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.

DELS-MVS		98.54 40	98.22 50	99.50 32	99.15 119	98.65 52	100.00 1	98.58 75	97.70 9	98.21 127	99.24 139	92.58 133	99.94 73	98.63 89	99.94 61	99.92 91
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
PVSNet_Blended		97.94 73	97.64 79	98.83 91	99.59 96	96.99 120	100.00 1	99.10 29	95.38 78	98.27 123	99.08 146	89.00 190	99.95 65	99.12 53	99.25 123	99.57 146

ET-MVSNet_ETH3D		94.37 188	93.28 204	97.64 153	98.30 165	97.99 77	99.99 3	97.61 232	94.35 115	71.57 363	99.45 121	96.23 31	95.34 338	96.91 148	85.14 282	99.59 139

CS-MVS		97.79 83	97.91 73	97.43 162	99.10 120	94.42 198	99.99 3	97.10 282	95.07 85	99.68 37	99.75 82	92.95 123	98.34 213	98.38 96	99.14 127	99.54 151

alignmvs		97.81 81	97.33 91	99.25 52	98.77 146	98.66 50	99.99 3	98.44 112	94.40 114	98.41 116	99.47 118	93.65 104	99.42 156	98.57 90	94.26 214	99.67 122

lupinMVS		97.85 78	97.60 81	98.62 103	97.28 225	97.70 88	99.99 3	97.55 238	95.50 77	99.43 60	99.67 102	90.92 164	98.71 183	98.40 95	99.62 104	99.45 164

DROMVSNet		97.38 100	97.24 93	97.80 144	97.41 215	95.64 169	99.99 3	97.06 287	94.59 104	99.63 40	99.32 130	89.20 188	98.14 227	98.76 79	99.23 124	99.62 133

IB-MVS	92.85 6	94.99 170	93.94 183	98.16 132	97.72 203	95.69 168	99.99 3	98.81 50	94.28 120	92.70 219	96.90 243	95.08 54	99.17 163	96.07 156	73.88 350	99.60 138
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
EIA-MVS		97.53 91	97.46 85	97.76 149	98.04 181	94.84 190	99.98 9	97.61 232	94.41 113	97.90 133	99.59 109	92.40 138	98.87 172	98.04 110	99.13 128	99.59 139

ETV-MVS		97.92 75	97.80 76	98.25 130	98.14 177	96.48 135	99.98 9	97.63 227	95.61 72	99.29 75	99.46 120	92.55 134	98.82 174	99.02 62	98.54 137	99.46 162

CANet		98.27 60	97.82 75	99.63 15	99.72 88	99.10 21	99.98 9	98.51 99	97.00 29	98.52 111	99.71 93	87.80 199	99.95 65	99.75 28	99.38 119	99.83 102

CS-MVS-test		97.88 76	97.94 71	97.70 152	99.28 115	95.20 183	99.98 9	97.15 277	95.53 75	99.62 43	99.79 64	92.08 146	98.38 209	98.75 80	99.28 122	99.52 155

MSLP-MVS++		99.13 8	99.01 11	99.49 34	99.94 14	98.46 63	99.98 9	98.86 47	97.10 26	99.80 17	99.94 4	95.92 37	100.00 1	99.51 40	100.00 1	100.00 1

CNVR-MVS		99.40 1	99.26 1	99.84 6	99.98 2	99.51 6	99.98 9	98.69 57	98.20 3	99.93 1	99.98 2	96.82 23	100.00 1	99.75 28	100.00 1	99.99 24

SteuartSystems-ACMMP		99.02 12	98.97 13	99.18 57	98.72 147	97.71 86	99.98 9	98.44 112	96.85 31	99.80 17	99.91 15	97.57 8	99.85 99	99.44 44	99.99 22	99.99 24
Skip Steuart: Steuart Systems R&D Blog.
PHI-MVS		98.41 50	98.21 51	99.03 78	99.86 59	97.10 116	99.98 9	98.80 52	90.78 241	99.62 43	99.78 70	95.30 50	100.00 1	99.80 24	99.93 67	99.99 24

CLD-MVS		94.06 195	93.90 184	94.55 250	96.02 260	90.69 280	99.98 9	97.72 220	96.62 42	91.05 233	98.85 177	77.21 288	98.47 194	98.11 106	89.51 235	94.48 243
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
thisisatest0515		97.41 98	97.02 103	98.59 107	97.71 205	97.52 95	99.97 18	98.54 89	91.83 213	97.45 142	99.04 148	97.50 9	99.10 166	94.75 182	96.37 185	99.16 190

Fast-Effi-MVS+		95.02 169	94.19 177	97.52 157	97.88 188	94.55 195	99.97 18	97.08 285	88.85 271	94.47 197	97.96 213	84.59 229	98.41 201	89.84 263	97.10 171	99.59 139

MCST-MVS		99.32 3	99.14 4	99.86 5	99.97 3	99.59 5	99.97 18	98.64 65	98.47 2	99.13 83	99.92 13	96.38 30	100.00 1	99.74 30	100.00 1	100.00 1

TSAR-MVS + GP.		98.60 34	98.51 30	98.86 90	99.73 86	96.63 130	99.97 18	97.92 207	98.07 5	98.76 101	99.55 112	95.00 60	99.94 73	99.91 16	97.68 159	99.99 24

jason		97.24 103	96.86 105	98.38 126	95.73 271	97.32 108	99.97 18	97.40 257	95.34 80	98.60 110	99.54 114	87.70 200	98.56 190	97.94 116	99.47 115	99.25 185
jason: jason.
NCCC		99.37 2	99.25 2	99.71 13	99.96 8	99.15 20	99.97 18	98.62 69	98.02 6	99.90 2	99.95 3	97.33 17	100.00 1	99.54 39	100.00 1	100.00 1

CP-MVS		98.45 47	98.32 46	98.87 89	99.96 8	96.62 131	99.97 18	98.39 143	94.43 110	98.90 94	99.87 28	94.30 85	100.00 1	99.04 60	99.99 22	99.99 24

GeoE		94.36 190	93.48 196	96.99 176	97.29 224	93.54 218	99.96 25	96.72 321	88.35 281	93.43 208	98.94 166	82.05 246	98.05 233	88.12 280	96.48 183	99.37 173

SED-MVS		99.28 5	99.11 7	99.77 8	99.93 27	99.30 11	99.96 25	98.43 120	97.27 21	99.80 17	99.94 4	96.71 24	100.00 1	100.00 1	100.00 1	100.00 1

OPU-MVS				99.93 2	99.89 50	99.80 2	99.96 25				99.80 60	97.44 14	100.00 1	100.00 1	99.98 35	100.00 1

xxxxxxxxxxxxxcwj		98.98 15	98.79 17	99.54 26	99.82 70	98.79 37	99.96 25	97.52 244	97.66 10	99.81 13	99.89 21	94.70 68	99.86 95	99.84 19	99.93 67	99.96 74

save fliter					99.82 70	98.79 37	99.96 25	98.40 140	97.66 10

test0726					99.93 27	99.29 14	99.96 25	98.42 132	97.28 19	99.86 5	99.94 4	97.22 19

DPM-MVS		98.83 22	98.46 32	99.97 1	99.33 113	99.92 1	99.96 25	98.44 112	97.96 7	99.55 49	99.94 4	97.18 21	100.00 1	93.81 203	99.94 61	99.98 55

TEST9					99.92 36	98.92 27	99.96 25	98.43 120	93.90 141	99.71 34	99.86 31	95.88 38	99.85 99

train_agg		98.88 20	98.65 22	99.59 21	99.92 36	98.92 27	99.96 25	98.43 120	94.35 115	99.71 34	99.86 31	95.94 35	99.85 99	99.69 37	99.98 35	99.99 24

test_8					99.92 36	98.88 30	99.96 25	98.43 120	94.35 115	99.69 36	99.85 35	95.94 35	99.85 99

agg_prior1		98.88 20	98.66 21	99.54 26	99.93 27	98.77 40	99.96 25	98.43 120	94.63 103	99.63 40	99.85 35	95.79 41	99.85 99	99.72 34	99.99 22	99.99 24

region2R		98.54 40	98.37 42	99.05 76	99.96 8	97.18 112	99.96 25	98.55 85	94.87 94	99.45 58	99.85 35	94.07 93	100.00 1	98.67 84	100.00 1	99.98 55

test-LLR		96.47 131	96.04 126	97.78 146	97.02 233	95.44 172	99.96 25	98.21 177	94.07 129	95.55 183	96.38 258	93.90 98	98.27 221	90.42 255	98.83 132	99.64 128

TESTMET0.1,1		96.74 122	96.26 122	98.16 132	97.36 218	96.48 135	99.96 25	98.29 166	91.93 210	95.77 181	98.07 206	95.54 44	98.29 217	90.55 252	98.89 130	99.70 117

test-mter		96.39 135	95.93 138	97.78 146	97.02 233	95.44 172	99.96 25	98.21 177	91.81 215	95.55 183	96.38 258	95.17 51	98.27 221	90.42 255	98.83 132	99.64 128

CPTT-MVS		97.64 89	97.32 92	98.58 108	99.97 3	95.77 162	99.96 25	98.35 155	89.90 254	98.36 119	99.79 64	91.18 160	99.99 40	98.37 97	99.99 22	99.99 24

cascas		94.64 180	93.61 189	97.74 151	97.82 193	96.26 144	99.96 25	97.78 219	85.76 313	94.00 203	97.54 222	76.95 291	99.21 159	97.23 136	95.43 203	97.76 224

DeepPCF-MVS	95.94 2	97.71 87	98.98 12	93.92 277	99.63 94	81.76 355	99.96 25	98.56 79	99.47 1	99.19 81	99.99 1	94.16 91	100.00 1	99.92 13	99.93 67	100.00 1

patch_mono-2		98.24 64	99.12 5	95.59 213	99.67 92	86.91 329	99.95 43	98.89 43	97.60 12	99.90 2	99.76 76	96.54 28	99.98 46	99.94 12	99.82 91	99.88 96

DVP-MVS++		99.26 6	99.09 9	99.77 8	99.91 44	99.31 9	99.95 43	98.43 120	96.48 43	99.80 17	99.93 11	97.44 14	100.00 1	99.92 13	99.98 35	100.00 1

FOURS1					99.92 36	97.66 90	99.95 43	98.36 152	95.58 73	99.52 54

DVP-MVS		99.30 4	99.16 3	99.73 11	99.93 27	99.29 14	99.95 43	98.32 160	97.28 19	99.83 11	99.91 15	97.22 19	100.00 1	99.99 5	100.00 1	99.89 94
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test_0728_SECOND				99.82 7	99.94 14	99.47 7	99.95 43	98.43 120					100.00 1	99.99 5	100.00 1	100.00 1

MSP-MVS		99.09 9	99.12 5	98.98 83	99.93 27	97.24 109	99.95 43	98.42 132	97.50 15	99.52 54	99.88 24	97.43 16	99.71 134	99.50 41	99.98 35	100.00 1
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
mvs-test1		95.53 160	95.97 133	94.20 264	97.77 196	85.44 337	99.95 43	97.06 287	94.92 91	96.58 162	98.72 180	85.81 218	98.98 169	94.80 179	98.11 150	98.18 215

HFP-MVS		98.56 38	98.37 42	99.14 66	99.96 8	97.43 104	99.95 43	98.61 71	94.77 96	99.31 71	99.85 35	94.22 87	100.00 1	98.70 82	99.98 35	99.98 55

#test#		98.59 36	98.41 35	99.14 66	99.96 8	97.43 104	99.95 43	98.61 71	95.00 87	99.31 71	99.85 35	94.22 87	100.00 1	98.78 77	99.98 35	99.98 55

