High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

LCM-MVSNet		95.70 1	96.40 1	93.61 2	98.67 1	85.39 22	95.54 3	97.36 1	96.97 1	99.04 1	99.05 1	96.61 1	95.92 6	85.07 28	99.27 3	99.54 1

TDRefinement		93.52 2	93.39 3	93.88 1	95.94 10	90.26 3	95.70 2	96.46 2	90.58 7	92.86 37	96.29 16	88.16 22	94.17 56	86.07 24	98.48 19	97.22 25

LTVRE_ROB	86.10 1	93.04 3	93.44 2	91.82 15	93.73 38	85.72 21	96.79 1	95.51 4	88.86 11	95.63 10	96.99 6	84.81 44	93.16 107	91.10 1	97.53 49	96.58 36
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
HPM-MVS_fast		92.50 4	92.54 4	92.37 3	95.93 11	85.81 20	92.99 10	94.23 15	85.21 22	92.51 44	95.13 40	90.65 9	95.34 27	88.06 5	98.15 30	95.95 48

HPM-MVS		92.13 5	92.20 6	91.91 12	95.58 17	84.67 31	93.51 6	94.85 9	82.88 36	91.77 53	93.94 78	90.55 11	95.73 14	88.50 4	98.23 27	95.33 66

APD-MVS_3200maxsize		92.05 6	92.24 5	91.48 16	93.02 53	85.17 24	92.47 18	95.05 8	87.65 17	93.21 33	94.39 61	90.09 12	95.08 34	86.67 16	97.60 48	94.18 88

COLMAP_ROB	83.01 3	91.97 7	91.95 7	92.04 7	93.68 39	86.15 12	93.37 8	95.10 7	90.28 8	92.11 48	95.03 42	89.75 13	94.93 37	79.95 92	98.27 25	95.04 72
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
ACMMP		91.91 8	91.87 12	92.03 8	95.53 18	85.91 15	93.35 9	94.16 18	82.52 40	92.39 47	94.14 69	89.15 15	95.62 16	87.35 11	98.24 26	94.56 77
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
mPP-MVS		91.69 9	91.47 17	92.37 3	96.04 9	88.48 5	92.72 12	92.60 63	83.09 33	91.54 56	94.25 65	87.67 27	95.51 24	87.21 15	98.11 31	93.12 116

CP-MVS		91.67 10	91.58 14	91.96 9	95.29 21	87.62 6	93.38 7	93.36 32	83.16 32	91.06 62	94.00 72	88.26 19	95.71 15	87.28 14	98.39 21	92.55 126

XVS		91.54 11	91.36 19	92.08 5	95.64 15	86.25 10	92.64 13	93.33 34	85.07 23	89.99 76	94.03 71	86.57 33	95.80 11	87.35 11	97.62 46	94.20 86

UA-Net		91.49 12	91.53 15	91.39 18	94.98 25	82.95 43	93.52 5	92.79 57	88.22 14	88.53 110	97.64 2	83.45 53	94.55 48	86.02 25	98.60 14	96.67 34

ACMMPR		91.49 12	91.35 21	91.92 11	95.74 13	85.88 17	92.58 16	93.25 40	81.99 46	91.40 59	94.17 68	87.51 28	95.87 8	87.74 6	97.76 41	93.99 91

LPG-MVS_test		91.47 14	91.68 13	90.82 27	94.75 28	81.69 44	90.00 33	94.27 12	82.35 41	93.67 27	94.82 46	91.18 4	95.52 22	85.36 26	98.73 8	95.23 69

region2R		91.44 15	91.30 23	91.87 13	95.75 12	85.90 16	92.63 15	93.30 37	81.91 48	90.88 67	94.21 66	87.75 24	95.87 8	87.60 9	97.71 44	93.83 95

HFP-MVS		91.30 16	91.39 18	91.02 23	95.43 19	84.66 32	92.58 16	93.29 38	81.99 46	91.47 57	93.96 75	88.35 17	95.56 19	87.74 6	97.74 42	92.85 119

APDe-MVS		91.22 17	91.92 8	89.14 46	92.97 55	78.04 66	92.84 11	94.14 19	83.33 30	93.90 23	95.73 26	88.77 16	96.41 1	87.60 9	97.98 37	92.98 118

PGM-MVS		91.20 18	90.95 27	91.93 10	95.67 14	85.85 18	90.00 33	93.90 24	80.32 62	91.74 54	94.41 60	88.17 21	95.98 4	86.37 17	97.99 36	93.96 93

SteuartSystems-ACMMP		91.16 19	91.36 19	90.55 30	93.91 37	80.97 51	91.49 25	93.48 31	82.82 37	92.60 43	93.97 73	88.19 20	96.29 2	87.61 8	98.20 29	94.39 84
Skip Steuart: Steuart Systems R&D Blog.
MP-MVS		91.14 20	90.91 28	91.83 14	96.18 8	86.88 8	92.20 19	93.03 49	82.59 39	88.52 111	94.37 62	86.74 32	95.41 25	86.32 18	98.21 28	93.19 115

ACMH+	77.89 11	90.73 21	91.50 16	88.44 51	93.00 54	76.26 84	89.65 42	95.55 3	87.72 16	93.89 24	94.94 44	91.62 3	93.44 94	78.35 105	98.76 5	95.61 61

ACMM	79.39 9	90.65 22	90.99 25	89.63 41	95.03 24	83.53 37	89.62 43	93.35 33	79.20 75	93.83 25	93.60 82	90.81 7	92.96 113	85.02 30	98.45 20	92.41 129
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LS3D		90.60 23	90.34 34	91.38 19	89.03 131	84.23 35	93.58 4	94.68 10	90.65 6	90.33 71	93.95 77	84.50 47	95.37 26	80.87 77	95.50 106	94.53 80

ACMP	79.16 10	90.54 24	90.60 31	90.35 34	94.36 31	80.98 50	89.16 52	94.05 21	79.03 79	92.87 36	93.74 81	90.60 10	95.21 32	82.87 56	98.76 5	94.87 73
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
#test#		90.49 25	90.31 35	91.02 23	95.43 19	84.66 32	90.65 30	93.29 38	77.00 101	91.47 57	93.96 75	88.35 17	95.56 19	84.88 31	97.74 42	92.85 119

v7n		90.13 26	90.96 26	87.65 63	91.95 80	71.06 117	89.99 35	93.05 46	86.53 19	94.29 19	96.27 17	82.69 60	94.08 59	86.25 21	97.63 45	97.82 10

PMVS	80.48 6	90.08 27	90.66 30	88.34 53	96.71 2	92.97 2	90.31 31	89.57 142	88.51 13	90.11 72	95.12 41	90.98 6	88.92 187	77.55 112	97.07 57	83.13 234
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PS-CasMVS		90.06 28	91.92 8	84.47 115	96.56 4	58.83 217	89.04 53	92.74 58	91.40 4	96.12 4	96.06 21	87.23 29	95.57 18	79.42 100	98.74 7	99.00 2

PEN-MVS		90.03 29	91.88 11	84.48 114	96.57 3	58.88 216	88.95 54	93.19 41	91.62 3	96.01 6	96.16 20	87.02 30	95.60 17	78.69 103	98.72 10	98.97 3

OurMVSNet-221017-0		90.01 30	89.74 39	90.83 26	93.16 50	80.37 52	91.91 23	93.11 43	81.10 55	95.32 12	97.24 4	72.94 165	94.85 39	85.07 28	97.78 40	97.26 22

DTE-MVSNet		89.98 31	91.91 10	84.21 122	96.51 5	57.84 220	88.93 55	92.84 56	91.92 2	96.16 3	96.23 18	86.95 31	95.99 3	79.05 101	98.57 16	98.80 6

XVG-ACMP-BASELINE		89.98 31	89.84 37	90.41 32	94.91 27	84.50 34	89.49 48	93.98 22	79.68 68	92.09 49	93.89 79	83.80 50	93.10 109	82.67 60	98.04 32	93.64 104

v52		89.97 33	90.60 31	88.07 57	88.69 136	72.01 109	91.35 26	92.64 61	82.22 43	95.97 8	96.31 14	84.82 43	93.98 63	88.59 2	94.83 127	98.23 7

V4		89.97 33	90.60 31	88.07 57	88.69 136	72.01 109	91.35 26	92.64 61	82.22 43	95.98 7	96.31 14	84.80 45	93.98 63	88.59 2	94.83 127	98.23 7

3Dnovator+	83.92 2	89.97 33	89.66 40	90.92 25	91.27 98	81.66 47	91.25 28	94.13 20	88.89 10	88.83 105	94.26 64	77.55 105	95.86 10	84.88 31	95.87 97	95.24 68

WR-MVS_H		89.91 36	91.31 22	85.71 93	96.32 7	62.39 182	89.54 46	93.31 36	90.21 9	95.57 11	95.66 28	81.42 79	95.90 7	80.94 76	98.80 4	98.84 5

mvs_tets		89.78 37	89.27 44	91.30 20	93.51 41	84.79 29	89.89 37	90.63 112	70.00 168	94.55 16	96.67 9	87.94 23	93.59 78	84.27 39	95.97 92	95.52 62

test_djsdf		89.62 38	89.01 46	91.45 17	92.36 70	82.98 42	91.98 21	90.08 132	71.54 156	94.28 20	96.54 11	81.57 77	94.27 50	86.26 19	96.49 74	97.09 29

