34 20	17.9 12	26.3 8	5.23 17	43.1 25	57.8 12	5.25 40	11.0 67	41.2 65	1.04 54	9.71 11	40.9 13	0.46 33	21.2 4	51.3 9	0.47 15
IROF++ [58]	28.0	5.37 56	16.8 75	0.14 8	8.87 21	17.4 17	0.45 16	6.41 49	14.6 21	0.43 57	17.5 1	25.8 2	5.22 12	42.9 6	57.8 12	5.19 25	10.5 11	39.4 13	0.87 12	10.0 55	42.4 61	0.47 49	21.4 19	51.5 12	0.50 63
TV-L1-MCT [64]	28.5	5.74 103	18.1 109	0.18 45	9.50 41	19.1 40	0.58 40	5.73 2	14.5 19	0.38 39	17.8 8	26.0 5	5.28 25	43.0 14	57.9 21	5.22 31	10.4 7	39.1 8	0.94 29	9.78 23	41.1 22	0.44 17	21.2 4	51.1 5	0.48 28
HAST [109]	29.7	5.12 16	15.2 15	0.16 25	8.74 14	17.1 12	0.43 10	6.62 74	15.3 46	0.39 44	17.7 4	26.4 10	4.98 1	43.0 14	58.0 30	5.05 9	11.0 67	41.4 74	1.06 61	9.53 3	40.4 5	0.42 4	22.0 81	52.8 78	0.47 15
ComponentFusion [96]	30.8	5.15 20	16.1 48	0.14 8	8.86 19	17.9 25	0.41 6	6.38 40	15.4 47	0.33 16	17.8 8	27.0 21	5.15 7	43.2 40	58.0 30	5.24 37	10.6 19	39.8 17	0.94 29	10.0 55	42.7 80	0.57 96	21.5 28	51.8 28	0.47 15
ProbFlowFields [128]	31.2	5.03 8	15.6 24	0.17 35	8.55 10	17.1 12	0.41 6	6.00 10	14.4 15	0.32 13	18.1 23	27.1 23	5.38 49	43.3 50	58.1 46	5.49 105	10.9 53	41.2 65	1.20 89	9.61 6	40.7 9	0.47 49	21.0 3	50.8 3	0.49 44
FMOF [94]	31.5	5.62 92	17.2 87	0.21 62	8.71 13	17.0 11	0.44 13	6.38 40	14.7 24	0.46 62	18.6 48	28.0 41	5.31 32	43.1 25	57.9 21	5.15 18	10.8 42	40.5 44	0.87 12	9.60 5	40.4 5	0.40 3	21.5 28	51.7 20	0.46 9
2DHMM-SAS [92]	34.7	5.62 92	17.6 103	0.18 45	10.1 64	19.7 55	0.64 55	5.73 2	14.4 15	0.37 37	17.7 4	25.9 4	5.30 29	43.0 14	57.8 12	5.26 43	10.7 26	40.0 23	0.82 5	9.83 29	41.3 25	0.48 56	21.6 40	52.0 39	0.47 15
CombBMOF [113]	35.2	5.46 70	16.2 55	0.22 72	8.89 22	18.0 27	0.45 16	6.29 30	14.7 24	0.40 47	18.5 42	28.0 41	5.24 20	43.0 14	57.7 8	5.08 11	10.8 42	40.2 30	0.82 5	11.7 125	42.9 85	0.47 49	21.2 4	50.9 4	0.45 3
LSM [39]	36.2	5.49 74	17.4 97	0.18 45	8.93 24	17.7 22	0.48 25	6.32 34	15.4 47	0.35 28	18.1 23	27.1 23	5.22 12	43.1 25	57.9 21	5.28 53	11.0 67	41.3 70	1.03 52	9.72 13	40.9 13	0.46 33	21.4 19	51.7 20	0.48 28
Ramp [62]	37.1	5.46 70	17.1 84	0.18 45	8.84 17	17.4 17	0.58 40	6.14 20	14.7 24	0.34 20	17.8 8	26.4 10	5.23 17	43.2 40	58.0 30	5.27 48	11.2 87	42.0 93	1.15 79	9.72 13	40.9 13	0.42 4	21.6 40	52.1 44	0.48 28
DeepFlow [86]	37.6	5.06 12	14.6 7	0.19 53	9.80 56	19.5 46	0.75 67	6.45 52	16.6 81	0.35 28	18.7 57	27.6 34	5.41 55	43.4 67	58.0 30	5.37 71	10.3 5	38.3 4	0.99 42	9.83 29	41.8 41	0.43 10	21.3 10	51.6 18	0.48 28
SuperFlow [81]	38.0	4.99 7	14.3 4	0.22 72	10.3 70	19.9 60	0.90 82	6.61 69	15.5 50	0.51 69	18.5 42	27.2 28	5.52 74	43.3 50	58.1 46	5.37 71	10.1 3	38.0 3	0.73 3	9.73 16	41.4 30	0.46 33	21.3 10	51.5 12	0.46 9
NNF-EAC [103]	38.1	5.52 78	15.7 26	0.34 113	9.27 37	18.1 29	0.48 25	6.53 58	13.8 8	0.40 47	18.2 29	27.0 21	5.71 87	43.0 14	57.7 8	5.11 13	10.4 7	39.1 8	0.83 7	9.89 37	41.6 36	0.52 80	21.7 51	52.2 51	0.49 44
DeepFlow2 [108]	38.2	5.16 21	14.9 12	0.21 62	9.81 57	19.7 55	0.65 57	6.38 40	16.3 70	0.34 20	18.6 48	28.1 46	5.29 27	43.4 67	58.0 30	5.37 71	10.2 4	38.4 5	0.85 10	9.96 52	42.1 54	0.44 17	21.4 19	51.8 28	0.49 44
LME [70]	38.4	5.13 18	15.8 32	0.14 8	9.15 34	18.4 38	0.51 31	6.32 34	15.7 54	0.34 20	17.9 12	27.1 23	5.34 40	43.8 110	58.8 109	5.79 123	10.8 42	41.2 65	0.93 24	9.86 33	41.3 25	0.43 10	21.3 10	51.5 12	0.47 15