HPM-MVS++		99.07 10	98.88 15	99.63 15	99.90 47	99.02 23	99.95 43	98.56 79	97.56 14	99.44 59	99.85 35	95.38 49	100.00 1	99.31 49	99.99 22	99.87 98

test_prior3		98.99 14	98.84 16	99.43 38	99.94 14	98.49 61	99.95 43	98.65 62	95.78 64	99.73 30	99.76 76	96.00 33	99.80 111	99.78 26	100.00 1	99.99 24

test_prior2							99.95 43		95.78 64	99.73 30	99.76 76	96.00 33		99.78 26	100.00 1

ACMMPR		98.50 43	98.32 46	99.05 76	99.96 8	97.18 112	99.95 43	98.60 73	94.77 96	99.31 71	99.84 48	93.73 102	100.00 1	98.70 82	99.98 35	99.98 55

MP-MVS		98.23 65	97.97 67	99.03 78	99.94 14	97.17 115	99.95 43	98.39 143	94.70 99	98.26 125	99.81 59	91.84 151	100.00 1	98.85 72	99.97 48	99.93 85
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
mPP-MVS		98.39 53	98.20 52	98.97 84	99.97 3	96.92 123	99.95 43	98.38 147	95.04 86	98.61 109	99.80 60	93.39 108	100.00 1	98.64 88	100.00 1	99.98 55

PVSNet_BlendedMVS		96.05 145	95.82 144	96.72 185	99.59 96	96.99 120	99.95 43	99.10 29	94.06 131	98.27 123	95.80 272	89.00 190	99.95 65	99.12 53	87.53 266	93.24 327

PAPR		98.52 42	98.16 55	99.58 23	99.97 3	98.77 40	99.95 43	98.43 120	95.35 79	98.03 129	99.75 82	94.03 94	99.98 46	98.11 106	99.83 85	99.99 24

PVSNet	91.05 13	97.13 106	96.69 111	98.45 120	99.52 103	95.81 160	99.95 43	99.65 11	94.73 98	99.04 86	99.21 141	84.48 230	99.95 65	94.92 174	98.74 134	99.58 145

ZNCC-MVS		98.31 57	98.03 63	99.17 60	99.88 54	97.59 91	99.94 61	98.44 112	94.31 118	98.50 113	99.82 55	93.06 121	99.99 40	98.30 100	99.99 22	99.93 85

test_prior4						98.05 74	99.94 61

XVS		98.70 29	98.55 27	99.15 64	99.94 14	97.50 99	99.94 61	98.42 132	96.22 53	99.41 62	99.78 70	94.34 80	99.96 58	98.92 66	99.95 55	99.99 24

X-MVStestdata		93.83 197	92.06 228	99.15 64	99.94 14	97.50 99	99.94 61	98.42 132	96.22 53	99.41 62	41.37 380	94.34 80	99.96 58	98.92 66	99.95 55	99.99 24

SD-MVS		98.92 17	98.70 19	99.56 24	99.70 90	98.73 45	99.94 61	98.34 157	96.38 48	99.81 13	99.76 76	94.59 70	99.98 46	99.84 19	99.96 52	99.97 67
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
PVSNet_0	88.03 19	91.80 248	90.27 260	96.38 198	98.27 169	90.46 288	99.94 61	99.61 12	93.99 135	86.26 315	97.39 228	71.13 328	99.89 84	98.77 78	67.05 362	98.79 208

ETH3 D test6400		98.81 23	98.54 28	99.59 21	99.93 27	98.93 26	99.93 67	98.46 107	94.56 105	99.84 9	99.92 13	94.32 84	99.86 95	99.96 9	99.98 35	100.00 1

GST-MVS		98.27 60	97.97 67	99.17 60	99.92 36	97.57 92	99.93 67	98.39 143	94.04 134	98.80 97	99.74 87	92.98 122	100.00 1	98.16 103	99.76 95	99.93 85

test0.0.03 1		93.86 196	93.61 189	94.64 244	95.02 287	92.18 251	99.93 67	98.58 75	94.07 129	87.96 289	98.50 192	93.90 98	94.96 343	81.33 328	93.17 224	96.78 230

MVS_111021_HR		98.72 28	98.62 24	99.01 81	99.36 112	97.18 112	99.93 67	99.90 1	96.81 35	98.67 105	99.77 72	93.92 96	99.89 84	99.27 51	99.94 61	99.96 74

thisisatest0530		97.10 107	96.72 110	98.22 131	97.60 208	96.70 128	99.92 71	98.54 89	91.11 233	97.07 150	98.97 158	97.47 12	99.03 167	93.73 208	96.09 188	98.92 200

PVSNet_Blended_VisFu		97.27 102	96.81 107	98.66 100	98.81 143	96.67 129	99.92 71	98.64 65	94.51 107	96.38 170	98.49 193	89.05 189	99.88 90	97.10 140	98.34 141	99.43 167

DP-MVS Recon		98.41 50	98.02 64	99.56 24	99.97 3	98.70 47	99.92 71	98.44 112	92.06 208	98.40 118	99.84 48	95.68 42	100.00 1	98.19 101	99.71 99	99.97 67

PLC	95.54 3	97.93 74	97.89 74	98.05 138	99.82 70	94.77 194	99.92 71	98.46 107	93.93 139	97.20 146	99.27 134	95.44 48	99.97 56	97.41 131	99.51 114	99.41 169
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
9.14			98.38 40		99.87 57		99.91 75	98.33 158	93.22 162	99.78 25	99.89 21	94.57 71	99.85 99	99.84 19	99.97 48

iter_conf05		96.07 144	95.95 137	96.44 194	98.43 160	97.52 95	99.91 75	96.85 311	94.16 124	92.49 222	97.98 211	98.20 4	97.34 259	97.26 135	88.29 253	94.45 250

APDe-MVS		99.06 11	98.91 14	99.51 31	99.94 14	98.76 44	99.91 75	98.39 143	97.20 25	99.46 57	99.85 35	95.53 46	99.79 114	99.86 18	100.00 1	99.99 24

MVSTER		95.53 160	95.22 157	96.45 192	98.56 152	97.72 85	99.91 75	97.67 224	92.38 197	91.39 228	97.14 233	97.24 18	97.30 264	94.80 179	87.85 260	94.34 262

PMMVS		96.76 120	96.76 109	96.76 183	98.28 168	92.10 252	99.91 75	97.98 200	94.12 126	99.53 51	99.39 126	86.93 209	98.73 181	96.95 146	97.73 157	99.45 164

SF-MVS		98.67 31	98.40 37	99.50 32	99.77 78	98.67 48	99.90 80	98.21 177	93.53 153	99.81 13	99.89 21	94.70 68	99.86 95	99.84 19	99.93 67	99.96 74

testtj		98.89 19	98.69 20	99.52 29	99.94 14	98.56 57	99.90 80	98.55 85	95.14 84	99.72 33	99.84 48	95.46 47	100.00 1	99.65 38	99.99 22	99.99 24

ETH3D-3000-0.1		98.68 30	98.42 33	99.47 37	99.83 68	98.57 55	99.90 80	98.37 150	93.81 144	99.81 13	99.90 19	94.34 80	99.86 95	99.84 19	99.98 35	99.97 67

原ACMM2							99.90 80

HPM-MVS		97.96 72	97.72 77	98.68 98	99.84 65	96.39 140	99.90 80	98.17 182	92.61 186	98.62 108	99.57 111	91.87 150	99.67 141	98.87 71	99.99 22	99.99 24
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
EPNet		98.49 44	98.40 37	98.77 93	99.62 95	96.80 127	99.90 80	99.51 16	97.60 12	99.20 78	99.36 129	93.71 103	99.91 79	97.99 113	98.71 135	99.61 136
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CSCG		97.10 107	97.04 101	97.27 171	99.89 50	91.92 257	99.90 80	99.07 32	88.67 274	95.26 189	99.82 55	93.17 119	99.98 46	98.15 104	99.47 115	99.90 93

PAPM		98.60 34	98.42 33	99.14 66	96.05 259	98.96 24	99.90 80	99.35 24	96.68 39	98.35 120	99.66 104	96.45 29	98.51 193	99.45 43	99.89 78	99.96 74

114514_t		97.41 98	96.83 106	99.14 66	99.51 105	97.83 83	99.89 88	98.27 170	88.48 278	99.06 85	99.66 104	90.30 172	99.64 144	96.32 154	99.97 48	99.96 74

WTY-MVS		98.10 69	97.60 81	99.60 20	98.92 134	99.28 16	99.89 88	99.52 14	95.58 73	98.24 126	99.39 126	93.33 110	99.74 130	97.98 115	95.58 201	99.78 109

GA-MVS		93.83 197	92.84 210	96.80 181	95.73 271	93.57 216	99.88 90	97.24 269	92.57 190	92.92 215	96.66 251	78.73 280	97.67 248	87.75 283	94.06 217	99.17 189

UniMVSNet (Re)		93.07 218	92.13 225	95.88 208	94.84 288	96.24 148	99.88 90	98.98 35	92.49 195	89.25 264	95.40 291	87.09 207	97.14 275	93.13 219	78.16 331	94.26 266

HPM-MVS_fast		97.80 82	97.50 84	98.68 98	99.79 75	96.42 137	99.88 90	98.16 185	91.75 217	98.94 92	99.54 114	91.82 152	99.65 143	97.62 128	99.99 22	99.99 24

iter_conf_final		96.01 147	95.93 138	96.28 200	98.38 162	97.03 118	99.87 93	97.03 291	94.05 133	92.61 220	97.98 211	98.01 5	97.34 259	97.02 142	88.39 252	94.47 244

DPE-MVS		99.26 6	99.10 8	99.74 10	99.89 50	99.24 18	99.87 93	98.44 112	97.48 16	99.64 39	99.94 4	96.68 26	99.99 40	99.99 5	100.00 1	99.99 24
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MTMP							99.87 93	96.49 328

Regformer-1		98.79 25	98.60 25	99.36 48	99.85 60	98.34 66	99.87 93	98.52 92	96.05 57	99.41 62	99.79 64	94.93 63	99.76 123	99.07 55	99.90 76	99.99 24

Regformer-2		98.78 26	98.59 26	99.36 48	99.85 60	98.32 67	99.87 93	98.52 92	96.04 58	99.41 62	99.79 64	94.92 64	99.76 123	99.05 56	99.90 76	99.98 55

CDPH-MVS		98.65 32	98.36 44	99.49 34	99.94 14	98.73 45	99.87 93	98.33 158	93.97 136	99.76 27	99.87 28	94.99 61	99.75 126	98.55 91	100.00 1	99.98 55

HQP-NCC					95.78 265		99.87 93		96.82 32	93.37 209

ACMP_Plane					95.78 265		99.87 93		96.82 32	93.37 209

APD-MVS		98.62 33	98.35 45	99.41 42	99.90 47	98.51 60	99.87 93	98.36 152	94.08 128	99.74 29	99.73 89	94.08 92	99.74 130	99.42 45	99.99 22	99.99 24
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MVS_111021_LR		98.42 49	98.38 40	98.53 115	99.39 110	95.79 161	99.87 93	99.86 2	96.70 38	98.78 98	99.79 64	92.03 147	99.90 80	99.17 52	99.86 83	99.88 96

HQP-MVS		94.61 181	94.50 172	94.92 234	95.78 265	91.85 258	99.87 93	97.89 209	96.82 32	93.37 209	98.65 183	80.65 264	98.39 205	97.92 117	89.60 230	94.53 239