XVG-OURS-SEG-HR		89.59 39	89.37 43	90.28 35	94.47 30	85.95 14	86.84 82	93.91 23	80.07 65	86.75 136	93.26 84	93.64 2	90.93 156	84.60 35	90.75 193	93.97 92

APD-MVS		89.54 40	89.63 41	89.26 45	92.57 63	81.34 49	90.19 32	93.08 45	80.87 57	91.13 61	93.19 85	86.22 38	95.97 5	82.23 64	97.18 55	90.45 164
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
jajsoiax		89.41 41	88.81 50	91.19 22	93.38 45	84.72 30	89.70 39	90.29 127	69.27 171	94.39 17	96.38 13	86.02 40	93.52 88	83.96 42	95.92 95	95.34 65

CPTT-MVS		89.39 42	88.98 48	90.63 29	95.09 23	86.95 7	92.09 20	92.30 68	79.74 67	87.50 124	92.38 105	81.42 79	93.28 102	83.07 53	97.24 53	91.67 142

ACMH	76.49 14	89.34 43	91.14 24	83.96 128	92.50 66	70.36 121	89.55 44	93.84 25	81.89 49	94.70 14	95.44 36	90.69 8	88.31 194	83.33 49	98.30 24	93.20 114
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CP-MVSNet		89.27 44	90.91 28	84.37 118	96.34 6	58.61 219	88.66 60	92.06 73	90.78 5	95.67 9	95.17 39	81.80 75	95.54 21	79.00 102	98.69 11	98.95 4

XVG-OURS		89.18 45	88.83 49	90.23 36	94.28 32	86.11 13	85.91 96	93.60 29	80.16 64	89.13 101	93.44 83	83.82 49	90.98 154	83.86 45	95.30 113	93.60 106

DeepC-MVS	82.31 4	89.15 46	89.08 45	89.37 44	93.64 40	79.07 59	88.54 61	94.20 16	73.53 128	89.71 86	94.82 46	85.09 42	95.77 13	84.17 41	98.03 34	93.26 112
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SD-MVS		88.96 47	89.88 36	86.22 81	91.63 86	77.07 76	89.82 38	93.77 26	78.90 80	92.88 35	92.29 109	86.11 39	90.22 174	86.24 22	97.24 53	91.36 148

HPM-MVS++		88.93 48	88.45 54	90.38 33	94.92 26	85.85 18	89.70 39	91.27 102	78.20 87	86.69 137	92.28 110	80.36 86	95.06 35	86.17 23	96.49 74	90.22 168

v748		88.91 49	89.82 38	86.19 85	90.06 121	68.53 132	88.81 57	91.48 87	84.36 26	94.19 21	95.98 22	82.52 63	92.67 121	84.30 38	96.67 68	97.37 19

test_0402		88.65 50	89.58 42	85.88 90	92.55 64	72.22 108	84.01 120	89.44 144	88.63 12	94.38 18	95.77 25	86.38 37	93.59 78	79.84 93	95.21 114	91.82 139

DP-MVS		88.60 51	89.01 46	87.36 67	91.30 96	77.50 71	87.55 70	92.97 51	87.95 15	89.62 92	92.87 94	84.56 46	93.89 66	77.65 111	96.62 69	90.70 157

wuykxyi23d		88.46 52	88.80 51	87.44 66	90.96 106	93.03 1	85.85 98	81.96 205	74.58 120	98.58 2	97.29 3	87.73 25	87.31 200	82.84 58	99.41 1	81.99 247

PS-MVSNAJss		88.31 53	87.90 57	89.56 43	93.31 46	77.96 67	87.94 65	91.97 76	70.73 162	94.19 21	96.67 9	76.94 112	94.57 46	83.07 53	96.28 79	96.15 39

OMC-MVS		88.19 54	87.52 61	90.19 37	91.94 82	81.68 46	87.49 72	93.17 42	76.02 109	88.64 108	91.22 132	84.24 48	93.37 97	77.97 110	97.03 58	95.52 62

RPSCF		88.00 55	86.93 73	91.22 21	90.08 119	89.30 4	89.68 41	91.11 105	79.26 74	89.68 87	94.81 49	82.44 64	87.74 197	76.54 120	88.74 210	96.61 35

AllTest		87.97 56	87.40 65	89.68 39	91.59 87	83.40 38	89.50 47	95.44 5	79.47 70	88.00 118	93.03 88	82.66 61	91.47 143	70.81 152	96.14 86	94.16 89

TranMVSNet+NR-MVSNet		87.86 57	88.76 52	85.18 99	94.02 34	64.13 153	84.38 116	91.29 101	84.88 25	92.06 50	93.84 80	86.45 35	93.73 69	73.22 142	98.66 12	97.69 11

nrg030		87.85 58	88.49 53	85.91 89	90.07 120	69.73 123	87.86 66	94.20 16	74.04 123	92.70 41	94.66 50	85.88 41	91.50 142	79.72 94	97.32 52	96.50 37

CNVR-MVS		87.81 59	87.68 60	88.21 54	92.87 57	77.30 75	85.25 105	91.23 103	77.31 97	87.07 131	91.47 128	82.94 58	94.71 42	84.67 34	96.27 81	92.62 125

HQP_MVS		87.75 60	87.43 64	88.70 49	93.45 42	76.42 82	89.45 49	93.61 27	79.44 72	86.55 138	92.95 92	74.84 129	95.22 30	80.78 79	95.83 99	94.46 81

NCCC		87.36 61	86.87 74	88.83 47	92.32 72	78.84 62	86.58 92	91.09 106	78.77 82	84.85 161	90.89 149	80.85 82	95.29 28	81.14 73	95.32 110	92.34 132

v13		87.31 62	88.10 55	84.94 101	88.84 133	63.75 157	87.85 67	91.47 90	79.12 76	93.72 26	95.82 24	75.20 123	93.58 81	84.76 33	96.16 84	97.48 16

DeepPCF-MVS	81.24 5	87.28 63	86.21 85	90.49 31	91.48 94	84.90 27	83.41 141	92.38 67	70.25 167	89.35 99	90.68 156	82.85 59	94.57 46	79.55 96	95.95 93	92.00 137

SixPastTwentyTwo		87.20 64	87.45 63	86.45 75	92.52 65	69.19 130	87.84 68	88.05 156	81.66 51	94.64 15	96.53 12	65.94 202	94.75 41	83.02 55	96.83 64	95.41 64

v12		87.15 65	87.91 56	84.84 103	88.69 136	63.52 160	87.58 69	91.46 91	78.74 83	93.57 29	95.66 28	74.94 127	93.57 82	84.50 36	96.08 89	97.43 17

v11		86.96 66	87.78 58	84.51 112	88.50 142	62.60 178	87.21 75	91.63 82	78.08 90	93.40 31	95.56 33	75.07 124	93.57 82	84.46 37	96.08 89	97.36 20

V9		86.96 66	87.70 59	84.74 107	88.52 141	63.27 166	87.31 74	91.45 93	78.28 86	93.43 30	95.45 35	74.59 135	93.57 82	84.23 40	96.01 91	97.38 18

UniMVSNet (Re)		86.87 68	86.98 71	86.55 73	93.11 52	68.48 133	83.80 128	92.87 53	80.37 60	89.61 94	91.81 119	77.72 102	94.18 54	75.00 129	98.53 17	96.99 32

Vis-MVSNet		86.86 69	86.58 78	87.72 61	92.09 76	77.43 72	87.35 73	92.09 72	78.87 81	84.27 171	94.05 70	78.35 97	93.65 72	80.54 83	91.58 181	92.08 136
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
UniMVSNet_NR-MVSNet		86.84 70	87.06 68	86.17 86	92.86 59	67.02 141	82.55 162	91.56 83	83.08 34	90.92 64	91.82 118	78.25 98	93.99 61	74.16 131	98.35 22	97.49 15

DU-MVS		86.80 71	86.99 70	86.21 83	93.24 48	67.02 141	83.16 148	92.21 69	81.73 50	90.92 64	91.97 112	77.20 107	93.99 61	74.16 131	98.35 22	97.61 12

V14		86.75 72	87.46 62	84.62 110	88.35 145	63.00 169	87.02 80	91.42 95	77.78 92	93.27 32	95.23 38	74.22 136	93.56 85	83.95 43	95.93 94	97.31 21

Regformer-2		86.74 73	86.08 87	88.73 48	84.18 211	79.20 58	83.52 136	89.33 145	83.33 30	89.92 82	85.07 228	83.23 56	93.16 107	83.39 48	92.72 169	93.83 95

IS-MVSNet		86.66 74	86.82 77	86.17 86	92.05 78	66.87 143	91.21 29	88.64 150	86.30 21	89.60 95	92.59 100	69.22 189	94.91 38	73.89 135	97.89 39	96.72 33

v15		86.56 75	87.25 66	84.51 112	88.15 149	62.72 174	86.72 89	91.40 97	77.38 95	93.11 34	95.00 43	73.93 142	93.55 86	83.67 47	95.86 98	97.26 22

v10		86.54 76	87.10 67	84.84 103	88.16 148	63.28 165	86.64 91	92.20 70	75.42 116	92.81 38	94.50 56	74.05 140	94.06 60	83.88 44	96.28 79	97.17 27

