Optical flow evaluation results: R2.5 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

R2.5 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
NNF-Local [87]	12.2	13.4 3	36.1 7	1.56 2	24.2 2	35.7 6	2.60 5	18.4 21	30.4 6	1.43 3	59.2 19	68.4 50	41.6 17	79.1 11	87.3 5	43.1 21	36.4 14	66.6 16	25.0 22	31.7 11	63.5 9	4.66 19	38.9 9	78.4 8	3.01 6
PH-Flow [101]	13.9	13.7 20	37.1 30	1.77 24	24.3 5	35.5 5	2.58 3	18.5 23	30.6 9	1.54 16	58.8 1	66.8 4	41.6 17	79.0 5	87.2 3	42.9 11	36.3 9	67.1 50	24.6 7	31.6 2	63.7 18	4.64 11	39.0 17	78.6 16	3.10 27
NN-field [71]	15.0	13.5 8	36.9 25	1.67 10	24.2 2	35.4 4	2.54 1	18.7 36	30.6 9	1.52 13	59.3 29	68.5 55	41.7 25	79.1 11	87.3 5	43.2 37	36.4 14	66.2 4	25.0 22	31.6 2	63.6 12	4.64 11	38.9 9	78.1 5	3.05 12
MDP-Flow2 [68]	17.2	13.3 2	35.1 3	1.62 5	24.6 13	36.5 13	2.63 10	18.5 23	30.5 7	1.42 2	59.0 9	67.8 26	41.4 4	79.1 11	87.3 5	43.4 62	36.5 20	66.4 9	25.0 22	32.0 49	63.9 24	4.64 11	39.3 42	78.7 21	3.08 19
PMMST [114]	17.8	13.4 3	35.0 2	1.70 14	25.1 32	37.1 25	2.73 19	18.5 23	30.5 7	1.39 1	58.9 4	67.4 13	41.5 11	79.2 30	87.4 12	43.4 62	36.3 9	66.2 4	24.9 16	31.8 20	63.8 20	4.67 21	39.2 36	78.7 21	3.09 23
COFM [59]	18.2	13.6 15	36.0 5	1.89 48	24.6 13	36.4 11	2.71 17	18.5 23	30.3 4	1.59 25	58.8 1	66.8 4	41.1 2	79.0 5	87.4 12	42.6 6	35.8 3	67.2 60	24.1 2	31.2 1	61.6 1	4.89 86	38.5 3	78.1 5	3.34 85
Layers++ [37]	18.7	14.0 48	37.5 42	1.91 51	24.3 5	35.3 3	2.75 21	18.3 19	31.0 18	1.56 20	59.2 19	67.5 16	41.7 25	79.2 30	87.4 12	43.1 21	36.4 14	66.5 13	25.0 22	31.6 2	63.2 4	4.60 1	38.7 6	77.7 4	3.12 33
AGIF+OF [85]	20.0	13.9 41	37.5 42	1.67 10	24.6 13	36.5 13	2.68 13	18.1 15	31.0 18	1.61 30	58.9 4	66.9 6	41.4 4	79.2 30	87.5 37	43.1 21	36.6 32	67.2 60	25.0 22	31.8 20	63.6 12	4.60 1	39.0 17	78.6 16	2.98 3
HAST [109]	20.3	13.7 20	36.2 12	1.93 57	24.7 21	37.0 23	2.77 30	18.8 39	32.2 42	1.66 38	59.1 13	67.9 34	41.4 4	79.0 5	87.4 12	42.6 6	36.3 9	66.9 32	24.6 7	31.6 2	63.3 6	4.71 37	39.0 17	78.4 8	3.06 13
Sparse-NonSparse [56]	20.3	13.8 29	37.3 35	1.81 28	24.4 8	36.0 8	2.61 6	18.0 13	31.2 23	1.52 13	59.0 9	67.1 9	42.0 45	79.2 30	87.4 12	43.1 21	36.7 46	66.7 20	25.3 62	31.7 11	63.6 12	4.63 6	38.9 9	78.5 14	3.08 19
nLayers [57]	20.8	13.9 41	36.7 21	1.85 39	24.5 10	36.1 9	2.76 24	17.7 6	30.0 3	1.44 4	59.2 19	67.6 18	41.6 17	79.3 54	87.5 37	43.3 47	36.4 14	66.8 27	25.1 36	31.7 11	63.2 4	4.72 42	38.7 6	77.6 3	3.03 8
ProbFlowFields [128]	23.4	13.5 8	36.6 16	1.82 30	24.4 8	36.4 11	2.68 13	18.5 23	31.2 23	1.49 8	59.2 19	67.2 10	42.1 49	79.3 54	87.5 37	43.6 86	36.5 20	67.0 40	25.2 46	31.6 2	63.5 9	4.64 11	39.0 17	78.4 8	3.06 13
2DHMM-SAS [92]	24.8	14.1 58	38.9 86	1.82 30	25.5 50	38.0 39	2.77 30	17.2 2	30.9 15	1.56 20	58.9 4	66.5 1	41.7 25	79.1 11	87.4 12	42.9 11	36.5 20	66.6 16	24.9 16	31.7 11	63.9 24	4.68 29	39.2 36	79.0 33	3.07 16
LSM [39]	25.0	13.9 41	38.0 59	1.78 26	24.6 13	36.5 13	2.61 6	18.1 15	32.0 37	1.55 18	59.2 19	67.6 18	42.1 49	79.2 30	87.4 12	43.1 21	36.7 46	66.9 32	25.3 62	31.7 11	63.6 12	4.65 18	38.9 9	78.6 16	3.07 16
OFLAF [77]	25.2	13.5 8	36.1 7	1.62 5	24.3 5	35.8 7	2.62 9	18.7 36	31.5 30	1.47 6	59.1 13	67.8 26	41.2 3	79.3 54	87.4 12	43.4 62	36.6 32	67.4 74	25.0 22	31.9 35	64.3 35	4.79 67	38.9 9	78.7 21	3.10 27
FMOF [94]	26.4	14.2 66	38.6 74	1.91 51	24.5 10	36.2 10	2.70 16	18.4 21	31.2 23	1.77 55	59.5 38	68.0 36	41.5 11	79.2 30	87.4 12	43.1 21	36.6 32	66.8 27	25.0 22	31.6 2	63.3 6	4.61 4	39.1 26	78.4 8	3.11 32
CombBMOF [113]	28.2	13.6 15	36.4 14	1.71 16	24.5 10	36.9 22	2.58 3	18.1 15	31.5 30	1.81 62	59.5 38	68.2 41	41.6 17	79.1 11	87.3 5	43.0 16	36.8 58	66.5 13	25.0 22	33.9 125	65.2 81	4.68 29	39.1 26	78.4 8	2.92 1
