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		86.90 1	88.67 1	81.57 20	91.50 1	63.30 105	84.80 25	87.77 7	86.18 1	96.26 2	96.06 2	90.32 1	84.49 50	68.08 83	97.05 3	96.93 1

TDRefinement		86.32 2	86.33 2	86.29 1	88.64 29	81.19 6	88.84 2	90.72 1	78.27 7	87.95 17	92.53 13	79.37 12	84.79 47	74.51 35	96.15 4	92.88 9

abl_6		84.92 3	85.70 3	82.57 14	86.72 40	79.27 8	87.56 5	86.08 16	77.48 9	88.12 16	91.53 31	81.18 6	84.31 55	78.12 22	94.47 35	84.15 114

HPM-MVS_fast		84.59 4	85.10 4	83.06 4	88.60 30	75.83 23	86.27 19	86.89 11	73.69 18	86.17 39	91.70 26	78.23 16	85.20 40	79.45 12	94.91 26	88.15 62

LTVRE_ROB	75.46 1	84.22 5	84.98 5	81.94 19	84.82 61	75.40 26	91.60 1	87.80 5	73.52 19	88.90 13	93.06 7	71.39 60	81.53 92	81.53 3	92.15 70	88.91 48
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
ACMMP		84.22 5	84.84 6	82.35 17	89.23 22	76.66 22	87.65 4	85.89 18	71.03 32	85.85 45	90.58 52	78.77 14	85.78 30	79.37 15	95.17 18	84.62 100
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
HPM-MVS		84.12 7	84.63 7	82.60 12	88.21 33	74.40 31	85.24 22	87.21 9	70.69 35	85.14 53	90.42 60	78.99 13	86.62 12	80.83 6	94.93 25	86.79 73
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CP-MVS		84.12 7	84.55 8	82.80 9	89.42 18	79.74 7	88.19 3	84.43 38	71.96 29	84.70 61	90.56 53	77.12 18	86.18 21	79.24 17	95.36 14	82.49 148

mPP-MVS		84.01 9	84.39 9	82.88 5	90.65 4	81.38 5	87.08 9	82.79 66	72.41 25	85.11 55	90.85 45	76.65 21	84.89 44	79.30 16	94.63 32	82.35 150

APD-MVS_3200maxsize		83.57 12	84.33 10	81.31 26	82.83 89	73.53 40	85.50 21	87.45 8	74.11 16	86.45 35	90.52 56	80.02 10	84.48 51	77.73 24	94.34 40	85.93 82

LPG-MVS_test		83.47 15	84.33 10	80.90 32	87.00 37	70.41 56	82.04 44	86.35 12	69.77 40	87.75 18	91.13 37	81.83 3	86.20 19	77.13 27	95.96 7	86.08 78

APDe-MVS		82.88 22	84.14 12	79.08 50	84.80 63	66.72 78	86.54 16	85.11 26	72.00 28	86.65 33	91.75 25	78.20 17	87.04 8	77.93 23	94.32 41	83.47 125

COLMAP_ROB	72.78 3	83.75 10	84.11 13	82.68 11	82.97 87	74.39 32	87.18 7	88.18 4	78.98 5	86.11 41	91.47 33	79.70 11	85.76 31	66.91 99	95.46 13	87.89 64
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
HFP-MVS		83.39 16	84.03 14	81.48 22	89.25 20	75.69 24	87.01 11	84.27 41	70.23 36	84.47 65	90.43 57	76.79 19	85.94 27	79.58 10	94.23 45	82.82 138

ACMMPR		83.62 11	83.93 15	82.69 10	89.78 11	77.51 18	87.01 11	84.19 45	70.23 36	84.49 64	90.67 51	75.15 33	86.37 15	79.58 10	94.26 43	84.18 113

MTAPA		83.19 17	83.87 16	81.13 29	91.16 2	78.16 12	84.87 24	80.63 107	72.08 26	84.93 56	90.79 46	74.65 37	84.42 52	80.98 4	94.75 28	80.82 178

region2R		83.54 13	83.86 17	82.58 13	89.82 10	77.53 16	87.06 10	84.23 44	70.19 38	83.86 70	90.72 50	75.20 30	86.27 18	79.41 14	94.25 44	83.95 117

XVS		83.51 14	83.73 18	82.85 7	89.43 16	77.61 14	86.80 13	84.66 34	72.71 23	82.87 77	90.39 62	73.86 43	86.31 16	78.84 18	94.03 47	84.64 98

SteuartSystems-ACMMP		83.07 19	83.64 19	81.35 25	85.14 57	71.00 50	85.53 20	84.78 32	70.91 33	85.64 46	90.41 61	75.55 28	87.69 3	79.75 7	95.08 21	85.36 89
Skip Steuart: Steuart Systems R&D Blog.
zzz-MVS		83.01 21	83.63 20	81.13 29	91.16 2	78.16 12	82.72 40	80.63 107	72.08 26	84.93 56	90.79 46	74.65 37	84.42 52	80.98 4	94.75 28	80.82 178

MP-MVS		83.19 17	83.54 21	82.14 18	90.54 5	79.00 9	86.42 18	83.59 55	71.31 30	81.26 96	90.96 42	74.57 39	84.69 48	78.41 20	94.78 27	82.74 141
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MP-MVS-pluss		82.54 24	83.46 22	79.76 41	88.88 28	68.44 71	81.57 47	86.33 14	63.17 94	85.38 52	91.26 36	76.33 22	84.67 49	83.30 1	94.96 24	86.17 77
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMP	69.50 8	82.64 23	83.38 23	80.40 37	86.50 42	69.44 63	82.30 41	86.08 16	66.80 52	86.70 32	89.99 71	81.64 5	85.95 26	74.35 36	96.11 5	85.81 84
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ACMM	69.25 9	82.11 28	83.31 24	78.49 58	88.17 34	73.96 34	83.11 37	84.52 37	66.40 56	87.45 23	89.16 86	81.02 7	80.52 130	74.27 37	95.73 9	80.98 175
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMMP_Plus		82.33 26	83.28 25	79.46 46	89.28 19	69.09 69	83.62 32	84.98 27	64.77 74	83.97 69	91.02 40	75.53 29	85.93 29	82.00 2	94.36 39	83.35 131

PGM-MVS		83.07 19	83.25 26	82.54 15	89.57 14	77.21 20	82.04 44	85.40 23	67.96 47	84.91 58	90.88 43	75.59 27	86.57 13	78.16 21	94.71 30	83.82 118

PMVS	70.70 6	81.70 29	83.15 27	77.36 70	90.35 6	82.82 3	82.15 42	79.22 130	74.08 17	87.16 27	91.97 19	84.80 2	76.97 182	64.98 115	93.61 52	72.28 256
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PEN-MVS		80.46 40	82.91 28	73.11 127	89.83 9	39.02 271	77.06 93	82.61 69	80.04 3	90.60 8	92.85 9	74.93 36	85.21 39	63.15 125	95.15 19	95.09 2

DTE-MVSNet		80.35 42	82.89 29	72.74 138	89.84 8	37.34 285	77.16 90	81.81 79	80.45 2	90.92 5	92.95 8	74.57 39	86.12 25	63.65 123	94.68 31	94.76 6

PS-CasMVS		80.41 41	82.86 30	73.07 128	89.93 7	39.21 268	77.15 91	81.28 91	79.74 4	90.87 6	92.73 11	75.03 35	84.93 43	63.83 122	95.19 17	95.07 3

SMA-MVS		82.23 27	82.82 31	80.48 36	88.90 27	69.66 61	85.12 23	84.95 28	63.53 90	84.31 68	91.47 33	72.87 50	87.16 6	79.74 9	94.47 35	84.61 101

#test#		82.40 25	82.71 32	81.48 22	89.25 20	75.69 24	84.47 27	84.27 41	64.45 77	84.47 65	90.43 57	76.79 19	85.94 27	76.01 31	94.23 45	82.82 138

ESAPD		81.57 30	82.55 33	78.63 56	85.90 46	66.44 80	83.39 34	84.94 30	73.27 20	84.61 62	89.25 82	75.17 31	86.96 10	72.56 46	93.83 49	82.50 146

ACMH+	66.64 10	81.20 33	82.48 34	77.35 71	81.16 109	62.39 109	80.51 52	87.80 5	73.02 22	87.57 21	91.08 39	80.28 9	82.44 79	64.82 116	96.10 6	87.21 71

UA-Net		81.56 31	82.28 35	79.40 47	88.91 26	69.16 67	84.67 26	80.01 122	75.34 13	79.80 116	94.91 3	69.79 70	80.25 134	72.63 44	94.46 37	88.78 52

WR-MVS_H		80.22 44	82.17 36	74.39 97	89.46 15	42.69 246	78.24 78	82.24 72	78.21 8	89.57 11	92.10 18	68.05 84	85.59 32	66.04 108	95.62 11	94.88 5

CPTT-MVS		81.51 32	81.76 37	80.76 34	89.20 23	78.75 10	86.48 17	82.03 75	68.80 43	80.92 104	88.52 98	72.00 55	82.39 80	74.80 32	93.04 58	81.14 171

APD-MVS		81.13 34	81.73 38	79.36 48	84.47 69	70.53 55	83.85 31	83.70 52	69.43 42	83.67 72	88.96 94	75.89 26	86.41 14	72.62 45	92.95 59	81.14 171
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CP-MVSNet		79.48 49	81.65 39	72.98 131	89.66 13	39.06 270	76.76 95	80.46 112	78.91 6	90.32 9	91.70 26	68.49 79	84.89 44	63.40 124	95.12 20	95.01 4

OPM-MVS		80.99 37	81.63 40	79.07 51	86.86 39	69.39 64	79.41 66	84.00 50	65.64 61	85.54 50	89.28 79	76.32 23	83.47 65	74.03 38	93.57 53	84.35 111

SD-MVS		80.28 43	81.55 41	76.47 75	83.57 78	67.83 75	83.39 34	85.35 25	64.42 80	86.14 40	87.07 114	74.02 42	80.97 117	77.70 25	92.32 69	80.62 183

XVG-ACMP-BASELINE		80.54 39	81.06 42	78.98 52	87.01 36	72.91 41	80.23 58	85.56 20	66.56 55	85.64 46	89.57 76	69.12 74	80.55 129	72.51 48	93.37 54	83.48 124

LS3D		80.99 37	80.85 43	81.41 24	78.37 137	71.37 46	87.45 6	85.87 19	77.48 9	81.98 84	89.95 72	69.14 73	85.26 37	66.15 106	91.24 84	87.61 67

DeepC-MVS	72.44 4	81.00 36	80.83 44	81.50 21	86.70 41	70.03 60	82.06 43	87.00 10	59.89 122	80.91 105	90.53 54	72.19 51	88.56 1	73.67 40	94.52 34	85.92 83
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
3Dnovator+	73.19 2	81.08 35	80.48 45	82.87 6	81.41 106	72.03 42	84.38 28	86.23 15	77.28 11	80.65 107	90.18 69	59.80 152	87.58 4	73.06 42	91.34 82	89.01 43

v7n		79.37 51	80.41 46	76.28 79	78.67 136	55.81 147	79.22 67	82.51 71	70.72 34	87.54 22	92.44 14	68.00 86	81.34 102	72.84 43	91.72 72	91.69 12

