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.

CHOSEN 1792x2688		97.12 129	96.80 126	98.08 141	99.30 72	94.56 236	98.05 264	99.71 1	93.57 253	97.09 171	98.91 126	88.17 230	99.89 54	96.87 141	99.56 94	99.81 18

HyFIR lowres test		96.90 138	96.49 144	98.14 132	99.33 63	95.56 180	97.38 327	99.65 2	92.34 303	97.61 158	98.20 205	89.29 199	99.10 225	96.97 129	97.60 201	99.77 30

MVS_111021_LR		98.34 58	98.23 54	98.67 84	99.27 82	96.90 114	97.95 275	99.58 3	97.14 66	98.44 102	99.01 111	95.03 79	99.62 145	97.91 80	99.75 48	99.50 95

MVS_111021_HR		98.47 43	98.34 41	98.88 72	99.22 95	97.32 91	97.91 281	99.58 3	97.20 61	98.33 108	99.00 112	95.99 40	99.64 138	98.05 73	99.76 42	99.69 60

PGM-MVS		98.49 40	98.23 54	99.27 38	99.72 12	98.08 62	98.99 86	99.49 5	95.43 145	99.03 55	99.32 55	95.56 52	99.94 10	96.80 147	99.77 36	99.78 24

ACMMP		98.23 64	97.95 72	99.09 56	99.74 7	97.62 77	99.03 76	99.41 6	95.98 118	97.60 159	99.36 48	94.45 91	99.93 29	97.14 123	98.85 148	99.70 57
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
fmvsm_s_conf0.5_n		98.42 49	98.51 22	98.13 135	99.30 72	95.25 198	98.85 126	99.39 7	97.94 21	99.74 13	99.62 3	92.59 117	99.91 45	99.65 10	99.52 100	99.25 141

fmvsm_s_conf0.5_n_a		98.38 52	98.42 30	98.27 120	99.09 112	95.41 188	98.86 122	99.37 8	97.69 28	99.78 9	99.61 4	92.38 120	99.91 45	99.58 14	99.43 112	99.49 100

test_fmvsm_n_1920		98.87 14	99.01 3	98.45 106	99.42 58	96.43 138	98.96 94	99.36 9	98.63 8	99.86 4	99.51 20	95.91 43	99.97 1	99.72 7	99.75 48	98.94 188

test_fmvsmconf_n		98.92 10	98.87 6	99.04 59	98.88 133	97.25 99	98.82 134	99.34 10	98.75 6	99.80 7	99.61 4	95.16 73	99.95 8	99.70 9	99.80 24	99.93 1

CSCG		97.85 80	97.74 78	98.20 129	99.67 25	95.16 202	99.22 36	99.32 11	93.04 277	97.02 177	98.92 125	95.36 61	99.91 45	97.43 115	99.64 76	99.52 90

fmvsm_l_conf0.5_n		99.07 4	99.05 2	99.14 51	99.41 59	97.54 81	98.89 110	99.31 12	98.49 12	99.86 4	99.42 35	96.45 24	99.96 4	99.86 1	99.74 52	99.90 3

fmvsm_l_conf0.5_n_a		99.09 1	99.08 1	99.11 55	99.43 57	97.48 83	98.88 116	99.30 13	98.47 13	99.85 6	99.43 34	96.71 17	99.96 4	99.86 1	99.80 24	99.89 4

patch_mono-2		98.36 55	98.87 6	96.82 229	99.53 36	90.68 337	98.64 182	99.29 14	97.88 22	99.19 48	99.52 18	96.80 15	99.97 1	99.11 22	99.86 2	99.82 17

fmvsm_s_conf0.5_n_3		98.53 36	98.45 28	98.79 75	99.23 93	97.32 91	98.80 143	99.26 15	98.82 2	99.87 2	99.60 8	90.95 165	99.93 29	99.76 6	99.73 55	99.12 163

PVSNet_BlendedMVS		96.73 144	96.60 139	97.12 208	99.25 85	95.35 193	98.26 236	99.26 15	94.28 207	97.94 132	97.46 269	92.74 115	99.81 88	96.88 138	93.32 294	96.20 360

PVSNet_Blended		97.38 114	97.12 111	98.14 132	99.25 85	95.35 193	97.28 338	99.26 15	93.13 273	97.94 132	98.21 204	92.74 115	99.81 88	96.88 138	99.40 117	99.27 136

fmvsm_l_conf0.5_n_3		98.90 12	98.74 14	99.37 22	99.36 60	98.25 50	98.89 110	99.24 18	98.77 5	99.89 1	99.59 10	93.39 106	99.96 4	99.78 5	99.76 42	99.89 4

fmvsm_s_conf0.1_n		98.18 67	98.21 56	98.11 139	98.54 169	95.24 199	98.87 119	99.24 18	97.50 39	99.70 17	99.67 1	91.33 154	99.89 54	99.47 16	99.54 97	99.21 147

UniMVSNet_NR-MVSNet		95.71 189	95.15 200	97.40 192	96.84 317	96.97 110	98.74 156	99.24 18	95.16 162	93.88 288	97.72 247	91.68 142	98.31 323	95.81 177	87.25 372	96.92 285

WR-MVS_H		95.05 232	94.46 237	96.81 230	96.86 316	95.82 173	99.24 30	99.24 18	93.87 229	92.53 337	96.84 331	90.37 174	98.24 331	93.24 263	87.93 363	96.38 353

SDMVSNet		96.85 140	96.42 145	98.14 132	99.30 72	96.38 141	99.21 39	99.23 22	95.92 120	95.96 223	98.76 148	85.88 277	99.44 182	97.93 78	95.59 258	98.60 220

FC-MVSNet-test		96.42 156	96.05 158	97.53 184	96.95 309	97.27 94	99.36 13	99.23 22	95.83 126	93.93 285	98.37 185	92.00 135	98.32 321	96.02 171	92.72 303	97.00 279

VPA-MVSNet		95.75 187	95.11 204	97.69 171	97.24 289	97.27 94	98.94 98	99.23 22	95.13 163	95.51 230	97.32 282	85.73 279	98.91 253	97.33 120	89.55 342	96.89 293

FIs		96.51 153	96.12 156	97.67 174	97.13 300	97.54 81	99.36 13	99.22 25	95.89 122	94.03 282	98.35 187	91.98 136	98.44 302	96.40 158	92.76 302	97.01 278

fmvsm_s_conf0.5_n_2		98.30 63	98.21 56	98.57 90	99.25 85	97.11 105	98.66 178	99.20 26	98.82 2	99.79 8	99.60 8	89.38 196	99.92 36	99.80 4	99.38 119	98.69 209

tfpnnormal		93.66 310	92.70 320	96.55 258	96.94 310	95.94 164	98.97 89	99.19 27	91.04 344	91.38 356	97.34 279	84.94 294	98.61 285	85.45 381	89.02 353	95.11 382

UniMVSNet (Re)		95.78 186	95.19 199	97.58 181	96.99 307	97.47 85	98.79 150	99.18 28	95.60 137	93.92 286	97.04 311	91.68 142	98.48 295	95.80 179	87.66 366	96.79 303

fmvsm_s_conf0.1_n_a		98.08 69	98.04 68	98.21 127	97.66 256	95.39 189	98.89 110	99.17 29	97.24 58	99.76 12	99.67 1	91.13 159	99.88 63	99.39 17	99.41 114	99.35 120

PVSNet_Blended_VisFu		97.70 88	97.46 94	98.44 108	99.27 82	95.91 169	98.63 185	99.16 30	94.48 202	97.67 151	98.88 130	92.80 114	99.91 45	97.11 124	99.12 131	99.50 95

test_fmvsmvis_n_1920		98.44 46	98.51 22	98.23 126	98.33 190	96.15 152	98.97 89	99.15 31	98.55 11	98.45 100	99.55 13	94.26 96	99.97 1	99.65 10	99.66 69	98.57 226

CHOSEN 280x420		97.18 125	97.18 110	97.20 199	98.81 142	93.27 285	95.78 393	99.15 31	95.25 158	96.79 190	98.11 211	92.29 123	99.07 228	98.56 41	99.85 6	99.25 141

D2MVS		95.18 224	95.08 205	95.48 312	97.10 302	92.07 309	98.30 230	99.13 33	94.02 217	92.90 325	96.73 336	89.48 191	98.73 275	94.48 225	93.60 288	95.65 373

PHI-MVS		98.34 58	98.06 66	99.18 46	99.15 106	98.12 61	99.04 73	99.09 34	93.32 263	98.83 74	99.10 93	96.54 21	99.83 76	97.70 97	99.76 42	99.59 83

sd_testset		96.17 167	95.76 169	97.42 189	99.30 72	94.34 245	98.82 134	99.08 35	95.92 120	95.96 223	98.76 148	82.83 327	99.32 194	95.56 188	95.59 258	98.60 220

UA-Net		97.96 73	97.62 81	98.98 63	98.86 137	97.47 85	98.89 110	99.08 35	96.67 92	98.72 83	99.54 15	93.15 110	99.81 88	94.87 209	98.83 149	99.65 73

PatchMatch-RL		96.59 149	96.03 160	98.27 120	99.31 68	96.51 134	97.91 281	99.06 37	93.72 240	96.92 182	98.06 214	88.50 225	99.65 136	91.77 306	99.00 139	98.66 215

3Dnovator	94.51 5	97.46 105	96.93 121	99.07 57	97.78 244	97.64 75	99.35 15	99.06 37	97.02 72	93.75 296	99.16 84	89.25 200	99.92 36	97.22 122	99.75 48	99.64 75

MSLP-MVS++		98.56 33	98.57 19	98.55 93	99.26 84	96.80 118	98.71 165	99.05 39	97.28 53	98.84 72	99.28 60	96.47 23	99.40 185	98.52 48	99.70 62	99.47 104

PS-CasMVS		94.67 257	93.99 269	96.71 234	96.68 327	95.26 197	99.13 57	99.03 40	93.68 246	92.33 343	97.95 225	85.35 286	98.10 339	93.59 255	88.16 362	96.79 303

TranMVSNet+NR-MVSNet		95.14 226	94.48 235	97.11 209	96.45 339	96.36 143	99.03 76	99.03 40	95.04 170	93.58 299	97.93 226	88.27 228	98.03 345	94.13 237	86.90 377	96.95 283

PEN-MVS		94.42 278	93.73 290	96.49 262	96.28 345	94.84 219	99.17 49	99.00 42	93.51 254	92.23 345	97.83 239	86.10 273	97.90 355	92.55 286	86.92 376	96.74 308

Vis-MVSNet		97.42 111	97.11 112	98.34 116	98.66 157	96.23 148	99.22 36	99.00 42	96.63 94	98.04 121	99.21 72	88.05 236	99.35 190	96.01 172	99.21 127	99.45 110
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
DU-MVS		95.42 206	94.76 219	97.40 192	96.53 333	96.97 110	98.66 178	98.99 44	95.43 145	93.88 288	97.69 250	88.57 220	98.31 323	95.81 177	87.25 372	96.92 285

test_fmvsmconf0.1_n		98.58 27	98.44 29	98.99 61	97.73 250	97.15 104	98.84 130	98.97 45	98.75 6	99.43 31	99.54 15	93.29 108	99.93 29	99.64 12	99.79 30	99.89 4

VPNet		94.99 236	94.19 251	97.40 192	97.16 298	96.57 131	98.71 165	98.97 45	95.67 135	94.84 243	98.24 203	80.36 347	98.67 281	96.46 155	87.32 371	96.96 281

OpenMVS	93.04 13	95.83 184	95.00 208	98.32 117	97.18 297	97.32 91	99.21 39	98.97 45	89.96 362	91.14 358	99.05 105	86.64 262	99.92 36	93.38 259	99.47 107	97.73 257

HFP-MVS		98.63 21	98.40 31	99.32 32	99.72 12	98.29 47	99.23 32	98.96 48	96.10 116	98.94 62	99.17 81	96.06 36	99.92 36	97.62 102	99.78 34	99.75 38

FOURS1					99.82 1	98.66 24	99.69 1	98.95 49	97.46 42	99.39 34

ACMMPR		98.59 25	98.36 35	99.29 33	99.74 7	98.15 58	99.23 32	98.95 49	96.10 116	98.93 66	99.19 79	95.70 49	99.94 10	97.62 102	99.79 30	99.78 24

CP-MVSNet		94.94 243	94.30 245	96.83 228	96.72 325	95.56 180	99.11 60	98.95 49	93.89 227	92.42 342	97.90 229	87.19 253	98.12 338	94.32 231	88.21 360	96.82 302

NR-MVSNet		94.98 238	94.16 254	97.44 187	96.53 333	97.22 101	98.74 156	98.95 49	94.96 176	89.25 376	97.69 250	89.32 198	98.18 333	94.59 222	87.40 369	96.92 285

region2R		98.61 22	98.38 33	99.29 33	99.74 7	98.16 57	99.23 32	98.93 53	96.15 113	98.94 62	99.17 81	95.91 43	99.94 10	97.55 109	99.79 30	99.78 24

APDe-MVS		99.02 6	98.84 8	99.55 9	99.57 33	98.96 16	99.39 10	98.93 53	97.38 47	99.41 32	99.54 15	96.66 18	99.84 74	98.86 29	99.85 6	99.87 7
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
VNet		97.79 83	97.40 98	98.96 66	98.88 133	97.55 79	98.63 185	98.93 53	96.74 86	99.02 56	98.84 134	90.33 176	99.83 76	98.53 42	96.66 225	99.50 95

