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		97.12 109	96.80 104	98.08 124	99.30 75	94.56 215	98.05 222	99.71 1	93.57 202	97.09 137	98.91 100	88.17 209	99.89 35	96.87 103	99.56 80	99.81 8

HyFIR lowres test		96.90 117	96.49 122	98.14 118	99.33 65	95.56 167	97.38 271	99.65 2	92.34 247	97.61 125	98.20 175	89.29 179	99.10 185	96.97 90	97.60 171	99.77 20

MVS_111021_LR		98.34 48	98.23 42	98.67 80	99.27 83	96.90 107	97.95 231	99.58 3	97.14 43	98.44 75	99.01 84	95.03 73	99.62 131	97.91 41	99.75 38	99.50 91

MVS_111021_HR		98.47 38	98.34 28	98.88 72	99.22 94	97.32 88	97.91 235	99.58 3	97.20 39	98.33 81	99.00 85	95.99 35	99.64 126	98.05 36	99.76 32	99.69 51

PGM-MVS		98.49 36	98.23 42	99.27 38	99.72 12	98.08 59	98.99 65	99.49 5	95.43 110	99.03 33	99.32 33	95.56 47	99.94 3	96.80 107	99.77 26	99.78 13

ACMMP		98.23 54	97.95 55	99.09 59	99.74 7	97.62 79	99.03 56	99.41 6	95.98 86	97.60 126	99.36 26	94.45 88	99.93 15	97.14 84	98.85 122	99.70 48
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
CSCG		97.85 67	97.74 63	98.20 115	99.67 26	95.16 182	99.22 27	99.32 7	93.04 222	97.02 143	98.92 99	95.36 58	99.91 30	97.43 75	99.64 62	99.52 85

PVSNet_BlendedMVS		96.73 122	96.60 117	97.12 181	99.25 86	95.35 177	98.26 196	99.26 8	94.28 163	97.94 103	97.46 237	92.74 109	99.81 71	96.88 100	93.32 247	96.20 313

PVSNet_Blended		97.38 96	97.12 90	98.14 118	99.25 86	95.35 177	97.28 282	99.26 8	93.13 219	97.94 103	98.21 174	92.74 109	99.81 71	96.88 100	99.40 100	99.27 125

UniMVSNet_NR-MVSNet		95.71 162	95.15 172	97.40 169	96.84 270	96.97 103	98.74 115	99.24 10	95.16 126	93.88 242	97.72 217	91.68 131	98.31 278	95.81 140	87.25 321	96.92 238

WR-MVS_H		95.05 200	94.46 204	96.81 201	96.86 269	95.82 160	99.24 22	99.24 10	93.87 181	92.53 288	96.84 290	90.37 160	98.24 287	93.24 221	87.93 313	96.38 306

FC-MVSNet-test		96.42 134	96.05 135	97.53 163	96.95 262	97.27 91	99.36 9	99.23 12	95.83 91	93.93 239	98.37 156	92.00 125	98.32 276	96.02 134	92.72 255	97.00 232

VPA-MVSNet		95.75 160	95.11 175	97.69 151	97.24 242	97.27 91	98.94 75	99.23 12	95.13 128	95.51 188	97.32 246	85.73 256	98.91 211	97.33 80	89.55 292	96.89 246

FIs		96.51 131	96.12 133	97.67 153	97.13 253	97.54 82	99.36 9	99.22 14	95.89 88	94.03 237	98.35 158	91.98 126	98.44 258	96.40 122	92.76 254	97.01 231

tfpnnormal		93.66 270	92.70 279	96.55 226	96.94 263	95.94 151	98.97 69	99.19 15	91.04 291	91.38 307	97.34 244	84.94 269	98.61 240	85.45 328	89.02 302	95.11 334

UniMVSNet (Re)		95.78 159	95.19 171	97.58 159	96.99 261	97.47 84	98.79 110	99.18 16	95.60 101	93.92 240	97.04 271	91.68 131	98.48 252	95.80 142	87.66 316	96.79 256

PVSNet_Blended_VisFu		97.70 73	97.46 77	98.44 98	99.27 83	95.91 156	98.63 140	99.16 17	94.48 159	97.67 119	98.88 102	92.80 108	99.91 30	97.11 85	99.12 111	99.50 91

CHOSEN 280x420		97.18 106	97.18 89	97.20 175	98.81 126	93.27 259	95.78 334	99.15 18	95.25 122	96.79 156	98.11 181	92.29 115	99.07 188	98.56 9	99.85 3	99.25 127

D2MVS		95.18 193	95.08 176	95.48 274	97.10 255	92.07 275	98.30 190	99.13 19	94.02 172	92.90 276	96.73 293	89.48 174	98.73 231	94.48 184	93.60 241	95.65 326

PHI-MVS		98.34 48	98.06 49	99.18 47	99.15 101	98.12 58	99.04 54	99.09 20	93.32 211	98.83 48	99.10 69	96.54 16	99.83 56	97.70 60	99.76 32	99.59 80

UA-Net		97.96 59	97.62 65	98.98 65	98.86 121	97.47 84	98.89 84	99.08 21	96.67 60	98.72 56	99.54 1	93.15 105	99.81 71	94.87 168	98.83 123	99.65 67

PatchMatch-RL		96.59 127	96.03 137	98.27 109	99.31 70	96.51 124	97.91 235	99.06 22	93.72 190	96.92 148	98.06 184	88.50 203	99.65 124	91.77 263	99.00 114	98.66 180

3Dnovator	94.51 5	97.46 87	96.93 100	99.07 60	97.78 202	97.64 77	99.35 11	99.06 22	97.02 49	93.75 249	99.16 61	89.25 180	99.92 21	97.22 82	99.75 38	99.64 70

MSLP-MVS++		98.56 28	98.57 8	98.55 87	99.26 85	96.80 110	98.71 124	99.05 24	97.28 31	98.84 46	99.28 40	96.47 18	99.40 155	98.52 14	99.70 51	99.47 98

PS-CasMVS		94.67 224	93.99 233	96.71 206	96.68 279	95.26 180	99.13 41	99.03 25	93.68 196	92.33 295	97.95 194	85.35 263	98.10 295	93.59 212	88.16 312	96.79 256

TranMVSNet+NR-MVSNet		95.14 195	94.48 202	97.11 182	96.45 290	96.36 131	99.03 56	99.03 25	95.04 134	93.58 252	97.93 196	88.27 206	98.03 302	94.13 195	86.90 326	96.95 237

PEN-MVS		94.42 241	93.73 252	96.49 230	96.28 296	94.84 199	99.17 35	99.00 27	93.51 203	92.23 297	97.83 209	86.10 251	97.90 311	92.55 244	86.92 325	96.74 262

Vis-MVSNet		97.42 93	97.11 91	98.34 105	98.66 139	96.23 136	99.22 27	99.00 27	96.63 62	98.04 91	99.21 48	88.05 214	99.35 158	96.01 135	99.21 107	99.45 104
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
DU-MVS		95.42 176	94.76 189	97.40 169	96.53 285	96.97 103	98.66 137	98.99 29	95.43 110	93.88 242	97.69 218	88.57 199	98.31 278	95.81 140	87.25 321	96.92 238

VPNet		94.99 203	94.19 218	97.40 169	97.16 251	96.57 121	98.71 124	98.97 30	95.67 98	94.84 199	98.24 173	80.36 312	98.67 236	96.46 118	87.32 320	96.96 235

OpenMVS	93.04 13	95.83 157	95.00 179	98.32 106	97.18 250	97.32 88	99.21 30	98.97 30	89.96 308	91.14 309	99.05 79	86.64 241	99.92 21	93.38 216	99.47 90	97.73 212

HFP-MVS		98.63 16	98.40 18	99.32 28	99.72 12	98.29 46	99.23 23	98.96 32	96.10 84	98.94 39	99.17 56	96.06 30	99.92 21	97.62 64	99.78 23	99.75 28

#test#		98.54 32	98.27 36	99.32 28	99.72 12	98.29 46	98.98 68	98.96 32	95.65 100	98.94 39	99.17 56	96.06 30	99.92 21	97.21 83	99.78 23	99.75 28

ACMMPR		98.59 20	98.36 22	99.29 31	99.74 7	98.15 56	99.23 23	98.95 34	96.10 84	98.93 43	99.19 55	95.70 44	99.94 3	97.62 64	99.79 19	99.78 13

CP-MVSNet		94.94 209	94.30 213	96.83 200	96.72 277	95.56 167	99.11 44	98.95 34	93.89 179	92.42 294	97.90 198	87.19 231	98.12 294	94.32 189	88.21 310	96.82 255

NR-MVSNet		94.98 205	94.16 221	97.44 165	96.53 285	97.22 96	98.74 115	98.95 34	94.96 138	89.25 326	97.69 218	89.32 178	98.18 289	94.59 180	87.40 319	96.92 238

region2R		98.61 17	98.38 20	99.29 31	99.74 7	98.16 55	99.23 23	98.93 37	96.15 79	98.94 39	99.17 56	95.91 39	99.94 3	97.55 71	99.79 19	99.78 13

APDe-MVS		99.02 3	98.84 2	99.55 6	99.57 33	98.96 12	99.39 5	98.93 37	97.38 26	99.41 11	99.54 1	96.66 13	99.84 53	98.86 1	99.85 3	99.87 1

VNet		97.79 69	97.40 81	98.96 67	98.88 119	97.55 81	98.63 140	98.93 37	96.74 57	99.02 34	98.84 107	90.33 162	99.83 56	98.53 10	96.66 187	99.50 91

UGNet		96.78 121	96.30 127	98.19 117	98.24 169	95.89 158	98.88 87	98.93 37	97.39 25	96.81 154	97.84 206	82.60 297	99.90 33	96.53 116	99.49 88	98.79 169
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 95	96.98 99	98.61 83	98.60 145	96.61 118	98.22 198	98.93 37	93.97 176	98.01 97	98.48 144	91.98 126	99.85 50	96.45 119	98.15 151	99.39 109

QAPM		96.29 138	95.40 156	98.96 67	97.85 199	97.60 80	99.23 23	98.93 37	89.76 312	93.11 272	99.02 80	89.11 185	99.93 15	91.99 258	99.62 66	99.34 112

DPE-MVS		98.92 4	98.67 6	99.65 2	99.58 32	99.20 7	98.42 172	98.91 43	97.58 14	99.54 7	99.46 9	97.10 9	99.94 3	97.64 63	99.84 8	99.83 5
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
114514_t		96.93 115	96.27 128	98.92 69	99.50 41	97.63 78	98.85 92	98.90 44	84.80 342	97.77 111	99.11 67	92.84 107	99.66 123	94.85 169	99.77 26	99.47 98

LS3D		97.16 107	96.66 116	98.68 79	98.53 149	97.19 97	98.93 77	98.90 44	92.83 232	95.99 184	99.37 22	92.12 122	99.87 44	93.67 210	99.57 75	98.97 158

DELS-MVS		98.40 42	98.20 44	98.99 63	99.00 110	97.66 76	97.75 251	98.89 46	97.71 8	98.33 81	98.97 87	94.97 74	99.88 43	98.42 21	99.76 32	99.42 108
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 66	97.46 77	99.06 61	99.53 36	98.35 43	98.33 182	98.89 46	92.62 236	98.05 89	98.94 96	95.34 59	99.65 124	96.04 133	99.42 97	99.19 133