Regformer-4		86.41 77	85.71 92	88.52 50	84.27 207	77.57 70	84.07 118	88.00 158	82.82 37	89.84 83	85.48 220	82.06 68	92.77 118	83.83 46	91.04 186	95.22 71

PHI-MVS		86.38 78	85.81 90	88.08 56	88.44 144	77.34 73	89.35 51	93.05 46	73.15 137	84.76 162	87.70 201	78.87 95	94.18 54	80.67 81	96.29 78	92.73 123

v17		86.32 79	86.95 72	84.44 116	88.00 151	62.62 177	86.74 87	91.48 87	77.17 98	92.74 39	94.56 52	73.74 146	93.53 87	83.27 50	94.87 126	97.18 26

test_prior3		86.31 80	86.31 82	86.32 77	90.59 113	71.99 111	83.37 142	92.85 54	75.43 114	84.58 163	91.57 123	81.92 73	94.17 56	79.54 97	96.97 59	92.80 121

CSCG		86.26 81	86.47 80	85.60 95	90.87 108	74.26 92	87.98 64	91.85 79	80.35 61	89.54 98	88.01 196	79.09 93	92.13 129	75.51 124	95.06 119	90.41 165

v16		86.24 82	86.85 75	84.43 117	87.96 153	62.59 179	86.73 88	91.48 87	77.17 98	92.67 42	94.55 53	73.63 147	93.52 88	83.26 51	94.16 139	97.17 27

DeepC-MVS_fast	80.27 8	86.23 83	85.65 94	87.96 60	91.30 96	76.92 77	87.19 76	91.99 75	70.56 163	84.96 157	90.69 155	80.01 89	95.14 33	78.37 104	95.78 101	91.82 139
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
v8		86.22 84	86.83 76	84.36 119	87.82 155	62.35 183	86.42 93	91.33 100	76.78 102	92.73 40	94.48 57	73.41 154	93.72 70	83.10 52	95.41 107	97.01 31

CDPH-MVS		86.17 85	85.54 95	88.05 59	92.25 73	75.45 87	83.85 125	92.01 74	65.91 194	86.19 144	91.75 121	83.77 51	94.98 36	77.43 114	96.71 67	93.73 101

Regformer-1		86.00 86	85.50 96	87.49 64	84.18 211	76.90 78	83.52 136	87.94 160	82.18 45	89.19 100	85.07 228	82.28 66	91.89 134	82.40 62	92.72 169	93.69 103

NR-MVSNet		86.00 86	86.22 84	85.34 97	93.24 48	64.56 152	82.21 172	90.46 116	80.99 56	88.42 113	91.97 112	77.56 104	93.85 67	72.46 147	98.65 13	97.61 12

v18		85.99 88	86.55 79	84.30 120	87.73 161	62.29 187	86.40 94	91.49 86	76.64 103	92.40 46	94.20 67	73.28 158	93.52 88	82.87 56	93.99 143	97.09 29

FC-MVSNet-test		85.93 89	87.05 69	82.58 154	92.25 73	56.44 231	85.75 99	93.09 44	77.33 96	91.94 51	94.65 51	74.78 131	93.41 96	75.11 127	98.58 15	97.88 9

agg_prior3		85.76 90	84.95 104	88.16 55	92.43 68	79.92 55	83.98 121	90.03 134	65.11 200	83.66 176	90.64 160	81.00 81	93.67 71	81.21 71	96.54 71	90.88 153

agg_prior1		85.72 91	85.20 99	87.28 68	91.58 90	77.69 68	83.69 131	90.30 124	66.29 190	84.32 168	91.07 143	82.13 67	93.18 105	81.02 74	96.36 77	90.98 149

TAPA-MVS	77.73 12	85.71 92	84.83 105	88.37 52	88.78 135	79.72 56	87.15 78	93.50 30	69.17 172	85.80 149	89.56 177	80.76 83	92.13 129	73.21 143	95.51 105	93.25 113
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
canonicalmvs		85.50 93	86.14 86	83.58 140	87.97 152	67.13 140	87.55 70	94.32 11	73.44 130	88.47 112	87.54 204	86.45 35	91.06 153	75.76 123	93.76 150	92.54 127

EPP-MVSNet		85.47 94	85.04 101	86.77 71	91.52 93	69.37 125	91.63 24	87.98 159	81.51 53	87.05 132	91.83 117	66.18 201	95.29 28	70.75 154	96.89 61	95.64 56

FIs		85.35 95	86.27 83	82.60 153	91.86 83	57.31 224	85.10 107	93.05 46	75.83 112	91.02 63	93.97 73	73.57 151	92.91 116	73.97 134	98.02 35	97.58 14

K. test v3		85.14 96	84.73 106	86.37 76	91.13 103	69.63 124	85.45 103	76.68 230	84.06 28	92.44 45	96.99 6	62.03 214	94.65 43	80.58 82	93.24 162	94.83 76

EI-MVSNet-Vis-set		85.12 97	84.53 118	86.88 69	84.01 213	72.76 100	83.91 124	85.18 188	80.44 59	88.75 106	85.49 219	80.08 88	91.92 132	82.02 65	90.85 191	95.97 46

Regformer-3		85.06 98	84.67 111	86.22 81	84.27 207	73.43 96	84.07 118	85.26 186	80.77 58	88.62 109	85.48 220	80.56 85	90.39 171	81.99 66	91.04 186	94.85 75

EI-MVSNet-UG-set		85.04 99	84.44 120	86.85 70	83.87 215	72.52 103	83.82 126	85.15 189	80.27 63	88.75 106	85.45 222	79.95 90	91.90 133	81.92 67	90.80 192	96.13 40

X-MVStestdata		85.04 99	82.70 141	92.08 5	95.64 15	86.25 10	92.64 13	93.33 34	85.07 23	89.99 76	16.05 295	86.57 33	95.80 11	87.35 11	97.62 46	94.20 86

MSLP-MVS		85.00 101	86.03 88	81.90 160	91.84 84	71.56 115	86.75 86	93.02 50	75.95 110	87.12 128	89.39 179	77.98 99	89.40 184	77.46 113	94.78 129	84.75 214

F-COLMAP		84.97 102	83.42 135	89.63 41	92.39 69	83.40 38	88.83 56	91.92 78	73.19 136	80.18 208	89.15 182	77.04 110	93.28 102	65.82 189	92.28 175	92.21 135

v7		84.81 103	85.00 102	84.23 121	88.15 149	63.27 166	83.79 129	91.39 98	71.10 160	90.07 73	91.28 130	74.04 141	93.63 74	81.48 70	93.67 153	95.79 49

3Dnovator	80.37 7	84.80 104	84.71 109	85.06 100	86.36 184	74.71 90	88.77 58	90.00 135	75.65 113	84.96 157	93.17 86	74.06 139	91.19 149	78.28 107	91.09 184	89.29 176

IterMVS-LS		84.73 105	84.98 103	83.96 128	87.35 167	63.66 158	83.25 145	89.88 137	76.06 107	89.62 92	92.37 108	73.40 156	92.52 123	78.16 108	94.77 131	95.69 54
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MVS_111021_HR		84.63 106	84.34 127	85.49 96	90.18 118	75.86 86	79.23 207	87.13 167	73.35 131	85.56 153	89.34 180	83.60 52	90.50 169	76.64 119	94.05 142	90.09 171

HQP-MVS		84.61 107	84.06 130	86.27 79	91.19 99	70.66 119	84.77 108	92.68 59	73.30 132	80.55 204	90.17 168	72.10 177	94.61 44	77.30 115	94.47 136	93.56 108

v1192		84.57 108	84.69 110	84.21 122	87.75 160	62.88 171	83.02 149	91.43 94	69.08 173	89.98 78	90.89 149	72.70 170	93.62 77	82.41 61	94.97 121	96.13 40

FMVSNet1		84.55 109	85.45 97	81.85 162	90.27 117	61.05 202	86.83 83	88.27 153	78.57 84	89.66 88	95.64 30	75.43 121	90.68 164	69.09 167	95.33 109	93.82 97

v1144		84.54 110	84.72 108	84.00 126	87.67 163	62.55 180	82.97 150	90.93 108	70.32 166	89.80 84	90.99 145	73.50 152	93.48 92	81.69 69	94.65 134	95.97 46

Gipuma		84.44 111	86.33 81	78.78 193	84.20 210	73.57 95	89.55 44	90.44 117	84.24 27	84.38 166	94.89 45	76.35 118	80.40 241	76.14 121	96.80 66	82.36 242
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
v1neww		84.43 112	84.66 112	83.75 133	87.81 156	62.34 184	83.59 132	90.27 128	72.33 146	89.93 80	91.22 132	73.28 158	93.29 99	80.25 88	93.25 160	95.62 57

v7new		84.43 112	84.66 112	83.75 133	87.81 156	62.34 184	83.59 132	90.27 128	72.33 146	89.93 80	91.22 132	73.28 158	93.29 99	80.25 88	93.25 160	95.62 57

v6		84.43 112	84.66 112	83.75 133	87.81 156	62.34 184	83.59 132	90.26 130	72.33 146	89.94 79	91.19 136	73.30 157	93.29 99	80.26 87	93.26 159	95.62 57

testing_2		84.36 115	84.64 115	83.50 145	86.74 178	63.97 156	84.56 113	90.31 122	66.22 191	91.62 55	94.55 53	75.88 119	91.95 131	77.02 118	94.89 123	94.56 77

