Optical flow evaluation results: A99 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

A99 interpolation error	avg.	Mequon (Hidden texture) im0 GT im1			Schefflera (Hidden texture) im0 GT im1			Urban (Synthetic) im0 GT im1			Teddy (Stereo) im0 GT im1			Backyard (High-speed camera) im0 GT im1			Basketball (High-speed camera) im0 GT im1			Dumptruck (High-speed camera) im0 GT im1			Evergreen (High-speed camera) im0 GT im1
	rank	all	disc	untext	all	disc	untext	all	disc	untext	all	disc	untext	all	disc	untext	all	disc	untext	all	disc	untext	all	disc	untext
PMMST [114]	10.3	11.2 10	21.1 6	2.71 2	13.8 8	19.7 7	3.65 22	10.3 1	19.2 7	2.71 1	16.8 4	30.8 22	7.53 31	41.1 5	51.1 7	10.0 17	24.6 2	43.0 6	4.93 12	34.2 10	70.9 12	4.04 8	28.8 14	45.4 24	3.42 9
MDP-Flow2 [68]	10.7	11.0 3	20.7 5	2.71 2	13.9 10	19.9 9	3.46 2	10.3 1	20.3 14	3.00 2	16.7 3	30.0 14	7.35 7	41.0 4	50.7 4	10.1 29	27.1 35	44.9 19	4.97 19	33.6 3	70.1 7	3.92 3	29.2 18	47.0 35	3.42 9
NNF-Local [87]	20.0	11.4 16	21.6 9	2.71 2	12.8 1	18.4 2	3.56 4	10.4 3	20.0 12	3.00 2	19.8 58	37.3 88	7.35 7	41.5 11	51.4 9	10.0 17	28.2 62	47.3 38	5.07 46	34.5 13	71.9 23	4.04 8	29.1 17	46.1 30	3.37 2
SepConv-v1 [127]	20.5	9.68 1	19.1 1	2.52 1	15.4 36	20.1 11	5.26 101	11.0 10	16.7 1	3.87 95	20.4 72	26.8 1	9.59 117	41.9 12	52.5 16	9.00 2	24.7 3	42.4 3	4.69 1	30.7 1	67.4 1	3.92 3	24.7 1	35.8 1	3.32 1
NN-field [71]	22.8	11.5 24	22.9 23	2.71 2	13.0 3	18.6 3	3.42 1	12.3 77	19.7 8	3.00 2	21.1 80	39.8 102	7.44 17	41.4 9	51.4 9	10.0 17	27.5 43	46.4 28	4.97 19	33.8 6	71.0 14	4.04 8	29.3 20	46.2 31	3.37 2
NNF-EAC [103]	26.8	11.5 24	21.7 10	3.11 73	14.5 22	21.0 24	3.70 24	12.3 77	22.6 35	3.00 2	17.7 16	32.4 41	7.55 38	43.2 38	55.1 42	10.1 29	25.1 6	43.8 9	4.90 6	34.0 8	70.5 9	4.08 38	29.4 22	47.5 40	3.42 9
DeepFlow2 [108]	28.7	11.4 16	23.5 27	3.00 60	16.7 58	23.0 60	4.04 56	11.0 10	20.3 14	3.00 2	19.0 47	29.8 12	7.53 31	42.7 25	54.0 25	10.3 52	25.0 4	43.0 6	4.93 12	35.2 27	73.8 34	4.04 8	28.9 15	44.9 21	3.56 67
DeepFlow [86]	28.9	11.3 14	24.2 40	3.00 60	16.6 57	23.0 60	4.32 69	11.0 10	20.3 14	3.00 2	19.3 49	28.1 6	7.59 46	42.7 25	54.5 30	10.2 47	25.2 8	44.1 10	5.00 40	32.9 2	68.2 2	4.04 8	28.4 10	44.6 17	3.56 67
SuperFlow [81]	30.0	11.0 3	22.1 17	3.11 73	17.1 63	22.7 52	4.69 82	11.7 46	18.7 2	3.37 67	18.7 40	27.4 3	7.70 66	41.3 7	51.2 8	9.98 13	26.3 22	46.9 32	4.80 3	34.7 16	76.0 46	4.08 38	28.1 8	41.5 5	3.42 9
PH-Flow [101]	30.6	11.9 56	25.7 70	2.83 17	13.3 5	19.7 7	3.56 4	10.7 5	22.7 36	3.00 2	16.5 2	30.2 15	7.33 3	42.3 18	52.1 13	10.1 29	28.7 78	50.9 92	5.20 70	35.6 34	77.0 56	4.04 8	29.6 28	47.0 35	3.51 51
DF-Auto [115]	30.9	10.9 2	19.2 2	3.11 73	17.2 66	23.4 68	4.43 74	10.4 3	20.6 21	3.00 2	18.1 27	29.7 9	7.55 38	41.4 9	52.1 13	10.0 17	26.2 17	47.2 35	4.97 19	35.2 27	79.3 68	4.08 38	29.6 28	44.7 18	3.56 67
CombBMOF [113]	31.2	12.0 62	24.3 41	2.83 17	14.3 16	20.6 16	3.56 4	11.3 27	25.7 61	3.00 2	20.3 71	34.9 66	7.55 38	43.2 38	54.0 25	10.1 29	26.4 23	47.7 45	4.90 6	36.2 52	71.4 18	4.08 38	29.5 25	45.7 27	3.37 2
CBF [12]	32.8	11.0 3	19.8 3	3.00 60	17.1 63	22.9 57	4.24 66	12.0 61	19.0 4	3.00 2	17.8 21	28.0 5	7.85 80	40.6 3	49.9 2	9.97 9	26.2 17	44.6 14	4.97 19	36.3 54	76.3 51	4.12 81	27.9 4	41.2 4	3.70 104
Aniso. Huber-L1 [22]	33.7	11.4 16	21.7 10	3.11 73	19.7 101	24.7 101	4.55 78	12.0 61	19.7 8	3.11 62	18.4 32	29.8 12	7.55 38	42.5 21	54.4 29	9.98 13	25.2 8	42.2 2	4.83 4	35.6 34	71.5 19	4.04 8	27.9 4	42.0 7	3.56 67
LME [70]	34.0	11.4 16	22.0 15	2.71 2	15.1 31	21.8 36	3.87 49	11.3 27	36.0 119	3.00 2	17.4 8	32.0 38	7.48 21	44.5 61	57.0 60	11.4 123	27.6 45	47.2 35	4.97 19	33.6 3	69.7 4	4.04 8	30.0 34	48.6 51	3.42 9