WLIF-Flow [93]	39.2	5.25 32	16.0 41	0.15 15	9.14 33	18.1 29	0.59 46	6.29 30	14.3 12	0.34 20	17.9 12	26.3 8	5.65 83	43.1 25	57.9 21	5.26 43	11.2 87	41.9 92	1.22 95	9.82 28	41.3 25	0.44 17	21.7 51	52.2 51	0.49 44
PGM-C [120]	39.2	5.18 24	16.0 41	0.15 15	8.97 27	18.2 31	0.46 22	6.51 55	16.4 75	0.33 16	18.4 35	28.5 61	5.36 46	43.4 67	58.1 46	5.40 87	10.7 26	40.5 44	0.96 38	9.92 42	41.9 43	0.45 25	21.4 19	51.8 28	0.48 28
FlowFields+ [130]	39.3	5.23 31	16.6 67	0.15 15	8.91 23	18.3 32	0.45 16	6.28 29	15.9 58	0.34 20	18.2 29	28.1 46	5.34 40	43.4 67	58.2 57	5.35 66	10.9 53	41.6 85	1.10 70	9.79 24	41.5 32	0.46 33	21.3 10	51.5 12	0.48 28
FlowFields [110]	40.0	5.22 29	16.5 62	0.16 25	8.95 25	18.3 32	0.42 8	6.29 30	15.9 58	0.35 28	18.4 35	28.5 61	5.41 55	43.4 67	58.1 46	5.33 58	10.9 53	41.3 70	1.08 64	9.79 24	41.5 32	0.45 25	21.3 10	51.6 18	0.49 44
Classic+NL [31]	40.4	5.56 84	17.4 97	0.22 72	8.99 29	17.6 21	0.54 33	6.02 14	14.7 24	0.36 33	18.1 23	26.8 15	5.41 55	43.1 25	58.0 30	5.23 34	11.1 84	41.5 79	1.06 61	9.72 13	41.0 16	0.46 33	21.6 40	52.0 39	0.47 15
DF-Auto [115]	41.4	5.03 8	13.8 2	0.17 35	10.2 65	19.3 42	0.79 71	6.09 16	14.4 15	0.34 20	18.7 57	28.1 46	5.24 20	43.2 40	57.9 21	5.31 56	10.4 7	39.3 11	0.93 24	10.1 64	42.3 58	0.49 62	21.9 73	52.9 84	0.53 96
S2F-IF [123]	42.3	5.22 29	16.5 62	0.15 15	8.84 17	18.0 27	0.44 13	6.27 28	15.7 54	0.33 16	18.3 33	28.3 54	5.14 6	43.4 67	58.2 57	5.41 90	11.0 67	41.5 79	1.11 71	9.91 41	41.9 43	0.47 49	21.3 10	51.5 12	0.51 75
FC-2Layers-FF [74]	42.4	5.40 61	17.0 81	0.17 35	8.15 1	15.3 1	0.42 8	6.14 20	14.9 32	0.35 28	18.1 23	27.2 28	5.31 32	43.3 50	58.2 57	5.36 69	11.2 87	42.2 97	1.20 89	9.75 17	41.0 16	0.49 62	21.7 51	52.1 44	0.48 28
AGIF+OF [85]	42.6	5.60 89	17.4 97	0.15 15	8.95 25	17.7 22	0.59 46	6.20 25	14.5 19	0.43 57	17.9 12	26.6 13	5.22 12	43.4 67	58.3 79	5.38 78	11.1 84	42.0 93	1.01 46	9.87 36	40.7 9	0.42 4	21.5 28	52.0 39	0.48 28
OFLAF [77]	42.9	5.16 21	15.9 34	0.14 8	8.28 5	16.1 6	0.40 5	6.34 38	14.9 32	0.30 7	18.0 17	27.3 30	5.11 2	43.3 50	58.1 46	5.39 80	11.2 87	42.4 98	1.21 92	10.1 64	42.4 61	0.60 102	21.9 73	52.6 69	0.45 3
MDP-Flow [26]	43.8	5.03 8	15.4 17	0.14 8	8.68 12	17.4 17	0.47 23	5.97 8	14.3 12	0.32 13	18.9 73	28.5 61	5.50 71	43.2 40	58.0 30	5.39 80	11.2 87	42.6 101	1.31 103	10.3 82	43.1 90	0.49 62	21.4 19	51.7 20	0.47 15
S2D-Matching [84]	44.8	5.56 84	17.3 91	0.18 45	9.96 61	19.9 60	0.66 58	5.99 9	14.7 24	0.41 52	17.9 12	26.4 10	5.40 54	43.2 40	58.0 30	5.17 20	11.2 87	42.0 93	1.17 84	9.93 45	41.1 22	0.43 10	21.5 28	51.8 28	0.48 28
TF+OM [100]	45.6	4.98 5	14.6 7	0.20 58	9.03 31	17.9 25	0.55 35	6.29 30	16.2 67	0.39 44	18.5 42	28.0 41	5.50 71	43.3 50	58.1 46	5.47 102	10.6 19	39.8 17	1.03 52	9.86 33	42.0 49	0.51 76	21.7 51	52.3 56	0.52 87
Brox et al. [5]	46.4	5.33 50	15.4 17	0.19 53	10.2 65	20.1 65	0.64 55	6.61 69	17.2 96	0.46 62	18.7 57	28.2 50	5.21 9	43.4 67	58.1 46	5.27 48	10.7 26	40.1 25	0.99 42	9.90 39	42.0 49	0.45 25	21.6 40	52.1 44	0.47 15
ALD-Flow [66]	46.4	5.37 56	16.1 48	0.23 80	9.53 43	19.2 41	0.57 38	6.51 55	16.7 85	0.34 20	18.2 29	27.9 37	5.32 34	43.4 67	58.3 79	5.46 100	10.7 26	39.9 20	0.99 42	9.76 22	41.2 24	0.44 17	21.8 62	52.7 74	0.47 15
CPM-Flow [116]	47.1	5.20 28	16.1 48	0.16 25	8.99 29	18.3 32	0.47 23	6.42 50	16.0 63	0.30 7	18.8 65	29.2 89	5.43 62	43.4 67	58.2 57	5.44 98	10.6 19	40.1 25	1.02 48	10.0 55	42.6 72	0.45 25	21.4 19	51.8 28	0.53 96