AdaColmap		97.15 108	96.70 112	98.48 95	99.16 99	96.69 115	98.01 226	98.89 46	94.44 161	96.83 151	98.68 123	90.69 156	99.76 103	94.36 186	99.29 106	98.98 157

test_0728_SECOND				99.71 1	99.72 12	99.35 1	98.97 69	98.88 49					99.94 3	98.47 16	99.81 10	99.84 4

test0726					99.72 12	99.25 2	99.06 52	98.88 49	97.62 11	99.56 5	99.50 4	97.42 6

MSP-MVS		98.74 8	98.55 10	99.29 31	99.75 3	98.23 49	99.26 20	98.88 49	97.52 15	99.41 11	98.78 113	96.00 34	99.79 92	97.79 51	99.59 71	99.85 2
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		94.10 261	93.26 270	96.61 216	99.11 104	94.28 224	99.01 61	98.88 49	86.43 333	92.81 278	97.57 230	81.66 303	98.68 235	94.83 170	89.02 302	96.88 247

XVS		98.70 9	98.49 16	99.34 23	99.70 23	98.35 43	99.29 16	98.88 49	97.40 23	98.46 71	99.20 52	95.90 40	99.89 35	97.85 47	99.74 41	99.78 13

X-MVStestdata		94.06 265	92.30 285	99.34 23	99.70 23	98.35 43	99.29 16	98.88 49	97.40 23	98.46 71	43.50 362	95.90 40	99.89 35	97.85 47	99.74 41	99.78 13

SED-MVS		99.09 1	98.91 1	99.63 3	99.71 20	99.24 4	99.02 59	98.87 55	97.65 9	99.73 1	99.48 6	97.53 4	99.94 3	98.43 19	99.81 10	99.70 48

test_241102_TWO								98.87 55	97.65 9	99.53 8	99.48 6	97.34 8	99.94 3	98.43 19	99.80 17	99.83 5

test_241102_ONE					99.71 20	99.24 4		98.87 55	97.62 11	99.73 1	99.39 14	97.53 4	99.74 107

CP-MVS		98.57 26	98.36 22	99.19 43	99.66 27	97.86 68	99.34 12	98.87 55	95.96 87	98.60 66	99.13 64	96.05 32	99.94 3	97.77 52	99.86 1	99.77 20

SteuartSystems-ACMMP		98.90 5	98.75 4	99.36 21	99.22 94	98.43 33	99.10 47	98.87 55	97.38 26	99.35 14	99.40 13	97.78 3	99.87 44	97.77 52	99.85 3	99.78 13
Skip Steuart: Steuart Systems R&D Blog.
DeepPCF-MVS	96.37 2	97.93 64	98.48 17	96.30 245	99.00 110	89.54 315	97.43 268	98.87 55	98.16 2	99.26 18	99.38 21	96.12 28	99.64 126	98.30 27	99.77 26	99.72 40

ZNCC-MVS		98.49 36	98.20 44	99.35 22	99.73 11	98.39 34	99.19 33	98.86 61	95.77 93	98.31 83	99.10 69	95.46 51	99.93 15	97.57 70	99.81 10	99.74 33

testtj		98.33 50	97.95 55	99.47 11	99.49 45	98.70 19	98.83 96	98.86 61	95.48 107	98.91 45	99.17 56	95.48 50	99.93 15	95.80 142	99.53 85	99.76 26

DTE-MVSNet		93.98 267	93.26 270	96.14 251	96.06 305	94.39 221	99.20 31	98.86 61	93.06 221	91.78 303	97.81 211	85.87 255	97.58 322	90.53 280	86.17 330	96.46 303

SD-MVS		98.64 14	98.68 5	98.53 91	99.33 65	98.36 42	98.90 80	98.85 64	97.28 31	99.72 3	99.39 14	96.63 15	97.60 321	98.17 29	99.85 3	99.64 70
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
ETH3D-3000-0.1		98.35 46	98.00 53	99.38 17	99.47 48	98.68 21	98.67 134	98.84 65	94.66 152	99.11 28	99.25 43	95.46 51	99.81 71	96.80 107	99.73 43	99.63 73

test_prior3		98.22 55	97.90 59	99.19 43	99.31 70	98.22 50	97.80 247	98.84 65	96.12 82	97.89 108	98.69 121	95.96 36	99.70 115	96.89 97	99.60 68	99.65 67

test_prior				99.19 43	99.31 70	98.22 50		98.84 65					99.70 115			99.65 67

test1172		98.56 28	98.35 24	99.16 50	99.53 36	97.94 66	99.09 48	98.83 68	96.52 66	99.05 32	99.34 31	95.34 59	99.82 64	97.86 46	99.64 62	99.73 36

Anonymous20240529		95.10 197	94.22 216	97.75 145	99.01 109	94.26 226	98.87 89	98.83 68	85.79 339	96.64 159	98.97 87	78.73 321	99.85 50	96.27 124	94.89 217	99.12 143

9.14			98.06 49		99.47 48		98.71 124	98.82 70	94.36 162	99.16 26	99.29 39	96.05 32	99.81 71	97.00 88	99.71 50

SR-MVS		98.57 26	98.35 24	99.24 40	99.53 36	98.18 53	99.09 48	98.82 70	96.58 63	99.10 29	99.32 33	95.39 55	99.82 64	97.70 60	99.63 64	99.72 40

GST-MVS		98.43 40	98.12 47	99.34 23	99.72 12	98.38 35	99.09 48	98.82 70	95.71 96	98.73 55	99.06 78	95.27 64	99.93 15	97.07 87	99.63 64	99.72 40

abl_6		98.30 53	98.03 51	99.13 54	99.56 34	97.76 75	99.13 41	98.82 70	96.14 80	99.26 18	99.37 22	93.33 102	99.93 15	96.96 92	99.67 54	99.69 51

HPM-MVS_fast		98.38 43	98.13 46	99.12 57	99.75 3	97.86 68	99.44 4	98.82 70	94.46 160	98.94 39	99.20 52	95.16 69	99.74 107	97.58 67	99.85 3	99.77 20

APD-MVS		98.35 46	98.00 53	99.42 15	99.51 39	98.72 17	98.80 106	98.82 70	94.52 157	99.23 20	99.25 43	95.54 49	99.80 80	96.52 117	99.77 26	99.74 33
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
SF-MVS		98.59 20	98.32 33	99.41 16	99.54 35	98.71 18	99.04 54	98.81 76	95.12 129	99.32 15	99.39 14	96.22 20	99.84 53	97.72 55	99.73 43	99.67 61

ETH3 D test6400		97.59 81	97.01 96	99.34 23	99.40 59	98.56 25	98.20 202	98.81 76	91.63 270	98.44 75	98.85 105	93.98 97	99.82 64	94.11 197	99.69 52	99.64 70

test_part1		94.82 213	93.82 243	97.82 139	98.84 124	97.82 72	99.03 56	98.81 76	92.31 251	92.51 290	97.89 200	81.96 300	98.67 236	94.80 173	88.24 309	96.98 233

MVS_0304		92.81 286	92.01 288	95.23 281	97.46 227	91.33 291	98.17 211	98.81 76	91.13 290	93.80 247	95.68 327	66.08 355	98.06 300	90.79 276	96.13 209	96.32 310

ACMMP_NAP		98.61 17	98.30 34	99.55 6	99.62 30	98.95 13	98.82 99	98.81 76	95.80 92	99.16 26	99.47 8	95.37 57	99.92 21	97.89 44	99.75 38	99.79 10

Regformer-2		98.69 11	98.52 12	99.19 43	99.35 60	98.01 62	98.37 176	98.81 76	97.48 18	99.21 21	99.21 48	96.13 27	99.80 80	98.40 23	99.73 43	99.75 28

APD-MVS_3200maxsize		98.53 34	98.33 32	99.15 53	99.50 41	97.92 67	99.15 37	98.81 76	96.24 76	99.20 22	99.37 22	95.30 62	99.80 80	97.73 54	99.67 54	99.72 40

WR-MVS		95.15 194	94.46 204	97.22 174	96.67 280	96.45 126	98.21 199	98.81 76	94.15 166	93.16 268	97.69 218	87.51 225	98.30 280	95.29 160	88.62 306	96.90 245

mPP-MVS		98.51 35	98.26 37	99.25 39	99.75 3	98.04 60	99.28 18	98.81 76	96.24 76	98.35 80	99.23 45	95.46 51	99.94 3	97.42 76	99.81 10	99.77 20

CNVR-MVS		98.78 6	98.56 9	99.45 14	99.32 68	98.87 15	98.47 164	98.81 76	97.72 6	98.76 52	99.16 61	97.05 10	99.78 96	98.06 34	99.66 57	99.69 51

CPTT-MVS		97.72 72	97.32 84	98.92 69	99.64 28	97.10 100	99.12 43	98.81 76	92.34 247	98.09 87	99.08 76	93.01 106	99.92 21	96.06 132	99.77 26	99.75 28

SR-MVS-dyc-post		98.54 32	98.35 24	99.13 54	99.49 45	97.86 68	99.11 44	98.80 87	96.49 68	99.17 24	99.35 28	95.34 59	99.82 64	97.72 55	99.65 58	99.71 44

RE-MVS-def			98.34 28		99.49 45	97.86 68	99.11 44	98.80 87	96.49 68	99.17 24	99.35 28	95.29 63		97.72 55	99.65 58	99.71 44

SMA-MVS		98.58 23	98.25 38	99.56 5	99.51 39	99.04 11	98.95 73	98.80 87	93.67 198	99.37 13	99.52 3	96.52 17	99.89 35	98.06 34	99.81 10	99.76 26
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 45	98.10 48	99.13 54	99.74 7	97.82 72	99.53 1	98.80 87	94.63 153	98.61 65	98.97 87	95.13 70	99.77 101	97.65 62	99.83 9	99.79 10
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
RPMNet		92.81 286	91.34 294	97.24 173	97.00 259	93.43 252	94.96 341	98.80 87	82.27 346	96.93 146	92.12 349	86.98 236	99.82 64	76.32 354	96.65 188	98.46 189

ZD-MVS					99.46 51	98.70 19		98.79 92	93.21 215	98.67 58	98.97 87	95.70 44	99.83 56	96.07 129	99.58 74

Regformer-4		98.64 14	98.53 11	98.99 63	99.43 57	97.37 87	98.40 174	98.79 92	97.46 21	99.09 30	99.31 35	95.86 42	99.80 80	98.64 4	99.76 32	99.79 10

MP-MVS		98.33 50	98.01 52	99.28 35	99.75 3	98.18 53	99.22 27	98.79 92	96.13 81	97.92 106	99.23 45	94.54 83	99.94 3	96.74 111	99.78 23	99.73 36
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CANet		98.05 56	97.76 62	98.90 71	98.73 130	97.27 91	98.35 179	98.78 95	97.37 28	97.72 116	98.96 93	91.53 138	99.92 21	98.79 2	99.65 58	99.51 89