Anonymous20231211		77.74 66	80.26 47	70.19 170	83.05 84	43.39 240	75.86 113	76.74 170	75.91 12	85.92 43	96.14 1	80.85 8	75.59 197	53.58 190	94.27 42	91.58 13

ACMH	63.62 14	77.50 69	80.11 48	69.68 177	79.61 118	56.28 145	78.81 69	83.62 54	63.41 93	87.14 28	90.23 68	76.11 24	73.32 212	67.58 91	94.44 38	79.44 198
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
XVG-OURS-SEG-HR		79.62 47	79.99 49	78.49 58	86.46 43	74.79 30	77.15 91	85.39 24	66.73 53	80.39 111	88.85 96	74.43 41	78.33 169	74.73 34	85.79 166	82.35 150

XVG-OURS		79.51 48	79.82 50	78.58 57	86.11 45	74.96 29	76.33 103	84.95 28	66.89 49	82.75 79	88.99 92	66.82 94	78.37 168	74.80 32	90.76 99	82.40 149

HPM-MVS++		79.89 45	79.80 51	80.18 39	89.02 24	78.44 11	83.49 33	80.18 119	64.71 76	78.11 136	88.39 101	65.46 104	83.14 70	77.64 26	91.20 85	78.94 203

v52		78.96 53	79.79 52	76.46 76	73.03 227	54.90 150	78.48 73	83.48 56	64.43 78	91.19 4	91.54 29	72.08 52	81.11 110	76.45 29	87.47 143	93.38 7

V4		78.96 53	79.79 52	76.46 76	73.02 228	54.90 150	78.48 73	83.47 57	64.43 78	91.20 3	91.54 29	72.08 52	81.11 110	76.45 29	87.46 145	93.38 7

test_0402		78.17 65	79.48 54	74.24 99	83.50 79	59.15 133	72.52 148	74.60 186	75.34 13	88.69 15	91.81 23	75.06 34	82.37 81	65.10 114	88.68 128	81.20 168

DP-MVS		78.44 63	79.29 55	75.90 84	81.86 101	65.33 88	79.05 68	84.63 36	74.83 15	80.41 110	86.27 141	71.68 56	83.45 66	62.45 129	92.40 67	78.92 204

HSP-MVS		79.69 46	79.17 56	81.27 28	89.70 12	77.46 19	87.16 8	80.58 110	64.94 73	81.05 101	88.38 102	57.10 198	87.10 7	79.75 7	83.87 198	79.24 200

TSAR-MVS + MP.		79.05 52	78.81 57	79.74 42	88.94 25	67.52 76	86.61 15	81.38 90	51.71 210	77.15 143	91.42 35	65.49 103	87.20 5	79.44 13	87.17 154	84.51 106

OMC-MVS		79.41 50	78.79 58	81.28 27	80.62 111	70.71 54	80.91 50	84.76 33	62.54 99	81.77 86	86.65 131	71.46 58	83.53 64	67.95 89	92.44 66	89.60 36

HQP_MVS		78.77 57	78.78 59	78.72 54	85.18 55	65.18 90	82.74 38	85.49 21	65.45 63	78.23 134	89.11 88	60.83 142	86.15 22	71.09 56	90.94 91	84.82 95

mvs_tets		78.93 55	78.67 60	79.72 43	84.81 62	73.93 35	80.65 51	76.50 171	51.98 208	87.40 24	91.86 22	76.09 25	78.53 159	68.58 78	90.20 106	86.69 75

CNVR-MVS		78.49 61	78.59 61	78.16 62	85.86 50	67.40 77	78.12 81	81.50 83	63.92 84	77.51 141	86.56 135	68.43 81	84.82 46	73.83 39	91.61 75	82.26 153

OurMVSNet-221017-0		78.57 59	78.53 62	78.67 55	80.48 112	64.16 97	80.24 57	82.06 74	61.89 103	88.77 14	93.32 5	57.15 196	82.60 78	70.08 68	92.80 60	89.25 38

test_djsdf		78.88 56	78.27 63	80.70 35	81.42 105	71.24 48	83.98 29	75.72 176	52.27 203	87.37 25	92.25 16	68.04 85	80.56 127	72.28 52	91.15 86	90.32 33

jajsoiax		78.51 60	78.16 64	79.59 45	84.65 65	73.83 37	80.42 54	76.12 172	51.33 215	87.19 26	91.51 32	73.79 45	78.44 163	68.27 81	90.13 110	86.49 76

NCCC		78.25 64	78.04 65	78.89 53	85.61 52	69.45 62	79.80 63	80.99 104	65.77 60	75.55 166	86.25 143	67.42 89	85.42 33	70.10 67	90.88 97	81.81 162

v748		76.93 72	77.95 66	73.87 104	73.94 201	52.44 166	75.90 111	79.98 123	65.34 68	86.97 30	91.77 24	67.40 90	78.40 166	70.23 65	90.01 111	90.76 31

anonymousdsp		78.60 58	77.80 67	81.00 31	78.01 142	74.34 33	80.09 59	76.12 172	50.51 228	89.19 12	90.88 43	71.45 59	77.78 177	73.38 41	90.60 101	90.90 27

TranMVSNet+NR-MVSNet		76.13 78	77.66 68	71.56 156	84.61 67	42.57 247	70.98 179	78.29 150	68.67 45	83.04 76	89.26 80	72.99 48	80.75 126	55.58 175	95.47 12	91.35 15

AllTest		77.66 67	77.43 69	78.35 60	79.19 127	70.81 51	78.60 71	88.64 2	65.37 66	80.09 114	88.17 105	70.33 65	78.43 164	55.60 172	90.90 95	85.81 84

PS-MVSNAJss		77.54 68	77.35 70	78.13 64	84.88 60	66.37 82	78.55 72	79.59 127	53.48 194	86.29 38	92.43 15	62.39 124	80.25 134	67.90 90	90.61 100	87.77 65

test_prior3		76.71 74	77.19 71	75.27 91	82.15 97	59.85 126	75.57 116	84.33 39	58.92 129	76.53 156	86.78 122	67.83 87	83.39 67	69.81 70	92.76 61	82.58 143

DeepPCF-MVS	71.07 5	78.48 62	77.14 72	82.52 16	84.39 73	77.04 21	76.35 101	84.05 48	56.66 152	80.27 112	85.31 156	68.56 78	87.03 9	67.39 94	91.26 83	83.50 123

CDPH-MVS		77.33 70	77.06 73	78.14 63	84.21 74	63.98 99	76.07 108	83.45 58	54.20 183	77.68 140	87.18 111	69.98 68	85.37 34	68.01 85	92.72 64	85.08 93

v13		76.23 77	77.02 74	73.86 106	74.61 187	48.80 185	76.91 94	81.10 98	62.66 97	87.02 29	91.01 41	59.76 153	81.41 97	71.29 55	88.78 127	91.38 14

v12		76.03 79	76.79 75	73.76 108	74.45 189	48.60 191	76.59 96	81.11 95	62.22 102	86.79 31	90.74 49	59.51 154	81.40 99	71.01 58	88.67 129	91.29 16

wuykxyi23d		75.33 89	76.75 76	71.04 160	78.83 134	85.01 1	71.78 164	61.00 258	53.47 195	96.33 1	93.38 4	73.07 46	68.04 266	65.65 111	97.28 2	60.07 327

train_agg		76.38 75	76.55 77	75.86 85	85.47 53	69.32 65	76.42 99	78.69 141	54.00 187	76.97 144	86.74 125	66.60 95	81.10 112	72.50 49	91.56 76	77.15 220

V9		75.82 81	76.53 78	73.66 109	74.28 193	48.37 192	76.26 104	81.10 98	61.73 105	86.59 34	90.43 57	59.16 160	81.42 96	70.71 61	88.56 130	91.21 19

v11		75.76 83	76.51 79	73.48 116	74.28 193	47.81 204	76.16 106	81.28 91	61.56 106	86.39 36	90.38 63	59.32 158	81.41 97	70.85 59	88.41 132	91.23 17

agg_prior1		75.89 80	76.41 80	74.31 98	84.44 71	66.02 84	76.12 107	78.62 144	54.40 181	76.95 146	86.85 119	66.44 98	80.34 132	72.45 51	91.42 80	76.57 225

SixPastTwentyTwo		75.77 82	76.34 81	74.06 102	81.69 103	54.84 152	76.47 98	75.49 178	64.10 83	87.73 20	92.24 17	50.45 226	81.30 104	67.41 93	91.46 79	86.04 80

agg_prior3		76.32 76	76.33 82	76.28 79	85.86 50	70.13 59	76.50 97	78.26 151	53.41 196	75.78 162	86.49 137	66.58 97	81.57 91	72.50 49	91.56 76	77.15 220

DeepC-MVS_fast	69.89 7	77.17 71	76.33 82	79.70 44	83.90 77	67.94 73	80.06 61	83.75 51	56.73 151	74.88 174	85.32 155	65.54 102	87.79 2	65.61 112	91.14 87	83.35 131
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
V14		75.58 86	76.26 84	73.55 114	74.10 200	48.13 197	75.91 110	81.07 101	61.19 109	86.34 37	90.11 70	58.80 164	81.40 99	70.40 63	88.43 131	91.12 20

Anonymous20240521		74.99 98	76.21 85	71.33 159	77.99 143	44.41 237	75.24 123	77.16 166	65.86 59	84.89 59	91.96 20	60.23 146	79.31 146	59.86 142	92.75 63	90.27 34

v10		75.69 85	76.20 86	74.16 100	74.44 191	48.69 187	75.84 114	82.93 65	59.02 128	85.92 43	89.17 85	58.56 169	82.74 76	70.73 60	89.14 123	91.05 21

v15		75.37 88	76.01 87	73.44 117	73.91 204	47.87 203	75.55 118	81.04 102	60.76 114	86.11 41	89.76 75	58.53 170	81.40 99	70.11 66	88.32 133	91.04 23

nrg030		74.87 103	75.99 88	71.52 157	74.90 176	49.88 180	74.10 138	82.58 70	54.55 180	83.50 74	89.21 84	71.51 57	75.74 195	61.24 133	92.34 68	88.94 47

MSLP-MVS++		74.48 108	75.78 89	70.59 164	84.66 64	62.40 108	78.65 70	84.24 43	60.55 118	77.71 139	81.98 202	63.12 117	77.64 178	62.95 126	88.14 135	71.73 261

