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 93	96.80 87	98.08 107	99.30 56	94.56 218	98.05 222	99.71 1	93.57 182	97.09 109	98.91 80	88.17 188	99.89 28	96.87 81	99.56 63	99.81 3

HyFIR lowres test		96.90 102	96.49 105	98.14 101	99.33 46	95.56 151	97.38 275	99.65 2	92.34 235	97.61 97	98.20 146	89.29 146	99.10 174	96.97 69	97.60 149	99.77 15

MVS_111021_LR		98.34 38	98.23 34	98.67 66	99.27 65	96.90 83	97.95 231	99.58 3	97.14 34	98.44 54	99.01 65	95.03 53	99.62 110	97.91 31	99.75 31	99.50 73

MVS_111021_HR		98.47 30	98.34 22	98.88 58	99.22 75	97.32 67	97.91 236	99.58 3	97.20 30	98.33 58	99.00 66	95.99 26	99.64 105	98.05 28	99.76 25	99.69 37

PGM-MVS		98.49 29	98.23 34	99.27 25	99.72 11	98.08 41	98.99 67	99.49 5	95.43 89	99.03 19	99.32 22	95.56 37	99.94 3	96.80 84	99.77 19	99.78 8

ACMMP		98.23 43	97.95 43	99.09 44	99.74 7	97.62 58	99.03 63	99.41 6	95.98 69	97.60 98	99.36 18	94.45 66	99.93 10	97.14 65	98.85 99	99.70 36
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 54	97.74 48	98.20 97	99.67 18	95.16 166	99.22 29	99.32 7	93.04 204	97.02 116	98.92 79	95.36 43	99.91 23	97.43 58	99.64 47	99.52 68

PVSNet_BlendedMVS		96.73 107	96.60 100	97.12 171	99.25 68	95.35 160	98.26 198	99.26 8	94.28 144	97.94 77	97.46 203	92.74 86	99.81 54	96.88 78	93.32 238	96.20 299

PVSNet_Blended		97.38 80	97.12 73	98.14 101	99.25 68	95.35 160	97.28 285	99.26 8	93.13 202	97.94 77	98.21 145	92.74 86	99.81 54	96.88 78	99.40 80	99.27 103

UniMVSNet_NR-MVSNet		95.71 142	95.15 150	97.40 159	96.84 240	96.97 79	98.74 125	99.24 10	95.16 110	93.88 232	97.72 186	91.68 109	98.31 272	95.81 116	87.25 311	96.92 224

WR-MVS_H		95.05 189	94.46 190	96.81 189	96.86 239	95.82 143	99.24 21	99.24 10	93.87 161	92.53 269	96.84 270	90.37 132	98.24 278	93.24 184	87.93 301	96.38 292

FC-MVSNet-test		96.42 118	96.05 117	97.53 147	96.95 232	97.27 69	99.36 8	99.23 12	95.83 73	93.93 230	98.37 127	92.00 103	98.32 270	96.02 109	92.72 246	97.00 219

VPA-MVSNet		95.75 139	95.11 151	97.69 132	97.24 215	97.27 69	98.94 75	99.23 12	95.13 112	95.51 173	97.32 214	85.73 245	98.91 197	97.33 62	89.55 276	96.89 232

FIs		96.51 115	96.12 116	97.67 134	97.13 225	97.54 61	99.36 8	99.22 14	95.89 71	94.03 228	98.35 129	91.98 104	98.44 251	96.40 99	92.76 245	97.01 218

tfpnnormal		93.66 259	92.70 266	96.55 221	96.94 233	95.94 126	98.97 71	99.19 15	91.04 273	91.38 287	97.34 212	84.94 258	98.61 222	85.45 321	89.02 285	95.11 319

UniMVSNet (Re)		95.78 138	95.19 149	97.58 144	96.99 231	97.47 63	98.79 114	99.18 16	95.60 81	93.92 231	97.04 244	91.68 109	98.48 241	95.80 118	87.66 306	96.79 243

PVSNet_Blended_VisFu		97.70 60	97.46 61	98.44 83	99.27 65	95.91 138	98.63 147	99.16 17	94.48 141	97.67 92	98.88 81	92.80 85	99.91 23	97.11 66	99.12 89	99.50 73

CHOSEN 280x420		97.18 90	97.18 72	97.20 165	98.81 118	93.27 254	95.78 330	99.15 18	95.25 105	96.79 131	98.11 151	92.29 93	99.07 177	98.56 9	99.85 2	99.25 105

PHI-MVS		98.34 38	98.06 39	99.18 34	99.15 82	98.12 40	99.04 62	99.09 19	93.32 196	98.83 33	99.10 51	96.54 9	99.83 46	97.70 45	99.76 25	99.59 63

UA-Net		97.96 47	97.62 50	98.98 51	98.86 114	97.47 63	98.89 82	99.08 20	96.67 50	98.72 39	99.54 1	93.15 81	99.81 54	94.87 143	98.83 100	99.65 52

PatchMatch-RL		96.59 112	96.03 119	98.27 93	99.31 51	96.51 101	97.91 236	99.06 21	93.72 170	96.92 122	98.06 155	88.50 183	99.65 103	91.77 229	99.00 92	98.66 155

3Dnovator	94.51 5	97.46 70	96.93 83	99.07 45	97.78 182	97.64 56	99.35 10	99.06 21	97.02 40	93.75 237	99.16 45	89.25 147	99.92 14	97.22 63	99.75 31	99.64 55

MSLP-MVS++		98.56 23	98.57 6	98.55 73	99.26 67	96.80 86	98.71 131	99.05 23	97.28 22	98.84 31	99.28 28	96.47 11	99.40 137	98.52 14	99.70 39	99.47 79

PS-CasMVS		94.67 217	93.99 219	96.71 193	96.68 249	95.26 163	99.13 49	99.03 24	93.68 176	92.33 275	97.95 164	85.35 252	98.10 283	93.59 177	88.16 300	96.79 243

TranMVSNet+NR-MVSNet		95.14 186	94.48 188	97.11 172	96.45 260	96.36 108	99.03 63	99.03 24	95.04 117	93.58 239	97.93 167	88.27 186	98.03 288	94.13 164	86.90 316	96.95 223

PEN-MVS		94.42 230	93.73 236	96.49 225	96.28 280	94.84 194	99.17 36	99.00 26	93.51 183	92.23 277	97.83 178	86.10 239	97.90 296	92.55 208	86.92 315	96.74 248

Vis-MVSNet		97.42 76	97.11 75	98.34 90	98.66 130	96.23 113	99.22 29	99.00 26	96.63 52	98.04 67	99.21 35	88.05 194	99.35 142	96.01 110	99.21 86	99.45 85
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
DU-MVS		95.42 166	94.76 176	97.40 159	96.53 255	96.97 79	98.66 145	98.99 28	95.43 89	93.88 232	97.69 187	88.57 178	98.31 272	95.81 116	87.25 311	96.92 224

VPNet		94.99 191	94.19 204	97.40 159	97.16 223	96.57 97	98.71 131	98.97 29	95.67 78	94.84 183	98.24 144	80.36 306	98.67 219	96.46 95	87.32 309	96.96 221

OpenMVS	93.04 13	95.83 136	95.00 155	98.32 91	97.18 222	97.32 67	99.21 32	98.97 29	89.96 289	91.14 290	99.05 60	86.64 224	99.92 14	93.38 180	99.47 71	97.73 193

HFP-MVS		98.63 13	98.40 15	99.32 18	99.72 11	98.29 28	99.23 23	98.96 31	96.10 67	98.94 25	99.17 42	96.06 22	99.92 14	97.62 49	99.78 16	99.75 22

#test#		98.54 26	98.27 28	99.32 18	99.72 11	98.29 28	98.98 70	98.96 31	95.65 80	98.94 25	99.17 42	96.06 22	99.92 14	97.21 64	99.78 16	99.75 22

ACMMPR		98.59 17	98.36 19	99.29 20	99.74 7	98.15 38	99.23 23	98.95 33	96.10 67	98.93 29	99.19 41	95.70 35	99.94 3	97.62 49	99.79 12	99.78 8

CP-MVSNet		94.94 197	94.30 197	96.83 188	96.72 247	95.56 151	99.11 52	98.95 33	93.89 159	92.42 274	97.90 169	87.19 215	98.12 282	94.32 159	88.21 298	96.82 242

NR-MVSNet		94.98 193	94.16 205	97.44 155	96.53 255	97.22 73	98.74 125	98.95 33	94.96 122	89.25 307	97.69 187	89.32 145	98.18 280	94.59 152	87.40 308	96.92 224

region2R		98.61 14	98.38 17	99.29 20	99.74 7	98.16 37	99.23 23	98.93 36	96.15 62	98.94 25	99.17 42	95.91 30	99.94 3	97.55 54	99.79 12	99.78 8

APDe-MVS		99.02 1	98.84 1	99.55 3	99.57 26	98.96 5	99.39 5	98.93 36	97.38 18	99.41 4	99.54 1	96.66 6	99.84 45	98.86 2	99.85 2	99.87 1

VNet		97.79 56	97.40 64	98.96 53	98.88 112	97.55 60	98.63 147	98.93 36	96.74 47	99.02 20	98.84 84	90.33 134	99.83 46	98.53 10	96.66 162	99.50 73

UGNet		96.78 106	96.30 110	98.19 100	98.24 152	95.89 140	98.88 85	98.93 36	97.39 17	96.81 129	97.84 175	82.60 290	99.90 26	96.53 93	99.49 69	98.79 147
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 79	96.98 82	98.61 69	98.60 136	96.61 95	98.22 200	98.93 36	93.97 156	98.01 72	98.48 117	91.98 104	99.85 42	96.45 96	98.15 130	99.39 89

QAPM		96.29 122	95.40 135	98.96 53	97.85 179	97.60 59	99.23 23	98.93 36	89.76 296	93.11 255	99.02 61	89.11 151	99.93 10	91.99 222	99.62 49	99.34 91

ESAPD		98.92 2	98.67 4	99.65 1	99.58 25	99.20 1	98.42 179	98.91 42	97.58 7	99.54 3	99.46 6	97.10 2	99.94 3	97.64 48	99.84 7	99.83 2

114514_t		96.93 100	96.27 111	98.92 55	99.50 31	97.63 57	98.85 94	98.90 43	84.80 334	97.77 84	99.11 49	92.84 84	99.66 102	94.85 144	99.77 19	99.47 79

LS3D		97.16 91	96.66 99	98.68 65	98.53 140	97.19 74	98.93 76	98.90 43	92.83 215	95.99 170	99.37 14	92.12 100	99.87 37	93.67 175	99.57 57	98.97 137

DELS-MVS		98.40 33	98.20 36	98.99 49	99.00 92	97.66 55	97.75 253	98.89 45	97.71 6	98.33 58	98.97 68	94.97 54	99.88 36	98.42 16	99.76 25	99.42 88
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 53	97.46 61	99.06 47	99.53 28	98.35 25	98.33 187	98.89 45	92.62 218	98.05 65	98.94 76	95.34 44	99.65 103	96.04 108	99.42 77	99.19 111

AdaColmap		97.15 92	96.70 95	98.48 80	99.16 80	96.69 92	98.01 226	98.89 45	94.44 143	96.83 126	98.68 99	90.69 129	99.76 85	94.36 157	99.29 85	98.98 136

