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		99.86 1	99.86 1	99.87 1	99.99 1	99.77 1	99.77 1	99.80 1	99.97 1	99.97 1	99.95 1	99.74 1	99.98 1	99.56 1	100.00 1	99.85 4

pmmvs6		99.07 4	99.24 4	98.56 45	99.81 3	96.38 57	98.87 9	99.30 9	99.01 15	99.63 9	99.66 4	99.27 2	99.68 103	97.75 41	99.89 33	99.62 31

Anonymous20231211		99.29 2	99.41 2	98.91 22	99.94 2	97.08 37	99.47 3	99.51 5	99.56 2	99.83 3	99.80 2	99.13 3	99.90 13	97.55 49	99.93 21	99.75 13

pcd1.5k->3k		41.47 331	44.19 332	33.29 344	99.65 11	0.00 362	0.00 353	99.07 34	0.00 357	0.00 358	0.00 359	99.04 4	0.00 360	0.00 357	99.96 11	99.87 2

XVG-OURS-SEG-HR		97.38 96	97.07 116	98.30 64	99.01 89	97.41 31	94.66 235	99.02 51	95.20 149	98.15 98	97.52 178	98.83 5	98.43 318	94.87 140	96.41 312	99.07 152

ACMH	93.61 9	98.44 25	98.76 16	97.51 109	99.43 37	93.54 148	98.23 34	99.05 38	97.40 79	99.37 19	99.08 46	98.79 6	99.47 182	97.74 42	99.71 63	99.50 50
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
mvs_tets		98.90 5	98.94 8	98.75 30	99.69 8	96.48 55	98.54 20	99.22 10	96.23 110	99.71 5	99.48 7	98.77 7	99.93 2	98.89 10	99.95 13	99.84 6

LTVRE_ROB	96.88 1	99.18 3	99.34 3	98.72 35	99.71 7	96.99 40	99.69 2	99.57 3	99.02 14	99.62 10	99.36 16	98.53 8	99.52 163	98.58 24	99.95 13	99.66 23
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
TransMVSNet (Re)		98.38 28	98.67 19	97.51 109	99.51 26	93.39 153	98.20 39	98.87 81	98.23 35	99.48 12	99.27 25	98.47 9	99.55 156	96.52 78	99.53 105	99.60 34

pm-mvs1		98.47 24	98.67 19	97.86 86	99.52 25	94.58 113	98.28 31	99.00 62	97.57 63	99.27 26	99.22 30	98.32 10	99.50 175	97.09 68	99.75 54	99.50 50

jajsoiax		98.77 11	98.79 15	98.74 32	99.66 10	96.48 55	98.45 25	99.12 22	95.83 125	99.67 7	99.37 14	98.25 11	99.92 4	98.77 14	99.94 19	99.82 7

ACMH+	93.58 10	98.23 35	98.31 37	97.98 81	99.39 42	95.22 93	97.55 81	99.20 13	98.21 36	99.25 27	98.51 82	98.21 12	99.40 210	94.79 145	99.72 59	99.32 107

HPM-MVS_fast		98.32 30	98.13 44	98.88 23	99.54 23	97.48 27	98.35 28	99.03 50	95.88 122	97.88 132	98.22 109	98.15 13	99.74 59	96.50 80	99.62 79	99.42 86

wuyk23d		93.25 254	95.20 186	87.40 335	96.07 306	95.38 86	97.04 107	94.97 287	95.33 142	99.70 6	98.11 124	98.14 14	91.94 352	77.76 341	99.68 71	74.89 352

wuykxyi23d		98.68 17	98.53 26	99.13 3	99.44 34	97.97 7	96.85 117	99.02 51	95.81 126	99.88 2	99.38 13	98.14 14	99.69 97	98.32 28	99.95 13	99.73 16

ACMM	93.33 11	98.05 42	97.79 59	98.85 24	99.15 67	97.55 23	96.68 124	98.83 95	95.21 148	98.36 77	98.13 121	98.13 16	99.62 128	96.04 92	99.54 103	99.39 94
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
HPM-MVS		98.11 40	97.83 58	98.92 19	99.42 39	97.46 28	98.57 17	99.05 38	95.43 140	97.41 156	97.50 180	97.98 17	99.79 38	95.58 114	99.57 95	99.50 50
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
testgi		96.07 165	96.50 149	94.80 249	99.26 51	87.69 268	95.96 161	98.58 143	95.08 159	98.02 114	96.25 252	97.92 18	97.60 340	88.68 270	98.74 223	99.11 145

LPG-MVS_test		97.94 52	97.67 68	98.74 32	99.15 67	97.02 38	97.09 105	99.02 51	95.15 153	98.34 79	98.23 106	97.91 19	99.70 88	94.41 156	99.73 56	99.50 50

LGP-MVS_train				98.74 32	99.15 67	97.02 38		99.02 51	95.15 153	98.34 79	98.23 106	97.91 19	99.70 88	94.41 156	99.73 56	99.50 50

abl_6		98.42 26	98.19 41	99.09 4	99.16 64	98.10 5	97.73 69	99.11 23	97.76 50	98.62 57	98.27 104	97.88 21	99.80 37	95.67 105	99.50 112	99.38 96

SD-MVS		97.37 97	97.70 65	96.35 179	98.14 192	95.13 96	96.54 125	98.92 73	95.94 120	99.19 29	98.08 126	97.74 22	95.06 350	95.24 125	99.54 103	98.87 182

DeepC-MVS	95.41 4	97.82 67	97.70 65	98.16 70	98.78 105	95.72 74	96.23 144	99.02 51	93.92 197	98.62 57	98.99 49	97.69 23	99.62 128	96.18 87	99.87 36	99.15 134
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
nrg030		98.54 21	98.62 23	98.32 61	99.22 56	95.66 78	97.90 56	99.08 30	98.31 32	99.02 37	98.74 65	97.68 24	99.61 134	97.77 40	99.85 39	99.70 19

ANet_high		98.31 31	98.94 8	96.41 177	99.33 47	89.64 214	97.92 55	99.56 4	99.27 5	99.66 8	99.50 6	97.67 25	99.83 30	97.55 49	99.98 3	99.77 9

canonicalmvs		97.23 107	97.21 104	97.30 129	97.65 247	94.39 118	97.84 59	99.05 38	97.42 71	96.68 187	93.85 305	97.63 26	99.33 231	96.29 85	98.47 242	98.18 243

TranMVSNet+NR-MVSNet		98.33 29	98.30 39	98.43 51	99.07 81	95.87 70	96.73 122	99.05 38	98.67 21	98.84 45	98.45 86	97.58 27	99.88 19	96.45 82	99.86 38	99.54 45

cdsmvs_eth3d_5k		24.22 332	32.30 333	0.00 347	0.00 361	0.00 362	0.00 353	98.10 201	0.00 357	0.00 358	95.06 283	97.54 28	0.00 360	0.00 357	0.00 358	0.00 358

ACMP	92.54 13	97.47 91	97.10 113	98.55 46	99.04 85	96.70 48	96.24 143	98.89 77	93.71 207	97.97 119	97.75 159	97.44 29	99.63 122	93.22 188	99.70 66	99.32 107
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test_djsdf		98.73 13	98.74 18	98.69 37	99.63 13	96.30 60	98.67 12	99.02 51	96.50 99	99.32 21	99.44 10	97.43 30	99.92 4	98.73 17	99.95 13	99.86 3

TDRefinement		98.90 5	98.86 11	99.02 8	99.54 23	98.06 6	99.34 5	99.44 7	98.85 19	99.00 40	99.20 31	97.42 31	99.59 144	97.21 62	99.76 50	99.40 91

anonymousdsp		98.72 16	98.63 21	98.99 10	99.62 14	97.29 34	98.65 15	99.19 14	95.62 131	99.35 20	99.37 14	97.38 32	99.90 13	98.59 23	99.91 27	99.77 9

PS-CasMVS		98.73 13	98.85 13	98.39 55	99.55 21	95.47 85	98.49 22	99.13 21	99.22 7	99.22 28	98.96 52	97.35 33	99.92 4	97.79 39	99.93 21	99.79 8

COLMAP_ROB	94.48 6	98.25 34	98.11 45	98.64 40	99.21 59	97.35 32	97.96 52	99.16 16	98.34 31	98.78 48	98.52 81	97.32 34	99.45 191	94.08 168	99.67 73	99.13 137
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
EG-PatchMatch MVS		97.69 76	97.79 59	97.40 124	99.06 82	93.52 150	95.96 161	98.97 69	94.55 177	98.82 46	98.76 63	97.31 35	99.29 238	97.20 64	99.44 131	99.38 96

XXY-MVS		97.54 87	97.70 65	97.07 140	99.46 32	92.21 171	97.22 95	99.00 62	94.93 164	98.58 62	98.92 56	97.31 35	99.41 207	94.44 154	99.43 140	99.59 35

PEN-MVS		98.75 12	98.85 13	98.44 50	99.58 18	95.67 77	98.45 25	99.15 19	99.33 4	99.30 24	99.00 48	97.27 37	99.92 4	97.64 44	99.92 24	99.75 13

DTE-MVSNet		98.79 10	98.86 11	98.59 43	99.55 21	96.12 64	98.48 24	99.10 25	99.36 3	99.29 25	99.06 47	97.27 37	99.93 2	97.71 43	99.91 27	99.70 19

MP-MVS-pluss		97.69 76	97.36 90	98.70 36	99.50 29	96.84 43	95.38 197	98.99 65	92.45 237	98.11 101	98.31 97	97.25 39	99.77 47	96.60 75	99.62 79	99.48 61
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMMP_Plus		97.89 59	97.63 74	98.67 38	99.35 46	96.84 43	96.36 135	98.79 102	95.07 160	97.88 132	98.35 93	97.24 40	99.72 70	96.05 91	99.58 92	99.45 71

Effi-MVS+		96.19 162	96.01 163	96.71 158	97.43 262	92.19 174	96.12 149	99.10 25	95.45 138	93.33 302	94.71 289	97.23 41	99.56 152	93.21 189	97.54 287	98.37 221

PGM-MVS		97.88 60	97.52 83	98.96 13	99.20 60	97.62 18	97.09 105	99.06 36	95.45 138	97.55 145	97.94 142	97.11 42	99.78 39	94.77 147	99.46 126	99.48 61

APD-MVS_3200maxsize		98.13 39	97.90 54	98.79 28	98.79 104	97.31 33	97.55 81	98.92 73	97.72 55	98.25 89	98.13 121	97.10 43	99.75 54	95.44 117	99.24 176	99.32 107

OPM-MVS		97.54 87	97.25 96	98.41 52	99.11 77	96.61 51	95.24 209	98.46 151	94.58 176	98.10 104	98.07 127	97.09 44	99.39 216	95.16 131	99.44 131	99.21 125

HFP-MVS		97.94 52	97.64 72	98.83 25	99.15 67	97.50 25	97.59 78	98.84 87	96.05 113	97.49 149	97.54 175	97.07 45	99.70 88	95.61 111	99.46 126	99.30 111