UniMVSNet_NR-MVSNet		74.90 101	75.65 90	72.64 140	83.04 85	45.79 230	69.26 197	78.81 139	66.66 54	81.74 88	86.88 118	63.26 116	81.07 114	56.21 168	94.98 22	91.05 21

v8		75.07 95	75.64 91	73.35 119	73.42 210	47.46 213	75.20 124	81.45 86	60.05 120	85.64 46	89.26 80	58.08 179	81.80 89	69.71 72	87.97 139	90.79 29

v17		75.03 96	75.59 92	73.36 118	73.56 206	47.66 208	75.48 119	81.45 86	60.58 116	85.55 49	89.02 90	58.36 172	81.47 93	69.69 73	86.59 160	90.96 24

DU-MVS		74.91 100	75.57 93	72.93 133	83.50 79	45.79 230	69.47 195	80.14 120	65.22 69	81.74 88	87.08 112	61.82 130	81.07 114	56.21 168	94.98 22	91.93 10

UniMVSNet (Re)		75.00 97	75.48 94	73.56 113	83.14 83	47.92 202	70.41 185	81.04 102	63.67 87	79.54 118	86.37 140	62.83 118	81.82 88	57.10 159	95.25 16	90.94 26

IS-MVSNet		75.10 94	75.42 95	74.15 101	79.23 125	48.05 200	79.43 64	78.04 156	70.09 39	79.17 123	88.02 109	53.04 213	83.60 62	58.05 152	93.76 51	90.79 29

v16		74.89 102	75.41 96	73.35 119	73.54 207	47.62 209	75.47 120	81.45 86	60.58 116	85.46 51	88.97 93	58.27 173	81.47 93	69.66 74	85.25 182	90.95 25

v18		74.60 106	75.06 97	73.22 124	73.29 216	47.36 217	75.02 125	81.47 85	60.01 121	85.13 54	88.44 99	57.93 187	81.47 93	69.26 75	85.02 186	90.84 28

HQP-MVS		75.24 92	75.01 98	75.94 83	82.37 92	58.80 135	77.32 87	84.12 46	59.08 125	71.58 210	85.96 152	58.09 177	85.30 36	67.38 95	89.16 121	83.73 121

X-MVStestdata		76.81 73	74.79 99	82.85 7	89.43 16	77.61 14	86.80 13	84.66 34	72.71 23	82.87 77	9.95 355	73.86 43	86.31 16	78.84 18	94.03 47	84.64 98

FC-MVSNet-test		73.32 123	74.78 100	68.93 188	79.21 126	36.57 287	71.82 163	79.54 128	57.63 140	82.57 80	90.38 63	59.38 157	78.99 149	57.91 153	94.56 33	91.23 17

Vis-MVSNet		74.85 104	74.56 101	75.72 86	81.63 104	64.64 94	76.35 101	79.06 135	62.85 96	73.33 192	88.41 100	62.54 122	79.59 144	63.94 121	82.92 206	82.94 135
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
AdaColmap		74.22 110	74.56 101	73.20 125	81.95 99	60.97 118	79.43 64	80.90 105	65.57 62	72.54 203	81.76 206	70.98 63	85.26 37	47.88 226	90.00 112	73.37 244

Regformer-2		75.32 90	74.47 103	77.88 65	74.22 196	66.65 79	72.77 145	77.54 160	68.47 46	80.44 109	72.08 295	70.60 64	80.97 117	70.08 68	84.02 196	86.01 81

CSCG		74.12 111	74.39 104	73.33 121	79.35 122	61.66 115	77.45 86	81.98 76	62.47 101	79.06 124	80.19 219	61.83 129	78.79 154	59.83 143	87.35 148	79.54 197

RPSCF		75.76 83	74.37 105	79.93 40	74.81 178	77.53 16	77.53 85	79.30 129	59.44 124	78.88 125	89.80 74	71.26 61	73.09 214	57.45 155	80.89 237	89.17 41

PHI-MVS		74.92 99	74.36 106	76.61 72	76.40 160	62.32 110	80.38 55	83.15 61	54.16 185	73.23 194	80.75 213	62.19 127	83.86 58	68.02 84	90.92 94	83.65 122

TAPA-MVS	65.27 12	75.16 93	74.29 107	77.77 67	74.86 177	68.08 72	77.89 82	84.04 49	55.15 166	76.19 161	83.39 179	66.91 92	80.11 138	60.04 140	90.14 109	85.13 91
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PAPM_NR		73.91 112	74.16 108	73.16 126	81.90 100	53.50 161	81.28 48	81.40 89	66.17 57	73.30 193	83.31 184	59.96 148	83.10 71	58.45 151	81.66 223	82.87 136

NR-MVSNet		73.62 116	74.05 109	72.33 149	83.50 79	43.71 239	65.65 244	77.32 164	64.32 81	75.59 165	87.08 112	62.45 123	81.34 102	54.90 178	95.63 10	91.93 10

F-COLMAP		75.29 91	73.99 110	79.18 49	81.73 102	71.90 43	81.86 46	82.98 63	59.86 123	72.27 205	84.00 174	64.56 112	83.07 72	51.48 198	87.19 153	82.56 145

FIs		72.56 143	73.80 111	68.84 192	78.74 135	37.74 281	71.02 178	79.83 124	56.12 154	80.88 106	89.45 77	58.18 174	78.28 170	56.63 161	93.36 55	90.51 32

v7		73.59 117	73.69 112	73.28 123	74.42 192	48.68 188	72.74 147	81.98 76	54.76 176	82.07 83	85.05 161	58.53 170	82.22 85	67.99 86	85.66 170	88.95 46

Regformer-4		74.64 105	73.67 113	77.55 68	74.74 180	64.49 96	72.91 142	75.42 181	67.45 48	80.24 113	72.07 298	68.98 75	80.19 137	70.29 64	80.91 235	87.98 63

pmmvs6		71.82 149	73.66 114	66.31 214	75.94 167	42.01 249	66.99 226	72.53 199	63.45 92	76.43 159	92.78 10	72.95 49	69.69 249	51.41 199	90.46 103	87.22 70

Regformer-1		74.28 109	73.63 115	76.21 82	74.22 196	64.12 98	72.77 145	75.46 180	66.86 51	79.27 121	72.08 295	69.29 72	78.74 155	68.73 77	84.02 196	85.77 87

K. test v3		73.67 115	73.61 116	73.87 104	79.78 116	55.62 148	74.69 134	62.04 255	66.16 58	84.76 60	93.23 6	49.47 228	80.97 117	65.66 110	86.67 159	85.02 94

MVS_0304		74.55 107	73.47 117	77.80 66	77.41 151	63.88 100	75.75 115	83.67 53	63.55 89	66.12 258	82.16 200	60.20 147	86.15 22	65.37 113	86.98 156	83.38 128

v1192		73.40 121	73.42 118	73.32 122	74.65 186	48.67 189	72.21 151	81.73 80	52.76 201	81.85 85	84.56 168	57.12 197	82.24 84	68.58 78	87.33 149	89.06 42

v1144		73.29 124	73.39 119	73.01 129	74.12 199	48.11 198	72.01 156	81.08 100	53.83 191	81.77 86	84.68 166	58.07 180	81.91 87	68.10 82	86.86 157	88.99 45

canonicalmvs		72.29 145	73.38 120	69.04 185	74.23 195	47.37 216	73.93 139	83.18 60	54.36 182	76.61 153	81.64 208	72.03 54	75.34 199	57.12 158	87.28 151	84.40 109

EPP-MVSNet		73.86 113	73.38 120	75.31 90	78.19 139	53.35 163	80.45 53	77.32 164	65.11 71	76.47 158	86.80 120	49.47 228	83.77 59	53.89 187	92.72 64	88.81 51

MCST-MVS		73.42 120	73.34 122	73.63 112	81.28 107	59.17 132	74.80 131	83.13 62	45.50 262	72.84 196	83.78 177	65.15 106	80.99 116	64.54 117	89.09 124	80.73 181

114514_t		73.40 121	73.33 123	73.64 111	84.15 76	57.11 142	78.20 79	80.02 121	43.76 275	72.55 202	86.07 150	64.00 114	83.35 69	60.14 139	91.03 90	80.45 186

Baseline_NR-MVSNet		70.62 159	73.19 124	62.92 237	76.97 155	34.44 306	68.84 200	70.88 218	60.25 119	79.50 119	90.53 54	61.82 130	69.11 251	54.67 181	95.27 15	85.22 90

v1240		73.06 126	73.14 125	72.84 135	74.74 180	47.27 219	71.88 162	81.11 95	51.80 209	82.28 82	84.21 171	56.22 204	82.34 82	68.82 76	87.17 154	88.91 48

VDDNet		71.60 151	73.13 126	67.02 206	86.29 44	41.11 255	69.97 188	66.50 236	68.72 44	74.74 176	91.70 26	59.90 149	75.81 193	48.58 220	91.72 72	84.15 114

IterMVS-LS		73.01 127	73.12 127	72.66 139	73.79 205	49.90 177	71.63 166	78.44 147	58.22 132	80.51 108	86.63 132	58.15 176	79.62 142	62.51 127	88.20 134	88.48 60
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v6		72.93 132	73.08 128	72.48 143	73.42 210	47.47 212	72.17 152	80.25 118	55.63 158	81.65 91	85.04 162	57.95 186	81.28 105	66.56 103	85.01 187	88.70 53

v1neww		72.93 132	73.07 129	72.48 143	73.41 212	47.46 213	72.17 152	80.26 116	55.63 158	81.63 92	85.07 159	57.97 183	81.28 105	66.55 104	84.98 188	88.70 53

v7new		72.93 132	73.07 129	72.48 143	73.41 212	47.46 213	72.17 152	80.26 116	55.63 158	81.63 92	85.07 159	57.97 183	81.28 105	66.55 104	84.98 188	88.70 53

v144192		72.99 129	73.06 131	72.77 136	74.58 188	47.48 211	71.90 161	80.44 113	51.57 212	81.46 95	84.11 173	58.04 181	82.12 86	67.98 87	87.47 143	88.70 53

CNLPA		73.44 119	73.03 132	74.66 93	78.27 138	75.29 27	75.99 109	78.49 146	65.39 65	75.67 164	83.22 189	61.23 138	66.77 278	53.70 189	85.33 180	81.92 161

v1921920		72.96 131	72.98 133	72.89 134	74.67 183	47.58 210	71.92 160	80.69 106	51.70 211	81.69 90	83.89 175	56.58 202	82.25 83	68.34 80	87.36 147	88.82 50

MVS_111021_HR		72.98 130	72.97 134	72.99 130	80.82 110	65.47 87	68.81 202	72.77 196	57.67 138	75.76 163	82.38 197	71.01 62	77.17 180	61.38 132	86.15 162	76.32 226