Anonymous20231211		94.10 248	93.26 257	96.61 210	99.11 85	94.28 228	99.01 65	98.88 48	86.43 322	92.81 262	97.57 198	81.66 295	98.68 218	94.83 145	89.02 285	96.88 234

XVS		98.70 6	98.49 13	99.34 15	99.70 15	98.35 25	99.29 15	98.88 48	97.40 15	98.46 49	99.20 38	95.90 31	99.89 28	97.85 36	99.74 34	99.78 8

X-MVStestdata		94.06 252	92.30 272	99.34 15	99.70 15	98.35 25	99.29 15	98.88 48	97.40 15	98.46 49	43.50 361	95.90 31	99.89 28	97.85 36	99.74 34	99.78 8

CP-MVS		98.57 22	98.36 19	99.19 30	99.66 19	97.86 49	99.34 11	98.87 51	95.96 70	98.60 45	99.13 47	96.05 24	99.94 3	97.77 41	99.86 1	99.77 15

SteuartSystems-ACMMP		98.90 3	98.75 2	99.36 14	99.22 75	98.43 19	99.10 53	98.87 51	97.38 18	99.35 7	99.40 8	97.78 1	99.87 37	97.77 41	99.85 2	99.78 8
Skip Steuart: Steuart Systems R&D Blog.
DeepPCF-MVS	96.37 2	97.93 50	98.48 14	96.30 240	99.00 92	89.54 304	97.43 272	98.87 51	98.16 2	99.26 10	99.38 13	96.12 19	99.64 105	98.30 21	99.77 19	99.72 32

DTE-MVSNet		93.98 254	93.26 257	96.14 246	96.06 293	94.39 223	99.20 33	98.86 54	93.06 203	91.78 283	97.81 180	85.87 243	97.58 307	90.53 251	86.17 320	96.46 290

SD-MVS		98.64 11	98.68 3	98.53 76	99.33 46	98.36 24	98.90 78	98.85 55	97.28 22	99.72 1	99.39 9	96.63 8	97.60 306	98.17 23	99.85 2	99.64 55

test_part1				0.00 355		0.00 370	0.00 361	98.84 56				0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

v1.0		41.12 338	54.83 339	0.00 355	99.63 21	0.00 370	0.00 361	98.84 56	96.40 58	99.27 8	99.31 23	0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

test_prior3		98.22 44	97.90 45	99.19 30	99.31 51	98.22 33	97.80 249	98.84 56	96.12 65	97.89 81	98.69 97	95.96 27	99.70 95	96.89 75	99.60 51	99.65 52

test_prior				99.19 30	99.31 51	98.22 33		98.84 56					99.70 95			99.65 52

Anonymous20240529		95.10 187	94.22 199	97.75 124	99.01 91	94.26 230	98.87 86	98.83 60	85.79 329	96.64 134	98.97 68	78.73 313	99.85 42	96.27 101	94.89 209	99.12 123

abl_6		98.30 42	98.03 40	99.13 40	99.56 27	97.76 54	99.13 49	98.82 61	96.14 63	99.26 10	99.37 14	93.33 78	99.93 10	96.96 71	99.67 41	99.69 37

HPM-MVS_fast		98.38 34	98.13 37	99.12 42	99.75 3	97.86 49	99.44 4	98.82 61	94.46 142	98.94 25	99.20 38	95.16 50	99.74 89	97.58 51	99.85 2	99.77 15

APD-MVS		98.35 37	98.00 42	99.42 11	99.51 29	98.72 10	98.80 109	98.82 61	94.52 138	99.23 12	99.25 31	95.54 39	99.80 61	96.52 94	99.77 19	99.74 27
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ACMMP_Plus		98.61 14	98.30 26	99.55 3	99.62 23	98.95 6	98.82 100	98.81 64	95.80 74	99.16 16	99.47 5	95.37 42	99.92 14	97.89 34	99.75 31	99.79 5

Regformer-2		98.69 8	98.52 9	99.19 30	99.35 41	98.01 44	98.37 183	98.81 64	97.48 12	99.21 13	99.21 35	96.13 18	99.80 61	98.40 18	99.73 36	99.75 22

APD-MVS_3200maxsize		98.53 27	98.33 25	99.15 39	99.50 31	97.92 48	99.15 44	98.81 64	96.24 60	99.20 14	99.37 14	95.30 45	99.80 61	97.73 43	99.67 41	99.72 32

WR-MVS		95.15 185	94.46 190	97.22 164	96.67 250	96.45 104	98.21 201	98.81 64	94.15 146	93.16 251	97.69 187	87.51 210	98.30 274	95.29 136	88.62 295	96.90 231

mPP-MVS		98.51 28	98.26 29	99.25 26	99.75 3	98.04 42	99.28 17	98.81 64	96.24 60	98.35 57	99.23 32	95.46 40	99.94 3	97.42 59	99.81 9	99.77 15

CNVR-MVS		98.78 4	98.56 7	99.45 10	99.32 49	98.87 8	98.47 172	98.81 64	97.72 4	98.76 37	99.16 45	97.05 3	99.78 78	98.06 26	99.66 44	99.69 37

CPTT-MVS		97.72 58	97.32 66	98.92 55	99.64 20	97.10 76	99.12 51	98.81 64	92.34 235	98.09 63	99.08 57	93.01 83	99.92 14	96.06 107	99.77 19	99.75 22

SMA-MVS		98.58 19	98.25 30	99.56 2	99.51 29	99.04 4	98.95 73	98.80 71	93.67 178	99.37 6	99.52 3	96.52 10	99.89 28	98.06 26	99.81 9	99.76 21

HPM-MVS		98.36 36	98.10 38	99.13 40	99.74 7	97.82 52	99.53 1	98.80 71	94.63 135	98.61 44	98.97 68	95.13 51	99.77 83	97.65 47	99.83 8	99.79 5
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
Regformer-4		98.64 11	98.53 8	98.99 49	99.43 39	97.37 66	98.40 181	98.79 73	97.46 13	99.09 17	99.31 23	95.86 33	99.80 61	98.64 4	99.76 25	99.79 5

MP-MVS		98.33 40	98.01 41	99.28 22	99.75 3	98.18 36	99.22 29	98.79 73	96.13 64	97.92 79	99.23 32	94.54 61	99.94 3	96.74 86	99.78 16	99.73 29
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CANet		98.05 45	97.76 47	98.90 57	98.73 122	97.27 69	98.35 185	98.78 75	97.37 20	97.72 89	98.96 73	91.53 116	99.92 14	98.79 3	99.65 45	99.51 71

MP-MVS-pluss		98.31 41	97.92 44	99.49 6	99.72 11	98.88 7	98.43 177	98.78 75	94.10 148	97.69 91	99.42 7	95.25 47	99.92 14	98.09 25	99.80 11	99.67 48
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
DeepC-MVS_fast	96.70 1	98.55 24	98.34 22	99.18 34	99.25 68	98.04 42	98.50 169	98.78 75	97.72 4	98.92 30	99.28 28	95.27 46	99.82 52	97.55 54	99.77 19	99.69 37
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 55	97.60 51	98.44 83	99.12 84	95.97 122	97.75 253	98.78 75	96.89 43	98.46 49	99.22 34	93.90 75	99.68 100	94.81 147	99.52 68	99.67 48

Regformer-1		98.66 9	98.51 11	99.12 42	99.35 41	97.81 53	98.37 183	98.76 79	97.49 11	99.20 14	99.21 35	96.08 21	99.79 73	98.42 16	99.73 36	99.75 22

NCCC		98.61 14	98.35 21	99.38 12	99.28 64	98.61 13	98.45 173	98.76 79	97.82 3	98.45 53	98.93 77	96.65 7	99.83 46	97.38 61	99.41 78	99.71 34

PLC	95.07 4	97.20 89	96.78 90	98.44 83	99.29 59	96.31 112	98.14 212	98.76 79	92.41 232	96.39 161	98.31 136	94.92 55	99.78 78	94.06 166	98.77 103	99.23 107
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
DeepC-MVS	95.98 3	97.88 51	97.58 52	98.77 61	99.25 68	96.93 81	98.83 98	98.75 82	96.96 42	96.89 124	99.50 4	90.46 131	99.87 37	97.84 38	99.76 25	99.52 68
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MVS_0304		97.70 60	97.25 68	99.07 45	98.90 103	97.83 51	98.20 202	98.74 83	97.51 9	98.03 68	99.06 59	86.12 232	99.93 10	99.02 1	99.64 47	99.44 86

zzz-MVS		98.55 24	98.25 30	99.46 8	99.76 1	98.64 11	98.55 161	98.74 83	97.27 26	98.02 69	99.39 9	94.81 56	99.96 1	97.91 31	99.79 12	99.77 15

MTGPA								98.74 83

MTAPA		98.58 19	98.29 27	99.46 8	99.76 1	98.64 11	98.90 78	98.74 83	97.27 26	98.02 69	99.39 9	94.81 56	99.96 1	97.91 31	99.79 12	99.77 15

ab-mvs		96.42 118	95.71 129	98.55 73	98.63 133	96.75 89	97.88 242	98.74 83	93.84 162	96.54 144	98.18 147	85.34 253	99.75 87	95.93 112	96.35 176	99.15 119

TEST9					99.31 51	98.50 15	97.92 233	98.73 88	92.63 217	97.74 87	98.68 99	96.20 14	99.80 61

train_agg		97.97 46	97.52 56	99.33 17	99.31 51	98.50 15	97.92 233	98.73 88	92.98 207	97.74 87	98.68 99	96.20 14	99.80 61	96.59 90	99.57 57	99.68 43

test_8					99.29 59	98.44 17	97.89 241	98.72 90	92.98 207	97.70 90	98.66 102	96.20 14	99.80 61

agg_prior3		97.87 52	97.42 63	99.23 29	99.29 59	98.23 31	97.92 233	98.72 90	92.38 234	97.59 99	98.64 104	96.09 20	99.79 73	96.59 90	99.57 57	99.68 43

agg_prior1		97.95 48	97.51 57	99.28 22	99.30 56	98.38 20	97.81 248	98.72 90	93.16 201	97.57 100	98.66 102	96.14 17	99.81 54	96.63 89	99.56 63	99.66 50

agg_prior					99.30 56	98.38 20		98.72 90		97.57 100			99.81 54

无先验							97.58 265	98.72 90	91.38 260				99.87 37	93.36 181		99.60 61

WTY-MVS		97.37 81	96.92 84	98.72 63	98.86 114	96.89 85	98.31 192	98.71 95	95.26 104	97.67 92	98.56 111	92.21 97	99.78 78	95.89 113	96.85 158	99.48 78

3Dnovator+	94.38 6	97.43 75	96.78 90	99.38 12	97.83 180	98.52 14	99.37 7	98.71 95	97.09 38	92.99 258	99.13 47	89.36 144	99.89 28	96.97 69	99.57 57	99.71 34

旧先验1					99.29 59	97.48 62		98.70 97			99.09 55	95.56 37			99.47 71	99.61 58

EI-MVSNet-Vis-set		98.47 30	98.39 16	98.69 64	99.46 36	96.49 102	98.30 194	98.69 98	97.21 29	98.84 31	99.36 18	95.41 41	99.78 78	98.62 6	99.65 45	99.80 4