CLG-TV [48]	35.4	11.1 8	21.8 13	3.11 73	18.8 85	24.0 82	4.43 74	11.3 27	20.0 12	3.70 82	18.6 38	28.9 7	7.72 71	42.8 28	55.0 41	10.0 17	25.0 4	42.9 5	4.93 12	36.0 46	71.6 20	4.04 8	29.0 16	44.0 13	3.56 67
IROF++ [58]	35.4	11.9 56	24.1 37	2.83 17	14.7 25	21.3 26	3.56 4	12.1 74	29.0 89	3.00 2	16.3 1	27.9 4	7.35 7	43.9 50	56.0 50	11.1 93	26.4 23	47.0 34	4.93 12	34.5 13	72.3 24	4.08 38	30.3 44	49.3 59	3.56 67
WLIF-Flow [93]	35.4	11.5 24	22.1 17	2.83 17	15.2 32	21.6 33	3.79 40	11.3 27	26.4 69	3.00 2	17.4 8	30.3 17	7.59 46	42.5 21	53.5 21	10.4 61	29.0 84	51.1 95	5.29 87	34.8 19	69.7 4	4.04 8	30.0 34	48.4 47	3.46 36
FMOF [94]	36.8	12.2 82	24.5 50	2.94 48	14.0 11	20.0 10	3.56 4	12.3 77	27.7 76	3.00 2	19.8 58	35.4 71	7.70 66	42.4 19	52.1 13	10.1 29	28.1 58	49.1 61	4.93 12	34.6 15	72.7 28	3.87 1	30.2 40	47.6 43	3.42 9
Brox et al. [5]	37.7	11.4 16	24.9 62	2.94 48	15.9 43	22.2 44	4.04 56	11.3 27	21.0 22	3.37 67	18.4 32	27.0 2	7.59 46	42.2 16	53.3 19	10.0 17	28.2 62	51.5 98	5.00 40	36.8 57	88.0 95	4.04 8	28.4 10	42.3 8	3.42 9
IROF-TV [53]	38.1	11.7 38	24.7 57	3.00 60	15.5 39	22.0 42	3.70 24	11.0 10	23.7 46	3.00 2	17.3 6	31.3 26	7.57 45	43.8 48	56.0 50	11.2 100	27.6 45	48.4 52	4.97 19	35.9 44	74.5 42	4.08 38	28.0 6	42.6 9	3.56 67
ALD-Flow [66]	39.0	12.0 62	28.4 95	3.11 73	16.3 50	22.8 54	3.83 44	11.0 10	21.7 28	3.00 2	17.9 24	33.6 54	7.39 12	43.4 43	54.6 34	10.8 79	25.8 12	44.8 18	5.00 40	34.1 9	70.4 8	4.04 8	31.9 74	50.3 67	3.46 36
nLayers [57]	39.6	11.8 46	22.9 23	2.83 17	14.1 12	20.4 13	3.56 4	11.0 10	19.7 8	3.00 2	18.3 30	34.2 62	7.39 12	46.7 112	60.1 108	11.0 87	27.9 52	50.1 68	5.20 70	35.5 32	72.6 27	4.08 38	30.8 49	49.3 59	3.42 9
LDOF [28]	41.2	11.4 16	22.5 20	3.56 114	16.1 45	21.4 31	6.35 118	12.0 61	20.3 14	3.70 82	19.0 47	29.7 9	7.94 85	41.2 6	50.9 5	10.1 29	26.8 28	50.2 70	4.90 6	34.8 19	80.2 72	4.08 38	29.4 22	44.5 15	3.46 36
p-harmonic [29]	41.3	11.4 16	23.5 27	2.83 17	19.1 89	24.3 90	4.80 87	11.3 27	22.0 30	3.70 82	20.9 78	31.7 32	7.62 52	42.6 24	54.2 27	10.1 29	25.7 11	43.5 8	5.07 46	36.1 50	71.8 21	4.08 38	29.6 28	46.5 32	3.51 51
Layers++ [37]	41.4	11.4 16	21.7 10	2.94 48	12.8 1	18.2 1	3.46 2	11.0 10	26.7 72	3.00 2	17.7 16	32.9 46	7.53 31	46.6 110	60.9 119	10.6 72	30.9 115	60.2 120	5.00 40	34.9 25	72.7 28	3.87 1	29.9 33	47.5 40	3.46 36
MDP-Flow [26]	41.6	11.2 10	21.2 7	2.71 2	14.2 14	20.5 15	3.70 24	10.7 5	19.0 4	3.00 2	19.7 56	32.4 41	7.70 66	44.2 53	57.0 60	11.2 100	30.0 104	51.4 97	5.51 115	36.1 50	72.9 31	4.08 38	30.8 49	48.4 47	3.42 9
Second-order prior [8]	41.9	11.3 14	22.0 15	3.11 73	19.0 88	24.2 88	4.32 69	13.3 89	27.7 76	3.70 82	18.8 43	31.6 31	7.51 25	42.9 30	54.7 36	10.0 17	26.2 17	45.0 20	4.97 19	35.6 34	71.2 15	4.04 8	29.5 25	45.4 24	3.56 67
SIOF [67]	42.5	11.7 38	23.1 25	3.11 73	19.4 96	24.8 104	4.76 84	11.3 27	25.7 61	3.11 62	18.4 32	31.4 28	8.04 90	40.3 2	50.3 3	9.95 7	25.8 12	45.3 22	4.97 19	33.9 7	71.2 15	4.08 38	30.0 34	47.4 37	3.70 104
Local-TV-L1 [65]	43.2	11.2 10	21.5 8	3.56 114	19.6 99	24.4 93	5.57 107	11.0 10	19.1 6	3.00 2	18.3 30	30.4 20	7.87 83	42.8 28	54.5 30	10.2 47	26.2 17	44.7 15	5.45 103	34.2 10	76.1 49	4.08 38	28.0 6	42.8 11	3.65 101
COFM [59]	43.9	11.8 46	24.3 41	2.94 48	14.5 22	20.9 21	3.65 22	11.0 10	26.4 69	3.00 2	17.4 8	32.3 39	7.35 7	44.2 53	55.1 42	10.1 29	30.0 104	54.4 114	5.20 70	35.8 40	79.3 68	4.08 38	31.2 56	48.8 54	3.51 51
ProbFlowFields [128]	45.3	11.6 30	25.4 67	2.83 17	14.4 18	21.1 25	3.56 4	10.7 5	23.7 46	3.00 2	18.4 32	33.4 51	7.59 46	46.2 100	59.2 95	11.2 100	28.5 74	50.7 86	5.32 91	34.7 16	76.9 54	4.08 38	29.4 22	46.5 32	3.46 36