Gipuma		69.55 169	72.83 135	59.70 268	63.63 303	53.97 158	80.08 60	75.93 174	64.24 82	73.49 190	88.93 95	57.89 189	62.46 293	59.75 145	91.55 78	62.67 321
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
v1141		72.59 142	72.73 136	72.19 150	73.10 223	47.00 223	71.48 167	79.11 132	55.01 167	81.23 98	84.94 164	57.45 193	80.89 123	66.58 101	85.65 171	88.68 57

divwei89l23v2f112		72.60 140	72.73 136	72.19 150	73.10 223	47.00 223	71.48 167	79.11 132	55.01 167	81.23 98	84.95 163	57.45 193	80.89 123	66.58 101	85.67 168	88.68 57

v1		72.60 140	72.73 136	72.19 150	73.12 222	47.01 222	71.48 167	79.10 134	55.01 167	81.24 97	84.92 165	57.46 192	80.90 122	66.59 100	85.67 168	88.68 57

DP-MVS Recon		73.57 118	72.69 139	76.23 81	82.85 88	63.39 103	74.32 136	82.96 64	57.75 135	70.35 228	81.98 202	64.34 113	84.41 54	49.69 211	89.95 113	80.89 176

v2v482		72.55 144	72.58 140	72.43 146	72.92 233	46.72 227	71.41 172	79.13 131	55.27 162	81.17 100	85.25 157	55.41 206	81.13 109	67.25 98	85.46 176	89.43 37

WR-MVS		71.20 153	72.48 141	67.36 203	84.98 59	35.70 296	64.43 257	68.66 228	65.05 72	81.49 94	86.43 139	57.57 191	76.48 188	50.36 207	93.32 56	89.90 35

FMVSNet1		71.06 154	72.48 141	66.81 208	77.65 149	40.68 257	71.96 157	73.03 192	61.14 110	79.45 120	90.36 65	60.44 144	75.20 201	50.20 208	88.05 136	84.54 103

testing_2		72.01 148	72.36 143	70.95 161	70.79 245	48.70 186	72.81 144	78.09 155	48.79 238	84.46 67	89.15 87	57.90 188	78.55 158	61.55 131	87.74 140	85.61 88

CLD-MVS		72.88 135	72.36 143	74.43 96	77.03 154	54.30 156	68.77 205	83.43 59	52.12 205	76.79 151	74.44 278	69.54 71	83.91 57	55.88 171	93.25 57	85.09 92
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Effi-MVS+-dtu		75.43 87	72.28 145	84.91 2	77.05 152	83.58 2	78.47 75	77.70 158	57.68 136	74.89 173	78.13 242	64.80 109	84.26 56	56.46 165	85.32 181	86.88 72

Effi-MVS+		72.10 146	72.28 145	71.58 155	74.21 198	50.33 173	74.72 133	82.73 67	62.62 98	70.77 222	76.83 249	69.96 69	80.97 117	60.20 137	78.43 262	83.45 127

Regformer-3		72.86 136	72.28 145	74.62 94	74.74 180	60.18 123	72.91 142	71.76 204	64.74 75	78.42 130	72.07 298	67.00 91	76.28 190	67.97 88	80.91 235	87.39 69

EI-MVSNet-Vis-set		72.78 137	71.87 148	75.54 88	74.77 179	59.02 134	72.24 150	71.56 207	63.92 84	78.59 126	71.59 305	66.22 99	78.60 157	67.58 91	80.32 243	89.00 44

CANet		73.00 128	71.84 149	76.48 74	75.82 168	61.28 116	74.81 129	80.37 114	63.17 94	62.43 277	80.50 216	61.10 140	85.16 42	64.00 120	84.34 192	83.01 134

MVS_111021_LR		72.10 146	71.82 150	72.95 132	79.53 120	73.90 36	70.45 184	66.64 235	56.87 148	76.81 150	81.76 206	68.78 76	71.76 237	61.81 130	83.74 200	73.18 246

PCF-MVS	63.80 13	72.70 138	71.69 151	75.72 86	78.10 140	60.01 125	73.04 141	81.50 83	45.34 265	79.66 117	84.35 170	65.15 106	82.65 77	48.70 218	89.38 120	84.50 107
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
EI-MVSNet-UG-set		72.63 139	71.68 152	75.47 89	74.67 183	58.64 138	72.02 155	71.50 208	63.53 90	78.58 128	71.39 308	65.98 100	78.53 159	67.30 97	80.18 244	89.23 39

TransMVSNet (Re)		69.62 167	71.63 153	63.57 231	76.51 159	35.93 294	65.75 243	71.29 212	61.05 111	75.02 171	89.90 73	65.88 101	70.41 247	49.79 210	89.48 118	84.38 110

TSAR-MVS + GP.		73.08 125	71.60 154	77.54 69	78.99 133	70.73 53	74.96 126	69.38 225	60.73 115	74.39 181	78.44 239	57.72 190	82.78 75	60.16 138	89.60 116	79.11 202

LCM-MVSNet-Re		69.10 175	71.57 155	61.70 250	70.37 251	34.30 307	61.45 282	79.62 125	56.81 149	89.59 10	88.16 107	68.44 80	72.94 215	42.30 262	87.33 149	77.85 217

API-MVS		70.97 156	71.51 156	69.37 178	75.20 172	55.94 146	80.99 49	76.84 167	62.48 100	71.24 217	77.51 245	61.51 134	80.96 121	52.04 194	85.76 167	71.22 265

VDD-MVS		70.81 157	71.44 157	68.91 190	79.07 132	46.51 228	67.82 216	70.83 219	61.23 108	74.07 185	88.69 97	59.86 150	75.62 196	51.11 201	90.28 105	84.61 101

MG-MVS		70.47 161	71.34 158	67.85 199	79.26 124	40.42 262	74.67 135	75.15 184	58.41 131	68.74 239	88.14 108	56.08 205	83.69 60	59.90 141	81.71 222	79.43 199

3Dnovator	65.95 11	71.50 152	71.22 159	72.34 148	73.16 218	63.09 106	78.37 76	78.32 148	57.67 138	72.22 207	84.61 167	54.77 207	78.47 161	60.82 136	81.07 234	75.45 231

alignmvs		70.54 160	71.00 160	69.15 184	73.50 208	48.04 201	69.85 191	79.62 125	53.94 190	76.54 155	82.00 201	59.00 162	74.68 205	57.32 156	87.21 152	84.72 97

EG-PatchMatch MVS		70.70 158	70.88 161	70.16 171	82.64 91	58.80 135	71.48 167	73.64 190	54.98 170	76.55 154	81.77 205	61.10 140	78.94 150	54.87 179	80.84 238	72.74 251

V42		71.06 154	70.83 162	71.72 154	67.25 282	47.14 220	65.94 240	80.35 115	51.35 214	83.40 75	83.23 187	59.25 159	78.80 153	65.91 109	80.81 239	89.23 39

MVS_Test		69.84 166	70.71 163	67.24 204	67.49 281	43.25 242	69.87 190	81.22 94	52.69 202	71.57 213	86.68 128	62.09 128	74.51 207	66.05 107	78.74 258	83.96 116

mvs-test1		73.81 114	70.69 164	83.18 3	77.05 152	81.39 4	75.39 121	77.70 158	57.68 136	71.19 219	74.72 274	64.80 109	83.66 61	56.46 165	81.19 233	84.50 107

VPA-MVSNet		68.71 183	70.37 165	63.72 230	76.13 164	38.06 279	64.10 259	71.48 209	56.60 153	74.10 184	88.31 103	64.78 111	69.72 248	47.69 228	90.15 108	83.37 130

PLC	62.01 16	71.79 150	70.28 166	76.33 78	80.31 114	68.63 70	78.18 80	81.24 93	54.57 179	67.09 255	80.63 214	59.44 155	81.74 90	46.91 233	84.17 193	78.63 205
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
ANet_high		67.08 196	69.94 167	58.51 275	57.55 336	27.09 340	58.43 299	76.80 168	63.56 88	82.40 81	91.93 21	59.82 151	64.98 285	50.10 209	88.86 126	83.46 126

pm-mvs1		68.40 185	69.85 168	64.04 227	73.10 223	39.94 264	64.61 255	70.50 220	55.52 161	73.97 186	89.33 78	63.91 115	68.38 263	49.68 212	88.02 137	83.81 119

BH-untuned		69.39 170	69.46 169	69.18 183	77.96 144	56.88 143	68.47 211	77.53 161	56.77 150	77.79 138	79.63 227	60.30 145	80.20 136	46.04 238	80.65 240	70.47 271

v148		69.38 171	69.39 170	69.36 179	69.14 261	44.56 234	68.83 201	72.70 197	54.79 174	78.59 126	84.12 172	54.69 208	76.74 187	59.40 146	82.20 211	86.79 73

TinyColmap		67.98 188	69.28 171	64.08 226	67.98 276	46.82 225	70.04 187	75.26 182	53.05 198	77.36 142	86.79 121	59.39 156	72.59 226	45.64 240	88.01 138	72.83 249

QAPM		69.18 174	69.26 172	68.94 187	71.61 243	52.58 165	80.37 56	78.79 140	49.63 233	73.51 189	85.14 158	53.66 212	79.12 147	55.11 177	75.54 278	75.11 235

MIMVSNet1		66.57 198	69.23 173	58.59 274	81.26 108	37.73 282	64.06 260	57.62 271	57.02 147	78.40 131	90.75 48	62.65 119	58.10 306	41.77 268	89.58 117	79.95 195

UGNet		70.20 162	69.05 174	73.65 110	76.24 162	63.64 101	75.87 112	72.53 199	61.48 107	60.93 289	86.14 147	52.37 217	77.12 181	50.67 204	85.21 183	80.17 194
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
DI_MVS_plusplus_test		69.01 178	69.04 175	68.93 188	69.54 256	46.74 226	70.14 186	75.49 178	46.64 255	78.30 132	83.18 190	58.80 164	78.86 151	57.14 157	82.15 212	81.18 169

MVSFormer		69.93 165	69.03 176	72.63 141	74.93 174	59.19 130	83.98 29	75.72 176	52.27 203	63.53 273	76.74 250	43.19 255	80.56 127	72.28 52	78.67 260	78.14 212

EI-MVSNet		69.61 168	69.01 177	71.41 158	73.94 201	49.90 177	71.31 175	71.32 210	58.22 132	75.40 169	70.44 309	58.16 175	75.85 191	62.51 127	79.81 249	88.48 60

Test4		69.04 177	68.95 178	69.32 182	69.52 257	48.10 199	70.69 183	78.25 152	45.90 259	80.99 102	82.24 198	51.91 218	78.11 175	58.46 150	82.58 209	81.74 163

test_normal		68.88 179	68.88 179	68.88 191	69.43 259	47.03 221	69.85 191	74.83 185	46.06 258	78.30 132	83.29 185	58.76 168	78.23 171	57.51 154	81.90 216	81.36 167

PVSNet_Blended_VisFu		70.04 163	68.88 179	73.53 115	82.71 90	63.62 102	74.81 129	81.95 78	48.53 240	67.16 254	79.18 235	51.42 223	78.38 167	54.39 185	79.72 252	78.60 206

GBi-Net		68.30 186	68.79 181	66.81 208	73.14 219	40.68 257	71.96 157	73.03 192	54.81 171	74.72 177	90.36 65	48.63 233	75.20 201	47.12 230	85.37 177	84.54 103

test1		68.30 186	68.79 181	66.81 208	73.14 219	40.68 257	71.96 157	73.03 192	54.81 171	74.72 177	90.36 65	48.63 233	75.20 201	47.12 230	85.37 177	84.54 103