SVFilterOh [111]	47.2	5.32 44	15.7 26	0.21 62	8.78 16	17.1 12	0.49 29	6.40 46	14.6 21	0.38 39	18.4 35	27.1 23	5.80 96	43.8 110	58.6 105	5.65 117	10.9 53	41.0 62	1.04 54	9.54 4	40.1 3	0.43 10	21.7 51	52.2 51	0.50 63
AggregFlow [97]	47.6	5.64 95	17.2 87	0.22 72	9.81 57	19.5 46	0.59 46	6.11 18	14.4 15	0.28 3	18.9 73	29.0 80	5.30 29	43.4 67	58.2 57	5.33 58	10.7 26	40.2 30	0.96 38	9.89 37	41.7 38	0.50 69	21.4 19	51.7 20	0.50 63
RNLOD-Flow [121]	48.9	5.32 44	16.6 67	0.16 25	9.70 52	19.6 52	0.60 50	6.57 62	15.5 50	0.51 69	18.2 29	27.4 31	5.22 12	43.1 25	58.0 30	5.28 53	11.0 67	41.4 74	1.08 64	9.85 32	41.3 25	0.50 69	21.9 73	52.7 74	0.49 44
Second-order prior [8]	49.1	5.29 40	15.3 16	0.27 97	10.8 79	21.1 79	0.78 70	7.14 94	17.8 103	0.62 92	18.6 48	28.3 54	5.21 9	42.9 6	57.7 8	5.16 19	10.5 11	39.6 15	0.93 24	10.2 75	42.8 82	0.44 17	21.6 40	52.3 56	0.49 44
IROF-TV [53]	50.5	5.35 55	16.6 67	0.21 62	9.10 32	17.8 24	0.57 38	6.61 69	16.8 87	0.44 59	17.8 8	26.9 18	5.37 48	43.5 90	58.4 91	5.50 107	10.5 11	40.1 25	0.90 18	9.98 54	42.2 56	0.46 33	21.6 40	52.1 44	0.51 75
DPOF [18]	51.2	5.51 77	17.9 107	0.22 72	8.45 8	16.5 7	0.43 10	6.87 81	15.1 41	0.59 84	18.9 73	29.5 93	5.43 62	42.9 6	57.8 12	5.05 9	11.0 67	40.9 56	0.84 9	10.3 82	42.5 68	0.45 25	21.9 73	52.8 78	0.48 28
TC-Flow [46]	52.9	5.19 25	15.9 34	0.21 62	9.57 44	19.6 52	0.63 52	6.78 79	17.0 93	0.36 33	18.1 23	27.4 31	5.61 79	43.3 50	58.2 57	5.46 100	11.0 67	41.6 85	1.18 85	9.93 45	41.7 38	0.45 25	21.5 28	52.0 39	0.49 44
Aniso. Huber-L1 [22]	53.1	5.41 63	16.0 41	0.23 80	11.2 89	21.1 79	0.90 82	6.72 76	15.4 47	0.46 62	18.5 42	28.1 46	5.39 53	43.0 14	57.8 12	5.23 34	10.5 11	40.1 25	0.81 4	10.2 75	42.6 72	0.46 33	21.9 73	52.7 74	0.52 87
Kuang [131]	53.4	5.34 52	17.0 81	0.16 25	9.50 41	19.4 45	0.49 29	6.58 65	16.4 75	0.38 39	18.6 48	28.9 75	5.42 59	43.4 67	58.2 57	5.39 80	10.7 26	40.7 51	0.90 18	10.3 82	43.5 96	0.52 80	21.4 19	51.8 28	0.49 44
OAR-Flow [125]	53.5	5.28 38	15.5 20	0.18 45	9.71 54	19.5 46	0.67 59	6.43 51	16.3 70	0.28 3	18.0 17	27.6 34	5.23 17	43.5 90	58.4 91	5.48 103	10.9 53	41.3 70	1.13 76	10.2 75	42.9 85	0.51 76	21.7 51	52.3 56	0.45 3
ComplOF-FED-GPU [35]	54.2	5.30 42	16.1 48	0.19 53	9.39 39	19.3 42	0.58 40	7.21 98	16.9 90	0.66 94	18.4 35	28.6 67	5.32 34	43.1 25	58.0 30	5.27 48	10.8 42	40.9 56	0.99 42	10.1 64	42.8 82	0.47 49	21.8 62	52.3 56	0.50 63
EpicFlow [102]	54.2	5.19 25	16.1 48	0.15 15	9.60 45	19.8 59	0.58 40	6.40 46	16.4 75	0.35 28	18.6 48	29.1 87	5.47 68	43.4 67	58.2 57	5.42 93	10.8 42	41.2 65	1.08 64	10.1 64	42.5 68	0.54 87	21.5 28	52.0 39	0.49 44
FESL [72]	56.0	5.65 98	17.3 91	0.17 35	9.18 35	18.3 32	0.55 35	6.22 26	15.0 38	0.44 59	18.8 65	28.4 57	5.38 49	43.4 67	58.2 57	5.41 90	11.3 94	42.8 105	1.19 87	9.92 42	41.5 32	0.42 4	21.8 62	52.3 56	0.48 28
Classic+CPF [83]	56.2	5.59 88	17.3 91	0.16 25	9.22 36	18.3 32	0.58 40	6.00 10	14.9 32	0.40 47	18.0 17	26.8 15	5.22 12	43.5 90	58.5 99	5.38 78	11.4 99	43.0 111	1.15 79	10.1 64	41.9 43	0.45 25	22.0 81	53.1 90	0.49 44
PMF [73]	56.6	5.32 44	16.6 67	0.14 8	9.67 51	19.9 60	0.45 16	6.89 86	18.2 107	0.49 66	18.4 35	27.9 37	5.21 9	43.5 90	58.4 91	5.22 31	11.0 67	40.5 44	1.27 100	9.86 33	41.8 41	0.46 33	22.1 89	53.1 90	0.50 63
RFlow [90]	57.8	5.19 25	16.1 48	0.23 80	10.8 79	21.2 83	0.85 77	6.59 67	16.0 63	0.51 69	18.8 65	28.8 73	5.47 68	43.1 25	58.0 30	5.21 29	10.5 11	40.0 23	0.93 24	10.0 55	42.6 72	0.49 62	22.1 89	53.2 94	0.51 75
