Optical flow evaluation results: R0.5 normalized interpolation error

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

R0.5 normalized interpolation error	avg.	Mequon (Hidden texture) im0 GT im1			Schefflera (Hidden texture) im0 GT im1			Urban (Synthetic) im0 GT im1			Teddy (Stereo) im0 GT im1			Backyard (High-speed camera) im0 GT im1			Basketball (High-speed camera) im0 GT im1			Dumptruck (High-speed camera) im0 GT im1			Evergreen (High-speed camera) im0 GT im1
	rank	all	disc	untext	all	disc	untext	all	disc	untext	all	disc	untext	all	disc	untext	all	disc	untext	all	disc	untext	all	disc	untext
MDP-Flow2 [68]	9.9	35.7 2	30.6 2	47.8 8	25.9 10	30.5 10	36.9 3	28.6 3	29.8 5	38.5 2	51.9 7	46.5 15	80.3 10	71.9 4	66.6 3	87.2 8	68.6 5	53.9 23	82.1 35	28.1 2	43.6 8	42.4 10	36.6 28	55.6 29	50.0 6
PMMST [114]	10.9	35.8 4	30.8 3	47.9 18	26.5 32	31.0 20	37.3 36	28.6 3	29.9 6	38.4 1	51.7 3	46.0 5	80.2 5	72.0 9	66.7 8	87.3 16	68.5 1	53.3 7	82.0 13	28.1 2	43.7 13	42.4 10	36.5 18	55.5 23	50.0 6
PH-Flow [101]	11.7	36.1 21	32.5 29	47.8 8	25.6 4	29.6 4	36.9 3	28.7 9	30.0 8	38.5 2	51.6 1	45.5 1	80.2 5	71.9 4	66.7 8	87.3 16	68.8 35	54.8 66	81.9 5	28.1 2	43.6 8	42.4 10	36.4 12	55.3 14	50.0 6
NNF-Local [87]	12.1	35.7 2	31.4 6	47.6 3	25.5 2	29.6 4	36.9 3	28.6 3	29.9 6	38.5 2	52.4 31	48.0 62	80.3 10	72.0 9	66.6 3	87.4 34	68.7 19	54.4 45	82.0 13	28.1 2	43.5 6	42.4 10	36.2 4	55.0 6	50.0 6
NN-field [71]	17.1	36.0 16	32.2 18	47.9 18	25.5 2	29.3 3	36.8 1	29.4 60	29.7 4	39.0 43	52.4 31	48.1 68	80.3 10	72.0 9	66.7 8	87.3 16	68.7 19	54.0 28	82.0 13	28.1 2	43.4 4	42.4 10	36.4 12	55.2 10	50.0 6
IROF++ [58]	20.9	36.2 28	33.0 43	47.8 8	26.1 15	30.9 17	36.9 3	29.1 30	31.0 29	38.9 28	51.6 1	45.6 2	80.4 19	72.0 9	66.8 17	87.2 8	68.6 5	53.4 8	82.2 57	28.3 17	44.6 39	42.4 10	36.5 18	55.3 14	50.4 76
ProbFlowFields [128]	21.1	35.9 9	32.4 23	48.0 29	25.8 8	30.5 10	37.2 31	28.6 3	30.3 11	38.5 2	52.1 20	46.4 11	80.7 56	72.3 64	67.1 65	87.5 78	68.6 5	53.8 15	82.1 35	28.0 1	42.8 1	42.3 1	36.1 2	54.6 2	50.1 24
Sparse-NonSparse [56]	22.5	36.2 28	32.8 37	48.0 29	25.9 10	30.4 8	37.0 11	29.0 22	30.9 26	38.8 13	52.0 13	46.1 7	80.6 41	72.1 24	66.8 17	87.3 16	68.9 45	54.6 56	82.1 35	28.3 17	44.0 21	42.4 10	36.4 12	55.4 18	50.1 24
CombBMOF [113]	23.8	35.9 9	31.0 4	47.8 8	25.8 8	30.5 10	36.8 1	29.2 37	30.8 23	39.5 80	52.4 31	47.4 41	80.3 10	72.1 24	66.8 17	87.4 34	68.9 45	54.5 49	82.1 35	28.5 57	44.6 39	42.3 1	36.0 1	54.6 2	50.0 6
nLayers [57]	25.7	36.4 53	32.0 13	48.2 57	26.0 13	30.4 8	37.3 36	28.7 9	29.4 2	38.8 13	52.2 27	46.8 22	80.4 19	72.3 64	67.1 65	87.4 34	68.8 35	54.7 60	82.0 13	28.3 17	43.7 13	42.4 10	36.4 12	55.4 18	49.9 3
AGIF+OF [85]	25.8	36.2 28	32.8 37	47.9 18	26.1 15	30.8 14	37.1 18	29.0 22	30.7 19	38.9 28	51.8 5	46.2 9	80.1 3	72.3 64	67.2 79	87.3 16	68.9 45	55.2 82	81.9 5	28.3 17	43.6 8	42.4 10	36.6 28	56.0 46	49.9 3
2DHMM-SAS [92]	27.5	36.4 53	33.9 80	47.9 18	27.1 57	32.6 43	37.0 11	28.5 2	30.4 14	38.9 28	51.8 5	45.6 2	80.4 19	72.1 24	66.9 29	87.4 34	68.8 35	54.5 49	82.0 13	28.3 17	44.2 27	42.3 1	36.7 40	56.1 54	50.0 6
NNF-EAC [103]	28.1	36.3 42	32.4 23	48.0 29	26.6 35	31.7 30	37.1 18	29.3 47	30.2 9	39.0 43	52.4 31	46.9 25	81.1 97	72.0 9	66.7 8	87.4 34	68.7 19	53.7 14	82.1 35	28.2 11	43.9 16	42.4 10	36.7 40	55.9 43	50.0 6
Layers++ [37]	28.7	36.3 42	32.4 23	48.2 57	25.7 5	29.2 2	37.3 36	28.9 18	30.6 16	38.9 28	52.0 13	46.4 11	80.4 19	72.2 36	67.0 42	87.5 78	68.9 45	55.2 82	82.0 13	28.3 17	44.0 21	42.4 10	36.6 28	55.5 23	50.1 24
LSM [39]	30.1	36.3 42	33.7 70	48.0 29	26.1 15	31.0 20	37.0 11	29.1 30	31.8 44	38.9 28	52.2 27	46.9 25	80.6 41	72.1 24	66.9 29	87.3 16	69.0 59	54.9 69	82.1 35	28.3 17	44.1 25	42.4 10	36.5 18	55.7 33	50.0 6