OpenMVS	62.51 15	68.76 182	68.75 183	68.78 193	70.56 249	53.91 159	78.29 77	77.35 163	48.85 237	70.22 230	83.52 178	52.65 216	76.93 183	55.31 176	81.99 214	75.49 230

Fast-Effi-MVS+-dtu		70.00 164	68.74 184	73.77 107	73.47 209	64.53 95	71.36 173	78.14 154	55.81 157	68.84 238	74.71 275	65.36 105	75.75 194	52.00 195	79.00 256	81.03 173

PAPR		69.20 173	68.66 185	70.82 162	75.15 173	47.77 205	75.31 122	81.11 95	49.62 234	66.33 257	79.27 232	61.53 133	82.96 73	48.12 224	81.50 225	81.74 163

DELS-MVS		68.83 180	68.31 186	70.38 165	70.55 250	48.31 193	63.78 262	82.13 73	54.00 187	68.96 237	75.17 270	58.95 163	80.06 139	58.55 149	82.74 207	82.76 140
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
Fast-Effi-MVS+		68.81 181	68.30 187	70.35 166	74.66 185	48.61 190	66.06 239	78.32 148	50.62 227	71.48 216	75.54 264	68.75 77	79.59 144	50.55 206	78.73 259	82.86 137

1121		69.23 172	68.26 188	72.12 153	88.36 32	71.40 45	68.59 206	62.06 253	43.80 274	74.75 175	86.18 144	52.92 214	76.85 185	54.47 182	83.27 204	68.12 293

FMVSNet2		67.48 194	68.21 189	65.29 219	73.14 219	38.94 272	68.81 202	71.21 216	54.81 171	76.73 152	86.48 138	48.63 233	74.60 206	47.98 225	86.11 164	82.35 150

BH-RMVSNet		68.69 184	68.20 190	70.14 172	76.40 160	53.90 160	64.62 254	73.48 191	58.01 134	73.91 187	81.78 204	59.09 161	78.22 172	48.59 219	77.96 267	78.31 209

tfpnnormal		66.48 199	67.93 191	62.16 248	73.40 214	36.65 286	63.45 264	64.99 243	55.97 155	72.82 197	87.80 110	57.06 199	69.10 252	48.31 223	87.54 142	80.72 182

LFMVS		67.06 197	67.89 192	64.56 222	78.02 141	38.25 277	70.81 182	59.60 263	65.18 70	71.06 220	86.56 135	43.85 251	75.22 200	46.35 237	89.63 115	80.21 189

VPNet		65.58 201	67.56 193	59.65 269	79.72 117	30.17 333	60.27 290	62.14 251	54.19 184	71.24 217	86.63 132	58.80 164	67.62 269	44.17 244	90.87 98	81.18 169

MSDG		67.47 195	67.48 194	67.46 202	70.70 248	54.69 154	66.90 228	78.17 153	60.88 113	70.41 227	74.76 272	61.22 139	73.18 213	47.38 229	76.87 271	74.49 238

EPNet		69.10 175	67.32 195	74.46 95	68.33 272	61.27 117	77.56 84	63.57 247	60.95 112	56.62 310	82.75 191	51.53 222	81.24 108	54.36 186	90.20 106	80.88 177
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
LF4IMVS		67.50 193	67.31 196	68.08 197	58.86 328	61.93 111	71.43 171	75.90 175	44.67 270	72.42 204	80.20 218	57.16 195	70.44 245	58.99 148	86.12 163	71.88 259

xiu_mvs_v1_base_debu		67.87 189	67.07 197	70.26 167	79.13 129	61.90 112	67.34 221	71.25 213	47.98 245	67.70 243	74.19 283	61.31 135	72.62 223	56.51 162	78.26 264	76.27 227

xiu_mvs_v1_base		67.87 189	67.07 197	70.26 167	79.13 129	61.90 112	67.34 221	71.25 213	47.98 245	67.70 243	74.19 283	61.31 135	72.62 223	56.51 162	78.26 264	76.27 227

xiu_mvs_v1_base_debi		67.87 189	67.07 197	70.26 167	79.13 129	61.90 112	67.34 221	71.25 213	47.98 245	67.70 243	74.19 283	61.31 135	72.62 223	56.51 162	78.26 264	76.27 227

wuyk23d		61.97 232	66.25 200	49.12 308	58.19 334	60.77 120	66.32 233	52.97 301	55.93 156	90.62 7	86.91 117	73.07 46	35.98 350	20.63 349	91.63 74	50.62 341

MAR-MVS		67.72 192	66.16 201	72.40 147	74.45 189	64.99 93	74.87 127	77.50 162	48.67 239	65.78 262	68.58 324	57.01 200	77.79 176	46.68 236	81.92 215	74.42 239
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
diffmvs		66.15 200	65.86 202	67.01 207	62.31 308	44.43 236	68.81 202	72.93 195	48.13 243	62.12 278	83.33 183	57.96 185	72.29 228	59.83 143	77.31 270	84.33 112

mvs_anonymous		65.08 204	65.49 203	63.83 229	63.79 301	37.60 283	66.52 232	69.82 224	43.44 279	73.46 191	86.08 149	58.79 167	71.75 238	51.90 196	75.63 277	82.15 154

FMVSNet3		65.00 205	65.16 204	64.52 223	69.47 258	37.56 284	66.63 230	70.38 221	51.55 213	74.72 177	83.27 186	37.89 284	74.44 208	47.12 230	85.37 177	81.57 165

VNet		64.01 217	65.15 205	60.57 261	73.28 217	35.61 297	57.60 302	67.08 233	54.61 178	66.76 256	83.37 181	56.28 203	66.87 274	42.19 263	85.20 184	79.23 201

ab-mvs		64.11 215	65.13 206	61.05 257	71.99 241	38.03 280	67.59 217	68.79 227	49.08 236	65.32 263	86.26 142	58.02 182	66.85 276	39.33 277	79.79 251	78.27 210

PVSNet_BlendedMVS		65.38 202	64.30 207	68.61 194	69.81 253	49.36 181	65.60 246	78.96 136	45.50 262	59.98 294	78.61 238	51.82 219	78.20 173	44.30 242	84.11 194	78.27 210

BH-w/o		64.81 206	64.29 208	66.36 213	76.08 166	54.71 153	65.61 245	75.23 183	50.10 231	71.05 221	71.86 304	54.33 210	79.02 148	38.20 288	76.14 274	65.36 309

xiu_mvs_v2_base		64.43 211	63.96 209	65.85 218	77.72 148	51.32 170	63.63 263	72.31 202	45.06 269	61.70 279	69.66 315	62.56 120	73.93 211	49.06 216	73.91 288	72.31 255

CANet_DTU		64.04 216	63.83 210	64.66 221	68.39 269	42.97 244	73.45 140	74.50 187	52.05 207	54.78 318	75.44 269	43.99 250	70.42 246	53.49 192	78.41 263	80.59 184

TAMVS		65.31 203	63.75 211	69.97 176	82.23 96	59.76 128	66.78 229	63.37 248	45.20 266	69.79 232	79.37 231	47.42 239	72.17 229	34.48 308	85.15 185	77.99 216

PS-MVSNAJ		64.27 214	63.73 212	65.90 217	77.82 146	51.42 169	63.33 266	72.33 201	45.09 268	61.60 280	68.04 325	62.39 124	73.95 210	49.07 215	73.87 289	72.34 254

PM-MVS		64.49 209	63.61 213	67.14 205	76.68 158	75.15 28	68.49 210	42.85 338	51.17 218	77.85 137	80.51 215	45.76 240	66.31 281	52.83 193	76.35 273	59.96 329

TR-MVS		64.59 207	63.54 214	67.73 201	75.75 170	50.83 172	63.39 265	70.29 222	49.33 235	71.55 214	74.55 276	50.94 224	78.46 162	40.43 275	75.69 276	73.89 242

OpenMVS_ROB	54.93 17	63.23 220	63.28 215	63.07 236	69.81 253	45.34 232	68.52 209	67.14 232	43.74 276	70.61 226	79.22 233	47.90 237	72.66 222	48.75 217	73.84 290	71.21 266

pmmvs-eth3d		64.41 212	63.27 216	67.82 200	75.81 169	60.18 123	69.49 194	62.05 254	38.81 299	74.13 183	82.23 199	43.76 252	68.65 261	42.53 261	80.63 242	74.63 237

Vis-MVSNet (Re-imp)		62.74 230	63.21 217	61.34 255	72.19 236	31.56 331	67.31 224	53.87 295	53.60 193	69.88 231	83.37 181	40.52 270	70.98 241	41.40 269	86.78 158	81.48 166

USDC		62.80 229	63.10 218	61.89 249	65.19 295	43.30 241	67.42 220	74.20 188	35.80 314	72.25 206	84.48 169	45.67 241	71.95 235	37.95 290	84.97 190	70.42 273

Patchmtry		60.91 241	63.01 219	54.62 293	66.10 291	26.27 344	67.47 219	56.40 283	54.05 186	72.04 208	86.66 129	33.19 296	60.17 300	43.69 245	87.45 146	77.42 218

view600		62.88 225	62.90 220	62.82 238	72.97 229	33.66 312	66.10 235	55.01 288	57.05 143	72.66 198	82.56 193	31.60 309	72.78 217	42.64 257	85.55 172	82.02 155

view800		62.88 225	62.90 220	62.82 238	72.97 229	33.66 312	66.10 235	55.01 288	57.05 143	72.66 198	82.56 193	31.60 309	72.78 217	42.64 257	85.55 172	82.02 155

conf0.05thres1000		62.88 225	62.90 220	62.82 238	72.97 229	33.66 312	66.10 235	55.01 288	57.05 143	72.66 198	82.56 193	31.60 309	72.78 217	42.64 257	85.55 172	82.02 155

tfpn		62.88 225	62.90 220	62.82 238	72.97 229	33.66 312	66.10 235	55.01 288	57.05 143	72.66 198	82.56 193	31.60 309	72.78 217	42.64 257	85.55 172	82.02 155

jason		64.47 210	62.84 224	69.34 181	76.91 157	59.20 129	67.15 225	65.67 237	35.29 316	65.16 264	76.74 250	44.67 246	70.68 242	54.74 180	79.28 255	78.14 212
jason: jason.
cascas		64.59 207	62.77 225	70.05 174	75.27 171	50.02 176	61.79 279	71.61 205	42.46 283	63.68 272	68.89 321	49.33 230	80.35 131	47.82 227	84.05 195	79.78 196