MP-MVS-pluss		98.31 52	97.92 58	99.49 9	99.72 12	98.88 14	98.43 170	98.78 95	94.10 168	97.69 118	99.42 12	95.25 66	99.92 21	98.09 33	99.80 17	99.67 61
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
DeepC-MVS_fast	96.70 1	98.55 30	98.34 28	99.18 47	99.25 86	98.04 60	98.50 161	98.78 95	97.72 6	98.92 44	99.28 40	95.27 64	99.82 64	97.55 71	99.77 26	99.69 51
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 68	97.60 66	98.44 98	99.12 103	95.97 148	97.75 251	98.78 95	96.89 52	98.46 71	99.22 47	93.90 98	99.68 121	94.81 172	99.52 87	99.67 61

Regformer-1		98.66 12	98.51 13	99.12 57	99.35 60	97.81 74	98.37 176	98.76 99	97.49 17	99.20 22	99.21 48	96.08 29	99.79 92	98.42 21	99.73 43	99.75 28

NCCC		98.61 17	98.35 24	99.38 17	99.28 82	98.61 24	98.45 165	98.76 99	97.82 5	98.45 74	98.93 97	96.65 14	99.83 56	97.38 78	99.41 98	99.71 44

PLC	95.07 4	97.20 105	96.78 107	98.44 98	99.29 78	96.31 135	98.14 213	98.76 99	92.41 245	96.39 174	98.31 165	94.92 76	99.78 96	94.06 199	98.77 126	99.23 128
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
hse-mvs3		96.17 143	95.62 152	97.81 140	99.03 108	94.45 217	98.64 139	98.75 102	97.48 18	98.67 58	98.72 120	89.76 169	99.86 49	97.95 38	81.59 340	99.11 144

DeepC-MVS	95.98 3	97.88 65	97.58 67	98.77 75	99.25 86	96.93 105	98.83 96	98.75 102	96.96 51	96.89 150	99.50 4	90.46 159	99.87 44	97.84 49	99.76 32	99.52 85
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
zzz-MVS		98.55 30	98.25 38	99.46 12	99.76 1	98.64 22	98.55 154	98.74 104	97.27 35	98.02 93	99.39 14	94.81 77	99.96 1	97.91 41	99.79 19	99.77 20

MTGPA								98.74 104

MTAPA		98.58 23	98.29 35	99.46 12	99.76 1	98.64 22	98.90 80	98.74 104	97.27 35	98.02 93	99.39 14	94.81 77	99.96 1	97.91 41	99.79 19	99.77 20

ab-mvs		96.42 134	95.71 147	98.55 87	98.63 142	96.75 113	97.88 240	98.74 104	93.84 182	96.54 167	98.18 177	85.34 264	99.75 105	95.93 136	96.35 197	99.15 139

TEST9					99.31 70	98.50 29	97.92 233	98.73 108	92.63 235	97.74 114	98.68 123	96.20 23	99.80 80

train_agg		97.97 58	97.52 72	99.33 27	99.31 70	98.50 29	97.92 233	98.73 108	92.98 224	97.74 114	98.68 123	96.20 23	99.80 80	96.59 113	99.57 75	99.68 57

test_8					99.29 78	98.44 31	97.89 239	98.72 110	92.98 224	97.70 117	98.66 126	96.20 23	99.80 80

agg_prior1		97.95 62	97.51 74	99.28 35	99.30 75	98.38 35	97.81 246	98.72 110	93.16 218	97.57 127	98.66 126	96.14 26	99.81 71	96.63 112	99.56 80	99.66 65

agg_prior					99.30 75	98.38 35		98.72 110		97.57 127			99.81 71

无先验							97.58 262	98.72 110	91.38 276				99.87 44	93.36 218		99.60 78

save fliter					99.46 51	98.38 35	98.21 199	98.71 114	97.95 3

WTY-MVS		97.37 97	96.92 101	98.72 77	98.86 121	96.89 109	98.31 188	98.71 114	95.26 121	97.67 119	98.56 137	92.21 119	99.78 96	95.89 137	96.85 182	99.48 96

3Dnovator+	94.38 6	97.43 92	96.78 107	99.38 17	97.83 200	98.52 27	99.37 8	98.71 114	97.09 47	92.99 275	99.13 64	89.36 177	99.89 35	96.97 90	99.57 75	99.71 44

旧先验1					99.29 78	97.48 83		98.70 117			99.09 74	95.56 47			99.47 90	99.61 75

EI-MVSNet-Vis-set		98.47 38	98.39 19	98.69 78	99.46 51	96.49 125	98.30 190	98.69 118	97.21 38	98.84 46	99.36 26	95.41 54	99.78 96	98.62 6	99.65 58	99.80 9

新几何1				99.16 50	99.34 62	98.01 62		98.69 118	90.06 307	98.13 85	98.95 95	94.60 82	99.89 35	91.97 259	99.47 90	99.59 80

API-MVS		97.41 94	97.25 86	97.91 133	98.70 135	96.80 110	98.82 99	98.69 118	94.53 155	98.11 86	98.28 167	94.50 87	99.57 135	94.12 196	99.49 88	97.37 222

ETH3D cwj APD-0.16		97.96 59	97.52 72	99.29 31	99.05 105	98.52 27	98.33 182	98.68 121	93.18 216	98.68 57	99.13 64	94.62 81	99.83 56	96.45 119	99.55 83	99.52 85

EI-MVSNet-UG-set		98.41 41	98.34 28	98.61 83	99.45 55	96.32 133	98.28 193	98.68 121	97.17 41	98.74 53	99.37 22	95.25 66	99.79 92	98.57 8	99.54 84	99.73 36

Regformer-3		98.59 20	98.50 14	98.86 73	99.43 57	97.05 101	98.40 174	98.68 121	97.43 22	99.06 31	99.31 35	95.80 43	99.77 101	98.62 6	99.76 32	99.78 13

testdata				98.26 111	99.20 97	95.36 175		98.68 121	91.89 262	98.60 66	99.10 69	94.44 89	99.82 64	94.27 191	99.44 95	99.58 82

1121		97.37 97	96.77 111	99.16 50	99.34 62	97.99 65	98.19 206	98.68 121	90.14 306	98.01 97	98.97 87	94.80 79	99.87 44	93.36 218	99.46 93	99.61 75

MCST-MVS		98.65 13	98.37 21	99.48 10	99.60 31	98.87 15	98.41 173	98.68 121	97.04 48	98.52 70	98.80 111	96.78 12	99.83 56	97.93 40	99.61 67	99.74 33

PVSNet	91.96 18	96.35 136	96.15 132	96.96 191	99.17 98	92.05 276	96.08 327	98.68 121	93.69 194	97.75 113	97.80 212	88.86 194	99.69 120	94.26 192	99.01 113	99.15 139

MAR-MVS		96.91 116	96.40 124	98.45 97	98.69 137	96.90 107	98.66 137	98.68 121	92.40 246	97.07 140	97.96 193	91.54 137	99.75 105	93.68 208	98.92 116	98.69 176
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 81	99.32 68	96.62 116		98.67 129	93.27 214	97.81 110	98.97 87	95.18 68	99.83 56	93.84 204	99.46 93	99.50 91

CDPH-MVS		97.94 63	97.49 75	99.28 35	99.47 48	98.44 31	97.91 235	98.67 129	92.57 239	98.77 51	98.85 105	95.93 38	99.72 109	95.56 152	99.69 52	99.68 57

UnsupCasMVSNet_eth		90.99 302	89.92 305	94.19 312	94.08 339	89.83 310	97.13 293	98.67 129	93.69 194	85.83 342	96.19 315	75.15 341	96.74 336	89.14 304	79.41 345	96.00 318

TSAR-MVS + MP.		98.78 6	98.62 7	99.24 40	99.69 25	98.28 48	99.14 38	98.66 132	96.84 53	99.56 5	99.31 35	96.34 19	99.70 115	98.32 26	99.73 43	99.73 36
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 23	98.25 38	99.55 6	99.50 41	99.08 9	98.72 123	98.66 132	97.51 16	98.15 84	98.83 108	95.70 44	99.92 21	97.53 73	99.67 54	99.66 65

test222					99.23 93	97.17 98	97.40 269	98.66 132	88.68 323	98.05 89	98.96 93	94.14 93			99.53 85	99.61 75

test11								98.66 132

XXY-MVS		95.20 192	94.45 206	97.46 164	96.75 275	96.56 122	98.86 91	98.65 136	93.30 213	93.27 265	98.27 170	84.85 271	98.87 218	94.82 171	91.26 271	96.96 235

IU-MVS					99.71 20	99.23 6		98.64 137	95.28 120	99.63 4				98.35 25	99.81 10	99.83 5

TAPA-MVS	93.98 7	95.35 183	94.56 198	97.74 146	99.13 102	94.83 201	98.33 182	98.64 137	86.62 331	96.29 176	98.61 129	94.00 96	99.29 162	80.00 346	99.41 98	99.09 146
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
F-COLMAP		97.09 111	96.80 104	97.97 130	99.45 55	94.95 196	98.55 154	98.62 139	93.02 223	96.17 179	98.58 134	94.01 95	99.81 71	93.95 201	98.90 117	99.14 141

EIA-MVS		97.75 70	97.58 67	98.27 109	98.38 156	96.44 127	99.01 61	98.60 140	95.88 89	97.26 132	97.53 233	94.97 74	99.33 160	97.38 78	99.20 108	99.05 151

PAPM_NR		97.46 87	97.11 91	98.50 93	99.50 41	96.41 129	98.63 140	98.60 140	95.18 125	97.06 141	98.06 184	94.26 92	99.57 135	93.80 206	98.87 121	99.52 85

cdsmvs_eth3d_5k		23.98 334	31.98 336	0.00 350	0.00 371	0.00 372	0.00 362	98.59 142	0.00 367	0.00 368	98.61 129	90.60 157	0.00 368	0.00 366	0.00 366	0.00 364

1314		96.25 142	95.73 143	97.79 141	97.13 253	95.55 169	98.19 206	98.59 142	93.47 205	92.03 301	97.82 210	91.33 142	99.49 146	94.62 177	98.44 141	98.32 196

CVMVSNet		95.43 175	96.04 136	93.57 316	97.93 194	83.62 348	98.12 216	98.59 142	95.68 97	96.56 163	99.02 80	87.51 225	97.51 325	93.56 214	97.44 173	99.60 78

OMC-MVS		97.55 85	97.34 83	98.20 115	99.33 65	95.92 155	98.28 193	98.59 142	95.52 106	97.97 100	99.10 69	93.28 104	99.49 146	95.09 165	98.88 119	99.19 133

LTVRE_ROB	92.95 15	94.60 227	93.90 238	96.68 210	97.41 235	94.42 219	98.52 156	98.59 142	91.69 268	91.21 308	98.35 158	84.87 270	99.04 192	91.06 272	93.44 245	96.60 280
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
DVP-MVS		99.03 2	98.83 3	99.63 3	99.72 12	99.25 2	98.97 69	98.58 147	97.62 11	99.45 9	99.46 9	97.42 6	99.94 3	98.47 16	99.81 10	99.69 51
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
UniMVSNet_ETH3D		94.24 251	93.33 267	96.97 190	97.19 249	93.38 256	98.74 115	98.57 148	91.21 288	93.81 246	98.58 134	72.85 349	98.77 229	95.05 166	93.93 234	98.77 171