新几何1				99.16 37	99.34 43	98.01 44		98.69 98	90.06 287	98.13 61	98.95 75	94.60 60	99.89 28	91.97 223	99.47 71	99.59 63

API-MVS		97.41 78	97.25 68	97.91 114	98.70 126	96.80 86	98.82 100	98.69 98	94.53 137	98.11 62	98.28 138	94.50 65	99.57 119	94.12 165	99.49 69	97.37 205

EI-MVSNet-UG-set		98.41 32	98.34 22	98.61 69	99.45 37	96.32 110	98.28 196	98.68 101	97.17 32	98.74 38	99.37 14	95.25 47	99.79 73	98.57 8	99.54 66	99.73 29

Regformer-3		98.59 17	98.50 12	98.86 59	99.43 39	97.05 77	98.40 181	98.68 101	97.43 14	99.06 18	99.31 23	95.80 34	99.77 83	98.62 6	99.76 25	99.78 8

testdata				98.26 94	99.20 78	95.36 158		98.68 101	91.89 246	98.60 45	99.10 51	94.44 67	99.82 52	94.27 161	99.44 76	99.58 65

1121		97.37 81	96.77 93	99.16 37	99.34 43	97.99 47	98.19 206	98.68 101	90.14 286	98.01 72	98.97 68	94.80 58	99.87 37	93.36 181	99.46 74	99.61 58

MCST-MVS		98.65 10	98.37 18	99.48 7	99.60 24	98.87 8	98.41 180	98.68 101	97.04 39	98.52 48	98.80 88	96.78 5	99.83 46	97.93 30	99.61 50	99.74 27

PVSNet	91.96 18	96.35 120	96.15 115	96.96 181	99.17 79	92.05 270	96.08 322	98.68 101	93.69 174	97.75 86	97.80 181	88.86 161	99.69 99	94.26 162	99.01 91	99.15 119

MAR-MVS		96.91 101	96.40 107	98.45 82	98.69 128	96.90 83	98.66 145	98.68 101	92.40 233	97.07 112	97.96 163	91.54 115	99.75 87	93.68 174	98.92 94	98.69 152
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 67	99.32 49	96.62 93		98.67 108	93.27 199	97.81 83	98.97 68	95.18 49	99.83 46	93.84 170	99.46 74	99.50 73

CDPH-MVS		97.94 49	97.49 58	99.28 22	99.47 35	98.44 17	97.91 236	98.67 108	92.57 221	98.77 36	98.85 83	95.93 29	99.72 90	95.56 127	99.69 40	99.68 43

UnsupCasMVSNet_eth		90.99 300	89.92 301	94.19 309	94.08 332	89.83 299	97.13 292	98.67 108	93.69 174	85.83 323	96.19 295	75.15 330	96.74 327	89.14 282	79.41 339	96.00 304

TSAR-MVS + MP.		98.78 4	98.62 5	99.24 27	99.69 17	98.28 30	99.14 45	98.66 111	96.84 44	99.56 2	99.31 23	96.34 12	99.70 95	98.32 20	99.73 36	99.73 29

HPM-MVS++		98.58 19	98.25 30	99.55 3	99.50 31	99.08 3	98.72 130	98.66 111	97.51 9	98.15 60	98.83 85	95.70 35	99.92 14	97.53 56	99.67 41	99.66 50

test222					99.23 74	97.17 75	97.40 273	98.66 111	88.68 311	98.05 65	98.96 73	94.14 71			99.53 67	99.61 58

test11								98.66 111

XXY-MVS		95.20 184	94.45 192	97.46 154	96.75 245	96.56 98	98.86 93	98.65 115	93.30 198	93.27 248	98.27 141	84.85 260	98.87 203	94.82 146	91.26 263	96.96 221

TAPA-MVS	93.98 7	95.35 174	94.56 185	97.74 125	99.13 83	94.83 196	98.33 187	98.64 116	86.62 320	96.29 163	98.61 105	94.00 74	99.29 147	80.00 333	99.41 78	99.09 126
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
F-COLMAP		97.09 95	96.80 87	97.97 112	99.45 37	94.95 178	98.55 161	98.62 117	93.02 205	96.17 165	98.58 110	94.01 73	99.81 54	93.95 168	98.90 95	99.14 121

PAPM_NR		97.46 70	97.11 75	98.50 78	99.50 31	96.41 106	98.63 147	98.60 118	95.18 108	97.06 113	98.06 155	94.26 70	99.57 119	93.80 172	98.87 98	99.52 68

cdsmvs_eth3d_5k		23.98 341	31.98 341	0.00 355	0.00 370	0.00 370	0.00 361	98.59 119	0.00 365	0.00 367	98.61 105	90.60 130	0.00 367	0.00 364	0.00 365	0.00 365

1314		96.25 126	95.73 125	97.79 122	97.13 225	95.55 153	98.19 206	98.59 119	93.47 185	92.03 282	97.82 179	91.33 118	99.49 131	94.62 150	98.44 117	98.32 176

CVMVSNet		95.43 164	96.04 118	93.57 313	97.93 174	83.62 336	98.12 215	98.59 119	95.68 77	96.56 140	99.02 61	87.51 210	97.51 309	93.56 178	97.44 150	99.60 61

OMC-MVS		97.55 69	97.34 65	98.20 97	99.33 46	95.92 136	98.28 196	98.59 119	95.52 85	97.97 75	99.10 51	93.28 80	99.49 131	95.09 141	98.88 96	99.19 111

LTVRE_ROB	92.95 15	94.60 220	93.90 224	96.68 199	97.41 208	94.42 221	98.52 164	98.59 119	91.69 251	91.21 288	98.35 129	84.87 259	99.04 182	91.06 242	93.44 236	96.60 273
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
HSP-MVS		98.70 6	98.52 9	99.24 27	99.75 3	98.23 31	99.26 18	98.58 124	97.52 8	99.41 4	98.78 90	96.00 25	99.79 73	97.79 40	99.59 54	99.69 37

PAPR		96.84 104	96.24 113	98.65 67	98.72 125	96.92 82	97.36 279	98.57 125	93.33 195	96.67 133	97.57 198	94.30 69	99.56 121	91.05 244	98.59 110	99.47 79

HQP_MVS		96.14 127	95.90 122	96.85 187	97.42 205	94.60 216	98.80 109	98.56 126	97.28 22	95.34 174	98.28 138	87.09 216	99.03 183	96.07 105	94.27 212	96.92 224

plane_prior5								98.56 126					99.03 183	96.07 105	94.27 212	96.92 224

mvs_tets		95.41 168	95.00 155	96.65 204	95.58 311	94.42 221	99.00 66	98.55 128	95.73 76	93.21 250	98.38 126	83.45 287	98.63 221	97.09 67	94.00 223	96.91 229

pcd1.5k->3k		39.42 339	41.78 340	32.35 351	96.17 287	0.00 370	0.00 361	98.54 129	0.00 365	0.00 367	0.00 367	87.78 203	0.00 367	0.00 364	93.56 232	97.06 215

LPG-MVS_test		95.62 147	95.34 140	96.47 227	97.46 201	93.54 248	98.99 67	98.54 129	94.67 131	94.36 205	98.77 92	85.39 250	99.11 171	95.71 122	94.15 218	96.76 246

LGP-MVS_train				96.47 227	97.46 201	93.54 248		98.54 129	94.67 131	94.36 205	98.77 92	85.39 250	99.11 171	95.71 122	94.15 218	96.76 246

test12				99.18 34	99.16 80	98.19 35		98.53 132		98.07 64		95.13 51	99.72 90		99.56 63	99.63 57

CNLPA		97.45 73	97.03 80	98.73 62	99.05 86	97.44 65	98.07 221	98.53 132	95.32 102	96.80 130	98.53 112	93.32 79	99.72 90	94.31 160	99.31 84	99.02 132

jajsoiax		95.45 163	95.03 154	96.73 192	95.42 318	94.63 211	99.14 45	98.52 134	95.74 75	93.22 249	98.36 128	83.87 284	98.65 220	96.95 72	94.04 221	96.91 229

XVG-OURS		96.55 114	96.41 106	96.99 178	98.75 121	93.76 242	97.50 269	98.52 134	95.67 78	96.83 126	99.30 27	88.95 158	99.53 128	95.88 114	96.26 185	97.69 196

xiu_mvs_v1_base_debu		97.60 64	97.56 53	97.72 126	98.35 143	95.98 118	97.86 244	98.51 136	97.13 35	99.01 21	98.40 123	91.56 112	99.80 61	98.53 10	98.68 104	97.37 205

xiu_mvs_v1_base		97.60 64	97.56 53	97.72 126	98.35 143	95.98 118	97.86 244	98.51 136	97.13 35	99.01 21	98.40 123	91.56 112	99.80 61	98.53 10	98.68 104	97.37 205

xiu_mvs_v1_base_debi		97.60 64	97.56 53	97.72 126	98.35 143	95.98 118	97.86 244	98.51 136	97.13 35	99.01 21	98.40 123	91.56 112	99.80 61	98.53 10	98.68 104	97.37 205

PS-MVSNAJ		97.73 57	97.77 46	97.62 138	98.68 129	95.58 149	97.34 281	98.51 136	97.29 21	98.66 41	97.88 171	94.51 62	99.90 26	97.87 35	99.17 88	97.39 203

cascas		94.63 219	93.86 226	96.93 184	96.91 236	94.27 229	96.00 326	98.51 136	85.55 330	94.54 191	96.23 292	84.20 278	98.87 203	95.80 118	96.98 157	97.66 197

PS-MVSNAJss		96.43 117	96.26 112	96.92 186	95.84 303	95.08 170	99.16 43	98.50 141	95.87 72	93.84 235	98.34 133	94.51 62	98.61 222	96.88 78	93.45 235	97.06 215

MVS		94.67 217	93.54 246	98.08 107	96.88 238	96.56 98	98.19 206	98.50 141	78.05 348	92.69 264	98.02 157	91.07 123	99.63 108	90.09 262	98.36 121	98.04 181

XVG-OURS-SEG-HR		96.51 115	96.34 108	97.02 177	98.77 120	93.76 242	97.79 251	98.50 141	95.45 88	96.94 119	99.09 55	87.87 200	99.55 127	96.76 85	95.83 202	97.74 192

PVSNet_0	88.72 19	91.28 296	90.03 299	95.00 287	97.99 171	87.29 330	94.84 339	98.50 141	92.06 242	89.86 301	95.19 311	79.81 308	99.39 139	92.27 213	69.79 351	98.33 175

ACMH	92.88 16	94.55 224	93.95 221	96.34 238	97.63 188	93.26 255	98.81 106	98.49 145	93.43 186	89.74 302	98.53 112	81.91 293	99.08 176	93.69 173	93.30 239	96.70 255
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
xiu_mvs_v2_base		97.66 63	97.70 49	97.56 146	98.61 135	95.46 155	97.44 270	98.46 146	97.15 33	98.65 42	98.15 148	94.33 68	99.80 61	97.84 38	98.66 108	97.41 201