Local-TV-L1 [65]	57.8	5.29 40	14.6 7	0.35 115	11.5 96	21.1 79	1.23 107	6.39 43	14.9 32	0.37 37	19.0 79	27.9 37	6.64 112	43.3 50	58.3 79	5.33 58	10.9 53	39.0 6	1.58 123	9.79 24	41.6 36	0.48 56	21.3 10	51.5 12	0.53 96
CLG-TV [48]	59.0	5.32 44	15.7 26	0.26 94	11.0 85	21.2 83	0.83 76	6.75 78	16.6 81	0.56 78	18.9 73	28.4 57	5.50 71	43.3 50	58.1 46	5.25 40	10.5 11	39.8 17	0.87 12	10.1 64	42.5 68	0.44 17	22.0 81	53.1 90	0.51 75
TriFlow [95]	60.0	5.42 64	17.0 81	0.24 86	10.9 82	21.2 83	0.91 84	6.61 69	16.8 87	0.36 33	18.9 73	29.0 80	5.28 25	43.2 40	58.2 57	5.37 71	11.0 67	40.9 56	0.95 33	9.96 52	41.7 38	0.49 62	21.7 51	52.2 51	0.47 15
Classic++ [32]	60.0	5.33 50	16.0 41	0.28 98	10.2 65	20.3 69	0.69 62	6.87 81	16.6 81	0.50 67	18.7 57	27.7 36	5.64 81	43.2 40	58.0 30	5.26 43	11.0 67	40.7 51	1.34 106	9.93 45	41.9 43	0.47 49	21.7 51	52.4 65	0.50 63
EPPM w/o HM [88]	60.1	5.34 52	17.3 91	0.13 4	9.73 55	20.1 65	0.53 32	7.33 105	18.7 113	0.63 93	18.5 42	29.1 87	5.33 38	43.1 25	58.0 30	5.20 28	11.0 67	41.4 74	0.96 38	10.3 82	42.3 58	0.56 93	21.8 62	52.4 65	0.49 44
SIOF [67]	60.5	5.64 95	16.5 62	0.28 98	11.3 91	21.6 90	0.91 84	6.32 34	15.9 58	0.42 53	18.7 57	28.4 57	5.36 46	43.0 14	57.9 21	5.17 20	10.7 26	40.2 30	0.95 33	10.1 64	42.4 61	0.50 69	22.2 98	53.2 94	0.53 96
LDOF [28]	61.1	5.53 82	15.6 24	0.32 110	11.1 87	20.3 69	1.45 121	6.89 86	17.3 98	0.59 84	19.0 79	28.9 75	5.63 80	43.4 67	58.2 57	5.40 87	10.4 7	39.0 6	0.83 7	9.92 42	42.4 61	0.46 33	21.6 40	52.3 56	0.46 9
Efficient-NL [60]	61.5	5.54 83	17.1 84	0.16 25	9.60 45	18.9 39	0.56 37	6.99 90	15.1 41	0.75 100	18.8 65	28.2 50	5.26 24	43.1 25	57.9 21	5.25 40	11.6 105	43.4 119	1.04 54	10.1 64	42.5 68	0.48 56	22.6 108	53.8 106	0.48 28
p-harmonic [29]	61.8	5.17 23	15.5 20	0.16 25	11.2 89	21.4 87	0.94 87	6.55 59	17.4 101	0.55 77	19.2 87	28.6 67	5.45 66	43.3 50	58.2 57	5.27 48	10.7 26	40.2 30	1.04 54	10.4 91	43.4 94	0.50 69	21.8 62	52.6 69	0.49 44
Complementary OF [21]	62.0	5.28 38	16.7 73	0.15 15	9.39 39	19.5 46	0.58 40	7.53 109	16.3 70	1.10 118	18.7 57	29.0 80	5.35 44	43.2 40	58.2 57	5.26 43	10.9 53	41.2 65	1.16 82	10.3 82	43.4 94	0.55 90	21.5 28	52.2 51	0.51 75
F-TV-L1 [15]	62.2	5.56 84	16.0 41	0.36 118	11.4 94	21.5 89	0.94 87	6.88 83	17.0 93	0.66 94	18.7 57	27.9 37	5.79 95	42.6 2	57.8 12	5.01 6	10.6 19	39.3 11	1.02 48	10.0 55	41.9 43	0.55 90	22.0 81	52.8 78	0.51 75
CostFilter [40]	62.6	5.44 66	17.7 104	0.13 4	9.64 48	20.1 65	0.45 16	6.96 89	19.1 115	0.47 65	18.5 42	28.9 75	5.13 5	43.6 104	58.5 99	5.32 57	11.1 84	40.5 44	1.48 117	9.94 49	42.1 54	0.45 25	21.8 62	52.6 69	0.49 44
OFH [38]	63.2	5.49 74	16.6 67	0.25 90	10.3 70	20.2 68	0.77 69	6.88 83	17.8 103	0.36 33	18.4 35	28.9 75	5.24 20	43.1 25	58.0 30	5.26 43	10.9 53	41.5 79	1.18 85	10.3 82	43.0 88	0.58 98	21.6 40	52.1 44	0.50 63
CBF [12]	63.7	4.98 5	14.8 11	0.18 45	10.2 65	19.9 60	0.71 64	6.63 75	15.2 45	0.42 53	19.0 79	28.5 61	6.39 109	43.4 67	58.3 79	5.49 105	10.7 26	40.4 40	0.95 33	10.1 64	42.6 72	0.50 69	22.3 102	53.5 103	0.53 96
HBM-GC [105]	63.7	5.52 78	17.1 84	0.22 72	9.64 48	19.3 42	0.59 46	5.93 6	13.2 3	0.31 12	18.8 65	28.0 41	5.83 100	44.3 121	59.2 115	5.71 119	11.5 102	43.3 117	1.32 104	9.75 17	40.6 8	0.39 1	22.0 81	52.9 84	0.50 63