#test#		97.62 80	97.22 103	98.83 25	99.15 67	97.50 25	96.81 119	98.84 87	94.25 188	97.49 149	97.54 175	97.07 45	99.70 88	94.37 159	99.46 126	99.30 111

SteuartSystems-ACMMP		98.02 44	97.76 62	98.79 28	99.43 37	97.21 36	97.15 96	98.90 75	96.58 98	98.08 107	97.87 149	97.02 47	99.76 48	95.25 124	99.59 89	99.40 91
Skip Steuart: Steuart Systems R&D Blog.
APDe-MVS		98.14 37	98.03 50	98.47 49	98.72 111	96.04 67	98.07 46	99.10 25	95.96 119	98.59 61	98.69 69	96.94 48	99.81 33	96.64 74	99.58 92	99.57 40

LCM-MVSNet-Re		97.33 101	97.33 91	97.32 128	98.13 195	93.79 139	96.99 109	99.65 2	96.74 94	99.47 13	98.93 55	96.91 49	99.84 28	90.11 248	99.06 196	98.32 228

VPA-MVSNet		98.27 32	98.46 29	97.70 95	99.06 82	93.80 138	97.76 64	99.00 62	98.40 29	99.07 35	98.98 50	96.89 50	99.75 54	97.19 65	99.79 47	99.55 44

ACMMP		98.05 42	97.75 63	98.93 18	99.23 55	97.60 19	98.09 45	98.96 70	95.75 128	97.91 127	98.06 130	96.89 50	99.76 48	95.32 122	99.57 95	99.43 84
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
PMVS	89.60 17	96.71 143	96.97 120	95.95 211	99.51 26	97.81 13	97.42 88	97.49 239	97.93 45	95.95 220	98.58 75	96.88 52	96.91 344	89.59 255	99.36 155	93.12 343
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
region2R		97.92 55	97.59 78	98.92 19	99.22 56	97.55 23	97.60 77	98.84 87	96.00 117	97.22 161	97.62 169	96.87 53	99.76 48	95.48 115	99.43 140	99.46 66

CP-MVS		97.92 55	97.56 81	98.99 10	98.99 90	97.82 12	97.93 54	98.96 70	96.11 112	96.89 182	97.45 183	96.85 54	99.78 39	95.19 127	99.63 78	99.38 96

test_0402		97.84 63	97.97 51	97.47 117	99.19 62	94.07 129	96.71 123	98.73 113	98.66 22	98.56 63	98.41 88	96.84 55	99.69 97	94.82 142	99.81 43	98.64 200

v52		98.85 8	99.01 5	98.37 56	99.61 15	95.53 83	99.01 7	99.04 45	98.48 26	99.31 22	99.41 11	96.82 56	99.87 21	99.44 2	99.95 13	99.70 19

V4		98.85 8	99.01 5	98.37 56	99.61 15	95.53 83	99.01 7	99.04 45	98.48 26	99.31 22	99.41 11	96.81 57	99.87 21	99.44 2	99.95 13	99.70 19

ACMMPR		97.95 50	97.62 76	98.94 15	99.20 60	97.56 22	97.59 78	98.83 95	96.05 113	97.46 154	97.63 168	96.77 58	99.76 48	95.61 111	99.46 126	99.49 58

Vis-MVSNet		98.27 32	98.34 35	98.07 74	99.33 47	95.21 95	98.04 48	99.46 6	97.32 82	97.82 141	99.11 43	96.75 59	99.86 23	97.84 36	99.36 155	99.15 134
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
Fast-Effi-MVS+		95.49 183	95.07 190	96.75 156	97.67 246	92.82 160	94.22 250	98.60 140	91.61 250	93.42 299	92.90 316	96.73 60	99.70 88	92.60 195	97.89 265	97.74 266

test_part1								98.84 87				96.69 61			99.44 131	99.37 101

ESAPD		97.22 108	96.82 129	98.40 54	99.03 86	96.07 65	95.64 181	98.84 87	94.84 165	98.08 107	97.60 171	96.69 61	99.76 48	91.22 220	99.44 131	99.37 101

tfpnnormal		97.72 73	97.97 51	96.94 147	99.26 51	92.23 170	97.83 60	98.45 152	98.25 34	99.13 32	98.66 71	96.65 63	99.69 97	93.92 174	99.62 79	98.91 173

DeepPCF-MVS	94.58 5	96.90 125	96.43 150	98.31 63	97.48 256	97.23 35	92.56 304	98.60 140	92.84 231	98.54 64	97.40 186	96.64 64	98.78 294	94.40 158	99.41 149	98.93 170

SMA-MVS		97.55 85	97.19 105	98.61 42	98.83 101	96.71 46	96.74 121	98.81 101	91.81 249	98.78 48	98.36 92	96.63 65	99.68 103	95.17 129	99.59 89	99.45 71

MVS_111021_LR		96.82 134	96.55 143	97.62 101	98.27 165	95.34 88	93.81 272	98.33 171	94.59 175	96.56 191	96.63 232	96.61 66	98.73 298	94.80 144	99.34 160	98.78 191

Gipuma		98.07 41	98.31 37	97.36 126	99.76 5	96.28 61	98.51 21	99.10 25	98.76 20	96.79 184	99.34 20	96.61 66	98.82 290	96.38 83	99.50 112	96.98 289
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MVS_111021_HR		96.73 140	96.54 145	97.27 130	98.35 157	93.66 145	93.42 285	98.36 166	94.74 170	96.58 189	96.76 225	96.54 68	98.99 270	94.87 140	99.27 174	99.15 134

v748		98.58 20	98.89 10	97.67 99	99.61 15	93.53 149	98.59 16	98.90 75	98.97 17	99.43 15	99.15 40	96.53 69	99.85 24	98.88 11	99.91 27	99.64 27

v7n		98.73 13	98.99 7	97.95 82	99.64 12	94.20 126	98.67 12	99.14 20	99.08 9	99.42 16	99.23 29	96.53 69	99.91 12	99.27 4	99.93 21	99.73 16

mPP-MVS		97.91 57	97.53 82	99.04 7	99.22 56	97.87 11	97.74 67	98.78 105	96.04 115	97.10 167	97.73 162	96.53 69	99.78 39	95.16 131	99.50 112	99.46 66

XVS		97.96 48	97.63 74	98.94 15	99.15 67	97.66 16	97.77 62	98.83 95	97.42 71	96.32 205	97.64 167	96.49 72	99.72 70	95.66 107	99.37 152	99.45 71

X-MVStestdata		92.86 257	90.83 291	98.94 15	99.15 67	97.66 16	97.77 62	98.83 95	97.42 71	96.32 205	36.50 354	96.49 72	99.72 70	95.66 107	99.37 152	99.45 71

UA-Net		98.88 7	98.76 16	99.22 2	99.11 77	97.89 10	99.47 3	99.32 8	99.08 9	97.87 137	99.67 3	96.47 74	99.92 4	97.88 34	99.98 3	99.85 4

xiu_mvs_v1_base_debu		95.62 177	95.96 166	94.60 257	98.01 203	88.42 247	93.99 263	98.21 186	92.98 224	95.91 221	94.53 291	96.39 75	99.72 70	95.43 119	98.19 250	95.64 322

xiu_mvs_v1_base		95.62 177	95.96 166	94.60 257	98.01 203	88.42 247	93.99 263	98.21 186	92.98 224	95.91 221	94.53 291	96.39 75	99.72 70	95.43 119	98.19 250	95.64 322

xiu_mvs_v1_base_debi		95.62 177	95.96 166	94.60 257	98.01 203	88.42 247	93.99 263	98.21 186	92.98 224	95.91 221	94.53 291	96.39 75	99.72 70	95.43 119	98.19 250	95.64 322

testing_2		97.43 92	97.71 64	96.60 163	98.91 97	90.85 195	96.01 154	98.54 144	94.78 169	98.78 48	98.96 52	96.35 78	99.54 158	97.25 60	99.82 42	99.40 91

MP-MVS		97.64 79	97.18 106	99.00 9	99.32 49	97.77 14	97.49 84	98.73 113	96.27 107	95.59 233	97.75 159	96.30 79	99.78 39	93.70 180	99.48 122	99.45 71
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
TinyColmap		96.00 168	96.34 153	94.96 243	97.90 214	87.91 263	94.13 258	98.49 149	94.41 181	98.16 96	97.76 156	96.29 80	98.68 305	90.52 240	99.42 143	98.30 231

Fast-Effi-MVS+-dtu		96.44 155	96.12 158	97.39 125	97.18 277	94.39 118	95.46 187	98.73 113	96.03 116	94.72 249	94.92 287	96.28 81	99.69 97	93.81 177	97.98 257	98.09 245

OMC-MVS		96.48 153	96.00 164	97.91 84	98.30 159	96.01 69	94.86 229	98.60 140	91.88 247	97.18 163	97.21 197	96.11 82	99.04 263	90.49 243	99.34 160	98.69 198

xiu_mvs_v2_base		94.22 229	94.63 208	92.99 300	97.32 271	84.84 302	92.12 311	97.84 216	91.96 244	94.17 268	93.43 306	96.07 83	99.71 80	91.27 217	97.48 290	94.42 332

CSCG		97.40 95	97.30 92	97.69 97	98.95 93	94.83 104	97.28 91	98.99 65	96.35 106	98.13 100	95.95 265	95.99 84	99.66 115	94.36 162	99.73 56	98.59 205

PHI-MVS		96.96 120	96.53 146	98.25 68	97.48 256	96.50 54	96.76 120	98.85 84	93.52 210	96.19 214	96.85 216	95.94 85	99.42 196	93.79 178	99.43 140	98.83 186

TSAR-MVS + MP.		97.42 93	97.23 102	98.00 80	99.38 43	95.00 99	97.63 73	98.20 189	93.00 223	98.16 96	98.06 130	95.89 86	99.72 70	95.67 105	99.10 190	99.28 118

XVG-ACMP-BASELINE		97.58 84	97.28 95	98.49 47	99.16 64	96.90 42	96.39 130	98.98 67	95.05 161	98.06 110	98.02 133	95.86 87	99.56 152	94.37 159	99.64 77	99.00 158

AllTest		97.20 109	96.92 124	98.06 75	99.08 79	96.16 62	97.14 98	99.16 16	94.35 185	97.78 142	98.07 127	95.84 88	99.12 253	91.41 214	99.42 143	98.91 173

TestCases				98.06 75	99.08 79	96.16 62		99.16 16	94.35 185	97.78 142	98.07 127	95.84 88	99.12 253	91.41 214	99.42 143	98.91 173

APD-MVS		97.00 112	96.53 146	98.41 52	98.55 136	96.31 59	96.32 138	98.77 106	92.96 229	97.44 155	97.58 174	95.84 88	99.74 59	91.96 202	99.35 158	99.19 127
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
pcd_1.5k_mvsjas		7.98 335	10.65 336	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	0.00 359	95.82 91	0.00 360	0.00 357	0.00 358	0.00 358