HQP3-MVS								98.46 146							94.18 216

HQP-MVS		95.72 140	95.40 135	96.69 196	97.20 219	94.25 231	98.05 222	98.46 146	96.43 55	94.45 195	97.73 184	86.75 222	98.96 190	95.30 134	94.18 216	96.86 238

CLD-MVS		95.62 147	95.34 140	96.46 230	97.52 198	93.75 244	97.27 286	98.46 146	95.53 84	94.42 203	98.00 161	86.21 230	98.97 187	96.25 103	94.37 210	96.66 264
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Anonymous20240521		94.80 205	94.03 214	97.11 172	96.56 253	96.46 103	99.30 14	98.44 150	92.86 213	91.21 288	97.01 248	89.59 140	98.58 227	92.03 220	89.23 281	96.30 296

XVG-ACMP-BASELINE		94.54 225	94.14 207	95.75 261	96.55 254	91.65 278	98.11 217	98.44 150	94.96 122	94.22 217	97.90 169	79.18 312	99.11 171	94.05 167	93.85 226	96.48 288

ACMP	93.49 10	95.34 175	94.98 157	96.43 231	97.67 186	93.48 250	98.73 128	98.44 150	94.94 125	92.53 269	98.53 112	84.50 269	99.14 165	95.48 130	94.00 223	96.66 264
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ACMM	93.85 9	95.69 144	95.38 139	96.61 210	97.61 190	93.84 240	98.91 77	98.44 150	95.25 105	94.28 213	98.47 118	86.04 242	99.12 167	95.50 129	93.95 225	96.87 236
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Effi-MVS+		97.12 93	96.69 96	98.39 88	98.19 157	96.72 90	97.37 277	98.43 154	93.71 171	97.65 96	98.02 157	92.20 98	99.25 149	96.87 81	97.79 143	99.19 111

anonymousdsp		95.42 166	94.91 165	96.94 183	95.10 322	95.90 139	99.14 45	98.41 155	93.75 166	93.16 251	97.46 203	87.50 212	98.41 261	95.63 126	94.03 222	96.50 286

PMMVS		96.60 110	96.33 109	97.41 157	97.90 176	93.93 237	97.35 280	98.41 155	92.84 214	97.76 85	97.45 205	91.10 122	99.20 160	96.26 102	97.91 137	99.11 124

MVSFormer		97.57 67	97.49 58	97.84 118	98.07 165	95.76 144	99.47 2	98.40 157	94.98 119	98.79 34	98.83 85	92.34 90	98.41 261	96.91 73	99.59 54	99.34 91

test_djsdf		96.00 129	95.69 131	96.93 184	95.72 307	95.49 154	99.47 2	98.40 157	94.98 119	94.58 190	97.86 172	89.16 150	98.41 261	96.91 73	94.12 220	96.88 234

OPM-MVS		95.69 144	95.33 142	96.76 191	96.16 290	94.63 211	98.43 177	98.39 159	96.64 51	95.02 180	98.78 90	85.15 255	99.05 178	95.21 140	94.20 215	96.60 273

canonicalmvs		97.67 62	97.23 70	98.98 51	98.70 126	98.38 20	99.34 11	98.39 159	96.76 46	97.67 92	97.40 207	92.26 94	99.49 131	98.28 22	96.28 184	99.08 129

DP-MVS		96.59 112	95.93 121	98.57 71	99.34 43	96.19 114	98.70 134	98.39 159	89.45 304	94.52 192	99.35 20	91.85 106	99.85 42	92.89 200	98.88 96	99.68 43

ACMH+	92.99 14	94.30 235	93.77 232	95.88 255	97.81 181	92.04 271	98.71 131	98.37 162	93.99 154	90.60 297	98.47 118	80.86 301	99.05 178	92.75 202	92.40 248	96.55 280

MSDG		95.93 132	95.30 145	97.83 119	98.90 103	95.36 158	96.83 309	98.37 162	91.32 265	94.43 202	98.73 96	90.27 135	99.60 111	90.05 265	98.82 101	98.52 161

CMPMVS	66.06 21	89.70 308	89.67 303	89.78 326	93.19 335	76.56 346	97.00 294	98.35 164	80.97 344	81.57 341	97.75 183	74.75 333	98.61 222	89.85 268	93.63 230	94.17 336
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
v7n		94.19 241	93.43 252	96.47 227	95.90 299	94.38 224	99.26 18	98.34 165	91.99 243	92.76 263	97.13 231	88.31 185	98.52 237	89.48 278	87.70 305	96.52 283

diffmvs1		97.35 83	97.07 78	98.20 97	98.25 151	96.13 116	98.61 150	98.34 165	95.47 86	97.66 95	98.01 159	92.54 88	99.30 143	96.44 97	98.29 125	99.17 117

CDS-MVSNet		96.99 98	96.69 96	97.90 115	98.05 168	95.98 118	98.20 202	98.33 167	93.67 178	96.95 117	98.49 116	93.54 76	98.42 254	95.24 139	97.74 146	99.31 95
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
casdiffmvs1		97.72 58	97.49 58	98.41 87	98.52 141	96.71 91	99.14 45	98.32 168	95.15 111	98.46 49	98.31 136	93.10 82	99.21 159	98.14 24	98.27 126	99.31 95

0601test		97.22 87	96.78 90	98.54 75	98.73 122	96.60 96	98.45 173	98.31 169	94.70 128	98.02 69	98.42 122	90.80 127	99.70 95	96.81 83	96.79 160	99.34 91

nrg030		96.28 124	95.72 126	97.96 113	96.90 237	98.15 38	99.39 5	98.31 169	95.47 86	94.42 203	98.35 129	92.09 101	98.69 215	97.50 57	89.05 283	97.04 217

TAMVS		97.02 97	96.79 89	97.70 131	98.06 167	95.31 162	98.52 164	98.31 169	93.95 157	97.05 114	98.61 105	93.49 77	98.52 237	95.33 133	97.81 142	99.29 101

EPP-MVSNet		97.46 70	97.28 67	97.99 111	98.64 132	95.38 157	99.33 13	98.31 169	93.61 181	97.19 107	99.07 58	94.05 72	99.23 151	96.89 75	98.43 119	99.37 90

UnsupCasMVSNet_bld		87.17 317	85.12 320	93.31 316	91.94 339	88.77 314	94.92 338	98.30 173	84.30 336	82.30 339	90.04 345	63.96 351	97.25 313	85.85 318	74.47 350	93.93 341

Vis-MVSNet (Re-imp)		96.87 103	96.55 102	97.83 119	98.73 122	95.46 155	99.20 33	98.30 173	94.96 122	96.60 139	98.87 82	90.05 137	98.59 225	93.67 175	98.60 109	99.46 83

TSAR-MVS + GP.		98.38 34	98.24 33	98.81 60	99.22 75	97.25 72	98.11 217	98.29 175	97.19 31	98.99 24	99.02 61	96.22 13	99.67 101	98.52 14	98.56 112	99.51 71

MS-PatchMatch		93.84 257	93.63 240	94.46 305	96.18 286	89.45 305	97.76 252	98.27 176	92.23 240	92.13 280	97.49 201	79.50 309	98.69 215	89.75 271	99.38 81	95.25 317

EI-MVSNet		95.96 130	95.83 124	96.36 235	97.93 174	93.70 247	98.12 215	98.27 176	93.70 173	95.07 178	99.02 61	92.23 96	98.54 230	94.68 148	93.46 233	96.84 239

MVSTER		96.06 128	95.72 126	97.08 175	98.23 153	95.93 129	98.73 128	98.27 176	94.86 126	95.07 178	98.09 153	88.21 187	98.54 230	96.59 90	93.46 233	96.79 243

FMVSNet2		94.47 228	93.61 242	97.04 176	98.21 154	96.43 105	98.79 114	98.27 176	92.46 222	93.50 244	97.09 234	81.16 296	98.00 290	91.09 240	91.93 253	96.70 255

FMVSNet3		94.97 194	94.26 198	97.11 172	98.18 159	96.62 93	98.56 159	98.26 180	93.67 178	94.09 224	97.10 232	84.25 275	98.01 289	92.08 216	92.14 249	96.70 255

Fast-Effi-MVS+		96.28 124	95.70 130	98.03 110	98.29 150	95.97 122	98.58 154	98.25 181	91.74 250	95.29 177	97.23 220	91.03 124	99.15 164	92.90 198	97.96 135	98.97 137

PAPM		94.95 195	94.00 217	97.78 123	97.04 228	95.65 147	96.03 325	98.25 181	91.23 270	94.19 219	97.80 181	91.27 119	98.86 205	82.61 328	97.61 148	98.84 145

v748		93.75 258	93.06 259	95.82 257	95.73 306	92.64 264	99.25 20	98.24 183	91.60 253	92.22 278	96.52 283	87.60 209	98.46 246	90.64 249	85.72 323	96.36 293

V4		94.18 243	93.52 247	96.13 247	95.89 300	94.31 226	99.23 23	98.22 184	91.42 258	92.82 261	96.89 263	87.93 197	98.52 237	91.51 235	87.81 302	95.58 314

CANet_DTU		96.96 99	96.55 102	98.21 96	98.17 161	96.07 117	97.98 229	98.21 185	97.24 28	97.13 108	98.93 77	86.88 221	99.91 23	95.00 142	99.37 82	98.66 155

v52		94.18 243	93.52 247	96.13 247	95.95 298	94.29 227	99.23 23	98.21 185	91.42 258	92.84 260	96.89 263	87.85 201	98.53 236	91.51 235	87.81 302	95.57 315

HY-MVS	93.96 8	96.82 105	96.23 114	98.57 71	98.46 142	97.00 78	98.14 212	98.21 185	93.95 157	96.72 132	97.99 162	91.58 111	99.76 85	94.51 155	96.54 167	98.95 141

casdiffmvs		97.42 76	97.12 73	98.31 92	98.35 143	96.55 100	99.05 59	98.20 188	94.97 121	97.55 102	98.11 151	92.33 92	99.18 162	97.70 45	97.85 141	99.18 115

PCF-MVS	93.45 11	94.68 216	93.43 252	98.42 86	98.62 134	96.77 88	95.48 332	98.20 188	84.63 335	93.34 247	98.32 135	88.55 180	99.81 54	84.80 324	98.96 93	98.68 153
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
v8		94.47 228	93.77 232	96.57 217	96.36 267	94.83 196	99.05 59	98.19 190	91.92 245	93.16 251	96.97 252	88.82 166	98.48 241	91.69 231	87.79 304	96.39 291

v10		94.29 236	93.55 245	96.51 224	96.39 263	94.80 201	98.99 67	98.19 190	91.35 263	93.02 257	96.99 250	88.09 192	98.41 261	90.50 258	88.41 297	96.33 295

mvs_anonymous		96.70 108	96.53 104	97.18 167	98.19 157	93.78 241	98.31 192	98.19 190	94.01 152	94.47 194	98.27 141	92.08 102	98.46 246	97.39 60	97.91 137	99.31 95

AllTest		95.24 181	94.65 181	96.99 178	99.25 68	93.21 257	98.59 152	98.18 193	91.36 261	93.52 242	98.77 92	84.67 261	99.72 90	89.70 273	97.87 139	98.02 182

TestCases				96.99 178	99.25 68	93.21 257		98.18 193	91.36 261	93.52 242	98.77 92	84.67 261	99.72 90	89.70 273	97.87 139	98.02 182