TC/T-Flow [76]	63.8	5.73 101	17.3 91	0.22 72	9.66 50	19.7 55	0.63 52	6.24 27	14.9 32	0.32 13	18.6 48	28.7 70	5.38 49	43.5 90	58.4 91	5.50 107	11.0 67	41.4 74	0.89 17	10.2 75	43.0 88	0.58 98	21.9 73	53.0 89	0.45 3
Steered-L1 [118]	63.9	5.12 16	16.0 41	0.17 35	9.62 47	19.5 46	0.88 79	7.15 95	15.6 52	1.00 110	19.4 91	28.5 61	6.39 109	43.5 90	58.5 99	5.19 25	10.8 42	40.8 55	1.20 89	9.95 51	42.6 72	0.52 80	21.7 51	52.6 69	0.48 28
GraphCuts [14]	64.5	5.98 111	17.5 101	0.24 86	10.0 62	19.5 46	0.76 68	8.24 120	14.6 21	1.06 113	19.7 94	29.0 80	5.69 85	42.9 6	57.9 21	4.97 4	10.5 11	40.3 35	0.87 12	10.0 55	42.4 61	0.58 98	22.1 89	53.2 94	0.51 75
AdaConv-v1 [126]	64.7	6.72 120	21.8 124	0.25 90	12.8 112	22.4 107	1.80 126	8.18 119	18.4 109	1.46 125	24.3 125	34.7 126	7.39 120	41.5 1	56.1 1	4.28 1	9.57 2	36.9 2	0.71 2	9.75 17	41.0 16	0.60 102	20.5 2	49.7 2	0.42 2
MLDP_OF [89]	66.1	5.44 66	17.2 87	0.17 35	9.84 59	19.9 60	0.62 51	6.19 23	14.8 30	0.28 3	18.6 48	27.4 31	5.71 87	43.3 50	58.2 57	5.34 63	11.9 114	43.3 117	1.57 122	10.4 91	42.6 72	0.56 93	21.7 51	52.3 56	0.59 120
BlockOverlap [61]	66.2	5.34 52	14.6 7	0.41 122	11.4 94	20.6 72	1.42 117	6.49 53	14.1 10	0.61 90	18.9 73	26.9 18	7.34 119	44.2 119	58.9 111	5.91 125	11.0 67	39.9 20	1.39 112	9.81 27	41.3 25	0.46 33	21.5 28	51.7 20	0.51 75
SRR-TVOF-NL [91]	66.8	5.70 99	16.9 79	0.23 80	10.3 70	21.0 78	0.88 79	6.57 62	16.1 65	0.39 44	19.2 87	28.7 70	5.12 4	43.2 40	58.3 79	5.27 48	10.8 42	40.9 56	0.86 11	10.6 101	42.3 58	0.46 33	22.5 104	53.8 106	0.54 107
Sparse Occlusion [54]	68.0	5.43 65	16.8 75	0.23 80	10.3 70	20.8 76	0.63 52	6.51 55	15.0 38	0.44 59	19.0 79	29.0 80	5.42 59	43.4 67	58.2 57	5.41 90	11.3 94	42.9 109	1.14 77	10.1 64	42.2 56	0.42 4	22.1 89	53.2 94	0.49 44
CRTflow [80]	68.5	5.48 73	16.5 62	0.34 113	10.7 77	20.7 73	0.86 78	7.25 100	18.6 112	0.60 89	18.8 65	28.8 73	5.98 103	43.4 67	58.2 57	5.43 95	10.7 26	40.4 40	0.95 33	9.93 45	42.0 49	0.49 62	21.7 51	52.3 56	0.49 44
SimpleFlow [49]	70.5	5.52 78	17.5 101	0.18 45	10.2 65	19.7 55	0.73 65	7.32 104	15.8 56	1.05 112	18.0 17	26.8 15	5.44 64	43.3 50	58.1 46	5.33 58	11.3 94	42.9 109	1.22 95	10.3 82	44.6 107	1.04 127	21.8 62	52.6 69	0.47 15
FlowNet2 [122]	70.7	6.90 121	21.5 123	0.25 90	10.6 76	20.7 73	0.82 73	7.10 93	17.3 98	0.54 74	19.4 91	31.8 117	5.57 76	43.4 67	58.3 79	5.39 80	10.7 26	40.3 35	0.90 18	10.0 55	42.0 49	0.46 33	21.6 40	51.9 35	0.51 75
IAOF [50]	71.3	5.97 110	16.8 75	0.29 103	14.1 125	24.8 125	1.41 116	6.05 15	16.2 67	0.61 90	20.1 101	29.5 93	5.47 68	43.0 14	57.8 12	5.19 25	10.7 26	40.3 35	0.94 29	10.4 91	43.3 92	0.46 33	22.0 81	52.8 78	0.54 107
Modified CLG [34]	71.6	5.05 11	15.1 13	0.19 53	12.3 109	22.2 102	1.30 110	6.81 80	18.3 108	0.66 94	19.3 90	29.7 96	5.34 40	43.4 67	58.2 57	5.29 55	10.8 42	40.6 48	1.15 79	10.2 75	43.6 97	0.47 49	21.9 73	52.7 74	0.53 96
Aniso-Texture [82]	72.8	5.09 14	15.7 26	0.15 15	11.1 87	21.7 91	1.00 92	7.30 103	15.9 58	0.59 84	18.7 57	28.6 67	5.90 101	43.6 104	58.4 91	5.53 110	11.6 105	44.0 124	1.44 115	9.90 39	41.4 30	0.43 10	22.1 89	53.1 90	0.49 44
FlowNetS+ft+v [112]	73.3	5.40 61	15.5 20	0.29 103	11.7 102	21.7 91	1.62 123	6.88 83	17.1 95	0.56 78	19.0 79	29.2 89	5.73 91	43.5 90	58.4 91	5.56 112	10.5 11	39.9 20	0.95 33	10.1 64	42.9 85	0.52 80	21.8 62	52.5 68	0.48 28
Occlusion-TV-L1 [63]	74.2	5.32 44	16.2 55	0.28 98	11.3 91	21.9 96	0.96 91	6.60 68	16.9 90	0.58 82	19.1 85	28.9 75	5.72 89	43.4 67	58.2 57	5.24 37	10.9 53	40.3 35	1.26 99	10.9 108	42.6 72	0.81 119	21.8 62	52.4 65	0.49 44