PS-MVSNAJss		98.53 22	98.63 21	98.21 69	99.68 9	94.82 105	98.10 44	99.21 11	96.91 87	99.75 4	99.45 9	95.82 91	99.92 4	98.80 13	99.96 11	99.89 1

PS-MVSNAJ		94.10 235	94.47 215	93.00 299	97.35 266	84.88 301	91.86 315	97.84 216	91.96 244	94.17 268	92.50 323	95.82 91	99.71 80	91.27 217	97.48 290	94.40 333

3Dnovator	96.53 2	97.61 81	97.64 72	97.50 112	97.74 238	93.65 146	98.49 22	98.88 79	96.86 91	97.11 166	98.55 79	95.82 91	99.73 64	95.94 98	99.42 143	99.13 137

zzz-MVS		98.01 46	97.66 69	99.06 5	99.44 34	97.90 8	95.66 177	98.73 113	97.69 57	97.90 128	97.96 138	95.81 95	99.82 31	96.13 88	99.61 84	99.45 71

MTAPA		98.14 37	97.84 57	99.06 5	99.44 34	97.90 8	97.25 92	98.73 113	97.69 57	97.90 128	97.96 138	95.81 95	99.82 31	96.13 88	99.61 84	99.45 71

DP-MVS		97.87 61	97.89 55	97.81 89	98.62 127	94.82 105	97.13 99	98.79 102	98.98 16	98.74 52	98.49 83	95.80 97	99.49 177	95.04 138	99.44 131	99.11 145

LS3D		97.77 70	97.50 85	98.57 44	96.24 299	97.58 21	98.45 25	98.85 84	98.58 24	97.51 147	97.94 142	95.74 98	99.63 122	95.19 127	98.97 201	98.51 211

CNVR-MVS		96.92 123	96.55 143	98.03 79	98.00 206	95.54 81	94.87 228	98.17 194	94.60 173	96.38 199	97.05 204	95.67 99	99.36 225	95.12 135	99.08 192	99.19 127

CLD-MVS		95.47 186	95.07 190	96.69 160	98.27 165	92.53 164	91.36 322	98.67 128	91.22 254	95.78 227	94.12 303	95.65 100	98.98 272	90.81 230	99.72 59	98.57 206
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Regformer-2		97.41 94	97.24 98	97.93 83	97.21 275	94.72 108	94.85 230	98.27 181	97.74 51	98.11 101	97.50 180	95.58 101	99.69 97	96.57 77	99.31 167	99.37 101

ITE_SJBPF				97.85 87	98.64 122	96.66 49		98.51 148	95.63 130	97.22 161	97.30 194	95.52 102	98.55 313	90.97 225	98.90 208	98.34 227

Regformer-4		97.53 89	97.47 87	97.71 94	97.35 266	93.91 134	95.26 207	98.14 198	97.97 44	98.34 79	97.89 147	95.49 103	99.71 80	97.41 57	99.42 143	99.51 49

DeepC-MVS_fast	94.34 7	96.74 138	96.51 148	97.44 121	97.69 242	94.15 127	96.02 153	98.43 156	93.17 219	97.30 159	97.38 191	95.48 104	99.28 239	93.74 179	99.34 160	98.88 180
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
WR-MVS_H		98.65 18	98.62 23	98.75 30	99.51 26	96.61 51	98.55 19	99.17 15	99.05 12	99.17 31	98.79 60	95.47 105	99.89 17	97.95 32	99.91 27	99.75 13

FMVSNet1		97.95 50	98.08 46	97.56 104	99.14 75	93.67 142	98.23 34	98.66 130	97.41 78	99.00 40	99.19 32	95.47 105	99.73 64	95.83 101	99.76 50	99.30 111

MIMVSNet1		98.51 23	98.45 31	98.67 38	99.72 6	96.71 46	98.76 10	98.89 77	98.49 25	99.38 18	99.14 41	95.44 107	99.84 28	96.47 81	99.80 46	99.47 64

CP-MVSNet		98.42 26	98.46 29	98.30 64	99.46 32	95.22 93	98.27 33	98.84 87	99.05 12	99.01 38	98.65 73	95.37 108	99.90 13	97.57 48	99.91 27	99.77 9

Regformer-1		97.27 104	97.16 108	97.61 102	97.21 275	93.86 136	94.85 230	98.04 209	97.62 61	98.03 113	97.50 180	95.34 109	99.63 122	96.52 78	99.31 167	99.35 105

segment_acmp												95.34 109

CDPH-MVS		95.45 189	94.65 207	97.84 88	98.28 163	94.96 101	93.73 274	98.33 171	85.03 314	95.44 234	96.60 233	95.31 111	99.44 194	90.01 250	99.13 186	99.11 145

3Dnovator+	96.13 3	97.73 72	97.59 78	98.15 71	98.11 196	95.60 79	98.04 48	98.70 122	98.13 38	96.93 180	98.45 86	95.30 112	99.62 128	95.64 109	98.96 202	99.24 123

MVS_Test		96.27 158	96.79 133	94.73 252	96.94 285	86.63 285	96.18 146	98.33 171	94.94 162	96.07 217	98.28 102	95.25 113	99.26 242	97.21 62	97.90 264	98.30 231

XVG-OURS		97.12 110	96.74 134	98.26 66	98.99 90	97.45 29	93.82 271	99.05 38	95.19 150	98.32 82	97.70 165	95.22 114	98.41 319	94.27 164	98.13 253	98.93 170

MCST-MVS		96.24 159	95.80 171	97.56 104	98.75 107	94.13 128	94.66 235	98.17 194	90.17 264	96.21 213	96.10 259	95.14 115	99.43 195	94.13 167	98.85 217	99.13 137

EI-MVSNet-Vis-set		97.32 102	97.39 89	97.11 137	97.36 265	92.08 177	95.34 200	97.65 231	97.74 51	98.29 87	98.11 124	95.05 116	99.68 103	97.50 53	99.50 112	99.56 41

Regformer-3		97.25 106	97.29 93	97.11 137	97.35 266	92.32 168	95.26 207	97.62 236	97.67 59	98.17 95	97.89 147	95.05 116	99.56 152	97.16 66	99.42 143	99.46 66

EI-MVSNet-UG-set		97.32 102	97.40 88	97.09 139	97.34 269	92.01 179	95.33 201	97.65 231	97.74 51	98.30 86	98.14 120	95.04 118	99.69 97	97.55 49	99.52 109	99.58 36

DELS-MVS		96.17 163	96.23 156	95.99 207	97.55 254	90.04 207	92.38 308	98.52 146	94.13 193	96.55 194	97.06 203	94.99 119	99.58 146	95.62 110	99.28 172	98.37 221
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
ab-mvs		96.59 148	96.59 139	96.60 163	98.64 122	92.21 171	98.35 28	97.67 227	94.45 178	96.99 172	98.79 60	94.96 120	99.49 177	90.39 245	99.07 194	98.08 246

MSLP-MVS++		96.42 157	96.71 135	95.57 224	97.82 222	90.56 203	95.71 172	98.84 87	94.72 171	96.71 186	97.39 189	94.91 121	98.10 334	95.28 123	99.02 198	98.05 251

v13		98.02 44	98.52 27	96.51 170	99.02 88	90.14 205	98.07 46	99.09 29	98.10 40	99.13 32	99.35 18	94.84 122	99.74 59	99.12 5	99.98 3	99.65 24

QAPM		95.88 172	95.57 177	96.80 153	97.90 214	91.84 183	98.18 41	98.73 113	88.41 277	96.42 197	98.13 121	94.73 123	99.75 54	88.72 268	98.94 206	98.81 187

RPSCF		97.87 61	97.51 84	98.95 14	99.15 67	98.43 3	97.56 80	99.06 36	96.19 111	98.48 69	98.70 68	94.72 124	99.24 244	94.37 159	99.33 165	99.17 130

v12		97.97 47	98.47 28	96.46 174	98.98 92	90.01 209	97.97 51	99.08 30	98.00 43	99.11 34	99.34 20	94.70 125	99.73 64	99.07 6	99.98 3	99.64 27

DU-MVS		97.79 69	97.60 77	98.36 59	98.73 109	95.78 72	95.65 179	98.87 81	97.57 63	98.31 84	97.83 150	94.69 126	99.85 24	97.02 70	99.71 63	99.46 66

Baseline_NR-MVSNet		97.72 73	97.79 59	97.50 112	99.56 19	93.29 154	95.44 188	98.86 83	98.20 37	98.37 76	99.24 27	94.69 126	99.55 156	95.98 97	99.79 47	99.65 24

TEST9					97.84 220	95.23 90	93.62 278	98.39 162	86.81 295	93.78 281	95.99 260	94.68 128	99.52 163

UniMVSNet (Re)		97.83 64	97.65 70	98.35 60	98.80 103	95.86 71	95.92 165	99.04 45	97.51 68	98.22 91	97.81 154	94.68 128	99.78 39	97.14 67	99.75 54	99.41 88

agg_prior1		95.39 191	94.60 210	97.75 92	97.80 227	94.96 101	93.39 286	98.36 166	87.20 291	93.49 294	95.97 263	94.65 130	99.53 160	91.69 212	98.86 215	98.77 192

UniMVSNet_NR-MVSNet		97.83 64	97.65 70	98.37 56	98.72 111	95.78 72	95.66 177	99.02 51	98.11 39	98.31 84	97.69 166	94.65 130	99.85 24	97.02 70	99.71 63	99.48 61

VPNet		97.26 105	97.49 86	96.59 165	99.47 31	90.58 201	96.27 139	98.53 145	97.77 49	98.46 71	98.41 88	94.59 132	99.68 103	94.61 150	99.29 171	99.52 48

train_agg		95.46 187	94.66 206	97.88 85	97.84 220	95.23 90	93.62 278	98.39 162	87.04 293	93.78 281	95.99 260	94.58 133	99.52 163	91.76 209	98.90 208	98.89 176

test_8					97.81 223	95.07 98	93.54 281	98.38 164	87.04 293	93.71 285	95.96 264	94.58 133	99.52 163

API-MVS		95.09 204	95.01 193	95.31 232	96.61 291	94.02 131	96.83 118	97.18 251	95.60 132	95.79 226	94.33 300	94.54 135	98.37 325	85.70 303	98.52 238	93.52 340

V9		97.90 58	98.40 32	96.40 178	98.93 94	89.86 211	97.86 58	99.07 34	97.88 47	99.05 36	99.30 23	94.53 136	99.72 70	99.01 8	99.98 3	99.63 29

Test By Simon												94.51 137

MSDG		95.33 194	95.13 188	95.94 213	97.40 264	91.85 182	91.02 325	98.37 165	95.30 143	96.31 207	95.99 260	94.51 137	98.38 323	89.59 255	97.65 283	97.60 271

TSAR-MVS + GP.		96.47 154	96.12 158	97.49 115	97.74 238	95.23 90	94.15 256	96.90 261	93.26 213	98.04 112	96.70 228	94.41 139	98.89 282	94.77 147	99.14 184	98.37 221