FlowFields [110]	45.5	11.8 46	25.6 69	2.83 17	14.4 18	20.9 21	3.56 4	11.3 27	24.3 52	3.00 2	20.0 64	38.1 93	7.51 25	43.6 45	54.5 30	11.0 87	28.2 62	50.7 86	5.16 65	34.8 19	75.1 45	4.04 8	32.0 79	52.0 91	3.46 36
TV-L1-MCT [64]	47.7	12.4 98	24.7 57	2.83 17	16.4 51	23.1 62	3.83 44	11.9 60	32.7 110	3.00 2	17.6 13	31.7 32	7.53 31	47.0 120	61.2 120	11.0 87	25.5 10	44.7 15	4.97 19	36.0 46	80.7 77	4.04 8	28.4 10	44.8 20	3.46 36
BlockOverlap [61]	47.9	11.1 8	20.1 4	3.56 114	19.3 93	23.7 75	6.16 114	11.3 27	20.4 19	3.70 82	18.4 32	29.6 8	8.72 109	43.1 35	54.5 30	10.2 47	27.4 40	48.6 55	5.35 98	34.8 19	72.8 30	4.08 38	27.2 3	40.9 3	3.56 67
HAST [109]	49.2	11.7 38	23.6 29	2.94 48	13.8 8	19.6 6	3.56 4	12.0 61	31.7 106	3.00 2	17.8 21	31.7 32	7.14 1	45.3 78	57.0 60	9.97 9	33.7 124	62.8 127	5.10 62	38.4 79	88.4 97	4.04 8	33.0 96	51.0 75	3.42 9
Sparse-NonSparse [56]	49.2	12.0 62	24.3 41	2.83 17	15.0 28	21.3 26	3.56 4	11.7 46	29.0 89	3.00 2	17.6 13	29.7 9	7.39 12	45.7 86	59.3 96	11.0 87	28.8 79	48.7 57	5.07 46	38.6 84	90.1 106	4.04 8	32.4 87	51.8 87	3.42 9
Kuang [131]	49.5	11.9 56	25.3 66	2.83 17	15.0 28	21.4 31	3.70 24	12.7 83	28.0 80	3.00 2	20.5 74	40.0 104	7.51 25	44.6 64	56.4 54	10.8 79	27.7 50	46.5 29	4.97 19	36.8 57	74.2 39	4.12 81	30.8 49	50.4 68	3.42 9
Modified CLG [34]	49.8	11.0 3	21.9 14	3.11 73	19.6 99	23.9 79	5.94 112	12.4 81	26.3 67	3.87 95	19.8 58	30.8 22	8.12 95	42.1 14	52.9 17	10.1 29	27.0 31	48.1 50	5.23 77	34.7 16	70.8 11	4.08 38	29.5 25	45.3 23	3.56 67
OAR-Flow [125]	50.1	12.0 62	24.9 62	3.00 60	16.4 51	22.4 47	4.08 61	11.0 10	20.5 20	3.00 2	17.4 8	33.6 54	7.33 3	46.2 100	60.0 107	11.3 116	27.0 31	47.6 42	5.23 77	37.6 68	74.0 36	4.08 38	31.0 55	49.2 57	3.46 36
CPM-Flow [116]	50.5	11.8 46	27.3 85	2.83 17	14.4 18	20.4 13	3.70 24	11.7 46	24.0 49	3.00 2	21.4 87	40.1 105	7.77 75	45.5 82	58.1 77	11.2 100	26.6 27	48.0 48	5.07 46	36.0 46	72.3 24	4.04 8	30.9 53	50.4 68	3.56 67
AdaConv-v1 [126]	50.9	15.0 123	28.2 94	3.70 118	17.6 71	20.7 19	7.68 126	17.4 106	22.0 30	7.00 120	27.5 116	33.7 58	17.0 127	39.9 1	49.8 1	8.19 1	23.8 1	39.5 1	4.76 2	34.2 10	68.5 3	4.12 81	26.9 2	39.5 2	3.42 9
F-TV-L1 [15]	51.5	12.0 62	26.5 81	3.56 114	19.2 91	24.7 101	4.83 89	11.7 46	21.5 26	4.00 97	19.3 49	32.7 44	7.68 61	43.1 35	55.3 45	9.83 3	25.1 6	42.8 4	5.07 46	34.8 19	74.0 36	4.16 90	28.5 13	42.7 10	3.56 67
2DHMM-SAS [92]	51.5	12.2 82	24.5 50	2.83 17	17.9 74	24.1 85	3.87 49	12.0 61	28.7 86	3.00 2	17.3 6	31.4 28	7.51 25	45.1 74	58.2 81	11.2 100	27.9 52	49.0 59	4.83 4	37.0 60	76.1 49	4.08 38	31.9 74	50.5 70	3.42 9
FlowFields+ [130]	51.7	11.8 46	26.1 78	2.71 2	14.1 12	20.6 16	3.70 24	11.2 26	24.8 57	3.00 2	20.1 66	40.2 107	7.53 31	45.5 82	58.0 74	11.2 100	28.6 77	50.6 82	5.20 70	35.6 34	77.5 61	4.04 8	32.2 82	52.5 95	3.42 9
ComponentFusion [96]	51.8	12.0 62	29.6 102	2.71 2	14.5 22	21.3 26	3.56 4	11.0 10	22.0 30	3.00 2	18.8 43	36.2 83	7.33 3	45.5 82	58.2 81	10.7 76	27.2 36	46.3 26	4.97 19	40.5 103	93.3 114	4.12 81	34.4 110	58.3 118	3.42 9
AGIF+OF [85]	52.1	12.2 82	24.3 41	2.71 2	15.2 32	21.8 36	3.70 24	11.7 46	27.7 76	3.00 2	18.0 25	33.0 48	7.55 38	45.8 90	58.8 92	11.2 100	30.0 104	53.4 109	5.07 46	35.4 29	74.8 44	3.92 3	32.2 82	52.6 98	3.37 2
TC/T-Flow [76]	53.4	12.4 98	26.4 80	2.83 17	16.5 55	23.1 62	3.83 44	11.0 10	22.4 34	3.00 2	18.9 45	34.5 63	7.33 3	45.5 82	58.1 77	11.4 123	27.3 39	47.6 42	4.93 12	41.1 105	80.4 75	4.20 96	30.9 53	49.7 63	3.37 2
DPOF [18]	53.5	12.3 91	29.4 101	3.11 73	13.3 5	19.1 5	3.56 4	15.7 97	25.2 59	3.70 82	19.4 51	37.5 90	7.59 46	43.1 35	54.6 34	10.0 17	29.1 89	49.7 64	4.90 6	36.6 55	77.0 56	4.08 38	31.5 64	50.5 70	3.51 51