ComponentFusion [96]	30.5	36.0 16	32.2 18	48.0 29	26.1 15	31.1 25	36.9 3	29.1 30	32.3 52	38.8 13	52.0 13	47.0 29	80.3 10	72.2 36	67.1 65	87.3 16	68.7 19	53.9 23	82.1 35	28.5 57	46.1 92	42.4 10	36.7 40	55.8 40	50.2 45
FlowFields [110]	30.5	36.0 16	32.7 34	47.9 18	26.4 29	32.0 32	37.3 36	29.0 22	32.6 58	38.7 8	52.5 36	47.9 59	80.7 56	72.3 64	67.0 42	87.5 78	68.6 5	54.4 45	82.0 13	28.2 11	44.0 21	42.4 10	36.3 6	55.2 10	50.1 24
S2F-IF [123]	31.4	35.9 9	32.5 29	47.8 8	26.2 22	31.6 29	37.2 31	29.0 22	31.9 49	38.6 7	52.3 29	47.6 46	80.4 19	72.4 91	67.2 79	87.5 78	68.7 19	54.5 49	81.9 5	28.4 38	44.7 44	42.4 10	36.3 6	55.2 10	50.1 24
TV-L1-MCT [64]	31.5	36.8 84	34.7 100	48.2 57	26.7 36	32.4 40	37.3 36	28.6 3	30.9 26	39.0 43	51.9 7	45.7 4	80.5 35	72.2 36	67.0 42	87.3 16	68.6 5	53.0 4	82.3 71	28.3 17	44.4 32	42.4 10	36.1 2	54.9 5	50.2 45
FlowFields+ [130]	31.5	35.9 9	32.6 33	47.9 18	26.4 29	32.2 35	37.4 47	29.0 22	32.6 58	38.7 8	52.3 29	47.7 51	80.6 41	72.3 64	67.1 65	87.5 78	68.7 19	54.6 56	82.0 13	28.2 11	44.0 21	42.4 10	36.3 6	55.2 10	50.1 24
WLIF-Flow [93]	32.3	36.1 21	32.5 29	47.8 8	26.3 27	31.2 26	37.1 18	29.1 30	30.7 19	39.1 50	52.0 13	46.4 11	80.6 41	72.1 24	66.8 17	87.4 34	69.0 59	54.9 69	82.3 71	28.3 17	43.9 16	42.5 62	36.8 47	55.9 43	50.1 24
LME [70]	34.6	35.8 4	31.0 4	47.8 8	26.9 46	32.2 35	38.4 85	29.2 37	32.6 58	38.8 13	51.9 7	46.7 20	80.4 19	72.6 111	67.4 103	87.7 119	68.8 35	54.9 69	82.0 13	28.1 2	43.5 6	42.4 10	36.3 6	55.3 14	50.0 6
COFM [59]	34.8	36.1 21	32.0 13	48.1 43	26.1 15	30.8 14	37.1 18	28.8 12	30.3 11	38.8 13	51.7 3	46.0 5	80.0 1	72.2 36	67.2 79	87.2 8	68.9 45	56.1 108	81.7 1	28.1 2	42.8 1	43.1 116	37.1 78	56.9 87	50.7 106
FMOF [94]	35.6	36.5 61	33.7 70	48.2 57	25.9 10	30.3 7	37.1 18	29.3 47	30.7 19	39.0 43	52.5 36	47.5 42	80.2 5	72.2 36	67.0 42	87.5 78	69.0 59	55.1 79	82.0 13	28.1 2	43.4 4	42.4 10	36.8 47	56.0 46	50.1 24
DeepFlow2 [108]	35.7	36.2 28	32.4 23	48.2 57	27.1 57	32.9 47	37.8 70	29.2 37	32.9 66	39.0 43	52.5 36	47.5 42	80.5 35	72.2 36	66.9 29	87.5 78	68.5 1	52.9 3	82.1 35	28.3 17	44.4 32	42.4 10	36.4 12	55.4 18	50.2 45
OFLAF [77]	36.0	35.8 4	31.5 7	47.8 8	25.7 5	29.8 6	37.0 11	29.0 22	31.2 32	38.7 8	52.0 13	46.8 22	80.1 3	72.4 91	67.3 93	87.4 34	68.9 45	55.3 87	82.0 13	28.6 75	45.4 77	42.4 10	37.1 78	57.1 97	50.1 24
RNLOD-Flow [121]	36.1	36.3 42	33.5 59	48.0 29	26.8 41	32.6 43	37.1 18	29.2 37	31.8 44	38.8 13	52.1 20	46.9 25	80.2 5	72.2 36	67.0 42	87.3 16	69.0 59	55.2 82	82.1 35	28.3 17	44.2 27	42.4 10	37.1 78	56.9 87	49.8 1
DeepFlow [86]	37.2	36.1 21	31.8 11	48.1 43	27.3 62	32.9 47	38.4 85	29.3 47	33.3 80	39.1 50	52.6 49	47.0 29	80.7 56	72.2 36	66.8 17	87.5 78	68.7 19	52.8 2	82.5 96	28.1 2	43.6 8	42.3 1	36.2 4	55.0 6	50.2 45
Ramp [62]	38.0	36.5 61	34.0 85	48.2 57	26.0 13	30.8 14	37.1 18	28.9 18	30.8 23	38.8 13	51.9 7	46.1 7	80.4 19	72.2 36	67.0 42	87.4 34	69.1 69	55.4 93	82.2 57	28.4 38	44.7 44	42.4 10	36.8 47	56.2 62	50.2 45
MDP-Flow [26]	38.2	35.8 4	31.5 7	48.0 29	26.2 22	31.4 28	37.4 47	29.0 22	31.1 30	38.9 28	52.7 58	47.8 56	80.7 56	72.2 36	66.9 29	87.5 78	68.9 45	55.2 82	82.1 35	28.5 57	45.3 76	42.5 62	36.3 6	55.4 18	50.0 6
IROF-TV [53]	39.3	36.3 42	33.6 66	48.2 57	26.2 22	31.0 20	37.0 11	29.3 47	33.6 86	39.1 50	51.9 7	46.5 15	80.8 69	72.3 64	67.0 42	87.6 110	68.5 1	53.9 23	81.9 5	28.3 17	44.9 56	42.3 1	36.6 28	55.6 29	50.4 76
PGM-C [120]	39.5	36.2 28	33.3 52	48.1 43	26.5 32	32.2 35	37.5 56	29.2 37	32.9 66	38.8 13	52.5 36	48.3 82	80.7 56	72.3 64	67.0 42	87.5 78	68.6 5	54.0 28	82.0 13	28.3 17	44.6 39	42.4 10	36.5 18	55.5 23	50.4 76