UGNet		96.78 143	96.30 150	98.19 131	98.24 198	95.89 171	98.88 116	98.93 53	97.39 46	96.81 188	97.84 236	82.60 328	99.90 52	96.53 153	99.49 104	98.79 198
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
sss		97.39 113	96.98 120	98.61 88	98.60 165	96.61 127	98.22 239	98.93 53	93.97 223	98.01 127	98.48 174	91.98 136	99.85 70	96.45 156	98.15 181	99.39 116

QAPM		96.29 162	95.40 184	98.96 66	97.85 240	97.60 78	99.23 32	98.93 53	89.76 366	93.11 321	99.02 107	89.11 205	99.93 29	91.99 300	99.62 79	99.34 122

DPE-MVS		98.92 10	98.67 16	99.65 2	99.58 32	99.20 9	98.42 218	98.91 59	97.58 34	99.54 26	99.46 31	97.10 12	99.94 10	97.64 101	99.84 11	99.83 13
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
114514_t		96.93 136	96.27 151	98.92 68	99.50 42	97.63 76	98.85 126	98.90 60	84.80 400	97.77 140	99.11 91	92.84 113	99.66 135	94.85 210	99.77 36	99.47 104

LS3D		97.16 126	96.66 138	98.68 83	98.53 170	97.19 102	98.93 101	98.90 60	92.83 286	95.99 221	99.37 44	92.12 131	99.87 65	93.67 253	99.57 88	98.97 184

DELS-MVS		98.40 51	98.20 58	98.99 61	99.00 120	97.66 74	97.75 302	98.89 62	97.71 26	98.33 108	98.97 114	94.97 80	99.88 63	98.42 56	99.76 42	99.42 115
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
DP-MVS Recon		97.86 78	97.46 94	99.06 58	99.53 36	98.35 44	98.33 223	98.89 62	92.62 292	98.05 119	98.94 122	95.34 62	99.65 136	96.04 170	99.42 113	99.19 152

AdaColmap		97.15 127	96.70 134	98.48 103	99.16 104	96.69 124	98.01 269	98.89 62	94.44 204	96.83 185	98.68 154	90.69 170	99.76 114	94.36 228	99.29 126	98.98 183

DVP-MVS++		99.08 3	98.89 5	99.64 3	99.17 100	99.23 7	99.69 1	98.88 65	97.32 50	99.53 27	99.47 27	97.81 3	99.94 10	98.47 50	99.72 59	99.74 40

test_0728_SECOND				99.71 1	99.72 12	99.35 1	98.97 89	98.88 65					99.94 10	98.47 50	99.81 15	99.84 12

test0726					99.72 12	99.25 2	99.06 67	98.88 65	97.62 31	99.56 24	99.50 22	97.42 9

MSP-MVS		98.74 17	98.55 21	99.29 33	99.75 3	98.23 51	99.26 27	98.88 65	97.52 37	99.41 32	98.78 141	96.00 39	99.79 105	97.79 88	99.59 84	99.85 10
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
Anonymous20231211		94.10 301	93.26 310	96.61 247	99.11 110	94.28 247	99.01 81	98.88 65	86.43 390	92.81 327	97.57 263	81.66 332	98.68 280	94.83 211	89.02 353	96.88 294

XVS		98.70 18	98.49 25	99.34 26	99.70 22	98.35 44	99.29 22	98.88 65	97.40 44	98.46 97	99.20 74	95.90 45	99.89 54	97.85 84	99.74 52	99.78 24

X-MVStestdata		94.06 305	92.30 330	99.34 26	99.70 22	98.35 44	99.29 22	98.88 65	97.40 44	98.46 97	43.50 426	95.90 45	99.89 54	97.85 84	99.74 52	99.78 24

SED-MVS		99.09 1	98.91 4	99.63 4	99.71 19	99.24 5	99.02 79	98.87 72	97.65 29	99.73 14	99.48 25	97.53 7	99.94 10	98.43 54	99.81 15	99.70 57

test_241102_TWO								98.87 72	97.65 29	99.53 27	99.48 25	97.34 11	99.94 10	98.43 54	99.80 24	99.83 13

test_241102_ONE					99.71 19	99.24 5		98.87 72	97.62 31	99.73 14	99.39 38	97.53 7	99.74 118

CP-MVS		98.57 31	98.36 35	99.19 44	99.66 26	97.86 69	99.34 16	98.87 72	95.96 119	98.60 92	99.13 89	96.05 37	99.94 10	97.77 89	99.86 2	99.77 30

SteuartSystems-ACMMP		98.90 12	98.75 13	99.36 24	99.22 95	98.43 33	99.10 63	98.87 72	97.38 47	99.35 36	99.40 37	97.78 5	99.87 65	97.77 89	99.85 6	99.78 24
Skip Steuart: Steuart Systems R&D Blog.
DeepPCF-MVS	96.37 2	97.93 76	98.48 27	96.30 279	99.00 120	89.54 360	97.43 324	98.87 72	98.16 15	99.26 44	99.38 43	96.12 35	99.64 138	98.30 61	99.77 36	99.72 49

test_one_0601					99.66 26	99.25 2		98.86 78	97.55 36	99.20 46	99.47 27	97.57 6

ZNCC-MVS		98.49 40	98.20 58	99.35 25	99.73 11	98.39 34	99.19 44	98.86 78	95.77 129	98.31 110	99.10 93	95.46 55	99.93 29	97.57 108	99.81 15	99.74 40

DTE-MVSNet		93.98 307	93.26 310	96.14 284	96.06 354	94.39 242	99.20 42	98.86 78	93.06 276	91.78 351	97.81 241	85.87 278	97.58 371	90.53 328	86.17 381	96.46 350

SD-MVS		98.64 20	98.68 15	98.53 97	99.33 63	98.36 43	98.90 106	98.85 81	97.28 53	99.72 16	99.39 38	96.63 20	97.60 369	98.17 66	99.85 6	99.64 75
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
reproduce_model		98.94 7	98.81 10	99.34 26	99.52 39	98.26 49	98.94 98	98.84 82	98.06 17	99.35 36	99.61 4	96.39 27	99.94 10	98.77 32	99.82 14	99.83 13

test_prior				99.19 44	99.31 68	98.22 52		98.84 82					99.70 126			99.65 73

reproduce-ours		98.93 8	98.78 11	99.38 18	99.49 46	98.38 35	98.86 122	98.83 84	98.06 17	99.29 40	99.58 11	96.40 25	99.94 10	98.68 34	99.81 15	99.81 18

our_new_method		98.93 8	98.78 11	99.38 18	99.49 46	98.38 35	98.86 122	98.83 84	98.06 17	99.29 40	99.58 11	96.40 25	99.94 10	98.68 34	99.81 15	99.81 18

Anonymous20240529		95.10 229	94.22 249	97.75 165	99.01 119	94.26 249	98.87 119	98.83 84	85.79 396	96.64 193	98.97 114	78.73 357	99.85 70	96.27 161	94.89 263	99.12 163

fmvsm_s_conf0.1_n_2		98.14 68	98.02 69	98.53 97	98.88 133	97.07 107	98.69 171	98.82 87	98.78 4	99.77 10	99.61 4	88.83 215	99.91 45	99.71 8	99.07 132	98.61 219

9.14			98.06 66		99.47 50		98.71 165	98.82 87	94.36 206	99.16 52	99.29 59	96.05 37	99.81 88	97.00 127	99.71 61

SR-MVS		98.57 31	98.35 37	99.24 40	99.53 36	98.18 55	99.09 64	98.82 87	96.58 95	99.10 54	99.32 55	95.39 58	99.82 83	97.70 97	99.63 77	99.72 49

GST-MVS		98.43 48	98.12 62	99.34 26	99.72 12	98.38 35	99.09 64	98.82 87	95.71 133	98.73 82	99.06 104	95.27 66	99.93 29	97.07 126	99.63 77	99.72 49

HPM-MVS_fast		98.38 52	98.13 61	99.12 54	99.75 3	97.86 69	99.44 9	98.82 87	94.46 203	98.94 62	99.20 74	95.16 73	99.74 118	97.58 105	99.85 6	99.77 30

APD-MVS		98.35 57	98.00 71	99.42 16	99.51 40	98.72 21	98.80 143	98.82 87	94.52 200	99.23 45	99.25 68	95.54 54	99.80 95	96.52 154	99.77 36	99.74 40
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
SF-MVS		98.59 25	98.32 46	99.41 17	99.54 35	98.71 22	99.04 73	98.81 93	95.12 164	99.32 39	99.39 38	96.22 30	99.84 74	97.72 92	99.73 55	99.67 69

ACMMP_NAP		98.61 22	98.30 47	99.55 9	99.62 30	98.95 17	98.82 134	98.81 93	95.80 127	99.16 52	99.47 27	95.37 60	99.92 36	97.89 82	99.75 48	99.79 22

APD-MVS_3200maxsize		98.53 36	98.33 45	99.15 50	99.50 42	97.92 68	99.15 51	98.81 93	96.24 109	99.20 46	99.37 44	95.30 64	99.80 95	97.73 91	99.67 66	99.72 49

WR-MVS		95.15 225	94.46 237	97.22 198	96.67 328	96.45 136	98.21 240	98.81 93	94.15 211	93.16 317	97.69 250	87.51 247	98.30 325	95.29 198	88.62 357	96.90 292

mPP-MVS		98.51 38	98.26 49	99.25 39	99.75 3	98.04 63	99.28 24	98.81 93	96.24 109	98.35 107	99.23 69	95.46 55	99.94 10	97.42 116	99.81 15	99.77 30

CNVR-MVS		98.78 15	98.56 20	99.45 15	99.32 66	98.87 19	98.47 210	98.81 93	97.72 24	98.76 79	99.16 84	97.05 13	99.78 108	98.06 71	99.66 69	99.69 60

CPTT-MVS		97.72 86	97.32 102	98.92 68	99.64 28	97.10 106	99.12 58	98.81 93	92.34 303	98.09 116	99.08 102	93.01 111	99.92 36	96.06 169	99.77 36	99.75 38

SR-MVS-dyc-post		98.54 35	98.35 37	99.13 52	99.49 46	97.86 69	99.11 60	98.80 100	96.49 98	99.17 49	99.35 50	95.34 62	99.82 83	97.72 92	99.65 72	99.71 53

RE-MVS-def			98.34 41		99.49 46	97.86 69	99.11 60	98.80 100	96.49 98	99.17 49	99.35 50	95.29 65		97.72 92	99.65 72	99.71 53

SMA-MVS		98.58 27	98.25 50	99.56 8	99.51 40	99.04 15	98.95 95	98.80 100	93.67 248	99.37 35	99.52 18	96.52 22	99.89 54	98.06 71	99.81 15	99.76 37
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
HPM-MVS		98.36 55	98.10 65	99.13 52	99.74 7	97.82 73	99.53 6	98.80 100	94.63 193	98.61 91	98.97 114	95.13 75	99.77 113	97.65 100	99.83 13	99.79 22
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
RPMNet		92.81 329	91.34 340	97.24 197	97.00 305	93.43 276	94.96 400	98.80 100	82.27 407	96.93 180	92.12 411	86.98 257	99.82 83	76.32 412	96.65 226	98.46 231

ZD-MVS					99.46 52	98.70 23		98.79 105	93.21 268	98.67 84	98.97 114	95.70 49	99.83 76	96.07 166	99.58 87

MP-MVS		98.33 60	98.01 70	99.28 36	99.75 3	98.18 55	99.22 36	98.79 105	96.13 114	97.92 135	99.23 69	94.54 86	99.94 10	96.74 150	99.78 34	99.73 45
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CANet		98.05 71	97.76 77	98.90 71	98.73 146	97.27 94	98.35 221	98.78 107	97.37 49	97.72 147	98.96 119	91.53 150	99.92 36	98.79 31	99.65 72	99.51 93

MP-MVS-pluss		98.31 61	97.92 73	99.49 12	99.72 12	98.88 18	98.43 216	98.78 107	94.10 213	97.69 150	99.42 35	95.25 68	99.92 36	98.09 70	99.80 24	99.67 69
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
DeepC-MVS_fast	96.70 1	98.55 34	98.34 41	99.18 46	99.25 85	98.04 63	98.50 207	98.78 107	97.72 24	98.92 68	99.28 60	95.27 66	99.82 83	97.55 109	99.77 36	99.69 60
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MG-MVS		97.81 82	97.60 82	98.44 108	99.12 108	95.97 161	97.75 302	98.78 107	96.89 78	98.46 97	99.22 71	93.90 102	99.68 132	94.81 213	99.52 100	99.67 69

NCCC		98.61 22	98.35 37	99.38 18	99.28 81	98.61 26	98.45 211	98.76 111	97.82 23	98.45 100	98.93 123	96.65 19	99.83 76	97.38 118	99.41 114	99.71 53

PLC	95.07 4	97.20 124	96.78 129	98.44 108	99.29 77	96.31 147	98.14 252	98.76 111	92.41 301	96.39 209	98.31 194	94.92 82	99.78 108	94.06 241	98.77 152	99.23 143
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
h-mvs33		96.17 167	95.62 180	97.81 158	99.03 116	94.45 238	98.64 182	98.75 113	97.48 40	98.67 84	98.72 151	89.76 184	99.86 69	97.95 76	81.59 397	99.11 166