Ad-TV-NDC [36]	54.9	12.2 82	22.5 20	4.32 125	20.6 120	24.8 104	5.80 108	11.7 46	21.6 27	3.37 67	21.6 88	31.8 35	8.04 90	42.5 21	53.4 20	9.97 9	26.4 23	47.6 42	5.16 65	36.8 57	70.9 12	4.08 38	28.3 9	41.8 6	3.70 104
PGM-C [120]	55.7	11.8 46	27.3 85	2.83 17	14.4 18	20.7 19	3.70 24	12.3 77	23.0 40	3.00 2	20.6 76	42.3 113	7.62 52	45.8 90	59.5 101	11.2 100	27.2 36	47.4 39	4.97 19	37.1 62	79.2 66	4.04 8	32.4 87	55.0 109	3.51 51
Ramp [62]	56.0	12.0 62	24.6 53	2.94 48	14.8 26	21.3 26	3.70 24	11.7 46	29.4 95	3.00 2	16.9 5	30.3 17	7.39 12	45.4 80	58.5 84	11.0 87	30.2 109	50.9 92	5.23 77	39.8 96	89.6 103	4.04 8	32.4 87	52.5 95	3.42 9
TF+OM [100]	56.1	11.6 30	30.1 107	3.11 73	15.0 28	21.6 33	4.04 56	11.7 46	24.0 49	3.00 2	21.3 84	39.0 101	7.68 61	44.3 55	56.7 58	10.3 52	28.8 79	50.4 77	5.07 46	37.7 69	83.5 89	4.08 38	29.2 18	46.0 28	3.56 67
PMF [73]	56.1	12.2 82	25.9 73	2.71 2	15.4 36	21.8 36	3.56 4	12.7 83	35.7 117	3.00 2	20.2 69	35.9 77	7.51 25	44.4 59	54.9 40	10.1 29	28.4 67	50.5 80	5.32 91	37.9 73	81.1 79	4.04 8	34.2 109	54.1 104	3.37 2
ComplOF-FED-GPU [35]	56.3	12.0 62	27.9 91	2.94 48	15.7 42	22.2 44	3.79 40	16.0 98	21.4 24	3.70 82	18.4 32	33.6 54	7.48 21	44.9 71	57.7 68	10.7 76	27.4 40	45.9 23	5.00 40	36.6 55	78.7 65	4.08 38	32.6 94	52.3 93	3.51 51
AggregFlow [97]	56.5	13.7 116	37.1 121	3.11 73	16.2 48	22.6 50	4.04 56	11.0 10	23.3 45	3.00 2	21.8 90	40.7 109	7.66 59	43.2 38	53.5 21	10.3 52	27.0 31	46.0 24	5.00 40	38.0 74	82.4 86	4.08 38	31.9 74	51.9 90	3.42 9
OFLAF [77]	57.4	11.7 38	24.5 50	2.71 2	13.6 7	20.3 12	3.56 4	11.0 10	23.0 40	3.00 2	17.6 13	31.3 26	7.39 12	47.3 122	61.7 123	11.2 100	29.6 99	51.9 104	5.32 91	41.8 110	95.6 119	4.16 90	33.6 102	52.1 92	3.42 9
S2F-IF [123]	57.6	12.1 75	29.8 104	2.71 2	14.2 14	20.6 16	3.56 4	11.3 27	26.3 67	3.00 2	20.2 69	40.1 105	7.53 31	45.9 94	58.7 91	11.3 116	28.4 67	50.7 86	5.20 70	35.7 38	76.0 46	4.08 38	32.3 85	53.1 99	3.46 36
Classic++ [32]	58.1	11.6 30	23.7 30	3.11 73	17.8 73	24.4 93	4.08 61	11.7 46	20.3 14	3.37 67	20.1 66	33.8 59	7.62 52	44.7 65	57.8 71	10.0 17	28.0 55	49.7 64	5.35 98	37.4 66	81.4 81	4.08 38	30.7 48	49.5 61	3.56 67
Classic+NL [31]	58.4	12.1 75	24.3 41	3.00 60	15.3 35	21.8 36	3.70 24	11.7 46	29.4 95	3.00 2	17.4 8	31.4 28	7.53 31	45.7 86	59.4 98	10.8 79	29.0 84	49.8 67	5.10 62	39.6 93	90.4 108	4.08 38	32.2 82	51.8 87	3.46 36
FlowNetS+ft+v [112]	58.9	11.5 24	23.7 30	3.46 112	19.9 106	24.6 99	7.87 128	12.0 61	21.1 23	3.37 67	19.5 53	30.6 21	8.91 112	43.7 46	56.6 57	11.2 100	26.0 14	44.5 13	4.97 19	38.6 84	87.8 93	4.08 38	30.0 34	46.0 28	3.51 51
FC-2Layers-FF [74]	60.0	12.1 75	26.0 77	2.83 17	13.0 3	18.7 4	3.56 4	11.4 43	25.7 61	3.00 2	17.8 21	33.5 53	7.48 21	46.5 106	60.3 113	11.2 100	30.4 112	52.3 108	5.32 91	39.8 96	90.0 105	4.08 38	31.8 70	51.6 83	3.46 36
MLDP_OF [89]	60.0	11.9 56	24.7 57	2.83 17	17.4 69	23.8 77	3.87 49	10.7 5	24.6 55	3.00 2	20.5 74	33.6 54	8.35 101	44.1 51	56.5 55	10.1 29	29.3 92	50.5 80	5.57 116	35.8 40	73.4 33	4.20 96	31.2 56	50.6 73	3.70 104
LSM [39]	60.1	12.3 91	24.7 57	2.83 17	15.4 36	21.9 40	3.56 4	12.0 61	30.3 101	3.00 2	18.7 40	33.2 50	7.44 17	46.1 98	59.4 98	11.1 93	29.3 92	51.9 104	5.07 46	39.2 89	91.0 110	4.04 8	32.3 85	52.5 95	3.42 9
TCOF [69]	61.1	12.0 62	24.7 57	2.83 17	20.3 115	26.4 128	5.07 94	11.1 25	29.0 89	3.00 2	17.7 16	32.4 41	7.68 61	43.2 38	55.5 46	9.97 9	28.8 79	46.3 26	5.07 46	41.2 108	94.9 117	4.08 38	31.8 70	51.3 79	3.70 104
CRTflow [80]	61.1	11.7 38	24.4 48	3.32 101	19.5 98	24.9 107	4.51 76	12.0 61	22.7 36	4.00 97	18.1 27	30.3 17	7.68 61	45.0 72	58.1 77	11.3 116	26.0 14	45.1 21	4.97 19	37.7 69	87.9 94	4.08 38	30.8 49	50.2 64	3.56 67