Classic+NL [31]	39.7	36.5 61	34.0 85	48.2 57	26.2 22	30.9 17	37.1 18	28.8 12	30.6 16	38.8 13	52.1 20	46.5 15	80.6 41	72.2 36	67.0 42	87.4 34	69.2 79	55.3 87	82.2 57	28.4 38	44.6 39	42.4 10	36.8 47	56.2 62	50.2 45
DF-Auto [115]	40.2	36.8 84	31.9 12	48.9 94	28.5 87	33.7 66	40.8 99	28.8 12	30.3 11	38.7 8	52.5 36	47.3 35	80.4 19	72.1 24	66.7 8	87.4 34	68.6 5	53.8 15	82.0 13	28.4 38	44.7 44	42.5 62	36.8 47	56.3 67	50.2 45
FC-2Layers-FF [74]	42.5	36.4 53	33.8 76	48.1 43	25.7 5	29.1 1	37.4 47	28.9 18	30.9 26	38.8 13	52.1 20	46.8 22	80.6 41	72.3 64	67.2 79	87.4 34	69.1 69	55.5 95	82.1 35	28.4 38	44.7 44	42.5 62	36.9 61	56.3 67	50.0 6
SuperFlow [81]	43.0	36.5 61	32.2 18	48.8 91	28.3 82	33.4 61	40.9 100	29.5 70	32.6 58	39.4 73	52.5 36	46.7 20	80.9 77	72.2 36	66.9 29	87.5 78	68.5 1	53.4 8	82.0 13	28.3 17	44.7 44	42.4 10	36.3 6	55.4 18	50.1 24
HAST [109]	43.6	36.1 21	31.7 9	48.1 43	26.1 15	31.0 20	37.0 11	29.3 47	31.7 41	39.2 62	51.9 7	46.5 15	80.3 10	72.3 64	67.3 93	87.2 8	69.3 90	56.4 116	82.0 13	28.4 38	45.0 63	42.5 62	37.3 91	57.3 101	50.0 6
CPM-Flow [116]	44.4	36.2 28	33.5 59	48.1 43	26.5 32	32.2 35	37.5 56	29.3 47	32.6 58	38.9 28	52.7 58	48.7 95	80.7 56	72.3 64	67.0 42	87.5 78	68.7 19	53.8 15	82.2 57	28.4 38	44.7 44	42.4 10	36.5 18	55.5 23	50.3 62
Second-order prior [8]	44.5	36.2 28	32.1 16	48.1 43	27.9 76	34.1 72	37.4 47	29.9 86	34.6 101	39.7 90	52.4 31	47.2 33	80.6 41	71.9 4	66.6 3	87.5 78	68.7 19	54.0 28	82.1 35	28.5 57	45.2 74	42.4 10	36.5 18	55.7 33	50.2 45
Kuang [131]	45.3	36.2 28	33.5 59	47.9 18	27.0 52	33.3 58	37.4 47	29.4 60	32.9 66	39.1 50	52.5 36	48.1 68	80.6 41	72.3 64	67.1 65	87.4 34	68.7 19	54.3 40	82.1 35	28.5 57	45.4 77	42.4 10	36.5 18	55.5 23	50.3 62
Aniso. Huber-L1 [22]	45.4	36.7 78	33.5 59	48.6 85	28.5 87	34.3 75	38.2 81	29.3 47	31.8 44	38.9 28	52.5 36	47.5 42	80.6 41	72.0 9	66.7 8	87.4 34	68.6 5	54.3 40	81.9 5	28.5 57	45.0 63	42.4 10	36.8 47	56.0 46	50.3 62
Classic+CPF [83]	45.5	36.4 53	33.6 66	47.9 18	26.3 27	31.3 27	37.0 11	28.8 12	31.1 30	38.9 28	52.0 13	46.5 15	80.0 1	72.5 103	67.4 103	87.4 34	69.2 79	56.1 108	82.0 13	28.6 75	45.2 74	42.4 10	37.2 85	57.3 101	50.0 6
RFlow [90]	46.8	36.2 28	33.0 43	48.2 57	27.6 68	33.7 66	37.1 18	29.3 47	32.5 57	39.2 62	52.6 49	47.8 56	80.6 41	72.0 9	66.8 17	87.3 16	68.6 5	53.8 15	81.9 5	28.5 57	45.5 83	42.6 90	37.2 85	56.9 87	50.3 62
EpicFlow [102]	47.0	36.2 28	33.3 52	48.1 43	26.9 46	33.1 53	37.5 56	29.4 60	33.0 72	39.0 43	52.6 49	48.5 86	80.8 69	72.3 64	67.0 42	87.5 78	68.6 5	54.1 32	82.0 13	28.4 38	44.8 53	42.4 10	36.6 28	55.7 33	50.4 76
Brox et al. [5]	47.8	36.3 42	32.4 23	48.2 57	27.8 74	34.1 72	38.0 78	29.8 82	33.9 92	39.6 88	52.5 36	47.0 29	80.4 19	72.2 36	66.9 29	87.5 78	68.7 19	53.8 15	82.1 35	28.4 38	44.9 56	42.5 62	36.5 18	55.5 23	50.2 45
S2D-Matching [84]	48.5	36.6 70	34.2 91	48.2 57	26.9 46	32.5 42	37.2 31	28.8 12	30.7 19	38.9 28	52.1 20	46.4 11	80.9 77	72.3 64	67.1 65	87.5 78	69.1 69	55.3 87	82.2 57	28.5 57	44.7 44	42.4 10	36.7 40	55.9 43	50.2 45
FESL [72]	49.2	36.6 70	33.9 80	48.0 29	26.4 29	31.7 30	37.3 36	29.1 30	31.3 33	38.9 28	52.6 49	47.6 46	80.3 10	72.4 91	67.3 93	87.4 34	69.3 90	55.9 103	82.1 35	28.4 38	44.9 56	42.3 1	37.0 68	56.6 78	50.1 24
p-harmonic [29]	49.3	35.9 9	32.1 16	47.9 18	28.2 79	34.3 75	37.8 70	29.4 60	34.2 96	39.4 73	53.0 80	47.7 51	80.7 56	72.2 36	67.0 42	87.3 16	68.8 35	54.1 32	82.3 71	28.5 57	45.5 83	42.4 10	36.6 28	56.0 46	50.2 45
SepConv-v1 [127]	49.8	27.1 1	27.5 1	36.4 1	24.9 1	31.0 20	40.3 94	27.6 1	28.4 1	48.9 128	54.0 104	47.3 35	83.0 119	72.0 9	66.7 8	87.1 3	69.1 69	52.6 1	83.6 124	32.2 130	43.9 16	55.7 130	37.0 68	53.8 1	55.4 130