Shiralkar [42]	75.2	5.73 101	18.1 109	0.21 62	11.6 98	22.0 98	0.88 79	6.74 77	19.9 119	0.73 98	20.3 104	30.1 101	5.46 67	42.6 2	57.5 3	4.99 5	11.3 94	41.5 79	1.35 107	11.0 110	44.9 109	0.67 107	21.5 28	51.7 20	0.48 28
CNN-flow-warp+ref [117]	76.0	4.95 4	14.4 5	0.22 72	10.9 82	21.2 83	1.23 107	7.43 107	18.0 105	0.79 102	20.9 112	29.8 97	6.84 115	43.5 90	58.3 79	5.57 113	10.7 26	40.3 35	1.22 95	10.3 82	44.4 106	0.67 107	21.6 40	52.1 44	0.47 15
TCOF [69]	76.2	5.56 84	16.8 75	0.17 35	11.8 103	22.1 100	1.02 96	6.09 16	15.0 38	0.30 7	19.0 79	29.4 92	5.67 84	43.4 67	58.3 79	5.17 20	11.4 99	43.1 113	1.02 48	11.0 110	43.9 99	0.48 56	23.1 119	55.1 123	0.52 87
HBpMotionGpu [43]	76.3	5.80 105	16.3 57	0.42 123	13.1 115	23.8 117	1.34 112	6.32 34	14.9 32	0.38 39	19.9 95	30.4 104	5.80 96	43.1 25	58.3 79	5.39 80	11.3 94	41.0 62	1.21 92	9.94 49	41.9 43	0.43 10	22.1 89	52.9 84	0.53 96
Fusion [6]	77.3	5.37 56	16.9 79	0.21 62	9.33 38	18.3 32	0.54 33	6.39 43	15.1 41	0.54 74	20.0 99	29.8 97	5.41 55	43.5 90	59.2 115	5.14 16	11.5 102	43.7 122	1.21 92	10.5 99	44.1 101	0.52 80	23.1 119	55.4 124	0.52 87
Adaptive [20]	77.4	5.50 76	16.7 73	0.30 105	11.8 103	22.2 102	1.02 96	6.58 65	16.5 80	0.53 73	18.6 48	28.0 41	5.60 78	43.5 90	58.3 79	5.21 29	11.0 67	41.3 70	1.09 68	10.4 91	42.8 82	0.46 33	22.2 98	53.5 103	0.54 107
BriefMatch [124]	79.0	5.45 68	16.5 62	0.31 109	9.84 59	19.6 52	1.43 118	7.55 110	15.6 52	1.08 115	20.3 104	29.2 89	7.97 127	43.3 50	58.3 79	5.43 95	12.0 117	41.5 79	2.37 129	9.84 31	41.5 32	0.56 93	21.4 19	51.7 20	0.52 87
Nguyen [33]	79.1	5.63 94	15.9 34	0.23 80	13.8 119	23.8 117	1.37 114	6.89 86	18.7 113	0.59 84	20.8 111	30.8 108	5.44 64	43.1 25	58.1 46	5.14 16	10.6 19	40.4 40	0.93 24	11.9 127	45.9 116	0.73 115	22.0 81	52.8 78	0.52 87
2D-CLG [1]	81.7	5.27 37	15.7 26	0.21 62	13.1 115	22.8 109	1.37 114	7.29 101	17.3 98	0.94 108	20.3 104	30.2 102	5.34 40	43.5 90	58.4 91	5.37 71	10.8 42	40.7 51	1.22 95	10.5 99	44.3 104	0.59 101	22.0 81	52.3 56	0.50 63
TV-L1-improved [17]	82.2	5.26 35	16.0 41	0.28 98	11.6 98	22.0 98	1.06 101	7.21 98	16.3 70	0.79 102	18.8 65	28.5 61	5.70 86	43.5 90	58.5 99	5.22 31	11.0 67	41.5 79	1.05 59	10.4 91	44.6 107	0.74 117	22.1 89	53.2 94	0.53 96
SegOF [10]	82.4	5.25 32	15.9 34	0.20 58	10.9 82	20.8 76	0.82 73	8.07 118	18.4 109	1.18 119	20.0 99	32.3 118	5.52 74	43.3 50	58.2 57	5.35 66	11.4 99	43.1 113	1.38 111	10.7 103	46.3 117	0.96 125	21.5 28	51.7 20	0.53 96
SPSA-learn [13]	82.4	5.45 68	15.4 17	0.25 90	11.6 98	21.4 87	1.15 105	7.65 112	16.6 81	1.26 120	20.1 101	28.2 50	5.30 29	43.3 50	58.2 57	5.42 93	10.9 53	41.0 62	1.14 77	11.6 122	50.4 131	1.71 131	22.2 98	53.3 102	0.49 44
TriangleFlow [30]	84.5	5.85 107	18.2 111	0.26 94	11.0 85	21.8 94	0.79 71	7.17 96	16.3 70	0.58 82	19.6 93	30.7 106	5.74 92	42.8 4	57.8 12	4.95 3	11.6 105	42.8 105	1.05 59	10.8 105	45.8 114	0.73 115	22.8 113	54.3 117	0.51 75
Black & Anandan [4]	85.2	5.71 100	15.5 20	0.35 115	12.7 111	22.3 106	1.12 103	7.89 114	18.1 106	1.06 113	20.5 109	30.3 103	5.42 59	43.6 104	58.6 105	5.35 66	10.6 19	39.7 16	0.91 21	10.9 108	44.1 101	0.50 69	22.2 98	52.9 84	0.53 96
Rannacher [23]	86.1	5.39 59	16.6 67	0.30 105	11.6 98	22.2 102	1.01 94	7.17 96	16.9 90	0.92 107	18.6 48	28.4 57	5.74 92	43.6 104	58.5 99	5.33 58	11.0 67	41.6 85	1.11 71	10.4 91	44.3 104	0.72 114	21.9 73	52.8 78	0.54 107