RNLOD-Flow [121]	61.2	11.8 46	24.6 53	2.89 44	17.3 68	24.0 82	3.74 38	12.7 83	36.0 119	3.11 62	18.1 27	31.2 25	7.48 21	45.8 90	59.6 102	11.1 93	29.3 92	50.6 82	5.16 65	35.4 29	74.1 38	4.08 38	32.0 79	51.6 83	3.42 9
RFlow [90]	61.8	11.6 30	24.3 41	3.00 60	19.3 93	24.8 104	4.36 71	11.6 45	29.7 97	3.37 67	20.0 64	36.1 79	7.72 71	43.0 31	55.2 44	10.1 29	27.9 52	51.8 103	4.97 19	37.1 62	82.8 88	4.08 38	31.6 66	49.5 61	3.56 67
Fusion [6]	61.9	11.6 30	24.3 41	2.89 44	15.6 40	21.9 40	3.83 44	11.0 10	23.7 46	3.37 67	21.0 79	33.4 51	7.62 52	44.1 51	56.3 53	10.1 29	30.3 111	54.1 112	5.45 103	38.0 74	83.7 90	4.08 38	34.0 107	54.7 106	3.56 67
Sparse Occlusion [54]	62.5	11.7 38	25.9 73	3.00 60	18.1 78	24.6 99	3.83 44	11.3 27	22.7 36	3.11 62	18.7 40	34.1 61	7.70 66	45.0 72	58.0 74	11.1 93	28.5 74	44.2 11	5.26 81	39.3 91	83.7 90	3.92 3	31.9 74	51.7 85	3.56 67
S2D-Matching [84]	63.0	12.3 91	25.7 70	2.94 48	17.2 66	23.7 75	4.00 54	11.7 46	28.7 86	3.00 2	17.7 16	31.9 37	7.55 38	46.8 115	60.1 108	10.4 61	30.0 104	51.5 98	5.29 87	37.0 60	77.7 62	4.04 8	31.8 70	50.9 74	3.46 36
TC-Flow [46]	63.5	12.0 62	30.3 109	2.89 44	16.8 60	23.4 68	3.92 53	11.7 46	21.4 24	3.00 2	19.5 53	36.1 79	8.12 95	46.5 106	59.8 105	11.3 116	27.0 31	48.4 52	5.26 81	35.5 32	74.6 43	4.04 8	33.3 99	54.5 105	3.51 51
Black & Anandan [4]	64.0	12.3 91	24.0 35	3.46 112	21.2 122	25.4 114	5.35 103	18.1 109	25.0 58	5.35 112	24.4 108	34.9 66	7.77 75	42.2 16	53.5 21	10.1 29	26.9 30	46.5 29	4.97 19	39.5 92	77.2 58	4.08 38	29.3 20	42.8 11	3.56 67
HBM-GC [105]	64.0	11.8 46	23.8 33	3.11 73	16.8 60	24.2 88	3.87 49	10.7 5	18.7 2	3.00 2	18.9 45	32.9 46	7.68 61	46.8 115	60.8 117	11.5 129	34.5 127	61.7 122	5.48 111	37.7 69	81.9 85	4.04 8	30.5 47	47.8 44	3.51 51
SVFilterOh [111]	64.0	11.9 56	26.1 78	2.94 48	14.3 16	20.9 21	3.70 24	12.0 61	26.7 72	3.00 2	19.9 62	36.1 79	7.62 52	46.7 112	59.8 105	11.4 123	30.7 114	55.1 115	5.07 46	36.0 46	77.2 58	4.04 8	32.4 87	53.2 100	3.51 51
Classic+CPF [83]	65.5	12.2 82	24.6 53	2.83 17	15.6 40	22.1 43	3.74 38	12.0 61	30.7 102	3.00 2	17.7 16	30.9 24	7.44 17	47.2 121	61.3 121	11.2 100	31.2 118	55.9 116	5.26 81	39.9 98	88.8 100	4.04 8	33.6 102	54.0 103	3.42 9
FESL [72]	66.2	12.2 82	25.1 65	2.83 17	14.9 27	21.6 33	3.70 24	12.1 74	33.7 113	3.00 2	19.7 56	35.0 68	7.72 71	46.2 100	60.2 112	11.3 116	29.3 92	50.4 77	5.32 91	39.6 93	88.6 99	3.92 3	32.4 87	51.2 77	3.42 9
CostFilter [40]	66.5	13.1 112	33.1 116	2.71 2	15.2 32	21.3 26	3.56 4	14.0 91	42.7 127	3.00 2	22.0 91	44.4 120	7.26 2	45.8 90	57.2 65	10.4 61	27.2 36	48.1 50	5.45 103	39.9 98	89.4 102	4.08 38	35.6 114	56.1 113	3.37 2
Efficient-NL [60]	66.7	11.8 46	23.8 33	2.83 17	16.7 58	23.3 66	3.70 24	18.4 111	29.0 89	3.70 82	19.4 51	34.0 60	7.51 25	45.1 74	58.5 84	11.1 93	30.0 104	51.5 98	5.07 46	40.1 101	88.9 101	4.08 38	33.0 96	52.4 94	3.42 9
Bartels [41]	67.0	12.2 82	29.9 105	3.37 105	17.4 69	24.3 90	4.83 89	11.3 27	24.7 56	3.70 82	21.2 81	35.4 71	9.15 115	41.3 7	51.0 6	9.87 4	29.7 101	50.2 70	6.32 128	33.7 5	70.7 10	4.20 96	30.2 40	48.4 47	3.79 121
2D-CLG [1]	67.0	11.6 30	24.1 37	3.11 73	19.4 96	23.3 66	6.24 116	18.7 112	24.3 52	4.69 106	22.4 95	31.8 35	8.66 108	43.3 42	56.1 52	10.4 61	26.0 14	44.2 11	5.35 98	40.2 102	91.5 112	4.20 96	29.6 28	44.5 15	3.51 51
EpicFlow [102]	67.1	11.9 56	27.6 89	2.83 17	16.0 44	22.2 44	3.79 40	11.8 59	21.7 28	3.00 2	21.3 84	42.9 115	7.85 80	46.3 103	59.4 98	11.2 100	27.4 40	47.5 41	5.16 65	38.2 76	76.6 52	4.12 81	35.2 113	58.0 116	3.56 67
Filter Flow [19]	67.2	11.8 46	23.1 25	3.37 105	20.0 107	25.1 109	5.23 100	12.2 76	26.0 64	3.70 82	22.1 92	32.7 44	7.94 85	42.1 14	51.9 11	10.4 61	28.1 58	49.0 59	5.07 46	38.4 79	81.6 82	4.16 90	30.0 34	45.5 26	3.74 118