NR-MVSNet		97.96 48	97.86 56	98.26 66	98.73 109	95.54 81	98.14 42	98.73 113	97.79 48	99.42 16	97.83 150	94.40 140	99.78 39	95.91 100	99.76 50	99.46 66

V14		97.83 64	98.33 36	96.35 179	98.88 100	89.72 212	97.75 65	99.05 38	97.74 51	99.01 38	99.27 25	94.35 141	99.71 80	98.95 9	99.97 8	99.62 31

AdaColmap		95.11 202	94.62 209	96.58 166	97.33 270	94.45 117	94.92 226	98.08 204	93.15 220	93.98 278	95.53 276	94.34 142	99.10 257	85.69 304	98.61 234	96.20 315

FC-MVSNet-test		98.16 36	98.37 33	97.56 104	99.49 30	93.10 157	98.35 28	99.21 11	98.43 28	98.89 44	98.83 59	94.30 143	99.81 33	97.87 35	99.91 27	99.77 9

v11		97.82 67	98.36 34	96.17 195	98.93 94	89.16 231	97.79 61	99.08 30	97.64 60	99.19 29	99.32 22	94.28 144	99.72 70	99.07 6	99.97 8	99.63 29

Effi-MVS+-dtu		96.81 135	96.09 160	98.99 10	96.90 287	98.69 2	96.42 129	98.09 202	95.86 123	95.15 240	95.54 275	94.26 145	99.81 33	94.06 169	98.51 240	98.47 213

mvs-test1		96.20 161	95.50 179	98.32 61	96.90 287	98.16 4	95.07 218	98.09 202	95.86 123	93.63 288	94.32 301	94.26 145	99.71 80	94.06 169	97.27 299	97.07 286

ambc				96.56 169	98.23 176	91.68 186	97.88 57	98.13 199		98.42 74	98.56 78	94.22 147	99.04 263	94.05 171	99.35 158	98.95 164

v15		97.77 70	98.26 40	96.30 184	98.81 102	89.59 219	97.62 74	99.04 45	97.59 62	98.97 42	99.24 27	94.19 148	99.70 88	98.88 11	99.97 8	99.61 33

test20.03		96.58 149	96.61 138	96.48 173	98.49 145	91.72 185	95.68 176	97.69 226	96.81 92	98.27 88	97.92 145	94.18 149	98.71 300	90.78 232	99.66 75	99.00 158

agg_prior3		95.30 196	94.46 218	97.80 90	97.80 227	95.00 99	93.63 277	98.34 170	86.33 299	93.40 301	95.84 267	94.15 150	99.50 175	91.76 209	98.90 208	98.89 176

HPM-MVS++		96.99 113	96.38 151	98.81 27	98.64 122	97.59 20	95.97 157	98.20 189	95.51 136	95.06 241	96.53 237	94.10 151	99.70 88	94.29 163	99.15 183	99.13 137

v17		97.70 75	98.17 42	96.28 187	98.77 106	89.59 219	97.62 74	99.01 60	97.54 65	98.72 54	99.18 35	94.06 152	99.68 103	98.74 16	99.92 24	99.58 36

v16		97.69 76	98.16 43	96.29 186	98.75 107	89.60 217	97.62 74	99.01 60	97.53 67	98.69 56	99.18 35	94.05 153	99.68 103	98.73 17	99.88 34	99.58 36

PM-MVS		97.36 100	97.10 113	98.14 72	98.91 97	96.77 45	96.20 145	98.63 137	93.82 204	98.54 64	98.33 95	93.98 154	99.05 262	95.99 96	99.45 130	98.61 204

OpenMVS	94.22 8	95.48 185	95.20 186	96.32 182	97.16 278	91.96 180	97.74 67	98.84 87	87.26 289	94.36 265	98.01 134	93.95 155	99.67 110	90.70 236	98.75 222	97.35 283

v18		97.60 82	98.06 48	96.23 188	98.68 121	89.46 222	97.48 85	98.98 67	97.33 81	98.60 60	99.13 42	93.86 156	99.67 110	98.62 21	99.87 36	99.56 41

v8		97.60 82	98.06 48	96.23 188	98.71 114	89.44 223	97.43 87	98.82 99	97.29 83	98.74 52	99.10 44	93.86 156	99.68 103	98.61 22	99.94 19	99.56 41

v6		96.97 117	97.24 98	96.15 196	98.71 114	89.44 223	95.97 157	98.33 171	95.25 145	97.89 130	98.15 117	93.86 156	99.61 134	97.51 52	99.50 112	99.42 86

v1neww		96.97 117	97.24 98	96.15 196	98.70 116	89.44 223	95.97 157	98.33 171	95.25 145	97.88 132	98.15 117	93.83 159	99.61 134	97.50 53	99.50 112	99.41 88

v7new		96.97 117	97.24 98	96.15 196	98.70 116	89.44 223	95.97 157	98.33 171	95.25 145	97.88 132	98.15 117	93.83 159	99.61 134	97.50 53	99.50 112	99.41 88

NCCC		96.52 151	95.99 165	98.10 73	97.81 223	95.68 76	95.00 224	98.20 189	95.39 141	95.40 236	96.36 248	93.81 161	99.45 191	93.55 183	98.42 243	99.17 130

TAPA-MVS	93.32 12	94.93 209	94.23 223	97.04 142	98.18 185	94.51 114	95.22 210	98.73 113	81.22 331	96.25 211	95.95 265	93.80 162	98.98 272	89.89 251	98.87 213	97.62 269
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
FIs		97.93 54	98.07 47	97.48 116	99.38 43	92.95 159	98.03 50	99.11 23	98.04 42	98.62 57	98.66 71	93.75 163	99.78 39	97.23 61	99.84 40	99.73 16

OurMVSNet-221017-0		98.61 19	98.61 25	98.63 41	99.77 4	96.35 58	99.17 6	99.05 38	98.05 41	99.61 11	99.52 5	93.72 164	99.88 19	98.72 20	99.88 34	99.65 24

test_prior3		95.91 170	95.39 182	97.46 118	97.79 232	94.26 124	93.33 289	98.42 159	94.21 190	94.02 275	96.25 252	93.64 165	99.34 228	91.90 203	98.96 202	98.79 189

test_prior2							93.33 289		94.21 190	94.02 275	96.25 252	93.64 165		91.90 203	98.96 202

旧先验1					97.80 227	93.87 135		97.75 221			97.04 205	93.57 167			98.68 229	98.72 196

v10		97.55 85	97.97 51	96.31 183	98.60 129	89.64 214	97.44 86	99.02 51	96.60 96	98.72 54	99.16 39	93.48 168	99.72 70	98.76 15	99.92 24	99.58 36

v7		96.93 121	97.17 107	96.23 188	98.59 131	89.64 214	95.96 161	98.66 130	94.41 181	97.87 137	98.38 91	93.47 169	99.64 119	97.93 33	99.24 176	99.43 84

v148		96.58 149	96.97 120	95.42 229	98.63 126	87.57 269	95.09 215	97.90 212	95.91 121	98.24 90	97.96 138	93.42 170	99.39 216	96.04 92	99.52 109	99.29 117

v1		96.86 127	97.14 110	96.04 203	98.55 136	89.06 234	95.44 188	98.33 171	95.14 155	97.94 122	98.18 115	93.39 171	99.61 134	97.61 45	99.69 67	99.44 80

v1141		96.86 127	97.14 110	96.04 203	98.55 136	89.06 234	95.44 188	98.33 171	95.14 155	97.93 125	98.19 111	93.36 172	99.62 128	97.61 45	99.69 67	99.44 80

divwei89l23v2f112		96.86 127	97.14 110	96.04 203	98.54 139	89.06 234	95.44 188	98.33 171	95.14 155	97.93 125	98.19 111	93.36 172	99.61 134	97.61 45	99.68 71	99.44 80

V42		97.04 111	97.16 108	96.68 161	98.59 131	91.05 192	96.33 137	98.36 166	94.60 173	97.99 115	98.30 100	93.32 174	99.62 128	97.40 58	99.53 105	99.38 96

new-patchmatchnet		95.67 176	96.58 140	92.94 301	97.48 256	80.21 328	92.96 295	98.19 193	94.83 167	98.82 46	98.79 60	93.31 175	99.51 173	95.83 101	99.04 197	99.12 142

test12				97.46 118	97.61 250	94.07 129		97.78 220		93.57 292		93.31 175	99.42 196		98.78 219	98.89 176

UGNet		96.81 135	96.56 142	97.58 103	96.64 290	93.84 137	97.75 65	97.12 254	96.47 102	93.62 289	98.88 58	93.22 177	99.53 160	95.61 111	99.69 67	99.36 104
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
pmmvs-eth3d		96.49 152	96.18 157	97.42 122	98.25 174	94.29 121	94.77 234	98.07 206	89.81 267	97.97 119	98.33 95	93.11 178	99.08 259	95.46 116	99.84 40	98.89 176

v1144		96.84 130	97.08 115	96.13 200	98.42 153	89.28 229	95.41 195	98.67 128	94.21 190	97.97 119	98.31 97	93.06 179	99.65 116	98.06 30	99.62 79	99.45 71

PVSNet_BlendedMVS		95.02 207	94.93 197	95.27 233	97.79 232	87.40 274	94.14 257	98.68 125	88.94 273	94.51 261	98.01 134	93.04 180	99.30 235	89.77 253	99.49 119	99.11 145

PVSNet_Blended		93.96 239	93.65 236	94.91 244	97.79 232	87.40 274	91.43 321	98.68 125	84.50 318	94.51 261	94.48 294	93.04 180	99.30 235	89.77 253	98.61 234	98.02 256

mvs_anonymous		95.36 193	96.07 162	93.21 294	96.29 298	81.56 323	94.60 237	97.66 229	93.30 212	96.95 179	98.91 57	93.03 182	99.38 221	96.60 75	97.30 298	98.69 198

v1192		96.83 133	97.06 117	96.15 196	98.28 163	89.29 228	95.36 198	98.77 106	93.73 206	98.11 101	98.34 94	93.02 183	99.67 110	98.35 26	99.58 92	99.50 50

F-COLMAP		95.30 196	94.38 220	98.05 78	98.64 122	96.04 67	95.61 185	98.66 130	89.00 272	93.22 303	96.40 247	92.90 184	99.35 227	87.45 292	97.53 288	98.77 192

WR-MVS		96.90 125	96.81 130	97.16 134	98.56 135	92.20 173	94.33 243	98.12 200	97.34 80	98.20 93	97.33 193	92.81 185	99.75 54	94.79 145	99.81 43	99.54 45

v1240		96.74 138	97.02 119	95.91 214	98.18 185	88.52 246	95.39 196	98.88 79	93.15 220	98.46 71	98.40 90	92.80 186	99.71 80	98.45 25	99.49 119	99.49 58