DeepC-MVS	95.98 3	97.88 77	97.58 83	98.77 76	99.25 85	96.93 112	98.83 132	98.75 113	96.96 75	96.89 184	99.50 22	90.46 173	99.87 65	97.84 86	99.76 42	99.52 90
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MTGPA								98.74 115

MTAPA		98.58 27	98.29 48	99.46 14	99.76 2	98.64 25	98.90 106	98.74 115	97.27 57	98.02 124	99.39 38	94.81 83	99.96 4	97.91 80	99.79 30	99.77 30

ab-mvs		96.42 156	95.71 174	98.55 93	98.63 162	96.75 121	97.88 288	98.74 115	93.84 230	96.54 202	98.18 207	85.34 287	99.75 116	95.93 173	96.35 235	99.15 159

TEST9					99.31 68	98.50 29	97.92 279	98.73 118	92.63 291	97.74 144	98.68 154	96.20 32	99.80 95

train_agg		97.97 72	97.52 89	99.33 30	99.31 68	98.50 29	97.92 279	98.73 118	92.98 279	97.74 144	98.68 154	96.20 32	99.80 95	96.59 151	99.57 88	99.68 65

test_8					99.29 77	98.44 31	97.89 287	98.72 120	92.98 279	97.70 149	98.66 157	96.20 32	99.80 95

agg_prior					99.30 72	98.38 35		98.72 120		97.57 161			99.81 88

无先验							97.58 316	98.72 120	91.38 330				99.87 65	93.36 261		99.60 81

save fliter					99.46 52	98.38 35	98.21 240	98.71 123	97.95 20

mamv4		97.13 128	98.11 63	94.17 358	98.97 126	83.70 401	98.66 178	98.71 123	94.63 193	97.83 138	98.90 127	96.25 29	99.55 161	99.27 19	99.76 42	99.27 136

WTY-MVS		97.37 116	96.92 122	98.72 80	98.86 137	96.89 116	98.31 228	98.71 123	95.26 157	97.67 151	98.56 168	92.21 128	99.78 108	95.89 174	96.85 220	99.48 102

3Dnovator+	94.38 6	97.43 110	96.78 129	99.38 18	97.83 241	98.52 28	99.37 12	98.71 123	97.09 70	92.99 324	99.13 89	89.36 197	99.89 54	96.97 129	99.57 88	99.71 53

旧先验1					99.29 77	97.48 83		98.70 127			99.09 100	95.56 52			99.47 107	99.61 79

EI-MVSNet-Vis-set		98.47 43	98.39 32	98.69 82	99.46 52	96.49 135	98.30 230	98.69 128	97.21 60	98.84 72	99.36 48	95.41 57	99.78 108	98.62 37	99.65 72	99.80 21

新几何1				99.16 49	99.34 61	98.01 65		98.69 128	90.06 361	98.13 113	98.95 121	94.60 85	99.89 54	91.97 302	99.47 107	99.59 83

API-MVS		97.41 112	97.25 105	97.91 151	98.70 151	96.80 118	98.82 134	98.69 128	94.53 198	98.11 114	98.28 196	94.50 90	99.57 151	94.12 238	99.49 104	97.37 270

EI-MVSNet-UG-set		98.41 50	98.34 41	98.61 88	99.45 55	96.32 145	98.28 233	98.68 131	97.17 63	98.74 80	99.37 44	95.25 68	99.79 105	98.57 39	99.54 97	99.73 45

testdata				98.26 123	99.20 98	95.36 191		98.68 131	91.89 317	98.60 92	99.10 93	94.44 92	99.82 83	94.27 233	99.44 111	99.58 87

MCST-MVS		98.65 19	98.37 34	99.48 13	99.60 31	98.87 19	98.41 219	98.68 131	97.04 71	98.52 95	98.80 139	96.78 16	99.83 76	97.93 78	99.61 80	99.74 40

PVSNet	91.96 18	96.35 160	96.15 155	96.96 219	99.17 100	92.05 310	96.08 386	98.68 131	93.69 244	97.75 143	97.80 242	88.86 214	99.69 131	94.26 234	99.01 137	99.15 159

MAR-MVS		96.91 137	96.40 147	98.45 106	98.69 154	96.90 114	98.66 178	98.68 131	92.40 302	97.07 174	97.96 224	91.54 149	99.75 116	93.68 251	98.92 141	98.69 209
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
原ACMM1				98.65 86	99.32 66	96.62 125		98.67 136	93.27 267	97.81 139	98.97 114	95.18 72	99.83 76	93.84 247	99.46 110	99.50 95

CDPH-MVS		97.94 75	97.49 91	99.28 36	99.47 50	98.44 31	97.91 281	98.67 136	92.57 295	98.77 78	98.85 133	95.93 42	99.72 120	95.56 188	99.69 63	99.68 65

UnsupCasMVSNet_eth		90.99 350	89.92 353	94.19 357	94.08 395	89.83 351	97.13 352	98.67 136	93.69 244	85.83 397	96.19 357	75.15 388	96.74 387	89.14 352	79.41 406	96.00 365

TSAR-MVS + MP.		98.78 15	98.62 17	99.24 40	99.69 24	98.28 48	99.14 54	98.66 139	96.84 79	99.56 24	99.31 57	96.34 28	99.70 126	98.32 60	99.73 55	99.73 45
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
HPM-MVS++		98.58 27	98.25 50	99.55 9	99.50 42	99.08 11	98.72 164	98.66 139	97.51 38	98.15 111	98.83 136	95.70 49	99.92 36	97.53 111	99.67 66	99.66 72

test222					99.23 93	97.17 103	97.40 325	98.66 139	88.68 380	98.05 119	98.96 119	94.14 98			99.53 99	99.61 79

test11								98.66 139

XXY-MVS		95.20 223	94.45 239	97.46 185	96.75 323	96.56 132	98.86 122	98.65 143	93.30 265	93.27 313	98.27 199	84.85 296	98.87 260	94.82 212	91.26 320	96.96 281

reproduce_monomvs		94.77 250	94.67 225	95.08 327	98.40 178	89.48 361	98.80 143	98.64 144	97.57 35	93.21 315	97.65 254	80.57 346	98.83 266	97.72 92	89.47 345	96.93 284

IU-MVS					99.71 19	99.23 7		98.64 144	95.28 156	99.63 22				98.35 59	99.81 15	99.83 13

TAPA-MVS	93.98 7	95.35 213	94.56 231	97.74 166	99.13 107	94.83 221	98.33 223	98.64 144	86.62 388	96.29 211	98.61 159	94.00 101	99.29 197	80.00 403	99.41 114	99.09 168
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MSC_two_6792asdad				99.62 6	99.17 100	99.08 11		98.63 147					99.94 10	98.53 42	99.80 24	99.86 8

No_MVS				99.62 6	99.17 100	99.08 11		98.63 147					99.94 10	98.53 42	99.80 24	99.86 8

F-COLMAP		97.09 131	96.80 126	97.97 148	99.45 55	94.95 215	98.55 199	98.62 149	93.02 278	96.17 216	98.58 164	94.01 100	99.81 88	93.95 243	98.90 142	99.14 161

test_fmvsmconf0.01_n		97.86 78	97.54 88	98.83 73	95.48 374	96.83 117	98.95 95	98.60 150	98.58 9	98.93 66	99.55 13	88.57 220	99.91 45	99.54 15	99.61 80	99.77 30

balanced_conf03		98.45 45	98.35 37	98.74 78	98.65 160	97.55 79	99.19 44	98.60 150	96.72 89	99.35 36	98.77 143	95.06 78	99.55 161	98.95 26	99.87 1	99.12 163

EIA-MVS		97.75 84	97.58 83	98.27 120	98.38 179	96.44 137	99.01 81	98.60 150	95.88 123	97.26 166	97.53 266	94.97 80	99.33 193	97.38 118	99.20 128	99.05 177

PAPM_NR		97.46 105	97.11 112	98.50 100	99.50 42	96.41 140	98.63 185	98.60 150	95.18 161	97.06 175	98.06 214	94.26 96	99.57 151	93.80 249	98.87 146	99.52 90

cdsmvs_eth3d_5k		23.98 396	31.98 398	0.00 414	0.00 437	0.00 439	0.00 425	98.59 154	0.00 432	0.00 433	98.61 159	90.60 171	0.00 433	0.00 432	0.00 431	0.00 429

1314		96.25 166	95.73 170	97.79 159	97.13 300	95.55 182	98.19 245	98.59 154	93.47 257	92.03 349	97.82 240	91.33 154	99.49 172	94.62 219	98.44 169	98.32 239

CVMVSNet		95.43 205	96.04 159	93.57 364	97.93 235	83.62 402	98.12 255	98.59 154	95.68 134	96.56 198	99.02 107	87.51 247	97.51 374	93.56 257	97.44 204	99.60 81

OMC-MVS		97.55 103	97.34 101	98.20 129	99.33 63	95.92 168	98.28 233	98.59 154	95.52 141	97.97 129	99.10 93	93.28 109	99.49 172	95.09 204	98.88 144	99.19 152

LTVRE_ROB	92.95 15	94.60 260	93.90 275	96.68 238	97.41 281	94.42 240	98.52 201	98.59 154	91.69 323	91.21 357	98.35 187	84.87 295	99.04 232	91.06 320	93.44 292	96.60 326
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
test_vis1_n_1920		96.71 145	96.84 125	96.31 278	99.11 110	89.74 354	99.05 69	98.58 159	98.08 16	99.87 2	99.37 44	78.48 360	99.93 29	99.29 18	99.69 63	99.27 136

DVP-MVS		99.03 5	98.83 9	99.63 4	99.72 12	99.25 2	98.97 89	98.58 159	97.62 31	99.45 29	99.46 31	97.42 9	99.94 10	98.47 50	99.81 15	99.69 60
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
MVSMamba_PlusPlus		98.31 61	98.19 60	98.67 84	98.96 127	97.36 89	99.24 30	98.57 161	94.81 185	98.99 60	98.90 127	95.22 71	99.59 148	99.15 21	99.84 11	99.07 176

UniMVSNet_ETH3D		94.24 289	93.33 307	96.97 218	97.19 296	93.38 281	98.74 156	98.57 161	91.21 342	93.81 292	98.58 164	72.85 398	98.77 273	95.05 206	93.93 280	98.77 204

PAPR		96.84 141	96.24 153	98.65 86	98.72 150	96.92 113	97.36 331	98.57 161	93.33 262	96.67 192	97.57 263	94.30 94	99.56 154	91.05 322	98.59 160	99.47 104

HQP_MVS		96.14 169	95.90 165	96.85 227	97.42 278	94.60 234	98.80 143	98.56 164	97.28 53	95.34 232	98.28 196	87.09 254	99.03 233	96.07 166	94.27 266	96.92 285

plane_prior5								98.56 164					99.03 233	96.07 166	94.27 266	96.92 285

ETV-MVS		97.96 73	97.81 75	98.40 113	98.42 175	97.27 94	98.73 160	98.55 166	96.84 79	98.38 104	97.44 272	95.39 58	99.35 190	97.62 102	98.89 143	98.58 225

mvs_tets		95.41 208	95.00 208	96.65 239	95.58 369	94.42 240	99.00 83	98.55 166	95.73 132	93.21 315	98.38 184	83.45 325	98.63 283	97.09 125	94.00 277	96.91 290

LPG-MVS_test		95.62 195	95.34 190	96.47 265	97.46 273	93.54 271	98.99 86	98.54 168	94.67 191	94.36 264	98.77 143	85.39 284	99.11 221	95.71 183	94.15 272	96.76 306

LGP-MVS_train				96.47 265	97.46 273	93.54 271		98.54 168	94.67 191	94.36 264	98.77 143	85.39 284	99.11 221	95.71 183	94.15 272	96.76 306

test_cas_vis1_n_1920		97.38 114	97.36 100	97.45 186	98.95 128	93.25 288	99.00 83	98.53 170	97.70 27	99.77 10	99.35 50	84.71 301	99.85 70	98.57 39	99.66 69	99.26 139

test12				99.18 46	99.16 104	98.19 54		98.53 170		98.07 117		95.13 75	99.72 120		99.56 94	99.63 77

CNLPA		97.45 108	97.03 116	98.73 79	99.05 114	97.44 87	98.07 262	98.53 170	95.32 154	96.80 189	98.53 169	93.32 107	99.72 120	94.31 232	99.31 125	99.02 179

GDP-MVS		97.64 93	97.28 103	98.71 81	98.30 195	97.33 90	99.05 69	98.52 173	96.34 106	98.80 75	99.05 105	89.74 186	99.51 168	96.86 144	98.86 147	99.28 135

jajsoiax		95.45 204	95.03 207	96.73 233	95.42 378	94.63 229	99.14 54	98.52 173	95.74 130	93.22 314	98.36 186	83.87 321	98.65 282	96.95 131	94.04 275	96.91 290

XVG-OURS		96.55 152	96.41 146	96.99 215	98.75 145	93.76 262	97.50 321	98.52 173	95.67 135	96.83 185	99.30 58	88.95 213	99.53 164	95.88 175	96.26 245	97.69 259

xiu_mvs_v1_base_debu		97.60 97	97.56 85	97.72 167	98.35 182	95.98 156	97.86 291	98.51 176	97.13 67	99.01 57	98.40 181	91.56 146	99.80 95	98.53 42	98.68 153	97.37 270

xiu_mvs_v1_base		97.60 97	97.56 85	97.72 167	98.35 182	95.98 156	97.86 291	98.51 176	97.13 67	99.01 57	98.40 181	91.56 146	99.80 95	98.53 42	98.68 153	97.37 270

xiu_mvs_v1_base_debi		97.60 97	97.56 85	97.72 167	98.35 182	95.98 156	97.86 291	98.51 176	97.13 67	99.01 57	98.40 181	91.56 146	99.80 95	98.53 42	98.68 153	97.37 270