SRR-TVOF-NL [91]	67.8	12.9 109	28.7 96	3.00 60	16.9 62	23.1 62	4.69 82	11.5 44	27.0 74	3.00 2	22.2 93	37.3 88	7.59 46	44.8 69	57.9 72	11.0 87	29.1 89	51.9 104	4.90 6	35.7 38	77.7 62	4.08 38	33.0 96	51.5 82	3.56 67
Steered-L1 [118]	68.0	11.2 10	22.6 22	2.89 44	16.2 48	22.6 50	4.55 78	21.7 115	32.4 109	5.00 109	23.4 102	38.3 95	10.7 119	44.7 65	57.4 66	9.88 5	28.0 55	48.5 54	5.32 91	37.1 62	79.2 66	4.12 81	31.4 60	51.1 76	3.51 51
Occlusion-TV-L1 [63]	70.8	11.6 30	25.0 64	3.11 73	19.8 104	26.0 122	4.83 89	11.3 27	23.0 40	3.46 79	22.5 98	43.0 117	7.94 85	43.0 31	54.8 39	9.88 5	28.0 55	50.7 86	5.32 91	39.6 93	76.6 52	4.62 119	31.5 64	50.5 70	3.56 67
EPPM w/o HM [88]	71.5	12.7 106	30.9 111	2.71 2	16.1 45	23.1 62	3.70 24	17.7 107	42.4 126	3.70 82	21.3 84	42.5 114	7.70 66	43.0 31	53.1 18	10.3 52	30.2 109	57.1 118	4.97 19	38.5 82	89.6 103	4.12 81	32.4 87	51.3 79	3.42 9
OFH [38]	71.8	12.0 62	27.3 85	3.00 60	18.1 78	23.4 68	4.20 65	12.4 81	32.7 110	3.00 2	18.6 38	35.4 71	7.35 7	46.5 106	60.1 108	10.8 79	27.5 43	47.2 35	5.26 81	41.1 105	81.1 79	4.20 96	35.7 115	56.1 113	3.46 36
CNN-flow-warp+ref [117]	72.0	11.0 3	22.4 19	3.11 73	17.6 71	22.9 57	5.92 111	16.1 99	28.3 85	4.00 97	23.5 103	30.2 15	10.7 119	44.8 69	58.5 84	11.3 116	26.5 26	46.5 29	5.29 87	41.5 109	91.5 112	4.32 108	30.4 45	47.5 40	3.51 51
Adaptive [20]	72.2	11.6 30	26.7 82	3.11 73	20.2 111	25.9 119	5.07 94	12.0 61	23.0 40	3.37 67	20.4 72	36.6 85	7.77 75	44.3 55	58.5 84	9.98 13	28.3 65	49.1 61	5.16 65	42.5 114	90.6 109	4.08 38	31.6 66	48.8 54	3.65 101
Horn & Schunck [3]	73.8	12.1 75	23.7 30	3.32 101	21.4 124	25.6 117	5.89 110	17.0 103	28.2 84	5.35 112	27.3 115	37.9 91	8.04 90	42.4 19	54.3 28	10.3 52	26.2 17	44.7 15	5.07 46	40.9 104	81.7 83	4.20 96	30.2 40	44.3 14	3.70 104
IAOF [50]	74.5	13.0 110	29.2 99	3.37 105	23.7 129	27.4 131	6.45 120	16.4 101	28.7 86	3.46 79	22.7 99	33.1 49	8.37 102	43.4 43	55.6 48	10.0 17	27.6 45	50.1 68	4.97 19	38.3 77	82.7 87	4.08 38	30.0 34	46.8 34	3.56 67
TV-L1-improved [17]	75.2	11.5 24	25.4 67	3.11 73	20.1 110	26.0 122	5.26 101	16.8 102	19.7 8	4.04 102	19.5 53	32.3 39	7.79 78	43.8 48	56.5 55	10.0 17	28.9 83	51.1 95	5.07 46	43.2 117	98.9 123	4.43 114	31.4 60	50.2 64	3.70 104
TriFlow [95]	75.6	12.5 102	36.7 120	3.00 60	18.7 83	24.5 96	4.76 84	11.7 46	28.1 83	3.00 2	21.7 89	41.4 111	7.62 52	46.8 115	60.4 114	11.2 100	29.9 102	51.7 102	4.97 19	37.8 72	76.9 54	4.08 38	31.7 69	48.6 51	3.51 51
HBpMotionGpu [43]	76.2	12.3 91	32.0 114	3.79 120	20.6 120	25.4 114	6.00 113	11.3 27	26.1 66	3.00 2	23.2 101	44.0 119	7.85 80	44.3 55	56.9 59	10.8 79	29.0 84	53.5 111	5.26 81	34.9 25	69.8 6	4.04 8	31.8 70	51.4 81	3.70 104
Nguyen [33]	76.5	12.0 62	25.9 73	3.37 105	21.2 122	24.5 96	6.27 117	12.7 83	28.0 80	3.70 82	23.8 104	34.7 64	8.58 106	43.0 31	54.7 36	10.1 29	27.7 50	50.7 86	4.97 19	43.4 119	93.7 115	4.43 114	30.2 40	47.4 37	3.56 67
BriefMatch [124]	77.1	12.1 75	29.2 99	3.11 73	16.5 55	22.5 48	6.61 122	18.0 108	22.7 36	5.69 115	26.2 111	35.5 75	18.2 129	43.7 46	54.7 36	10.4 61	29.6 99	50.2 70	5.94 125	35.8 40	72.5 26	4.16 90	32.1 81	50.2 64	3.56 67
GraphCuts [14]	77.4	13.9 119	30.2 108	3.32 101	16.4 51	22.5 48	4.36 71	33.4 127	24.1 51	5.35 112	22.3 94	34.7 64	7.87 83	44.5 61	57.0 60	9.98 13	28.3 65	50.3 75	4.90 6	38.5 82	88.2 96	4.20 96	33.9 106	53.6 102	3.56 67
FlowNet2 [122]	78.7	19.1 127	47.5 128	3.11 73	17.1 63	24.1 85	4.55 78	14.2 93	29.8 99	3.37 67	23.8 104	42.9 115	8.33 99	45.9 94	58.1 77	10.6 72	27.6 45	49.4 63	4.93 12	39.2 89	81.0 78	4.08 38	31.6 66	49.2 57	3.56 67