PAPR		96.84 119	96.24 130	98.65 81	98.72 134	96.92 106	97.36 275	98.57 148	93.33 210	96.67 158	97.57 230	94.30 91	99.56 137	91.05 274	98.59 133	99.47 98

HQP_MVS		96.14 144	95.90 140	96.85 199	97.42 232	94.60 213	98.80 106	98.56 150	97.28 31	95.34 189	98.28 167	87.09 233	99.03 193	96.07 129	94.27 220	96.92 238

plane_prior5								98.56 150					99.03 193	96.07 129	94.27 220	96.92 238

ETV-MVS		97.96 59	97.81 60	98.40 102	98.42 154	97.27 91	98.73 119	98.55 152	96.84 53	98.38 78	97.44 240	95.39 55	99.35 158	97.62 64	98.89 118	98.58 186

mvs_tets		95.41 178	95.00 179	96.65 211	95.58 319	94.42 219	99.00 63	98.55 152	95.73 95	93.21 267	98.38 155	83.45 295	98.63 239	97.09 86	94.00 231	96.91 243

LPG-MVS_test		95.62 168	95.34 162	96.47 232	97.46 227	93.54 247	98.99 65	98.54 154	94.67 150	94.36 219	98.77 115	85.39 261	99.11 182	95.71 147	94.15 226	96.76 260

LGP-MVS_train				96.47 232	97.46 227	93.54 247		98.54 154	94.67 150	94.36 219	98.77 115	85.39 261	99.11 182	95.71 147	94.15 226	96.76 260

test12				99.18 47	99.16 99	98.19 52		98.53 156		98.07 88		95.13 70	99.72 109		99.56 80	99.63 73

CNLPA		97.45 90	97.03 95	98.73 76	99.05 105	97.44 86	98.07 220	98.53 156	95.32 118	96.80 155	98.53 138	93.32 103	99.72 109	94.31 190	99.31 105	99.02 153

xxxxxxxxxxxxxcwj		98.70 9	98.50 14	99.30 30	99.46 51	98.38 35	98.21 199	98.52 158	97.95 3	99.32 15	99.39 14	96.22 20	99.84 53	97.72 55	99.73 43	99.67 61

jajsoiax		95.45 174	95.03 178	96.73 205	95.42 326	94.63 208	99.14 38	98.52 158	95.74 94	93.22 266	98.36 157	83.87 291	98.65 238	96.95 93	94.04 229	96.91 243

XVG-OURS		96.55 130	96.41 123	96.99 187	98.75 129	93.76 238	97.50 265	98.52 158	95.67 98	96.83 151	99.30 38	88.95 193	99.53 143	95.88 138	96.26 204	97.69 214

xiu_mvs_v1_base_debu		97.60 78	97.56 69	97.72 147	98.35 158	95.98 143	97.86 242	98.51 161	97.13 44	99.01 35	98.40 152	91.56 134	99.80 80	98.53 10	98.68 127	97.37 222

xiu_mvs_v1_base		97.60 78	97.56 69	97.72 147	98.35 158	95.98 143	97.86 242	98.51 161	97.13 44	99.01 35	98.40 152	91.56 134	99.80 80	98.53 10	98.68 127	97.37 222

xiu_mvs_v1_base_debi		97.60 78	97.56 69	97.72 147	98.35 158	95.98 143	97.86 242	98.51 161	97.13 44	99.01 35	98.40 152	91.56 134	99.80 80	98.53 10	98.68 127	97.37 222

PS-MVSNAJ		97.73 71	97.77 61	97.62 157	98.68 138	95.58 166	97.34 277	98.51 161	97.29 30	98.66 62	97.88 201	94.51 84	99.90 33	97.87 45	99.17 110	97.39 220

cascas		94.63 226	93.86 241	96.93 193	96.91 266	94.27 225	96.00 331	98.51 161	85.55 340	94.54 208	96.23 312	84.20 284	98.87 218	95.80 142	96.98 181	97.66 215

PS-MVSNAJss		96.43 133	96.26 129	96.92 196	95.84 313	95.08 188	99.16 36	98.50 166	95.87 90	93.84 245	98.34 162	94.51 84	98.61 240	96.88 100	93.45 244	97.06 229

MVS		94.67 224	93.54 261	98.08 124	96.88 268	96.56 122	98.19 206	98.50 166	78.05 351	92.69 283	98.02 186	91.07 149	99.63 129	90.09 285	98.36 146	98.04 203

XVG-OURS-SEG-HR		96.51 131	96.34 125	97.02 186	98.77 128	93.76 238	97.79 249	98.50 166	95.45 109	96.94 145	99.09 74	87.87 219	99.55 142	96.76 110	95.83 213	97.74 211

PVSNet_0	88.72 19	91.28 298	90.03 304	95.00 289	97.99 191	87.29 342	94.84 344	98.50 166	92.06 258	89.86 320	95.19 330	79.81 315	99.39 156	92.27 250	69.79 354	98.33 195

ACMH	92.88 16	94.55 232	93.95 235	96.34 243	97.63 212	93.26 260	98.81 105	98.49 170	93.43 207	89.74 321	98.53 138	81.91 301	99.08 187	93.69 207	93.30 248	96.70 269
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
xiu_mvs_v2_base		97.66 75	97.70 64	97.56 161	98.61 144	95.46 172	97.44 266	98.46 171	97.15 42	98.65 63	98.15 178	94.33 90	99.80 80	97.84 49	98.66 131	97.41 218

HQP3-MVS								98.46 171							94.18 224

HQP-MVS		95.72 161	95.40 156	96.69 209	97.20 246	94.25 227	98.05 222	98.46 171	96.43 70	94.45 212	97.73 215	86.75 239	98.96 204	95.30 158	94.18 224	96.86 251

CLD-MVS		95.62 168	95.34 162	96.46 235	97.52 224	93.75 240	97.27 283	98.46 171	95.53 104	94.42 217	98.00 189	86.21 249	98.97 200	96.25 126	94.37 218	96.66 275
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		94.54 233	94.14 223	95.75 268	96.55 284	91.65 285	98.11 218	98.44 175	94.96 138	94.22 227	97.90 198	79.18 319	99.11 182	94.05 200	93.85 235	96.48 301

ACMP	93.49 10	95.34 184	94.98 181	96.43 237	97.67 209	93.48 251	98.73 119	98.44 175	94.94 141	92.53 288	98.53 138	84.50 278	99.14 177	95.48 155	94.00 231	96.66 275
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ACMM	93.85 9	95.69 165	95.38 160	96.61 216	97.61 213	93.84 236	98.91 79	98.44 175	95.25 122	94.28 223	98.47 145	86.04 254	99.12 179	95.50 154	93.95 233	96.87 249
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Effi-MVS+		97.12 109	96.69 113	98.39 103	98.19 175	96.72 114	97.37 273	98.43 178	93.71 191	97.65 122	98.02 186	92.20 120	99.25 164	96.87 103	97.79 163	99.19 133

RRT_test8_iter05		94.56 231	94.19 218	95.67 270	97.60 214	91.34 289	98.93 77	98.42 179	94.75 145	93.39 261	97.87 202	79.00 320	98.61 240	96.78 109	90.99 275	97.07 228

CS-MVS		98.04 57	97.95 55	98.32 106	98.14 181	97.15 99	99.39 5	98.41 180	96.51 67	98.59 68	98.51 142	93.89 99	99.03 193	98.66 3	99.43 96	98.77 171

anonymousdsp		95.42 176	94.91 184	96.94 192	95.10 328	95.90 157	99.14 38	98.41 180	93.75 186	93.16 268	97.46 237	87.50 227	98.41 267	95.63 151	94.03 230	96.50 299

PMMVS		96.60 125	96.33 126	97.41 167	97.90 196	93.93 233	97.35 276	98.41 180	92.84 231	97.76 112	97.45 239	91.10 148	99.20 170	96.26 125	97.91 158	99.11 144

MVSFormer		97.57 83	97.49 75	97.84 136	98.07 185	95.76 162	99.47 2	98.40 183	94.98 136	98.79 49	98.83 108	92.34 113	98.41 267	96.91 94	99.59 71	99.34 112

test_djsdf		96.00 149	95.69 149	96.93 193	95.72 315	95.49 171	99.47 2	98.40 183	94.98 136	94.58 207	97.86 203	89.16 183	98.41 267	96.91 94	94.12 228	96.88 247

OPM-MVS		95.69 165	95.33 164	96.76 203	96.16 302	94.63 208	98.43 170	98.39 185	96.64 61	95.02 195	98.78 113	85.15 266	99.05 189	95.21 164	94.20 223	96.60 280

canonicalmvs		97.67 74	97.23 87	98.98 65	98.70 135	98.38 35	99.34 12	98.39 185	96.76 56	97.67 119	97.40 243	92.26 116	99.49 146	98.28 28	96.28 203	99.08 149

DP-MVS		96.59 127	95.93 139	98.57 85	99.34 62	96.19 139	98.70 128	98.39 185	89.45 317	94.52 209	99.35 28	91.85 128	99.85 50	92.89 235	98.88 119	99.68 57

diffmvs		97.58 82	97.40 81	98.13 120	98.32 166	95.81 161	98.06 221	98.37 188	96.20 78	98.74 53	98.89 101	91.31 143	99.25 164	98.16 30	98.52 136	99.34 112

ACMH+	92.99 14	94.30 247	93.77 248	95.88 263	97.81 201	92.04 277	98.71 124	98.37 188	93.99 175	90.60 315	98.47 145	80.86 309	99.05 189	92.75 237	92.40 257	96.55 288

MSDG		95.93 152	95.30 167	97.83 137	98.90 117	95.36 175	96.83 314	98.37 188	91.32 281	94.43 216	98.73 119	90.27 163	99.60 132	90.05 288	98.82 124	98.52 187

DPM-MVS		97.55 85	96.99 98	99.23 42	99.04 107	98.55 26	97.17 290	98.35 191	94.85 143	97.93 105	98.58 134	95.07 72	99.71 114	92.60 239	99.34 103	99.43 106

CMPMVS	66.06 21	89.70 311	89.67 307	89.78 332	93.19 345	76.56 355	97.00 298	98.35 191	80.97 348	81.57 349	97.75 214	74.75 343	98.61 240	89.85 291	93.63 239	94.17 343
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
v7n		94.19 254	93.43 265	96.47 232	95.90 310	94.38 222	99.26 20	98.34 193	91.99 259	92.76 280	97.13 257	88.31 205	98.52 250	89.48 300	87.70 315	96.52 294