MCST-MVS		84.36 115	83.93 132	85.63 94	91.59 87	71.58 114	83.52 136	92.13 71	61.82 219	83.96 172	89.75 174	79.93 91	93.46 93	78.33 106	94.34 138	91.87 138

VDDNet		84.35 117	85.39 98	81.25 171	95.13 22	59.32 213	85.42 104	81.11 211	86.41 20	87.41 125	96.21 19	73.61 150	90.61 167	66.33 183	96.85 62	93.81 100

v1240		84.30 118	84.51 119	83.65 138	87.65 164	61.26 198	82.85 153	91.54 84	67.94 181	90.68 69	90.65 158	71.71 182	93.64 73	82.84 58	94.78 129	96.07 42

MVS_111021_LR		84.28 119	83.76 133	85.83 92	89.23 128	83.07 41	80.99 192	83.56 198	72.71 141	86.07 145	89.07 183	81.75 76	86.19 211	77.11 117	93.36 155	88.24 184

v144192		84.24 120	84.41 121	83.71 137	87.59 166	61.57 195	82.95 151	91.03 107	67.82 183	89.80 84	90.49 161	73.28 158	93.51 91	81.88 68	94.89 123	96.04 44

v1921920		84.23 121	84.37 126	83.79 131	87.64 165	61.71 190	82.91 152	91.20 104	67.94 181	90.06 74	90.34 164	72.04 180	93.59 78	82.32 63	94.91 122	96.07 42

VDD-MVS		84.23 121	84.58 117	83.20 148	91.17 102	65.16 150	83.25 145	84.97 194	79.79 66	87.18 127	94.27 63	74.77 132	90.89 158	69.24 165	96.54 71	93.55 110

v1141		84.16 123	84.38 123	83.52 142	87.32 169	61.70 192	82.79 155	89.74 138	71.90 153	89.64 89	91.12 139	72.68 171	93.10 109	80.39 86	93.80 148	95.75 51

divwei89l23v2f112		84.16 123	84.38 123	83.52 142	87.32 169	61.70 192	82.79 155	89.74 138	71.90 153	89.64 89	91.12 139	72.68 171	93.10 109	80.40 84	93.81 147	95.75 51

v1		84.16 123	84.38 123	83.52 142	87.33 168	61.71 190	82.79 155	89.73 140	71.89 155	89.64 89	91.11 141	72.72 168	93.10 109	80.40 84	93.79 149	95.75 51

v2v482		84.09 126	84.24 128	83.62 139	87.13 173	61.40 196	82.71 159	89.71 141	72.19 149	89.55 96	91.41 129	70.70 186	93.20 104	81.02 74	93.76 150	96.25 38

EG-PatchMatch MVS		84.08 127	84.11 129	83.98 127	92.22 75	72.61 102	82.20 174	87.02 170	72.63 142	88.86 103	91.02 144	78.52 96	91.11 151	73.41 141	91.09 184	88.21 185

DP-MVS Recon		84.05 128	83.22 137	86.52 74	91.73 85	75.27 88	83.23 147	92.40 66	72.04 150	82.04 189	88.33 192	77.91 101	93.95 65	66.17 184	95.12 117	90.34 167

TransMVSNet (Re)		84.02 129	85.74 91	78.85 192	91.00 105	55.20 239	82.29 168	87.26 165	79.65 69	88.38 115	95.52 34	83.00 57	86.88 204	67.97 175	96.60 70	94.45 83

Baseline_NR-MVSNet		84.00 130	85.90 89	78.29 199	91.47 95	53.44 247	82.29 168	87.00 171	79.06 78	89.55 96	95.72 27	77.20 107	86.14 212	72.30 148	98.51 18	95.28 67

TSAR-MVS		83.95 131	82.69 142	87.72 61	89.27 127	81.45 48	83.72 130	81.58 210	74.73 119	85.66 150	86.06 216	72.56 174	92.69 120	75.44 125	95.21 114	89.01 181

alignmvs		83.94 132	83.98 131	83.80 130	87.80 159	67.88 137	84.54 114	91.42 95	73.27 135	88.41 114	87.96 197	72.33 176	90.83 159	76.02 122	94.11 140	92.69 124

AdaColmap		83.66 133	83.69 134	83.57 141	90.05 122	72.26 107	86.29 95	90.00 135	78.19 88	81.65 193	87.16 206	83.40 54	94.24 53	61.69 207	94.76 132	84.21 220

MIMVSNet1		83.63 134	84.59 116	80.74 176	94.06 33	62.77 173	82.72 158	84.53 196	77.57 94	90.34 70	95.92 23	76.88 115	85.83 216	61.88 205	97.42 50	93.62 105

WR-MVS		83.56 135	84.40 122	81.06 174	93.43 44	54.88 240	78.67 211	85.02 192	81.24 54	90.74 68	91.56 125	72.85 166	91.08 152	68.00 174	98.04 32	97.23 24

CNLPA		83.55 136	83.10 140	84.90 102	89.34 126	83.87 36	84.54 114	88.77 147	79.09 77	83.54 178	88.66 188	74.87 128	81.73 237	66.84 181	92.29 174	89.11 177

LCM-MVSNet-Re		83.48 137	85.06 100	78.75 194	85.94 189	55.75 235	80.05 197	94.27 12	76.47 104	96.09 5	94.54 55	83.31 55	89.75 180	59.95 219	94.89 123	90.75 156

V42		83.47 138	83.37 136	83.75 133	83.16 220	63.33 164	81.31 187	90.23 131	69.51 170	90.91 66	90.81 152	74.16 138	92.29 127	80.06 90	90.22 200	95.62 57

VPA-MVSNet		83.47 138	84.73 106	79.69 184	90.29 116	57.52 223	81.30 189	88.69 149	76.29 105	87.58 122	94.44 58	80.60 84	87.20 201	66.60 182	96.82 65	94.34 85

PAPM_NR		83.23 140	83.19 139	83.33 146	90.90 107	65.98 147	88.19 63	90.78 109	78.13 89	80.87 200	87.92 199	73.49 153	92.42 124	70.07 159	88.40 212	91.60 144

CLD-MVS		83.18 141	82.64 143	84.79 105	89.05 130	67.82 138	77.93 215	92.52 64	68.33 177	85.07 156	81.54 254	82.06 68	92.96 113	69.35 164	97.91 38	93.57 107
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
ANet_high		83.17 142	85.68 93	75.65 220	81.24 230	45.26 278	79.94 199	92.91 52	83.83 29	91.33 60	96.88 8	80.25 87	85.92 214	68.89 169	95.89 96	95.76 50

114514_t		83.10 143	82.54 146	84.77 106	92.90 56	69.10 131	86.65 90	90.62 113	54.66 247	81.46 195	90.81 152	76.98 111	94.38 49	72.62 146	96.18 83	90.82 155

UGNet		82.78 144	81.64 155	86.21 83	86.20 187	76.24 85	86.86 81	85.68 182	77.07 100	73.76 239	92.82 95	69.64 187	91.82 137	69.04 168	93.69 152	90.56 162
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
LF4IMVS		82.75 145	81.93 153	85.19 98	82.08 225	80.15 54	85.53 102	88.76 148	68.01 179	85.58 152	87.75 200	71.80 181	86.85 205	74.02 133	93.87 146	88.58 183

EI-MVSNet		82.61 146	82.42 148	83.20 148	83.25 218	63.66 158	83.50 139	85.07 190	76.06 107	86.55 138	85.10 226	73.41 154	90.25 172	78.15 109	90.67 195	95.68 55

QAPM		82.59 147	82.59 145	82.58 154	86.44 179	66.69 144	89.94 36	90.36 120	67.97 180	84.94 159	92.58 102	72.71 169	92.18 128	70.63 157	87.73 218	88.85 182

MVS_Test		82.47 148	83.22 137	80.22 180	82.62 224	57.75 222	82.54 163	91.96 77	71.16 159	82.89 182	92.52 104	77.41 106	90.50 169	80.04 91	87.84 217	92.40 130

v148		82.31 149	82.48 147	81.81 165	85.59 190	59.66 211	81.47 185	86.02 179	72.85 139	88.05 117	90.65 158	70.73 185	90.91 157	75.15 126	91.79 179	94.87 73

API-MVS		82.28 150	82.61 144	81.30 169	86.29 185	69.79 122	88.71 59	87.67 162	78.42 85	82.15 188	84.15 237	77.98 99	91.59 141	65.39 190	92.75 168	82.51 241

MVSFormer		82.23 151	81.57 157	84.19 124	85.54 191	69.26 127	91.98 21	90.08 132	71.54 156	76.23 223	85.07 228	58.69 228	94.27 50	86.26 19	88.77 208	89.03 179