ComponentFusion [96]	28.4	13.4 3	36.1 7	1.72 18	24.6 13	36.8 21	2.57 2	18.9 46	32.9 55	1.69 41	59.1 13	67.8 26	41.4 4	79.2 30	87.4 12	43.6 86	36.5 20	66.3 6	25.1 36	32.0 49	64.8 57	4.76 62	39.1 26	78.7 21	3.10 27
IROF++ [58]	29.3	13.8 29	37.8 51	1.72 18	24.6 13	36.6 18	2.61 6	18.6 32	31.3 26	1.64 36	58.8 1	66.7 3	41.8 32	79.0 5	87.3 5	42.7 9	36.5 20	66.6 16	25.0 22	32.0 49	65.0 65	4.74 54	39.5 67	79.2 46	3.30 81
SepConv-v1 [127]	30.9	9.23 1	28.0 1	1.08 1	20.5 1	32.4 1	3.35 81	8.95 1	20.5 1	2.08 82	60.8 96	66.9 6	44.2 109	79.1 11	87.1 2	43.2 37	35.6 1	62.4 1	25.1 36	32.2 74	62.3 2	5.34 117	37.6 1	76.4 1	3.28 78
Ramp [62]	31.3	14.1 58	38.7 78	1.92 55	24.6 13	36.6 18	2.69 15	17.9 10	31.0 18	1.47 6	58.9 4	67.0 8	41.9 37	79.2 30	87.5 37	43.1 21	37.0 74	67.4 74	25.5 75	31.6 2	63.5 9	4.63 6	39.1 26	78.9 29	3.19 48
S2F-IF [123]	32.1	13.5 8	36.6 16	1.70 14	24.9 26	37.9 35	2.77 30	18.8 39	32.7 51	1.54 16	59.1 13	67.7 22	41.6 17	79.3 54	87.5 37	43.3 47	36.5 20	67.1 50	25.0 22	31.9 35	64.7 53	4.74 54	39.3 42	79.1 43	3.10 27
TV-L1-MCT [64]	33.3	14.5 87	39.7 106	1.86 42	25.2 35	37.8 33	2.78 33	17.3 3	31.1 22	1.59 25	58.9 4	66.6 2	41.6 17	79.1 11	87.4 12	42.9 11	36.8 58	66.4 9	25.6 80	31.8 20	64.0 29	4.73 46	39.1 26	79.0 33	3.20 56
FlowFields+ [130]	33.8	13.5 8	37.0 28	1.69 13	25.0 29	38.2 45	2.78 33	18.9 46	33.3 66	1.55 18	59.1 13	67.6 18	41.9 37	79.3 54	87.5 37	43.3 47	36.6 32	67.2 60	25.1 36	31.8 20	64.5 45	4.67 21	39.3 42	79.2 46	3.07 16
RNLOD-Flow [121]	34.0	13.9 41	37.9 56	1.86 42	25.2 35	37.9 35	2.78 33	19.0 52	32.1 39	1.78 56	59.2 19	67.8 26	41.5 11	79.1 11	87.4 12	43.1 21	36.7 46	66.8 27	25.2 46	31.9 35	64.2 32	4.75 58	39.2 36	79.0 33	3.06 13
FC-2Layers-FF [74]	34.8	14.0 48	38.6 74	1.84 35	24.2 2	35.1 2	2.82 43	17.9 10	31.3 26	1.51 11	59.3 29	67.7 22	42.1 49	79.3 54	87.6 70	43.3 47	36.7 46	67.4 74	25.3 62	31.6 2	63.6 12	4.67 21	39.1 26	78.7 21	3.19 48
Classic+NL [31]	35.0	14.2 66	38.8 80	1.98 60	24.6 13	36.5 13	2.65 11	17.7 6	30.9 15	1.51 11	59.2 19	67.5 16	42.2 60	79.2 30	87.5 37	43.3 47	37.0 74	67.1 50	25.5 75	31.7 11	63.6 12	4.67 21	39.2 36	79.0 33	3.18 45
Classic+CPF [83]	35.8	14.1 58	38.3 67	1.74 21	24.9 26	37.1 25	2.73 19	17.6 5	31.4 28	1.60 28	59.0 9	67.3 11	41.4 4	79.3 54	87.6 70	43.3 47	36.9 65	67.9 99	25.2 46	31.9 35	64.3 35	4.64 11	39.3 42	79.2 46	3.04 9
FlowFields [110]	36.2	13.6 15	37.1 30	1.74 21	25.0 29	38.1 40	2.75 21	18.8 39	33.2 64	1.53 15	59.4 33	68.0 36	42.3 69	79.3 54	87.5 37	43.2 37	36.5 20	67.0 40	25.0 22	31.8 20	64.7 53	4.69 32	39.4 54	79.3 54	3.13 34
NNF-EAC [103]	38.6	14.2 66	37.3 35	2.09 71	25.3 40	37.6 31	2.76 24	18.9 46	30.6 9	1.61 30	59.8 60	68.5 55	43.3 102	79.1 11	87.3 5	43.1 21	36.5 20	66.5 13	25.0 22	32.1 61	64.3 35	4.73 46	39.4 54	79.0 33	3.14 36
LME [70]	39.0	13.5 8	36.1 7	1.62 5	25.3 40	37.8 33	3.44 86	19.0 52	32.8 53	1.63 34	59.0 9	67.8 26	41.5 11	79.7 115	87.9 110	44.4 116	36.5 20	67.0 40	24.9 16	32.0 49	64.2 32	4.66 19	39.0 17	78.6 16	3.09 23
S2D-Matching [84]	39.7	14.2 66	38.9 86	1.96 58	25.3 40	37.9 35	2.76 24	17.5 4	31.0 18	1.60 28	59.3 29	67.4 13	42.8 88	79.2 30	87.5 37	43.2 37	36.9 65	67.3 69	25.4 72	31.8 20	63.8 20	4.64 11	39.1 26	78.6 16	3.21 60
WLIF-Flow [93]	39.7	13.8 29	37.4 39	1.73 20	24.9 26	37.1 25	2.81 40	18.5 23	30.9 15	1.49 8	59.4 33	67.8 26	42.5 81	79.2 30	87.4 12	43.8 107	37.2 87	67.5 81	25.9 96	31.8 20	63.9 24	4.64 11	39.4 54	78.9 29	3.14 36
FESL [72]	42.3	14.4 83	39.1 92	1.83 33	25.0 29	37.4 29	2.76 24	18.2 18	31.6 32	1.70 42	59.7 51	68.5 55	41.7 25	79.3 54	87.6 70	43.3 47	36.9 65	67.9 99	25.2 46	31.8 20	63.8 20	4.61 4	39.3 42	78.8 26	3.04 9