ROF-ND [107]	86.4	6.15 113	16.4 59	0.14 8	10.4 74	21.1 79	0.70 63	7.09 92	15.9 58	0.40 47	20.7 110	32.9 121	5.82 98	43.4 67	58.2 57	5.37 71	11.6 105	43.4 119	1.16 82	11.6 122	46.4 118	0.55 90	22.6 108	53.8 106	0.54 107
Ad-TV-NDC [36]	87.6	6.08 112	15.9 34	0.60 125	13.0 113	22.8 109	1.36 113	6.55 59	16.4 75	0.56 78	20.9 112	30.6 105	6.29 107	44.1 115	59.0 113	5.43 95	10.7 26	39.4 13	1.11 71	10.4 91	43.3 92	0.51 76	22.1 89	52.9 84	0.53 96
IAOF2 [51]	89.5	6.17 114	18.3 112	0.30 105	12.0 105	23.3 114	0.93 86	5.90 5	16.1 65	0.42 53	20.4 108	31.2 115	5.75 94	43.7 109	58.9 111	5.39 80	11.2 87	42.0 93	1.08 64	10.3 82	42.7 80	0.48 56	22.7 110	54.2 114	0.52 87
Correlation Flow [75]	90.1	5.61 90	17.8 106	0.15 15	10.8 79	21.7 91	0.82 73	6.40 46	14.8 30	0.42 53	19.1 85	29.0 80	6.04 105	43.9 112	58.6 105	6.05 127	12.0 117	43.9 123	1.29 102	11.0 110	45.3 111	0.70 112	22.5 104	54.1 112	0.51 75
Filter Flow [19]	90.8	5.64 95	16.4 59	0.32 110	12.2 108	22.2 102	1.08 102	6.61 69	16.2 67	0.57 81	20.3 104	29.0 80	6.32 108	44.1 115	59.1 114	5.74 121	10.9 53	40.7 51	1.04 54	10.2 75	43.2 91	0.54 87	22.7 110	54.3 117	0.54 107
Bartels [41]	92.5	5.52 78	17.2 87	0.40 121	10.0 62	20.7 73	0.94 87	6.50 54	15.8 56	0.54 74	19.9 95	30.0 100	7.79 124	44.8 125	59.2 115	6.72 130	12.8 129	42.4 98	3.06 131	10.0 55	42.0 49	0.54 87	22.1 89	53.2 94	0.54 107
Dynamic MRF [7]	93.5	5.39 59	17.4 97	0.20 58	10.5 75	21.8 94	0.74 66	7.60 111	20.3 123	0.99 109	21.3 115	31.1 113	7.06 117	43.0 14	58.1 46	5.34 63	11.6 105	43.0 111	1.49 118	10.7 103	45.8 114	0.85 120	22.5 104	53.2 94	0.55 114
LocallyOriented [52]	93.5	5.79 104	17.9 107	0.26 94	12.1 107	23.2 113	1.01 94	7.05 91	17.6 102	0.51 69	19.9 95	30.9 111	5.72 89	43.3 50	58.2 57	5.23 34	11.9 114	42.6 101	1.52 120	10.8 105	44.0 100	0.53 85	22.5 104	54.0 110	0.52 87
StereoOF-V1MT [119]	96.2	5.94 109	18.8 115	0.20 58	11.3 91	22.6 108	0.94 87	7.95 115	19.6 117	1.00 110	21.6 116	30.7 106	6.76 113	43.3 50	58.3 79	5.37 71	12.1 120	42.6 101	1.82 126	11.6 122	46.7 120	0.90 122	21.8 62	51.8 28	0.50 63
ACK-Prior [27]	97.1	5.46 70	17.7 104	0.15 15	9.70 52	20.3 69	0.67 59	7.76 113	16.4 75	1.08 115	19.9 95	31.0 112	6.01 104	44.7 124	59.6 121	5.78 122	12.1 120	44.2 127	1.33 105	10.6 101	44.2 103	0.53 85	23.4 124	56.1 128	0.52 87
TI-DOFE [24]	98.4	6.39 116	18.7 114	0.36 118	14.8 126	25.5 128	1.66 124	7.45 108	20.2 121	0.78 101	22.8 122	32.5 119	6.04 105	43.2 40	58.4 91	5.17 20	10.9 53	40.4 40	0.92 22	11.2 115	45.6 113	0.65 106	23.2 121	54.2 114	0.65 125
UnFlow [129]	99.1	6.39 116	20.9 119	0.21 62	13.0 113	24.4 124	1.15 105	8.06 117	21.1 124	0.82 105	19.2 87	29.6 95	5.64 81	43.1 25	58.0 30	5.40 87	11.8 113	42.8 105	1.36 109	11.0 110	42.4 61	0.70 112	24.3 130	54.8 121	0.70 127
StereoFlow [44]	99.4	10.4 131	27.1 131	0.35 115	16.3 130	28.4 131	1.03 98	6.55 59	16.8 87	0.50 67	18.8 65	28.2 50	5.38 49	45.7 130	62.1 130	5.58 114	13.6 130	50.3 131	1.28 101	10.0 55	42.4 61	0.49 62	23.0 115	55.5 125	0.56 118
Horn & Schunck [3]	99.5	5.81 106	17.3 91	0.21 62	13.1 115	23.5 115	1.26 109	8.03 116	19.7 118	1.08 115	22.6 120	32.7 120	5.59 77	43.6 104	58.7 108	5.39 80	10.9 53	40.6 48	1.02 48	11.7 125	46.5 119	0.60 102	22.8 113	53.9 109	0.55 114
2bit-BM-tele [98]	99.5	5.61 90	15.9 34	0.50 124	11.5 96	21.9 96	1.04 99	6.57 62	15.1 41	0.79 102	20.1 101	29.8 97	7.50 121	44.8 125	59.6 121	6.26 128	12.2 123	42.8 105	2.11 128	11.2 115	49.2 130	1.26 129	21.8 62	52.1 44	0.55 114