PCF-MVS	74.62 15	82.15 152	80.92 164	85.84 91	89.43 124	72.30 106	80.53 194	91.82 80	57.36 236	87.81 120	89.92 171	77.67 103	93.63 74	58.69 224	95.08 118	91.58 145
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PLC	73.85 16	82.09 153	80.31 168	87.45 65	90.86 109	80.29 53	85.88 97	90.65 111	68.17 178	76.32 222	86.33 213	73.12 163	92.61 122	61.40 211	90.02 202	89.44 174
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
GBi-Net		82.02 154	82.07 150	81.85 162	86.38 181	61.05 202	86.83 83	88.27 153	72.43 143	86.00 146	95.64 30	63.78 210	90.68 164	65.95 185	93.34 156	93.82 97

test1		82.02 154	82.07 150	81.85 162	86.38 181	61.05 202	86.83 83	88.27 153	72.43 143	86.00 146	95.64 30	63.78 210	90.68 164	65.95 185	93.34 156	93.82 97

OpenMVS	76.72 13	81.98 156	82.00 152	81.93 159	84.42 205	68.22 135	88.50 62	89.48 143	66.92 186	81.80 192	91.86 114	72.59 173	90.16 176	71.19 151	91.25 182	87.40 195

PVSNet_Blended_VisFu		81.55 157	80.49 167	84.70 109	91.58 90	73.24 98	84.21 117	91.67 81	62.86 212	80.94 198	87.16 206	67.27 196	92.87 117	69.82 161	88.94 207	87.99 189

DELS-MVS		81.44 158	81.25 159	82.03 158	84.27 207	62.87 172	76.47 229	92.49 65	70.97 161	81.64 194	83.83 238	75.03 125	92.70 119	74.29 130	92.22 177	90.51 163
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
Test4		81.31 159	81.13 161	81.88 161	84.89 197	63.05 168	82.37 165	90.50 115	62.75 213	89.00 102	88.29 193	67.55 195	91.68 139	73.55 139	91.24 183	90.89 152

FMVSNet2		81.31 159	81.61 156	80.41 178	86.38 181	58.75 218	83.93 123	86.58 174	72.43 143	87.65 121	92.98 90	63.78 210	90.22 174	66.86 179	93.92 145	92.27 133

DI_MVS_test_dynamic		81.27 161	81.26 158	81.29 170	84.98 196	61.65 194	81.98 177	87.25 166	63.56 206	87.56 123	89.60 176	73.62 148	91.83 136	72.20 149	90.59 199	90.38 166

TinyColmap		81.25 162	82.34 149	77.99 203	85.33 194	60.68 206	82.32 167	88.33 152	71.26 158	86.97 133	92.22 111	77.10 109	86.98 203	62.37 203	95.17 116	86.31 202

DI_MVS_test_normal		81.23 163	81.16 160	81.43 168	84.77 199	61.99 189	81.46 186	86.95 172	63.16 211	87.22 126	89.63 175	73.62 148	91.65 140	72.92 144	90.70 194	90.65 160

BH-untuned		80.96 164	80.99 162	80.84 175	88.55 140	68.23 134	80.33 195	88.46 151	72.79 140	86.55 138	86.76 210	74.72 133	91.77 138	61.79 206	88.99 206	82.52 240

liao		80.86 165	79.81 172	84.02 125	93.93 36	78.70 63	81.64 182	80.18 215	55.43 244	83.67 175	91.15 137	71.29 183	91.41 147	67.95 176	93.06 164	81.96 248

BH-RMVSNet		80.53 166	80.22 170	81.49 167	87.19 172	66.21 146	77.79 217	86.23 176	74.21 122	83.69 174	88.50 190	73.25 162	90.75 161	63.18 202	87.90 216	87.52 193

EPNet		80.37 167	78.41 179	86.23 80	76.75 262	73.28 97	87.18 77	77.45 223	76.24 106	68.14 263	88.93 185	65.41 204	93.85 67	69.47 163	96.12 88	91.55 146
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MG-MVS		80.32 168	80.94 163	78.47 198	88.18 146	52.62 253	82.29 168	85.01 193	72.01 151	79.24 211	92.54 103	69.36 188	93.36 98	70.65 156	89.19 205	89.45 173

VPNet		80.25 169	81.68 154	75.94 218	92.46 67	47.98 273	76.70 225	81.67 209	73.45 129	84.87 160	92.82 95	74.66 134	86.51 208	61.66 208	96.85 62	93.33 111

MAR-MVS		80.24 170	78.74 177	84.73 108	86.87 177	78.18 65	85.75 99	87.81 161	65.67 197	77.84 214	78.50 265	73.79 145	90.53 168	61.59 210	90.87 190	85.49 209
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
PM-MVS		80.20 171	79.00 175	83.78 132	88.17 147	86.66 9	81.31 187	66.81 276	69.64 169	88.33 116	90.19 167	64.58 206	83.63 230	71.99 150	90.03 201	81.06 263

LFMVS		80.15 172	80.56 165	78.89 191	89.19 129	55.93 233	85.22 106	73.78 241	82.96 35	84.28 170	92.72 99	57.38 234	90.07 178	63.80 197	95.75 102	90.68 158

MSDG		80.06 173	79.99 171	80.25 179	83.91 214	68.04 136	77.51 220	89.19 146	77.65 93	81.94 190	83.45 241	76.37 117	86.31 210	63.31 201	86.59 226	86.41 200

ab-mvs		79.67 174	80.56 165	76.99 209	88.48 143	56.93 226	84.70 111	86.06 178	68.95 175	80.78 201	93.08 87	75.30 122	84.62 223	56.78 231	90.90 189	89.43 175

VNet		79.31 175	80.27 169	76.44 213	87.92 154	53.95 243	75.58 235	84.35 197	74.39 121	82.23 186	90.72 154	72.84 167	84.39 225	60.38 218	93.98 144	90.97 150

diffmvs		79.20 176	79.04 174	79.69 184	78.64 251	58.90 215	81.79 178	87.61 163	65.07 201	73.65 241	89.80 172	73.10 164	87.79 196	75.02 128	86.63 225	92.38 131

PAPR		78.84 177	78.10 180	81.07 173	85.17 195	60.22 209	82.21 172	90.57 114	62.51 215	75.32 232	84.61 235	74.99 126	92.30 126	59.48 222	88.04 215	90.68 158

PVSNet_BlendedMVS		78.80 178	77.84 181	81.65 166	84.43 203	63.41 161	79.49 203	90.44 117	61.70 221	75.43 230	87.07 208	69.11 190	91.44 145	60.68 216	92.24 176	90.11 170

FMVSNet3		78.80 178	78.55 178	79.57 187	82.89 222	56.89 228	81.76 179	85.77 181	69.04 174	86.00 146	90.44 163	51.75 249	90.09 177	65.95 185	93.34 156	91.72 141

mvs_anonymous		78.13 180	78.76 176	76.23 217	79.24 245	50.31 266	78.69 210	84.82 195	61.60 222	83.09 181	92.82 95	73.89 144	87.01 202	68.33 173	86.41 227	91.37 147

TAMVS		78.08 181	76.36 189	83.23 147	90.62 112	72.87 99	79.08 208	80.01 217	61.72 220	81.35 197	86.92 209	63.96 209	88.78 188	50.61 251	93.01 166	88.04 188

Vis-MVSNet (Re-imp)		77.82 182	77.79 182	77.92 204	88.82 134	51.29 258	83.28 144	71.97 253	74.04 123	82.23 186	89.78 173	57.38 234	89.41 183	57.22 229	95.41 107	93.05 117

OpenMVS_ROB	70.19 17	77.77 183	77.46 183	78.71 195	84.39 206	61.15 200	81.18 191	82.52 201	62.45 216	83.34 179	87.37 205	66.20 200	88.66 191	64.69 193	85.02 239	86.32 201

MDA-MVSNet-bldmvs		77.47 184	76.90 186	79.16 190	79.03 247	64.59 151	66.58 268	75.67 234	73.15 137	88.86 103	88.99 184	66.94 197	81.23 238	64.71 192	88.22 214	91.64 143

jason		77.42 185	75.75 195	82.43 156	87.10 174	69.27 126	77.99 214	81.94 207	51.47 264	77.84 214	85.07 228	60.32 218	89.00 185	70.74 155	89.27 204	89.03 179
jason: jason.
CDS-MVSNet		77.32 186	75.40 197	83.06 150	89.00 132	72.48 104	77.90 216	82.17 204	60.81 224	78.94 212	83.49 240	59.30 225	88.76 189	54.64 244	92.37 173	87.93 191
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MVSTER		77.09 187	75.70 196	81.25 171	75.27 273	61.08 201	77.49 221	85.07 190	60.78 225	86.55 138	88.68 187	43.14 273	90.25 172	73.69 137	90.67 195	92.42 128

Patchmatch-RL test		77.08 188	77.10 185	77.01 208	84.83 198	66.54 145	72.29 251	69.16 267	57.70 234	86.76 135	90.46 162	72.42 175	82.59 234	69.63 162	90.65 197	81.90 250

PS-MVSNAJ		77.04 189	76.53 188	78.56 197	87.09 175	61.40 196	75.26 236	87.13 167	61.25 223	74.38 237	77.22 270	76.94 112	90.94 155	64.63 194	84.83 242	83.35 231