PGM-C [120]	46.1	13.8 29	37.7 50	1.85 39	25.1 32	38.1 40	2.90 50	19.1 62	33.6 70	1.59 25	59.3 29	68.2 41	41.9 37	79.3 54	87.5 37	43.5 69	36.6 32	67.2 60	25.2 46	31.9 35	64.8 57	4.67 21	39.5 67	79.4 62	3.22 62
PMF [73]	46.8	13.7 20	37.1 30	1.66 9	25.5 50	39.3 67	2.71 17	19.0 52	34.9 98	1.74 52	59.4 33	68.4 50	41.8 32	79.4 84	87.6 70	43.3 47	37.3 91	66.9 32	26.2 104	31.9 35	64.3 35	4.73 46	39.3 42	78.8 26	2.93 2
MDP-Flow [26]	46.9	13.4 3	36.1 7	1.67 10	24.8 24	37.2 28	2.79 37	18.8 39	32.0 37	1.70 42	59.8 60	68.9 84	42.1 49	79.3 54	87.6 70	43.5 69	36.7 46	67.7 91	25.2 46	32.5 92	65.5 91	4.77 65	39.1 26	79.0 33	3.09 23
SuperFlow [81]	47.6	13.8 29	36.2 12	2.27 87	26.3 76	38.7 56	4.39 98	19.1 62	33.1 61	1.99 77	59.6 44	67.7 22	42.2 60	79.4 84	87.5 37	43.7 100	36.1 7	65.9 3	24.8 13	31.7 11	64.5 45	4.80 73	38.9 9	78.9 29	3.19 48
Efficient-NL [60]	48.5	14.3 78	38.7 78	1.77 24	25.2 35	37.6 31	2.76 24	19.0 52	31.8 33	2.08 82	59.8 60	68.7 74	41.4 4	79.1 11	87.4 12	43.0 16	36.9 65	68.4 112	24.6 7	32.1 61	64.7 53	4.69 32	40.1 98	79.8 86	3.14 36
SVFilterOh [111]	48.9	14.1 58	37.3 35	1.96 58	24.7 21	36.6 18	2.87 48	18.3 19	30.8 13	1.63 34	59.9 69	68.5 55	43.1 101	79.5 109	87.7 96	44.5 117	36.6 32	66.7 20	25.3 62	31.6 2	62.8 3	5.05 100	38.6 4	78.2 7	3.37 92
TC-Flow [46]	49.2	13.7 20	36.9 25	1.91 51	25.3 40	38.5 51	3.05 62	19.3 80	34.1 87	1.73 48	59.2 19	67.8 26	42.2 60	79.3 54	87.5 37	43.5 69	37.1 80	68.0 102	25.6 80	31.9 35	64.3 35	4.71 37	39.0 17	79.0 33	3.13 34
AggregFlow [97]	50.9	14.5 87	38.3 67	2.20 81	25.7 64	38.5 51	3.23 75	18.6 32	30.8 13	1.44 4	59.7 51	68.4 50	41.7 25	79.4 84	87.6 70	43.8 107	37.5 96	66.9 32	26.4 109	31.8 20	64.2 32	4.70 36	38.9 9	78.4 8	3.08 19
Second-order prior [8]	51.0	14.0 48	37.1 30	2.11 72	26.2 73	39.3 67	2.93 52	19.4 85	35.1 101	2.16 92	59.4 33	67.8 26	41.8 32	79.1 11	87.3 5	43.1 21	36.5 20	66.7 20	25.0 22	32.3 81	65.4 87	4.74 54	39.5 67	79.6 77	3.19 48
EPPM w/o HM [88]	51.3	13.4 3	36.6 16	1.61 3	25.5 50	39.3 67	2.76 24	19.4 85	35.7 107	1.99 77	59.6 44	69.3 93	41.9 37	79.2 30	87.4 12	43.1 21	37.0 74	67.5 81	25.3 62	32.8 102	65.0 65	4.85 82	39.4 54	79.0 33	3.04 9
IROF-TV [53]	51.7	14.0 48	38.1 61	1.99 61	24.7 21	36.5 13	2.65 11	19.1 62	34.2 88	1.78 56	59.1 13	67.4 13	42.4 76	79.4 84	87.7 96	43.6 86	36.0 4	66.4 9	24.4 5	32.1 61	64.6 49	4.75 58	39.8 87	79.9 90	3.35 88
TF+OM [100]	52.0	13.7 20	36.5 15	2.17 74	25.2 35	37.4 29	3.76 88	17.9 10	32.7 51	1.76 54	59.8 60	68.5 55	42.3 69	79.3 54	87.5 37	43.7 100	36.9 65	66.7 20	25.7 89	31.8 20	64.3 35	4.79 67	39.3 42	79.3 54	3.47 104
DeepFlow2 [108]	52.1	13.9 41	36.6 16	2.07 69	25.6 58	38.4 48	3.08 64	19.1 62	33.6 70	1.70 42	59.6 44	68.5 55	41.9 37	79.4 84	87.5 37	43.7 100	36.7 46	66.3 6	25.4 72	31.9 35	64.7 53	4.67 21	39.4 54	79.4 62	3.26 75
CPM-Flow [116]	52.7	13.8 29	37.8 51	1.87 46	25.1 32	38.2 45	2.93 52	19.0 52	33.4 68	1.61 30	59.6 44	68.7 74	42.1 49	79.3 54	87.5 37	43.5 69	36.8 58	66.9 32	25.5 75	32.0 49	65.2 81	4.68 29	39.5 67	79.5 70	3.25 71
TriFlow [95]	53.6	14.2 66	39.0 90	2.20 81	26.6 81	39.3 67	4.59 102	19.0 52	33.7 73	1.71 47	59.9 69	68.7 74	41.4 4	79.2 30	87.5 37	43.5 69	36.7 46	67.1 50	25.2 46	31.8 20	63.9 24	4.69 32	39.1 26	79.0 33	3.23 67
SimpleFlow [49]	53.9	14.1 58	38.9 86	1.92 55	25.5 50	37.9 35	2.85 46	19.0 52	32.3 44	2.26 97	59.2 19	67.3 11	42.4 76	79.2 30	87.5 37	43.2 37	36.7 46	67.6 87	25.1 36	32.0 49	66.1 102	5.29 113	39.3 42	79.2 46	3.15 39
EpicFlow [102]	53.9	13.8 29	37.6 45	1.87 46	25.5 50	38.9 59	2.96 54	18.9 46	33.7 73	1.64 36	59.5 38	68.5 55	42.3 69	79.4 84	87.6 70	43.5 69	36.5 20	67.5 81	24.9 16	32.0 49	65.1 72	4.74 54	39.4 54	79.4 62	3.22 62