PS-MVSNAJ		97.73 85	97.77 76	97.62 179	98.68 155	95.58 179	97.34 333	98.51 176	97.29 52	98.66 88	97.88 232	94.51 87	99.90 52	97.87 83	99.17 130	97.39 268

cascas		94.63 259	93.86 279	96.93 221	96.91 313	94.27 248	96.00 390	98.51 176	85.55 397	94.54 252	96.23 354	84.20 314	98.87 260	95.80 179	96.98 217	97.66 260

SPE-MVS-test		98.49 40	98.50 24	98.46 105	99.20 98	97.05 108	99.64 4	98.50 181	97.45 43	98.88 69	99.14 88	95.25 68	99.15 213	98.83 30	99.56 94	99.20 148

PS-MVSNAJss		96.43 155	96.26 152	96.92 224	95.84 363	95.08 207	99.16 50	98.50 181	95.87 124	93.84 291	98.34 191	94.51 87	98.61 285	96.88 138	93.45 291	97.06 276

MVS		94.67 257	93.54 300	98.08 141	96.88 315	96.56 132	98.19 245	98.50 181	78.05 412	92.69 332	98.02 217	91.07 163	99.63 141	90.09 333	98.36 175	98.04 248

XVG-OURS-SEG-HR		96.51 153	96.34 148	97.02 214	98.77 144	93.76 262	97.79 300	98.50 181	95.45 144	96.94 179	99.09 100	87.87 241	99.55 161	96.76 149	95.83 257	97.74 256

PVSNet_0	88.72 19	91.28 345	90.03 352	95.00 329	97.99 228	87.29 392	94.84 403	98.50 181	92.06 313	89.86 370	95.19 383	79.81 351	99.39 188	92.27 292	69.79 419	98.33 238

ACMH	92.88 16	94.55 265	93.95 271	96.34 276	97.63 258	93.26 286	98.81 142	98.49 186	93.43 259	89.74 371	98.53 169	81.91 330	99.08 227	93.69 250	93.30 295	96.70 315
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CS-MVS		98.44 46	98.49 25	98.31 118	99.08 113	96.73 122	99.67 3	98.47 187	97.17 63	98.94 62	99.10 93	95.73 48	99.13 216	98.71 33	99.49 104	99.09 168

xiu_mvs_v2_base		97.66 92	97.70 79	97.56 183	98.61 164	95.46 186	97.44 322	98.46 188	97.15 65	98.65 89	98.15 208	94.33 93	99.80 95	97.84 86	98.66 157	97.41 266

HQP3-MVS								98.46 188							94.18 270

HQP-MVS		95.72 188	95.40 184	96.69 237	97.20 293	94.25 250	98.05 264	98.46 188	96.43 100	94.45 256	97.73 245	86.75 260	98.96 244	95.30 196	94.18 270	96.86 299

CLD-MVS		95.62 195	95.34 190	96.46 268	97.52 270	93.75 264	97.27 339	98.46 188	95.53 140	94.42 261	98.00 220	86.21 271	98.97 240	96.25 164	94.37 264	96.66 321
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
XVG-ACMP-BASELINE		94.54 266	94.14 256	95.75 303	96.55 332	91.65 318	98.11 257	98.44 192	94.96 176	94.22 272	97.90 229	79.18 356	99.11 221	94.05 242	93.85 281	96.48 348

casdiffmvs_mvg		97.72 86	97.48 93	98.44 108	98.42 175	96.59 130	98.92 103	98.44 192	96.20 111	97.76 141	99.20 74	91.66 144	99.23 203	98.27 65	98.41 172	99.49 100
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
ACMP	93.49 10	95.34 214	94.98 210	96.43 270	97.67 254	93.48 275	98.73 160	98.44 192	94.94 180	92.53 337	98.53 169	84.50 307	99.14 215	95.48 192	94.00 277	96.66 321
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ACMM	93.85 9	95.69 192	95.38 188	96.61 247	97.61 259	93.84 260	98.91 105	98.44 192	95.25 158	94.28 268	98.47 175	86.04 276	99.12 219	95.50 191	93.95 279	96.87 297
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Effi-MVS+		97.12 129	96.69 135	98.39 114	98.19 206	96.72 123	97.37 329	98.43 196	93.71 241	97.65 155	98.02 217	92.20 129	99.25 200	96.87 141	97.79 193	99.19 152

EC-MVSNet		98.21 66	98.11 63	98.49 102	98.34 187	97.26 98	99.61 5	98.43 196	96.78 82	98.87 70	98.84 134	93.72 103	99.01 238	98.91 28	99.50 102	99.19 152

anonymousdsp		95.42 206	94.91 213	96.94 220	95.10 382	95.90 170	99.14 54	98.41 198	93.75 235	93.16 317	97.46 269	87.50 249	98.41 311	95.63 187	94.03 276	96.50 345

PMMVS		96.60 148	96.33 149	97.41 190	97.90 237	93.93 257	97.35 332	98.41 198	92.84 285	97.76 141	97.45 271	91.10 162	99.20 207	96.26 162	97.91 188	99.11 166

MVSFormer		97.57 101	97.49 91	97.84 154	98.07 217	95.76 175	99.47 7	98.40 200	94.98 174	98.79 76	98.83 136	92.34 121	98.41 311	96.91 132	99.59 84	99.34 122

test_djsdf		96.00 173	95.69 177	96.93 221	95.72 365	95.49 185	99.47 7	98.40 200	94.98 174	94.58 251	97.86 233	89.16 203	98.41 311	96.91 132	94.12 274	96.88 294

sasdasda		97.67 90	97.23 106	98.98 63	98.70 151	98.38 35	99.34 16	98.39 202	96.76 84	97.67 151	97.40 276	92.26 124	99.49 172	98.28 62	96.28 243	99.08 172

OPM-MVS		95.69 192	95.33 192	96.76 232	96.16 351	94.63 229	98.43 216	98.39 202	96.64 93	95.02 240	98.78 141	85.15 291	99.05 229	95.21 203	94.20 269	96.60 326
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
canonicalmvs		97.67 90	97.23 106	98.98 63	98.70 151	98.38 35	99.34 16	98.39 202	96.76 84	97.67 151	97.40 276	92.26 124	99.49 172	98.28 62	96.28 243	99.08 172

DP-MVS		96.59 149	95.93 164	98.57 90	99.34 61	96.19 151	98.70 169	98.39 202	89.45 372	94.52 253	99.35 50	91.85 139	99.85 70	92.89 277	98.88 144	99.68 65

MGCFI-Net		97.62 96	97.19 109	98.92 68	98.66 157	98.20 53	99.32 21	98.38 206	96.69 90	97.58 160	97.42 275	92.10 132	99.50 171	98.28 62	96.25 246	99.08 172

dcpmvs_2		98.08 69	98.59 18	96.56 254	99.57 33	90.34 346	99.15 51	98.38 206	96.82 81	99.29 40	99.49 24	95.78 47	99.57 151	98.94 27	99.86 2	99.77 30

diffmvs		97.58 100	97.40 98	98.13 135	98.32 193	95.81 174	98.06 263	98.37 208	96.20 111	98.74 80	98.89 129	91.31 156	99.25 200	98.16 67	98.52 164	99.34 122
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
ACMH+	92.99 14	94.30 284	93.77 286	95.88 298	97.81 243	92.04 311	98.71 165	98.37 208	93.99 222	90.60 364	98.47 175	80.86 343	99.05 229	92.75 279	92.40 306	96.55 334

MSDG		95.93 178	95.30 195	97.83 155	98.90 131	95.36 191	96.83 373	98.37 208	91.32 335	94.43 260	98.73 150	90.27 178	99.60 147	90.05 336	98.82 150	98.52 227

DPM-MVS		97.55 103	96.99 118	99.23 42	99.04 115	98.55 27	97.17 348	98.35 211	94.85 184	97.93 134	98.58 164	95.07 77	99.71 125	92.60 281	99.34 123	99.43 113

RRT-MVS		97.03 132	96.78 129	97.77 163	97.90 237	94.34 245	99.12 58	98.35 211	95.87 124	98.06 118	98.70 152	86.45 267	99.63 141	98.04 74	98.54 163	99.35 120

CMPMVS	66.06 21	89.70 359	89.67 355	89.78 385	93.19 401	76.56 411	97.00 357	98.35 211	80.97 409	81.57 407	97.75 244	74.75 390	98.61 285	89.85 339	93.63 286	94.17 396
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
v7n		94.19 292	93.43 305	96.47 265	95.90 360	94.38 243	99.26 27	98.34 214	91.99 314	92.76 329	97.13 295	88.31 227	98.52 293	89.48 348	87.70 365	96.52 340

CDS-MVSNet		96.99 134	96.69 135	97.90 152	98.05 222	95.98 156	98.20 242	98.33 215	93.67 248	96.95 178	98.49 173	93.54 104	98.42 304	95.24 201	97.74 196	99.31 128
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
casdiffmvs		97.63 95	97.41 97	98.28 119	98.33 190	96.14 153	98.82 134	98.32 216	96.38 105	97.95 130	99.21 72	91.23 158	99.23 203	98.12 68	98.37 173	99.48 102
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
baseline		97.64 93	97.44 96	98.25 124	98.35 182	96.20 149	99.00 83	98.32 216	96.33 108	98.03 122	99.17 81	91.35 153	99.16 210	98.10 69	98.29 179	99.39 116

cl22		94.68 254	94.19 251	96.13 285	98.11 215	93.60 269	96.94 360	98.31 218	92.43 300	93.32 312	96.87 329	86.51 263	98.28 329	94.10 240	91.16 321	96.51 343

test_yl		97.22 121	96.78 129	98.54 95	98.73 146	96.60 128	98.45 211	98.31 218	94.70 187	98.02 124	98.42 179	90.80 167	99.70 126	96.81 145	96.79 222	99.34 122

DCV-MVSNet		97.22 121	96.78 129	98.54 95	98.73 146	96.60 128	98.45 211	98.31 218	94.70 187	98.02 124	98.42 179	90.80 167	99.70 126	96.81 145	96.79 222	99.34 122

nrg030		96.28 164	95.72 171	97.96 150	96.90 314	98.15 58	99.39 10	98.31 218	95.47 143	94.42 261	98.35 187	92.09 133	98.69 277	97.50 113	89.05 351	97.04 277

TAMVS		97.02 133	96.79 128	97.70 170	98.06 220	95.31 196	98.52 201	98.31 218	93.95 224	97.05 176	98.61 159	93.49 105	98.52 293	95.33 195	97.81 192	99.29 133

EPP-MVSNet		97.46 105	97.28 103	97.99 147	98.64 161	95.38 190	99.33 20	98.31 218	93.61 252	97.19 168	99.07 103	94.05 99	99.23 203	96.89 136	98.43 171	99.37 118

UnsupCasMVSNet_bld		87.17 371	85.12 378	93.31 369	91.94 407	88.77 374	94.92 402	98.30 224	84.30 402	82.30 405	90.04 413	63.96 412	97.25 378	85.85 378	74.47 418	93.93 402

Vis-MVSNet (Re-imp)		96.87 139	96.55 141	97.83 155	98.73 146	95.46 186	99.20 42	98.30 224	94.96 176	96.60 197	98.87 131	90.05 180	98.59 288	93.67 253	98.60 159	99.46 108

TSAR-MVS + GP.		98.38 52	98.24 52	98.81 74	99.22 95	97.25 99	98.11 257	98.29 226	97.19 62	98.99 60	99.02 107	96.22 30	99.67 133	98.52 48	98.56 162	99.51 93

MS-PatchMatch		93.84 309	93.63 295	94.46 352	96.18 348	89.45 362	97.76 301	98.27 227	92.23 308	92.13 347	97.49 267	79.50 353	98.69 277	89.75 341	99.38 119	95.25 378

EI-MVSNet		95.96 174	95.83 167	96.36 274	97.93 235	93.70 268	98.12 255	98.27 227	93.70 243	95.07 238	99.02 107	92.23 127	98.54 291	94.68 215	93.46 289	96.84 300

MVSTER		96.06 171	95.72 171	97.08 211	98.23 200	95.93 167	98.73 160	98.27 227	94.86 182	95.07 238	98.09 212	88.21 229	98.54 291	96.59 151	93.46 289	96.79 303

FMVSNet2		94.47 275	93.61 296	97.04 213	98.21 202	96.43 138	98.79 150	98.27 227	92.46 296	93.50 305	97.09 300	81.16 336	98.00 348	91.09 317	91.93 310	96.70 315

FMVSNet3		94.97 240	94.26 247	97.11 209	98.18 208	96.62 125	98.56 198	98.26 231	93.67 248	94.09 278	97.10 296	84.25 310	98.01 346	92.08 295	92.14 307	96.70 315

Fast-Effi-MVS+		96.28 164	95.70 176	98.03 144	98.29 196	95.97 161	98.58 191	98.25 232	91.74 320	95.29 236	97.23 289	91.03 164	99.15 213	92.90 275	97.96 187	98.97 184