ComplOF-FED-GPU [35]	50.5	36.3 42	33.4 56	48.0 29	26.8 41	33.0 50	37.3 36	30.4 100	34.0 93	39.6 88	52.5 36	48.1 68	80.9 77	72.1 24	66.8 17	87.4 34	68.7 19	54.3 40	82.1 35	28.5 57	45.1 69	42.5 62	36.8 47	56.0 46	50.2 45
TC-Flow [46]	51.3	36.2 28	33.2 49	48.2 57	26.9 46	33.5 63	37.5 56	29.5 70	33.6 86	38.9 28	52.1 20	47.1 32	80.6 41	72.3 64	67.2 79	87.5 78	69.0 59	54.8 66	82.3 71	28.4 38	44.4 32	42.5 62	36.6 28	56.1 54	50.1 24
EPPM w/o HM [88]	51.6	35.8 4	32.3 22	47.6 3	26.7 36	33.0 50	36.9 3	30.0 89	35.5 111	39.4 73	52.6 49	48.9 100	80.4 19	72.2 36	67.1 65	87.4 34	69.3 90	55.9 103	82.3 71	28.4 38	44.9 56	42.5 62	36.8 47	56.1 54	50.1 24
DPOF [18]	52.4	36.7 78	34.5 95	48.6 85	26.1 15	30.6 13	37.6 62	29.8 82	31.4 34	39.3 68	52.8 68	48.6 89	80.8 69	72.0 9	66.8 17	87.3 16	69.1 69	55.3 87	81.9 5	28.5 57	44.5 36	42.5 62	36.9 61	56.5 74	50.0 6
OFH [38]	53.8	36.4 53	33.8 76	48.2 57	27.4 64	33.3 58	37.4 47	29.7 77	35.0 106	39.0 43	52.5 36	48.3 82	80.9 77	72.2 36	67.0 42	87.4 34	68.7 19	54.2 38	82.1 35	28.6 75	45.4 77	42.5 62	36.6 28	56.0 46	50.1 24
Efficient-NL [60]	54.0	36.5 61	33.6 66	48.0 29	26.7 36	32.0 32	37.1 18	29.9 86	31.4 34	39.3 68	52.7 58	47.7 51	80.4 19	72.2 36	67.0 42	87.3 16	69.5 101	57.0 120	81.9 5	28.6 75	45.9 89	42.4 10	37.9 107	58.1 114	50.1 24
Local-TV-L1 [65]	55.6	37.5 100	33.0 43	49.7 106	29.3 99	34.5 83	40.3 94	29.2 37	31.6 39	39.1 50	53.3 91	47.3 35	83.1 122	72.1 24	66.9 29	87.4 34	69.3 90	53.4 8	83.2 118	28.2 11	43.9 16	42.4 10	36.4 12	55.1 8	50.4 76
PMF [73]	55.9	35.9 9	32.0 13	47.7 6	26.9 46	33.5 63	36.9 3	29.6 75	34.5 99	39.1 50	52.5 36	47.8 56	80.4 19	72.5 103	67.5 109	87.4 34	69.2 79	55.0 77	82.4 85	28.5 57	45.0 63	42.5 62	37.3 91	57.3 101	50.0 6
Sparse Occlusion [54]	56.7	36.5 61	33.7 70	48.2 57	27.6 68	34.1 72	37.3 36	29.3 47	31.8 44	38.8 13	52.8 68	48.1 68	80.5 35	72.3 64	67.1 65	87.4 34	69.2 79	56.1 108	82.0 13	28.5 57	45.4 77	42.3 1	37.2 85	57.0 93	50.2 45
TC/T-Flow [76]	56.8	36.6 70	33.7 70	47.9 18	26.8 41	32.9 47	37.1 18	29.1 30	31.9 49	38.8 13	52.7 58	48.5 86	80.4 19	72.5 103	67.4 103	87.5 78	69.1 69	55.2 82	82.1 35	28.6 75	45.4 77	42.5 62	37.0 68	56.9 87	50.0 6
OAR-Flow [125]	56.8	36.5 61	33.0 43	48.4 75	27.0 52	33.0 50	37.8 70	29.2 37	33.3 80	38.9 28	52.1 20	47.6 46	80.5 35	72.4 91	67.3 93	87.6 110	68.9 45	54.5 49	82.2 57	28.5 57	44.7 44	42.4 10	36.9 61	56.5 74	50.4 76
TF+OM [100]	57.2	36.3 42	33.0 43	48.5 78	26.9 46	32.2 35	39.2 89	28.6 3	32.4 55	38.9 28	52.8 68	48.2 80	80.7 56	72.3 64	67.1 65	87.4 34	69.0 59	54.5 49	82.3 71	28.4 38	45.1 69	42.5 62	37.0 68	56.4 71	50.6 100
SRR-TVOF-NL [91]	57.5	36.6 70	33.5 59	48.2 57	27.7 70	34.3 75	37.9 74	29.5 70	33.2 76	39.1 50	53.1 84	48.1 68	80.2 5	72.2 36	67.1 65	87.3 16	68.9 45	55.7 99	81.8 3	28.5 57	44.9 56	42.4 10	37.5 98	58.0 113	50.1 24
SIOF [67]	58.7	36.7 78	34.1 87	48.2 57	29.1 95	35.4 101	39.7 91	29.4 60	32.9 66	39.1 50	52.7 58	47.7 51	80.9 77	71.9 4	66.6 3	87.4 34	69.1 69	54.3 40	82.4 85	28.3 17	44.6 39	42.4 10	37.3 91	56.8 84	50.3 62
CLG-TV [48]	59.0	36.6 70	33.4 56	48.5 78	28.2 79	34.4 80	38.2 81	29.7 77	33.6 86	39.4 73	52.8 68	48.0 62	80.9 77	72.2 36	66.9 29	87.5 78	68.7 19	54.0 28	82.1 35	28.4 38	45.1 69	42.4 10	37.0 68	56.5 74	50.2 45
ALD-Flow [66]	59.2	36.7 78	33.9 80	48.6 85	27.0 52	33.2 55	37.9 74	29.3 47	33.4 83	38.9 28	52.5 36	48.0 62	80.9 77	72.4 91	67.2 79	87.6 110	68.9 45	54.4 45	82.2 57	28.2 11	43.6 8	42.4 10	37.0 68	56.6 78	50.3 62
SimpleFlow [49]	59.8	36.5 61	34.2 91	48.2 57	27.2 60	32.8 46	37.3 36	30.1 95	31.7 41	39.4 73	52.0 13	46.3 10	80.7 56	72.3 64	67.2 79	87.4 34	69.0 59	55.4 93	82.0 13	28.7 86	47.1 103	42.6 90	37.0 68	56.8 84	50.1 24