SRR-TVOF-NL [91]	54.1	14.2 66	37.6 45	2.07 69	26.1 70	39.8 80	3.30 79	19.4 85	33.9 80	1.82 63	59.8 60	68.6 68	41.0 1	79.1 11	87.5 37	42.9 11	36.0 4	66.9 32	24.1 2	32.9 105	64.8 57	4.81 76	39.6 74	79.4 62	3.22 62
Kuang [131]	54.1	13.8 29	38.3 67	1.78 26	25.6 58	39.2 65	2.84 45	19.1 62	33.4 68	1.66 38	59.5 38	68.5 55	42.2 60	79.3 54	87.6 70	43.2 37	36.5 20	67.1 50	24.8 13	32.2 74	65.9 98	4.80 73	39.6 74	79.6 77	3.19 48
DeepFlow [86]	54.3	13.7 20	35.7 4	2.03 66	25.6 58	38.2 45	3.30 79	19.2 73	33.9 80	1.74 52	59.7 51	68.0 36	42.2 60	79.4 84	87.5 37	43.7 100	37.3 91	66.4 9	26.2 104	31.8 20	64.8 57	4.63 6	39.3 42	79.3 54	3.26 75
CostFilter [40]	55.1	13.6 15	37.4 39	1.63 8	25.5 50	39.7 78	2.75 21	19.0 52	36.0 110	1.79 58	59.4 33	68.8 80	42.0 45	79.4 84	87.6 70	43.7 100	38.6 113	67.1 50	28.1 122	31.9 35	64.6 49	4.81 76	39.0 17	78.5 14	3.00 4
OFH [38]	55.4	14.1 58	38.2 66	2.03 66	25.6 58	38.4 48	3.01 58	19.4 85	35.1 101	1.79 58	59.5 38	68.8 80	42.3 69	79.1 11	87.4 12	43.1 21	36.7 46	67.6 87	25.2 46	32.1 61	65.1 72	4.79 67	39.2 36	79.2 46	3.15 39
Complementary OF [21]	55.7	13.7 20	37.8 51	1.71 16	25.2 35	38.6 54	2.81 40	19.8 106	33.7 73	2.38 102	59.9 69	69.2 92	42.8 88	79.2 30	87.5 37	43.1 21	36.6 32	67.4 74	25.2 46	32.3 81	65.4 87	4.79 67	38.8 8	78.9 29	3.29 79
RFlow [90]	55.9	13.8 29	37.8 51	2.02 64	26.0 67	39.1 64	2.85 46	19.0 52	33.1 61	1.86 66	59.7 51	68.4 50	42.2 60	79.2 30	87.6 70	43.4 62	36.1 7	66.8 27	24.5 6	32.2 74	65.1 72	4.82 81	39.7 81	79.8 86	3.34 85
DPOF [18]	56.1	14.2 66	39.1 92	2.19 80	24.8 24	37.0 23	2.80 38	19.3 80	31.9 34	2.01 79	60.2 84	69.5 101	42.3 69	79.1 11	87.4 12	43.1 21	36.7 46	67.1 50	24.6 7	32.4 87	65.3 85	4.81 76	39.5 67	79.5 70	3.18 45
Aniso. Huber-L1 [22]	56.3	14.3 78	38.5 72	2.17 74	26.6 81	39.5 76	3.21 74	19.2 73	32.5 49	1.83 65	59.7 51	68.7 74	41.9 37	79.2 30	87.4 12	43.2 37	36.3 9	67.1 50	24.6 7	32.2 74	64.9 63	4.71 37	39.7 81	79.6 77	3.24 70
OAR-Flow [125]	56.9	14.0 48	36.9 25	2.05 68	25.3 40	38.1 40	3.11 67	19.1 62	34.0 86	1.70 42	59.2 19	68.6 68	41.9 37	79.4 84	87.6 70	43.5 69	36.9 65	67.8 94	25.3 62	32.0 49	65.1 72	4.75 58	39.3 42	79.3 54	3.18 45
TC/T-Flow [76]	57.1	14.3 78	38.8 80	1.84 35	25.3 40	38.6 54	2.81 40	18.9 46	32.4 48	1.58 23	59.9 69	69.5 101	42.1 49	79.3 54	87.5 37	43.5 69	37.1 80	68.0 102	25.2 46	32.1 61	65.2 81	4.81 76	39.2 36	79.4 62	3.00 4
Brox et al. [5]	57.4	14.0 48	37.4 39	1.90 49	26.4 78	40.1 87	3.08 64	19.3 80	35.0 100	1.97 74	59.7 51	68.2 41	41.7 25	79.4 84	87.6 70	43.6 86	36.6 32	66.9 32	25.1 36	31.9 35	64.8 57	4.73 46	39.4 54	79.5 70	3.15 39
Sparse Occlusion [54]	58.3	14.2 66	38.6 74	1.99 61	25.8 66	39.2 65	2.78 33	19.3 80	32.3 44	1.80 61	59.8 60	68.8 80	41.7 25	79.3 54	87.5 37	43.2 37	37.1 80	68.4 112	25.3 62	32.1 61	64.4 44	4.60 1	39.7 81	79.6 77	3.15 39
ComplOF-FED-GPU [35]	58.8	14.0 48	38.0 59	1.91 51	25.3 40	38.5 51	2.90 50	20.2 110	34.6 94	2.16 92	59.5 38	68.5 55	42.5 81	79.2 30	87.4 12	43.2 37	36.6 32	67.4 74	25.0 22	32.2 74	65.4 87	4.75 58	39.7 81	79.8 86	3.19 48
Aniso-Texture [82]	59.0	13.6 15	36.6 16	1.82 30	26.2 73	39.3 67	3.20 73	19.6 95	33.0 58	1.96 73	59.7 51	68.5 55	42.6 84	79.4 84	87.6 70	43.6 86	37.0 74	68.4 112	25.7 89	31.9 35	63.8 20	4.63 6	39.4 54	79.3 54	3.16 43
GraphCuts [14]	59.1	15.1 103	39.3 96	2.68 99	26.4 78	39.4 74	4.50 100	19.2 73	30.7 12	2.69 108	60.7 94	68.6 68	42.8 88	79.0 5	87.4 12	42.5 5	35.6 1	66.7 20	23.7 1	32.0 49	65.0 65	5.04 99	39.0 17	79.2 46	3.48 105