IterMVS		76.91 190	76.34 190	78.64 196	80.91 233	64.03 154	76.30 230	79.03 218	64.88 203	83.11 180	89.16 181	59.90 221	84.46 224	68.61 171	85.15 238	87.42 194
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TR-MVS		76.77 191	75.79 193	79.72 183	86.10 188	65.79 149	77.14 222	83.02 199	65.20 199	81.40 196	82.10 250	66.30 199	90.73 163	55.57 238	85.27 235	82.65 236

USDC		76.63 192	76.73 187	76.34 215	83.46 216	57.20 225	80.02 198	88.04 157	52.14 259	83.65 177	91.25 131	63.24 213	86.65 207	54.66 243	94.11 140	85.17 210

BH-w/o		76.57 193	76.07 192	78.10 201	86.88 176	65.92 148	77.63 218	86.33 175	65.69 196	80.89 199	79.95 261	68.97 192	90.74 162	53.01 248	85.25 236	77.62 268

Patchmtry		76.56 194	77.46 183	73.83 228	79.37 244	46.60 276	82.41 164	76.90 226	73.81 126	85.56 153	92.38 105	48.07 258	83.98 228	63.36 200	95.31 112	90.92 151

PVSNet_Blended		76.49 195	75.40 197	79.76 182	84.43 203	63.41 161	75.14 238	90.44 117	57.36 236	75.43 230	78.30 266	69.11 190	91.44 145	60.68 216	87.70 219	84.42 218

lupinMVS		76.37 196	74.46 203	82.09 157	85.54 191	69.26 127	76.79 223	80.77 213	50.68 271	76.23 223	82.82 247	58.69 228	88.94 186	69.85 160	88.77 208	88.07 186

cascas		76.29 197	74.81 199	80.72 177	84.47 202	62.94 170	73.89 245	87.34 164	55.94 242	75.16 234	76.53 273	63.97 208	91.16 150	65.00 191	90.97 188	88.06 187

RPMNet		76.06 198	75.79 193	76.85 211	79.58 240	62.64 175	82.58 160	71.75 257	74.80 118	75.72 228	92.59 100	48.69 256	84.07 226	73.48 140	82.91 253	83.85 222

GA-MVS		75.83 199	74.61 200	79.48 189	81.87 227	59.25 214	73.42 249	82.88 200	68.68 176	79.75 209	81.80 252	50.62 251	89.46 181	66.85 180	85.64 232	89.72 172

MV-PatchMatch		75.81 200	73.51 207	82.71 152	89.35 125	73.62 94	80.06 196	85.20 187	60.30 227	73.96 238	87.94 198	57.89 232	89.45 182	52.02 250	74.87 276	85.06 212

wuyk23d		75.13 201	79.30 173	62.63 264	75.56 269	75.18 89	80.89 193	73.10 246	75.06 117	94.76 13	95.32 37	87.73 25	52.85 289	34.16 286	97.11 56	59.85 287

EU-MVSNet		75.12 202	74.43 204	77.18 207	83.11 221	59.48 212	85.71 101	82.43 202	39.76 290	85.64 151	88.76 186	44.71 269	87.88 195	73.86 136	85.88 230	84.16 221

CMPMVS	59.41 20	75.12 202	73.57 206	79.77 181	75.84 267	67.22 139	81.21 190	82.18 203	50.78 269	76.50 219	87.66 202	55.20 241	82.99 232	62.17 204	90.64 198	89.09 178
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
1112_ss		74.82 204	73.74 205	78.04 202	89.57 123	60.04 210	76.49 228	87.09 169	54.31 248	73.66 240	79.80 262	60.25 219	86.76 206	58.37 225	84.15 245	87.32 196

PatchMatch-RL		74.48 205	73.22 208	78.27 200	87.70 162	85.26 23	75.92 231	70.09 262	64.34 205	76.09 226	81.25 256	65.87 203	78.07 246	53.86 245	83.82 247	71.48 279

XXY-MVS		74.44 206	76.19 191	69.21 246	84.61 201	52.43 254	71.70 253	77.18 224	60.73 226	80.60 202	90.96 147	75.44 120	69.35 264	56.13 234	88.33 213	85.86 206

CR-MVSNet		74.00 207	73.04 210	76.85 211	79.58 240	62.64 175	82.58 160	76.90 226	50.50 272	75.72 228	92.38 105	48.07 258	84.07 226	68.72 170	82.91 253	83.85 222

Test_1112_low_res		73.90 208	73.08 209	76.35 214	90.35 115	55.95 232	73.40 250	86.17 177	50.70 270	73.14 243	85.94 217	58.31 230	85.90 215	56.51 232	83.22 250	87.20 197

test20.03		73.75 209	74.59 202	71.22 240	81.11 231	51.12 260	70.15 257	72.10 252	70.42 164	80.28 207	91.50 127	64.21 207	74.72 256	46.96 267	94.58 135	87.82 192

liao1		73.22 210	72.56 213	75.20 221	80.41 239	57.84 220	81.64 182	85.36 185	51.68 262	73.10 244	76.65 272	61.45 216	85.19 219	63.54 198	79.21 269	82.59 237

HC-MVS		73.21 211	72.59 212	75.06 222	80.97 232	60.81 205	81.64 182	85.92 180	46.03 283	71.68 249	77.54 267	68.47 193	89.77 179	55.70 237	85.39 233	74.60 274

HY-MVS	64.64 18	73.03 212	72.47 214	74.71 224	83.36 217	54.19 241	82.14 175	81.96 205	56.76 241	69.57 259	86.21 215	60.03 220	84.83 222	49.58 256	82.65 255	85.11 211

EPNet_dtu		72.87 213	71.33 221	77.49 206	77.72 257	60.55 207	82.35 166	75.79 232	66.49 189	58.39 291	81.06 257	53.68 245	85.98 213	53.55 246	92.97 167	85.95 204
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CVMVSNet		72.62 214	71.41 220	76.28 216	83.25 218	60.34 208	83.50 139	79.02 219	37.77 291	76.33 221	85.10 226	49.60 254	87.41 199	70.54 158	77.54 274	81.08 261

testgi		72.36 215	74.61 200	65.59 257	80.56 237	42.82 284	68.29 260	73.35 245	66.87 187	81.84 191	89.93 170	72.08 179	66.92 271	46.05 269	92.54 171	87.01 199

FPMVS		72.29 216	72.00 216	73.14 231	88.63 139	85.00 25	74.65 241	67.39 270	71.94 152	77.80 216	87.66 202	50.48 252	75.83 252	49.95 253	79.51 265	58.58 289

FMVSNet5		72.10 217	71.69 218	73.32 229	81.57 229	53.02 250	76.77 224	78.37 220	63.31 209	76.37 220	91.85 115	36.68 284	78.98 245	47.87 263	92.45 172	87.95 190

PAPM		71.77 218	70.06 227	76.92 210	86.39 180	53.97 242	76.62 226	86.62 173	53.44 253	63.97 279	84.73 234	57.79 233	92.34 125	39.65 277	81.33 260	84.45 217

IB-MVS	62.13 19	71.64 219	68.97 232	79.66 186	80.80 236	62.26 188	73.94 244	76.90 226	63.27 210	68.63 262	76.79 271	33.83 286	91.84 135	59.28 223	87.26 221	84.88 213
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
UnsupCasMVSNet_eth		71.63 220	72.30 215	69.62 244	76.47 263	52.70 252	70.03 258	80.97 212	59.18 230	79.36 210	88.21 194	60.50 217	69.12 265	58.33 227	77.62 273	87.04 198

no-one		71.52 221	70.43 225	74.81 223	78.45 253	63.41 161	57.73 285	77.03 225	51.46 265	77.17 218	90.33 165	54.96 243	80.35 242	47.41 264	99.29 2	80.68 265

HyFIR		71.49 222	71.95 217	70.08 241	84.74 200	46.83 275	63.41 273	76.88 229	47.99 279	76.15 225	88.34 191	65.15 205	79.77 244	60.78 215	84.14 246	83.64 225

MIMVSNet		71.09 223	71.59 219	69.57 245	87.23 171	50.07 267	78.91 209	71.83 255	60.20 228	71.26 251	91.76 120	55.08 242	76.09 250	41.06 275	87.02 224	82.54 239

MS-PatchMatch		70.93 224	70.22 226	73.06 232	81.85 228	62.50 181	73.82 246	77.90 221	52.44 257	75.92 227	81.27 255	55.67 239	81.75 236	55.37 240	77.70 272	74.94 273

pm-mvs1		70.57 225	69.48 230	73.85 227	80.42 238	56.56 230	73.77 247	71.49 259	48.39 278	73.32 242	88.60 189	52.30 248	80.42 240	48.53 260	88.51 211	84.55 215

PatchT		70.52 226	72.76 211	63.79 263	79.38 243	33.53 292	77.63 218	65.37 278	73.61 127	71.77 248	92.79 98	44.38 270	75.65 253	64.53 195	85.37 234	82.18 245

testmv		70.47 227	70.70 222	69.77 243	86.22 186	53.89 244	67.32 265	71.91 254	63.32 208	78.16 213	89.47 178	56.12 237	73.10 257	36.43 283	87.33 220	82.33 243