CDS-MVSNet		96.99 113	96.69 113	97.90 134	98.05 188	95.98 143	98.20 202	98.33 194	93.67 198	96.95 144	98.49 143	93.54 100	98.42 260	95.24 163	97.74 166	99.31 118
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
casdiffmvs		97.63 77	97.41 80	98.28 108	98.33 164	96.14 140	98.82 99	98.32 195	96.38 73	97.95 101	99.21 48	91.23 145	99.23 167	98.12 31	98.37 144	99.48 96

baseline		97.64 76	97.44 79	98.25 112	98.35 158	96.20 137	99.00 63	98.32 195	96.33 75	98.03 92	99.17 56	91.35 141	99.16 173	98.10 32	98.29 149	99.39 109

cl-mvsnet2		94.68 221	94.19 218	96.13 252	98.11 183	93.60 245	96.94 301	98.31 197	92.43 244	93.32 264	96.87 288	86.51 242	98.28 285	94.10 198	91.16 272	96.51 297

test_yl		97.22 102	96.78 107	98.54 89	98.73 130	96.60 119	98.45 165	98.31 197	94.70 146	98.02 93	98.42 150	90.80 153	99.70 115	96.81 105	96.79 184	99.34 112

DCV-MVSNet		97.22 102	96.78 107	98.54 89	98.73 130	96.60 119	98.45 165	98.31 197	94.70 146	98.02 93	98.42 150	90.80 153	99.70 115	96.81 105	96.79 184	99.34 112

nrg030		96.28 140	95.72 144	97.96 132	96.90 267	98.15 56	99.39 5	98.31 197	95.47 108	94.42 217	98.35 158	92.09 123	98.69 232	97.50 74	89.05 300	97.04 230

TAMVS		97.02 112	96.79 106	97.70 150	98.06 187	95.31 179	98.52 156	98.31 197	93.95 177	97.05 142	98.61 129	93.49 101	98.52 250	95.33 157	97.81 162	99.29 123

EPP-MVSNet		97.46 87	97.28 85	97.99 129	98.64 141	95.38 174	99.33 15	98.31 197	93.61 201	97.19 134	99.07 77	94.05 94	99.23 167	96.89 97	98.43 143	99.37 111

UnsupCasMVSNet_bld		87.17 319	85.12 323	93.31 321	91.94 349	88.77 326	94.92 343	98.30 203	84.30 344	82.30 348	90.04 350	63.96 357	97.25 328	85.85 325	74.47 353	93.93 348

Vis-MVSNet (Re-imp)		96.87 118	96.55 119	97.83 137	98.73 130	95.46 172	99.20 31	98.30 203	94.96 138	96.60 162	98.87 103	90.05 165	98.59 244	93.67 210	98.60 132	99.46 102

TSAR-MVS + GP.		98.38 43	98.24 41	98.81 74	99.22 94	97.25 95	98.11 218	98.29 205	97.19 40	98.99 38	99.02 80	96.22 20	99.67 122	98.52 14	98.56 135	99.51 89

MS-PatchMatch		93.84 269	93.63 257	94.46 308	96.18 299	89.45 316	97.76 250	98.27 206	92.23 253	92.13 299	97.49 235	79.50 316	98.69 232	89.75 293	99.38 101	95.25 330

EI-MVSNet		95.96 150	95.83 142	96.36 241	97.93 194	93.70 244	98.12 216	98.27 206	93.70 193	95.07 193	99.02 80	92.23 118	98.54 248	94.68 174	93.46 242	96.84 252

MVSTER		96.06 146	95.72 144	97.08 184	98.23 170	95.93 154	98.73 119	98.27 206	94.86 142	95.07 193	98.09 182	88.21 207	98.54 248	96.59 113	93.46 242	96.79 256

FMVSNet2		94.47 239	93.61 258	97.04 185	98.21 172	96.43 128	98.79 110	98.27 206	92.46 240	93.50 258	97.09 262	81.16 304	98.00 305	91.09 270	91.93 261	96.70 269

FMVSNet3		94.97 206	94.26 215	97.11 182	98.18 177	96.62 116	98.56 152	98.26 210	93.67 198	94.09 233	97.10 258	84.25 281	98.01 303	92.08 253	92.14 258	96.70 269

Fast-Effi-MVS+		96.28 140	95.70 148	98.03 127	98.29 168	95.97 148	98.58 146	98.25 211	91.74 265	95.29 192	97.23 252	91.03 150	99.15 176	92.90 233	97.96 157	98.97 158

PAPM		94.95 207	94.00 231	97.78 142	97.04 258	95.65 164	96.03 330	98.25 211	91.23 286	94.19 229	97.80 212	91.27 144	98.86 220	82.61 340	97.61 170	98.84 167

CANet_DTU		96.96 114	96.55 119	98.21 114	98.17 179	96.07 142	97.98 229	98.21 213	97.24 37	97.13 136	98.93 97	86.88 238	99.91 30	95.00 167	99.37 102	98.66 180

HY-MVS	93.96 8	96.82 120	96.23 131	98.57 85	98.46 153	97.00 102	98.14 213	98.21 213	93.95 177	96.72 157	97.99 190	91.58 133	99.76 103	94.51 183	96.54 192	98.95 161

PCF-MVS	93.45 11	94.68 221	93.43 265	98.42 101	98.62 143	96.77 112	95.48 339	98.20 215	84.63 343	93.34 263	98.32 164	88.55 201	99.81 71	84.80 333	98.96 115	98.68 177
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
v8		94.47 239	93.77 248	96.57 222	96.36 293	94.83 201	99.05 53	98.19 216	91.92 261	93.16 268	96.97 278	88.82 196	98.48 252	91.69 265	87.79 314	96.39 305

v10		94.29 248	93.55 260	96.51 229	96.39 292	94.80 203	98.99 65	98.19 216	91.35 279	93.02 274	96.99 276	88.09 212	98.41 267	90.50 281	88.41 308	96.33 309

mvs_anonymous		96.70 123	96.53 121	97.18 177	98.19 175	93.78 237	98.31 188	98.19 216	94.01 173	94.47 211	98.27 170	92.08 124	98.46 255	97.39 77	97.91 158	99.31 118

AllTest		95.24 189	94.65 194	96.99 187	99.25 86	93.21 262	98.59 144	98.18 219	91.36 277	93.52 255	98.77 115	84.67 274	99.72 109	89.70 295	97.87 160	98.02 204

TestCases				96.99 187	99.25 86	93.21 262		98.18 219	91.36 277	93.52 255	98.77 115	84.67 274	99.72 109	89.70 295	97.87 160	98.02 204

GBi-Net		94.49 237	93.80 245	96.56 223	98.21 172	95.00 190	98.82 99	98.18 219	92.46 240	94.09 233	97.07 265	81.16 304	97.95 307	92.08 253	92.14 258	96.72 265

test1		94.49 237	93.80 245	96.56 223	98.21 172	95.00 190	98.82 99	98.18 219	92.46 240	94.09 233	97.07 265	81.16 304	97.95 307	92.08 253	92.14 258	96.72 265

FMVSNet1		93.19 282	92.07 287	96.56 223	97.54 221	95.00 190	98.82 99	98.18 219	90.38 301	92.27 296	97.07 265	73.68 347	97.95 307	89.36 302	91.30 269	96.72 265

v1192		94.32 246	93.58 259	96.53 227	96.10 303	94.45 217	98.50 161	98.17 224	91.54 272	94.19 229	97.06 268	86.95 237	98.43 259	90.14 284	89.57 290	96.70 269

v1240		94.06 265	93.29 269	96.34 243	96.03 307	93.90 234	98.44 168	98.17 224	91.18 289	94.13 232	97.01 275	86.05 252	98.42 260	89.13 305	89.50 294	96.70 269

v144192		94.39 243	93.70 254	96.48 231	96.06 305	94.35 223	98.58 146	98.16 226	91.45 274	94.33 221	97.02 273	87.50 227	98.45 256	91.08 271	89.11 299	96.63 277

Fast-Effi-MVS+-dtu		95.87 154	95.85 141	95.91 260	97.74 206	91.74 283	98.69 130	98.15 227	95.56 103	94.92 197	97.68 221	88.98 191	98.79 227	93.19 223	97.78 164	97.20 226

v1921920		94.20 253	93.47 264	96.40 239	95.98 308	94.08 230	98.52 156	98.15 227	91.33 280	94.25 225	97.20 255	86.41 246	98.42 260	90.04 289	89.39 296	96.69 274

v1144		94.59 229	93.92 236	96.60 218	96.21 297	94.78 205	98.59 144	98.14 229	91.86 264	94.21 228	97.02 273	87.97 215	98.41 267	91.72 264	89.57 290	96.61 279

IterMVS-LS		95.46 172	95.21 170	96.22 248	98.12 182	93.72 243	98.32 187	98.13 230	93.71 191	94.26 224	97.31 247	92.24 117	98.10 295	94.63 175	90.12 283	96.84 252
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
GeoE		96.58 129	96.07 134	98.10 123	98.35 158	95.89 158	99.34 12	98.12 231	93.12 220	96.09 180	98.87 103	89.71 171	98.97 200	92.95 231	98.08 154	99.43 106

EU-MVSNet		93.66 270	94.14 223	92.25 328	95.96 309	83.38 349	98.52 156	98.12 231	94.69 148	92.61 285	98.13 180	87.36 230	96.39 344	91.82 261	90.00 285	96.98 233

IterMVS		94.09 262	93.85 242	94.80 297	97.99 191	90.35 307	97.18 288	98.12 231	93.68 196	92.46 293	97.34 244	84.05 286	97.41 326	92.51 246	91.33 268	96.62 278
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS-SCA-FT		94.11 260	93.87 240	94.85 294	97.98 193	90.56 305	97.18 288	98.11 234	93.75 186	92.58 286	97.48 236	83.97 288	97.41 326	92.48 248	91.30 269	96.58 282

COLMAP_ROB	93.27 12	95.33 185	94.87 186	96.71 206	99.29 78	93.24 261	98.58 146	98.11 234	89.92 309	93.57 253	99.10 69	86.37 247	99.79 92	90.78 277	98.10 153	97.09 227
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
hse-mvs2		95.71 162	95.30 167	96.93 193	98.50 150	93.53 249	98.36 178	98.10 236	97.48 18	98.67 58	97.99 190	89.76 169	99.02 197	97.95 38	80.91 344	98.22 198

AUN-MVS		94.53 234	93.73 252	96.92 196	98.50 150	93.52 250	98.34 180	98.10 236	93.83 184	95.94 186	97.98 192	85.59 259	99.03 193	94.35 187	80.94 343	98.22 198

Effi-MVS+-dtu		96.29 138	96.56 118	95.51 273	97.89 197	90.22 308	98.80 106	98.10 236	96.57 64	96.45 173	96.66 296	90.81 151	98.91 211	95.72 145	97.99 156	97.40 219

mvs-test1		96.60 125	96.68 115	96.37 240	97.89 197	91.81 279	98.56 152	98.10 236	96.57 64	96.52 169	97.94 195	90.81 151	99.45 153	95.72 145	98.01 155	97.86 208

1112_ss		96.63 124	96.00 138	98.50 93	98.56 146	96.37 130	98.18 210	98.10 236	92.92 227	94.84 199	98.43 148	92.14 121	99.58 134	94.35 187	96.51 193	99.56 84

RRT_MVS		96.04 147	95.53 153	97.56 161	97.07 257	97.32 88	98.57 151	98.09 241	95.15 127	95.02 195	98.44 147	88.20 208	98.58 246	96.17 128	93.09 251	96.79 256