MVE	73.61 22	86.48 324	85.92 324	88.18 333	96.23 301	85.28 295	81.78 352	75.79 355	86.01 301	82.53 352	91.88 328	92.74 187	87.47 355	71.42 350	94.86 327	91.78 346
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DP-MVS Recon		95.55 180	95.13 188	96.80 153	98.51 143	93.99 133	94.60 237	98.69 123	90.20 263	95.78 227	96.21 255	92.73 188	98.98 272	90.58 239	98.86 215	97.42 276

CANet		95.86 173	95.65 175	96.49 172	96.41 297	90.82 197	94.36 242	98.41 161	94.94 162	92.62 314	96.73 226	92.68 189	99.71 80	95.12 135	99.60 87	98.94 166

v1921920		96.72 141	96.96 122	95.99 207	98.21 178	88.79 243	95.42 193	98.79 102	93.22 214	98.19 94	98.26 105	92.68 189	99.70 88	98.34 27	99.55 101	99.49 58

BH-untuned		94.69 217	94.75 205	94.52 262	97.95 213	87.53 270	94.07 260	97.01 257	93.99 195	97.10 167	95.65 271	92.65 191	98.95 277	87.60 289	96.74 307	97.09 285

LF4IMVS		96.07 165	95.63 176	97.36 126	98.19 182	95.55 80	95.44 188	98.82 99	92.29 239	95.70 231	96.55 235	92.63 192	98.69 302	91.75 211	99.33 165	97.85 261

v2v482		96.78 137	97.06 117	95.95 211	98.57 134	88.77 244	95.36 198	98.26 183	95.18 151	97.85 139	98.23 106	92.58 193	99.63 122	97.80 38	99.69 67	99.45 71

EI-MVSNet		96.63 147	96.93 123	95.74 219	97.26 273	88.13 254	95.29 205	97.65 231	96.99 84	97.94 122	98.19 111	92.55 194	99.58 146	96.91 72	99.56 97	99.50 50

IterMVS-LS		96.92 123	97.29 93	95.79 218	98.51 143	88.13 254	95.10 213	98.66 130	96.99 84	98.46 71	98.68 70	92.55 194	99.74 59	96.91 72	99.79 47	99.50 50
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
VDD-MVS		97.37 97	97.25 96	97.74 93	98.69 120	94.50 116	97.04 107	95.61 284	98.59 23	98.51 66	98.72 66	92.54 196	99.58 146	96.02 94	99.49 119	99.12 142

MVS		90.02 301	89.20 308	92.47 307	94.71 326	86.90 281	95.86 166	96.74 266	64.72 352	90.62 328	92.77 318	92.54 196	98.39 321	79.30 334	95.56 324	92.12 345

v144192		96.69 144	96.90 125	96.03 206	98.25 174	88.92 237	95.49 186	98.77 106	93.05 222	98.09 105	98.29 101	92.51 198	99.70 88	98.11 29	99.56 97	99.47 64

testmv		95.51 181	95.33 183	96.05 202	98.23 176	89.51 221	93.50 283	98.63 137	94.25 188	98.22 91	97.73 162	92.51 198	99.47 182	85.22 309	99.72 59	99.17 130

原ACMM1				96.58 166	98.16 189	92.12 175		98.15 197	85.90 304	93.49 294	96.43 244	92.47 200	99.38 221	87.66 282	98.62 233	98.23 237

VNet		96.84 130	96.83 128	96.88 151	98.06 198	92.02 178	96.35 136	97.57 238	97.70 56	97.88 132	97.80 155	92.40 201	99.54 158	94.73 149	98.96 202	99.08 150

114514_t		93.96 239	93.22 244	96.19 193	99.06 82	90.97 194	95.99 155	98.94 72	73.88 350	93.43 298	96.93 212	92.38 202	99.37 224	89.09 262	99.28 172	98.25 236

CPTT-MVS		96.69 144	96.08 161	98.49 47	98.89 99	96.64 50	97.25 92	98.77 106	92.89 230	96.01 219	97.13 200	92.23 203	99.67 110	92.24 200	99.34 160	99.17 130

HSP-MVS		97.37 97	96.85 126	98.92 19	99.26 51	97.70 15	97.66 70	98.23 185	95.65 129	98.51 66	96.46 241	92.15 204	99.81 33	95.14 133	98.58 237	99.26 122

MAR-MVS		94.21 232	93.03 246	97.76 91	96.94 285	97.44 30	96.97 116	97.15 252	87.89 287	92.00 319	92.73 321	92.14 205	99.12 253	83.92 317	97.51 289	96.73 300
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
PVSNet_Blended_VisFu		95.95 169	95.80 171	96.42 176	99.28 50	90.62 200	95.31 203	99.08 30	88.40 278	96.97 178	98.17 116	92.11 206	99.78 39	93.64 181	99.21 178	98.86 183

BH-RMVSNet		94.56 223	94.44 219	94.91 244	97.57 251	87.44 273	93.78 273	96.26 270	93.69 208	96.41 198	96.50 240	92.10 207	99.00 269	85.96 301	97.71 277	98.31 229

新几何1				97.25 133	98.29 160	94.70 110		97.73 223	77.98 343	94.83 248	96.67 230	92.08 208	99.45 191	88.17 277	98.65 231	97.61 270

diffmvs		95.00 208	95.00 194	95.01 242	96.53 293	87.96 262	95.73 170	98.32 180	90.67 259	91.89 321	97.43 184	92.07 209	98.90 279	95.44 117	96.88 302	98.16 244

testdata				95.70 222	98.16 189	90.58 201		97.72 224	80.38 334	95.62 232	97.02 206	92.06 210	98.98 272	89.06 264	98.52 238	97.54 273

YYNet1		94.73 214	94.84 201	94.41 264	97.47 260	85.09 299	90.29 331	95.85 279	92.52 234	97.53 146	97.76 156	91.97 211	99.18 249	93.31 185	96.86 303	98.95 164

Anonymous20231206		95.27 198	95.06 192	95.88 215	98.72 111	89.37 227	95.70 173	97.85 215	88.00 285	96.98 173	97.62 169	91.95 212	99.34 228	89.21 260	99.53 105	98.94 166

MS-PatchMatch		94.83 212	94.91 198	94.57 260	96.81 289	87.10 280	94.23 249	97.34 245	88.74 275	97.14 165	97.11 201	91.94 213	98.23 330	92.99 193	97.92 262	98.37 221

1121		94.26 227	93.26 242	97.27 130	98.26 173	94.73 107	95.86 166	97.71 225	77.96 344	94.53 260	96.71 227	91.93 214	99.40 210	87.71 279	98.64 232	97.69 267

MDA-MVSNet_test_wron		94.73 214	94.83 203	94.42 263	97.48 256	85.15 297	90.28 332	95.87 277	92.52 234	97.48 152	97.76 156	91.92 215	99.17 251	93.32 184	96.80 306	98.94 166

HQP_MVS		96.66 146	96.33 154	97.68 98	98.70 116	94.29 121	96.50 127	98.75 110	96.36 104	96.16 215	96.77 223	91.91 216	99.46 187	92.59 196	99.20 179	99.28 118

plane_prior6					98.38 154	94.37 120						91.91 216

MVS_0304		96.22 160	95.94 169	97.04 142	97.07 281	92.54 163	94.19 252	99.04 45	95.17 152	93.74 284	96.92 213	91.77 218	99.73 64	95.76 103	99.81 43	98.85 185

MVP-Stereo		95.69 174	95.28 184	96.92 148	98.15 191	93.03 158	95.64 181	98.20 189	90.39 261	96.63 188	97.73 162	91.63 219	99.10 257	91.84 207	97.31 297	98.63 202
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
test_normal		95.51 181	95.46 180	95.68 223	97.97 208	89.12 233	93.73 274	95.86 278	91.98 243	97.17 164	96.94 210	91.55 220	99.42 196	95.21 126	98.73 226	98.51 211

no-one		94.84 211	94.76 204	95.09 239	98.29 160	87.49 271	91.82 316	97.49 239	88.21 281	97.84 140	98.75 64	91.51 221	99.27 240	88.96 265	99.99 2	98.52 210

DI_MVS_plusplus_test		95.46 187	95.43 181	95.55 225	98.05 199	88.84 241	94.18 253	95.75 280	91.92 246	97.32 158	96.94 210	91.44 222	99.39 216	94.81 143	98.48 241	98.43 217

PatchMatch-RL		94.61 221	93.81 234	97.02 145	98.19 182	95.72 74	93.66 276	97.23 248	88.17 282	94.94 245	95.62 273	91.43 223	98.57 310	87.36 293	97.68 280	96.76 299

MDA-MVSNet-bldmvs		95.69 174	95.67 174	95.74 219	98.48 147	88.76 245	92.84 296	97.25 247	96.00 117	97.59 144	97.95 141	91.38 224	99.46 187	93.16 190	96.35 313	98.99 161

PAPR		92.22 270	91.27 275	95.07 240	95.73 314	88.81 242	91.97 314	97.87 214	85.80 305	90.91 326	92.73 321	91.16 225	98.33 327	79.48 333	95.76 321	98.08 246

1314		92.38 267	92.30 258	92.64 306	95.42 320	85.15 297	95.86 166	96.97 259	85.40 311	90.62 328	93.06 314	91.12 226	97.80 338	86.74 297	95.49 325	94.97 330

ppachtmachnet_test		94.49 225	94.84 201	93.46 288	96.16 304	82.10 322	90.59 329	97.48 241	90.53 260	97.01 171	97.59 173	91.01 227	99.36 225	93.97 173	99.18 182	98.94 166

PLC	91.02 16	94.05 238	92.90 248	97.51 109	98.00 206	95.12 97	94.25 247	98.25 184	86.17 300	91.48 324	95.25 279	91.01 227	99.19 248	85.02 311	96.69 308	98.22 238
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
test222					98.17 187	93.24 156	92.74 301	97.61 237	75.17 348	94.65 251	96.69 229	90.96 229			98.66 230	97.66 268

USDC		94.56 223	94.57 213	94.55 261	97.78 236	86.43 287	92.75 299	98.65 136	85.96 302	96.91 181	97.93 144	90.82 230	98.74 297	90.71 235	99.59 89	98.47 213

PCF-MVS	89.43 18	92.12 273	90.64 294	96.57 168	97.80 227	93.48 151	89.88 337	98.45 152	74.46 349	96.04 218	95.68 270	90.71 231	99.31 233	73.73 345	99.01 200	96.91 293
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PAPM_NR		94.61 221	94.17 227	95.96 209	98.36 156	91.23 190	95.93 164	97.95 210	92.98 224	93.42 299	94.43 299	90.53 232	98.38 323	87.60 289	96.29 314	98.27 234

OpenMVS_ROB	91.80 14	93.64 246	93.05 245	95.42 229	97.31 272	91.21 191	95.08 217	96.68 268	81.56 328	96.88 183	96.41 245	90.44 233	99.25 243	85.39 308	97.67 281	95.80 320