NL-TV-NCC [25]	111.2	6.44 118	20.3 118	0.24 86	10.7 77	22.1 100	0.68 61	7.38 106	17.2 96	0.59 84	22.2 119	34.7 126	6.82 114	45.5 129	60.2 128	6.68 129	12.3 126	44.6 129	1.19 87	14.4 131	48.1 127	0.67 107	24.0 129	56.4 129	0.55 114
SILK [79]	111.9	6.21 115	19.3 117	0.39 120	13.8 119	24.0 119	1.73 125	8.85 123	20.2 121	1.41 122	21.8 117	31.1 113	7.10 118	43.5 90	58.5 99	5.45 99	11.9 114	41.4 74	2.03 127	10.8 105	45.5 112	0.77 118	22.4 103	53.2 94	0.60 121
HCIC-L [99]	111.9	8.84 130	25.2 129	1.06 130	14.0 123	24.1 121	1.43 118	9.42 126	19.3 116	0.69 97	24.3 125	34.1 124	6.48 111	45.1 128	60.1 126	5.86 124	12.1 120	44.1 126	1.06 61	10.2 75	42.6 72	0.51 76	23.6 126	56.0 127	0.51 75
Adaptive flow [45]	112.2	7.18 125	19.2 116	0.69 126	15.0 127	25.0 126	2.11 128	7.29 101	16.7 85	0.87 106	22.6 120	31.3 116	7.85 126	44.8 125	60.2 128	5.63 116	11.7 112	43.4 119	1.36 109	10.4 91	43.7 98	0.57 96	23.0 115	54.7 120	0.50 63
Learning Flow [11]	115.0	5.91 108	18.6 113	0.30 105	12.0 105	22.9 111	1.00 92	8.30 121	20.0 120	1.33 121	21.9 118	32.9 121	6.94 116	44.5 123	59.7 124	5.97 126	11.5 102	42.6 101	1.35 107	11.3 117	46.8 121	0.69 111	23.7 127	55.9 126	0.62 123
GroupFlow [9]	115.2	7.04 123	22.5 125	0.28 98	12.5 110	24.0 119	1.13 104	9.10 124	22.0 126	1.45 123	21.0 114	33.6 123	5.93 102	44.1 115	59.3 119	5.50 107	12.2 123	44.4 128	1.42 114	11.1 114	45.2 110	0.61 105	22.7 110	54.1 112	0.56 118
SLK [47]	116.6	6.55 119	21.1 121	0.32 110	13.5 118	23.1 112	1.44 120	9.16 125	21.2 125	1.49 127	24.9 127	34.2 125	7.81 125	43.5 90	58.8 109	5.34 63	12.2 123	43.1 113	1.45 116	11.9 127	48.9 129	0.96 125	23.0 115	54.0 110	0.64 124
Heeger++ [104]	118.0	7.79 128	25.2 129	0.17 35	13.9 122	24.2 122	1.33 111	11.8 129	28.7 130	1.49 127	23.4 123	30.8 108	7.63 122	44.4 122	59.9 125	5.62 115	12.6 128	43.1 113	1.77 125	12.6 130	46.9 122	0.87 121	23.2 121	53.5 103	0.60 121
FFV1MT [106]	118.6	6.93 122	22.8 126	0.24 86	14.0 123	23.5 115	1.48 122	11.2 128	27.7 129	1.52 129	23.4 123	30.8 108	7.63 122	44.0 113	59.2 115	5.69 118	12.0 117	41.6 85	1.56 121	12.1 129	47.3 125	0.95 124	23.4 124	54.2 114	0.79 129
FOLKI [16]	121.6	7.10 124	21.1 121	0.94 129	15.3 128	25.5 128	2.28 129	8.49 122	22.2 127	1.47 126	26.3 129	35.2 128	10.6 130	44.0 113	59.6 121	5.54 111	11.6 105	41.8 91	1.49 118	11.4 119	47.7 126	0.90 122	23.3 123	54.9 122	0.67 126
Pyramid LK [2]	122.9	7.19 126	21.0 120	0.93 128	16.2 129	25.1 127	2.91 130	14.0 130	18.5 111	2.57 130	32.5 131	46.2 131	13.7 131	44.2 119	60.1 126	5.48 103	11.6 105	42.5 100	1.40 113	11.4 119	47.2 124	1.28 130	23.7 127	56.7 130	1.08 130
PGAM+LK [55]	123.2	7.51 127	23.5 128	0.73 127	13.8 119	24.2 122	1.92 127	9.44 127	22.7 128	1.45 123	26.4 130	36.9 129	10.5 129	44.1 115	59.5 120	5.72 120	12.4 127	44.0 124	1.75 124	11.3 117	47.0 123	0.68 110	23.0 115	54.5 119	0.76 128
Periodicity [78]	129.5	8.05 129	23.2 127	1.34 131	20.5 131	27.4 130	3.39 131	15.2 131	30.5 131	4.22 131	26.2 128	43.5 130	9.47 128	46.4 131	62.7 131	6.92 131	13.7 131	44.6 129	2.88 130	11.4 119	48.3 128	1.18 128	25.7 131	59.2 131	1.29 131

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

References

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

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