V42		94.78 217	94.14 223	96.70 208	96.33 295	95.22 181	98.97 69	98.09 241	92.32 249	94.31 222	97.06 268	88.39 204	98.55 247	92.90 233	88.87 304	96.34 307

miper_enhance_ethall		95.10 197	94.75 190	96.12 253	97.53 223	93.73 242	96.61 321	98.08 243	92.20 256	93.89 241	96.65 298	92.44 112	98.30 280	94.21 193	91.16 272	96.34 307

v2v482		94.69 219	94.03 227	96.65 211	96.17 300	94.79 204	98.67 134	98.08 243	92.72 233	94.00 238	97.16 256	87.69 224	98.45 256	92.91 232	88.87 304	96.72 265

CL-MVSNet_2432*1600		90.11 308	89.14 311	93.02 324	91.86 350	88.23 335	96.51 324	98.07 245	90.49 296	90.49 316	94.41 336	84.75 273	95.34 348	80.79 344	74.95 351	95.50 327

miper_ehance_all_eth		95.01 201	94.69 193	95.97 257	97.70 208	93.31 258	97.02 297	98.07 245	92.23 253	93.51 257	96.96 280	91.85 128	98.15 291	93.68 208	91.16 272	96.44 304

eth_miper_zixun_eth		94.68 221	94.41 209	95.47 275	97.64 211	91.71 284	96.73 318	98.07 245	92.71 234	93.64 250	97.21 254	90.54 158	98.17 290	93.38 216	89.76 287	96.54 289

MVS_Test		97.28 100	97.00 97	98.13 120	98.33 164	95.97 148	98.74 115	98.07 245	94.27 164	98.44 75	98.07 183	92.48 111	99.26 163	96.43 121	98.19 150	99.16 138

Test_1112_low_res		96.34 137	95.66 151	98.36 104	98.56 146	95.94 151	97.71 253	98.07 245	92.10 257	94.79 203	97.29 248	91.75 130	99.56 137	94.17 194	96.50 194	99.58 82

alignmvs		97.56 84	97.07 94	99.01 62	98.66 139	98.37 41	98.83 96	98.06 250	96.74 57	98.00 99	97.65 222	90.80 153	99.48 150	98.37 24	96.56 191	99.19 133

RPSCF		94.87 212	95.40 156	93.26 322	98.89 118	82.06 353	98.33 182	98.06 250	90.30 303	96.56 163	99.26 42	87.09 233	99.49 146	93.82 205	96.32 199	98.24 197

miper_lstm_enhance		94.33 245	94.07 226	95.11 286	97.75 203	90.97 297	97.22 285	98.03 252	91.67 269	92.76 280	96.97 278	90.03 166	97.78 317	92.51 246	89.64 289	96.56 286

cl_fuxian		94.79 216	94.43 208	95.89 262	97.75 203	93.12 265	97.16 291	98.03 252	92.23 253	93.46 260	97.05 270	91.39 139	98.01 303	93.58 213	89.21 298	96.53 291

pm-mvs1		93.94 268	93.06 272	96.59 219	96.49 288	95.16 182	98.95 73	98.03 252	92.32 249	91.08 310	97.84 206	84.54 277	98.41 267	92.16 251	86.13 332	96.19 314

v148		94.29 248	93.76 250	95.91 260	96.10 303	92.93 267	98.58 146	97.97 255	92.59 238	93.47 259	96.95 282	88.53 202	98.32 276	92.56 243	87.06 323	96.49 300

IS-MVSNet		97.22 102	96.88 102	98.25 112	98.85 123	96.36 131	99.19 33	97.97 255	95.39 112	97.23 133	98.99 86	91.11 147	98.93 209	94.60 178	98.59 133	99.47 98

cl-mvsnet____		94.51 236	94.01 230	96.02 254	97.58 216	93.40 255	97.05 295	97.96 257	91.73 267	92.76 280	97.08 264	89.06 187	98.13 293	92.61 238	90.29 282	96.52 294

DIV-MVS_2432*1600		90.38 306	89.38 309	93.40 319	92.85 347	88.94 325	97.95 231	97.94 258	90.35 302	90.25 317	93.96 341	79.82 314	95.94 345	84.62 335	76.69 349	95.33 329

cl-mvsnet1		94.52 235	94.03 227	95.99 255	97.57 220	93.38 256	97.05 295	97.94 258	91.74 265	92.81 278	97.10 258	89.12 184	98.07 299	92.60 239	90.30 281	96.53 291

pmmvs6		91.77 294	90.63 298	95.17 284	94.69 335	91.24 294	98.67 134	97.92 260	86.14 335	89.62 322	97.56 232	75.79 339	98.34 274	90.75 278	84.56 334	95.94 320

jason		97.32 99	97.08 93	98.06 126	97.45 231	95.59 165	97.87 241	97.91 261	94.79 144	98.55 69	98.83 108	91.12 146	99.23 167	97.58 67	99.60 68	99.34 112
jason: jason.
ppachtmachnet_test		93.22 280	92.63 280	94.97 290	95.45 324	90.84 298	96.88 310	97.88 262	90.60 295	92.08 300	97.26 249	88.08 213	97.86 316	85.12 330	90.33 280	96.22 312

tpm cat1		93.36 274	92.80 276	95.07 288	97.58 216	87.97 337	96.76 316	97.86 263	82.17 347	93.53 254	96.04 318	86.13 250	99.13 178	89.24 303	95.87 212	98.10 202

EG-PatchMatch MVS		91.13 300	90.12 303	94.17 313	94.73 334	89.00 324	98.13 215	97.81 264	89.22 320	85.32 344	96.46 304	67.71 352	98.42 260	87.89 314	93.82 236	95.08 335

BH-untuned		95.95 151	95.72 144	96.65 211	98.55 148	92.26 272	98.23 197	97.79 265	93.73 189	94.62 206	98.01 188	88.97 192	99.00 199	93.04 228	98.51 137	98.68 177

lupinMVS		97.44 91	97.22 88	98.12 122	98.07 185	95.76 162	97.68 255	97.76 266	94.50 158	98.79 49	98.61 129	92.34 113	99.30 161	97.58 67	99.59 71	99.31 118

VDDNet		95.36 182	94.53 199	97.86 135	98.10 184	95.13 186	98.85 92	97.75 267	90.46 298	98.36 79	99.39 14	73.27 348	99.64 126	97.98 37	96.58 190	98.81 168

ADS-MVSNet		95.00 202	94.45 206	96.63 214	98.00 189	91.91 278	96.04 328	97.74 268	90.15 304	96.47 171	96.64 299	87.89 217	98.96 204	90.08 286	97.06 178	99.02 153

tpmvs		94.60 227	94.36 211	95.33 280	97.46 227	88.60 329	96.88 310	97.68 269	91.29 283	93.80 247	96.42 307	88.58 198	99.24 166	91.06 272	96.04 211	98.17 200

pmmvs4		94.69 219	93.99 233	96.81 201	95.74 314	95.94 151	97.40 269	97.67 270	90.42 300	93.37 262	97.59 228	89.08 186	98.20 288	92.97 230	91.67 264	96.30 311

our_test_3		93.65 272	93.30 268	94.69 299	95.45 324	89.68 314	96.91 304	97.65 271	91.97 260	91.66 305	96.88 286	89.67 172	97.93 310	88.02 312	91.49 266	96.48 301

MVP-Stereo		94.28 250	93.92 236	95.35 279	94.95 330	92.60 270	97.97 230	97.65 271	91.61 271	90.68 314	97.09 262	86.32 248	98.42 260	89.70 295	99.34 103	95.02 337
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
KD-MVS_2432*1600		89.61 313	87.96 317	94.54 303	94.06 340	91.59 286	95.59 337	97.63 273	89.87 310	88.95 328	94.38 338	78.28 324	96.82 334	84.83 331	68.05 355	95.21 331

miper_refine_blended		89.61 313	87.96 317	94.54 303	94.06 340	91.59 286	95.59 337	97.63 273	89.87 310	88.95 328	94.38 338	78.28 324	96.82 334	84.83 331	68.05 355	95.21 331

SCA		95.46 172	95.13 173	96.46 235	97.67 209	91.29 293	97.33 278	97.60 275	94.68 149	96.92 148	97.10 258	83.97 288	98.89 215	92.59 241	98.32 148	99.20 130

GA-MVS		94.81 215	94.03 227	97.14 179	97.15 252	93.86 235	96.76 316	97.58 276	94.00 174	94.76 204	97.04 271	80.91 307	98.48 252	91.79 262	96.25 205	99.09 146

Anonymous20240521		91.18 299	90.44 300	93.42 317	93.70 343	88.47 331	98.94 75	97.56 277	88.46 324	89.56 324	95.08 333	77.15 335	96.97 332	83.92 336	89.55 292	94.82 339

test20.03		90.89 303	90.38 301	92.43 326	93.48 344	88.14 336	98.33 182	97.56 277	93.40 208	87.96 333	96.71 295	80.69 311	94.13 354	79.15 349	86.17 330	95.01 338

CR-MVSNet		94.76 218	94.15 222	96.59 219	97.00 259	93.43 252	94.96 341	97.56 277	92.46 240	96.93 146	96.24 310	88.15 210	97.88 315	87.38 315	96.65 188	98.46 189

Patchmtry		93.22 280	92.35 284	95.84 264	96.77 272	93.09 266	94.66 346	97.56 277	87.37 329	92.90 276	96.24 310	88.15 210	97.90 311	87.37 316	90.10 284	96.53 291

tpmrst		95.63 167	95.69 149	95.44 277	97.54 221	88.54 330	96.97 299	97.56 277	93.50 204	97.52 129	96.93 284	89.49 173	99.16 173	95.25 162	96.42 196	98.64 182

FMVSNet5		91.81 293	90.92 296	94.49 305	97.21 245	92.09 274	98.00 228	97.55 282	89.31 319	90.86 312	95.61 328	74.48 344	95.32 349	85.57 326	89.70 288	96.07 317

testgi		93.06 284	92.45 283	94.88 293	96.43 291	89.90 309	98.75 112	97.54 283	95.60 101	91.63 306	97.91 197	74.46 345	97.02 331	86.10 322	93.67 237	97.72 213

PatchmatchNet		95.71 162	95.52 154	96.29 246	97.58 216	90.72 302	96.84 313	97.52 284	94.06 169	97.08 138	96.96 280	89.24 181	98.90 214	92.03 257	98.37 144	99.26 126
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MDA-MVSNet-bldmvs		89.97 310	88.35 315	94.83 296	95.21 327	91.34 289	97.64 258	97.51 285	88.36 325	71.17 357	96.13 316	79.22 318	96.63 341	83.65 337	86.27 329	96.52 294

USDC		93.33 277	92.71 278	95.21 282	96.83 271	90.83 299	96.91 304	97.50 286	93.84 182	90.72 313	98.14 179	77.69 329	98.82 224	89.51 299	93.21 250	95.97 319

ITE_SJBPF				95.44 277	97.42 232	91.32 292		97.50 286	95.09 133	93.59 251	98.35 158	81.70 302	98.88 217	89.71 294	93.39 246	96.12 315