Complementary OF [21]	60.3	36.1 21	33.3 52	47.8 8	26.7 36	33.2 55	37.3 36	30.4 100	32.9 66	39.5 80	52.8 68	48.7 95	81.1 97	72.3 64	67.2 79	87.3 16	68.8 35	54.7 60	82.2 57	28.7 86	45.6 86	42.5 62	36.8 47	56.7 80	50.3 62
AggregFlow [97]	60.6	37.1 93	34.8 103	48.5 78	27.3 62	33.2 55	38.1 79	28.7 9	30.2 9	38.5 2	52.9 76	48.6 89	80.3 10	72.4 91	67.2 79	87.6 110	69.3 90	54.5 49	82.6 102	28.3 17	44.2 27	42.5 62	36.7 40	56.0 46	50.4 76
LDOF [28]	60.7	37.1 93	33.7 70	48.8 91	29.5 100	35.3 99	40.6 98	30.0 89	34.3 97	39.7 90	52.8 68	47.9 59	80.9 77	72.2 36	66.9 29	87.4 34	68.8 35	53.6 13	82.3 71	28.3 17	44.5 36	42.4 10	36.6 28	55.8 40	50.4 76
MLDP_OF [89]	61.5	36.2 28	32.9 41	48.0 29	27.0 52	32.7 45	37.2 31	29.1 30	31.8 44	38.8 13	52.6 49	47.3 35	80.8 69	72.3 64	67.1 65	87.5 78	70.5 123	56.6 117	83.6 124	28.6 75	44.8 53	42.8 106	36.9 61	56.1 54	50.5 90
F-TV-L1 [15]	61.6	37.4 98	34.6 97	49.2 97	28.8 93	34.9 93	38.3 83	29.7 77	34.1 95	39.5 80	52.7 58	47.6 46	81.0 89	71.7 2	66.5 2	87.4 34	68.8 35	53.5 12	82.4 85	28.3 17	44.3 30	42.4 10	37.1 78	56.3 67	50.6 100
IAOF [50]	61.8	38.0 111	34.2 91	49.8 107	31.7 113	37.9 114	41.1 103	28.9 18	32.6 58	39.4 73	53.7 97	48.1 68	80.8 69	72.0 9	66.7 8	87.5 78	68.9 45	54.1 32	82.2 57	28.3 17	45.1 69	42.3 1	36.8 47	56.1 54	50.2 45
Aniso-Texture [82]	62.3	36.1 21	32.4 23	48.2 57	28.0 77	34.4 80	37.6 62	30.0 89	32.8 65	39.3 68	52.7 58	48.1 68	80.7 56	72.4 91	67.3 93	87.3 16	69.2 79	56.2 114	82.3 71	28.4 38	44.5 36	42.4 10	37.2 85	57.0 93	50.2 45
Classic++ [32]	63.2	36.4 53	33.5 59	48.4 75	27.4 64	33.7 66	37.6 62	29.6 75	33.6 86	39.2 62	52.7 58	47.3 35	80.9 77	72.2 36	67.0 42	87.5 78	69.1 69	54.5 49	82.5 96	28.5 57	44.9 56	42.6 90	36.8 47	56.2 62	50.3 62
Shiralkar [42]	64.5	36.5 61	34.6 97	48.1 43	28.3 82	34.3 75	37.2 31	29.8 82	36.9 118	40.0 98	53.9 101	49.0 101	80.5 35	71.8 3	66.6 3	87.2 8	69.2 79	55.1 79	82.4 85	29.2 108	48.0 112	42.5 62	36.6 28	55.7 33	50.1 24
CostFilter [40]	65.0	35.9 9	32.7 34	47.6 3	26.8 41	33.5 63	37.1 18	29.7 77	35.6 113	39.2 62	52.9 76	49.4 108	80.3 10	72.6 111	67.6 111	87.4 34	69.6 106	54.8 66	83.1 117	28.6 75	45.6 86	42.6 90	37.0 68	56.7 80	49.9 3
Fusion [6]	65.2	36.0 16	32.7 34	47.8 8	26.8 41	32.1 34	37.5 56	29.5 70	31.5 38	39.5 80	53.5 93	48.6 89	80.7 56	72.6 111	68.0 119	87.1 3	69.3 90	57.6 126	81.8 3	28.7 86	47.1 103	42.5 62	38.2 114	59.9 126	50.0 6
FlowNetS+ft+v [112]	65.2	36.8 84	33.0 43	48.7 88	29.5 100	35.6 102	40.5 96	29.8 82	34.3 97	39.5 80	52.8 68	48.2 80	80.8 69	72.2 36	67.0 42	87.4 34	68.7 19	53.9 23	82.1 35	28.6 75	45.9 89	42.5 62	36.7 40	56.0 46	50.4 76
TriFlow [95]	65.5	37.0 90	35.3 109	48.8 91	28.7 91	34.5 83	41.0 101	29.2 37	33.4 83	38.8 13	53.0 80	48.8 99	80.4 19	72.3 64	67.3 93	87.4 34	69.2 79	55.5 95	82.1 35	28.5 57	44.8 53	42.4 10	36.9 61	56.4 71	50.1 24
SVFilterOh [111]	65.6	36.3 42	32.2 18	48.1 43	26.2 22	30.9 17	37.4 47	29.2 37	30.6 16	39.3 68	52.6 49	47.5 42	81.0 89	72.6 111	67.6 111	87.6 110	69.3 90	55.9 103	82.3 71	28.5 57	43.7 13	43.3 120	37.3 91	57.1 97	51.0 110
Occlusion-TV-L1 [63]	65.8	36.6 70	33.8 76	48.5 78	28.4 85	34.8 90	37.7 67	29.5 70	33.0 72	39.5 80	53.0 80	48.1 68	81.1 97	72.1 24	66.8 17	87.5 78	68.9 45	53.4 8	82.4 85	29.0 104	44.7 44	42.6 90	36.8 47	55.6 29	50.4 76
CNN-flow-warp+ref [117]	66.3	36.3 42	31.7 9	48.7 88	28.5 87	34.7 86	39.5 90	30.4 100	35.0 106	39.8 93	54.0 104	48.1 68	81.2 101	72.3 64	67.0 42	87.4 34	68.6 5	53.2 5	82.4 85	28.8 94	47.1 103	42.5 62	36.6 28	55.7 33	50.3 62
CRTflow [80]	66.5	36.7 78	33.8 76	48.5 78	27.7 70	33.8 69	37.4 47	30.7 106	35.3 108	40.9 115	52.9 76	48.1 68	81.8 111	72.2 36	66.9 29	87.4 34	68.9 45	54.1 32	82.3 71	28.4 38	44.9 56	42.5 62	36.8 47	56.1 54	50.5 90