HQP2-MVS												90.33 234

N_pmnet		95.18 200	94.23 223	98.06 75	97.85 216	96.55 53	92.49 305	91.63 320	89.34 269	98.09 105	97.41 185	90.33 234	99.06 261	91.58 213	99.31 167	98.56 207

HQP-MVS		95.17 201	94.58 212	96.92 148	97.85 216	92.47 165	94.26 244	98.43 156	93.18 216	92.86 307	95.08 281	90.33 234	99.23 246	90.51 241	98.74 223	99.05 155

CNLPA		95.04 205	94.47 215	96.75 156	97.81 223	95.25 89	94.12 259	97.89 213	94.41 181	94.57 258	95.69 269	90.30 237	98.35 326	86.72 298	98.76 221	96.64 303

PMMVS		92.39 266	91.08 279	96.30 184	93.12 345	92.81 161	90.58 330	95.96 275	79.17 339	91.85 322	92.27 324	90.29 238	98.66 307	89.85 252	96.68 309	97.43 275

TR-MVS		92.54 265	92.20 259	93.57 285	96.49 295	86.66 284	93.51 282	94.73 289	89.96 266	94.95 244	93.87 304	90.24 239	98.61 308	81.18 330	94.88 326	95.45 326

TAMVS		95.49 183	94.94 195	97.16 134	98.31 158	93.41 152	95.07 218	96.82 263	91.09 255	97.51 147	97.82 153	89.96 240	99.42 196	88.42 273	99.44 131	98.64 200

PMMVS2		93.66 245	94.07 229	92.45 308	97.57 251	80.67 327	86.46 344	96.00 273	93.99 195	97.10 167	97.38 191	89.90 241	97.82 337	88.76 267	99.47 124	98.86 183

BH-w/o		92.14 272	91.94 265	92.73 304	97.13 279	85.30 294	92.46 306	95.64 283	89.33 270	94.21 267	92.74 320	89.60 242	98.24 329	81.68 328	94.66 328	94.66 331

UnsupCasMVSNet_bld		94.72 216	94.26 222	96.08 201	98.62 127	90.54 204	93.38 287	98.05 208	90.30 262	97.02 170	96.80 221	89.54 243	99.16 252	88.44 272	96.18 315	98.56 207

MG-MVS		94.08 237	94.00 232	94.32 266	97.09 280	85.89 288	93.19 293	95.96 275	92.52 234	94.93 246	97.51 179	89.54 243	98.77 295	87.52 291	97.71 277	98.31 229

UnsupCasMVSNet_eth		95.91 170	95.73 173	96.44 175	98.48 147	91.52 188	95.31 203	98.45 152	95.76 127	97.48 152	97.54 175	89.53 245	98.69 302	94.43 155	94.61 329	99.13 137

GBi-Net		96.99 113	96.80 131	97.56 104	97.96 209	93.67 142	98.23 34	98.66 130	95.59 133	97.99 115	99.19 32	89.51 246	99.73 64	94.60 151	99.44 131	99.30 111

test1		96.99 113	96.80 131	97.56 104	97.96 209	93.67 142	98.23 34	98.66 130	95.59 133	97.99 115	99.19 32	89.51 246	99.73 64	94.60 151	99.44 131	99.30 111

FMVSNet2		96.72 141	96.67 137	96.87 152	97.96 209	91.88 181	97.15 96	98.06 207	95.59 133	98.50 68	98.62 74	89.51 246	99.65 116	94.99 139	99.60 87	99.07 152

pmmvs4		94.82 213	94.19 226	96.70 159	97.42 263	92.75 162	92.09 313	96.76 264	86.80 296	95.73 230	97.22 196	89.28 249	98.89 282	93.28 186	99.14 184	98.46 215

cascas		91.89 279	91.35 273	93.51 286	94.27 333	85.60 290	88.86 340	98.61 139	79.32 338	92.16 318	91.44 336	89.22 250	98.12 333	90.80 231	97.47 292	96.82 296

DSMNet-mixed		92.19 271	91.83 267	93.25 293	96.18 303	83.68 318	96.27 139	93.68 299	76.97 347	92.54 315	99.18 35	89.20 251	98.55 313	83.88 318	98.60 236	97.51 274

testus		90.90 297	90.51 296	92.06 312	96.07 306	79.45 330	88.99 338	98.44 155	85.46 309	94.15 270	90.77 340	89.12 252	98.01 336	73.66 346	97.95 258	98.71 197

CANet_DTU		94.65 219	94.21 225	95.96 209	95.90 309	89.68 213	93.92 267	97.83 218	93.19 215	90.12 335	95.64 272	88.52 253	99.57 151	93.27 187	99.47 124	98.62 203

EPP-MVSNet		96.84 130	96.58 140	97.65 100	99.18 63	93.78 140	98.68 11	96.34 269	97.91 46	97.30 159	98.06 130	88.46 254	99.85 24	93.85 176	99.40 150	99.32 107

SixPastTwentyTwo		97.49 90	97.57 80	97.26 132	99.56 19	92.33 167	98.28 31	96.97 259	98.30 33	99.45 14	99.35 18	88.43 255	99.89 17	98.01 31	99.76 50	99.54 45

IS-MVSNet		96.93 121	96.68 136	97.70 95	99.25 54	94.00 132	98.57 17	96.74 266	98.36 30	98.14 99	97.98 137	88.23 256	99.71 80	93.10 191	99.72 59	99.38 96

jason		94.39 226	94.04 230	95.41 231	98.29 160	87.85 265	92.74 301	96.75 265	85.38 312	95.29 237	96.15 256	88.21 257	99.65 116	94.24 165	99.34 160	98.74 194
jason: jason.
Patchmatch-test1		93.38 252	93.59 237	92.73 304	96.24 299	81.40 324	93.24 291	94.00 295	91.58 251	94.57 258	96.67 230	87.94 258	99.03 266	90.42 244	97.66 282	97.77 265

sss		94.22 229	93.72 235	95.74 219	97.71 241	89.95 210	93.84 270	96.98 258	88.38 280	93.75 283	95.74 268	87.94 258	98.89 282	91.02 223	98.10 254	98.37 221

IterMVS		95.42 190	95.83 170	94.20 269	97.52 255	83.78 317	92.41 307	97.47 244	95.49 137	98.06 110	98.49 83	87.94 258	99.58 146	96.02 94	99.02 198	99.23 124
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CHOSEN 1792x2688		94.10 235	93.41 240	96.18 194	99.16 64	90.04 207	92.15 310	98.68 125	79.90 336	96.22 212	97.83 150	87.92 261	99.42 196	89.18 261	99.65 76	99.08 150

VDDNet		96.98 116	96.84 127	97.41 123	99.40 41	93.26 155	97.94 53	95.31 286	99.26 6	98.39 75	99.18 35	87.85 262	99.62 128	95.13 134	99.09 191	99.35 105

pmmvs5		94.63 220	94.34 221	95.50 227	97.63 249	88.34 250	94.02 261	97.13 253	87.15 292	95.22 239	97.15 199	87.50 263	99.27 240	93.99 172	99.26 175	98.88 180

semantic-postprocess				94.85 248	97.68 243	85.53 291		97.63 235	96.99 84	98.36 77	98.54 80	87.44 264	99.75 54	97.07 69	99.08 192	99.27 121

PVSNet	86.72 19	91.10 292	90.97 288	91.49 315	97.56 253	78.04 336	87.17 342	94.60 291	84.65 316	92.34 316	92.20 325	87.37 265	98.47 316	85.17 310	97.69 279	97.96 258

Test4		95.39 191	95.24 185	95.82 217	98.07 197	89.60 217	94.40 241	98.49 149	91.39 253	97.40 157	96.32 250	87.32 266	99.41 207	95.09 137	98.71 228	98.44 216

MVSFormer		96.14 164	96.36 152	95.49 228	97.68 243	87.81 266	98.67 12	99.02 51	96.50 99	94.48 263	96.15 256	86.90 267	99.92 4	98.73 17	99.13 186	98.74 194

lupinMVS		93.77 241	93.28 241	95.24 234	97.68 243	87.81 266	92.12 311	96.05 272	84.52 317	94.48 263	95.06 283	86.90 267	99.63 122	93.62 182	99.13 186	98.27 234

WTY-MVS		93.55 248	93.00 247	95.19 235	97.81 223	87.86 264	93.89 268	96.00 273	89.02 271	94.07 273	95.44 277	86.27 269	99.33 231	87.69 281	96.82 304	98.39 220

CDS-MVSNet		94.88 210	94.12 228	97.14 136	97.64 248	93.57 147	93.96 266	97.06 256	90.05 265	96.30 208	96.55 235	86.10 270	99.47 182	90.10 249	99.31 167	98.40 218
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
1112_ss		94.12 234	93.42 239	96.23 188	98.59 131	90.85 195	94.24 248	98.85 84	85.49 307	92.97 305	94.94 285	86.01 271	99.64 119	91.78 208	97.92 262	98.20 240

new_pmnet		92.34 268	91.69 269	94.32 266	96.23 301	89.16 231	92.27 309	92.88 309	84.39 320	95.29 237	96.35 249	85.66 272	96.74 347	84.53 314	97.56 286	97.05 287

alignmvs		96.01 167	95.52 178	97.50 112	97.77 237	94.71 109	96.07 150	96.84 262	97.48 69	96.78 185	94.28 302	85.50 273	99.40 210	96.22 86	98.73 226	98.40 218

lessismore_v0				97.05 141	99.36 45	92.12 175		84.07 353		98.77 51	98.98 50	85.36 274	99.74 59	97.34 59	99.37 152	99.30 111

test1235678		92.95 256	92.40 256	94.61 256	96.95 284	86.87 282	90.75 327	97.75 221	91.00 257	96.33 201	95.38 278	85.21 275	98.92 278	79.00 335	99.20 179	98.03 254

HY-MVS	91.43 15	92.58 260	91.81 268	94.90 246	96.49 295	88.87 239	97.31 89	94.62 290	85.92 303	90.50 332	96.84 217	85.05 276	99.40 210	83.77 320	95.78 320	96.43 312

EPNet		93.72 243	92.62 255	97.03 144	87.61 357	92.25 169	96.27 139	91.28 322	96.74 94	87.65 345	97.39 189	85.00 277	99.64 119	92.14 201	99.48 122	99.20 126
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Test_1112_low_res		93.53 249	92.86 249	95.54 226	98.60 129	88.86 240	92.75 299	98.69 123	82.66 325	92.65 312	96.92 213	84.75 278	99.56 152	90.94 226	97.76 266	98.19 241

MVS-HIRNet		88.40 315	90.20 301	82.99 339	97.01 282	60.04 357	93.11 294	85.61 351	84.45 319	88.72 341	99.09 45	84.72 279	98.23 330	82.52 323	96.59 310	90.69 350

K. test v3		96.44 155	96.28 155	96.95 146	99.41 40	91.53 187	97.65 71	90.31 333	98.89 18	98.93 43	99.36 16	84.57 280	99.92 4	97.81 37	99.56 97	99.39 94