CNLPA		97.76 85	97.38 87	98.92 88	99.53 102	96.84 125	99.87 93	98.14 188	93.78 146	96.55 164	99.69 98	92.28 141	99.98 46	97.13 138	99.44 117	99.93 85

ETH3D cwj APD-0.16		98.40 52	98.07 62	99.40 44	99.59 96	98.41 64	99.86 105	98.24 173	92.18 203	99.73 30	99.87 28	93.47 107	99.85 99	99.74 30	99.95 55	99.93 85

SMA-MVS		98.76 27	98.48 31	99.62 18	99.87 57	98.87 31	99.86 105	98.38 147	93.19 163	99.77 26	99.94 4	95.54 44	100.00 1	99.74 30	99.99 22	100.00 1
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
plane_prior						91.74 262	99.86 105		96.76 36						89.59 232

tttt0517		96.85 115	96.49 117	97.92 142	97.48 214	95.89 159	99.85 108	98.54 89	90.72 242	96.63 160	98.93 168	97.47 12	99.02 168	93.03 221	95.76 197	98.85 204

ACMMP_NAP		98.49 44	98.14 56	99.54 26	99.66 93	98.62 54	99.85 108	98.37 150	94.68 100	99.53 51	99.83 51	92.87 125	100.00 1	98.66 87	99.84 84	99.99 24

thres200		96.96 111	96.21 123	99.22 53	98.97 128	98.84 34	99.85 108	99.71 6	93.17 164	96.26 172	98.88 170	89.87 177	99.51 148	94.26 194	94.91 208	99.31 180

F-COLMAP		96.93 113	96.95 104	96.87 180	99.71 89	91.74 262	99.85 108	97.95 203	93.11 166	95.72 182	99.16 143	92.35 139	99.94 73	95.32 166	99.35 120	98.92 200

SR-MVS		98.46 46	98.30 48	98.93 87	99.88 54	97.04 117	99.84 112	98.35 155	94.92 91	99.32 70	99.80 60	93.35 109	99.78 116	99.30 50	99.95 55	99.96 74

CANet_DTU		96.76 120	96.15 124	98.60 105	98.78 145	97.53 94	99.84 112	97.63 227	97.25 24	99.20 78	99.64 106	81.36 255	99.98 46	92.77 223	98.89 130	98.28 214

casdiffmvs		96.42 134	95.97 133	97.77 148	97.30 223	94.98 186	99.84 112	97.09 284	93.75 148	96.58 162	99.26 137	85.07 226	98.78 177	97.77 124	97.04 173	99.54 151

HQP_MVS		94.49 185	94.36 174	94.87 235	95.71 274	91.74 262	99.84 112	97.87 211	96.38 48	93.01 213	98.59 187	80.47 268	98.37 211	97.79 122	89.55 233	94.52 241

plane_prior2							99.84 112		96.38 48

BH-w/o		95.71 155	95.38 153	96.68 186	98.49 158	92.28 247	99.84 112	97.50 247	92.12 205	92.06 224	98.79 178	84.69 228	98.67 186	95.29 167	99.66 102	99.09 196

UniMVSNet_NR-MVSNet		92.95 220	92.11 226	95.49 214	94.61 293	95.28 178	99.83 118	99.08 31	91.49 222	89.21 267	96.86 246	87.14 206	96.73 303	93.20 215	77.52 336	94.46 245

APD-MVS_3200maxsize		98.25 63	98.08 61	98.78 92	99.81 73	96.60 132	99.82 119	98.30 165	93.95 138	99.37 68	99.77 72	92.84 126	99.76 123	98.95 63	99.92 71	99.97 67

PAPM_NR		98.12 68	97.93 72	98.70 97	99.94 14	96.13 152	99.82 119	98.43 120	94.56 105	97.52 140	99.70 95	94.40 74	99.98 46	97.00 143	99.98 35	99.99 24

nrg030		93.51 207	92.53 219	96.45 192	94.36 295	97.20 111	99.81 121	97.16 276	91.60 219	89.86 248	97.46 224	86.37 214	97.68 247	95.88 160	80.31 319	94.46 245

diffmvs		97.00 110	96.64 112	98.09 136	97.64 206	96.17 151	99.81 121	97.19 271	94.67 102	98.95 91	99.28 131	86.43 213	98.76 179	98.37 97	97.42 165	99.33 178

DU-MVS		92.46 233	91.45 242	95.49 214	94.05 300	95.28 178	99.81 121	98.74 54	92.25 201	89.21 267	96.64 253	81.66 251	96.73 303	93.20 215	77.52 336	94.46 245

ACMP	92.05 9	92.74 225	92.42 222	93.73 282	95.91 264	88.72 311	99.81 121	97.53 242	94.13 125	87.00 302	98.23 202	74.07 316	98.47 194	96.22 155	88.86 242	93.99 295
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test1172		98.38 54	98.25 49	98.77 93	99.88 54	96.56 134	99.80 125	98.36 152	94.68 100	99.20 78	99.80 60	93.28 114	99.78 116	99.34 48	99.92 71	99.98 55

Fast-Effi-MVS+-dtu		93.72 204	93.86 186	93.29 293	97.06 230	86.16 331	99.80 125	96.83 313	92.66 183	92.58 221	97.83 215	81.39 254	97.67 248	89.75 264	96.87 177	96.05 237

zzz-MVS		98.33 56	98.00 65	99.30 50	99.85 60	97.93 81	99.80 125	98.28 167	95.76 66	97.18 147	99.88 24	92.74 129	100.00 1	98.67 84	99.88 80	99.99 24

BH-untuned		95.18 165	94.83 167	96.22 202	98.36 164	91.22 274	99.80 125	97.32 264	90.91 237	91.08 232	98.67 182	83.51 237	98.54 192	94.23 195	99.61 107	98.92 200

tfpn200view9		96.79 118	95.99 128	99.19 56	98.94 130	98.82 35	99.78 129	99.71 6	92.86 169	96.02 175	98.87 172	89.33 183	99.50 150	93.84 200	94.57 209	99.27 183

thres400		96.78 119	95.99 128	99.16 62	98.94 130	98.82 35	99.78 129	99.71 6	92.86 169	96.02 175	98.87 172	89.33 183	99.50 150	93.84 200	94.57 209	99.16 190

TAPA-MVS	92.12 8	94.42 186	93.60 191	96.90 179	99.33 113	91.78 261	99.78 129	98.00 197	89.89 255	94.52 195	99.47 118	91.97 148	99.18 162	69.90 358	99.52 112	99.73 114
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
TSAR-MVS + MP.		98.93 16	98.77 18	99.41 42	99.74 82	98.67 48	99.77 132	98.38 147	96.73 37	99.88 4	99.74 87	94.89 65	99.59 145	99.80 24	99.98 35	99.97 67
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
OPM-MVS		93.21 212	92.80 211	94.44 257	93.12 321	90.85 279	99.77 132	97.61 232	96.19 55	91.56 227	98.65 183	75.16 310	98.47 194	93.78 206	89.39 236	93.99 295
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
v2v482		91.30 254	90.07 266	95.01 230	93.13 319	93.79 211	99.77 132	97.02 292	88.05 283	89.25 264	95.37 295	80.73 262	97.15 274	87.28 289	80.04 322	94.09 286

Baseline_NR-MVSNet		90.33 277	89.51 275	92.81 303	92.84 328	89.95 299	99.77 132	93.94 367	84.69 328	89.04 271	95.66 278	81.66 251	96.52 310	90.99 244	76.98 342	91.97 345

ACMM	91.95 10	92.88 221	92.52 220	93.98 276	95.75 270	89.08 309	99.77 132	97.52 244	93.00 167	89.95 245	97.99 210	76.17 300	98.46 197	93.63 211	88.87 241	94.39 254
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
SR-MVS-dyc-post		98.31 57	98.17 54	98.71 96	99.79 75	96.37 141	99.76 137	98.31 162	94.43 110	99.40 66	99.75 82	93.28 114	99.78 116	98.90 69	99.92 71	99.97 67

RE-MVS-def			98.13 57		99.79 75	96.37 141	99.76 137	98.31 162	94.43 110	99.40 66	99.75 82	92.95 123		98.90 69	99.92 71	99.97 67

BH-RMVSNet		95.18 165	94.31 176	97.80 144	98.17 175	95.23 181	99.76 137	97.53 242	92.52 193	94.27 200	99.25 138	76.84 292	98.80 175	90.89 248	99.54 111	99.35 176

v148		90.70 267	89.63 270	93.92 277	92.97 326	90.97 276	99.75 140	96.89 308	87.51 288	88.27 286	95.01 308	81.67 250	97.04 285	87.40 287	77.17 341	93.75 312

PGM-MVS		98.34 55	98.13 57	98.99 82	99.92 36	97.00 119	99.75 140	99.50 17	93.90 141	99.37 68	99.76 76	93.24 117	100.00 1	97.75 126	99.96 52	99.98 55

LPG-MVS_test		92.96 219	92.71 213	93.71 284	95.43 280	88.67 312	99.75 140	97.62 229	92.81 173	90.05 241	98.49 193	75.24 306	98.40 203	95.84 161	89.12 237	94.07 287

thres100view900		96.74 122	95.92 140	99.18 57	98.90 137	98.77 40	99.74 143	99.71 6	92.59 188	95.84 178	98.86 174	89.25 185	99.50 150	93.84 200	94.57 209	99.27 183

MP-MVS-pluss		98.07 70	97.64 79	99.38 47	99.74 82	98.41 64	99.74 143	98.18 181	93.35 157	96.45 166	99.85 35	92.64 132	99.97 56	98.91 68	99.89 78	99.77 110
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
pmmvs5		90.17 283	89.09 282	93.40 291	92.10 338	89.77 302	99.74 143	95.58 347	85.88 312	87.24 301	95.74 274	73.41 319	96.48 312	88.54 273	83.56 294	93.95 298

thres600view7		96.69 125	95.87 143	99.14 66	98.90 137	98.78 39	99.74 143	99.71 6	92.59 188	95.84 178	98.86 174	89.25 185	99.50 150	93.44 213	94.50 212	99.16 190

baseline2		96.71 124	96.49 117	97.37 166	95.63 278	95.96 157	99.74 143	98.88 45	92.94 168	91.61 226	98.97 158	97.72 7	98.62 188	94.83 178	98.08 154	97.53 228

miper_enhance_ethall		94.36 190	93.98 182	95.49 214	98.68 149	95.24 180	99.73 148	97.29 266	93.28 161	89.86 248	95.97 270	94.37 79	97.05 282	92.20 227	84.45 287	94.19 272

testgi		89.01 298	88.04 299	91.90 313	93.49 311	84.89 340	99.73 148	95.66 345	93.89 143	85.14 322	98.17 203	59.68 360	94.66 347	77.73 343	88.88 240	96.16 236

sss		97.57 90	97.03 102	99.18 57	98.37 163	98.04 75	99.73 148	99.38 22	93.46 155	98.76 101	99.06 147	91.21 156	99.89 84	96.33 153	97.01 174	99.62 133

canonicalmvs		97.09 109	96.32 121	99.39 46	98.93 132	98.95 25	99.72 151	97.35 260	94.45 108	97.88 134	99.42 122	86.71 210	99.52 147	98.48 93	93.97 218	99.72 116

3Dnovator+	91.53 11	96.31 138	95.24 156	99.52 29	96.88 241	98.64 53	99.72 151	98.24 173	95.27 82	88.42 285	98.98 156	82.76 242	99.94 73	97.10 140	99.83 85	99.96 74

HyFIR lowres test		96.66 127	96.43 119	97.36 168	99.05 122	93.91 210	99.70 153	99.80 3	90.54 243	96.26 172	98.08 205	92.15 144	98.23 224	96.84 149	95.46 202	99.93 85