Fusion [6]	59.9	13.8 29	38.4 71	1.84 35	25.3 40	38.1 40	2.88 49	19.1 62	32.2 42	1.90 69	60.9 98	69.8 105	41.8 32	79.1 11	87.9 110	42.1 2	36.0 4	67.8 94	24.1 2	32.7 100	66.3 106	4.88 85	39.5 67	80.4 109	3.26 75
DF-Auto [115]	60.4	14.2 66	36.7 21	2.25 85	26.5 80	39.0 62	4.23 94	18.8 39	31.4 28	1.58 23	60.1 80	69.3 93	41.6 17	79.3 54	87.5 37	43.6 86	36.6 32	67.0 40	25.1 36	32.3 81	65.1 72	4.81 76	39.9 88	80.1 98	3.22 62
Classic++ [32]	60.6	14.0 48	38.1 61	2.17 74	25.7 64	38.8 57	2.96 54	19.3 80	33.9 80	1.93 70	59.7 51	67.9 34	42.8 88	79.2 30	87.5 37	43.3 47	37.4 95	67.0 40	26.6 111	31.8 20	64.3 35	4.78 66	39.4 54	79.5 70	3.36 89
Steered-L1 [118]	60.8	13.7 20	37.5 42	1.84 35	25.5 50	38.9 59	3.17 72	19.7 102	33.1 61	2.40 103	60.2 84	68.5 55	42.8 88	79.4 84	87.7 96	43.5 69	36.6 32	67.0 40	25.6 80	31.8 20	64.6 49	4.96 93	38.6 4	79.0 33	3.36 89
ALD-Flow [66]	61.9	14.1 58	37.9 56	2.17 74	25.4 48	38.4 48	3.14 69	19.1 62	33.9 80	1.73 48	59.6 44	69.0 88	42.6 84	79.4 84	87.6 70	43.6 86	37.0 74	67.5 81	25.6 80	31.7 11	64.0 29	4.69 32	39.4 54	79.5 70	3.20 56
p-harmonic [29]	62.1	13.5 8	36.7 21	1.85 39	26.7 88	39.9 84	3.25 77	19.4 85	35.2 103	2.10 85	60.1 80	68.7 74	42.2 60	79.3 54	87.5 37	43.3 47	36.7 46	66.7 20	25.3 62	32.6 96	65.8 95	4.76 62	39.4 54	79.5 70	3.17 44
Shiralkar [42]	65.1	14.2 66	39.0 90	2.02 64	26.8 89	40.3 91	2.98 56	18.5 23	38.0 122	2.48 106	60.1 80	67.7 22	41.8 32	78.8 2	87.2 3	42.3 4	37.7 102	67.2 60	26.2 104	33.2 111	67.1 110	4.94 90	39.4 54	79.3 54	3.10 27
HBM-GC [105]	68.0	14.7 93	39.4 101	2.41 94	25.4 48	38.1 40	3.07 63	18.0 13	29.8 2	1.56 20	59.8 60	68.2 41	42.8 88	80.1 121	88.0 116	45.9 124	37.5 96	68.2 108	26.1 102	31.9 35	63.3 6	4.99 95	39.3 42	79.1 43	3.30 81
FlowNet2 [122]	68.1	15.9 115	41.4 114	2.76 102	27.1 93	40.2 89	4.29 96	19.6 95	34.3 90	1.88 67	60.0 74	70.2 109	42.0 45	79.4 84	87.7 96	43.3 47	36.4 14	66.3 6	24.9 16	32.1 61	64.5 45	4.71 37	39.6 74	79.2 46	3.08 19
SIOF [67]	69.2	14.7 93	39.5 103	2.23 84	27.1 93	40.3 91	4.25 95	19.1 62	32.9 55	1.82 63	59.8 60	68.6 68	42.1 49	79.1 11	87.4 12	43.0 16	37.1 80	67.1 50	25.5 75	32.4 87	64.9 63	4.79 67	40.1 98	79.9 90	3.40 96
CLG-TV [48]	69.5	14.3 78	38.8 80	2.17 74	26.6 81	39.8 80	3.24 76	19.5 92	33.9 80	2.11 87	60.0 74	69.0 88	42.4 76	79.3 54	87.6 70	43.5 69	36.6 32	66.9 32	25.1 36	32.1 61	65.1 72	4.71 37	39.9 88	80.0 94	3.20 56
MLDP_OF [89]	70.1	13.9 41	38.1 61	1.81 28	25.6 58	38.9 59	2.80 38	18.8 39	32.3 44	1.61 30	59.6 44	68.3 47	42.3 69	79.3 54	87.6 70	43.9 110	39.6 125	68.7 117	28.5 124	33.0 109	65.3 85	5.09 103	39.6 74	79.2 46	3.51 107
Local-TV-L1 [65]	71.9	14.9 98	37.3 35	3.21 114	27.3 98	39.5 76	4.67 103	18.9 46	32.3 44	1.70 42	61.3 109	68.6 68	47.1 123	79.3 54	87.6 70	43.6 86	39.0 117	66.7 20	28.9 126	31.7 11	64.3 35	4.79 67	39.3 42	79.1 43	3.41 99
IAOF [50]	72.6	15.5 111	39.2 95	2.93 111	29.4 113	43.0 115	5.18 112	17.8 8	33.0 58	2.04 80	60.8 96	68.9 84	42.2 60	79.2 30	87.4 12	43.3 47	36.8 58	67.2 60	25.1 36	32.7 100	65.6 94	4.67 21	40.0 92	80.0 94	3.20 56
F-TV-L1 [15]	72.7	15.0 99	39.3 96	2.88 109	27.2 96	40.2 89	3.69 87	19.2 73	34.5 93	2.19 94	59.7 51	68.4 50	42.8 88	78.9 3	87.4 12	42.7 9	37.3 91	67.0 40	25.6 80	32.1 61	64.5 45	4.89 86	40.1 98	80.0 94	3.42 100
TCOF [69]	73.6	14.4 83	39.3 96	1.83 33	27.3 98	40.9 101	3.35 81	18.7 36	32.1 39	1.50 10	60.2 84	70.2 109	42.1 49	79.3 54	87.6 70	43.2 37	36.9 65	68.5 115	24.8 13	33.3 112	65.8 95	4.72 42	41.2 120	81.4 122	3.46 102
BriefMatch [124]	74.6	14.0 48	37.0 28	2.17 74	25.6 58	38.8 57	3.98 92	19.7 102	33.0 58	2.69 108	61.1 105	69.0 88	46.4 120	79.3 54	87.6 70	43.8 107	40.5 128	67.9 99	30.6 128	31.8 20	64.0 29	4.94 90	39.0 17	78.8 26	3.34 85