FlowNet2 [122]	68.5	39.4 116	38.2 120	50.4 113	29.2 98	34.8 90	41.9 110	30.0 89	34.6 101	39.4 73	53.3 91	51.0 120	80.6 41	72.5 103	67.4 103	87.4 34	68.8 35	54.3 40	82.0 13	28.4 38	45.0 63	42.3 1	36.5 18	55.7 33	49.8 1
Modified CLG [34]	69.0	36.9 89	32.8 37	49.4 101	30.9 110	36.3 108	42.8 112	30.0 89	34.8 104	39.9 94	53.0 80	47.9 59	80.7 56	72.2 36	66.9 29	87.5 78	68.7 19	53.8 15	82.2 57	28.4 38	45.1 69	42.5 62	36.9 61	56.2 62	50.5 90
Adaptive [20]	69.7	36.8 84	34.4 94	48.5 78	28.8 93	35.2 98	37.7 67	29.4 60	33.2 76	39.2 62	52.6 49	47.6 46	80.6 41	72.3 64	67.0 42	87.5 78	69.1 69	54.7 60	82.3 71	28.7 86	46.0 91	42.4 10	37.3 91	56.9 87	50.4 76
TCOF [69]	69.8	36.6 70	33.9 80	48.1 43	29.1 95	35.7 103	38.3 83	29.0 22	31.4 34	38.7 8	52.8 68	48.7 95	80.6 41	72.2 36	67.1 65	87.4 34	69.3 90	56.0 106	82.1 35	28.7 86	46.2 94	42.5 62	38.2 114	58.7 122	50.5 90
Nguyen [33]	71.8	39.6 117	33.9 80	52.6 121	32.5 118	37.9 114	43.3 115	30.0 89	35.5 111	40.2 103	54.1 107	49.0 101	80.9 77	72.0 9	66.8 17	87.4 34	68.6 5	53.8 15	82.0 13	28.8 94	47.8 110	42.4 10	36.8 47	56.1 54	50.3 62
Steered-L1 [118]	72.0	36.0 16	32.9 41	47.9 18	27.0 52	33.3 58	37.7 67	30.3 99	32.3 52	39.9 94	53.2 86	48.0 62	81.0 89	72.5 103	67.5 109	87.5 78	68.9 45	55.0 77	82.2 57	28.8 94	46.7 100	42.7 101	37.0 68	57.3 101	50.3 62
StereoOF-V1MT [119]	73.1	36.8 84	35.3 109	48.1 43	28.3 82	35.1 95	36.9 3	31.4 114	36.6 115	40.5 106	54.6 115	48.6 89	81.3 102	72.0 9	66.8 17	87.2 8	69.5 101	54.9 69	82.6 102	29.7 121	48.8 119	42.7 101	36.5 18	55.1 8	50.1 24
BriefMatch [124]	73.1	36.3 42	33.3 52	48.0 29	27.2 60	33.4 61	38.5 87	30.6 105	32.6 58	40.6 108	54.0 104	48.6 89	82.8 118	72.4 91	67.3 93	87.3 16	70.2 118	55.6 98	83.9 127	28.3 17	44.3 30	42.7 101	36.6 28	55.7 33	50.5 90
SPSA-learn [13]	76.5	37.4 98	33.6 66	49.4 101	29.8 104	35.1 95	41.4 107	30.9 109	33.2 76	40.7 109	53.5 93	47.2 33	80.4 19	72.2 36	67.0 42	87.4 34	68.8 35	54.1 32	82.2 57	29.5 116	52.2 130	42.9 111	37.1 78	57.0 93	50.3 62
GraphCuts [14]	77.1	38.0 111	35.1 106	49.5 103	28.4 85	33.9 71	41.3 105	31.3 113	30.8 23	40.7 109	53.7 97	48.3 82	81.0 89	72.1 24	67.1 65	87.1 3	68.6 5	54.9 69	81.7 1	28.8 94	46.3 95	42.8 106	37.7 101	58.5 118	50.4 76
HBpMotionGpu [43]	77.8	38.8 114	35.9 112	50.9 117	32.1 115	38.2 116	44.4 119	29.2 37	31.7 41	39.3 68	53.9 101	49.6 110	81.5 106	72.1 24	67.0 42	87.1 3	69.5 101	54.9 69	82.4 85	28.3 17	44.4 32	42.5 62	37.3 91	56.5 74	51.1 111
Dynamic MRF [7]	78.0	36.2 28	34.1 87	48.0 29	27.5 66	34.6 85	37.4 47	30.9 109	36.8 117	40.4 105	54.5 113	49.3 107	81.9 112	71.9 4	66.8 17	87.2 8	69.4 99	55.5 95	82.5 96	29.0 104	47.8 110	42.5 62	37.5 98	56.8 84	50.5 90
2D-CLG [1]	78.2	37.9 106	33.5 59	50.5 115	32.5 118	37.4 111	45.0 120	30.8 108	34.8 104	40.7 109	53.7 97	48.3 82	80.5 35	72.3 64	67.1 65	87.6 110	68.6 5	53.2 5	82.2 57	28.8 94	46.7 100	42.5 62	36.9 61	55.6 29	50.3 62
Black & Anandan [4]	78.5	37.9 106	34.1 87	49.6 104	30.7 108	36.0 104	41.2 104	31.0 111	34.7 103	40.3 104	53.9 101	48.6 89	80.7 56	72.3 64	67.0 42	87.4 34	69.0 59	53.8 15	82.5 96	28.8 94	46.5 96	42.4 10	37.0 68	56.1 54	50.4 76
ROF-ND [107]	78.6	37.0 90	32.8 37	48.1 43	27.7 70	34.7 86	37.6 62	29.7 77	32.3 52	39.1 50	54.2 109	51.4 121	80.4 19	72.4 91	67.3 93	87.4 34	69.5 101	56.9 118	82.0 13	29.7 121	49.0 122	43.2 118	37.8 105	57.7 110	50.2 45
TV-L1-improved [17]	79.2	36.6 70	34.1 87	48.4 75	28.6 90	35.1 95	37.8 70	30.5 103	33.2 76	40.0 98	52.7 58	48.0 62	80.9 77	72.3 64	67.2 79	87.4 34	69.1 69	54.9 69	82.3 71	28.8 94	47.3 106	42.6 90	37.2 85	56.7 80	50.6 100
CBF [12]	80.9	36.4 53	32.5 29	48.9 94	27.5 66	33.8 69	37.9 74	29.3 47	31.6 39	39.1 50	53.2 86	48.1 68	82.6 115	72.4 91	67.2 79	87.7 119	69.2 79	55.3 87	82.3 71	28.7 86	46.1 92	42.9 111	37.9 107	57.7 110	51.7 120