Patchmatch-test		94.42 241	93.68 256	96.63 214	97.60 214	91.76 281	94.83 345	97.49 288	89.45 317	94.14 231	97.10 258	88.99 188	98.83 223	85.37 329	98.13 152	99.29 123

YYNet1		90.70 305	89.39 308	94.62 302	94.79 333	90.65 303	97.20 286	97.46 289	87.54 328	72.54 355	95.74 321	86.51 242	96.66 340	86.00 323	86.76 328	96.54 289

MDA-MVSNet_test_wron		90.71 304	89.38 309	94.68 300	94.83 332	90.78 301	97.19 287	97.46 289	87.60 327	72.41 356	95.72 324	86.51 242	96.71 339	85.92 324	86.80 327	96.56 286

BH-RMVSNet		95.92 153	95.32 165	97.69 151	98.32 166	94.64 207	98.19 206	97.45 291	94.56 154	96.03 182	98.61 129	85.02 267	99.12 179	90.68 279	99.06 112	99.30 121

MIMVSNet1		89.67 312	88.28 316	93.82 314	92.81 348	91.08 296	98.01 226	97.45 291	87.95 326	87.90 334	95.87 320	67.63 353	94.56 353	78.73 351	88.18 311	95.83 322

OurMVSNet-221017-0		94.21 252	94.00 231	94.85 294	95.60 318	89.22 320	98.89 84	97.43 293	95.29 119	92.18 298	98.52 141	82.86 296	98.59 244	93.46 215	91.76 263	96.74 262

BH-w/o		95.38 179	95.08 176	96.26 247	98.34 163	91.79 280	97.70 254	97.43 293	92.87 230	94.24 226	97.22 253	88.66 197	98.84 221	91.55 267	97.70 168	98.16 201

VDD-MVS		95.82 158	95.23 169	97.61 158	98.84 124	93.98 232	98.68 131	97.40 295	95.02 135	97.95 101	99.34 31	74.37 346	99.78 96	98.64 4	96.80 183	99.08 149

Gipuma		78.40 324	76.75 327	83.38 338	95.54 320	80.43 354	79.42 360	97.40 295	64.67 356	73.46 354	80.82 357	45.65 361	93.14 355	66.32 357	87.43 318	76.56 358
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
new-patchmatchnet		88.50 317	87.45 320	91.67 330	90.31 354	85.89 345	97.16 291	97.33 297	89.47 316	83.63 347	92.77 345	76.38 336	95.06 351	82.70 339	77.29 348	94.06 346

ADS-MVSNet2		94.58 230	94.40 210	95.11 286	98.00 189	88.74 327	96.04 328	97.30 298	90.15 304	96.47 171	96.64 299	87.89 217	97.56 323	90.08 286	97.06 178	99.02 153

MDTV_nov1_ep13			95.40 156		97.48 225	88.34 333	96.85 312	97.29 299	93.74 188	97.48 130	97.26 249	89.18 182	99.05 189	91.92 260	97.43 174

pmmvs5		93.65 272	92.97 274	95.68 269	95.49 322	92.37 271	98.20 202	97.28 300	89.66 314	92.58 286	97.26 249	82.14 298	98.09 297	93.18 224	90.95 276	96.58 282

EPNet_dtu		95.21 191	94.95 183	95.99 255	96.17 300	90.45 306	98.16 212	97.27 301	96.77 55	93.14 271	98.33 163	90.34 161	98.42 260	85.57 326	98.81 125	99.09 146
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Anonymous20231206		91.66 295	91.10 295	93.33 320	94.02 342	87.35 341	98.58 146	97.26 302	90.48 297	90.16 318	96.31 308	83.83 292	96.53 342	79.36 348	89.90 286	96.12 315

test_0402		91.32 297	90.27 302	94.48 306	96.60 282	91.12 295	98.50 161	97.22 303	86.10 336	88.30 332	96.98 277	77.65 331	97.99 306	78.13 352	92.94 253	94.34 341

dp		94.15 257	93.90 238	94.90 292	97.31 239	86.82 344	96.97 299	97.19 304	91.22 287	96.02 183	96.61 301	85.51 260	99.02 197	90.00 290	94.30 219	98.85 165

thres200		95.25 188	94.57 197	97.28 172	98.81 126	94.92 197	98.20 202	97.11 305	95.24 124	96.54 167	96.22 314	84.58 276	99.53 143	87.93 313	96.50 194	97.39 220

PatchT		93.06 284	91.97 289	96.35 242	96.69 278	92.67 269	94.48 347	97.08 306	86.62 331	97.08 138	92.23 348	87.94 216	97.90 311	78.89 350	96.69 186	98.49 188

TDRefinement		91.06 301	89.68 306	95.21 282	85.35 358	91.49 288	98.51 160	97.07 307	91.47 273	88.83 330	97.84 206	77.31 333	99.09 186	92.79 236	77.98 347	95.04 336

LF4IMVS		93.14 283	92.79 277	94.20 311	95.88 311	88.67 328	97.66 257	97.07 307	93.81 185	91.71 304	97.65 222	77.96 328	98.81 225	91.47 268	91.92 262	95.12 333

Anonymous202405211		95.28 187	94.49 201	97.67 153	99.00 110	93.75 240	98.70 128	97.04 309	90.66 294	96.49 170	98.80 111	78.13 326	99.83 56	96.21 127	95.36 216	99.44 105

baseline1		95.84 156	95.12 174	98.01 128	98.49 152	95.98 143	98.73 119	97.03 310	95.37 115	96.22 177	98.19 176	89.96 167	99.16 173	94.60 178	87.48 317	98.90 164

MIMVSNet		93.26 279	92.21 286	96.41 238	97.73 207	93.13 264	95.65 336	97.03 310	91.27 285	94.04 236	96.06 317	75.33 340	97.19 329	86.56 319	96.23 206	98.92 163

EPNet		97.28 100	96.87 103	98.51 92	94.98 329	96.14 140	98.90 80	97.02 312	98.28 1	95.99 184	99.11 67	91.36 140	99.89 35	96.98 89	99.19 109	99.50 91
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TR-MVS		94.94 209	94.20 217	97.17 178	97.75 203	94.14 229	97.59 261	97.02 312	92.28 252	95.75 187	97.64 224	83.88 290	98.96 204	89.77 292	96.15 208	98.40 191

JIA-IIPM		93.35 275	92.49 282	95.92 259	96.48 289	90.65 303	95.01 340	96.96 314	85.93 337	96.08 181	87.33 353	87.70 223	98.78 228	91.35 269	95.58 215	98.34 194

pmmvs-eth3d		90.36 307	89.05 312	94.32 310	91.10 352	92.12 273	97.63 260	96.95 315	88.86 322	84.91 345	93.13 344	78.32 323	96.74 336	88.70 307	81.81 339	94.09 345

tfpn200view9		95.32 186	94.62 195	97.43 166	98.94 115	94.98 193	98.68 131	96.93 316	95.33 116	96.55 165	96.53 302	84.23 282	99.56 137	88.11 309	96.29 200	97.76 209

thres400		95.38 179	94.62 195	97.65 156	98.94 115	94.98 193	98.68 131	96.93 316	95.33 116	96.55 165	96.53 302	84.23 282	99.56 137	88.11 309	96.29 200	98.40 191

thres100view900		95.38 179	94.70 192	97.41 167	98.98 113	94.92 197	98.87 89	96.90 318	95.38 113	96.61 161	96.88 286	84.29 279	99.56 137	88.11 309	96.29 200	97.76 209

thres600view7		95.49 171	94.77 188	97.67 153	98.98 113	95.02 189	98.85 92	96.90 318	95.38 113	96.63 160	96.90 285	84.29 279	99.59 133	88.65 308	96.33 198	98.40 191

test_method		79.03 322	78.17 325	81.63 339	86.06 357	54.40 367	82.75 359	96.89 320	39.54 362	80.98 350	95.57 329	58.37 358	94.73 352	84.74 334	78.61 346	95.75 323

CostFormer		94.95 207	94.73 191	95.60 272	97.28 240	89.06 322	97.53 264	96.89 320	89.66 314	96.82 153	96.72 294	86.05 252	98.95 208	95.53 153	96.13 209	98.79 169

new_pmnet		90.06 309	89.00 313	93.22 323	94.18 337	88.32 334	96.42 326	96.89 320	86.19 334	85.67 343	93.62 342	77.18 334	97.10 330	81.61 342	89.29 297	94.23 342

OpenMVS_ROB	86.42 20	89.00 316	87.43 321	93.69 315	93.08 346	89.42 317	97.91 235	96.89 320	78.58 350	85.86 341	94.69 335	69.48 351	98.29 283	77.13 353	93.29 249	93.36 350

tpm2		94.19 254	93.76 250	95.46 276	97.23 243	89.04 323	97.31 280	96.85 324	87.08 330	96.21 178	96.79 292	83.75 294	98.74 230	92.43 249	96.23 206	98.59 184

TransMVSNet (Re)		92.67 288	91.51 293	96.15 250	96.58 283	94.65 206	98.90 80	96.73 325	90.86 293	89.46 325	97.86 203	85.62 258	98.09 297	86.45 320	81.12 341	95.71 324

ambc				89.49 333	86.66 356	75.78 356	92.66 352	96.72 326		86.55 339	92.50 347	46.01 360	97.90 311	90.32 282	82.09 336	94.80 340

LCM-MVSNet		78.70 323	76.24 328	86.08 335	77.26 364	71.99 359	94.34 348	96.72 326	61.62 357	76.53 352	89.33 351	33.91 366	92.78 356	81.85 341	74.60 352	93.46 349

TinyColmap		92.31 291	91.53 292	94.65 301	96.92 264	89.75 311	96.92 302	96.68 328	90.45 299	89.62 322	97.85 205	76.06 338	98.81 225	86.74 318	92.51 256	95.41 328

Baseline_NR-MVSNet		94.35 244	93.81 244	95.96 258	96.20 298	94.05 231	98.61 143	96.67 329	91.44 275	93.85 244	97.60 227	88.57 199	98.14 292	94.39 185	86.93 324	95.68 325

SixPastTwentyTwo		93.34 276	92.86 275	94.75 298	95.67 316	89.41 318	98.75 112	96.67 329	93.89 179	90.15 319	98.25 172	80.87 308	98.27 286	90.90 275	90.64 278	96.57 284

DWT-MVSNet_test		94.82 213	94.36 211	96.20 249	97.35 237	90.79 300	98.34 180	96.57 331	92.91 228	95.33 191	96.44 306	82.00 299	99.12 179	94.52 182	95.78 214	98.70 175

LFMVS		95.86 155	94.98 181	98.47 96	98.87 120	96.32 133	98.84 95	96.02 332	93.40 208	98.62 64	99.20 52	74.99 342	99.63 129	97.72 55	97.20 177	99.46 102

IB-MVS	91.98 17	93.27 278	91.97 289	97.19 176	97.47 226	93.41 254	97.09 294	95.99 333	93.32 211	92.47 292	95.73 322	78.06 327	99.53 143	94.59 180	82.98 335	98.62 183
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		94.08 263	93.51 262	95.80 265	95.53 321	92.89 268	97.38 271	95.97 334	95.11 130	92.51 290	96.66 296	87.71 221	96.94 333	87.03 317	93.67 237	97.57 216