CNN-flow-warp+ref [117]	76.2	13.8 29	36.0 5	2.35 91	26.6 81	39.8 80	3.83 89	20.0 108	35.5 106	2.34 99	60.9 98	68.9 84	43.0 99	79.4 84	87.6 70	43.7 100	36.8 58	67.0 40	25.6 80	32.1 61	66.2 104	4.94 90	39.4 54	79.5 70	3.19 48
Adaptive [20]	76.2	14.5 87	39.6 105	2.31 89	27.1 93	40.4 94	3.35 81	18.6 32	33.7 73	1.98 75	59.6 44	68.2 41	42.4 76	79.4 84	87.6 70	43.4 62	37.1 80	67.5 81	25.7 89	32.4 87	64.8 57	4.73 46	40.0 92	80.1 98	3.38 94
FlowNetS+ft+v [112]	77.0	14.7 93	38.1 61	2.80 105	27.5 100	40.6 98	4.81 106	19.6 95	34.9 98	2.07 81	60.1 80	69.5 101	42.2 60	79.4 84	87.7 96	43.4 62	36.6 32	67.1 50	25.2 46	32.0 49	65.4 87	4.73 46	39.6 74	79.7 84	3.21 60
SPSA-learn [13]	77.6	14.8 97	37.8 51	2.72 101	27.6 101	40.1 87	4.71 104	20.5 112	33.7 73	2.97 115	60.4 88	67.6 18	41.5 11	79.3 54	87.5 37	43.5 69	36.8 58	67.2 60	25.2 46	33.4 114	70.8 131	6.21 130	39.7 81	79.6 77	3.19 48
AdaConv-v1 [126]	78.1	16.5 119	42.3 117	4.36 121	30.4 119	43.8 118	9.06 127	20.6 115	36.3 114	4.45 127	64.5 125	71.3 123	45.3 115	78.4 1	86.7 1	42.2 3	36.3 9	64.9 2	25.4 72	32.5 92	63.7 18	5.53 123	38.0 2	77.4 2	3.53 109
LDOF [28]	78.8	15.0 99	38.8 80	2.92 110	28.0 106	41.1 103	5.03 109	19.7 102	34.8 97	2.15 90	60.0 74	68.9 84	42.6 84	79.4 84	87.6 70	43.5 69	36.9 65	66.8 27	25.5 75	31.9 35	65.1 72	4.73 46	39.5 67	79.6 77	3.23 67
HBpMotionGpu [43]	79.2	15.8 114	40.2 110	3.66 119	29.5 114	42.8 114	6.27 117	18.5 23	31.9 34	1.73 48	61.3 109	69.9 106	43.9 107	79.1 11	87.6 70	43.0 16	37.6 101	67.6 87	25.9 96	32.0 49	64.3 35	4.67 21	40.0 92	79.9 90	3.75 118
ROF-ND [107]	79.2	15.1 103	37.9 56	1.86 42	26.3 76	40.5 97	3.12 68	19.6 95	32.8 53	1.68 40	60.9 98	71.1 122	41.9 37	79.3 54	87.5 37	43.5 69	37.0 74	68.2 108	24.9 16	34.3 128	68.3 119	5.28 112	40.5 114	80.5 112	3.25 71
CRTflow [80]	79.4	14.4 83	38.9 86	2.38 92	26.0 67	39.0 62	3.14 69	20.2 110	36.2 113	2.37 101	60.5 91	69.5 101	44.1 108	79.3 54	87.5 37	43.4 62	37.1 80	67.3 69	25.7 89	32.0 49	64.6 49	4.85 82	39.6 74	79.6 77	3.45 101
Occlusion-TV-L1 [63]	79.8	14.3 78	39.1 92	2.21 83	26.6 81	40.0 85	3.14 69	19.2 73	34.2 88	2.15 90	60.0 74	68.5 55	42.8 88	79.3 54	87.5 37	43.6 86	37.5 96	67.0 40	26.2 104	32.9 105	65.1 72	5.16 107	40.0 92	79.8 86	3.30 81
Modified CLG [34]	79.9	14.1 58	37.6 45	2.33 90	28.5 110	41.4 107	5.68 113	19.6 95	35.8 109	2.31 98	60.2 84	68.6 68	42.1 49	79.4 84	87.5 37	43.5 69	36.7 46	67.2 60	25.2 46	32.3 81	66.0 100	4.76 62	40.2 102	80.4 109	3.40 96
CBF [12]	81.1	13.7 20	37.2 34	2.15 73	26.0 67	39.4 74	3.28 78	19.1 62	32.1 39	1.79 58	61.0 103	70.0 108	45.8 117	79.6 112	87.8 109	44.9 120	36.8 58	67.4 74	25.2 46	32.2 74	65.5 91	5.22 109	40.0 92	80.2 105	3.99 123
TriangleFlow [30]	82.9	14.7 93	40.0 109	2.29 88	26.6 81	40.8 99	3.03 60	19.4 85	33.3 66	2.10 85	60.4 88	69.9 106	42.8 88	79.0 5	87.4 12	42.6 6	37.7 102	68.3 111	25.3 62	33.1 110	67.8 114	5.24 111	40.4 110	80.6 114	3.32 84
2D-CLG [1]	83.0	14.5 87	37.6 45	2.76 102	29.8 116	42.4 111	6.69 121	19.7 102	35.2 103	2.74 111	60.7 94	68.7 74	41.5 11	79.4 84	87.7 96	43.5 69	36.6 32	67.0 40	25.1 36	32.5 92	66.7 108	4.90 88	40.2 102	80.1 98	3.25 71
Nguyen [33]	83.4	15.6 112	38.5 72	3.62 118	30.1 118	43.2 116	6.04 115	19.6 95	36.3 114	2.25 96	61.1 105	69.4 98	42.0 45	79.2 30	87.5 37	43.1 21	36.4 14	67.2 60	24.7 12	34.3 128	67.4 113	5.00 96	40.2 102	80.3 106	3.29 79
BlockOverlap [61]	83.5	15.1 103	37.6 45	3.31 116	27.7 103	39.3 67	5.73 114	18.6 32	30.3 4	2.09 84	60.9 98	68.2 41	47.1 123	80.2 122	87.9 110	46.5 125	39.0 117	67.3 69	28.4 123	31.9 35	63.9 24	5.09 103	39.7 81	79.3 54	3.55 110