CDS-MVSNet		64.33 213	62.66 226	69.35 180	80.44 113	58.28 139	65.26 250	65.66 238	44.36 271	67.30 253	75.54 264	43.27 254	71.77 236	37.68 291	84.44 191	78.01 215
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IterMVS		63.12 221	62.48 227	65.02 220	66.34 289	52.86 164	63.81 261	62.25 250	46.57 256	71.51 215	80.40 217	44.60 247	66.82 277	51.38 200	75.47 279	75.38 233
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MDA-MVSNet-bldmvs		62.34 231	61.73 228	64.16 224	61.64 312	49.90 177	48.11 326	57.24 277	53.31 197	80.95 103	79.39 230	49.00 231	61.55 297	45.92 239	80.05 246	81.03 173

GA-MVS		62.91 223	61.66 229	66.66 212	67.09 284	44.49 235	61.18 286	69.36 226	51.33 215	69.33 234	74.47 277	36.83 285	74.94 204	50.60 205	74.72 285	80.57 185

tfpn111		61.91 233	61.65 230	62.68 243	72.14 237	35.01 300	65.42 247	56.99 278	55.23 163	70.71 223	79.90 221	32.07 304	72.85 216	38.80 281	83.61 201	80.18 190

PVSNet_Blended		62.90 224	61.64 231	66.69 211	69.81 253	49.36 181	61.23 285	78.96 136	42.04 285	59.98 294	68.86 322	51.82 219	78.20 173	44.30 242	77.77 269	72.52 252

MVSTER		63.29 219	61.60 232	68.36 196	59.77 323	46.21 229	60.62 288	71.32 210	41.83 286	75.40 169	79.12 236	30.25 324	75.85 191	56.30 167	79.81 249	83.03 133

RPMNet		61.25 239	61.55 233	60.36 265	66.37 287	48.24 195	70.93 180	54.45 293	54.66 177	61.35 282	86.77 124	33.29 295	63.22 290	55.93 170	70.17 306	69.62 284

lupinMVS		63.36 218	61.49 234	68.97 186	74.93 174	59.19 130	65.80 242	64.52 245	34.68 321	63.53 273	74.25 281	43.19 255	70.62 243	53.88 188	78.67 260	77.10 222

thres600view7		61.82 234	61.38 235	63.12 235	71.81 242	34.93 303	64.64 253	56.99 278	54.78 175	70.33 229	79.74 226	32.07 304	72.42 227	38.61 284	83.46 202	82.02 155

conf200view11		61.42 238	61.09 236	62.43 246	72.14 237	35.01 300	65.42 247	56.99 278	55.23 163	70.71 223	79.90 221	32.07 304	72.09 230	35.61 303	81.73 218	80.18 190

thres100view900		61.17 240	61.09 236	61.39 254	72.14 237	35.01 300	65.42 247	56.99 278	55.23 163	70.71 223	79.90 221	32.07 304	72.09 230	35.61 303	81.73 218	77.08 223

CMPMVS	48.73 20	61.54 237	60.89 238	63.52 232	61.08 315	51.55 168	68.07 214	68.00 231	33.88 323	65.87 260	81.25 210	37.91 283	67.71 267	49.32 214	82.60 208	71.31 264
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
EU-MVSNet		60.82 242	60.80 239	60.86 260	68.37 270	41.16 254	72.27 149	68.27 230	26.96 348	69.08 235	75.71 262	32.09 303	67.44 270	55.59 174	78.90 257	73.97 240

HyFIR lowres test		63.01 222	60.47 240	70.61 163	83.04 85	54.10 157	59.93 292	72.24 203	33.67 327	69.00 236	75.63 263	38.69 277	76.93 183	36.60 298	75.45 280	80.81 180

PAPM		61.79 235	60.37 241	66.05 215	76.09 165	41.87 250	69.30 196	76.79 169	40.64 293	53.80 324	79.62 228	44.38 248	82.92 74	29.64 328	73.11 292	73.36 245

FPMVS		59.43 252	60.07 242	57.51 280	77.62 150	71.52 44	62.33 270	50.92 313	57.40 141	69.40 233	80.00 220	39.14 275	61.92 296	37.47 294	66.36 322	39.09 351

tfpn200view9		60.35 246	59.97 243	61.51 252	70.78 246	35.35 298	63.27 267	57.47 272	53.00 199	68.31 240	77.09 247	32.45 301	72.09 230	35.61 303	81.73 218	77.08 223

MVS		60.62 245	59.97 243	62.58 244	68.13 274	47.28 218	68.59 206	73.96 189	32.19 332	59.94 296	68.86 322	50.48 225	77.64 178	41.85 266	75.74 275	62.83 319

thres400		60.77 244	59.97 243	63.15 234	70.78 246	35.35 298	63.27 267	57.47 272	53.00 199	68.31 240	77.09 247	32.45 301	72.09 230	35.61 303	81.73 218	82.02 155

ppachtmachnet_test		60.26 247	59.61 246	62.20 247	67.70 279	44.33 238	58.18 300	60.96 259	40.75 291	65.80 261	72.57 293	41.23 264	63.92 288	46.87 234	82.42 210	78.33 208

MVP-Stereo		61.56 236	59.22 247	68.58 195	79.28 123	60.44 121	69.20 198	71.57 206	43.58 278	56.42 311	78.37 240	39.57 274	76.46 189	34.86 307	60.16 334	68.86 291
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
Patchmatch-RL test		59.95 248	59.12 248	62.44 245	72.46 235	54.61 155	59.63 293	47.51 325	41.05 290	74.58 180	74.30 280	31.06 318	65.31 282	51.61 197	79.85 248	67.39 297

pmmvs4		60.78 243	59.04 249	66.00 216	73.06 226	57.67 141	64.53 256	60.22 261	36.91 309	65.96 259	77.27 246	39.66 273	68.54 262	38.87 280	74.89 284	71.80 260

1112_ss		59.48 251	58.99 250	60.96 259	77.84 145	42.39 248	61.42 283	68.45 229	37.96 304	59.93 297	67.46 327	45.11 244	65.07 284	40.89 272	71.81 297	75.41 232

conf0.01		59.26 253	58.88 251	60.40 263	68.66 262	31.96 325	62.04 272	51.95 305	50.99 219	67.57 246	75.91 256	28.59 334	69.07 253	42.77 251	81.40 226	80.18 190

conf0.002		59.26 253	58.88 251	60.40 263	68.66 262	31.96 325	62.04 272	51.95 305	50.99 219	67.57 246	75.91 256	28.59 334	69.07 253	42.77 251	81.40 226	80.18 190

thresconf0.02		58.38 260	58.88 251	56.91 283	68.66 262	31.96 325	62.04 272	51.95 305	50.99 219	67.57 246	75.91 256	28.59 334	69.07 253	42.77 251	81.40 226	69.70 279

tfpn_n400		58.38 260	58.88 251	56.91 283	68.66 262	31.96 325	62.04 272	51.95 305	50.99 219	67.57 246	75.91 256	28.59 334	69.07 253	42.77 251	81.40 226	69.70 279

tfpnconf		58.38 260	58.88 251	56.91 283	68.66 262	31.96 325	62.04 272	51.95 305	50.99 219	67.57 246	75.91 256	28.59 334	69.07 253	42.77 251	81.40 226	69.70 279

tfpnview11		58.38 260	58.88 251	56.91 283	68.66 262	31.96 325	62.04 272	51.95 305	50.99 219	67.57 246	75.91 256	28.59 334	69.07 253	42.77 251	81.40 226	69.70 279

tfpn1000		58.28 264	58.86 257	56.53 287	68.05 275	32.26 322	62.58 269	51.67 312	51.25 217	67.38 252	75.95 255	27.24 341	68.83 259	43.51 248	82.11 213	68.49 292

1314		59.83 249	58.86 257	62.74 242	65.71 293	44.78 233	68.59 206	72.63 198	33.54 330	61.05 286	67.29 329	43.62 253	71.26 240	49.49 213	67.84 319	72.19 257

Test_1112_low_res		58.78 258	58.69 259	59.04 272	79.41 121	38.13 278	57.62 301	66.98 234	34.74 319	59.62 298	77.56 244	42.92 257	63.65 289	38.66 283	70.73 303	75.35 234

EPNet_dtu		58.93 257	58.52 260	60.16 267	67.91 277	47.70 207	69.97 188	58.02 268	49.73 232	47.28 339	73.02 292	38.14 280	62.34 294	36.57 299	85.99 165	70.43 272
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CR-MVSNet		58.96 256	58.49 261	60.36 265	66.37 287	48.24 195	70.93 180	56.40 283	32.87 331	61.35 282	86.66 129	33.19 296	63.22 290	48.50 221	70.17 306	69.62 284

CVMVSNet		59.21 255	58.44 262	61.51 252	73.94 201	47.76 206	71.31 175	64.56 244	26.91 349	60.34 291	70.44 309	36.24 287	67.65 268	53.57 191	68.66 316	69.12 289

Patchmatch-test1		57.81 267	58.04 263	57.13 281	70.17 252	41.07 256	65.19 251	53.38 299	43.34 282	61.00 287	71.94 302	45.20 243	62.69 292	41.81 267	70.31 305	67.63 296

PatchMatch-RL		58.68 259	57.72 264	61.57 251	76.21 163	73.59 39	61.83 278	49.00 320	47.30 253	61.08 284	68.97 319	50.16 227	59.01 303	36.06 302	68.84 314	52.10 340

HY-MVS	49.31 19	57.96 266	57.59 265	59.10 271	66.85 285	36.17 291	65.13 252	65.39 241	39.24 297	54.69 320	78.14 241	44.28 249	67.18 273	33.75 313	70.79 302	73.95 241

test20.03		55.74 277	57.51 266	50.42 301	59.89 322	32.09 323	50.63 319	49.01 319	50.11 230	65.07 265	83.23 187	45.61 242	48.11 321	30.22 324	83.82 199	71.07 269

XXY-MVS		55.19 279	57.40 267	48.56 311	64.45 299	34.84 305	51.54 318	53.59 297	38.99 298	63.79 271	79.43 229	56.59 201	45.57 326	36.92 297	71.29 299	65.25 310

tfpn_ndepth		56.91 271	57.30 268	55.71 289	67.22 283	33.26 317	61.72 280	53.98 294	48.49 241	64.16 268	71.94 302	27.65 340	68.71 260	40.49 274	80.08 245	65.17 311

thres200		57.55 269	57.02 269	59.17 270	67.89 278	34.93 303	58.91 297	57.25 276	50.24 229	64.01 269	71.46 307	32.49 300	71.39 239	31.31 319	79.57 253	71.19 267

IB-MVS	49.67 18	59.69 250	56.96 270	67.90 198	68.19 273	50.30 174	61.42 283	65.18 242	47.57 251	55.83 314	67.15 330	23.77 349	79.60 143	43.56 247	79.97 247	73.79 243
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
testgi		54.00 286	56.86 271	45.45 319	58.20 333	25.81 345	49.05 322	49.50 318	45.43 264	67.84 242	81.17 211	51.81 221	43.20 339	29.30 329	79.41 254	67.34 299

gg-mvs-nofinetune		55.75 276	56.75 272	52.72 297	62.87 304	28.04 339	68.92 199	41.36 346	71.09 31	50.80 330	92.63 12	20.74 352	66.86 275	29.97 326	72.41 294	63.25 317

our_test_3		56.46 272	56.51 273	56.30 288	67.70 279	39.66 266	55.36 309	52.34 304	40.57 294	63.85 270	69.91 314	40.04 272	58.22 305	43.49 249	75.29 283	71.03 270