D2MVS		92.76 224	92.59 218	93.27 294	95.13 283	89.54 305	99.69 154	99.38 22	92.26 200	87.59 293	94.61 322	85.05 227	97.79 244	91.59 234	88.01 258	92.47 339

TranMVSNet+NR-MVSNet		91.68 252	90.61 253	94.87 235	93.69 307	93.98 208	99.69 154	98.65 62	91.03 235	88.44 281	96.83 250	80.05 271	96.18 323	90.26 259	76.89 344	94.45 250

V42		91.28 256	90.12 265	94.74 240	93.42 313	93.46 220	99.68 156	97.02 292	87.36 291	89.85 250	95.05 306	81.31 256	97.34 259	87.34 288	80.07 321	93.40 322

testmvs		40.60 345	44.45 348	29.05 362	19.49 386	14.11 387	99.68 156	18.47 385	20.74 378	64.59 366	98.48 196	10.95 383	17.09 382	56.66 372	11.01 378	55.94 375

abl_6		97.67 88	97.34 90	98.66 100	99.68 91	96.11 155	99.68 156	98.14 188	93.80 145	99.27 76	99.70 95	88.65 195	99.98 46	97.46 130	99.72 98	99.89 94

mvsmamba		94.10 193	93.72 188	95.25 224	93.57 308	94.13 203	99.67 159	96.45 330	93.63 152	91.34 230	97.77 217	86.29 215	97.22 270	96.65 151	88.10 256	94.40 252

RRT_MVS		93.14 215	92.92 208	93.78 281	93.31 316	90.04 296	99.66 160	97.69 222	92.53 192	88.91 274	97.76 218	84.36 231	96.93 293	95.10 169	86.99 269	94.37 255

Regformer-3		98.58 37	98.41 35	99.10 72	99.84 65	97.57 92	99.66 160	98.52 92	95.79 63	99.01 88	99.77 72	94.40 74	99.75 126	98.82 73	99.83 85	99.98 55

Regformer-4		98.56 38	98.39 39	99.08 74	99.84 65	97.52 95	99.66 160	98.52 92	95.76 66	99.01 88	99.77 72	94.33 83	99.75 126	98.80 76	99.83 85	99.98 55

DeepC-MVS	94.51 4	96.92 114	96.40 120	98.45 120	99.16 118	95.90 158	99.66 160	98.06 194	96.37 51	94.37 198	99.49 117	83.29 240	99.90 80	97.63 127	99.61 107	99.55 148
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CHOSEN 1792x2688		96.81 117	96.53 116	97.64 153	98.91 136	93.07 228	99.65 164	99.80 3	95.64 71	95.39 186	98.86 174	84.35 233	99.90 80	96.98 144	99.16 126	99.95 82

Test_1112_low_res		95.72 153	94.83 167	98.42 123	97.79 195	96.41 138	99.65 164	96.65 324	92.70 180	92.86 218	96.13 267	92.15 144	99.30 157	91.88 231	93.64 220	99.55 148

1112_ss		96.01 147	95.20 158	98.42 123	97.80 194	96.41 138	99.65 164	96.66 323	92.71 179	92.88 217	99.40 124	92.16 143	99.30 157	91.92 230	93.66 219	99.55 148

OMC-MVS		97.28 101	97.23 94	97.41 163	99.76 79	93.36 226	99.65 164	97.95 203	96.03 59	97.41 143	99.70 95	89.61 179	99.51 148	96.73 150	98.25 146	99.38 171

test_yl		97.83 79	97.37 88	99.21 54	99.18 116	97.98 78	99.64 168	99.27 26	91.43 226	97.88 134	98.99 154	95.84 39	99.84 108	98.82 73	95.32 205	99.79 106

DCV-MVSNet		97.83 79	97.37 88	99.21 54	99.18 116	97.98 78	99.64 168	99.27 26	91.43 226	97.88 134	98.99 154	95.84 39	99.84 108	98.82 73	95.32 205	99.79 106

MG-MVS		98.91 18	98.65 22	99.68 14	99.94 14	99.07 22	99.64 168	99.44 19	97.33 18	99.00 90	99.72 91	94.03 94	99.98 46	98.73 81	100.00 1	100.00 1

v1144		91.09 259	89.83 267	94.87 235	93.25 317	93.69 215	99.62 171	96.98 297	86.83 301	89.64 256	94.99 311	80.94 259	97.05 282	85.08 307	81.16 308	93.87 305

cl22		93.77 201	93.25 205	95.33 221	99.49 106	94.43 197	99.61 172	98.09 191	90.38 245	89.16 270	95.61 279	90.56 170	97.34 259	91.93 229	84.45 287	94.21 271

WR-MVS		92.31 236	91.25 244	95.48 217	94.45 294	95.29 177	99.60 173	98.68 58	90.10 250	88.07 288	96.89 244	80.68 263	96.80 301	93.14 218	79.67 323	94.36 257

Effi-MVS+-dtu		94.53 184	95.30 155	92.22 308	97.77 196	82.54 348	99.59 174	97.06 287	94.92 91	95.29 188	95.37 295	85.81 218	97.89 242	94.80 179	97.07 172	96.23 235

test_low_dy_conf_001		93.16 213	92.88 209	94.01 272	93.16 318	90.65 283	99.58 175	97.66 226	92.21 202	91.34 230	97.80 216	82.45 244	97.05 282	93.64 210	88.05 257	94.32 263

DIV-MVS_self_test		92.32 235	91.60 236	94.47 255	97.31 222	92.74 235	99.58 175	96.75 319	86.99 298	87.64 292	95.54 283	89.55 180	96.50 311	88.58 272	82.44 298	94.17 273

FIs		94.10 193	93.43 197	96.11 204	94.70 291	96.82 126	99.58 175	98.93 41	92.54 191	89.34 262	97.31 229	87.62 201	97.10 279	94.22 196	86.58 271	94.40 252

cl____		92.31 236	91.58 237	94.52 251	97.33 221	92.77 233	99.57 178	96.78 318	86.97 299	87.56 294	95.51 286	89.43 181	96.62 307	88.60 271	82.44 298	94.16 278

EPNet_dtu		95.71 155	95.39 152	96.66 187	98.92 134	93.41 223	99.57 178	98.90 42	96.19 55	97.52 140	98.56 191	92.65 131	97.36 257	77.89 342	98.33 142	99.20 188
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
v144192		90.79 266	89.52 274	94.59 247	93.11 322	92.77 233	99.56 180	96.99 295	86.38 305	89.82 251	94.95 313	80.50 267	97.10 279	83.98 313	80.41 317	93.90 302

OpenMVS	90.15 15	94.77 175	93.59 192	98.33 127	96.07 258	97.48 101	99.56 180	98.57 77	90.46 244	86.51 308	98.95 164	78.57 281	99.94 73	93.86 199	99.74 96	97.57 227

MVSFormer		96.94 112	96.60 113	97.95 140	97.28 225	97.70 88	99.55 182	97.27 267	91.17 230	99.43 60	99.54 114	90.92 164	96.89 295	94.67 185	99.62 104	99.25 185

test_djsdf		92.83 222	92.29 223	94.47 255	91.90 340	92.46 244	99.55 182	97.27 267	91.17 230	89.96 244	96.07 269	81.10 257	96.89 295	94.67 185	88.91 239	94.05 289

PS-MVSNAJ		98.44 48	98.20 52	99.16 62	98.80 144	98.92 27	99.54 184	98.17 182	97.34 17	99.85 7	99.85 35	91.20 157	99.89 84	99.41 46	99.67 101	98.69 211

CDS-MVSNet		96.34 136	96.07 125	97.13 173	97.37 217	94.96 187	99.53 185	97.91 208	91.55 221	95.37 187	98.32 201	95.05 57	97.13 276	93.80 204	95.75 198	99.30 181
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
xiu_mvs_v2_base		98.23 65	97.97 67	99.02 80	98.69 148	98.66 50	99.52 186	98.08 193	97.05 27	99.86 5	99.86 31	90.65 168	99.71 134	99.39 47	98.63 136	98.69 211

PatchMatch-RL		96.04 146	95.40 151	97.95 140	99.59 96	95.22 182	99.52 186	99.07 32	93.96 137	96.49 165	98.35 200	82.28 245	99.82 110	90.15 260	99.22 125	98.81 207

test_method		80.79 329	79.70 332	84.08 346	92.83 329	67.06 371	99.51 188	95.42 349	54.34 369	81.07 342	93.53 335	44.48 372	92.22 362	78.90 339	77.23 340	92.94 332

baseline		96.43 133	95.98 130	97.76 149	97.34 219	95.17 184	99.51 188	97.17 274	93.92 140	96.90 153	99.28 131	85.37 224	98.64 187	97.50 129	96.86 178	99.46 162

miper_ehance_all_eth		93.16 213	92.60 215	94.82 239	97.57 209	93.56 217	99.50 190	97.07 286	88.75 272	88.85 275	95.52 285	90.97 163	96.74 302	90.77 250	84.45 287	94.17 273

v1192		90.62 271	89.25 279	94.72 242	93.13 319	93.07 228	99.50 190	97.02 292	86.33 306	89.56 258	95.01 308	79.22 275	97.09 281	82.34 323	81.16 308	94.01 292

v1921920		90.46 273	89.12 281	94.50 253	92.96 327	92.46 244	99.49 192	96.98 297	86.10 308	89.61 257	95.30 298	78.55 282	97.03 288	82.17 324	80.89 315	94.01 292

无先验							99.49 192	98.71 55	93.46 155				100.00 1	94.36 190		99.99 24

pmmvs4		92.10 241	91.07 247	95.18 226	92.82 330	94.96 187	99.48 194	96.83 313	87.45 290	88.66 279	96.56 256	83.78 236	96.83 299	89.29 266	84.77 285	93.75 312

Vis-MVSNet (Re-imp)		96.32 137	95.98 130	97.35 169	97.93 186	94.82 191	99.47 195	98.15 187	91.83 213	95.09 190	99.11 144	91.37 155	97.47 255	93.47 212	97.43 163	99.74 113

API-MVS		97.86 77	97.66 78	98.47 118	99.52 103	95.41 174	99.47 195	98.87 46	91.68 218	98.84 95	99.85 35	92.34 140	99.99 40	98.44 94	99.96 52	100.00 1

旧先验2							99.46 197		94.21 123	99.85 7			99.95 65	96.96 145

IterMVS-LS		92.69 228	92.11 226	94.43 259	96.80 245	92.74 235	99.45 198	96.89 308	88.98 265	89.65 255	95.38 294	88.77 192	96.34 317	90.98 245	82.04 301	94.22 269
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
3Dnovator	91.47 12	96.28 141	95.34 154	99.08 74	96.82 244	97.47 102	99.45 198	98.81 50	95.52 76	89.39 260	99.00 153	81.97 247	99.95 65	97.27 134	99.83 85	99.84 101

FC-MVSNet-test		93.81 199	93.15 206	95.80 211	94.30 297	96.20 149	99.42 200	98.89 43	92.33 199	89.03 272	97.27 231	87.39 204	96.83 299	93.20 215	86.48 272	94.36 257

c3_l		92.53 231	91.87 233	94.52 251	97.40 216	92.99 231	99.40 201	96.93 305	87.86 285	88.69 278	95.44 289	89.95 176	96.44 313	90.45 254	80.69 316	94.14 282

EI-MVSNet-Vis-set		98.27 60	98.11 59	98.75 95	99.83 68	96.59 133	99.40 201	98.51 99	95.29 81	98.51 112	99.76 76	93.60 106	99.71 134	98.53 92	99.52 112	99.95 82