Vis-MVSNet (Re-imp)		95.11 202	94.85 200	95.87 216	99.12 76	89.17 230	97.54 83	94.92 288	96.50 99	96.58 189	97.27 195	83.64 281	99.48 180	88.42 273	99.67 73	98.97 162

PVSNet_0	81.89 21	84.49 326	83.21 329	88.34 332	95.76 313	74.97 346	83.49 348	92.70 313	78.47 342	87.94 344	86.90 351	83.38 282	96.63 348	73.44 347	66.86 354	93.40 341

CMPMVS	73.10 23	92.74 259	91.39 271	96.77 155	93.57 342	94.67 111	94.21 251	97.67 227	80.36 335	93.61 290	96.60 233	82.85 283	97.35 341	84.86 312	98.78 219	98.29 233
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
EU-MVSNet		94.25 228	94.47 215	93.60 284	98.14 192	82.60 320	97.24 94	92.72 312	85.08 313	98.48 69	98.94 54	82.59 284	98.76 296	97.47 56	99.53 105	99.44 80

LP		93.12 255	92.78 253	94.14 270	94.50 330	85.48 292	95.73 170	95.68 282	92.97 228	95.05 242	97.17 198	81.93 285	99.40 210	93.06 192	88.96 344	97.55 272

CVMVSNet		92.33 269	92.79 251	90.95 321	97.26 273	75.84 343	95.29 205	92.33 315	81.86 326	96.27 209	98.19 111	81.44 286	98.46 317	94.23 166	98.29 245	98.55 209

EPNet_dtu		91.39 291	90.75 292	93.31 291	90.48 355	82.61 319	94.80 232	92.88 309	93.39 211	81.74 353	94.90 288	81.36 287	99.11 256	88.28 275	98.87 213	98.21 239
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MIMVSNet		93.42 250	92.86 249	95.10 238	98.17 187	88.19 251	98.13 43	93.69 297	92.07 240	95.04 243	98.21 110	80.95 288	99.03 266	81.42 329	98.06 255	98.07 248

PAPM		87.64 322	85.84 325	93.04 297	96.54 292	84.99 300	88.42 341	95.57 285	79.52 337	83.82 350	93.05 315	80.57 289	98.41 319	62.29 353	92.79 335	95.71 321

HyFIR lowres test		93.72 243	92.65 254	96.91 150	98.93 94	91.81 184	91.23 324	98.52 146	82.69 324	96.46 196	96.52 239	80.38 290	99.90 13	90.36 246	98.79 218	99.03 156

FMVSNet3		95.26 199	94.94 195	96.22 192	96.53 293	90.06 206	95.99 155	97.66 229	94.11 194	97.99 115	97.91 146	80.22 291	99.63 122	94.60 151	99.44 131	98.96 163

RPMNet		94.22 229	94.03 231	94.78 250	95.44 318	88.15 252	96.18 146	93.73 296	97.43 70	94.10 271	98.49 83	79.40 292	99.39 216	95.69 104	95.81 317	96.81 297

LFMVS		95.32 195	94.88 199	96.62 162	98.03 200	91.47 189	97.65 71	90.72 328	99.11 8	97.89 130	98.31 97	79.20 293	99.48 180	93.91 175	99.12 189	98.93 170

ADS-MVSNet2		91.47 290	90.51 296	94.36 265	95.51 316	85.63 289	95.05 221	95.70 281	83.46 322	92.69 310	96.84 217	79.15 294	99.41 207	85.66 305	90.52 339	98.04 252

ADS-MVSNet		90.95 296	90.26 299	93.04 297	95.51 316	82.37 321	95.05 221	93.41 303	83.46 322	92.69 310	96.84 217	79.15 294	98.70 301	85.66 305	90.52 339	98.04 252

MDTV_nov1_ep13_2view						57.28 358	94.89 227		80.59 333	94.02 275		78.66 296		85.50 307		97.82 263

PatchmatchNet		91.98 275	91.87 266	92.30 310	94.60 328	79.71 329	95.12 212	93.59 302	89.52 268	93.61 290	97.02 206	77.94 297	99.18 249	90.84 229	94.57 330	98.01 257
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
sam_mvs1												77.80 298				98.06 249

CR-MVSNet		93.29 253	92.79 251	94.78 250	95.44 318	88.15 252	96.18 146	97.20 249	84.94 315	94.10 271	98.57 76	77.67 299	99.39 216	95.17 129	95.81 317	96.81 297

Patchmtry		95.03 206	94.59 211	96.33 181	94.83 325	90.82 197	96.38 134	97.20 249	96.59 97	97.49 149	98.57 76	77.67 299	99.38 221	92.95 194	99.62 79	98.80 188

tpmrst		90.31 299	90.61 295	89.41 328	94.06 337	72.37 350	95.06 220	93.69 297	88.01 284	92.32 317	96.86 215	77.45 301	98.82 290	91.04 222	87.01 347	97.04 288

sam_mvs												77.38 302

patchmatchnet-post											96.84 217	77.36 303	99.42 196

Patchmatch-RL test		94.66 218	94.49 214	95.19 235	98.54 139	88.91 238	92.57 303	98.74 112	91.46 252	98.32 82	97.75 159	77.31 304	98.81 292	96.06 90	99.61 84	97.85 261

tpmvs		90.79 298	90.87 289	90.57 324	92.75 349	76.30 341	95.79 169	93.64 300	91.04 256	91.91 320	96.26 251	77.19 305	98.86 288	89.38 258	89.85 342	96.56 306

test_post											10.87 357	76.83 306	99.07 260

Patchmatch-test		93.60 247	93.25 243	94.63 255	96.14 305	87.47 272	96.04 152	94.50 292	93.57 209	96.47 195	96.97 208	76.50 307	98.61 308	90.67 237	98.41 244	97.81 264

MDTV_nov1_ep13			91.28 274		94.31 332	73.51 347	94.80 232	93.16 306	86.75 297	93.45 297	97.40 186	76.37 308	98.55 313	88.85 266	96.43 311

EMVS		89.06 310	89.22 306	88.61 331	93.00 346	77.34 339	82.91 350	90.92 325	94.64 172	92.63 313	91.81 329	76.30 309	97.02 343	83.83 319	96.90 301	91.48 348

test_post1							94.98 225				10.37 358	76.21 310	99.04 263	89.47 257

GA-MVS		92.83 258	92.15 260	94.87 247	96.97 283	87.27 277	90.03 333	96.12 271	91.83 248	94.05 274	94.57 290	76.01 311	98.97 276	92.46 198	97.34 296	98.36 226

PatchT		93.75 242	93.57 238	94.29 268	95.05 323	87.32 276	96.05 151	92.98 307	97.54 65	94.25 266	98.72 66	75.79 312	99.24 244	95.92 99	95.81 317	96.32 313

E-PMN		89.52 308	89.78 303	88.73 330	93.14 344	77.61 338	83.26 349	92.02 316	94.82 168	93.71 285	93.11 309	75.31 313	96.81 345	85.81 302	96.81 305	91.77 347

DeepMVS_CX				77.17 341	90.94 354	85.28 295		74.08 358	52.51 353	80.87 354	88.03 349	75.25 314	70.63 356	59.23 354	84.94 349	75.62 351

CHOSEN 280x420		89.98 303	89.19 309	92.37 309	95.60 315	81.13 325	86.22 345	97.09 255	81.44 330	87.44 346	93.15 308	73.99 315	99.47 182	88.69 269	99.07 194	96.52 307

thres200		91.00 294	90.42 298	92.77 303	97.47 260	83.98 316	94.01 262	91.18 324	95.12 158	95.44 234	91.21 338	73.93 316	99.31 233	77.76 341	97.63 285	95.01 329

test-LLR		89.97 304	89.90 302	90.16 325	94.24 334	74.98 344	89.89 334	89.06 339	92.02 241	89.97 336	90.77 340	73.92 317	98.57 310	91.88 205	97.36 294	96.92 291

test0.0.03 1		90.11 300	89.21 307	92.83 302	93.89 338	86.87 282	91.74 317	88.74 341	92.02 241	94.71 250	91.14 339	73.92 317	94.48 351	83.75 321	92.94 333	97.16 284

tpm cat1		88.01 318	87.33 318	90.05 327	94.48 331	76.28 342	94.47 240	94.35 294	73.84 351	89.26 339	95.61 274	73.64 319	98.30 328	84.13 315	86.20 348	95.57 325

tfpn200view9		91.55 289	91.00 280	93.21 294	98.02 201	84.35 313	95.70 173	90.79 326	96.26 108	95.90 224	92.13 326	73.62 320	99.42 196	78.85 337	97.74 267	95.85 318

view600		92.56 261	92.11 261	93.91 275	98.45 149	84.76 304	97.10 101	90.23 334	97.42 71	96.98 173	94.48 294	73.62 320	99.60 140	82.49 324	98.28 246	97.36 277

view800		92.56 261	92.11 261	93.91 275	98.45 149	84.76 304	97.10 101	90.23 334	97.42 71	96.98 173	94.48 294	73.62 320	99.60 140	82.49 324	98.28 246	97.36 277

conf0.05thres1000		92.56 261	92.11 261	93.91 275	98.45 149	84.76 304	97.10 101	90.23 334	97.42 71	96.98 173	94.48 294	73.62 320	99.60 140	82.49 324	98.28 246	97.36 277

tfpn		92.56 261	92.11 261	93.91 275	98.45 149	84.76 304	97.10 101	90.23 334	97.42 71	96.98 173	94.48 294	73.62 320	99.60 140	82.49 324	98.28 246	97.36 277

thres400		91.68 288	91.00 280	93.71 282	98.02 201	84.35 313	95.70 173	90.79 326	96.26 108	95.90 224	92.13 326	73.62 320	99.42 196	78.85 337	97.74 267	97.36 277

tfpn111		91.92 276	91.39 271	93.49 287	98.21 178	84.50 309	96.39 130	90.39 329	96.87 88	96.33 201	93.08 311	73.44 326	99.51 173	79.87 332	97.94 261	96.46 308

conf200view11		91.81 281	91.26 276	93.46 288	98.21 178	84.50 309	96.39 130	90.39 329	96.87 88	96.33 201	93.08 311	73.44 326	99.42 196	78.85 337	97.74 267	96.46 308

thres100view900		91.76 283	91.26 276	93.26 292	98.21 178	84.50 309	96.39 130	90.39 329	96.87 88	96.33 201	93.08 311	73.44 326	99.42 196	78.85 337	97.74 267	95.85 318

thres600view7		92.03 274	91.43 270	93.82 280	98.19 182	84.61 308	96.27 139	90.39 329	96.81 92	96.37 200	93.11 309	73.44 326	99.49 177	80.32 331	97.95 258	97.36 277

MVSTER		94.21 232	93.93 233	95.05 241	95.83 311	86.46 286	95.18 211	97.65 231	92.41 238	97.94 122	98.00 136	72.39 330	99.58 146	96.36 84	99.56 97	99.12 142