FPMVS		77.62 326	77.14 326	79.05 341	79.25 362	60.97 363	95.79 333	95.94 335	65.96 355	67.93 358	94.40 337	37.73 364	88.88 359	68.83 356	88.46 307	87.29 353

Patchmatch-RL test		91.49 296	90.85 297	93.41 318	91.37 351	84.40 346	92.81 351	95.93 336	91.87 263	87.25 335	94.87 334	88.99 188	96.53 342	92.54 245	82.00 337	99.30 121

tpm		94.13 258	93.80 245	95.12 285	96.50 287	87.91 338	97.44 266	95.89 337	92.62 236	96.37 175	96.30 309	84.13 285	98.30 280	93.24 221	91.66 265	99.14 141

LCM-MVSNet-Re		95.22 190	95.32 165	94.91 291	98.18 177	87.85 339	98.75 112	95.66 338	95.11 130	88.96 327	96.85 289	90.26 164	97.65 319	95.65 150	98.44 141	99.22 129

bset_n11_16_dypcd		94.89 211	94.27 214	96.76 203	94.41 336	95.15 184	95.67 335	95.64 339	95.53 104	94.65 205	97.52 234	87.10 232	98.29 283	96.58 115	91.35 267	96.83 254

ET-MVSNet_ETH3D		94.13 258	92.98 273	97.58 159	98.22 171	96.20 137	97.31 280	95.37 340	94.53 155	79.56 351	97.63 226	86.51 242	97.53 324	96.91 94	90.74 277	99.02 153

test-LLR		95.10 197	94.87 186	95.80 265	96.77 272	89.70 312	96.91 304	95.21 341	95.11 130	94.83 201	95.72 324	87.71 221	98.97 200	93.06 226	98.50 138	98.72 173

test-mter		94.08 263	93.51 262	95.80 265	96.77 272	89.70 312	96.91 304	95.21 341	92.89 229	94.83 201	95.72 324	77.69 329	98.97 200	93.06 226	98.50 138	98.72 173

PM-MVS		87.77 318	86.55 322	91.40 331	91.03 353	83.36 350	96.92 302	95.18 343	91.28 284	86.48 340	93.42 343	53.27 359	96.74 336	89.43 301	81.97 338	94.11 344

DeepMVS_CX				86.78 334	97.09 256	72.30 358		95.17 344	75.92 352	84.34 346	95.19 330	70.58 350	95.35 347	79.98 347	89.04 301	92.68 351

K. test v3		92.55 289	91.91 291	94.48 306	95.64 317	89.24 319	99.07 51	94.88 345	94.04 170	86.78 337	97.59 228	77.64 332	97.64 320	92.08 253	89.43 295	96.57 284

TESTMET0.1,1		94.18 256	93.69 255	95.63 271	96.92 264	89.12 321	96.91 304	94.78 346	93.17 217	94.88 198	96.45 305	78.52 322	98.92 210	93.09 225	98.50 138	98.85 165

pmmvs3		86.67 321	84.86 324	92.11 329	88.16 355	87.19 343	96.63 320	94.75 347	79.88 349	87.22 336	92.75 346	66.56 354	95.20 350	81.24 343	76.56 350	93.96 347

door								94.64 348

thisisatest0515		95.61 170	94.89 185	97.76 144	98.15 180	95.15 184	96.77 315	94.41 349	92.95 226	97.18 135	97.43 241	84.78 272	99.45 153	94.63 175	97.73 167	98.68 177

door-mid								94.37 350

tttt0517		96.07 145	95.51 155	97.78 142	98.41 155	94.84 199	99.28 18	94.33 351	94.26 165	97.64 123	98.64 128	84.05 286	99.47 151	95.34 156	97.60 171	99.03 152

DSMNet-mixed		92.52 290	92.58 281	92.33 327	94.15 338	82.65 351	98.30 190	94.26 352	89.08 321	92.65 284	95.73 322	85.01 268	95.76 346	86.24 321	97.76 165	98.59 184

thisisatest0530		96.01 148	95.36 161	97.97 130	98.38 156	95.52 170	98.88 87	94.19 353	94.04 170	97.64 123	98.31 165	83.82 293	99.46 152	95.29 160	97.70 168	98.93 162

MTMP							98.89 84	94.14 354

baseline2		95.11 196	94.52 200	96.87 198	96.65 281	93.56 246	98.27 195	94.10 355	93.45 206	92.02 302	97.43 241	87.45 229	99.19 171	93.88 203	97.41 175	97.87 207

PMVS	61.03 23	65.95 329	63.57 333	73.09 344	57.90 367	51.22 368	85.05 358	93.93 356	54.45 358	44.32 364	83.57 354	13.22 368	89.15 358	58.68 359	81.00 342	78.91 357
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PMMVS2		77.95 325	75.44 329	85.46 336	82.54 359	74.95 357	94.23 349	93.08 357	72.80 354	74.68 353	87.38 352	36.36 365	91.56 357	73.95 355	63.94 357	89.87 352

MVS-HIRNet		89.46 315	88.40 314	92.64 325	97.58 216	82.15 352	94.16 350	93.05 358	75.73 353	90.90 311	82.52 355	79.42 317	98.33 275	83.53 338	98.68 127	97.43 217

EPMVS		94.99 203	94.48 202	96.52 228	97.22 244	91.75 282	97.23 284	91.66 359	94.11 167	97.28 131	96.81 291	85.70 257	98.84 221	93.04 228	97.28 176	98.97 158

lessismore_v0				94.45 309	94.93 331	88.44 332		91.03 360		86.77 338	97.64 224	76.23 337	98.42 260	90.31 283	85.64 333	96.51 297

ANet_high		69.08 327	65.37 331	80.22 340	65.99 366	71.96 360	90.91 355	90.09 361	82.62 345	49.93 363	78.39 358	29.36 367	81.75 360	62.49 358	38.52 361	86.95 355

gg-mvs-nofinetune		92.21 292	90.58 299	97.13 180	96.75 275	95.09 187	95.85 332	89.40 362	85.43 341	94.50 210	81.98 356	80.80 310	98.40 273	92.16 251	98.33 147	97.88 206

GG-mvs-BLEND				96.59 219	96.34 294	94.98 193	96.51 324	88.58 363		93.10 273	94.34 340	80.34 313	98.05 301	89.53 298	96.99 180	96.74 262

E-PMN		64.94 330	64.25 332	67.02 345	82.28 360	59.36 365	91.83 354	85.63 364	52.69 359	60.22 360	77.28 359	41.06 363	80.12 362	46.15 361	41.14 359	61.57 360

EMVS		64.07 331	63.26 334	66.53 346	81.73 361	58.81 366	91.85 353	84.75 365	51.93 361	59.09 361	75.13 360	43.32 362	79.09 363	42.03 362	39.47 360	61.69 359

tmp_tt		68.90 328	66.97 330	74.68 343	50.78 368	59.95 364	87.13 356	83.47 366	38.80 363	62.21 359	96.23 312	64.70 356	76.91 364	88.91 306	30.49 362	87.19 354

MVE	62.14 22	63.28 332	59.38 335	74.99 342	74.33 365	65.47 361	85.55 357	80.50 367	52.02 360	51.10 362	75.00 361	10.91 371	80.50 361	51.60 360	53.40 358	78.99 356
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
N_pmnet		87.12 320	87.77 319	85.17 337	95.46 323	61.92 362	97.37 273	70.66 368	85.83 338	88.73 331	96.04 318	85.33 265	97.76 318	80.02 345	90.48 279	95.84 321

wuyk23d		30.17 333	30.18 337	30.16 347	78.61 363	43.29 369	66.79 361	14.21 369	17.31 364	14.82 367	11.93 367	11.55 370	41.43 365	37.08 363	19.30 363	5.76 363

testmvs		21.48 335	24.95 338	11.09 349	14.89 369	6.47 371	96.56 322	9.87 370	7.55 365	17.93 365	39.02 363	9.43 372	5.90 367	16.56 365	12.72 364	20.91 362

test123		20.95 336	23.72 339	12.64 348	13.54 370	8.19 370	96.55 323	6.13 371	7.48 366	16.74 366	37.98 364	12.97 369	6.05 366	16.69 364	5.43 365	23.68 361

uanet_test		0.00 339	0.00 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

pcd_1.5k_mvsjas		7.88 338	10.50 341	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	94.51 84	0.00 368	0.00 366	0.00 366	0.00 364

sosnet-low-res		0.00 339	0.00 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

sosnet		0.00 339	0.00 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

uncertanet		0.00 339	0.00 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

Regformer		0.00 339	0.00 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

n2								0.00 372

nn								0.00 372

ab-mvs-re		8.20 337	10.94 340	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	98.43 148	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

uanet		0.00 339	0.00 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

OPU-MVS				99.37 20	99.24 92	99.05 10	99.02 59				99.16 61	97.81 2	99.37 157	97.24 81	99.73 43	99.70 48

test_0728_THIRD									97.32 29	99.45 9	99.46 9	97.88 1	99.94 3	98.47 16	99.86 1	99.85 2

GSMVS																99.20 130

test_part2					99.63 29	99.18 8				99.27 17

sam_mvs1												89.45 175				99.20 130

sam_mvs												88.99 188

test_post1							96.68 319				30.43 366	87.85 220	98.69 232	92.59 241

test_post											31.83 365	88.83 195	98.91 211

patchmatchnet-post											95.10 332	89.42 176	98.89 215

gm-plane-assit					95.88 311	87.47 340			89.74 313		96.94 283		99.19 171	93.32 220

test9_res														96.39 123	99.57 75	99.69 51

agg_prior2														95.87 139	99.57 75	99.68 57

test_prior4						98.01 62	97.86 242

test_prior2							97.80 247		96.12 82	97.89 108	98.69 121	95.96 36		96.89 97	99.60 68

旧先验2							97.57 263		91.30 282	98.67 58			99.80 80	95.70 149

新几何2							97.64 258

原ACMM2							97.67 256

testdata2													99.89 35	91.65 266

segment_acmp												96.85 11

testdata1							97.32 279		96.34 74

plane_prior7					97.42 232	94.63 208

plane_prior6					97.35 237	94.61 211						87.09 233

plane_prior4											98.28 167

plane_prior3						94.61 211			97.02 49	95.34 189

plane_prior2							98.80 106		97.28 31

plane_prior1					97.37 236

plane_prior						94.60 213	98.44 168		96.74 57						94.22 222

HQP5-MVS						94.25 227

HQP-NCC					97.20 246		98.05 222		96.43 70	94.45 212

ACMP_Plane					97.20 246		98.05 222		96.43 70	94.45 212

BP-MVS														95.30 158

HQP4-MVS										94.45 212			98.96 204			96.87 249

HQP2-MVS												86.75 239

NP-MVS					97.28 240	94.51 216					97.73 215

MDTV_nov1_ep13_2view						84.26 347	96.89 309		90.97 292	97.90 107		89.89 168		93.91 202		99.18 137

ACMMP++_ref															92.97 252

ACMMP++															93.61 240

Test By Simon												94.64 80

High-resolution multi-view benchmark