GBi-Net		94.49 226	93.80 229	96.56 218	98.21 154	95.00 172	98.82 100	98.18 193	92.46 222	94.09 224	97.07 236	81.16 296	97.95 292	92.08 216	92.14 249	96.72 251

test1		94.49 226	93.80 229	96.56 218	98.21 154	95.00 172	98.82 100	98.18 193	92.46 222	94.09 224	97.07 236	81.16 296	97.95 292	92.08 216	92.14 249	96.72 251

FMVSNet1		93.19 271	92.07 274	96.56 218	97.54 196	95.00 172	98.82 100	98.18 193	90.38 282	92.27 276	97.07 236	73.68 337	97.95 292	89.36 280	91.30 261	96.72 251

v1192		94.32 234	93.58 244	96.53 222	96.10 291	94.45 220	98.50 169	98.17 198	91.54 254	94.19 219	97.06 239	86.95 220	98.43 253	90.14 261	89.57 274	96.70 255

v1240		94.06 252	93.29 256	96.34 238	96.03 295	93.90 238	98.44 175	98.17 198	91.18 272	94.13 223	97.01 248	86.05 240	98.42 254	89.13 283	89.50 277	96.70 255

v144192		94.39 232	93.70 237	96.48 226	96.06 293	94.35 225	98.58 154	98.16 200	91.45 256	94.33 207	97.02 246	87.50 212	98.45 248	91.08 241	89.11 282	96.63 269

Fast-Effi-MVS+-dtu		95.87 134	95.85 123	95.91 253	97.74 184	91.74 277	98.69 136	98.15 201	95.56 83	94.92 181	97.68 190	88.98 156	98.79 212	93.19 186	97.78 144	97.20 213

v1921920		94.20 240	93.47 251	96.40 233	95.98 296	94.08 234	98.52 164	98.15 201	91.33 264	94.25 215	97.20 222	86.41 227	98.42 254	90.04 266	89.39 279	96.69 260

v1144		94.59 222	93.92 222	96.60 212	96.21 284	94.78 206	98.59 152	98.14 203	91.86 249	94.21 218	97.02 246	87.97 195	98.41 261	91.72 230	89.57 274	96.61 271

v7		94.69 213	94.04 213	96.62 209	96.41 262	94.79 204	98.78 116	98.13 204	91.89 246	94.30 211	97.16 223	88.13 191	98.45 248	91.96 224	89.65 273	96.61 271

diffmvs		97.03 96	96.75 94	97.88 116	98.14 162	95.25 164	98.54 163	98.13 204	95.17 109	97.03 115	97.94 165	91.83 107	99.30 143	96.01 110	97.94 136	99.11 124

IterMVS-LS		95.46 162	95.21 148	96.22 243	98.12 163	93.72 246	98.32 191	98.13 204	93.71 171	94.26 214	97.31 215	92.24 95	98.10 283	94.63 149	90.12 268	96.84 239
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1141		94.75 209	94.11 211	96.67 202	96.27 282	94.86 184	98.69 136	98.12 207	92.43 230	94.31 209	96.94 256	88.78 171	98.48 241	92.63 205	88.85 292	96.67 261

divwei89l23v2f112		94.76 207	94.12 210	96.67 202	96.28 280	94.85 185	98.69 136	98.12 207	92.44 229	94.29 212	96.94 256	88.85 163	98.48 241	92.67 203	88.79 294	96.67 261

v1		94.75 209	94.11 211	96.69 196	96.27 282	94.87 183	98.69 136	98.12 207	92.43 230	94.32 208	96.94 256	88.71 175	98.54 230	92.66 204	88.84 293	96.67 261

EU-MVSNet		93.66 259	94.14 207	92.25 322	95.96 297	83.38 337	98.52 164	98.12 207	94.69 129	92.61 266	98.13 150	87.36 214	96.39 335	91.82 226	90.00 270	96.98 220

IterMVS		94.09 249	93.85 227	94.80 295	97.99 171	90.35 296	97.18 290	98.12 207	93.68 176	92.46 273	97.34 212	84.05 280	97.41 311	92.51 210	91.33 260	96.62 270
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
semantic-postprocess				94.85 292	97.98 173	90.56 294		98.11 212	93.75 166	92.58 267	97.48 202	83.91 282	97.41 311	92.48 211	91.30 261	96.58 275

v1neww		94.83 200	94.22 199	96.68 199	96.39 263	94.85 185	98.87 86	98.11 212	92.45 227	94.45 195	97.06 239	88.82 166	98.54 230	92.93 195	88.91 288	96.65 266

v7new		94.83 200	94.22 199	96.68 199	96.39 263	94.85 185	98.87 86	98.11 212	92.45 227	94.45 195	97.06 239	88.82 166	98.54 230	92.93 195	88.91 288	96.65 266

v6		94.83 200	94.21 202	96.69 196	96.36 267	94.85 185	98.87 86	98.11 212	92.46 222	94.44 201	97.05 243	88.76 172	98.57 228	92.95 194	88.92 287	96.65 266

COLMAP_ROB	93.27 12	95.33 176	94.87 167	96.71 193	99.29 59	93.24 256	98.58 154	98.11 212	89.92 292	93.57 240	99.10 51	86.37 228	99.79 73	90.78 246	98.10 132	97.09 214
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Effi-MVS+-dtu		96.29 122	96.56 101	95.51 265	97.89 177	90.22 297	98.80 109	98.10 217	96.57 53	96.45 160	96.66 276	90.81 125	98.91 197	95.72 120	97.99 134	97.40 202

mvs-test1		96.60 110	96.68 98	96.37 234	97.89 177	91.81 273	98.56 159	98.10 217	96.57 53	96.52 146	97.94 165	90.81 125	99.45 136	95.72 120	98.01 133	97.86 189

1112_ss		96.63 109	96.00 120	98.50 78	98.56 137	96.37 107	98.18 210	98.10 217	92.92 209	94.84 183	98.43 120	92.14 99	99.58 118	94.35 158	96.51 168	99.56 67

V42		94.78 206	94.14 207	96.70 195	96.33 274	95.22 165	98.97 71	98.09 220	92.32 237	94.31 209	97.06 239	88.39 184	98.55 229	92.90 198	88.87 290	96.34 294

v2v482		94.69 213	94.03 214	96.65 204	96.17 287	94.79 204	98.67 143	98.08 221	92.72 216	94.00 229	97.16 223	87.69 207	98.45 248	92.91 197	88.87 290	96.72 251

MVS_Test		97.28 85	97.00 81	98.13 103	98.33 148	95.97 122	98.74 125	98.07 222	94.27 145	98.44 54	98.07 154	92.48 89	99.26 148	96.43 98	98.19 129	99.16 118

Test_1112_low_res		96.34 121	95.66 133	98.36 89	98.56 137	95.94 126	97.71 255	98.07 222	92.10 241	94.79 187	97.29 216	91.75 108	99.56 121	94.17 163	96.50 169	99.58 65

alignmvs		97.56 68	97.07 78	99.01 48	98.66 130	98.37 23	98.83 98	98.06 224	96.74 47	98.00 74	97.65 191	90.80 127	99.48 135	98.37 19	96.56 166	99.19 111

testing_2		90.61 304	88.50 311	96.95 182	90.08 345	95.57 150	97.69 257	98.06 224	93.02 205	76.55 346	92.48 341	61.18 352	98.44 251	95.45 131	91.98 252	96.84 239

RPSCF		94.87 199	95.40 135	93.26 317	98.89 111	82.06 342	98.33 187	98.06 224	90.30 283	96.56 140	99.26 30	87.09 216	99.49 131	93.82 171	96.32 178	98.24 177

Test4		92.21 280	90.34 296	97.82 121	92.83 337	95.87 142	97.94 232	98.05 227	94.50 139	82.12 340	94.48 318	59.54 353	98.54 230	95.39 132	98.22 127	99.06 131

pm-mvs1		93.94 255	93.06 259	96.59 213	96.49 258	95.16 166	98.95 73	98.03 228	92.32 237	91.08 291	97.84 175	84.54 268	98.41 261	92.16 214	86.13 322	96.19 300

v148		94.29 236	93.76 234	95.91 253	96.10 291	92.93 261	98.58 154	97.97 229	92.59 220	93.47 245	96.95 254	88.53 181	98.32 270	92.56 207	87.06 313	96.49 287

IS-MVSNet		97.22 87	96.88 85	98.25 95	98.85 116	96.36 108	99.19 35	97.97 229	95.39 91	97.23 106	98.99 67	91.11 121	98.93 195	94.60 151	98.59 110	99.47 79

pmmvs6		91.77 292	90.63 293	95.17 283	94.69 329	91.24 283	98.67 143	97.92 231	86.14 324	89.62 303	97.56 200	75.79 328	98.34 268	90.75 247	84.56 327	95.94 306

jason		97.32 84	97.08 77	98.06 109	97.45 204	95.59 148	97.87 243	97.91 232	94.79 127	98.55 47	98.83 85	91.12 120	99.23 151	97.58 51	99.60 51	99.34 91
jason: jason.
ppachtmachnet_test		93.22 269	92.63 267	94.97 288	95.45 316	90.84 286	96.88 305	97.88 233	90.60 277	92.08 281	97.26 217	88.08 193	97.86 302	85.12 323	90.33 267	96.22 298

tpm cat1		93.36 263	92.80 263	95.07 286	97.58 193	87.97 325	96.76 310	97.86 234	82.17 342	93.53 241	96.04 299	86.13 231	99.13 166	89.24 281	95.87 201	98.10 180

EG-PatchMatch MVS		91.13 297	90.12 298	94.17 310	94.73 328	89.00 313	98.13 214	97.81 235	89.22 308	85.32 326	96.46 284	67.71 346	98.42 254	87.89 306	93.82 227	95.08 320

BH-untuned		95.95 131	95.72 126	96.65 204	98.55 139	92.26 267	98.23 199	97.79 236	93.73 169	94.62 189	98.01 159	88.97 157	99.00 186	93.04 191	98.51 113	98.68 153

lupinMVS		97.44 74	97.22 71	98.12 104	98.07 165	95.76 144	97.68 258	97.76 237	94.50 139	98.79 34	98.61 105	92.34 90	99.30 143	97.58 51	99.59 54	99.31 95

VDDNet		95.36 173	94.53 186	97.86 117	98.10 164	95.13 168	98.85 94	97.75 238	90.46 279	98.36 56	99.39 9	73.27 338	99.64 105	97.98 29	96.58 165	98.81 146

ADS-MVSNet		95.00 190	94.45 192	96.63 207	98.00 169	91.91 272	96.04 323	97.74 239	90.15 284	96.47 158	96.64 278	87.89 198	98.96 190	90.08 263	97.06 154	99.02 132

tpmvs		94.60 220	94.36 195	95.33 280	97.46 201	88.60 318	96.88 305	97.68 240	91.29 267	93.80 236	96.42 287	88.58 177	99.24 150	91.06 242	96.04 199	98.17 178

pmmvs4		94.69 213	93.99 219	96.81 189	95.74 305	95.94 126	97.40 273	97.67 241	90.42 281	93.37 246	97.59 196	89.08 152	98.20 279	92.97 193	91.67 257	96.30 296

our_test_3		93.65 261	93.30 255	94.69 297	95.45 316	89.68 303	96.91 299	97.65 242	91.97 244	91.66 285	96.88 265	89.67 139	97.93 295	88.02 304	91.49 259	96.48 288