新几何2							99.40 201

QAPM		95.40 163	94.17 178	99.10 72	96.92 236	97.71 86	99.40 201	98.68 58	89.31 259	88.94 273	98.89 169	82.48 243	99.96 58	93.12 220	99.83 85	99.62 133

MTAPA		98.29 59	97.96 70	99.30 50	99.85 60	97.93 81	99.39 205	98.28 167	95.76 66	97.18 147	99.88 24	92.74 129	100.00 1	98.67 84	99.88 80	99.99 24

miper_lstm_enhance		91.81 245	91.39 243	93.06 300	97.34 219	89.18 308	99.38 206	96.79 317	86.70 302	87.47 296	95.22 303	90.00 175	95.86 333	88.26 276	81.37 306	94.15 279

v1240		90.20 281	88.79 288	94.44 257	93.05 325	92.27 248	99.38 206	96.92 306	85.89 310	89.36 261	94.87 315	77.89 287	97.03 288	80.66 331	81.08 311	94.01 292

EPP-MVSNet		96.69 125	96.60 113	96.96 177	97.74 199	93.05 230	99.37 208	98.56 79	88.75 272	95.83 180	99.01 151	96.01 32	98.56 190	96.92 147	97.20 170	99.25 185

MSDG		94.37 188	93.36 202	97.40 164	98.88 139	93.95 209	99.37 208	97.38 258	85.75 315	90.80 235	99.17 142	84.11 235	99.88 90	86.35 298	98.43 140	98.36 213

EI-MVSNet-UG-set		98.14 67	97.99 66	98.60 105	99.80 74	96.27 143	99.36 210	98.50 103	95.21 83	98.30 122	99.75 82	93.29 113	99.73 133	98.37 97	99.30 121	99.81 104

test222					99.55 101	97.41 107	99.34 211	98.55 85	91.86 212	99.27 76	99.83 51	93.84 100			99.95 55	99.99 24

our_test_3		90.39 274	89.48 277	93.12 297	92.40 334	89.57 304	99.33 212	96.35 332	87.84 286	85.30 321	94.99 311	84.14 234	96.09 327	80.38 332	84.56 286	93.71 317

ppachtmachnet_test		89.58 292	88.35 294	93.25 295	92.40 334	90.44 289	99.33 212	96.73 320	85.49 319	85.90 319	95.77 273	81.09 258	96.00 331	76.00 350	82.49 297	93.30 325

mvs_anonymous		95.65 158	95.03 163	97.53 156	98.19 173	95.74 164	99.33 212	97.49 248	90.87 238	90.47 238	97.10 235	88.23 197	97.16 273	95.92 159	97.66 160	99.68 120

AUN-MVS		93.28 211	92.60 215	95.34 220	98.29 166	90.09 295	99.31 215	98.56 79	91.80 216	96.35 171	98.00 208	89.38 182	98.28 219	92.46 224	69.22 357	97.64 225

xiu_mvs_v1_base_debu		97.43 93	97.06 98	98.55 110	97.74 199	98.14 70	99.31 215	97.86 213	96.43 45	99.62 43	99.69 98	85.56 221	99.68 138	99.05 56	98.31 143	97.83 220

xiu_mvs_v1_base		97.43 93	97.06 98	98.55 110	97.74 199	98.14 70	99.31 215	97.86 213	96.43 45	99.62 43	99.69 98	85.56 221	99.68 138	99.05 56	98.31 143	97.83 220

xiu_mvs_v1_base_debi		97.43 93	97.06 98	98.55 110	97.74 199	98.14 70	99.31 215	97.86 213	96.43 45	99.62 43	99.69 98	85.56 221	99.68 138	99.05 56	98.31 143	97.83 220

MVS_Test		96.46 132	95.74 145	98.61 104	98.18 174	97.23 110	99.31 215	97.15 277	91.07 234	98.84 95	97.05 239	88.17 198	98.97 170	94.39 189	97.50 162	99.61 136

hse-mvs2		94.38 187	94.08 180	95.31 222	98.27 169	90.02 297	99.29 220	98.56 79	95.90 60	98.77 99	98.00 208	90.89 166	98.26 223	97.80 119	69.20 358	97.64 225

testdata1							99.28 221		96.35 52

Vis-MVSNet		95.72 153	95.15 160	97.45 160	97.62 207	94.28 201	99.28 221	98.24 173	94.27 122	96.84 155	98.94 166	79.39 274	98.76 179	93.25 214	98.49 138	99.30 181
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
FMVSNet3		92.69 228	91.58 237	95.99 206	98.29 166	97.42 106	99.26 223	97.62 229	89.80 256	89.68 252	95.32 297	81.62 253	96.27 320	87.01 294	85.65 276	94.29 265

DeepC-MVS_fast	96.59 1	98.81 23	98.54 28	99.62 18	99.90 47	98.85 33	99.24 224	98.47 105	98.14 4	99.08 84	99.91 15	93.09 120	100.00 1	99.04 60	99.99 22	100.00 1
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
dcpmvs_2		97.42 97	98.09 60	95.42 218	99.58 100	87.24 326	99.23 225	96.95 300	94.28 120	98.93 93	99.73 89	94.39 78	99.16 164	99.89 17	99.82 91	99.86 100

YYNet1		85.50 315	83.33 320	92.00 311	90.89 350	88.38 319	99.22 226	96.55 326	79.60 350	57.26 370	92.72 341	79.09 278	93.78 355	77.25 345	77.37 339	93.84 307

v8		90.54 272	89.17 280	94.66 243	93.43 312	93.40 224	99.20 227	96.94 304	85.76 313	87.56 294	94.51 323	81.96 248	97.19 272	84.94 308	78.25 330	93.38 324

MDA-MVSNet_test_wron		85.51 314	83.32 321	92.10 310	90.96 349	88.58 315	99.20 227	96.52 327	79.70 349	57.12 371	92.69 343	79.11 277	93.86 354	77.10 346	77.46 338	93.86 306

ACMMP		97.74 86	97.44 86	98.66 100	99.92 36	96.13 152	99.18 229	99.45 18	94.84 95	96.41 169	99.71 93	91.40 154	99.99 40	97.99 113	98.03 155	99.87 98
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
WR-MVS_H		91.30 254	90.35 257	94.15 265	94.17 299	92.62 242	99.17 230	98.94 37	88.87 270	86.48 310	94.46 327	84.36 231	96.61 308	88.19 277	78.51 329	93.21 328

TAMVS		95.85 150	95.58 148	96.65 188	97.07 229	93.50 219	99.17 230	97.82 217	91.39 229	95.02 191	98.01 207	92.20 142	97.30 264	93.75 207	95.83 195	99.14 193

bld_raw_dy_0_64		92.74 225	92.03 229	94.87 235	93.09 323	93.46 220	99.12 232	95.41 350	92.84 172	90.44 239	97.54 222	78.08 285	97.04 285	93.94 198	87.77 262	94.11 284

PS-MVSNAJss		93.64 206	93.31 203	94.61 245	92.11 337	92.19 250	99.12 232	97.38 258	92.51 194	88.45 280	96.99 242	91.20 157	97.29 267	94.36 190	87.71 263	94.36 257

DTE-MVSNet		89.40 293	88.24 297	92.88 302	92.66 332	89.95 299	99.10 234	98.22 176	87.29 292	85.12 323	96.22 264	76.27 299	95.30 340	83.56 317	75.74 347	93.41 321

CP-MVSNet		91.23 257	90.22 261	94.26 262	93.96 302	92.39 246	99.09 235	98.57 77	88.95 268	86.42 311	96.57 255	79.19 276	96.37 315	90.29 258	78.95 326	94.02 290

AdaColmap		97.23 104	96.80 108	98.51 116	99.99 1	95.60 170	99.09 235	98.84 49	93.32 159	96.74 158	99.72 91	86.04 217	100.00 1	98.01 111	99.43 118	99.94 84

v10		90.25 280	88.82 287	94.57 249	93.53 310	93.43 222	99.08 237	96.87 310	85.00 323	87.34 300	94.51 323	80.93 260	97.02 290	82.85 320	79.23 324	93.26 326

XVG-OURS-SEG-HR		94.79 173	94.70 170	95.08 228	98.05 180	89.19 306	99.08 237	97.54 240	93.66 150	94.87 192	99.58 110	78.78 279	99.79 114	97.31 133	93.40 222	96.25 233

XVG-OURS		94.82 172	94.74 169	95.06 229	98.00 182	89.19 306	99.08 237	97.55 238	94.10 127	94.71 193	99.62 107	80.51 266	99.74 130	96.04 157	93.06 226	96.25 233

IS-MVSNet		96.29 140	95.90 141	97.45 160	98.13 178	94.80 192	99.08 237	97.61 232	92.02 209	95.54 185	98.96 160	90.64 169	98.08 230	93.73 208	97.41 166	99.47 161

v7n		89.65 291	88.29 296	93.72 283	92.22 336	90.56 286	99.07 241	97.10 282	85.42 321	86.73 304	94.72 316	80.06 270	97.13 276	81.14 329	78.12 332	93.49 320

EI-MVSNet		93.73 203	93.40 201	94.74 240	96.80 245	92.69 238	99.06 242	97.67 224	88.96 267	91.39 228	99.02 149	88.75 193	97.30 264	91.07 240	87.85 260	94.22 269

CVMVSNet		94.68 179	94.94 165	93.89 279	96.80 245	86.92 328	99.06 242	98.98 35	94.45 108	94.23 201	99.02 149	85.60 220	95.31 339	90.91 247	95.39 204	99.43 167

baseline1		95.78 152	94.86 166	98.54 113	98.47 159	98.07 73	99.06 242	97.99 198	92.68 182	94.13 202	98.62 186	93.28 114	98.69 185	93.79 205	85.76 275	98.84 205

PEN-MVS		90.19 282	89.06 283	93.57 289	93.06 324	90.90 278	99.06 242	98.47 105	88.11 282	85.91 318	96.30 262	76.67 293	95.94 332	87.07 291	76.91 343	93.89 303

Anonymous20231206		86.32 309	85.42 311	89.02 334	89.11 360	80.53 363	99.05 246	95.28 353	85.43 320	82.82 332	93.92 331	74.40 314	93.44 358	66.99 363	81.83 303	93.08 330

MAR-MVS		97.43 93	97.19 95	98.15 135	99.47 107	94.79 193	99.05 246	98.76 53	92.65 184	98.66 106	99.82 55	88.52 196	99.98 46	98.12 105	99.63 103	99.67 122
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
VNet		97.21 105	96.57 115	99.13 71	98.97 128	97.82 84	99.03 248	99.21 28	94.31 118	99.18 82	98.88 170	86.26 216	99.89 84	98.93 65	94.32 213	99.69 119

LCM-MVSNet-Re		92.31 236	92.60 215	91.43 316	97.53 210	79.27 365	99.02 249	91.83 372	92.07 206	80.31 344	94.38 328	83.50 238	95.48 335	97.22 137	97.58 161	99.54 151

jajsoiax		91.92 243	91.18 245	94.15 265	91.35 346	90.95 277	99.00 250	97.42 254	92.61 186	87.38 298	97.08 236	72.46 321	97.36 257	94.53 188	88.77 243	94.13 283

test_part1		92.15 240	90.72 250	96.44 194	98.87 140	97.46 103	98.99 251	98.26 171	85.89 310	86.34 313	96.34 261	81.71 249	97.48 254	91.06 241	78.99 325	94.37 255

bld_raw_conf005		92.79 223	92.18 224	94.61 245	93.38 315	92.27 248	98.99 251	95.20 356	93.34 158	89.25 264	97.67 220	78.03 286	97.21 271	95.81 163	87.99 259	94.35 260