SegOF [10]	84.0	14.2 66	36.8 24	2.54 96	27.0 92	40.0 85	4.18 93	21.1 118	36.1 112	3.15 120	60.5 91	70.7 118	41.6 17	79.4 84	87.6 70	43.6 86	36.9 65	68.2 108	25.2 46	32.5 92	68.0 118	5.31 116	39.6 74	79.4 62	3.22 62
ACK-Prior [27]	84.3	13.8 29	38.1 61	1.74 21	25.5 50	39.3 67	2.82 43	19.6 95	33.8 79	2.45 105	60.5 91	70.3 111	42.3 69	80.2 122	88.0 116	45.8 123	38.2 107	67.8 94	26.9 115	32.6 96	66.2 104	5.35 118	38.9 9	79.7 84	3.60 114
IAOF2 [51]	84.9	15.6 112	41.3 113	2.58 98	27.6 101	41.4 107	4.29 96	17.8 8	33.6 70	1.94 71	61.2 108	70.8 119	42.8 88	79.4 84	87.7 96	43.3 47	37.2 87	67.5 81	25.6 80	32.3 81	65.0 65	4.63 6	40.6 115	80.4 109	3.40 96
StereoOF-V1MT [119]	86.0	14.6 91	39.9 107	2.00 63	27.2 96	41.9 109	3.04 61	20.9 117	37.8 120	2.85 114	61.3 109	68.3 47	43.8 105	79.2 30	87.5 37	42.9 11	38.2 107	67.8 94	26.3 108	33.8 121	68.5 120	5.36 119	40.0 92	79.4 62	3.09 23
Dynamic MRF [7]	86.5	13.9 41	38.6 74	1.90 49	26.1 70	40.4 94	3.08 64	20.0 108	37.7 119	2.73 110	61.3 109	69.3 93	44.6 110	79.1 11	87.6 70	43.0 16	37.7 102	68.0 102	25.9 96	32.6 96	67.2 111	5.08 102	40.4 110	80.5 112	3.49 106
TV-L1-improved [17]	86.7	14.2 66	38.8 80	2.25 85	26.9 90	40.3 91	3.40 85	19.5 92	33.9 80	2.44 104	59.9 69	69.0 88	42.7 87	79.4 84	87.7 96	43.5 69	37.2 87	67.6 87	25.8 93	32.1 61	66.1 102	5.05 100	39.9 88	80.0 94	3.46 102
Correlation Flow [75]	86.7	14.0 48	38.3 67	1.61 3	26.2 73	39.8 80	2.98 56	19.1 62	31.9 34	1.73 48	60.4 88	69.4 98	43.6 104	80.2 122	87.9 110	47.8 128	38.0 106	68.7 117	26.0 100	33.4 114	67.2 111	5.29 113	40.1 98	80.3 106	3.39 95
Black & Anandan [4]	89.5	15.3 107	38.8 80	2.96 112	28.4 108	40.9 101	4.78 105	20.5 112	35.2 103	2.74 111	60.9 98	69.3 93	42.1 49	79.4 84	87.7 96	43.6 86	37.1 80	66.6 16	25.6 80	32.9 105	65.9 98	4.72 42	40.3 105	80.3 106	3.25 71
Rannacher [23]	90.0	14.4 83	39.3 96	2.38 92	26.9 90	40.4 94	3.36 84	19.5 92	34.6 94	2.58 107	59.8 60	68.8 80	42.8 88	79.4 84	87.7 96	43.6 86	37.2 87	67.8 94	25.8 93	32.2 74	66.0 100	5.02 97	39.9 88	79.9 90	3.56 111
LocallyOriented [52]	91.5	15.0 99	40.3 111	2.53 95	27.7 103	41.3 105	3.86 90	19.4 85	34.4 91	1.95 72	61.1 105	70.6 115	43.3 102	79.2 30	87.5 37	43.3 47	39.1 121	68.1 106	27.6 120	32.9 105	65.8 95	4.72 42	40.6 115	80.6 114	3.37 92
UnFlow [129]	93.0	16.0 116	42.8 119	2.87 108	30.6 121	45.2 126	4.52 101	21.3 121	39.4 125	2.81 113	60.0 74	68.3 47	42.1 49	79.2 30	87.4 12	43.5 69	37.5 96	68.0 102	25.2 46	33.8 121	65.1 72	4.98 94	43.2 130	81.8 125	3.67 116
Filter Flow [19]	96.8	15.0 99	39.4 101	2.78 104	28.4 108	40.8 99	6.31 118	18.5 23	32.9 55	2.14 88	61.7 113	69.3 93	45.3 115	79.7 115	88.0 116	44.5 117	37.3 91	67.7 91	26.1 102	32.1 61	65.2 81	4.93 89	40.3 105	80.7 117	3.97 122
StereoFlow [44]	98.5	22.8 130	51.1 131	4.80 122	36.2 130	51.1 131	6.57 120	19.2 73	34.6 94	1.89 68	60.0 74	68.5 55	42.4 76	80.3 125	89.1 130	43.9 110	39.0 117	74.1 131	25.3 62	32.1 61	65.0 65	4.73 46	40.3 105	80.9 118	3.36 89
Ad-TV-NDC [36]	101.9	17.2 121	39.9 107	5.26 124	29.6 115	42.1 110	6.18 116	19.2 73	33.7 73	1.98 75	62.4 115	70.3 111	45.2 114	79.6 112	87.9 110	43.9 110	38.3 110	67.3 69	27.2 119	32.3 81	65.5 91	4.80 73	40.3 105	80.1 98	3.58 113
Bartels [41]	103.1	14.6 91	39.3 96	2.80 105	26.1 70	39.7 78	4.45 99	19.0 52	33.2 64	2.14 88	62.1 114	70.9 120	48.9 125	80.7 129	88.1 119	49.2 130	43.7 130	69.0 123	34.8 130	32.4 87	65.0 65	5.76 127	40.4 110	80.1 98	4.26 125
TI-DOFE [24]	104.5	17.9 124	43.0 120	5.41 125	32.3 126	46.2 128	7.98 125	20.5 112	38.1 123	2.97 115	63.1 122	70.6 115	43.8 105	79.1 11	87.6 70	43.1 21	37.7 102	67.4 74	25.8 93	33.4 114	67.8 114	5.09 103	41.6 124	81.5 124	3.68 117