N_pmnet		70.20 228	68.80 234	74.38 225	80.91 233	84.81 28	59.12 282	76.45 231	55.06 245	75.31 233	82.36 249	55.74 238	54.82 288	47.02 266	87.24 222	83.52 227

tpmvs		70.16 229	69.56 229	71.96 238	74.71 277	48.13 271	79.63 201	75.45 235	65.02 202	70.26 256	81.88 251	45.34 266	85.68 217	58.34 226	75.39 275	82.08 246

YYNet1		70.06 230	70.44 223	68.90 247	73.76 279	53.42 248	58.99 283	67.20 272	58.42 232	87.10 129	85.39 224	59.82 222	67.32 268	59.79 220	83.50 249	85.96 203

MDA-MVSNet_test_wron		70.05 231	70.44 223	68.88 248	73.84 278	53.47 246	58.93 284	67.28 271	58.43 231	87.09 130	85.40 223	59.80 223	67.25 269	59.66 221	83.54 248	85.92 205

CostFormer		69.98 232	68.68 235	73.87 226	77.14 260	50.72 264	79.26 206	74.51 239	51.94 261	70.97 254	84.75 233	45.16 268	87.49 198	55.16 241	79.23 268	83.40 230

PatchmatchNet		69.71 233	68.83 233	72.33 236	77.66 258	53.60 245	79.29 205	69.99 263	57.66 235	72.53 245	82.93 246	46.45 262	80.08 243	60.91 214	72.09 281	83.31 232
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpmp4_e23		69.43 234	67.33 240	75.72 219	78.53 252	52.75 251	82.13 176	74.91 236	49.23 276	66.37 268	84.17 236	41.28 277	88.67 190	49.73 255	79.63 264	85.75 207

LP		69.42 235	68.30 237	72.77 233	71.48 289	56.84 229	73.66 248	74.84 237	63.52 207	70.95 255	83.35 242	49.55 255	77.15 248	57.13 230	70.21 284	84.33 219

JIA-IIPM		69.41 236	66.64 245	77.70 205	73.19 281	71.24 116	75.67 233	65.56 277	70.42 164	65.18 274	92.97 91	33.64 287	83.06 231	53.52 247	69.61 288	78.79 267

UnsupCasMVSNet_bld		69.21 237	69.68 228	67.82 253	79.42 242	51.15 259	67.82 264	75.79 232	54.15 249	77.47 217	85.36 225	59.26 226	70.64 260	48.46 261	79.35 267	81.66 253

gg-mvs-nofinetune		68.96 238	69.11 231	68.52 252	76.12 266	45.32 277	83.59 132	55.88 291	86.68 18	64.62 278	97.01 5	30.36 290	83.97 229	44.78 271	82.94 252	76.26 271

tpm2		68.45 239	66.83 242	73.30 230	78.93 248	48.50 270	79.76 200	71.76 256	47.50 280	69.92 258	83.60 239	42.07 276	88.40 192	48.44 262	79.51 265	83.01 235

tpm		67.95 240	68.08 239	67.55 254	78.74 250	43.53 283	75.60 234	67.10 275	54.92 246	72.23 247	88.10 195	42.87 274	75.97 251	52.21 249	80.95 263	83.15 233

PatchFormer-LS_test		67.91 241	66.49 246	72.17 237	75.29 272	51.85 256	75.68 232	73.62 243	57.23 238	68.64 260	68.13 287	42.19 275	82.76 233	64.06 196	73.51 278	81.89 251

WTY-MVS		67.91 241	68.35 236	66.58 255	80.82 235	48.12 272	65.96 269	72.60 248	53.67 252	71.20 252	81.68 253	58.97 227	69.06 266	48.57 259	81.67 258	82.55 238

test-LLR		67.21 243	66.74 243	68.63 250	76.45 264	55.21 237	67.89 261	67.14 273	62.43 217	65.08 275	72.39 281	43.41 271	69.37 262	61.00 212	84.89 240	81.31 256

sss		66.92 244	67.26 241	65.90 256	77.23 259	51.10 261	64.79 270	71.72 258	52.12 260	70.13 257	80.18 259	57.96 231	65.36 278	50.21 252	81.01 262	81.25 258

tpm cat1		66.76 245	65.21 249	71.42 239	77.09 261	50.62 265	78.01 213	73.68 242	44.89 285	68.64 260	79.00 264	45.51 264	82.42 235	49.91 254	70.15 285	81.23 260

DWT-MVSNet_test		66.43 246	64.37 250	72.63 234	74.86 276	50.86 263	76.52 227	72.74 247	54.06 250	65.50 272	68.30 286	32.13 288	84.84 221	61.63 209	73.59 277	82.19 244

PVSNet	58.17 21	66.41 247	65.63 248	68.75 249	81.96 226	49.88 268	62.19 276	72.51 250	51.03 267	68.04 264	75.34 277	50.84 250	74.77 254	45.82 270	82.96 251	81.60 254

tpmrst		66.28 248	66.69 244	65.05 261	72.82 285	39.33 287	78.20 212	70.69 261	53.16 255	67.88 265	80.36 258	48.18 257	74.75 255	58.13 228	70.79 283	81.08 261

ADS-MVSNet2		65.87 249	63.64 254	72.55 235	73.16 282	56.92 227	67.10 266	74.81 238	49.74 273	66.04 270	82.97 244	46.71 260	77.26 247	42.29 272	69.96 286	83.46 228

test1235678		65.57 250	65.73 247	65.06 260	82.84 223	50.90 262	62.90 274	69.26 265	57.17 239	72.36 246	83.04 243	46.02 263	70.10 261	32.79 288	85.24 237	74.19 275

test-mter		65.00 251	63.79 252	68.63 250	76.45 264	55.21 237	67.89 261	67.14 273	50.98 268	65.08 275	72.39 281	28.27 292	69.37 262	61.00 212	84.89 240	81.31 256

test0.0.03 1		64.66 252	64.36 251	65.57 258	75.03 275	46.89 274	64.69 271	61.58 285	62.43 217	71.18 253	77.54 267	43.41 271	68.47 267	40.75 276	82.65 255	81.35 255

pmmvs3		62.47 253	60.02 266	69.80 242	71.58 288	64.00 155	70.52 256	58.44 288	39.77 289	66.05 269	75.84 274	27.10 294	72.28 258	46.15 268	84.77 243	73.11 277

EPMVS		62.47 253	62.63 257	62.01 265	70.63 290	38.74 288	74.76 239	52.86 292	53.91 251	67.71 266	80.01 260	39.40 281	66.60 273	55.54 239	68.81 290	80.68 265

testus		62.33 255	63.03 255	60.20 271	78.78 249	40.74 285	59.14 280	69.80 264	49.26 275	71.41 250	74.72 279	52.33 247	63.52 281	29.84 290	82.01 257	76.36 270

ADS-MVSNet		61.90 256	62.19 258	61.03 270	73.16 282	36.42 290	67.10 266	61.75 283	49.74 273	66.04 270	82.97 244	46.71 260	63.21 283	42.29 272	69.96 286	83.46 228

1111		61.71 257	63.77 253	55.55 276	78.05 254	25.74 296	60.62 277	67.52 268	66.09 192	74.68 235	86.50 211	16.00 297	59.22 286	38.79 278	85.65 231	81.70 252

PMMVS		61.65 258	60.38 263	65.47 259	65.40 295	69.26 127	63.97 272	61.73 284	36.80 292	60.11 285	68.43 284	59.42 224	66.35 274	48.97 258	78.57 270	60.81 286

E-PMN		61.59 259	61.62 259	61.49 268	66.81 292	55.40 236	53.77 289	60.34 286	66.80 188	58.90 290	65.50 290	40.48 280	66.12 275	55.72 236	86.25 228	62.95 285

TESTMET0.1,1		61.29 260	60.32 264	64.19 262	72.06 286	51.30 257	67.89 261	62.09 281	45.27 284	60.65 284	69.01 283	27.93 293	64.74 280	56.31 233	81.65 259	76.53 269

MVS-HIRNet		61.16 261	62.92 256	55.87 274	79.09 246	35.34 291	71.83 252	57.98 290	46.56 281	59.05 289	91.14 138	49.95 253	76.43 249	38.74 280	71.92 282	55.84 290

EMVS		61.10 262	60.81 262	61.99 266	65.96 294	55.86 234	53.10 290	58.97 287	67.06 184	56.89 293	63.33 291	40.98 278	67.03 270	54.79 242	86.18 229	63.08 284

DSMNet-mixed		60.98 263	61.61 260	59.09 273	72.88 284	45.05 280	74.70 240	46.61 297	26.20 294	65.34 273	90.32 166	55.46 240	63.12 284	41.72 274	81.30 261	69.09 282

dp		60.70 264	60.29 265	61.92 267	72.04 287	38.67 289	70.83 254	64.08 279	51.28 266	60.75 283	77.28 269	36.59 285	71.58 259	47.41 264	62.34 293	75.52 272

testpf		58.55 265	61.58 261	49.48 280	66.03 293	40.05 286	74.40 242	58.07 289	64.72 204	59.36 287	72.67 280	22.76 295	66.92 271	67.07 178	69.15 289	41.46 292

test2356		56.69 266	55.15 270	61.32 269	73.20 280	44.11 281	54.95 287	62.52 280	48.75 277	62.45 281	68.42 285	21.10 296	65.67 277	26.86 292	78.08 271	74.19 275