VPNet		91.81 245	90.46 254	95.85 210	94.74 290	95.54 171	98.98 253	98.59 74	92.14 204	90.77 236	97.44 225	68.73 335	97.54 252	94.89 177	77.89 333	94.46 245

PS-CasMVS		90.63 270	89.51 275	93.99 275	93.83 304	91.70 266	98.98 253	98.52 92	88.48 278	86.15 316	96.53 257	75.46 304	96.31 318	88.83 270	78.86 328	93.95 298

FMVSNet2		91.02 260	89.56 272	95.41 219	97.53 210	95.74 164	98.98 253	97.41 256	87.05 295	88.43 283	95.00 310	71.34 325	96.24 322	85.12 306	85.21 281	94.25 268

K. test v3		88.05 303	87.24 305	90.47 324	91.82 342	82.23 351	98.96 256	97.42 254	89.05 262	76.93 354	95.60 280	68.49 336	95.42 336	85.87 303	81.01 313	93.75 312

tfpnnormal		89.29 295	87.61 302	94.34 261	94.35 296	94.13 203	98.95 257	98.94 37	83.94 330	84.47 325	95.51 286	74.84 311	97.39 256	77.05 347	80.41 317	91.48 349

AllTest		92.48 232	91.64 235	95.00 231	99.01 124	88.43 316	98.94 258	96.82 315	86.50 303	88.71 276	98.47 197	74.73 312	99.88 90	85.39 304	96.18 186	96.71 231

h-mvs33		94.92 171	94.36 174	96.59 189	98.85 141	91.29 273	98.93 259	98.94 37	95.90 60	98.77 99	98.42 199	90.89 166	99.77 120	97.80 119	70.76 352	98.72 210

anonymousdsp		91.79 250	90.92 248	94.41 260	90.76 351	92.93 232	98.93 259	97.17 274	89.08 261	87.46 297	95.30 298	78.43 284	96.92 294	92.38 225	88.73 244	93.39 323

DP-MVS		94.54 182	93.42 198	97.91 143	99.46 109	94.04 205	98.93 259	97.48 249	81.15 344	90.04 243	99.55 112	87.02 208	99.95 65	88.97 269	98.11 150	99.73 114

IterMVS-SCA-FT		90.85 265	90.16 264	92.93 301	96.72 250	89.96 298	98.89 262	96.99 295	88.95 268	86.63 306	95.67 277	76.48 296	95.00 342	87.04 292	84.04 293	93.84 307

IterMVS		90.91 262	90.17 263	93.12 297	96.78 248	90.42 290	98.89 262	97.05 290	89.03 263	86.49 309	95.42 290	76.59 295	95.02 341	87.22 290	84.09 290	93.93 300
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Anonymous202405211		93.10 217	91.99 230	96.40 196	99.10 120	89.65 303	98.88 264	97.93 205	83.71 333	94.00 203	98.75 179	68.79 333	99.88 90	95.08 170	91.71 228	99.68 120

VPA-MVSNet		92.70 227	91.55 239	96.16 203	95.09 284	96.20 149	98.88 264	99.00 34	91.02 236	91.82 225	95.29 301	76.05 302	97.96 238	95.62 164	81.19 307	94.30 264

test20.03		84.72 320	83.99 314	86.91 342	88.19 363	80.62 362	98.88 264	95.94 339	88.36 280	78.87 348	94.62 321	68.75 334	89.11 369	66.52 364	75.82 346	91.00 351

XXY-MVS		91.82 244	90.46 254	95.88 208	93.91 303	95.40 175	98.87 267	97.69 222	88.63 276	87.87 290	97.08 236	74.38 315	97.89 242	91.66 233	84.07 291	94.35 260

test1111		95.57 159	94.98 164	97.37 166	98.56 152	93.37 225	98.86 268	98.45 109	94.95 88	96.63 160	98.95 164	75.21 309	99.11 165	95.02 171	98.14 149	99.64 128

SCA		94.69 177	93.81 187	97.33 170	97.10 228	94.44 196	98.86 268	98.32 160	93.30 160	96.17 174	95.59 281	76.48 296	97.95 239	91.06 241	97.43 163	99.59 139

ECVR-MVS		95.66 157	95.05 162	97.51 158	98.66 150	93.71 214	98.85 270	98.45 109	94.93 89	96.86 154	98.96 160	75.22 308	99.20 160	95.34 165	98.15 147	99.64 128

eth_miper_zixun_eth		92.41 234	91.93 231	93.84 280	97.28 225	90.68 281	98.83 271	96.97 299	88.57 277	89.19 269	95.73 276	89.24 187	96.69 305	89.97 262	81.55 304	94.15 279

CL-MVSNet_self_test		84.50 321	83.15 323	88.53 338	86.00 366	81.79 354	98.82 272	97.35 260	85.12 322	83.62 330	90.91 354	76.66 294	91.40 364	69.53 359	60.36 367	92.40 340

test2506		97.53 91	97.19 95	98.58 108	98.66 150	96.90 124	98.81 273	99.77 5	94.93 89	97.95 131	98.96 160	92.51 135	99.20 160	94.93 173	98.15 147	99.64 128

ACMH+	89.98 16	90.35 276	89.54 273	92.78 304	95.99 261	86.12 332	98.81 273	97.18 273	89.38 258	83.14 331	97.76 218	68.42 337	98.43 199	89.11 268	86.05 274	93.78 311

Anonymous20240521		85.15 317	83.81 318	89.16 333	88.32 361	82.69 346	98.80 275	95.74 342	79.72 348	81.53 339	90.99 352	65.38 348	94.16 350	72.69 354	81.11 310	90.63 355

N_pmnet		80.06 332	80.78 330	77.89 350	91.94 339	45.28 381	98.80 275	56.82 384	78.10 353	80.08 346	93.33 336	77.03 289	95.76 334	68.14 362	82.81 296	92.64 335

VDD-MVS		93.77 201	92.94 207	96.27 201	98.55 154	90.22 292	98.77 277	97.79 218	90.85 239	96.82 156	99.42 122	61.18 359	99.77 120	98.95 63	94.13 215	98.82 206

LFMVS		94.75 176	93.56 194	98.30 128	99.03 123	95.70 167	98.74 278	97.98 200	87.81 287	98.47 114	99.39 126	67.43 341	99.53 146	98.01 111	95.20 207	99.67 122

LS3D		95.84 151	95.11 161	98.02 139	99.85 60	95.10 185	98.74 278	98.50 103	87.22 294	93.66 207	99.86 31	87.45 203	99.95 65	90.94 246	99.81 93	99.02 198

Anonymous20240529		92.10 241	90.65 252	96.47 190	98.82 142	90.61 284	98.72 280	98.67 61	75.54 359	93.90 205	98.58 189	66.23 344	99.90 80	94.70 184	90.67 229	98.90 203

TR-MVS		94.54 182	93.56 194	97.49 159	97.96 184	94.34 200	98.71 281	97.51 246	90.30 249	94.51 196	98.69 181	75.56 303	98.77 178	92.82 222	95.99 190	99.35 176

USDC		90.00 286	88.96 285	93.10 299	94.81 289	88.16 320	98.71 281	95.54 348	93.66 150	83.75 329	97.20 232	65.58 346	98.31 216	83.96 314	87.49 267	92.85 334

VDDNet		93.12 216	91.91 232	96.76 183	96.67 252	92.65 241	98.69 283	98.21 177	82.81 338	97.75 137	99.28 131	61.57 357	99.48 154	98.09 108	94.09 216	98.15 216

EU-MVSNet		90.14 284	90.34 258	89.54 331	92.55 333	81.06 359	98.69 283	98.04 196	91.41 228	86.59 307	96.84 249	80.83 261	93.31 359	86.20 299	81.91 302	94.26 266

mvs_tets		91.81 245	91.08 246	94.00 274	91.63 344	90.58 285	98.67 285	97.43 252	92.43 196	87.37 299	97.05 239	71.76 323	97.32 263	94.75 182	88.68 245	94.11 284

MDA-MVSNet-bldmvs		84.09 323	81.52 329	91.81 314	91.32 347	88.00 323	98.67 285	95.92 340	80.22 347	55.60 372	93.32 337	68.29 338	93.60 357	73.76 352	76.61 345	93.82 309

UGNet		95.33 164	94.57 171	97.62 155	98.55 154	94.85 189	98.67 285	99.32 25	95.75 69	96.80 157	96.27 263	72.18 322	99.96 58	94.58 187	99.05 129	98.04 218
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
pm-mvs1		89.36 294	87.81 301	94.01 272	93.40 314	91.93 256	98.62 288	96.48 329	86.25 307	83.86 328	96.14 266	73.68 318	97.04 285	86.16 300	75.73 348	93.04 331

test_0402		85.58 312	83.94 316	90.50 323	93.81 305	85.04 339	98.55 289	95.20 356	76.01 356	79.72 347	95.13 304	64.15 352	96.26 321	66.04 366	86.88 270	90.21 358

ACMH	89.72 17	90.64 269	89.63 270	93.66 288	95.64 277	88.64 314	98.55 289	97.45 250	89.03 263	81.62 338	97.61 221	69.75 331	98.41 201	89.37 265	87.62 265	93.92 301
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Anonymous20231211		89.86 287	88.44 293	94.13 267	98.93 132	90.68 281	98.54 291	98.26 171	76.28 355	86.73 304	95.54 283	70.60 329	97.56 251	90.82 249	80.27 320	94.15 279

TransMVSNet (Re)		87.25 307	85.28 312	93.16 296	93.56 309	91.03 275	98.54 291	94.05 366	83.69 334	81.09 341	96.16 265	75.32 305	96.40 314	76.69 348	68.41 359	92.06 343

XVG-ACMP-BASELINE		91.22 258	90.75 249	92.63 305	93.73 306	85.61 334	98.52 293	97.44 251	92.77 177	89.90 247	96.85 247	66.64 343	98.39 205	92.29 226	88.61 246	93.89 303

CHOSEN 280x420		99.01 13	99.03 10	98.95 86	99.38 111	98.87 31	98.46 294	99.42 21	97.03 28	99.02 87	99.09 145	99.35 1	98.21 225	99.73 33	99.78 94	99.77 110

OpenMVS_ROB	79.82 20	83.77 325	81.68 328	90.03 328	88.30 362	82.82 345	98.46 294	95.22 355	73.92 363	76.00 357	91.29 351	55.00 365	96.94 292	68.40 361	88.51 250	90.34 356

GBi-Net		90.88 263	89.82 268	94.08 268	97.53 210	91.97 253	98.43 296	96.95 300	87.05 295	89.68 252	94.72 316	71.34 325	96.11 324	87.01 294	85.65 276	94.17 273

test1		90.88 263	89.82 268	94.08 268	97.53 210	91.97 253	98.43 296	96.95 300	87.05 295	89.68 252	94.72 316	71.34 325	96.11 324	87.01 294	85.65 276	94.17 273

FMVSNet1		88.50 300	86.64 306	94.08 268	95.62 279	91.97 253	98.43 296	96.95 300	83.00 336	86.08 317	94.72 316	59.09 361	96.11 324	81.82 327	84.07 291	94.17 273

COLMAP_ROB	90.47 14	92.18 239	91.49 241	94.25 263	99.00 126	88.04 322	98.42 299	96.70 322	82.30 341	88.43 283	99.01 151	76.97 290	99.85 99	86.11 301	96.50 182	94.86 238
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
test123		37.68 346	39.14 349	33.31 361	19.94 385	24.83 386	98.36 300	9.75 386	15.53 379	51.31 374	87.14 361	19.62 381	17.74 381	47.10 374	3.47 380	57.36 374