Horn & Schunck [3]	105.5	15.3 107	40.4 112	2.69 100	29.0 111	42.7 112	5.10 110	21.1 118	37.9 121	3.33 121	62.5 117	70.3 111	43.0 99	79.3 54	87.7 96	43.6 86	37.5 96	67.3 69	25.9 96	33.9 125	68.5 120	5.03 98	41.2 120	81.2 121	3.57 112
GroupFlow [9]	107.6	16.8 120	43.4 121	3.43 117	29.1 112	43.9 119	5.11 111	22.2 124	39.3 124	3.53 122	61.0 103	70.6 115	42.5 81	79.7 115	88.1 119	44.0 113	39.0 117	69.4 127	26.8 114	32.8 102	66.8 109	4.87 84	40.4 110	80.1 98	3.01 6
2bit-BM-tele [98]	109.3	15.3 107	39.5 103	3.22 115	27.8 105	41.2 104	4.90 107	18.8 39	32.5 49	2.34 99	62.4 115	71.0 121	49.0 126	80.6 127	88.2 124	47.9 129	42.8 129	69.3 126	32.9 129	33.4 114	70.0 129	6.77 131	40.3 105	79.4 62	4.33 128
SLK [47]	109.9	17.4 122	43.9 123	4.90 123	30.5 120	44.0 120	7.18 123	22.5 125	39.8 126	4.15 126	64.5 125	70.5 114	46.7 121	78.9 3	87.7 96	41.6 1	38.5 112	68.8 119	26.0 100	33.8 121	70.1 130	5.50 122	41.6 124	81.4 122	3.91 120
SILK [79]	110.9	16.3 117	42.0 116	4.01 120	29.9 117	43.5 117	6.44 119	21.6 122	37.4 118	3.55 123	62.6 118	69.4 98	47.0 122	79.3 54	87.7 96	43.6 86	39.9 126	68.1 106	29.2 127	32.8 102	67.8 114	5.14 106	40.6 115	80.6 114	3.52 108
NL-TV-NCC [25]	111.1	15.1 103	41.6 115	1.86 42	26.6 81	41.3 105	3.02 59	20.8 116	35.7 107	2.24 95	63.2 123	73.9 127	45.9 118	81.3 131	88.7 129	49.9 131	38.6 113	69.8 129	25.6 80	37.6 131	69.5 126	5.62 125	42.4 129	82.1 128	4.00 124
HCIC-L [99]	112.2	23.2 131	49.0 130	11.0 131	32.1 125	44.4 121	9.93 128	23.2 127	36.4 116	3.02 117	64.4 124	72.1 124	44.9 111	80.6 127	88.5 127	46.6 126	39.1 121	68.9 121	27.1 117	32.4 87	65.0 65	5.53 123	39.1 26	79.3 54	3.65 115
Heeger++ [104]	113.4	17.5 123	47.2 129	2.80 105	31.1 123	44.9 124	4.93 108	26.6 129	47.7 130	4.79 128	62.6 118	68.0 36	45.1 112	79.8 119	88.4 126	44.1 114	39.1 121	68.9 121	26.5 110	34.8 130	67.9 117	5.23 110	41.5 123	80.1 98	3.23 67
FFV1MT [106]	115.5	16.4 118	44.7 125	3.13 113	31.9 124	44.7 122	7.15 122	25.4 128	45.6 129	5.04 129	62.6 118	68.0 36	45.1 112	79.6 112	87.9 110	44.1 114	38.9 116	67.7 91	27.1 117	34.0 127	68.5 120	5.29 113	41.8 127	81.0 119	4.48 130
Learning Flow [11]	115.8	15.3 107	42.7 118	2.55 97	28.0 106	42.7 112	3.95 91	21.1 118	37.0 117	3.03 119	63.0 121	73.3 126	46.2 119	80.0 120	88.2 124	45.1 121	38.2 107	68.6 116	26.7 112	33.8 121	68.5 120	5.21 108	41.9 128	82.3 129	3.95 121
Adaptive flow [45]	118.8	19.6 126	44.1 124	6.76 126	32.8 127	45.7 127	10.2 129	19.8 106	34.4 91	3.02 117	64.7 127	72.1 124	49.4 127	80.3 125	88.6 128	45.6 122	38.3 110	69.2 124	26.7 112	32.6 96	66.5 107	5.45 121	41.0 118	81.1 120	3.75 118
Pyramid LK [2]	120.5	21.2 129	43.7 122	10.7 130	33.1 129	45.1 125	11.9 130	27.3 130	36.0 110	6.46 130	70.7 131	78.5 131	57.7 131	79.5 109	88.1 119	43.3 47	38.6 113	68.8 119	27.0 116	33.5 119	68.8 124	6.00 128	41.0 118	81.8 125	4.31 127
FOLKI [16]	123.8	20.9 127	46.0 126	9.48 129	32.8 127	47.4 129	8.75 126	21.6 122	40.7 127	4.10 125	67.2 130	74.2 129	53.7 130	79.5 109	88.1 119	43.7 100	39.2 124	69.2 124	27.9 121	33.4 114	69.5 126	5.65 126	41.7 126	82.3 129	4.28 126
PGAM+LK [55]	124.0	19.4 125	46.4 127	6.81 127	30.9 122	44.8 123	7.52 124	22.7 126	40.9 128	3.99 124	66.6 129	73.9 127	52.4 129	79.7 115	88.1 119	44.5 117	40.2 127	69.7 128	28.8 125	33.3 112	69.3 125	5.42 120	41.4 122	81.8 125	4.36 129
Periodicity [78]	129.3	21.0 128	47.0 128	9.32 128	38.1 131	48.1 130	14.7 131	29.8 131	47.9 131	9.27 131	66.0 128	77.1 130	50.7 128	80.8 130	89.3 131	46.8 127	45.1 131	70.6 130	35.5 131	33.5 119	69.6 128	6.07 129	43.5 131	84.0 131	6.51 131

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.