TI-DOFE [24]	78.8	12.7 106	27.6 89	3.87 124	22.2 127	25.3 111	6.66 123	14.1 92	25.3 60	4.36 104	27.7 117	38.7 99	9.06 113	42.7 25	53.6 24	10.1 29	26.8 28	48.8 58	4.97 19	38.3 77	76.0 46	4.24 106	31.9 74	44.7 18	3.87 123
ROF-ND [107]	81.7	12.4 98	24.4 48	2.83 17	17.9 74	23.9 79	4.08 61	12.0 61	26.6 71	3.00 2	29.5 121	48.9 124	8.72 109	45.4 80	58.6 88	11.1 93	31.1 117	53.4 109	5.26 81	38.9 87	74.2 39	4.20 96	38.0 118	60.3 121	3.56 67
NL-TV-NCC [25]	83.0	13.7 116	27.3 85	2.94 48	18.5 80	24.7 101	4.04 56	15.0 95	29.0 89	3.70 82	25.6 110	46.4 122	7.94 85	42.0 13	51.9 11	10.4 61	30.6 113	51.9 104	5.29 87	41.9 111	81.7 83	4.40 110	31.3 59	48.6 51	3.79 121
TriangleFlow [30]	83.4	12.5 102	25.9 73	3.11 73	18.8 85	24.3 90	4.24 66	13.2 88	29.7 97	3.46 79	21.2 81	35.4 71	7.94 85	44.4 59	57.7 68	9.95 7	29.4 97	48.6 55	5.07 46	43.9 120	99.9 124	4.43 114	42.1 126	69.7 129	3.56 67
Complementary OF [21]	83.7	12.4 98	34.5 119	2.83 17	16.4 51	23.5 72	3.79 40	30.7 121	32.2 108	7.05 123	19.9 62	43.9 118	7.44 17	46.9 118	60.4 114	10.7 76	28.1 58	47.7 45	5.23 77	41.1 105	80.3 74	4.12 81	42.0 125	62.0 123	3.56 67
Correlation Flow [75]	84.0	12.6 105	28.0 92	2.71 2	20.0 107	25.8 118	4.36 71	11.3 27	22.3 33	3.00 2	20.7 77	38.6 98	7.72 71	45.7 86	59.0 94	10.3 52	33.4 123	60.4 121	5.45 103	45.6 124	99.9 124	4.40 110	33.4 100	54.9 108	3.56 67
LocallyOriented [52]	84.6	12.2 82	28.1 93	3.27 99	20.5 118	25.9 119	5.07 94	14.3 94	30.0 100	3.37 67	24.2 107	41.7 112	7.66 59	44.7 65	57.1 64	10.1 29	28.8 79	47.4 39	5.48 111	42.4 112	80.6 76	4.12 81	32.4 87	51.2 77	3.56 67
IAOF2 [51]	85.4	12.7 106	28.7 96	3.32 101	20.4 116	25.9 119	4.76 84	12.7 83	31.7 106	3.11 62	22.4 95	35.8 76	8.06 94	45.9 94	59.6 102	10.8 79	29.9 102	51.5 98	5.10 62	39.0 88	79.7 70	4.08 38	31.2 56	49.0 56	3.56 67
Aniso-Texture [82]	86.3	11.5 24	24.1 37	2.83 17	20.2 111	26.0 122	4.97 92	20.0 114	24.4 54	3.37 67	26.9 113	50.7 125	9.11 114	46.1 98	60.5 116	11.4 123	32.7 122	62.2 126	5.94 125	37.3 65	80.2 72	4.04 8	34.1 108	55.0 109	3.42 9
ACK-Prior [27]	88.2	12.5 102	29.7 103	2.83 17	16.1 45	22.7 52	4.00 54	25.6 117	27.7 76	5.72 117	22.4 95	36.0 78	10.7 119	45.7 86	59.3 96	11.4 123	31.8 120	50.6 82	5.35 98	38.8 86	79.9 71	4.16 90	33.5 101	51.7 85	3.70 104
Rannacher [23]	88.8	11.7 38	28.7 96	3.16 98	20.4 116	26.3 126	5.07 94	19.0 113	26.0 64	4.80 108	19.8 58	38.1 93	7.79 78	44.5 61	57.4 66	10.1 29	29.0 84	50.3 75	5.20 70	42.6 115	97.0 120	4.40 110	33.7 104	55.9 112	3.70 104
Learning Flow [11]	90.1	12.1 75	24.6 53	3.27 99	19.7 101	25.2 110	5.00 93	39.7 129	47.7 130	7.68 125	24.6 109	35.0 68	8.19 98	45.2 77	58.6 88	10.5 71	28.4 67	48.0 48	5.45 103	38.4 79	77.8 64	4.40 110	32.6 94	48.4 47	3.92 125
2bit-BM-tele [98]	90.8	11.7 38	27.0 84	3.79 120	20.2 111	26.3 126	5.07 94	12.0 61	23.2 44	4.00 97	21.2 81	36.1 79	8.16 97	45.3 78	58.0 74	10.3 52	34.0 126	61.8 123	5.92 124	54.1 131	99.9 124	5.72 129	29.8 32	47.4 37	3.74 118
FOLKI [16]	91.4	13.0 110	30.9 111	4.97 129	22.2 127	24.9 107	9.00 129	17.3 105	33.0 112	7.00 120	33.4 126	38.7 99	17.0 127	44.3 55	55.8 49	10.4 61	27.6 45	49.7 64	5.48 111	36.2 52	74.2 39	4.80 122	30.4 45	44.9 21	4.08 127
SimpleFlow [49]	92.4	12.0 62	24.0 35	2.94 48	18.5 80	24.4 93	4.24 66	32.7 124	39.0 121	5.69 115	18.0 25	36.2 83	7.55 38	46.9 118	60.8 117	11.1 93	31.4 119	58.1 119	5.35 98	49.4 128	99.9 124	5.16 127	40.0 122	63.0 126	3.46 36
SILK [79]	92.5	13.3 114	30.7 110	3.83 123	22.0 126	25.3 111	7.16 124	34.7 128	40.0 123	7.77 127	26.6 112	36.6 85	8.60 107	45.1 74	57.9 72	10.0 17	28.4 67	50.9 92	6.03 127	34.8 19	71.8 21	4.51 118	31.4 60	48.0 45	3.74 118
StereoFlow [44]	94.0	22.8 131	48.3 129	3.74 119	20.5 118	26.8 129	5.07 94	11.3 27	29.3 94	3.37 67	20.1 66	37.0 87	7.62 52	59.3 129	75.2 129	10.8 79	39.3 131	71.4 130	5.45 103	35.8 40	73.9 35	4.08 38	35.7 115	55.1 111	3.70 104