1314		96.84 116	95.96 135	99.48 36	96.74 249	98.52 59	98.31 301	98.86 47	95.82 62	89.91 246	98.98 156	87.49 202	99.96 58	97.80 119	99.73 97	99.96 74

1121		98.03 71	97.57 83	99.40 44	99.74 82	98.21 69	98.31 301	98.62 69	92.78 176	99.53 51	99.83 51	95.08 54	100.00 1	94.36 190	99.92 71	99.99 24

MVS		96.60 128	95.56 149	99.72 12	96.85 242	99.22 19	98.31 301	98.94 37	91.57 220	90.90 234	99.61 108	86.66 211	99.96 58	97.36 132	99.88 80	99.99 24

NR-MVSNet		91.56 253	90.22 261	95.60 212	94.05 300	95.76 163	98.25 304	98.70 56	91.16 232	80.78 343	96.64 253	83.23 241	96.57 309	91.41 235	77.73 335	94.46 245

MS-PatchMatch		90.65 268	90.30 259	91.71 315	94.22 298	85.50 336	98.24 305	97.70 221	88.67 274	86.42 311	96.37 260	67.82 339	98.03 234	83.62 316	99.62 104	91.60 347

pmmvs3		80.27 331	77.77 335	87.76 341	80.32 372	82.43 349	98.23 306	91.97 371	72.74 364	78.75 349	87.97 359	57.30 364	90.99 366	70.31 357	62.37 365	89.87 359

SixPastTwentyTwo		88.73 299	88.01 300	90.88 319	91.85 341	82.24 350	98.22 307	95.18 358	88.97 266	82.26 334	96.89 244	71.75 324	96.67 306	84.00 312	82.98 295	93.72 316

EG-PatchMatch MVS		85.35 316	83.81 318	89.99 329	90.39 353	81.89 353	98.21 308	96.09 337	81.78 343	74.73 360	93.72 334	51.56 369	97.12 278	79.16 338	88.61 246	90.96 352

OurMVSNet-221017-0		89.81 288	89.48 277	90.83 321	91.64 343	81.21 357	98.17 309	95.38 352	91.48 223	85.65 320	97.31 229	72.66 320	97.29 267	88.15 278	84.83 284	93.97 297

LF4IMVS		89.25 297	88.85 286	90.45 325	92.81 331	81.19 358	98.12 310	94.79 360	91.44 225	86.29 314	97.11 234	65.30 349	98.11 229	88.53 274	85.25 280	92.07 342

RPSCF		91.80 248	92.79 212	88.83 335	98.15 176	69.87 369	98.11 311	96.60 325	83.93 331	94.33 199	99.27 134	79.60 273	99.46 155	91.99 228	93.16 225	97.18 229

pmmvs-eth3d		84.03 324	81.97 327	90.20 326	84.15 369	87.09 327	98.10 312	94.73 362	83.05 335	74.10 361	87.77 360	65.56 347	94.01 351	81.08 330	69.24 356	89.49 362

DSMNet-mixed		88.28 302	88.24 297	88.42 339	89.64 358	75.38 367	98.06 313	89.86 375	85.59 317	88.20 287	92.14 349	76.15 301	91.95 363	78.46 340	96.05 189	97.92 219

MVP-Stereo		90.93 261	90.45 256	92.37 307	91.25 348	88.76 310	98.05 314	96.17 335	87.27 293	84.04 326	95.30 298	78.46 283	97.27 269	83.78 315	99.70 100	91.09 350
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
UA-Net		96.54 129	95.96 135	98.27 129	98.23 171	95.71 166	98.00 315	98.45 109	93.72 149	98.41 116	99.27 134	88.71 194	99.66 142	91.19 238	97.69 158	99.44 166

new-patchmatchnet		81.19 328	79.34 333	86.76 343	82.86 371	80.36 364	97.92 316	95.27 354	82.09 342	72.02 362	86.87 362	62.81 355	90.74 367	71.10 356	63.08 364	89.19 364

PCF-MVS	94.20 5	95.18 165	94.10 179	98.43 122	98.55 154	95.99 156	97.91 317	97.31 265	90.35 247	89.48 259	99.22 140	85.19 225	99.89 84	90.40 257	98.47 139	99.41 169
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
pmmvs6		85.69 311	83.84 317	91.26 318	90.00 357	84.41 342	97.82 318	96.15 336	75.86 357	81.29 340	95.39 293	61.21 358	96.87 297	83.52 318	73.29 351	92.50 338

UniMVSNet_ETH3D		90.06 285	88.58 291	94.49 254	94.67 292	88.09 321	97.81 319	97.57 237	83.91 332	88.44 281	97.41 226	57.44 363	97.62 250	91.41 235	88.59 248	97.77 223

MVS_0304		89.28 296	88.31 295	92.21 309	97.05 231	86.53 330	97.76 320	99.57 13	85.58 318	93.86 206	92.71 342	51.04 370	96.30 319	84.49 310	92.72 227	93.79 310

TinyColmap		87.87 306	86.51 307	91.94 312	95.05 286	85.57 335	97.65 321	94.08 365	84.40 329	81.82 337	96.85 247	62.14 356	98.33 214	80.25 333	86.37 273	91.91 346

HY-MVS	92.50 7	97.79 83	97.17 97	99.63 15	98.98 127	99.32 8	97.49 322	99.52 14	95.69 70	98.32 121	97.41 226	93.32 111	99.77 120	98.08 109	95.75 198	99.81 104

Effi-MVS+		96.30 139	95.69 146	98.16 132	97.85 191	96.26 144	97.41 323	97.21 270	90.37 246	98.65 107	98.58 189	86.61 212	98.70 184	97.11 139	97.37 167	99.52 155

TDRefinement		84.76 318	82.56 325	91.38 317	74.58 374	84.80 341	97.36 324	94.56 363	84.73 327	80.21 345	96.12 268	63.56 353	98.39 205	87.92 281	63.97 363	90.95 353

FMVSNet5		88.32 301	87.47 303	90.88 319	96.90 240	88.39 318	97.28 325	95.68 344	82.60 340	84.67 324	92.40 347	79.83 272	91.16 365	76.39 349	81.51 305	93.09 329

KD-MVS_self_test		83.59 326	82.06 326	88.20 340	86.93 364	80.70 361	97.21 326	96.38 331	82.87 337	82.49 333	88.97 357	67.63 340	92.32 361	73.75 353	62.30 366	91.58 348

LTVRE_ROB	88.28 18	90.29 279	89.05 284	94.02 271	95.08 285	90.15 294	97.19 327	97.43 252	84.91 326	83.99 327	97.06 238	74.00 317	98.28 219	84.08 311	87.71 263	93.62 318
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
KD-MVS_2432*1600		88.00 304	86.10 308	93.70 286	96.91 237	94.04 205	97.17 328	97.12 280	84.93 324	81.96 335	92.41 345	92.48 136	94.51 348	79.23 335	52.68 370	92.56 336

miper_refine_blended		88.00 304	86.10 308	93.70 286	96.91 237	94.04 205	97.17 328	97.12 280	84.93 324	81.96 335	92.41 345	92.48 136	94.51 348	79.23 335	52.68 370	92.56 336

CostFormer		96.10 143	95.88 142	96.78 182	97.03 232	92.55 243	97.08 330	97.83 216	90.04 253	98.72 103	94.89 314	95.01 59	98.29 217	96.54 152	95.77 196	99.50 159

tpm		93.70 205	93.41 200	94.58 248	95.36 282	87.41 325	97.01 331	96.90 307	90.85 239	96.72 159	94.14 330	90.40 171	96.84 298	90.75 251	88.54 249	99.51 157

CMPMVS	61.59 21	84.75 319	85.14 313	83.57 347	90.32 354	62.54 373	96.98 332	97.59 236	74.33 362	69.95 365	96.66 251	64.17 351	98.32 215	87.88 282	88.41 251	89.84 360
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
tpm2		95.47 162	95.18 159	96.35 199	96.91 237	91.70 266	96.96 333	97.93 205	88.04 284	98.44 115	95.40 291	93.32 111	97.97 236	94.00 197	95.61 200	99.38 171

new_pmnet		84.49 322	82.92 324	89.21 332	90.03 356	82.60 347	96.89 334	95.62 346	80.59 346	75.77 359	89.17 356	65.04 350	94.79 346	72.12 355	81.02 312	90.23 357

UnsupCasMVSNet_eth		85.52 313	83.99 314	90.10 327	89.36 359	83.51 344	96.65 335	97.99 198	89.14 260	75.89 358	93.83 332	63.25 354	93.92 352	81.92 326	67.90 361	92.88 333

MIMVSNet1		82.58 327	80.51 331	88.78 336	86.68 365	84.20 343	96.65 335	95.41 350	78.75 351	78.59 350	92.44 344	51.88 368	89.76 368	65.26 367	78.95 326	92.38 341

ab-mvs		94.69 177	93.42 198	98.51 116	98.07 179	96.26 144	96.49 337	98.68 58	90.31 248	94.54 194	97.00 241	76.30 298	99.71 134	95.98 158	93.38 223	99.56 147

EPMVS		96.53 130	96.01 127	98.09 136	98.43 160	96.12 154	96.36 338	99.43 20	93.53 153	97.64 138	95.04 307	94.41 73	98.38 209	91.13 239	98.11 150	99.75 112

tpmrst		96.27 142	95.98 130	97.13 173	97.96 184	93.15 227	96.34 339	98.17 182	92.07 206	98.71 104	95.12 305	93.91 97	98.73 181	94.91 176	96.62 179	99.50 159

dp		95.05 168	94.43 173	96.91 178	97.99 183	92.73 237	96.29 340	97.98 200	89.70 257	95.93 177	94.67 320	93.83 101	98.45 198	86.91 297	96.53 181	99.54 151

EGC-MVSNET		69.38 334	63.76 341	86.26 344	90.32 354	81.66 356	96.24 341	93.85 368	0.99 381	3.22 382	92.33 348	52.44 367	92.92 360	59.53 371	84.90 283	84.21 367

tpm cat1		93.51 207	92.52 220	96.47 190	97.77 196	91.47 272	96.13 342	98.06 194	80.98 345	92.91 216	93.78 333	89.66 178	98.87 172	87.03 293	96.39 184	99.09 196

MDTV_nov1_ep13_2view						96.26 144	96.11 343		91.89 211	98.06 128		94.40 74		94.30 193		99.67 122

PatchmatchNet		95.94 149	95.45 150	97.39 165	97.83 192	94.41 199	96.05 344	98.40 140	92.86 169	97.09 149	95.28 302	94.21 90	98.07 232	89.26 267	98.11 150	99.70 117
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MDTV_nov1_ep13			95.69 146		97.90 187	94.15 202	95.98 345	98.44 112	93.12 165	97.98 130	95.74 274	95.10 53	98.58 189	90.02 261	96.92 176

FPMVS		68.72 335	68.72 337	68.71 355	65.95 378	44.27 383	95.97 346	94.74 361	51.13 370	53.26 373	90.50 355	25.11 378	83.00 373	60.80 369	80.97 314	78.87 369

PM-MVS		80.47 330	78.88 334	85.26 345	83.79 370	72.22 368	95.89 347	91.08 373	85.71 316	76.56 356	88.30 358	36.64 373	93.90 353	82.39 322	69.57 355	89.66 361

test_post1							95.78 348				59.23 379	93.20 118	97.74 246	91.06 241

tpmvs		94.28 192	93.57 193	96.40 196	98.55 154	91.50 271	95.70 349	98.55 85	87.47 289	92.15 223	94.26 329	91.42 153	98.95 171	88.15 278	95.85 194	98.76 209