MVP-Stereo		94.28 238	93.92 222	95.35 279	94.95 324	92.60 265	97.97 230	97.65 242	91.61 252	90.68 296	97.09 234	86.32 229	98.42 254	89.70 273	99.34 83	95.02 322
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
Patchmatch-test1		95.32 177	94.97 159	96.35 236	97.67 186	91.29 282	97.33 282	97.60 244	94.68 130	96.92 122	96.95 254	83.97 281	98.50 240	91.33 239	98.32 123	99.25 105

GA-MVS		94.81 204	94.03 214	97.14 169	97.15 224	93.86 239	96.76 310	97.58 245	94.00 153	94.76 188	97.04 244	80.91 299	98.48 241	91.79 227	96.25 186	99.09 126

test20.03		90.89 301	90.38 295	92.43 320	93.48 334	88.14 324	98.33 187	97.56 246	93.40 193	87.96 313	96.71 275	80.69 303	94.13 343	79.15 336	86.17 320	95.01 323

CR-MVSNet		94.76 207	94.15 206	96.59 213	97.00 229	93.43 251	94.96 336	97.56 246	92.46 222	96.93 120	96.24 290	88.15 189	97.88 300	87.38 307	96.65 163	98.46 164

Patchmtry		93.22 269	92.35 271	95.84 256	96.77 242	93.09 260	94.66 342	97.56 246	87.37 318	92.90 259	96.24 290	88.15 189	97.90 296	87.37 308	90.10 269	96.53 282

tpmrst		95.63 146	95.69 131	95.44 271	97.54 196	88.54 320	96.97 295	97.56 246	93.50 184	97.52 103	96.93 260	89.49 141	99.16 163	95.25 138	96.42 171	98.64 157

FMVSNet5		91.81 291	90.92 285	94.49 302	97.21 218	92.09 269	98.00 228	97.55 250	89.31 307	90.86 294	95.61 310	74.48 334	95.32 339	85.57 319	89.70 272	96.07 303

testgi		93.06 273	92.45 270	94.88 291	96.43 261	89.90 298	98.75 121	97.54 251	95.60 81	91.63 286	97.91 168	74.46 335	97.02 316	86.10 315	93.67 228	97.72 194

v18		92.10 282	90.97 282	95.50 266	96.34 270	94.85 185	98.82 100	97.52 252	89.99 288	85.31 328	93.26 326	88.90 160	96.92 318	88.82 289	79.77 337	94.73 325

v17		92.08 283	90.94 283	95.48 268	96.34 270	94.83 196	98.81 106	97.52 252	89.95 290	85.32 326	93.24 327	88.91 159	96.91 319	88.76 290	79.63 338	94.71 327

PatchmatchNet		95.71 142	95.52 134	96.29 241	97.58 193	90.72 290	96.84 308	97.52 252	94.06 150	97.08 110	96.96 253	89.24 148	98.90 200	92.03 220	98.37 120	99.26 104
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v16		92.08 283	90.94 283	95.49 267	96.38 266	94.84 194	98.81 106	97.51 255	89.94 291	85.25 329	93.28 325	88.86 161	96.91 319	88.70 291	79.78 336	94.72 326

V14		91.93 286	90.76 288	95.42 276	96.33 274	94.81 200	98.77 117	97.51 255	89.86 294	85.09 331	93.13 328	88.80 170	96.83 323	88.32 296	79.06 342	94.60 332

MDA-MVSNet-bldmvs		89.97 307	88.35 313	94.83 294	95.21 321	91.34 280	97.64 261	97.51 255	88.36 313	71.17 352	96.13 297	79.22 311	96.63 332	83.65 325	86.27 319	96.52 283

v15		91.94 285	90.77 287	95.43 273	96.31 278	94.83 196	98.77 117	97.50 258	89.92 292	85.13 330	93.08 330	88.76 172	96.86 321	88.40 295	79.10 340	94.61 331

v13		91.88 289	90.69 291	95.43 273	96.33 274	94.78 206	98.75 121	97.50 258	89.68 299	84.93 335	92.98 334	88.84 164	96.83 323	88.14 299	79.09 341	94.69 328

v12		91.89 288	90.70 290	95.43 273	96.31 278	94.80 201	98.76 120	97.50 258	89.76 296	84.95 334	93.00 333	88.82 166	96.82 325	88.23 298	79.00 344	94.68 330

V9		91.91 287	90.73 289	95.45 270	96.32 277	94.80 201	98.77 117	97.50 258	89.81 295	85.03 333	93.08 330	88.76 172	96.86 321	88.24 297	79.03 343	94.69 328

USDC		93.33 266	92.71 265	95.21 281	96.83 241	90.83 287	96.91 299	97.50 258	93.84 162	90.72 295	98.14 149	77.69 319	98.82 209	89.51 277	93.21 242	95.97 305

ITE_SJBPF				95.44 271	97.42 205	91.32 281		97.50 258	95.09 116	93.59 238	98.35 129	81.70 294	98.88 202	89.71 272	93.39 237	96.12 301

Patchmatch-test		94.42 230	93.68 239	96.63 207	97.60 191	91.76 275	94.83 340	97.49 264	89.45 304	94.14 222	97.10 232	88.99 153	98.83 208	85.37 322	98.13 131	99.29 101

YYNet1		90.70 303	89.39 304	94.62 300	94.79 327	90.65 292	97.20 288	97.46 265	87.54 317	72.54 350	95.74 304	86.51 225	96.66 331	86.00 316	86.76 318	96.54 281

MDA-MVSNet_test_wron		90.71 302	89.38 305	94.68 298	94.83 326	90.78 289	97.19 289	97.46 265	87.60 316	72.41 351	95.72 307	86.51 225	96.71 330	85.92 317	86.80 317	96.56 279

BH-RMVSNet		95.92 133	95.32 143	97.69 132	98.32 149	94.64 210	98.19 206	97.45 267	94.56 136	96.03 168	98.61 105	85.02 256	99.12 167	90.68 248	99.06 90	99.30 99

MIMVSNet1		89.67 309	88.28 314	93.82 311	92.81 338	91.08 285	98.01 226	97.45 267	87.95 314	87.90 314	95.87 303	67.63 347	94.56 342	78.73 338	88.18 299	95.83 308

v11		91.85 290	90.68 292	95.36 278	96.34 270	94.74 208	98.80 109	97.43 269	89.60 302	85.09 331	93.03 332	88.53 181	96.75 326	87.37 308	79.96 335	94.58 333

OurMVSNet-221017-0		94.21 239	94.00 217	94.85 292	95.60 310	89.22 309	98.89 82	97.43 269	95.29 103	92.18 279	98.52 115	82.86 289	98.59 225	93.46 179	91.76 256	96.74 248

BH-w/o		95.38 170	95.08 153	96.26 242	98.34 147	91.79 274	97.70 256	97.43 269	92.87 212	94.24 216	97.22 221	88.66 176	98.84 206	91.55 233	97.70 147	98.16 179

VDD-MVS		95.82 137	95.23 147	97.61 143	98.84 117	93.98 236	98.68 140	97.40 272	95.02 118	97.95 76	99.34 21	74.37 336	99.78 78	98.64 4	96.80 159	99.08 129

Gipuma		78.40 325	76.75 326	83.38 339	95.54 312	80.43 343	79.42 359	97.40 272	64.67 353	73.46 349	80.82 354	45.65 357	93.14 348	66.32 352	87.43 307	76.56 358
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_normal		94.72 212	93.59 243	98.11 105	95.30 320	95.95 125	97.91 236	97.39 274	94.64 134	85.70 324	95.88 302	80.52 304	99.36 141	96.69 87	98.30 124	99.01 135

LP		91.12 298	89.99 300	94.53 301	96.35 269	88.70 316	93.86 347	97.35 275	84.88 333	90.98 292	94.77 316	84.40 270	97.43 310	75.41 344	91.89 255	97.47 199

DI_MVS_plusplus_test		94.74 211	93.62 241	98.09 106	95.34 319	95.92 136	98.09 220	97.34 276	94.66 133	85.89 321	95.91 301	80.49 305	99.38 140	96.66 88	98.22 127	98.97 137

PatchFormer-LS_test		95.47 161	95.27 146	96.08 249	97.59 192	90.66 291	98.10 219	97.34 276	93.98 155	96.08 166	96.15 296	87.65 208	99.12 167	95.27 137	95.24 207	98.44 166

tpmp4_e23		93.91 256	93.42 254	95.38 277	97.62 189	88.59 319	97.52 268	97.34 276	87.94 315	94.17 221	96.79 272	82.91 288	99.05 178	90.62 250	95.91 200	98.50 162

new-patchmatchnet		88.50 315	87.45 316	91.67 324	90.31 344	85.89 333	97.16 291	97.33 279	89.47 303	83.63 338	92.77 338	76.38 325	95.06 341	82.70 327	77.29 346	94.06 339

ADS-MVSNet2		94.58 223	94.40 194	95.11 285	98.00 169	88.74 315	96.04 323	97.30 280	90.15 284	96.47 158	96.64 278	87.89 198	97.56 308	90.08 263	97.06 154	99.02 132

MDTV_nov1_ep13			95.40 135		97.48 199	88.34 322	96.85 307	97.29 281	93.74 168	97.48 104	97.26 217	89.18 149	99.05 178	91.92 225	97.43 151

pmmvs5		93.65 261	92.97 261	95.68 262	95.49 314	92.37 266	98.20 202	97.28 282	89.66 300	92.58 267	97.26 217	82.14 291	98.09 285	93.18 187	90.95 264	96.58 275

EPNet_dtu		95.21 183	94.95 160	95.99 250	96.17 287	90.45 295	98.16 211	97.27 283	96.77 45	93.14 254	98.33 134	90.34 133	98.42 254	85.57 319	98.81 102	99.09 126
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Anonymous20231206		91.66 293	91.10 281	93.33 315	94.02 333	87.35 329	98.58 154	97.26 284	90.48 278	90.16 299	96.31 288	83.83 285	96.53 333	79.36 335	89.90 271	96.12 301

RPMNet		92.52 277	91.17 280	96.59 213	97.00 229	93.43 251	94.96 336	97.26 284	82.27 341	96.93 120	92.12 344	86.98 219	97.88 300	76.32 342	96.65 163	98.46 164

test_0402		91.32 295	90.27 297	94.48 303	96.60 251	91.12 284	98.50 169	97.22 286	86.10 325	88.30 312	96.98 251	77.65 321	97.99 291	78.13 339	92.94 244	94.34 334

dp		94.15 246	93.90 224	94.90 290	97.31 212	86.82 332	96.97 295	97.19 287	91.22 271	96.02 169	96.61 280	85.51 249	99.02 185	90.00 267	94.30 211	98.85 143

thres200		95.25 180	94.57 184	97.28 163	98.81 118	94.92 179	98.20 202	97.11 288	95.24 107	96.54 144	96.22 294	84.58 263	99.53 128	87.93 305	96.50 169	97.39 203