PatchFormer-LS_test		89.62 307	89.12 310	91.11 320	93.62 340	78.42 333	94.57 239	93.62 301	88.39 279	90.54 331	88.40 348	72.33 331	99.03 266	92.41 199	88.20 345	95.89 317

JIA-IIPM		91.79 282	90.69 293	95.11 237	93.80 339	90.98 193	94.16 255	91.78 319	96.38 103	90.30 334	99.30 23	72.02 332	98.90 279	88.28 275	90.17 341	95.45 326

tpm		91.08 293	90.85 290	91.75 314	95.33 321	78.09 334	95.03 223	91.27 323	88.75 274	93.53 293	97.40 186	71.24 333	99.30 235	91.25 219	93.87 331	97.87 260

CostFormer		89.75 306	89.25 305	91.26 318	94.69 327	78.00 337	95.32 202	91.98 317	81.50 329	90.55 330	96.96 209	71.06 334	98.89 282	88.59 271	92.63 336	96.87 294

FPMVS		89.92 305	88.63 312	93.82 280	98.37 155	96.94 41	91.58 318	93.34 304	88.00 285	90.32 333	97.10 202	70.87 335	91.13 353	71.91 349	96.16 316	93.39 342

EPMVS		89.26 309	88.55 313	91.39 316	92.36 350	79.11 331	95.65 179	79.86 354	88.60 276	93.12 304	96.53 237	70.73 336	98.10 334	90.75 233	89.32 343	96.98 289

test12356		87.98 319	88.41 314	86.69 337	95.84 310	63.49 354	87.15 343	97.32 246	87.21 290	91.78 323	93.36 307	70.66 337	98.39 321	74.70 344	97.64 284	98.19 241

tmp_tt		57.23 330	62.50 331	41.44 343	34.77 358	49.21 359	83.93 347	60.22 360	15.31 354	71.11 355	79.37 353	70.09 338	44.86 357	64.76 352	82.93 352	30.25 354

conf0.01		91.90 277	90.98 282	94.67 253	98.27 165	88.03 256	96.98 110	88.58 342	93.90 198	94.64 252	91.45 330	69.62 339	99.52 163	87.62 283	97.74 267	96.46 308

conf0.002		91.90 277	90.98 282	94.67 253	98.27 165	88.03 256	96.98 110	88.58 342	93.90 198	94.64 252	91.45 330	69.62 339	99.52 163	87.62 283	97.74 267	96.46 308

thresconf0.02		91.72 284	90.98 282	93.97 271	98.27 165	88.03 256	96.98 110	88.58 342	93.90 198	94.64 252	91.45 330	69.62 339	99.52 163	87.62 283	97.74 267	94.35 334

tfpn_n400		91.72 284	90.98 282	93.97 271	98.27 165	88.03 256	96.98 110	88.58 342	93.90 198	94.64 252	91.45 330	69.62 339	99.52 163	87.62 283	97.74 267	94.35 334

tfpnconf		91.72 284	90.98 282	93.97 271	98.27 165	88.03 256	96.98 110	88.58 342	93.90 198	94.64 252	91.45 330	69.62 339	99.52 163	87.62 283	97.74 267	94.35 334

tfpnview11		91.72 284	90.98 282	93.97 271	98.27 165	88.03 256	96.98 110	88.58 342	93.90 198	94.64 252	91.45 330	69.62 339	99.52 163	87.62 283	97.74 267	94.35 334

dp		88.08 317	88.05 315	88.16 334	92.85 347	68.81 352	94.17 254	92.88 309	85.47 308	91.38 325	96.14 258	68.87 345	98.81 292	86.88 296	83.80 351	96.87 294

tfpn1000		91.88 280	91.20 278	93.89 279	97.96 209	87.13 279	97.13 99	88.16 349	94.41 181	94.87 247	92.77 318	68.34 346	99.47 182	89.24 259	97.95 258	95.06 328

tfpn_ndepth		90.98 295	90.24 300	93.20 296	97.72 240	87.18 278	96.52 126	88.20 348	92.63 233	93.69 287	90.70 343	68.22 347	99.42 196	86.98 295	97.47 292	93.00 344

tpm2		88.47 313	87.69 316	90.79 322	94.98 324	77.34 339	95.09 215	91.83 318	77.51 346	89.40 338	96.41 245	67.83 348	98.73 298	83.58 322	92.60 337	96.29 314

pmmvs3		90.00 302	88.90 311	93.32 290	94.20 336	85.34 293	91.25 323	92.56 314	78.59 341	93.82 280	95.17 280	67.36 349	98.69 302	89.08 263	98.03 256	95.92 316

tpmp4_e23		88.46 314	87.54 317	91.22 319	94.56 329	78.08 335	95.63 184	93.17 305	79.08 340	85.85 348	96.80 221	65.86 350	98.85 289	84.10 316	92.85 334	96.72 301

FMVSNet5		93.39 251	92.35 257	96.50 171	95.83 311	90.81 199	97.31 89	98.27 181	92.74 232	96.27 209	98.28 102	62.23 351	99.67 110	90.86 228	99.36 155	99.03 156

DWT-MVSNet_test		87.92 320	86.77 322	91.39 316	93.18 343	78.62 332	95.10 213	91.42 321	85.58 306	88.00 343	88.73 347	60.60 352	98.90 279	90.60 238	87.70 346	96.65 302

IB-MVS	85.98 20	88.63 312	86.95 321	93.68 283	95.12 322	84.82 303	90.85 326	90.17 338	87.55 288	88.48 342	91.34 337	58.01 353	99.59 144	87.24 294	93.80 332	96.63 305
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
gg-mvs-nofinetune		88.28 316	86.96 320	92.23 311	92.84 348	84.44 312	98.19 40	74.60 356	99.08 9	87.01 347	99.47 8	56.93 354	98.23 330	78.91 336	95.61 323	94.01 338

GG-mvs-BLEND				90.60 323	91.00 353	84.21 315	98.23 34	72.63 359		82.76 351	84.11 352	56.14 355	96.79 346	72.20 348	92.09 338	90.78 349

TESTMET0.1,1		87.20 323	86.57 323	89.07 329	93.62 340	72.84 349	89.89 334	87.01 350	85.46 309	89.12 340	90.20 345	56.00 356	97.72 339	90.91 227	96.92 300	96.64 303

test-mter		87.92 320	87.17 319	90.16 325	94.24 334	74.98 344	89.89 334	89.06 339	86.44 298	89.97 336	90.77 340	54.96 357	98.57 310	91.88 205	97.36 294	96.92 291

test2356		85.45 325	83.26 328	92.01 313	91.12 352	80.76 326	85.16 346	92.90 308	83.90 321	90.63 327	87.71 350	53.10 358	97.24 342	69.20 351	95.65 322	98.03 254

PNet_i23d		83.82 327	83.39 327	85.10 338	96.07 306	65.16 353	81.87 351	94.37 293	90.87 258	93.92 279	92.89 317	52.80 359	96.44 349	77.52 343	70.22 353	93.70 339

testpf		82.70 328	84.35 326	77.74 340	88.97 356	73.23 348	93.85 269	84.33 352	88.10 283	85.06 349	90.42 344	52.62 360	91.05 354	91.00 224	84.82 350	68.93 353

1111		88.78 311	89.39 304	86.96 336	98.53 141	62.84 355	91.49 319	97.48 241	94.45 178	96.56 191	96.45 242	43.83 361	98.87 286	86.33 299	99.40 150	99.18 129

.test1245		73.49 329	79.27 330	56.15 342	98.53 141	62.84 355	91.49 319	97.48 241	94.45 178	96.56 191	96.45 242	43.83 361	98.87 286	86.33 299	8.32 356	6.75 356

test123		12.59 333	15.49 334	3.87 345	6.07 359	2.55 360	90.75 327	2.59 362	2.52 355	5.20 357	13.02 356	4.96 363	1.85 359	5.20 355	9.09 355	7.23 355

testmvs		12.33 334	15.23 335	3.64 346	5.77 360	2.23 361	88.99 338	3.62 361	2.30 356	5.29 356	13.09 355	4.52 364	1.95 358	5.16 356	8.32 356	6.75 356

sosnet-low-res		0.00 337	0.00 338	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	0.00 359	0.00 365	0.00 360	0.00 357	0.00 358	0.00 358

sosnet		0.00 337	0.00 338	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	0.00 359	0.00 365	0.00 360	0.00 357	0.00 358	0.00 358

uncertanet		0.00 337	0.00 338	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	0.00 359	0.00 365	0.00 360	0.00 357	0.00 358	0.00 358

Regformer		0.00 337	0.00 338	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	0.00 359	0.00 365	0.00 360	0.00 357	0.00 358	0.00 358

ab-mvs-re		7.91 336	10.55 337	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	94.94 285	0.00 365	0.00 360	0.00 357	0.00 358	0.00 358

uanet		0.00 337	0.00 338	0.00 347	0.00 361	0.00 362	0.00 353	0.00 363	0.00 357	0.00 358	0.00 359	0.00 365	0.00 360	0.00 357	0.00 358	0.00 358

GSMVS																98.06 249

test_part3							95.64 181		94.84 165		97.60 171		99.76 48	91.22 220

test_part2					99.03 86	96.07 65				98.08 107

MTGPA								98.73 113

MTMP								74.60 356

gm-plane-assit					91.79 351	71.40 351			81.67 327		90.11 346		98.99 270	84.86 312

test9_res														91.29 216	98.89 212	99.00 158

agg_prior2														90.34 247	98.90 208	99.10 149

agg_prior					97.80 227	94.96 101		98.36 166		93.49 294			99.53 160

test_prior4						95.38 86	93.61 280

test_prior				97.46 118	97.79 232	94.26 124		98.42 159					99.34 228			98.79 189

旧先验2							93.35 288		77.95 345	95.77 229			98.67 306	90.74 234

新几何2							93.43 284

无先验							93.20 292	97.91 211	80.78 332				99.40 210	87.71 279		97.94 259

原ACMM2							92.82 297

testdata2													99.46 187	87.84 278

testdata1							92.77 298		93.78 205

plane_prior7					98.70 116	94.67 111

plane_prior5								98.75 110					99.46 187	92.59 196	99.20 179	99.28 118

plane_prior4											96.77 223

plane_prior3						94.51 114			95.29 144	96.16 215

plane_prior2							96.50 127		96.36 104

plane_prior1					98.49 145

plane_prior						94.29 121	95.42 193		94.31 187						98.93 207

n2								0.00 363

nn								0.00 363

door-mid								98.17 194

test11								98.08 204

door								97.81 219

HQP5-MVS						92.47 165

HQP-NCC					97.85 216		94.26 244		93.18 216	92.86 307

ACMP_Plane					97.85 216		94.26 244		93.18 216	92.86 307

BP-MVS														90.51 241

HQP4-MVS										92.87 306			99.23 246			99.06 154

HQP3-MVS								98.43 156							98.74 223

NP-MVS					98.14 192	93.72 141					95.08 281

ACMMP++_ref															99.52 109

ACMMP++															99.55 101

High-resolution multi-view benchmark