UnFlow [129]	82.4	39.2 115	37.9 118	50.6 116	32.3 116	38.9 121	41.3 105	31.6 119	38.5 123	40.8 114	53.2 86	48.7 95	80.9 77	72.0 9	66.7 8	87.4 34	69.5 101	54.6 56	82.4 85	28.2 11	43.2 3	42.4 10	39.3 127	58.3 116	51.2 112
Rannacher [23]	82.4	36.7 78	34.5 95	48.7 88	28.7 91	35.3 99	38.1 79	30.5 103	34.0 93	39.9 94	52.7 58	48.0 62	80.8 69	72.4 91	67.2 79	87.5 78	69.0 59	54.6 56	82.3 71	28.8 94	47.0 102	42.6 90	37.1 78	56.4 71	50.6 100
Correlation Flow [75]	82.9	36.2 28	33.4 56	47.7 6	27.7 70	34.3 75	37.3 36	29.4 60	31.4 34	38.8 13	53.1 84	48.5 86	81.3 102	72.8 116	67.6 111	88.6 129	70.1 113	57.1 121	82.6 102	29.4 113	48.8 119	43.0 113	37.7 101	57.9 112	50.5 90
HBM-GC [105]	84.0	37.7 103	34.7 100	49.8 107	27.1 57	32.4 40	37.9 74	28.8 12	29.6 3	39.2 62	52.6 49	47.3 35	80.8 69	73.2 122	68.1 120	88.2 124	70.0 111	57.3 123	82.7 106	28.9 103	45.0 63	43.5 121	37.6 100	57.2 99	51.3 114
SegOF [10]	84.6	37.6 102	33.2 49	50.0 110	29.1 95	34.7 86	41.0 101	31.4 114	35.3 108	40.7 109	53.6 95	50.7 118	80.6 41	72.3 64	67.2 79	87.5 78	69.0 59	55.3 87	82.2 57	29.0 104	48.6 117	42.7 101	36.7 40	55.8 40	50.4 76
TriangleFlow [30]	84.7	37.0 90	34.9 104	48.5 78	28.0 77	34.7 86	37.5 56	30.2 97	33.0 72	39.9 94	53.2 86	49.0 101	81.1 97	72.0 9	66.9 29	87.1 3	69.8 110	56.1 108	82.4 85	29.2 108	48.5 116	42.8 106	38.1 112	58.5 118	50.5 90
BlockOverlap [61]	85.1	38.5 113	33.2 49	51.3 118	30.0 106	34.4 80	42.8 112	29.4 60	30.4 14	40.0 98	53.2 86	46.9 25	83.0 119	72.9 119	67.6 111	88.3 125	69.7 108	54.1 32	83.3 122	28.7 86	44.1 25	43.5 121	37.1 78	55.3 14	51.8 121
IAOF2 [51]	87.4	37.9 106	35.9 112	49.1 96	29.6 102	36.1 106	40.0 93	29.3 47	33.4 83	40.0 98	54.1 107	50.2 115	81.0 89	72.4 91	67.4 103	87.4 34	69.2 79	54.9 69	82.4 85	28.6 75	45.5 83	42.4 10	37.9 107	57.6 107	50.6 100
Ad-TV-NDC [36]	87.5	40.4 121	35.1 106	53.1 122	31.9 114	36.7 110	43.8 117	29.4 60	32.9 66	39.1 50	54.5 113	49.2 106	82.1 113	72.5 103	67.3 93	87.5 78	69.3 90	53.9 23	82.7 106	28.6 75	45.4 77	42.4 10	37.2 85	56.2 62	50.6 100
AdaConv-v1 [126]	93.9	37.2 97	36.6 115	47.5 2	34.3 123	39.1 123	51.1 129	36.1 127	39.4 125	52.9 131	58.2 127	53.1 125	83.8 124	70.9 1	65.4 1	86.6 2	69.7 108	54.7 60	84.4 128	38.6 131	46.5 96	77.4 131	38.2 114	54.6 2	60.3 131
LocallyOriented [52]	95.4	37.5 100	35.9 112	49.2 97	29.6 102	36.2 107	39.1 88	30.1 95	33.8 90	39.5 80	53.7 97	50.0 113	81.3 102	72.3 64	67.2 79	87.5 78	70.2 118	56.2 114	82.9 113	28.8 94	45.6 86	42.5 62	37.7 101	57.6 107	50.5 90
ACK-Prior [27]	96.4	36.4 53	33.7 70	48.1 43	26.7 36	33.1 53	37.1 18	30.7 106	33.3 80	39.7 90	53.6 95	50.0 113	81.0 89	73.5 126	68.6 123	88.3 125	70.8 125	59.8 129	82.7 106	29.7 121	48.7 118	43.6 124	39.5 128	62.1 129	51.3 114
Horn & Schunck [3]	96.6	37.9 106	35.1 106	49.6 104	31.4 111	37.7 113	41.8 109	31.7 120	37.4 120	41.5 117	55.8 119	50.6 116	81.3 102	72.2 36	67.0 42	87.4 34	69.2 79	54.2 38	82.7 106	29.5 116	48.9 121	42.6 90	37.8 105	57.2 99	50.9 109
StereoFlow [44]	96.7	46.3 130	45.9 131	54.3 123	38.3 130	45.4 131	45.7 122	29.3 47	33.8 90	39.1 50	52.9 76	47.7 51	81.0 89	74.4 130	70.5 131	87.6 110	72.0 129	66.3 131	82.4 85	28.4 38	45.0 63	42.4 10	38.0 111	59.1 123	50.5 90
Filter Flow [19]	97.8	37.8 105	34.6 97	49.8 107	30.8 109	36.0 104	44.3 118	29.4 60	32.4 55	39.5 80	54.2 109	48.1 68	82.2 114	72.7 115	67.7 117	87.6 110	69.2 79	55.1 79	82.5 96	28.7 86	46.5 96	42.6 90	38.3 120	58.4 117	51.4 117
TI-DOFE [24]	98.7	42.0 124	37.5 117	54.8 126	35.2 125	41.1 128	46.8 125	31.4 114	37.7 121	41.6 119	56.1 121	50.6 116	81.6 107	72.0 9	66.9 29	87.2 8	69.4 99	54.4 45	82.6 102	29.2 108	47.6 109	42.6 90	38.2 114	57.5 105	50.8 108