PAPM		94.95 241	94.00 267	97.78 160	97.04 304	95.65 177	96.03 389	98.25 232	91.23 340	94.19 274	97.80 242	91.27 157	98.86 262	82.61 396	97.61 200	98.84 195

test_fmvs1_n		95.90 180	95.99 162	95.63 307	98.67 156	88.32 383	99.26 27	98.22 234	96.40 103	99.67 18	99.26 63	73.91 395	99.70 126	99.02 25	99.50 102	98.87 192

CANet_DTU		96.96 135	96.55 141	98.21 127	98.17 211	96.07 155	97.98 273	98.21 235	97.24 58	97.13 170	98.93 123	86.88 259	99.91 45	95.00 207	99.37 121	98.66 215

HY-MVS	93.96 8	96.82 142	96.23 154	98.57 90	98.46 174	97.00 109	98.14 252	98.21 235	93.95 224	96.72 191	97.99 221	91.58 145	99.76 114	94.51 224	96.54 230	98.95 187

test_fmvs1		96.42 156	96.67 137	95.66 306	98.82 141	88.53 379	98.80 143	98.20 237	96.39 104	99.64 21	99.20 74	80.35 348	99.67 133	99.04 24	99.57 88	98.78 201

PCF-MVS	93.45 11	94.68 254	93.43 305	98.42 112	98.62 163	96.77 120	95.48 397	98.20 237	84.63 401	93.34 311	98.32 193	88.55 223	99.81 88	84.80 388	98.96 140	98.68 211
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
v8		94.47 275	93.77 286	96.57 253	96.36 342	94.83 221	99.05 69	98.19 239	91.92 316	93.16 317	96.97 319	88.82 217	98.48 295	91.69 308	87.79 364	96.39 352

v10		94.29 286	93.55 299	96.51 261	96.39 341	94.80 223	98.99 86	98.19 239	91.35 333	93.02 323	96.99 317	88.09 233	98.41 311	90.50 329	88.41 359	96.33 356

mvs_anonymous		96.70 146	96.53 143	97.18 202	98.19 206	93.78 261	98.31 228	98.19 239	94.01 220	94.47 255	98.27 199	92.08 134	98.46 299	97.39 117	97.91 188	99.31 128

WBMVS		94.56 264	94.04 261	96.10 287	98.03 224	93.08 296	97.82 297	98.18 242	94.02 217	93.77 295	96.82 332	81.28 335	98.34 318	95.47 193	91.00 324	96.88 294

AllTest		95.24 220	94.65 226	96.99 215	99.25 85	93.21 290	98.59 189	98.18 242	91.36 331	93.52 302	98.77 143	84.67 302	99.72 120	89.70 343	97.87 190	98.02 249

TestCases				96.99 215	99.25 85	93.21 290		98.18 242	91.36 331	93.52 302	98.77 143	84.67 302	99.72 120	89.70 343	97.87 190	98.02 249

GBi-Net		94.49 272	93.80 283	96.56 254	98.21 202	95.00 209	98.82 134	98.18 242	92.46 296	94.09 278	97.07 303	81.16 336	97.95 351	92.08 295	92.14 307	96.72 311

test1		94.49 272	93.80 283	96.56 254	98.21 202	95.00 209	98.82 134	98.18 242	92.46 296	94.09 278	97.07 303	81.16 336	97.95 351	92.08 295	92.14 307	96.72 311

FMVSNet1		93.19 323	92.07 332	96.56 254	97.54 267	95.00 209	98.82 134	98.18 242	90.38 356	92.27 344	97.07 303	73.68 396	97.95 351	89.36 350	91.30 318	96.72 311

v1192		94.32 283	93.58 297	96.53 259	96.10 352	94.45 238	98.50 207	98.17 248	91.54 326	94.19 274	97.06 307	86.95 258	98.43 303	90.14 332	89.57 340	96.70 315

v1240		94.06 305	93.29 309	96.34 276	96.03 356	93.90 258	98.44 214	98.17 248	91.18 343	94.13 277	97.01 316	86.05 274	98.42 304	89.13 353	89.50 344	96.70 315

v144192		94.39 280	93.70 292	96.48 264	96.06 354	94.35 244	98.58 191	98.16 250	91.45 328	94.33 266	97.02 314	87.50 249	98.45 300	91.08 319	89.11 350	96.63 323

Fast-Effi-MVS+-dtu		95.87 181	95.85 166	95.91 295	97.74 249	91.74 316	98.69 171	98.15 251	95.56 139	94.92 241	97.68 253	88.98 211	98.79 271	93.19 265	97.78 194	97.20 274

v1921920		94.20 291	93.47 303	96.40 273	95.98 357	94.08 254	98.52 201	98.15 251	91.33 334	94.25 270	97.20 292	86.41 268	98.42 304	90.04 337	89.39 347	96.69 320

v1144		94.59 262	93.92 272	96.60 249	96.21 346	94.78 225	98.59 189	98.14 253	91.86 319	94.21 273	97.02 314	87.97 237	98.41 311	91.72 307	89.57 340	96.61 325

IterMVS-LS		95.46 202	95.21 198	96.22 282	98.12 214	93.72 267	98.32 227	98.13 254	93.71 241	94.26 269	97.31 283	92.24 126	98.10 339	94.63 217	90.12 333	96.84 300
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
GeoE		96.58 151	96.07 157	98.10 140	98.35 182	95.89 171	99.34 16	98.12 255	93.12 274	96.09 217	98.87 131	89.71 187	98.97 240	92.95 273	98.08 184	99.43 113

EU-MVSNet		93.66 310	94.14 256	92.25 380	95.96 359	83.38 404	98.52 201	98.12 255	94.69 189	92.61 334	98.13 210	87.36 252	96.39 396	91.82 304	90.00 335	96.98 280

IterMVS		94.09 302	93.85 280	94.80 338	97.99 228	90.35 345	97.18 346	98.12 255	93.68 246	92.46 341	97.34 279	84.05 316	97.41 376	92.51 288	91.33 317	96.62 324
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
test_vis1_n		95.47 201	95.13 201	96.49 262	97.77 245	90.41 344	99.27 26	98.11 258	96.58 95	99.66 19	99.18 80	67.00 408	99.62 145	99.21 20	99.40 117	99.44 111

IterMVS-SCA-FT		94.11 300	93.87 278	94.85 335	97.98 230	90.56 341	97.18 346	98.11 258	93.75 235	92.58 335	97.48 268	83.97 318	97.41 376	92.48 290	91.30 318	96.58 328

COLMAP_ROB	93.27 12	95.33 215	94.87 216	96.71 234	99.29 77	93.24 289	98.58 191	98.11 258	89.92 363	93.57 300	99.10 93	86.37 269	99.79 105	90.78 325	98.10 183	97.09 275
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
hse-mvs2		95.71 189	95.30 195	96.93 221	98.50 171	93.53 273	98.36 220	98.10 261	97.48 40	98.67 84	97.99 221	89.76 184	99.02 236	97.95 76	80.91 402	98.22 242

AUN-MVS		94.53 268	93.73 290	96.92 224	98.50 171	93.52 274	98.34 222	98.10 261	93.83 232	95.94 225	97.98 223	85.59 282	99.03 233	94.35 229	80.94 401	98.22 242

Effi-MVS+-dtu		96.29 162	96.56 140	95.51 311	97.89 239	90.22 347	98.80 143	98.10 261	96.57 97	96.45 207	96.66 339	90.81 166	98.91 253	95.72 182	97.99 185	97.40 267

1112_ss		96.63 147	96.00 161	98.50 100	98.56 166	96.37 142	98.18 250	98.10 261	92.92 282	94.84 243	98.43 177	92.14 130	99.58 150	94.35 229	96.51 231	99.56 89

V42		94.78 249	94.14 256	96.70 236	96.33 344	95.22 200	98.97 89	98.09 265	92.32 305	94.31 267	97.06 307	88.39 226	98.55 290	92.90 275	88.87 355	96.34 354

miper_enhance_ethall		95.10 229	94.75 220	96.12 286	97.53 269	93.73 266	96.61 380	98.08 266	92.20 311	93.89 287	96.65 341	92.44 119	98.30 325	94.21 235	91.16 321	96.34 354

v2v482		94.69 252	94.03 263	96.65 239	96.17 349	94.79 224	98.67 176	98.08 266	92.72 288	94.00 283	97.16 293	87.69 246	98.45 300	92.91 274	88.87 355	96.72 311

CL-MVSNet_self_test		90.11 356	89.14 359	93.02 373	91.86 408	88.23 385	96.51 383	98.07 268	90.49 351	90.49 365	94.41 392	84.75 299	95.34 405	80.79 401	74.95 416	95.50 374

miper_ehance_all_eth		95.01 233	94.69 224	95.97 292	97.70 252	93.31 284	97.02 356	98.07 268	92.23 308	93.51 304	96.96 321	91.85 139	98.15 335	93.68 251	91.16 321	96.44 351

eth_miper_zixun_eth		94.68 254	94.41 242	95.47 313	97.64 257	91.71 317	96.73 377	98.07 268	92.71 289	93.64 297	97.21 291	90.54 172	98.17 334	93.38 259	89.76 337	96.54 335

MVS_Test		97.28 118	97.00 117	98.13 135	98.33 190	95.97 161	98.74 156	98.07 268	94.27 208	98.44 102	98.07 213	92.48 118	99.26 199	96.43 157	98.19 180	99.16 158

Test_1112_low_res		96.34 161	95.66 179	98.36 115	98.56 166	95.94 164	97.71 305	98.07 268	92.10 312	94.79 247	97.29 284	91.75 141	99.56 154	94.17 236	96.50 232	99.58 87

alignmvs		97.56 102	97.07 115	99.01 60	98.66 157	98.37 42	98.83 132	98.06 273	96.74 86	98.00 128	97.65 254	90.80 167	99.48 177	98.37 58	96.56 229	99.19 152

RPSCF		94.87 245	95.40 184	93.26 370	98.89 132	82.06 408	98.33 223	98.06 273	90.30 358	96.56 198	99.26 63	87.09 254	99.49 172	93.82 248	96.32 237	98.24 240

miper_lstm_enhance		94.33 282	94.07 260	95.11 325	97.75 246	90.97 328	97.22 341	98.03 275	91.67 324	92.76 329	96.97 319	90.03 181	97.78 363	92.51 288	89.64 339	96.56 332

c3_l		94.79 248	94.43 241	95.89 297	97.75 246	93.12 294	97.16 350	98.03 275	92.23 308	93.46 307	97.05 310	91.39 151	98.01 346	93.58 256	89.21 349	96.53 337

pm-mvs1		93.94 308	93.06 312	96.59 250	96.49 336	95.16 202	98.95 95	98.03 275	92.32 305	91.08 359	97.84 236	84.54 306	98.41 311	92.16 293	86.13 384	96.19 361

v148		94.29 286	93.76 288	95.91 295	96.10 352	92.93 298	98.58 191	97.97 278	92.59 294	93.47 306	96.95 323	88.53 224	98.32 321	92.56 285	87.06 374	96.49 346

IS-MVSNet		97.22 121	96.88 123	98.25 124	98.85 139	96.36 143	99.19 44	97.97 278	95.39 148	97.23 167	98.99 113	91.11 161	98.93 250	94.60 220	98.59 160	99.47 104

cl____		94.51 270	94.01 266	96.02 289	97.58 262	93.40 280	97.05 354	97.96 280	91.73 322	92.76 329	97.08 302	89.06 207	98.13 337	92.61 280	90.29 331	96.52 340

KD-MVS_self_test		90.38 354	89.38 357	93.40 367	92.85 403	88.94 373	97.95 275	97.94 281	90.35 357	90.25 366	93.96 397	79.82 350	95.94 401	84.62 390	76.69 414	95.33 376

DIV-MVS_self_test		94.52 269	94.03 263	95.99 290	97.57 266	93.38 281	97.05 354	97.94 281	91.74 320	92.81 327	97.10 296	89.12 204	98.07 343	92.60 281	90.30 330	96.53 337

pmmvs6		91.77 340	90.63 345	95.17 323	94.69 390	91.24 325	98.67 176	97.92 283	86.14 392	89.62 372	97.56 265	75.79 386	98.34 318	90.75 326	84.56 386	95.94 367

jason		97.32 117	97.08 114	98.06 143	97.45 276	95.59 178	97.87 289	97.91 284	94.79 186	98.55 94	98.83 136	91.12 160	99.23 203	97.58 105	99.60 82	99.34 122
jason: jason.
ppachtmachnet_test		93.22 321	92.63 321	94.97 330	95.45 376	90.84 333	96.88 369	97.88 285	90.60 350	92.08 348	97.26 285	88.08 234	97.86 360	85.12 384	90.33 329	96.22 359

tpm cat1		93.36 315	92.80 317	95.07 328	97.58 262	87.97 387	96.76 375	97.86 286	82.17 408	93.53 301	96.04 363	86.13 272	99.13 216	89.24 351	95.87 256	98.10 247

tt0805		94.54 266	93.85 280	96.63 244	97.98 230	93.06 297	98.77 152	97.84 287	93.67 248	93.80 293	98.04 216	76.88 380	98.96 244	94.79 214	92.86 300	97.86 253

EG-PatchMatch MVS		91.13 348	90.12 351	94.17 358	94.73 389	89.00 370	98.13 254	97.81 288	89.22 376	85.32 401	96.46 347	67.71 406	98.42 304	87.89 367	93.82 282	95.08 383