StereoOF-V1MT [119]	95.8	13.7 116	32.7 115	3.00 60	18.7 83	23.6 73	4.80 87	21.8 116	28.0 80	5.07 111	31.6 122	40.6 108	9.57 116	46.5 106	58.9 93	11.5 129	29.2 91	50.2 70	6.45 130	42.4 112	94.7 116	4.80 122	31.4 60	48.3 46	3.46 36
Shiralkar [42]	96.4	13.2 113	31.6 113	3.00 60	19.7 101	24.5 96	4.65 81	17.0 103	30.7 102	4.08 103	32.1 124	53.1 127	8.04 90	46.3 103	59.7 104	10.3 52	28.4 67	50.2 70	5.45 103	45.5 123	95.2 118	4.24 106	39.2 121	62.6 124	3.42 9
Dynamic MRF [7]	98.3	12.1 75	26.8 83	2.94 48	18.0 77	23.9 79	4.16 64	18.3 110	30.7 102	5.00 109	28.9 118	39.8 102	10.5 118	45.9 94	58.6 88	11.2 100	30.9 115	56.0 117	5.80 123	43.0 116	90.3 107	4.65 120	33.7 104	51.8 87	3.70 104
Adaptive flow [45]	98.8	13.4 115	25.8 72	4.51 126	21.8 125	25.4 114	7.26 125	13.7 90	27.5 75	4.69 106	24.1 106	35.2 70	8.76 111	47.3 122	61.5 122	10.2 47	33.8 125	61.9 124	5.45 103	35.9 44	73.2 32	4.20 96	34.7 112	54.7 106	3.70 104
UnFlow [129]	100.2	14.9 122	40.2 125	3.11 73	18.5 80	23.4 68	5.48 105	15.3 96	31.3 105	4.36 104	22.8 100	38.0 92	8.45 104	48.3 125	63.0 125	10.9 86	32.4 121	62.0 125	5.72 118	35.4 29	71.2 15	4.32 108	45.5 129	66.0 128	3.87 123
SPSA-learn [13]	103.4	12.3 91	33.7 118	3.37 105	19.2 91	23.6 73	5.45 104	30.0 120	39.7 122	7.00 120	26.9 113	41.3 110	8.41 103	46.7 112	60.1 108	10.2 47	29.4 97	50.6 82	5.20 70	53.7 130	99.9 124	8.43 131	51.4 130	72.0 130	3.51 51
HCIC-L [99]	105.0	21.0 130	41.8 126	5.07 130	20.2 111	26.1 125	5.80 108	16.3 100	42.3 125	4.00 97	31.7 123	51.0 126	8.50 105	44.7 65	55.5 46	10.4 61	35.2 128	69.8 129	5.07 46	39.9 98	91.2 111	4.16 90	40.4 124	58.0 116	3.65 101
SegOF [10]	105.4	12.3 91	33.1 116	3.11 73	17.9 74	23.8 77	4.51 76	29.0 119	34.3 115	6.16 118	32.8 125	78.9 131	8.33 99	48.1 124	63.6 126	11.2 100	28.5 74	54.3 113	5.72 118	44.6 121	99.9 124	4.97 125	37.9 117	61.4 122	3.51 51
FFV1MT [106]	106.7	17.0 125	37.6 123	3.37 105	19.3 93	22.9 57	6.40 119	28.2 118	46.7 129	6.95 119	29.3 119	38.4 96	11.4 123	46.3 103	58.2 81	10.4 61	29.0 84	50.4 77	5.72 118	46.7 125	88.5 98	4.93 124	39.0 120	56.9 115	4.43 129
PGAM+LK [55]	107.2	15.5 124	39.4 124	4.55 127	19.8 104	24.0 82	7.68 126	33.1 126	43.4 128	8.00 128	34.5 128	45.7 121	11.2 122	46.6 110	57.7 68	10.6 72	29.3 92	50.8 91	5.74 122	37.4 66	77.2 58	4.43 114	34.4 110	53.3 101	4.24 128
Heeger++ [104]	107.4	19.8 128	44.7 127	3.11 73	18.9 87	22.8 54	6.45 120	33.0 125	35.2 116	7.16 124	29.3 119	38.4 96	11.4 123	51.5 127	65.2 127	11.3 116	28.4 67	46.9 32	6.78 131	47.9 127	84.5 92	4.69 121	40.1 123	58.8 119	3.70 104
SLK [47]	109.0	13.9 119	29.9 105	3.79 120	20.0 107	22.8 54	6.22 115	32.0 123	33.7 113	7.72 126	33.4 126	46.4 122	16.1 126	48.5 126	61.7 123	10.3 52	28.4 67	47.9 47	5.72 118	43.2 117	97.9 121	4.97 125	38.7 119	59.8 120	4.04 126
Pyramid LK [2]	116.8	14.4 121	37.3 122	4.93 128	23.7 129	25.3 111	9.98 131	42.2 130	35.7 117	12.3 130	56.2 131	64.2 129	35.8 131	65.6 130	83.9 130	10.6 72	28.1 58	46.1 25	5.48 111	45.2 122	99.9 124	5.89 130	53.6 131	75.1 131	5.42 130
GroupFlow [9]	117.5	19.9 129	49.6 130	3.42 111	19.1 89	24.1 85	5.48 105	31.4 122	40.0 123	8.19 129	36.2 129	61.9 128	12.1 125	55.6 128	71.3 128	11.4 123	36.3 129	67.0 128	5.60 117	46.7 125	98.5 122	4.20 96	43.6 127	62.8 125	3.56 67
Periodicity [78]	129.7	17.6 126	55.7 131	5.45 131	26.8 131	27.0 130	9.75 130	49.4 131	51.5 131	17.7 131	51.3 130	70.3 130	27.9 130	66.6 131	86.3 131	11.7 131	38.7 130	82.5 131	6.38 129	51.8 129	99.9 124	5.48 128	44.3 128	65.5 127	5.80 131

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.