PatchT		93.06 273	91.97 275	96.35 236	96.69 248	92.67 263	94.48 343	97.08 289	86.62 320	97.08 110	92.23 343	87.94 196	97.90 296	78.89 337	96.69 161	98.49 163

TDRefinement		91.06 299	89.68 302	95.21 281	85.35 352	91.49 279	98.51 168	97.07 290	91.47 255	88.83 310	97.84 175	77.31 323	99.09 175	92.79 201	77.98 345	95.04 321

LF4IMVS		93.14 272	92.79 264	94.20 308	95.88 301	88.67 317	97.66 260	97.07 290	93.81 164	91.71 284	97.65 191	77.96 318	98.81 210	91.47 237	91.92 254	95.12 318

Anonymous202405211		95.28 179	94.49 187	97.67 134	99.00 92	93.75 244	98.70 134	97.04 292	90.66 276	96.49 157	98.80 88	78.13 316	99.83 46	96.21 104	95.36 206	99.44 86

MIMVSNet		93.26 268	92.21 273	96.41 232	97.73 185	93.13 259	95.65 331	97.03 293	91.27 269	94.04 227	96.06 298	75.33 329	97.19 314	86.56 312	96.23 187	98.92 142

EPNet		97.28 85	96.87 86	98.51 77	94.98 323	96.14 115	98.90 78	97.02 294	98.28 1	95.99 170	99.11 49	91.36 117	99.89 28	96.98 68	99.19 87	99.50 73
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TR-MVS		94.94 197	94.20 203	97.17 168	97.75 183	94.14 233	97.59 264	97.02 294	92.28 239	95.75 172	97.64 193	83.88 283	98.96 190	89.77 269	96.15 190	98.40 167

view600		95.60 149	94.93 161	97.62 138	99.05 86	94.85 185	99.09 54	97.01 296	95.36 96	96.52 146	97.37 208	84.55 264	99.59 112	89.07 284	96.39 172	98.40 167

view800		95.60 149	94.93 161	97.62 138	99.05 86	94.85 185	99.09 54	97.01 296	95.36 96	96.52 146	97.37 208	84.55 264	99.59 112	89.07 284	96.39 172	98.40 167

conf0.05thres1000		95.60 149	94.93 161	97.62 138	99.05 86	94.85 185	99.09 54	97.01 296	95.36 96	96.52 146	97.37 208	84.55 264	99.59 112	89.07 284	96.39 172	98.40 167

tfpn		95.60 149	94.93 161	97.62 138	99.05 86	94.85 185	99.09 54	97.01 296	95.36 96	96.52 146	97.37 208	84.55 264	99.59 112	89.07 284	96.39 172	98.40 167

JIA-IIPM		93.35 264	92.49 269	95.92 252	96.48 259	90.65 292	95.01 335	96.96 300	85.93 327	96.08 166	87.33 348	87.70 206	98.78 213	91.35 238	95.58 204	98.34 174

pmmvs-eth3d		90.36 305	89.05 308	94.32 307	91.10 342	92.12 268	97.63 263	96.95 301	88.86 310	84.91 336	93.13 328	78.32 315	96.74 327	88.70 291	81.81 332	94.09 338

tfpn200view9		95.32 177	94.62 182	97.43 156	98.94 101	94.98 175	98.68 140	96.93 302	95.33 100	96.55 142	96.53 281	84.23 276	99.56 121	88.11 300	96.29 180	97.76 190

thres400		95.38 170	94.62 182	97.65 137	98.94 101	94.98 175	98.68 140	96.93 302	95.33 100	96.55 142	96.53 281	84.23 276	99.56 121	88.11 300	96.29 180	98.40 167

tfpn111		95.43 164	94.74 177	97.51 148	98.98 96	94.92 179	98.87 86	96.90 304	95.38 92	96.61 136	96.88 265	84.29 271	99.59 112	88.43 294	96.32 178	98.02 182

conf200view11		95.40 169	94.70 179	97.50 153	98.98 96	94.92 179	98.87 86	96.90 304	95.38 92	96.61 136	96.88 265	84.29 271	99.56 121	88.11 300	96.29 180	98.02 182

thres100view900		95.38 170	94.70 179	97.41 157	98.98 96	94.92 179	98.87 86	96.90 304	95.38 92	96.61 136	96.88 265	84.29 271	99.56 121	88.11 300	96.29 180	97.76 190

thres600view7		95.49 160	94.77 175	97.67 134	98.98 96	95.02 171	98.85 94	96.90 304	95.38 92	96.63 135	96.90 262	84.29 271	99.59 112	88.65 293	96.33 177	98.40 167

CostFormer		94.95 195	94.73 178	95.60 264	97.28 213	89.06 311	97.53 267	96.89 308	89.66 300	96.82 128	96.72 274	86.05 240	98.95 194	95.53 128	96.13 191	98.79 147

new_pmnet		90.06 306	89.00 309	93.22 318	94.18 330	88.32 323	96.42 321	96.89 308	86.19 323	85.67 325	93.62 323	77.18 324	97.10 315	81.61 330	89.29 280	94.23 335

OpenMVS_ROB	86.42 20	89.00 311	87.43 317	93.69 312	93.08 336	89.42 306	97.91 236	96.89 308	78.58 347	85.86 322	94.69 317	69.48 343	98.29 276	77.13 340	93.29 240	93.36 343

tpm2		94.19 241	93.76 234	95.46 269	97.23 216	89.04 312	97.31 284	96.85 311	87.08 319	96.21 164	96.79 272	83.75 286	98.74 214	92.43 212	96.23 187	98.59 159

TransMVSNet (Re)		92.67 275	91.51 279	96.15 245	96.58 252	94.65 209	98.90 78	96.73 312	90.86 275	89.46 305	97.86 172	85.62 247	98.09 285	86.45 313	81.12 333	95.71 311

ambc				89.49 327	86.66 351	75.78 348	92.66 349	96.72 313		86.55 319	92.50 340	46.01 356	97.90 296	90.32 259	82.09 329	94.80 324

LCM-MVSNet		78.70 324	76.24 328	86.08 334	77.26 362	71.99 354	94.34 344	96.72 313	61.62 355	76.53 347	89.33 346	33.91 363	92.78 349	81.85 329	74.60 349	93.46 342

TinyColmap		92.31 279	91.53 278	94.65 299	96.92 234	89.75 300	96.92 297	96.68 315	90.45 280	89.62 303	97.85 174	76.06 327	98.81 210	86.74 311	92.51 247	95.41 316

Baseline_NR-MVSNet		94.35 233	93.81 228	95.96 251	96.20 285	94.05 235	98.61 150	96.67 316	91.44 257	93.85 234	97.60 195	88.57 178	98.14 281	94.39 156	86.93 314	95.68 312

SixPastTwentyTwo		93.34 265	92.86 262	94.75 296	95.67 308	89.41 307	98.75 121	96.67 316	93.89 159	90.15 300	98.25 143	80.87 300	98.27 277	90.90 245	90.64 265	96.57 277

test2356		88.68 314	88.61 310	88.87 328	89.90 346	78.23 344	95.11 334	96.66 318	88.66 312	89.06 308	94.33 322	73.14 339	92.56 350	75.56 343	95.11 208	95.81 309

DWT-MVSNet_test		94.82 203	94.36 195	96.20 244	97.35 210	90.79 288	98.34 186	96.57 319	92.91 210	95.33 176	96.44 286	82.00 292	99.12 167	94.52 154	95.78 203	98.70 151

testus		88.91 312	89.08 307	88.40 329	91.39 340	76.05 347	96.56 316	96.48 320	89.38 306	89.39 306	95.17 313	70.94 341	93.56 346	77.04 341	95.41 205	95.61 313

1111		84.94 321	84.30 322	86.86 332	87.59 348	75.10 349	96.63 313	96.43 321	82.53 339	80.75 343	92.91 336	68.94 344	93.79 344	68.24 350	84.66 326	91.70 345

.test1245		73.05 329	76.31 327	63.27 350	87.59 348	75.10 349	96.63 313	96.43 321	82.53 339	80.75 343	92.91 336	68.94 344	93.79 344	68.24 350	12.72 362	20.91 362

test1235678		86.26 320	85.81 319	87.62 331	86.97 350	75.00 351	96.55 318	96.32 323	86.08 326	81.32 342	92.98 334	73.10 340	92.05 351	71.64 347	87.32 309	95.81 309

LFMVS		95.86 135	94.98 157	98.47 81	98.87 113	96.32 110	98.84 97	96.02 324	93.40 193	98.62 43	99.20 38	74.99 331	99.63 108	97.72 44	97.20 153	99.46 83

IB-MVS	91.98 17	93.27 267	91.97 275	97.19 166	97.47 200	93.41 253	97.09 293	95.99 325	93.32 196	92.47 272	95.73 305	78.06 317	99.53 128	94.59 152	82.98 328	98.62 158
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		94.08 250	93.51 249	95.80 258	95.53 313	92.89 262	97.38 275	95.97 326	95.11 113	92.51 271	96.66 276	87.71 204	96.94 317	87.03 310	93.67 228	97.57 198

FPMVS		77.62 327	77.14 325	79.05 342	79.25 358	60.97 361	95.79 329	95.94 327	65.96 352	67.93 354	94.40 319	37.73 360	88.88 356	68.83 349	88.46 296	87.29 349

Patchmatch-RL test		91.49 294	90.85 286	93.41 314	91.37 341	84.40 334	92.81 348	95.93 328	91.87 248	87.25 315	94.87 315	88.99 153	96.53 333	92.54 209	82.00 330	99.30 99

tpm		94.13 247	93.80 229	95.12 284	96.50 257	87.91 326	97.44 270	95.89 329	92.62 218	96.37 162	96.30 289	84.13 279	98.30 274	93.24 184	91.66 258	99.14 121

conf0.01		95.56 153	94.84 169	97.72 126	98.90 103	95.93 129	99.17 36	95.70 330	93.42 187	96.50 151	97.16 223	86.12 232	99.22 153	90.51 252	96.06 193	98.02 182

conf0.002		95.56 153	94.84 169	97.72 126	98.90 103	95.93 129	99.17 36	95.70 330	93.42 187	96.50 151	97.16 223	86.12 232	99.22 153	90.51 252	96.06 193	98.02 182

thresconf0.02		95.50 156	94.84 169	97.51 148	98.90 103	95.93 129	99.17 36	95.70 330	93.42 187	96.50 151	97.16 223	86.12 232	99.22 153	90.51 252	96.06 193	97.37 205

tfpn_n400		95.50 156	94.84 169	97.51 148	98.90 103	95.93 129	99.17 36	95.70 330	93.42 187	96.50 151	97.16 223	86.12 232	99.22 153	90.51 252	96.06 193	97.37 205

tfpnconf		95.50 156	94.84 169	97.51 148	98.90 103	95.93 129	99.17 36	95.70 330	93.42 187	96.50 151	97.16 223	86.12 232	99.22 153	90.51 252	96.06 193	97.37 205

tfpnview11		95.50 156	94.84 169	97.51 148	98.90 103	95.93 129	99.17 36	95.70 330	93.42 187	96.50 151	97.16 223	86.12 232	99.22 153	90.51 252	96.06 193	97.37 205