PatchT		53.35 288	56.47 274	43.99 326	64.19 300	17.46 353	59.15 294	43.10 336	52.11 206	54.74 319	86.95 116	29.97 327	49.98 318	43.62 246	74.40 286	64.53 316

CHOSEN 1792x2688		58.09 265	56.30 275	63.45 233	79.95 115	50.93 171	54.07 312	65.59 239	28.56 345	61.53 281	74.33 279	41.09 266	66.52 280	33.91 312	67.69 320	72.92 248

CostFormer		57.35 270	56.14 276	60.97 258	63.76 302	38.43 274	67.50 218	60.22 261	37.14 308	59.12 299	76.34 252	32.78 298	71.99 234	39.12 279	69.27 312	72.47 253

MIMVSNet		54.39 282	56.12 277	49.20 306	72.57 234	30.91 332	59.98 291	48.43 322	41.66 287	55.94 313	83.86 176	41.19 265	50.42 315	26.05 335	75.38 281	66.27 305

Anonymous20231206		54.13 283	55.82 278	49.04 309	70.89 244	35.96 293	51.73 317	50.87 314	34.86 317	62.49 276	79.22 233	42.52 259	44.29 335	27.95 333	81.88 217	66.88 301

new-patchmatchnet		52.89 292	55.76 279	44.26 325	59.94 321	6.31 359	37.36 349	50.76 315	41.10 288	64.28 267	79.82 224	44.77 245	48.43 320	36.24 300	87.61 141	78.03 214

no-one		56.11 274	55.62 280	57.60 279	62.68 305	49.23 183	39.12 345	58.99 266	33.72 325	60.98 288	80.90 212	36.07 288	60.36 299	30.68 321	97.40 1	63.22 318

FMVSNet5		55.08 280	55.54 281	53.71 294	65.80 292	33.50 316	56.22 304	52.50 303	43.72 277	61.06 285	83.38 180	25.46 346	54.87 309	30.11 325	81.64 224	72.75 250

tpmp4_e23		57.57 268	55.46 282	63.93 228	66.48 286	41.56 253	71.68 165	60.65 260	35.64 315	55.35 317	76.25 253	29.53 330	75.41 198	34.40 309	69.12 313	74.83 236

tpmvs		55.84 275	55.45 283	57.01 282	60.33 319	33.20 318	65.89 241	59.29 265	47.52 252	56.04 312	73.60 286	31.05 319	68.06 265	40.64 273	64.64 325	69.77 278

MS-PatchMatch		55.59 278	54.89 284	57.68 278	69.18 260	49.05 184	61.00 287	62.93 249	35.98 312	58.36 302	68.93 320	36.71 286	66.59 279	37.62 293	63.30 328	57.39 333

tpm2		56.12 273	54.64 285	60.55 262	66.24 290	36.01 292	68.14 213	56.77 282	33.60 329	58.25 303	75.52 266	30.25 324	74.33 209	33.27 314	69.76 311	71.32 263

testmv		52.91 291	54.31 286	48.71 310	72.13 240	36.18 290	50.26 320	47.78 323	44.15 272	64.61 266	79.78 225	38.18 279	50.20 317	21.96 346	69.93 308	59.75 330

PatchmatchNet		54.60 281	54.27 287	55.59 290	65.17 297	39.08 269	66.92 227	51.80 311	39.89 295	58.39 301	73.12 291	31.69 308	58.33 304	43.01 250	58.38 343	69.38 287
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MDTV_nov1_ep13			54.05 288		65.54 294	29.30 335	59.00 296	55.22 285	35.96 313	52.44 326	75.98 254	30.77 321	59.62 301	38.21 287	73.33 291

YYNet1		52.58 293	53.50 289	49.85 302	54.15 351	36.45 289	40.53 340	46.55 328	38.09 303	75.52 167	73.31 289	41.08 267	43.88 336	41.10 270	71.14 301	69.21 288

MDA-MVSNet_test_wron		52.57 294	53.49 290	49.81 303	54.24 350	36.47 288	40.48 341	46.58 327	38.13 302	75.47 168	73.32 288	41.05 268	43.85 337	40.98 271	71.20 300	69.10 290

UnsupCasMVSNet_eth		52.26 296	53.29 291	49.16 307	55.08 347	33.67 311	50.03 321	58.79 267	37.67 305	63.43 275	74.75 273	41.82 262	45.83 325	38.59 285	59.42 337	67.98 295

PatchFormer-LS_test		53.94 287	52.64 292	57.85 277	61.87 310	39.59 267	61.60 281	57.63 270	40.65 292	54.52 321	58.64 344	29.07 333	64.18 286	46.78 235	62.98 330	69.78 277

tpm cat1		54.02 285	52.63 293	58.19 276	64.85 298	39.86 265	66.26 234	57.28 275	32.16 333	56.90 308	70.39 311	32.75 299	65.30 283	34.29 310	58.79 339	69.41 286

pmmvs5		52.49 295	52.58 294	52.21 299	54.99 348	32.38 321	55.45 308	53.84 296	32.15 334	55.49 316	74.81 271	38.08 281	57.37 307	34.02 311	74.40 286	66.88 301

tpm		50.60 299	52.42 295	45.14 321	65.18 296	26.29 343	60.30 289	43.50 335	37.41 306	57.01 306	79.09 237	30.20 326	42.32 341	32.77 316	66.36 322	66.81 303

LP		53.02 290	52.27 296	55.27 291	55.76 345	40.55 260	55.64 307	55.07 286	42.46 283	56.95 307	73.21 290	33.67 294	54.18 313	38.41 286	59.29 338	71.08 268

JIA-IIPM		54.03 284	51.62 297	61.25 256	59.14 327	55.21 149	59.10 295	47.72 324	50.85 226	50.31 334	85.81 153	20.10 354	63.97 287	36.16 301	55.41 348	64.55 315

tpmrst		50.15 301	51.38 298	46.45 316	56.05 341	24.77 347	64.40 258	49.98 316	36.14 311	53.32 325	69.59 316	35.16 290	48.69 319	39.24 278	58.51 342	65.89 306

PVSNet	43.83 21	51.56 298	51.17 299	52.73 296	68.34 271	38.27 276	48.22 325	53.56 298	36.41 310	54.29 322	64.94 333	34.60 291	54.20 312	30.34 323	69.87 309	65.71 308

DWT-MVSNet_test		53.04 289	51.12 300	58.77 273	61.23 313	38.67 273	62.16 271	57.74 269	38.24 301	51.76 328	59.07 343	21.36 351	67.40 271	44.80 241	63.76 327	70.25 274

N_pmnet		52.06 297	51.11 301	54.92 292	59.64 324	71.03 49	37.42 348	61.62 257	33.68 326	57.12 305	72.10 294	37.94 282	31.03 354	29.13 332	71.35 298	62.70 320

UnsupCasMVSNet_bld		50.01 302	51.03 302	46.95 312	58.61 330	32.64 320	48.31 324	53.27 300	34.27 322	60.47 290	71.53 306	41.40 263	47.07 323	30.68 321	60.78 333	61.13 325

test-LLR		50.43 300	50.69 303	49.64 304	60.76 316	41.87 250	53.18 314	45.48 333	43.41 280	49.41 335	60.47 341	29.22 331	44.73 332	42.09 264	72.14 295	62.33 323

WTY-MVS		49.39 303	50.31 304	46.62 315	61.22 314	32.00 324	46.61 330	49.77 317	33.87 324	54.12 323	69.55 317	41.96 261	45.40 328	31.28 320	64.42 326	62.47 322

Patchmatch-test		47.93 307	49.96 305	41.84 330	57.42 337	24.26 348	48.75 323	41.49 345	39.30 296	56.79 309	73.48 287	30.48 323	33.87 353	29.29 330	72.61 293	67.39 297

test1235678		48.41 306	49.60 306	44.83 323	68.52 268	33.81 310	46.33 332	45.89 330	38.72 300	58.46 300	72.08 295	29.85 329	47.82 322	19.67 350	66.91 321	52.88 338

testpf		45.32 313	48.47 307	35.88 339	53.56 353	26.84 341	58.86 298	42.95 337	47.78 249	46.18 341	63.70 334	13.73 360	50.29 316	50.81 203	58.61 341	30.51 354

sss		47.59 309	48.32 308	45.40 320	56.73 340	33.96 308	45.17 334	48.51 321	32.11 336	52.37 327	65.79 331	40.39 271	41.91 344	31.85 317	61.97 331	60.35 326

test0.0.03 1		47.72 308	48.31 309	45.93 317	55.53 346	29.39 334	46.40 331	41.21 347	43.41 280	55.81 315	67.65 326	29.22 331	43.77 338	25.73 338	69.87 309	64.62 314

test-mter		48.56 305	48.20 310	49.64 304	60.76 316	41.87 250	53.18 314	45.48 333	31.91 338	49.41 335	60.47 341	18.34 355	44.73 332	42.09 264	72.14 295	62.33 323

1111		45.08 316	47.96 311	36.43 338	59.56 325	14.82 355	43.56 335	45.65 331	45.60 260	60.04 292	75.47 267	9.31 362	34.46 351	23.66 342	68.76 315	60.02 328

MVS-HIRNet		45.53 312	47.29 312	40.24 334	62.29 309	26.82 342	56.02 305	37.41 352	29.74 344	43.69 350	81.27 209	33.96 293	55.48 308	24.46 341	56.79 344	38.43 352

ADS-MVSNet2		48.76 304	47.25 313	53.29 295	55.90 343	40.54 261	47.34 328	54.99 292	31.41 340	50.48 331	72.06 300	31.23 315	54.26 311	25.93 336	55.93 345	65.07 312

EPMVS		45.74 311	46.53 314	43.39 327	54.14 352	22.33 350	55.02 310	35.00 354	34.69 320	51.09 329	70.20 313	25.92 344	42.04 343	37.19 295	55.50 347	65.78 307

testus		45.03 317	46.49 315	40.65 333	62.53 306	25.24 346	42.54 337	46.23 329	31.16 342	57.69 304	62.90 336	34.60 291	42.33 340	17.72 352	63.01 329	54.37 337

ADS-MVSNet		44.62 319	45.58 316	41.73 331	55.90 343	20.83 351	47.34 328	39.94 350	31.41 340	50.48 331	72.06 300	31.23 315	39.31 347	25.93 336	55.93 345	65.07 312

E-PMN		45.17 314	45.36 317	44.60 324	50.07 354	42.75 245	38.66 346	42.29 342	46.39 257	39.55 352	51.15 351	26.00 343	45.37 329	37.68 291	76.41 272	45.69 347

pmmvs3		46.71 310	45.09 318	51.55 300	56.76 339	48.25 194	55.78 306	39.53 351	24.13 352	50.35 333	63.40 335	15.90 359	51.08 314	29.29 330	70.69 304	55.33 336