SILK [79]	100.3	39.6 117	38.1 119	51.5 120	32.4 117	38.5 119	43.6 116	32.4 121	37.2 119	41.5 117	55.4 117	49.7 111	83.0 119	72.2 36	67.0 42	87.4 34	70.0 111	54.7 60	83.4 123	29.0 104	46.5 96	42.8 106	37.4 97	56.7 80	50.7 106
Bartels [41]	102.0	37.1 93	35.0 105	49.3 100	28.2 79	34.8 90	40.5 96	29.9 86	33.1 75	40.5 106	54.2 109	49.7 111	83.9 125	73.0 120	67.6 111	88.7 130	71.8 128	56.1 108	85.6 130	28.6 75	43.9 16	43.6 124	38.1 112	57.0 93	53.2 127
SLK [47]	108.6	41.6 123	38.7 122	54.4 124	33.0 120	38.3 118	45.5 121	33.3 122	38.6 124	42.8 121	57.8 125	51.8 123	83.5 123	72.1 24	67.3 93	86.5 1	70.1 113	55.8 101	82.7 106	30.0 124	51.4 128	43.0 113	38.2 114	57.5 105	51.5 118
NL-TV-NCC [25]	109.9	37.1 93	35.7 111	48.0 29	27.8 74	35.0 94	37.6 62	31.0 111	35.4 110	40.0 98	56.0 120	54.2 128	82.6 115	73.8 128	68.6 123	89.1 131	70.6 124	58.4 128	82.5 96	30.4 128	50.0 125	44.0 128	39.8 129	60.2 127	52.4 125
GroupFlow [9]	110.6	40.3 120	40.1 124	51.3 118	31.5 112	38.9 121	42.6 111	33.5 124	39.5 126	43.8 123	54.7 116	52.3 124	81.0 89	73.2 122	68.6 123	87.6 110	70.4 122	57.3 123	83.0 115	29.3 111	48.1 113	42.5 62	37.9 107	58.2 115	50.1 24
FFV1MT [106]	111.2	39.6 117	40.8 126	50.3 112	34.8 124	38.8 120	46.6 124	36.5 128	45.8 129	44.6 125	56.2 122	49.0 101	81.7 108	72.5 103	67.4 103	87.4 34	70.2 118	54.7 60	83.2 118	30.1 126	49.3 124	42.8 106	38.6 121	57.6 107	51.3 114
Learning Flow [11]	111.6	37.7 103	37.0 116	49.2 97	29.9 105	37.5 112	39.7 91	31.5 118	36.3 114	40.7 109	55.4 117	51.6 122	82.6 115	72.8 116	67.8 118	87.8 121	69.6 106	55.7 99	82.8 111	29.3 111	48.4 114	42.7 101	39.2 125	59.8 125	51.2 112
Heeger++ [104]	111.7	40.6 122	42.5 128	50.4 113	33.5 121	38.2 116	43.0 114	37.6 129	48.1 130	44.9 126	56.2 122	49.0 101	81.7 108	73.4 125	68.9 128	87.5 78	70.1 113	56.0 106	82.8 111	30.3 127	49.1 123	42.6 90	37.7 101	56.9 87	50.3 62
2bit-BM-tele [98]	112.5	37.9 106	34.7 100	50.1 111	30.1 107	36.4 109	41.7 108	30.2 97	32.2 51	41.3 116	54.3 112	49.4 108	84.1 127	73.3 124	68.1 120	88.3 125	72.2 130	57.2 122	85.5 129	30.4 128	52.7 131	44.4 129	38.2 114	56.3 67	54.1 129
FOLKI [16]	116.3	44.6 128	40.4 125	58.4 129	35.7 126	42.3 129	47.3 126	33.3 122	40.7 127	44.9 126	59.4 130	53.6 126	86.5 130	72.5 103	67.6 111	87.3 16	70.1 113	55.8 101	83.2 118	29.4 113	48.4 114	43.1 116	38.7 122	58.5 118	52.0 122
Pyramid LK [2]	120.0	46.1 129	38.9 123	61.0 130	36.7 129	40.4 126	50.9 128	39.9 130	36.6 115	49.4 129	64.1 131	61.2 131	87.7 131	73.1 121	68.6 123	87.4 34	70.1 113	56.1 108	83.0 115	29.6 119	50.7 127	43.2 118	39.2 125	61.2 128	51.5 118
Adaptive flow [45]	120.8	43.8 126	38.2 120	56.5 127	35.8 127	40.5 127	50.2 127	31.4 114	34.5 99	42.5 120	56.5 124	50.9 119	83.9 125	73.5 126	68.7 127	88.1 123	70.2 118	57.4 125	82.9 113	29.4 113	47.5 107	43.6 124	39.0 124	59.1 123	52.0 122
PGAM+LK [55]	121.4	42.5 125	41.4 127	54.6 125	33.7 122	40.1 125	45.8 123	33.9 125	41.1 128	43.3 122	59.3 129	55.2 129	85.5 129	72.8 116	68.1 120	87.5 78	70.8 125	56.9 118	83.6 124	29.6 119	50.0 125	43.0 113	38.7 122	58.6 121	52.1 124
HCIC-L [99]	125.0	49.1 131	42.6 129	63.0 131	35.8 127	39.4 124	52.5 130	34.6 126	37.7 121	43.9 124	58.0 126	53.9 127	81.7 108	74.0 129	69.3 129	88.5 128	71.7 127	60.5 130	83.2 118	29.5 116	47.5 107	43.9 127	40.5 130	62.9 130	52.8 126
Periodicity [78]	128.8	44.4 127	43.3 130	56.9 128	42.8 131	43.4 130	56.2 131	40.9 131	49.1 131	49.5 130	58.9 128	58.6 130	84.9 128	74.4 130	70.2 130	88.0 122	73.1 131	57.9 127	86.2 131	30.0 124	51.5 129	43.5 121	41.8 131	63.4 131	53.7 128

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.