BH-untuned		95.95 175	95.72 171	96.65 239	98.55 168	92.26 304	98.23 238	97.79 289	93.73 238	94.62 250	98.01 219	88.97 212	99.00 239	93.04 270	98.51 165	98.68 211

lupinMVS		97.44 109	97.22 108	98.12 138	98.07 217	95.76 175	97.68 307	97.76 290	94.50 201	98.79 76	98.61 159	92.34 121	99.30 196	97.58 105	99.59 84	99.31 128

VDDNet		95.36 212	94.53 232	97.86 153	98.10 216	95.13 205	98.85 126	97.75 291	90.46 353	98.36 105	99.39 38	73.27 397	99.64 138	97.98 75	96.58 228	98.81 197

ADS-MVSNet		95.00 234	94.45 239	96.63 244	98.00 226	91.91 312	96.04 387	97.74 292	90.15 359	96.47 205	96.64 342	87.89 239	98.96 244	90.08 334	97.06 212	99.02 179

BP-MVS1		97.82 81	97.51 90	98.76 77	98.25 197	97.39 88	99.15 51	97.68 293	96.69 90	98.47 96	99.10 93	90.29 177	99.51 168	98.60 38	99.35 122	99.37 118

tpmvs		94.60 260	94.36 244	95.33 319	97.46 273	88.60 377	96.88 369	97.68 293	91.29 337	93.80 293	96.42 349	88.58 219	99.24 202	91.06 320	96.04 252	98.17 244

pmmvs4		94.69 252	93.99 269	96.81 230	95.74 364	95.94 164	97.40 325	97.67 295	90.42 355	93.37 310	97.59 261	89.08 206	98.20 332	92.97 272	91.67 314	96.30 357

our_test_3		93.65 312	93.30 308	94.69 340	95.45 376	89.68 357	96.91 363	97.65 296	91.97 315	91.66 354	96.88 327	89.67 188	97.93 354	88.02 364	91.49 316	96.48 348

MVP-Stereo		94.28 288	93.92 272	95.35 318	94.95 384	92.60 301	97.97 274	97.65 296	91.61 325	90.68 363	97.09 300	86.32 270	98.42 304	89.70 343	99.34 123	95.02 386
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
KD-MVS_2432*1600		89.61 361	87.96 369	94.54 347	94.06 396	91.59 319	95.59 395	97.63 298	89.87 364	88.95 378	94.38 394	78.28 362	96.82 385	84.83 386	68.05 420	95.21 379

miper_refine_blended		89.61 361	87.96 369	94.54 347	94.06 396	91.59 319	95.59 395	97.63 298	89.87 364	88.95 378	94.38 394	78.28 362	96.82 385	84.83 386	68.05 420	95.21 379

SCA		95.46 202	95.13 201	96.46 268	97.67 254	91.29 324	97.33 334	97.60 300	94.68 190	96.92 182	97.10 296	83.97 318	98.89 257	92.59 283	98.32 178	99.20 148

testing91		94.98 238	94.25 248	97.20 199	97.94 233	93.41 278	98.00 271	97.58 301	94.99 173	95.45 231	96.04 363	77.20 374	99.42 184	94.97 208	96.02 253	98.78 201

FA-MVS(test-final)		96.41 159	95.94 163	97.82 157	98.21 202	95.20 201	97.80 298	97.58 301	93.21 268	97.36 164	97.70 248	89.47 192	99.56 154	94.12 238	97.99 185	98.71 208

GA-MVS		94.81 247	94.03 263	97.14 205	97.15 299	93.86 259	96.76 375	97.58 301	94.00 221	94.76 249	97.04 311	80.91 341	98.48 295	91.79 305	96.25 246	99.09 168

Anonymous20240521		91.18 347	90.44 347	93.42 365	93.70 399	88.47 380	98.94 98	97.56 304	88.46 381	89.56 374	95.08 386	77.15 376	96.97 382	83.92 391	89.55 342	94.82 388

test20.03		90.89 351	90.38 348	92.43 376	93.48 400	88.14 386	98.33 223	97.56 304	93.40 260	87.96 384	96.71 338	80.69 345	94.13 411	79.15 406	86.17 381	95.01 387

CR-MVSNet		94.76 251	94.15 255	96.59 250	97.00 305	93.43 276	94.96 400	97.56 304	92.46 296	96.93 180	96.24 352	88.15 231	97.88 359	87.38 368	96.65 226	98.46 231

Patchmtry		93.22 321	92.35 329	95.84 299	96.77 320	93.09 295	94.66 407	97.56 304	87.37 386	92.90 325	96.24 352	88.15 231	97.90 355	87.37 369	90.10 334	96.53 337

tpmrst		95.63 194	95.69 177	95.44 315	97.54 267	88.54 378	96.97 358	97.56 304	93.50 255	97.52 162	96.93 325	89.49 190	99.16 210	95.25 200	96.42 234	98.64 217

FMVSNet5		91.81 339	90.92 342	94.49 349	97.21 292	92.09 308	98.00 271	97.55 309	89.31 375	90.86 361	95.61 378	74.48 392	95.32 406	85.57 379	89.70 338	96.07 364

testgi		93.06 327	92.45 328	94.88 334	96.43 340	89.90 350	98.75 153	97.54 310	95.60 137	91.63 355	97.91 228	74.46 393	97.02 381	86.10 375	93.67 284	97.72 258

mvsany_test1		97.69 89	97.70 79	97.66 177	98.24 198	94.18 252	97.53 318	97.53 311	95.52 141	99.66 19	99.51 20	94.30 94	99.56 154	98.38 57	98.62 158	99.23 143

PatchmatchNet		95.71 189	95.52 181	96.29 280	97.58 262	90.72 336	96.84 372	97.52 312	94.06 214	97.08 172	96.96 321	89.24 201	98.90 256	92.03 299	98.37 173	99.26 139
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MDA-MVSNet-bldmvs		89.97 358	88.35 364	94.83 337	95.21 380	91.34 322	97.64 311	97.51 313	88.36 382	71.17 420	96.13 359	79.22 355	96.63 392	83.65 392	86.27 380	96.52 340

USDC		93.33 318	92.71 319	95.21 321	96.83 318	90.83 334	96.91 363	97.50 314	93.84 230	90.72 362	98.14 209	77.69 368	98.82 268	89.51 347	93.21 297	95.97 366

ITE_SJBPF				95.44 315	97.42 278	91.32 323		97.50 314	95.09 168	93.59 298	98.35 187	81.70 331	98.88 259	89.71 342	93.39 293	96.12 362

Patchmatch-test		94.42 278	93.68 294	96.63 244	97.60 260	91.76 314	94.83 404	97.49 316	89.45 372	94.14 276	97.10 296	88.99 208	98.83 266	85.37 382	98.13 182	99.29 133

mvsmamba		97.25 120	96.99 118	98.02 145	98.34 187	95.54 183	99.18 48	97.47 317	95.04 170	98.15 111	98.57 167	89.46 193	99.31 195	97.68 99	99.01 137	99.22 145

Syy-MVS		92.55 334	92.61 322	92.38 377	97.39 282	83.41 403	97.91 281	97.46 318	93.16 271	93.42 308	95.37 381	84.75 299	96.12 398	77.00 411	96.99 214	97.60 262

myMVS_eth3d		92.73 331	92.01 333	94.89 333	97.39 282	90.94 329	97.91 281	97.46 318	93.16 271	93.42 308	95.37 381	68.09 404	96.12 398	88.34 360	96.99 214	97.60 262

YYNet1		90.70 353	89.39 356	94.62 345	94.79 388	90.65 338	97.20 343	97.46 318	87.54 385	72.54 418	95.74 370	86.51 263	96.66 391	86.00 376	86.76 379	96.54 335

MDA-MVSNet_test_wron		90.71 352	89.38 357	94.68 341	94.83 386	90.78 335	97.19 345	97.46 318	87.60 384	72.41 419	95.72 374	86.51 263	96.71 390	85.92 377	86.80 378	96.56 332

BH-RMVSNet		95.92 179	95.32 193	97.69 171	98.32 193	94.64 228	98.19 245	97.45 322	94.56 196	96.03 219	98.61 159	85.02 292	99.12 219	90.68 327	99.06 133	99.30 131

MIMVSNet1		89.67 360	88.28 365	93.82 361	92.81 404	91.08 327	98.01 269	97.45 322	87.95 383	87.90 385	95.87 368	67.63 407	94.56 410	78.73 408	88.18 361	95.83 369

OurMVSNet-221017-0		94.21 290	94.00 267	94.85 335	95.60 368	89.22 366	98.89 110	97.43 324	95.29 155	92.18 346	98.52 172	82.86 326	98.59 288	93.46 258	91.76 312	96.74 308

BH-w/o		95.38 209	95.08 205	96.26 281	98.34 187	91.79 313	97.70 306	97.43 324	92.87 284	94.24 271	97.22 290	88.66 218	98.84 263	91.55 310	97.70 198	98.16 245

VDD-MVS		95.82 185	95.23 197	97.61 180	98.84 140	93.98 256	98.68 173	97.40 326	95.02 172	97.95 130	99.34 54	74.37 394	99.78 108	98.64 36	96.80 221	99.08 172

Gipuma		78.40 385	76.75 388	83.38 398	95.54 370	80.43 410	79.42 423	97.40 326	64.67 420	73.46 417	80.82 421	45.65 420	93.14 415	66.32 419	87.43 368	76.56 423
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
FE-MVS		95.62 195	94.90 214	97.78 160	98.37 181	94.92 216	97.17 348	97.38 328	90.95 346	97.73 146	97.70 248	85.32 289	99.63 141	91.18 314	98.33 176	98.79 198

MonoMVSNet		95.51 199	95.45 183	95.68 304	95.54 370	90.87 331	98.92 103	97.37 329	95.79 128	95.53 229	97.38 278	89.58 189	97.68 366	96.40 158	92.59 304	98.49 229

new-patchmatchnet		88.50 367	87.45 372	91.67 382	90.31 413	85.89 397	97.16 350	97.33 330	89.47 371	83.63 404	92.77 407	76.38 381	95.06 408	82.70 395	77.29 412	94.06 400

myMVS_eth3d28		95.12 227	94.62 227	96.64 243	98.17 211	92.17 305	98.02 268	97.32 331	95.41 147	96.22 212	96.05 362	78.01 366	99.13 216	95.22 202	97.16 209	98.60 220

mmtdpeth		93.12 326	92.61 322	94.63 344	97.60 260	89.68 357	99.21 39	97.32 331	94.02 217	97.72 147	94.42 391	77.01 378	99.44 182	99.05 23	77.18 413	94.78 391

ADS-MVSNet2		94.58 263	94.40 243	95.11 325	98.00 226	88.74 375	96.04 387	97.30 333	90.15 359	96.47 205	96.64 342	87.89 239	97.56 372	90.08 334	97.06 212	99.02 179

ttmdpeth		92.61 333	91.96 336	94.55 346	94.10 394	90.60 340	98.52 201	97.29 334	92.67 290	90.18 367	97.92 227	79.75 352	97.79 362	91.09 317	86.15 383	95.26 377

MDTV_nov1_ep13			95.40 184		97.48 271	88.34 382	96.85 371	97.29 334	93.74 237	97.48 163	97.26 285	89.18 202	99.05 229	91.92 303	97.43 205

pmmvs5		93.65 312	92.97 315	95.68 304	95.49 373	92.37 302	98.20 242	97.28 336	89.66 368	92.58 335	97.26 285	82.14 329	98.09 341	93.18 266	90.95 325	96.58 328

EPNet_dtu		95.21 222	94.95 212	95.99 290	96.17 349	90.45 342	98.16 251	97.27 337	96.77 83	93.14 320	98.33 192	90.34 175	98.42 304	85.57 379	98.81 151	99.09 168
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Anonymous20231206		91.66 341	91.10 341	93.33 368	94.02 398	87.35 391	98.58 191	97.26 338	90.48 352	90.16 368	96.31 350	83.83 322	96.53 393	79.36 405	89.90 336	96.12 362

test_fmvs2		93.43 314	93.58 297	92.95 374	96.97 308	83.91 400	99.19 44	97.24 339	95.74 130	95.20 237	98.27 199	69.65 401	98.72 276	96.26 162	93.73 283	96.24 358

test_0402		91.32 343	90.27 349	94.48 350	96.60 330	91.12 326	98.50 207	97.22 340	86.10 393	88.30 383	96.98 318	77.65 370	97.99 349	78.13 409	92.94 299	94.34 392

UBG		95.32 216	94.72 222	97.13 206	98.05 222	93.26 286	97.87 289	97.20 341	94.96 176	96.18 215	95.66 377	80.97 340	99.35 190	94.47 226	97.08 211	98.78 201

dp		94.15 296	93.90 275	94.90 332	97.31 286	86.82 394	96.97 358	97.19 342	91.22 341	96.02 220	96.61 344	85.51 283	99.02 236	90.00 338	94.30 265	98.85 193

testing99		94.83 246	94.08 259	97.07 212	97.94 233	93.13 292	98.10 259	97.17 343	94.86 182	95.34 232	96.00 366	76.31 382	99.40 185	95.08 205	95.90 254	98.68 211

testing3		93.19 323	92.48 327	95.30 320	98.07 217	92.27 303	98.64 182	97.17 343	93.94 226	93.98 284	97.04 311	67.97 405	96.01 400	88.40 359	97.14 210	97.63 261