TESTMET0.1,1		45.17 314	44.93 319	45.89 318	56.02 342	38.31 275	53.18 314	41.94 344	27.85 346	44.86 345	56.47 346	17.93 356	41.50 346	38.08 289	68.06 317	57.85 332

dp		44.09 321	44.88 320	41.72 332	58.53 331	23.18 349	54.70 311	42.38 341	34.80 318	44.25 348	65.61 332	24.48 348	44.80 331	29.77 327	49.42 351	57.18 334

DSMNet-mixed		43.18 322	44.66 321	38.75 336	54.75 349	28.88 337	57.06 303	27.42 358	13.47 354	47.27 340	77.67 243	38.83 276	39.29 348	25.32 340	60.12 335	48.08 343

EMVS		44.61 320	44.45 322	45.10 322	48.91 356	43.00 243	37.92 347	41.10 348	46.75 254	38.00 354	48.43 353	26.42 342	46.27 324	37.11 296	75.38 281	46.03 346

PMMVS		44.69 318	43.95 323	46.92 313	50.05 355	53.47 162	48.08 327	42.40 340	22.36 353	44.01 349	53.05 348	42.60 258	45.49 327	31.69 318	61.36 332	41.79 349

PMMVS2		37.74 327	40.87 324	28.36 343	42.41 358	5.35 360	24.61 352	27.75 357	32.15 334	47.85 338	70.27 312	35.85 289	29.51 355	19.08 351	67.85 318	50.22 342

test12356		38.35 326	40.80 325	31.01 340	58.31 332	9.09 358	36.67 350	46.65 326	33.65 328	44.39 347	60.94 340	17.56 357	39.23 349	16.01 353	53.03 349	44.72 348

PVSNet_0	36.71 22	41.12 324	40.78 326	42.14 328	59.97 320	40.13 263	40.97 339	42.24 343	30.81 343	44.86 345	49.41 352	40.70 269	45.12 330	23.15 344	34.96 353	41.16 350

test2356		40.85 325	40.47 327	41.98 329	58.78 329	28.65 338	39.45 343	40.98 349	31.95 337	48.47 337	56.63 345	12.54 361	44.41 334	15.84 354	59.58 336	52.88 338

.test1245		34.47 332	40.38 328	16.73 344	59.56 325	14.82 355	43.56 335	45.65 331	45.60 260	60.04 292	75.47 267	9.31 362	34.46 351	23.66 342	0.55 358	0.90 357

CHOSEN 280x420		41.62 323	39.89 329	46.80 314	61.81 311	51.59 167	33.56 351	35.74 353	27.48 347	37.64 355	53.53 347	23.24 350	42.09 342	27.39 334	58.64 340	46.72 345

new_pmnet		37.55 328	39.80 330	30.79 341	56.83 338	16.46 354	39.35 344	30.65 356	25.59 350	45.26 343	61.60 339	24.54 347	28.02 356	21.60 347	52.80 350	47.90 344

pcd1.5k->3k		35.00 331	36.93 331	29.21 342	84.62 66	0.00 364	0.00 355	78.90 138	0.00 359	0.00 360	0.00 361	78.26 15	0.00 362	0.00 359	90.55 102	87.62 66

PNet_i23d		36.76 329	36.63 332	37.12 337	58.19 334	33.00 319	39.86 342	32.55 355	48.44 242	39.64 351	51.31 350	6.89 364	41.83 345	22.29 345	30.55 354	36.54 353

MVE	27.91 23	36.69 330	35.64 333	39.84 335	43.37 357	35.85 295	19.49 353	24.61 359	24.68 351	39.05 353	62.63 338	38.67 278	27.10 357	21.04 348	47.25 352	56.56 335
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
cdsmvs_eth3d_5k		17.71 333	23.62 334	0.00 349	0.00 363	0.00 364	0.00 355	70.17 223	0.00 359	0.00 360	74.25 281	68.16 83	0.00 362	0.00 359	0.00 360	0.00 360

tmp_tt		11.98 334	14.73 335	3.72 346	2.28 360	4.62 361	19.44 354	14.50 361	0.47 356	21.55 356	9.58 356	25.78 345	4.57 359	11.61 355	27.37 355	1.96 356

ab-mvs-re		5.62 335	7.50 336	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	67.46 327	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

pcd_1.5k_mvsjas		5.20 336	6.93 337	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	62.39 124	0.00 362	0.00 359	0.00 360	0.00 360

test123		4.43 337	5.78 338	0.39 348	0.97 361	0.28 362	46.33 332	0.45 363	0.31 357	0.62 358	1.50 359	0.61 366	0.11 361	0.56 357	0.63 357	0.77 359

testmvs		4.06 338	5.28 339	0.41 347	0.64 362	0.16 363	42.54 337	0.31 364	0.26 358	0.50 359	1.40 360	0.77 365	0.17 360	0.56 357	0.55 358	0.90 357

sosnet-low-res		0.00 339	0.00 340	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

sosnet		0.00 339	0.00 340	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

uncertanet		0.00 339	0.00 340	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

Regformer		0.00 339	0.00 340	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

uanet		0.00 339	0.00 340	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	0.00 361	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

GSMVS																70.05 275

test_part3							83.39 34		73.27 20		89.25 82		86.96 10	72.56 46

test_part2					85.90 46	66.44 80				84.61 62

test_part1								84.94 30				75.17 31			93.83 49	82.50 146

sam_mvs1												31.41 313				70.05 275

sam_mvs												31.21 317

semantic-postprocess				72.49 142	73.34 215	58.20 140		65.55 240	48.10 244	76.91 148	82.64 192	42.25 260	78.84 152	61.20 134	77.89 268	80.44 187

ambc				70.10 173	77.74 147	50.21 175	74.28 137	77.93 157		79.26 122	88.29 104	54.11 211	79.77 141	64.43 118	91.10 88	80.30 188

MTGPA								80.63 107

test_post1							66.63 230				2.08 357	30.66 322	59.33 302	40.34 276

test_post											1.99 358	30.91 320	54.76 310

patchmatchnet-post											68.99 318	31.32 314	69.38 250

GG-mvs-BLEND				52.24 298	60.64 318	29.21 336	69.73 193	42.41 339		45.47 342	52.33 349	20.43 353	68.16 264	25.52 339	65.42 324	59.36 331

MTMP								19.26 360

gm-plane-assit					62.51 307	33.91 309			37.25 307		62.71 337		72.74 221	38.70 282

test9_res														72.12 54	91.37 81	77.40 219

TEST9					85.47 53	69.32 65	76.42 99	78.69 141	53.73 192	76.97 144	86.74 125	66.84 93	81.10 112

test_8					85.09 58	67.89 74	76.26 104	78.66 143	54.00 187	76.89 149	86.72 127	66.60 95	80.89 123

agg_prior2														70.70 62	90.93 93	78.55 207

agg_prior					84.44 71	66.02 84		78.62 144		76.95 146			80.34 132

TestCases				78.35 60	79.19 127	70.81 51		88.64 2	65.37 66	80.09 114	88.17 105	70.33 65	78.43 164	55.60 172	90.90 95	85.81 84

test_prior4						70.14 58	77.57 83

test_prior2							75.57 116		58.92 129	76.53 156	86.78 122	67.83 87		69.81 70	92.76 61

test_prior				75.27 91	82.15 97	59.85 126		84.33 39					83.39 67			82.58 143

旧先验2							71.17 177		45.11 267	78.54 129			61.28 298	59.19 147

新几何2							71.33 174

新几何1				69.99 175	88.37 31	71.34 47		62.08 252	43.85 273	74.99 172	86.11 148	52.85 215	70.57 244	50.99 202	83.23 205	68.05 294

旧先验1					84.55 68	60.36 122		63.69 246			87.05 115	54.65 209			83.34 203	69.66 283

无先验							74.82 128	70.94 217	47.75 250				76.85 185	54.47 182		72.09 258

原ACMM2							74.78 132

原ACMM1				73.90 103	85.90 46	65.15 92		81.67 81	50.97 225	74.25 182	86.16 146	61.60 132	83.54 63	56.75 160	91.08 89	73.00 247

test222					87.30 35	69.15 68	67.85 215	59.59 264	41.06 289	73.05 195	85.72 154	48.03 236			80.65 240	66.92 300

testdata2													67.30 272	48.34 222

segment_acmp												68.30 82

testdata				64.13 225	85.87 49	63.34 104		61.80 256	47.83 248	76.42 160	86.60 134	48.83 232	62.31 295	54.46 184	81.26 232	66.74 304

testdata1							68.34 212		57.24 142

test12				76.51 73	82.28 95	60.94 119		81.64 82		73.60 188		64.88 108	85.19 41		90.42 104	83.38 128

plane_prior7					85.18 55	66.21 83

plane_prior6					84.18 75	65.31 89						60.83 142

plane_prior5								85.49 21					86.15 22	71.09 56	90.94 91	84.82 95

plane_prior4											89.11 88

plane_prior3						65.67 86			63.82 86	78.23 134

plane_prior2							82.74 38		65.45 63

plane_prior1					84.46 70

plane_prior						65.18 90	80.06 61		61.88 104						89.91 114

n2								0.00 365

nn								0.00 365

door-mid								55.02 287

lessismore_v0				72.75 137	79.60 119	56.83 144		57.37 274		83.80 71	89.01 91	47.45 238	78.74 155	64.39 119	86.49 161	82.69 142

LGP-MVS_train				80.90 32	87.00 37	70.41 56		86.35 12	69.77 40	87.75 18	91.13 37	81.83 3	86.20 19	77.13 27	95.96 7	86.08 78

test11								82.71 68

door								52.91 302

HQP5-MVS						58.80 135

HQP-NCC					82.37 92		77.32 87		59.08 125	71.58 210

ACMP_Plane					82.37 92		77.32 87		59.08 125	71.58 210

BP-MVS														67.38 95

HQP4-MVS										71.59 209			85.31 35			83.74 120

HQP3-MVS								84.12 46							89.16 121

HQP2-MVS												58.09 177

NP-MVS					83.34 82	63.07 107					85.97 151

MDTV_nov1_ep13_2view						18.41 352	53.74 313		31.57 339	44.89 344		29.90 328		32.93 315		71.48 262

ACMMP++_ref															89.47 119

ACMMP++															91.96 71

Test By Simon												62.56 120

ITE_SJBPF				80.35 38	76.94 156	73.60 38		80.48 111	66.87 50	83.64 73	86.18 144	70.25 67	79.90 140	61.12 135	88.95 125	87.56 68

DeepMVS_CX				11.83 345	15.51 359	13.86 357		11.25 362	5.76 355	20.85 357	26.46 354	17.06 358	9.22 358	9.69 356	13.82 356	12.42 355

High-resolution multi-view benchmark