LCM-MVSNet-Re		95.22 182	95.32 143	94.91 289	98.18 159	87.85 327	98.75 121	95.66 336	95.11 113	88.96 309	96.85 269	90.26 136	97.65 304	95.65 125	98.44 117	99.22 108

test12356		83.47 322	83.37 323	83.78 338	84.43 353	70.09 356	95.12 333	95.60 337	82.98 337	78.89 345	92.43 342	64.99 349	91.41 353	70.36 348	85.55 325	89.82 347

tfpn1000		95.72 140	95.11 151	97.58 144	99.00 92	95.73 146	99.24 21	95.49 338	94.08 149	96.87 125	97.45 205	85.81 244	99.30 143	91.78 228	96.22 189	97.71 195

tfpn_ndepth		95.53 155	94.90 166	97.39 162	98.96 100	95.88 141	99.05 59	95.27 339	93.80 165	96.95 117	96.93 260	85.53 248	99.40 137	91.54 234	96.10 192	96.89 232

test-LLR		95.10 187	94.87 167	95.80 258	96.77 242	89.70 301	96.91 299	95.21 340	95.11 113	94.83 185	95.72 307	87.71 204	98.97 187	93.06 189	98.50 114	98.72 149

test-mter		94.08 250	93.51 249	95.80 258	96.77 242	89.70 301	96.91 299	95.21 340	92.89 211	94.83 185	95.72 307	77.69 319	98.97 187	93.06 189	98.50 114	98.72 149

PM-MVS		87.77 316	86.55 318	91.40 325	91.03 343	83.36 338	96.92 297	95.18 342	91.28 268	86.48 320	93.42 324	53.27 354	96.74 327	89.43 279	81.97 331	94.11 337

DeepMVS_CX				86.78 333	97.09 227	72.30 353		95.17 343	75.92 349	84.34 337	95.19 311	70.58 342	95.35 338	79.98 334	89.04 284	92.68 344

K. test v3		92.55 276	91.91 277	94.48 303	95.64 309	89.24 308	99.07 58	94.88 344	94.04 151	86.78 317	97.59 196	77.64 322	97.64 305	92.08 216	89.43 278	96.57 277

TESTMET0.1,1		94.18 243	93.69 238	95.63 263	96.92 234	89.12 310	96.91 299	94.78 345	93.17 200	94.88 182	96.45 285	78.52 314	98.92 196	93.09 188	98.50 114	98.85 143

pmmvs3		86.67 319	84.86 321	92.11 323	88.16 347	87.19 331	96.63 313	94.75 346	79.88 346	87.22 316	92.75 339	66.56 348	95.20 340	81.24 331	76.56 348	93.96 340

testmv		78.74 323	77.35 324	82.89 340	78.16 361	69.30 357	95.87 327	94.65 347	81.11 343	70.98 353	87.11 349	46.31 355	90.42 354	65.28 353	76.72 347	88.95 348

door								94.64 348

door-mid								94.37 349

DSMNet-mixed		92.52 277	92.58 268	92.33 321	94.15 331	82.65 340	98.30 194	94.26 350	89.08 309	92.65 265	95.73 305	85.01 257	95.76 337	86.24 314	97.76 145	98.59 159

no-one		74.41 328	70.76 330	85.35 336	79.88 357	76.83 345	94.68 341	94.22 351	80.33 345	63.81 355	79.73 355	35.45 362	93.36 347	71.78 346	36.99 359	85.86 352

MTMP							98.89 82	94.14 352

testpf		88.74 313	89.09 306	87.69 330	95.78 304	83.16 339	84.05 358	94.13 353	85.22 332	90.30 298	94.39 320	74.92 332	95.80 336	89.77 269	93.28 241	84.10 353

PMVS	61.03 23	65.95 333	63.57 335	73.09 347	57.90 366	51.22 366	85.05 357	93.93 354	54.45 357	44.32 362	83.57 350	13.22 366	89.15 355	58.68 357	81.00 334	78.91 357
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PMMVS2		77.95 326	75.44 329	85.46 335	82.54 354	74.95 352	94.23 345	93.08 355	72.80 351	74.68 348	87.38 347	36.36 361	91.56 352	73.95 345	63.94 352	89.87 346

MVS-HIRNet		89.46 310	88.40 312	92.64 319	97.58 193	82.15 341	94.16 346	93.05 356	75.73 350	90.90 293	82.52 351	79.42 310	98.33 269	83.53 326	98.68 104	97.43 200

EPMVS		94.99 191	94.48 188	96.52 223	97.22 217	91.75 276	97.23 287	91.66 357	94.11 147	97.28 105	96.81 271	85.70 246	98.84 206	93.04 191	97.28 152	98.97 137

lessismore_v0				94.45 306	94.93 325	88.44 321		91.03 358		86.77 318	97.64 193	76.23 326	98.42 254	90.31 260	85.64 324	96.51 285

ANet_high		69.08 330	65.37 332	80.22 341	65.99 365	71.96 355	90.91 352	90.09 359	82.62 338	49.93 361	78.39 356	29.36 364	81.75 359	62.49 356	38.52 358	86.95 351

gg-mvs-nofinetune		92.21 280	90.58 294	97.13 170	96.75 245	95.09 169	95.85 328	89.40 360	85.43 331	94.50 193	81.98 352	80.80 302	98.40 267	92.16 214	98.33 122	97.88 188

GG-mvs-BLEND				96.59 213	96.34 270	94.98 175	96.51 320	88.58 361		93.10 256	94.34 321	80.34 307	98.05 287	89.53 276	96.99 156	96.74 248

PNet_i23d		67.70 332	65.07 333	75.60 344	78.61 359	59.61 363	89.14 353	88.24 362	61.83 354	52.37 359	80.89 353	18.91 365	84.91 358	62.70 355	52.93 354	82.28 354

wuykxyi23d		63.73 336	58.86 338	78.35 343	67.62 364	67.90 358	86.56 355	87.81 363	58.26 356	42.49 363	70.28 360	11.55 368	85.05 357	63.66 354	41.50 355	82.11 355

E-PMN		64.94 334	64.25 334	67.02 348	82.28 355	59.36 364	91.83 351	85.63 364	52.69 358	60.22 357	77.28 357	41.06 359	80.12 361	46.15 359	41.14 356	61.57 360

EMVS		64.07 335	63.26 336	66.53 349	81.73 356	58.81 365	91.85 350	84.75 365	51.93 360	59.09 358	75.13 358	43.32 358	79.09 362	42.03 360	39.47 357	61.69 359

tmp_tt		68.90 331	66.97 331	74.68 346	50.78 367	59.95 362	87.13 354	83.47 366	38.80 361	62.21 356	96.23 292	64.70 350	76.91 363	88.91 288	30.49 360	87.19 350

MVE	62.14 22	63.28 337	59.38 337	74.99 345	74.33 363	65.47 359	85.55 356	80.50 367	52.02 359	51.10 360	75.00 359	10.91 370	80.50 360	51.60 358	53.40 353	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		87.12 318	87.77 315	85.17 337	95.46 315	61.92 360	97.37 277	70.66 368	85.83 328	88.73 311	96.04 299	85.33 254	97.76 303	80.02 332	90.48 266	95.84 307

wuyk23d		30.17 340	30.18 342	30.16 352	78.61 359	43.29 367	66.79 360	14.21 369	17.31 362	14.82 366	11.93 366	11.55 368	41.43 364	37.08 361	19.30 361	5.76 364

testmvs		21.48 342	24.95 343	11.09 354	14.89 368	6.47 369	96.56 316	9.87 370	7.55 363	17.93 364	39.02 362	9.43 371	5.90 366	16.56 363	12.72 362	20.91 362

test123		20.95 343	23.72 344	12.64 353	13.54 369	8.19 368	96.55 318	6.13 371	7.48 364	16.74 365	37.98 363	12.97 367	6.05 365	16.69 362	5.43 364	23.68 361

pcd_1.5k_mvsjas		7.88 345	10.50 346	0.00 355	0.00 370	0.00 370	0.00 361	0.00 372	0.00 365	0.00 367	0.00 367	94.51 62	0.00 367	0.00 364	0.00 365	0.00 365

sosnet-low-res		0.00 346	0.00 347	0.00 355	0.00 370	0.00 370	0.00 361	0.00 372	0.00 365	0.00 367	0.00 367	0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

sosnet		0.00 346	0.00 347	0.00 355	0.00 370	0.00 370	0.00 361	0.00 372	0.00 365	0.00 367	0.00 367	0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

uncertanet		0.00 346	0.00 347	0.00 355	0.00 370	0.00 370	0.00 361	0.00 372	0.00 365	0.00 367	0.00 367	0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

Regformer		0.00 346	0.00 347	0.00 355	0.00 370	0.00 370	0.00 361	0.00 372	0.00 365	0.00 367	0.00 367	0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

n2								0.00 372

nn								0.00 372

ab-mvs-re		8.20 344	10.94 345	0.00 355	0.00 370	0.00 370	0.00 361	0.00 372	0.00 365	0.00 367	98.43 120	0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

uanet		0.00 346	0.00 347	0.00 355	0.00 370	0.00 370	0.00 361	0.00 372	0.00 365	0.00 367	0.00 367	0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

GSMVS																99.20 109

test_part2					99.63 21	99.18 2				99.27 8

sam_mvs1												89.45 142				99.20 109

sam_mvs												88.99 153

test_post1							96.68 312				30.43 365	87.85 201	98.69 215	92.59 206

test_post											31.83 364	88.83 165	98.91 197

patchmatchnet-post											95.10 314	89.42 143	98.89 201

gm-plane-assit					95.88 301	87.47 328			89.74 298		96.94 256		99.19 161	93.32 183

test9_res														96.39 100	99.57 57	99.69 37

agg_prior2														95.87 115	99.57 57	99.68 43

test_prior4						98.01 44	97.86 244

test_prior2							97.80 249		96.12 65	97.89 81	98.69 97	95.96 27		96.89 75	99.60 51

旧先验2							97.57 266		91.30 266	98.67 40			99.80 61	95.70 124

新几何2							97.64 261

原ACMM2							97.67 259

testdata2													99.89 28	91.65 232

segment_acmp												96.85 4

testdata1							97.32 283		96.34 59

plane_prior7					97.42 205	94.63 211

plane_prior6					97.35 210	94.61 214						87.09 216

plane_prior4											98.28 138

plane_prior3						94.61 214			97.02 40	95.34 174

plane_prior2							98.80 109		97.28 22

plane_prior1					97.37 209

plane_prior						94.60 216	98.44 175		96.74 47						94.22 214

HQP5-MVS						94.25 231

HQP-NCC					97.20 219		98.05 222		96.43 55	94.45 195

ACMP_Plane					97.20 219		98.05 222		96.43 55	94.45 195

BP-MVS														95.30 134

HQP4-MVS										94.45 195			98.96 190			96.87 236

HQP2-MVS												86.75 222

NP-MVS					97.28 213	94.51 219					97.73 184

MDTV_nov1_ep13_2view						84.26 335	96.89 304		90.97 274	97.90 80		89.89 138		93.91 169		99.18 115

ACMMP++_ref															92.97 243

ACMMP++															93.61 231

Test By Simon												94.64 59

High-resolution multi-view benchmark