ETVMVS		94.50 271	93.44 304	97.68 173	98.18 208	95.35 193	98.19 245	97.11 345	93.73 238	96.40 208	95.39 380	74.53 391	98.84 263	91.10 316	96.31 238	98.84 195

thres200		95.25 219	94.57 230	97.28 196	98.81 142	94.92 216	98.20 242	97.11 345	95.24 160	96.54 202	96.22 356	84.58 305	99.53 164	87.93 366	96.50 232	97.39 268

dmvs_re		94.48 274	94.18 253	95.37 317	97.68 253	90.11 349	98.54 200	97.08 347	94.56 196	94.42 261	97.24 288	84.25 310	97.76 364	91.02 323	92.83 301	98.24 240

PatchT		93.06 327	91.97 334	96.35 275	96.69 326	92.67 300	94.48 410	97.08 347	86.62 388	97.08 172	92.23 410	87.94 238	97.90 355	78.89 407	96.69 224	98.49 229

TDRefinement		91.06 349	89.68 354	95.21 321	85.35 424	91.49 321	98.51 206	97.07 349	91.47 327	88.83 381	97.84 236	77.31 372	99.09 226	92.79 278	77.98 411	95.04 385

LF4IMVS		93.14 325	92.79 318	94.20 356	95.88 361	88.67 376	97.66 309	97.07 349	93.81 233	91.71 352	97.65 254	77.96 367	98.81 269	91.47 311	91.92 311	95.12 381

testing11		95.00 234	94.28 246	97.16 204	97.96 232	93.36 283	98.09 260	97.06 351	94.94 180	95.33 235	96.15 358	76.89 379	99.40 185	95.77 181	96.30 239	98.72 205

Anonymous202405211		95.28 218	94.49 234	97.67 174	99.00 120	93.75 264	98.70 169	97.04 352	90.66 349	96.49 204	98.80 139	78.13 364	99.83 76	96.21 165	95.36 262	99.44 111

baseline1		95.84 183	95.12 203	98.01 146	98.49 173	95.98 156	98.73 160	97.03 353	95.37 151	96.22 212	98.19 206	89.96 182	99.16 210	94.60 220	87.48 367	98.90 191

MIMVSNet		93.26 320	92.21 331	96.41 271	97.73 250	93.13 292	95.65 394	97.03 353	91.27 339	94.04 281	96.06 361	75.33 387	97.19 379	86.56 372	96.23 248	98.92 190

MM		98.51 38	98.24 52	99.33 30	99.12 108	98.14 60	98.93 101	97.02 355	98.96 1	99.17 49	99.47 27	91.97 138	99.94 10	99.85 3	99.69 63	99.91 2

EPNet		97.28 118	96.87 124	98.51 99	94.98 383	96.14 153	98.90 106	97.02 355	98.28 14	95.99 221	99.11 91	91.36 152	99.89 54	96.98 128	99.19 129	99.50 95
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TR-MVS		94.94 243	94.20 250	97.17 203	97.75 246	94.14 253	97.59 315	97.02 355	92.28 307	95.75 227	97.64 257	83.88 320	98.96 244	89.77 340	96.15 250	98.40 233

JIA-IIPM		93.35 316	92.49 326	95.92 294	96.48 337	90.65 338	95.01 399	96.96 358	85.93 394	96.08 218	87.33 416	87.70 245	98.78 272	91.35 312	95.58 260	98.34 237

pmmvs-eth3d		90.36 355	89.05 360	94.32 355	91.10 411	92.12 307	97.63 314	96.95 359	88.86 379	84.91 402	93.13 405	78.32 361	96.74 387	88.70 356	81.81 396	94.09 398

tfpn200view9		95.32 216	94.62 227	97.43 188	98.94 129	94.98 212	98.68 173	96.93 360	95.33 152	96.55 200	96.53 345	84.23 312	99.56 154	88.11 361	96.29 240	97.76 254

thres400		95.38 209	94.62 227	97.65 178	98.94 129	94.98 212	98.68 173	96.93 360	95.33 152	96.55 200	96.53 345	84.23 312	99.56 154	88.11 361	96.29 240	98.40 233

thres100view900		95.38 209	94.70 223	97.41 190	98.98 124	94.92 216	98.87 119	96.90 362	95.38 149	96.61 196	96.88 327	84.29 308	99.56 154	88.11 361	96.29 240	97.76 254

thres600view7		95.49 200	94.77 218	97.67 174	98.98 124	95.02 208	98.85 126	96.90 362	95.38 149	96.63 194	96.90 326	84.29 308	99.59 148	88.65 358	96.33 236	98.40 233

test_method		79.03 380	78.17 382	81.63 402	86.06 423	54.40 434	82.75 422	96.89 364	39.54 426	80.98 410	95.57 379	58.37 416	94.73 409	84.74 389	78.61 408	95.75 370

CostFormer		94.95 241	94.73 221	95.60 309	97.28 287	89.06 368	97.53 318	96.89 364	89.66 368	96.82 187	96.72 337	86.05 274	98.95 249	95.53 190	96.13 251	98.79 198

new_pmnet		90.06 357	89.00 361	93.22 371	94.18 392	88.32 383	96.42 385	96.89 364	86.19 391	85.67 398	93.62 399	77.18 375	97.10 380	81.61 398	89.29 348	94.23 394

OpenMVS_ROB	86.42 20	89.00 365	87.43 373	93.69 362	93.08 402	89.42 363	97.91 281	96.89 364	78.58 411	85.86 396	94.69 388	69.48 402	98.29 328	77.13 410	93.29 296	93.36 405

tpm2		94.19 292	93.76 288	95.46 314	97.23 290	89.04 369	97.31 336	96.85 368	87.08 387	96.21 214	96.79 334	83.75 324	98.74 274	92.43 291	96.23 248	98.59 223

MVStest1		89.53 363	87.99 368	94.14 360	94.39 391	90.42 343	98.25 237	96.84 369	82.81 404	81.18 409	97.33 281	77.09 377	96.94 383	85.27 383	78.79 407	95.06 384

TransMVSNet (Re)		92.67 332	91.51 339	96.15 283	96.58 331	94.65 227	98.90 106	96.73 370	90.86 347	89.46 375	97.86 233	85.62 281	98.09 341	86.45 373	81.12 399	95.71 371

ambc				89.49 386	86.66 421	75.78 413	92.66 415	96.72 371		86.55 394	92.50 409	46.01 419	97.90 355	90.32 330	82.09 393	94.80 390

LCM-MVSNet		78.70 383	76.24 389	86.08 391	77.26 430	71.99 421	94.34 411	96.72 371	61.62 421	76.53 413	89.33 414	33.91 429	92.78 416	81.85 397	74.60 417	93.46 404

TinyColmap		92.31 337	91.53 338	94.65 343	96.92 311	89.75 353	96.92 361	96.68 373	90.45 354	89.62 372	97.85 235	76.06 385	98.81 269	86.74 371	92.51 305	95.41 375

Baseline_NR-MVSNet		94.35 281	93.81 282	95.96 293	96.20 347	94.05 255	98.61 188	96.67 374	91.44 329	93.85 290	97.60 260	88.57 220	98.14 336	94.39 227	86.93 375	95.68 372

SixPastTwentyTwo		93.34 317	92.86 316	94.75 339	95.67 366	89.41 364	98.75 153	96.67 374	93.89 227	90.15 369	98.25 202	80.87 342	98.27 330	90.90 324	90.64 327	96.57 330

testing222		94.12 299	93.03 313	97.37 195	98.02 225	94.66 226	97.94 277	96.65 376	94.63 193	95.78 226	95.76 369	71.49 399	98.92 251	91.17 315	95.88 255	98.52 227

test_fmvs3		87.17 371	87.06 374	87.50 389	91.21 410	75.66 414	99.05 69	96.61 377	92.79 287	88.85 380	92.78 406	43.72 421	93.49 412	93.95 243	84.56 386	93.34 406

mvs5depth		91.23 346	90.17 350	94.41 354	92.09 406	89.79 352	95.26 398	96.50 378	90.73 348	91.69 353	97.06 307	76.12 384	98.62 284	88.02 364	84.11 389	94.82 388

EGC-MVSNET		75.22 388	69.54 391	92.28 379	94.81 387	89.58 359	97.64 311	96.50 378	1.82 431	5.57 432	95.74 370	68.21 403	96.26 397	73.80 414	91.71 313	90.99 409

APD_test1		88.22 368	88.01 367	88.86 387	95.98 357	74.66 419	97.21 342	96.44 380	83.96 403	86.66 393	97.90 229	60.95 415	97.84 361	82.73 394	90.23 332	94.09 398

WB-MVS		84.86 376	85.33 377	83.46 397	89.48 415	69.56 423	98.19 245	96.42 381	89.55 370	81.79 406	94.67 389	84.80 297	90.12 419	52.44 423	80.64 403	90.69 410

test_f		86.07 375	85.39 376	88.10 388	89.28 416	75.57 415	97.73 304	96.33 382	89.41 374	85.35 400	91.56 412	43.31 423	95.53 403	91.32 313	84.23 388	93.21 407

SSC-MVS		84.27 377	84.71 380	82.96 401	89.19 417	68.83 424	98.08 261	96.30 383	89.04 378	81.37 408	94.47 390	84.60 304	89.89 420	49.80 425	79.52 405	90.15 411

LFMVS		95.86 182	94.98 210	98.47 104	98.87 136	96.32 145	98.84 130	96.02 384	93.40 260	98.62 90	99.20 74	74.99 389	99.63 141	97.72 92	97.20 208	99.46 108

IB-MVS	91.98 17	93.27 319	91.97 334	97.19 201	97.47 272	93.41 278	97.09 353	95.99 385	93.32 263	92.47 340	95.73 372	78.06 365	99.53 164	94.59 222	82.98 392	98.62 218
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
test0.0.03 1		94.08 303	93.51 301	95.80 300	95.53 372	92.89 299	97.38 327	95.97 386	95.11 165	92.51 339	96.66 339	87.71 243	96.94 383	87.03 370	93.67 284	97.57 264

WB-MVSnew		94.19 292	94.04 261	94.66 342	96.82 319	92.14 306	97.86 291	95.96 387	93.50 255	95.64 228	96.77 335	88.06 235	97.99 349	84.87 385	96.86 218	93.85 403

FPMVS		77.62 387	77.14 387	79.05 405	79.25 428	60.97 430	95.79 392	95.94 388	65.96 419	67.93 421	94.40 393	37.73 425	88.88 422	68.83 418	88.46 358	87.29 416

Patchmatch-RL test		91.49 342	90.85 343	93.41 366	91.37 409	84.40 398	92.81 414	95.93 389	91.87 318	87.25 387	94.87 387	88.99 208	96.53 393	92.54 287	82.00 394	99.30 131

tpm		94.13 297	93.80 283	95.12 324	96.50 335	87.91 388	97.44 322	95.89 390	92.62 292	96.37 210	96.30 351	84.13 315	98.30 325	93.24 263	91.66 315	99.14 161

LCM-MVSNet-Re		95.22 221	95.32 193	94.91 331	98.18 208	87.85 389	98.75 153	95.66 391	95.11 165	88.96 377	96.85 330	90.26 179	97.65 367	95.65 186	98.44 169	99.22 145

MVS_0304		98.23 64	97.91 74	99.21 43	98.06 220	97.96 67	98.58 191	95.51 392	98.58 9	98.87 70	99.26 63	92.99 112	99.95 8	99.62 13	99.67 66	99.73 45

mvsany_test3		88.80 366	88.04 366	91.09 384	89.78 414	81.57 409	97.83 296	95.49 393	93.81 233	87.53 386	93.95 398	56.14 417	97.43 375	94.68 215	83.13 391	94.26 393

ET-MVSNet_ETH3D		94.13 297	92.98 314	97.58 181	98.22 201	96.20 149	97.31 336	95.37 394	94.53 198	79.56 412	97.63 259	86.51 263	97.53 373	96.91 132	90.74 326	99.02 179

test-LLR		95.10 229	94.87 216	95.80 300	96.77 320	89.70 355	96.91 363	95.21 395	95.11 165	94.83 245	95.72 374	87.71 243	98.97 240	93.06 268	98.50 166	98.72 205

test-mter		94.08 303	93.51 301	95.80 300	96.77 320	89.70 355	96.91 363	95.21 395	92.89 283	94.83 245	95.72 374	77.69 368	98.97 240	93.06 268	98.50 166	98.72 205

PM-MVS		87.77 369	86.55 375	91.40 383	91.03 412	83.36 405	96.92 361	95.18 397	91.28 338	86.48 395	93.42 401	53.27 418	96.74 387	89.43 349	81.97 395	94.11 397

DeepMVS_CX				86.78 390	97.09 303	72.30 420		95.17 398	75.92 414	84.34 403	95.19 383	70.58 400	95.35 404	79.98 404	89.04 352	92.68 408

K. test v3		92.55 334	91.91 337	94.48 350	95.64 367	89.24 365	99.07 66	94.88 399	94.04 215	86.78 391	97.59 261	77.64 371	97.64 368	92.08 295	89.43 346	96.57 330