PVSNet_0	51.08 22	56.10 267	54.97 271	59.48 272	75.12 274	53.28 249	55.16 286	61.89 282	44.30 286	59.16 288	62.48 292	54.22 244	65.91 276	35.40 285	47.01 294	59.25 288

new_pmnet		55.69 268	57.66 268	49.76 279	75.47 271	30.59 293	59.56 279	51.45 294	43.62 287	62.49 280	75.48 275	40.96 279	49.15 291	37.39 281	72.52 279	69.55 281

PMMVS2		55.64 269	59.27 267	44.74 282	64.30 296	12.32 300	40.60 292	49.79 295	53.19 254	65.06 277	84.81 232	53.60 246	49.76 290	32.68 289	89.41 203	72.15 278

test12356		54.91 270	57.14 269	48.22 281	75.83 268	17.47 299	52.31 291	69.20 266	51.66 263	60.11 285	75.40 276	29.77 291	62.62 285	27.64 291	72.37 280	64.59 283

PNet_i23d		52.13 271	51.24 274	54.79 277	75.56 269	45.26 278	54.54 288	52.55 293	66.95 185	57.19 292	65.82 289	13.15 299	63.40 282	36.39 284	39.04 296	55.71 291

MVE	40.22 23	51.82 272	50.47 275	55.87 274	62.66 298	51.91 255	31.61 294	39.28 298	40.65 288	50.76 295	74.98 278	56.24 236	44.67 292	33.94 287	64.11 292	71.04 280
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
HyFIR lowres test		51.64 273	51.77 273	51.24 278	64.21 297	49.39 269	68.80 259	46.62 296	28.12 293	52.64 294	65.93 288	38.34 283	64.88 279	37.16 282	64.13 291	39.44 293

.test1245		48.02 274	54.41 272	28.84 284	78.05 254	25.74 296	60.62 277	67.52 268	66.09 192	74.68 235	86.50 211	16.00 297	59.22 286	38.79 278	1.47 298	1.55 297

pcd1.5k->3k		38.83 275	41.11 276	32.01 283	93.13 51	0.00 304	0.00 295	91.38 99	0.00 299	0.00 300	0.00 298	89.24 14	0.00 297	0.00 297	96.24 82	96.02 45

cdsmvs_eth3d_5k		20.81 276	27.75 277	0.00 289	0.00 303	0.00 304	0.00 295	85.44 184	0.00 299	0.00 300	82.82 247	81.46 78	0.00 297	0.00 297	0.00 301	0.00 299

DUST3R		20.25 277	24.50 278	7.49 286	4.47 300	8.70 301	34.17 293	25.16 299	1.00 296	32.43 297	18.49 294	39.37 282	9.21 294	21.64 293	43.75 295	4.57 295

ab-mvs-re		6.65 278	8.87 279	0.00 289	0.00 303	0.00 304	0.00 295	0.00 303	0.00 299	0.00 300	79.80 262	0.00 302	0.00 297	0.00 297	0.00 301	0.00 299

pcd_1.5k_mvsjas		6.41 279	8.55 280	0.00 289	0.00 303	0.00 304	0.00 295	0.00 303	0.00 299	0.00 300	0.00 298	76.94 112	0.00 297	0.00 297	0.00 301	0.00 299

test123		6.27 280	8.08 281	0.84 287	1.11 302	0.57 302	62.90 274	0.82 301	0.54 297	1.07 299	2.75 297	1.26 300	0.30 295	1.04 295	1.26 300	1.66 296

testmvs		5.91 281	7.65 282	0.72 288	1.20 301	0.37 303	59.14 280	0.67 302	0.49 298	1.11 298	2.76 296	0.94 301	0.24 296	1.02 296	1.47 298	1.55 297

sosnet-low-res		0.00 282	0.00 283	0.00 289	0.00 303	0.00 304	0.00 295	0.00 303	0.00 299	0.00 300	0.00 298	0.00 302	0.00 297	0.00 297	0.00 301	0.00 299

sosnet		0.00 282	0.00 283	0.00 289	0.00 303	0.00 304	0.00 295	0.00 303	0.00 299	0.00 300	0.00 298	0.00 302	0.00 297	0.00 297	0.00 301	0.00 299

uncertanet		0.00 282	0.00 283	0.00 289	0.00 303	0.00 304	0.00 295	0.00 303	0.00 299	0.00 300	0.00 298	0.00 302	0.00 297	0.00 297	0.00 301	0.00 299

Regformer		0.00 282	0.00 283	0.00 289	0.00 303	0.00 304	0.00 295	0.00 303	0.00 299	0.00 300	0.00 298	0.00 302	0.00 297	0.00 297	0.00 301	0.00 299

uanet		0.00 282	0.00 283	0.00 289	0.00 303	0.00 304	0.00 295	0.00 303	0.00 299	0.00 300	0.00 298	0.00 302	0.00 297	0.00 297	0.00 301	0.00 299

test9_res														80.83 78	96.45 76	90.57 161

TEST9					92.34 71	79.70 57	83.94 122	90.32 121	65.41 198	84.49 165	90.97 146	82.03 70	93.63 74

train_agg														81.21 71	96.54 71	90.88 153

Patchmatch-test1					85.44 193	61.17 199	75.24 237	73.42 244		86.97 133	91.56 125	74.17 137			87.04 223

Patchmatch-test					67.85 291	25.43 298					86.31 214

test_8					92.09 76	78.87 61	83.82 126	90.31 122	65.79 195	84.36 167	90.96 147	81.93 72	93.44 94

agg_prior2														79.68 95	96.16 84	90.22 168

agg_prior					91.58 90	77.69 68		90.30 124		84.32 168			93.18 105

TestCases				89.68 39	91.59 87	83.40 38		95.44 5	79.47 70	88.00 118	93.03 88	82.66 61	91.47 143	70.81 152	96.14 86	94.16 89

test_prior4						78.97 60	84.59 112

test_prior2							83.37 142		75.43 114	84.58 163	91.57 123	81.92 73		79.54 97	96.97 59

test_prior				86.32 77	90.59 113	71.99 111		92.85 54					94.17 56			92.80 121

旧先验2							81.73 180		56.88 240	86.54 143			84.90 220	72.81 145

新几何2							81.72 181

新几何1				82.95 151	93.96 35	78.56 64		80.24 214	55.45 243	83.93 173	91.08 142	71.19 184	88.33 193	65.84 188	93.07 163	81.95 249

旧先验1					91.97 79	71.77 113		81.78 208			91.84 116	73.92 143			93.65 154	83.61 226

无先验							82.81 154	85.62 183	58.09 233				91.41 147	67.95 176		84.48 216

原ACMM2							82.26 171

原ACMM1				84.60 111	92.81 61	74.01 93		91.50 85	62.59 214	82.73 184	90.67 157	76.53 116	94.25 52	69.24 165	95.69 104	85.55 208

test222					93.31 46	76.54 80	79.38 204	77.79 222	52.59 256	82.36 185	90.84 151	66.83 198			91.69 180	81.25 258

testdata2													86.43 209	63.52 199

segment_acmp												81.94 71

testdata				79.54 188	92.87 57	72.34 105		80.14 216	59.91 229	85.47 155	91.75 121	67.96 194	85.24 218	68.57 172	92.18 178	81.06 263

testdata1							79.62 202		73.95 125

test12				86.57 72	90.74 110	72.63 101		90.69 110		82.76 183		79.20 92	94.80 40		95.32 110	92.27 133

plane_prior7					93.45 42	77.31 74

plane_prior6					92.61 62	76.54 80						74.84 129

plane_prior5								93.61 27					95.22 30	80.78 79	95.83 99	94.46 81

plane_prior4											92.95 92

plane_prior3						76.85 79			77.79 91	86.55 138

plane_prior2							89.45 49		79.44 72

plane_prior1					92.83 60

plane_prior						76.42 82	87.15 78		75.94 111						95.03 120

abl_6																93.71 102

n2								0.00 303

nn								0.00 303

door-mid								74.45 240

lessismore_v0				85.95 88	91.10 104	70.99 118		70.91 260		91.79 52	94.42 59	61.76 215	92.93 115	79.52 99	93.03 165	93.93 94

LGP-MVS_train				90.82 27	94.75 28	81.69 44		94.27 12	82.35 41	93.67 27	94.82 46	91.18 4	95.52 22	85.36 26	98.73 8	95.23 69

test11								91.46 91

door								72.57 249

HQP5-MVS						70.66 119

HQP-NCC					91.19 99		84.77 108		73.30 132	80.55 204

ACMP_Plane					91.19 99		84.77 108		73.30 132	80.55 204

BP-MVS														77.30 115

HQP4-MVS										80.56 203			94.61 44			93.56 108

HQP3-MVS								92.68 59							94.47 136

HQP2-MVS												72.10 177

NP-MVS					91.95 80	74.55 91					90.17 168

MDTV_nov1_ep13_2view						27.60 295	70.76 255

High-resolution multi-view benchmark