ADS-MVSNet2		93.80 200	93.88 185	93.55 290	97.87 189	85.94 333	94.24 350	96.84 312	90.07 251	96.43 167	94.48 325	90.29 173	95.37 337	87.44 285	97.23 168	99.36 174

ADS-MVSNet		94.79 173	94.02 181	97.11 175	97.87 189	93.79 211	94.24 350	98.16 185	90.07 251	96.43 167	94.48 325	90.29 173	98.19 226	87.44 285	97.23 168	99.36 174

EMVS		51.44 344	51.22 346	52.11 360	70.71 376	44.97 382	94.04 352	75.66 383	35.34 377	42.40 377	61.56 378	28.93 376	65.87 379	27.64 379	24.73 375	45.49 376

PMMVS2		67.15 337	64.15 340	76.14 352	70.56 377	62.07 374	93.89 353	87.52 379	58.09 368	60.02 368	78.32 368	22.38 379	84.54 372	59.56 370	47.03 372	81.80 368

GG-mvs-BLEND				98.54 113	98.21 172	98.01 76	93.87 354	98.52 92		97.92 132	97.92 214	99.02 2	97.94 241	98.17 102	99.58 109	99.67 122

UnsupCasMVSNet_bld		79.97 333	77.03 336	88.78 336	85.62 367	81.98 352	93.66 355	97.35 260	75.51 360	70.79 364	83.05 366	48.70 371	94.91 344	78.31 341	60.29 368	89.46 363

E-PMN		52.30 342	52.18 344	52.67 359	71.51 375	45.40 380	93.62 356	76.60 382	36.01 375	43.50 376	64.13 375	27.11 377	67.31 378	31.06 378	26.06 374	45.30 377

JIA-IIPM		91.76 251	90.70 251	94.94 233	96.11 257	87.51 324	93.16 357	98.13 190	75.79 358	97.58 139	77.68 369	92.84 126	97.97 236	88.47 275	96.54 180	99.33 178

gg-mvs-nofinetune		93.51 207	91.86 234	98.47 118	97.72 203	97.96 80	92.62 358	98.51 99	74.70 361	97.33 144	69.59 372	98.91 3	97.79 244	97.77 124	99.56 110	99.67 122

MIMVSNet		90.30 278	88.67 290	95.17 227	96.45 253	91.64 268	92.39 359	97.15 277	85.99 309	90.50 237	93.19 340	66.95 342	94.86 345	82.01 325	93.43 221	99.01 199

MVS-HIRNet		86.22 310	83.19 322	95.31 222	96.71 251	90.29 291	92.12 360	97.33 263	62.85 367	86.82 303	70.37 371	69.37 332	97.49 253	75.12 351	97.99 156	98.15 216

CR-MVSNet		93.45 210	92.62 214	95.94 207	96.29 254	92.66 239	92.01 361	96.23 333	92.62 185	96.94 151	93.31 338	91.04 161	96.03 329	79.23 335	95.96 191	99.13 194

RPMNet		89.76 289	87.28 304	97.19 172	96.29 254	92.66 239	92.01 361	98.31 162	70.19 366	96.94 151	85.87 365	87.25 205	99.78 116	62.69 368	95.96 191	99.13 194

Patchmatch-test		92.65 230	91.50 240	96.10 205	96.85 242	90.49 287	91.50 363	97.19 271	82.76 339	90.23 240	95.59 281	95.02 58	98.00 235	77.41 344	96.98 175	99.82 103

Patchmtry		89.70 290	88.49 292	93.33 292	96.24 256	89.94 301	91.37 364	96.23 333	78.22 352	87.69 291	93.31 338	91.04 161	96.03 329	80.18 334	82.10 300	94.02 290

PatchT		90.38 275	88.75 289	95.25 224	95.99 261	90.16 293	91.22 365	97.54 240	76.80 354	97.26 145	86.01 364	91.88 149	96.07 328	66.16 365	95.91 193	99.51 157

Patchmatch-RL test		86.90 308	85.98 310	89.67 330	84.45 368	75.59 366	89.71 366	92.43 370	86.89 300	77.83 352	90.94 353	94.22 87	93.63 356	87.75 283	69.61 354	99.79 106

LCM-MVSNet		67.77 336	64.73 339	76.87 351	62.95 380	56.25 377	89.37 367	93.74 369	44.53 372	61.99 367	80.74 367	20.42 380	86.53 371	69.37 360	59.50 369	87.84 365

ambc				83.23 348	77.17 373	62.61 372	87.38 368	94.55 364		76.72 355	86.65 363	30.16 374	96.36 316	84.85 309	69.86 353	90.73 354

ANet_high		56.10 340	52.24 343	67.66 356	49.27 382	56.82 376	83.94 369	82.02 380	70.47 365	33.28 379	64.54 374	17.23 382	69.16 377	45.59 375	23.85 376	77.02 370

tmp_tt		65.23 339	62.94 342	72.13 354	44.90 383	50.03 379	81.05 370	89.42 378	38.45 373	48.51 375	99.90 19	54.09 366	78.70 375	91.84 232	18.26 377	87.64 366

MVE	53.74 22	51.54 343	47.86 347	62.60 357	59.56 381	50.93 378	79.41 371	77.69 381	35.69 376	36.27 378	61.76 377	5.79 386	69.63 376	37.97 377	36.61 373	67.24 371
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMVS	49.05 23	53.75 341	51.34 345	60.97 358	40.80 384	34.68 384	74.82 372	89.62 377	37.55 374	28.67 380	72.12 370	7.09 384	81.63 374	43.17 376	68.21 360	66.59 372
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gipuma		66.95 338	65.00 338	72.79 353	91.52 345	67.96 370	66.16 373	95.15 359	47.89 371	58.54 369	67.99 373	29.74 375	87.54 370	50.20 373	77.83 334	62.87 373
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
wuyk23d		20.37 348	20.84 351	18.99 363	65.34 379	27.73 385	50.43 374	7.67 387	9.50 380	8.01 381	6.34 381	6.13 385	26.24 380	23.40 380	10.69 379	2.99 378

test_blank		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.02 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

uanet_test		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 383	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

DCPMVS		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 383	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

cdsmvs_eth3d_5k		23.43 347	31.24 350	0.00 364	0.00 387	0.00 388	0.00 375	98.09 191	0.00 382	0.00 383	99.67 102	83.37 239	0.00 383	0.00 381	0.00 381	0.00 379

pcd_1.5k_mvsjas		7.60 350	10.13 353	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 383	91.20 157	0.00 383	0.00 381	0.00 381	0.00 379

sosnet-low-res		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 383	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

sosnet		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 383	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

uncertanet		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 383	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

Regformer		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 383	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

ab-mvs-re		8.28 349	11.04 352	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	99.40 124	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

uanet		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 383	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

MSC_two_6792asdad				99.93 2	99.91 44	99.80 2		98.41 136					100.00 1	99.96 9	100.00 1	100.00 1

PC_three_1452									96.96 30	99.80 17	99.79 64	97.49 10	100.00 1	99.99 5	99.98 35	100.00 1

No_MVS				99.93 2	99.91 44	99.80 2		98.41 136					100.00 1	99.96 9	100.00 1	100.00 1

test_one_0601					99.94 14	99.30 11		98.41 136	96.63 40	99.75 28	99.93 11	97.49 10

eth-test2					0.00 387

eth-test					0.00 387

ZD-MVS					99.92 36	98.57 55		98.52 92	92.34 198	99.31 71	99.83 51	95.06 56	99.80 111	99.70 36	99.97 48

IU-MVS					99.93 27	99.31 9		98.41 136	97.71 8	99.84 9				100.00 1	100.00 1	100.00 1

test_241102_TWO								98.43 120	97.27 21	99.80 17	99.94 4	97.18 21	100.00 1	100.00 1	100.00 1	100.00 1

test_241102_ONE					99.93 27	99.30 11		98.43 120	97.26 23	99.80 17	99.88 24	96.71 24	100.00 1

test_0728_THIRD									96.48 43	99.83 11	99.91 15	97.87 6	100.00 1	99.92 13	100.00 1	100.00 1

GSMVS																99.59 139

test_part2					99.89 50	99.25 17				99.49 56

sam_mvs1												94.72 67				99.59 139

sam_mvs												94.25 86

MTGPA								98.28 167

test_post											63.35 376	94.43 72	98.13 228

patchmatchnet-post											91.70 350	95.12 52	97.95 239

gm-plane-assit					96.97 235	93.76 213			91.47 224		98.96 160		98.79 176	94.92 174

test9_res														99.71 35	99.99 22	100.00 1

agg_prior2														99.48 42	100.00 1	100.00 1

agg_prior					99.93 27	98.77 40		98.43 120		99.63 40			99.85 99

TestCases				95.00 231	99.01 124	88.43 316		96.82 315	86.50 303	88.71 276	98.47 197	74.73 312	99.88 90	85.39 304	96.18 186	96.71 231

test_prior				99.43 38	99.94 14	98.49 61		98.65 62					99.80 111			99.99 24

新几何1				99.42 41	99.75 81	98.27 68		98.63 68	92.69 181	99.55 49	99.82 55	94.40 74	100.00 1	91.21 237	99.94 61	99.99 24

旧先验1					99.76 79	97.52 95		98.64 65			99.85 35	95.63 43			99.94 61	99.99 24

原ACMM1				98.96 85	99.73 86	96.99 120		98.51 99	94.06 131	99.62 43	99.85 35	94.97 62	99.96 58	95.11 168	99.95 55	99.92 91

testdata2													99.99 40	90.54 253

segment_acmp												96.68 26

testdata				98.42 123	99.47 107	95.33 176		98.56 79	93.78 146	99.79 24	99.85 35	93.64 105	99.94 73	94.97 172	99.94 61	100.00 1

test12				99.43 38	99.74 82	98.56 57		98.40 140		99.65 38		94.76 66	99.75 126		99.98 35	99.99 24

plane_prior7					95.71 274	91.59 270

plane_prior6					95.76 269	91.72 265						80.47 268

plane_prior5								97.87 211					98.37 211	97.79 122	89.55 233	94.52 241

plane_prior4											98.59 187

plane_prior3						91.64 268			96.63 40	93.01 213

plane_prior1					95.73 271

n2								0.00 388

nn								0.00 388

door-mid								89.69 376

lessismore_v0				90.53 322	90.58 352	80.90 360		95.80 341		77.01 353	95.84 271	66.15 345	96.95 291	83.03 319	75.05 349	93.74 315

LGP-MVS_train				93.71 284	95.43 280	88.67 312		97.62 229	92.81 173	90.05 241	98.49 193	75.24 306	98.40 203	95.84 161	89.12 237	94.07 287

test11								98.44 112

door								90.31 374

HQP5-MVS						91.85 258

BP-MVS														97.92 117

HQP4-MVS										93.37 209			98.39 205			94.53 239

HQP3-MVS								97.89 209							89.60 230

HQP2-MVS												80.65 264

NP-MVS					95.77 268	91.79 260					98.65 183

ACMMP++_ref															87.04 268

ACMMP++															88.23 254

Test By Simon												92.82 128

ITE_SJBPF				92.38 306	95.69 276	85.14 338		95.71 343	92.81 173	89.33 263	98.11 204	70.23 330	98.42 200	85.91 302	88.16 255	93.59 319

DeepMVS_CX				82.92 349	95.98 263	58.66 375		96.01 338	92.72 178	78.34 351	95.51 286	58.29 362	98.08 230	82.57 321	85.29 279	92.03 344

High-resolution multi-view benchmark