TESTMET0.1,1		94.18 295	93.69 293	95.63 307	96.92 311	89.12 367	96.91 363	94.78 400	93.17 270	94.88 242	96.45 348	78.52 359	98.92 251	93.09 267	98.50 166	98.85 193

pmmvs3		86.67 374	84.86 379	92.11 381	88.16 418	87.19 393	96.63 379	94.75 401	79.88 410	87.22 388	92.75 408	66.56 409	95.20 407	81.24 400	76.56 415	93.96 401

door								94.64 402

thisisatest0515		95.61 198	94.89 215	97.76 164	98.15 213	95.15 204	96.77 374	94.41 403	92.95 281	97.18 169	97.43 273	84.78 298	99.45 181	94.63 217	97.73 197	98.68 211

door-mid								94.37 404

tttt0517		96.07 170	95.51 182	97.78 160	98.41 177	94.84 219	99.28 24	94.33 405	94.26 209	97.64 156	98.64 158	84.05 316	99.47 179	95.34 194	97.60 201	99.03 178

DSMNet-mixed		92.52 336	92.58 324	92.33 378	94.15 393	82.65 406	98.30 230	94.26 406	89.08 377	92.65 333	95.73 372	85.01 293	95.76 402	86.24 374	97.76 195	98.59 223

thisisatest0530		96.01 172	95.36 189	97.97 148	98.38 179	95.52 184	98.88 116	94.19 407	94.04 215	97.64 156	98.31 194	83.82 323	99.46 180	95.29 198	97.70 198	98.93 189

MTMP							98.89 110	94.14 408

baseline2		95.11 228	94.52 233	96.87 226	96.65 329	93.56 270	98.27 235	94.10 409	93.45 258	92.02 350	97.43 273	87.45 251	99.19 208	93.88 246	97.41 206	97.87 252

PMVS	61.03 23	65.95 391	63.57 395	73.09 408	57.90 433	51.22 435	85.05 421	93.93 410	54.45 422	44.32 428	83.57 417	13.22 432	89.15 421	58.68 422	81.00 400	78.91 422
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
UWE-MVS		94.30 284	93.89 277	95.53 310	97.83 241	88.95 372	97.52 320	93.25 411	94.44 204	96.63 194	97.07 303	78.70 358	99.28 198	91.99 300	97.56 203	98.36 236

testf1		79.02 381	77.70 383	82.99 399	88.10 419	66.90 425	94.67 405	93.11 412	71.08 417	74.02 415	93.41 402	34.15 427	93.25 413	72.25 415	78.50 409	88.82 413

APD_test2		79.02 381	77.70 383	82.99 399	88.10 419	66.90 425	94.67 405	93.11 412	71.08 417	74.02 415	93.41 402	34.15 427	93.25 413	72.25 415	78.50 409	88.82 413

PMMVS2		77.95 386	75.44 390	85.46 392	82.54 425	74.95 417	94.23 412	93.08 414	72.80 416	74.68 414	87.38 415	36.36 426	91.56 417	73.95 413	63.94 422	89.87 412

MVS-HIRNet		89.46 364	88.40 363	92.64 375	97.58 262	82.15 407	94.16 413	93.05 415	75.73 415	90.90 360	82.52 418	79.42 354	98.33 320	83.53 393	98.68 153	97.43 265

UWE-MVS-28		92.79 330	92.51 325	93.62 363	96.46 338	86.28 395	97.93 278	92.71 416	94.17 210	94.78 248	97.16 293	81.05 339	96.43 395	81.45 399	96.86 218	98.14 246

test1111		95.94 177	95.78 168	96.41 271	98.99 123	90.12 348	99.04 73	92.45 417	96.99 74	98.03 122	99.27 62	81.40 333	99.48 177	96.87 141	99.04 134	99.63 77

ECVR-MVS		95.95 175	95.71 174	96.65 239	99.02 117	90.86 332	99.03 76	91.80 418	96.96 75	98.10 115	99.26 63	81.31 334	99.51 168	96.90 135	99.04 134	99.59 83

EPMVS		94.99 236	94.48 235	96.52 260	97.22 291	91.75 315	97.23 340	91.66 419	94.11 212	97.28 165	96.81 333	85.70 280	98.84 263	93.04 270	97.28 207	98.97 184

dmvs_testset		87.64 370	88.93 362	83.79 396	95.25 379	63.36 428	97.20 343	91.17 420	93.07 275	85.64 399	95.98 367	85.30 290	91.52 418	69.42 417	87.33 370	96.49 346

lessismore_v0				94.45 353	94.93 385	88.44 381		91.03 421		86.77 392	97.64 257	76.23 383	98.42 304	90.31 331	85.64 385	96.51 343

test_vis1_rt		91.29 344	90.65 344	93.19 372	97.45 276	86.25 396	98.57 197	90.90 422	93.30 265	86.94 390	93.59 400	62.07 414	99.11 221	97.48 114	95.58 260	94.22 395

ANet_high		69.08 389	65.37 393	80.22 404	65.99 432	71.96 422	90.91 418	90.09 423	82.62 406	49.93 427	78.39 422	29.36 430	81.75 424	62.49 420	38.52 426	86.95 418

gg-mvs-nofinetune		92.21 338	90.58 346	97.13 206	96.75 323	95.09 206	95.85 391	89.40 424	85.43 398	94.50 254	81.98 419	80.80 344	98.40 317	92.16 293	98.33 176	97.88 251

GG-mvs-BLEND				96.59 250	96.34 343	94.98 212	96.51 383	88.58 425		93.10 322	94.34 396	80.34 349	98.05 344	89.53 346	96.99 214	96.74 308

E-PMN		64.94 392	64.25 394	67.02 409	82.28 426	59.36 432	91.83 417	85.63 426	52.69 423	60.22 424	77.28 423	41.06 424	80.12 426	46.15 426	41.14 424	61.57 425

EMVS		64.07 393	63.26 396	66.53 410	81.73 427	58.81 433	91.85 416	84.75 427	51.93 425	59.09 425	75.13 424	43.32 422	79.09 428	42.03 428	39.47 425	61.69 424

tmp_tt		68.90 390	66.97 392	74.68 407	50.78 434	59.95 431	87.13 419	83.47 428	38.80 427	62.21 423	96.23 354	64.70 410	76.91 429	88.91 355	30.49 427	87.19 417

test_vis3_rt		79.22 379	77.40 386	84.67 394	86.44 422	74.85 418	97.66 309	81.43 429	84.98 399	67.12 422	81.91 420	28.09 431	97.60 369	88.96 354	80.04 404	81.55 420

MVE	62.14 22	63.28 394	59.38 397	74.99 406	74.33 431	65.47 427	85.55 420	80.50 430	52.02 424	51.10 426	75.00 425	10.91 435	80.50 425	51.60 424	53.40 423	78.99 421
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test2506		94.44 277	93.91 274	96.04 288	99.02 117	88.99 371	99.06 67	79.47 431	96.96 75	98.36 105	99.26 63	77.21 373	99.52 167	96.78 148	99.04 134	99.59 83

kuosan		78.45 384	77.69 385	80.72 403	92.73 405	75.32 416	94.63 408	74.51 432	75.96 413	80.87 411	93.19 404	63.23 413	79.99 427	42.56 427	81.56 398	86.85 419

dongtai		82.47 378	81.88 381	84.22 395	95.19 381	76.03 412	94.59 409	74.14 433	82.63 405	87.19 389	96.09 360	64.10 411	87.85 423	58.91 421	84.11 389	88.78 415

N_pmnet		87.12 373	87.77 371	85.17 393	95.46 375	61.92 429	97.37 329	70.66 434	85.83 395	88.73 382	96.04 363	85.33 288	97.76 364	80.02 402	90.48 328	95.84 368

wuyk23d		30.17 395	30.18 399	30.16 411	78.61 429	43.29 436	66.79 424	14.21 435	17.31 428	14.82 431	11.93 431	11.55 434	41.43 430	37.08 429	19.30 428	5.76 428

testmvs		21.48 397	24.95 400	11.09 413	14.89 435	6.47 438	96.56 381	9.87 436	7.55 429	17.93 429	39.02 427	9.43 436	5.90 432	16.56 431	12.72 429	20.91 427

test123		20.95 398	23.72 401	12.64 412	13.54 436	8.19 437	96.55 382	6.13 437	7.48 430	16.74 430	37.98 428	12.97 433	6.05 431	16.69 430	5.43 430	23.68 426

mmdepth		0.00 401	0.00 404	0.00 414	0.00 437	0.00 439	0.00 425	0.00 438	0.00 432	0.00 433	0.00 432	0.00 437	0.00 433	0.00 432	0.00 431	0.00 429

monomultidepth		0.00 401	0.00 404	0.00 414	0.00 437	0.00 439	0.00 425	0.00 438	0.00 432	0.00 433	0.00 432	0.00 437	0.00 433	0.00 432	0.00 431	0.00 429

test_blank		0.00 401	0.00 404	0.00 414	0.00 437	0.00 439	0.00 425	0.00 438	0.00 432	0.00 433	0.00 432	0.00 437	0.00 433	0.00 432	0.00 431	0.00 429

uanet_test		0.00 401	0.00 404	0.00 414	0.00 437	0.00 439	0.00 425	0.00 438	0.00 432	0.00 433	0.00 432	0.00 437	0.00 433	0.00 432	0.00 431	0.00 429

DCPMVS		0.00 401	0.00 404	0.00 414	0.00 437	0.00 439	0.00 425	0.00 438	0.00 432	0.00 433	0.00 432	0.00 437	0.00 433	0.00 432	0.00 431	0.00 429

pcd_1.5k_mvsjas		7.88 400	10.50 403	0.00 414	0.00 437	0.00 439	0.00 425	0.00 438	0.00 432	0.00 433	0.00 432	94.51 87	0.00 433	0.00 432	0.00 431	0.00 429

sosnet-low-res		0.00 401	0.00 404	0.00 414	0.00 437	0.00 439	0.00 425	0.00 438	0.00 432	0.00 433	0.00 432	0.00 437	0.00 433	0.00 432	0.00 431	0.00 429

sosnet		0.00 401	0.00 404	0.00 414	0.00 437	0.00 439	0.00 425	0.00 438	0.00 432	0.00 433	0.00 432	0.00 437	0.00 433	0.00 432	0.00 431	0.00 429

uncertanet		0.00 401	0.00 404	0.00 414	0.00 437	0.00 439	0.00 425	0.00 438	0.00 432	0.00 433	0.00 432	0.00 437	0.00 433	0.00 432	0.00 431	0.00 429

Regformer		0.00 401	0.00 404	0.00 414	0.00 437	0.00 439	0.00 425	0.00 438	0.00 432	0.00 433	0.00 432	0.00 437	0.00 433	0.00 432	0.00 431	0.00 429

n2								0.00 438

nn								0.00 438

ab-mvs-re		8.20 399	10.94 402	0.00 414	0.00 437	0.00 439	0.00 425	0.00 438	0.00 432	0.00 433	98.43 177	0.00 437	0.00 433	0.00 432	0.00 431	0.00 429

uanet		0.00 401	0.00 404	0.00 414	0.00 437	0.00 439	0.00 425	0.00 438	0.00 432	0.00 433	0.00 432	0.00 437	0.00 433	0.00 432	0.00 431	0.00 429

WAC-MVS						90.94 329								88.66 357

PC_three_1452									95.08 169	99.60 23	99.16 84	97.86 2	98.47 298	97.52 112	99.72 59	99.74 40

eth-test2					0.00 437

eth-test					0.00 437

OPU-MVS				99.37 22	99.24 92	99.05 14	99.02 79				99.16 84	97.81 3	99.37 189	97.24 121	99.73 55	99.70 57

test_0728_THIRD									97.32 50	99.45 29	99.46 31	97.88 1	99.94 10	98.47 50	99.86 2	99.85 10

GSMVS																99.20 148

test_part2					99.63 29	99.18 10				99.27 43

sam_mvs1												89.45 194				99.20 148

sam_mvs												88.99 208

test_post1							96.68 378				30.43 430	87.85 242	98.69 277	92.59 283

test_post											31.83 429	88.83 215	98.91 253

patchmatchnet-post											95.10 385	89.42 195	98.89 257

gm-plane-assit					95.88 361	87.47 390			89.74 367		96.94 324		99.19 208	93.32 262

test9_res														96.39 160	99.57 88	99.69 60

agg_prior2														95.87 176	99.57 88	99.68 65

test_prior4						98.01 65	97.86 291

test_prior2							97.80 298		96.12 115	97.89 137	98.69 153	95.96 41		96.89 136	99.60 82

旧先验2							97.57 317		91.30 336	98.67 84			99.80 95	95.70 185

新几何2							97.64 311

原ACMM2							97.67 308

testdata2													99.89 54	91.65 309

segment_acmp												96.85 14

testdata1							97.32 335		96.34 106

plane_prior7					97.42 278	94.63 229

plane_prior6					97.35 285	94.61 232						87.09 254

plane_prior4											98.28 196

plane_prior3						94.61 232			97.02 72	95.34 232

plane_prior2							98.80 143		97.28 53

plane_prior1					97.37 284

plane_prior						94.60 234	98.44 214		96.74 86						94.22 268

HQP5-MVS						94.25 250

HQP-NCC					97.20 293		98.05 264		96.43 100	94.45 256

ACMP_Plane					97.20 293		98.05 264		96.43 100	94.45 256

BP-MVS														95.30 196

HQP4-MVS										94.45 256			98.96 244			96.87 297

HQP2-MVS												86.75 260

NP-MVS					97.28 287	94.51 237					97.73 245

MDTV_nov1_ep13_2view						84.26 399	96.89 368		90.97 345	97.90 136		89.89 183		93.91 245		99.18 157

ACMMP++_ref															92.97 298

ACMMP++															93.61 287

Test By Simon												94.64 84

High-resolution multi-view benchmark