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.

MM		99.63 51	99.52 61	99.94 63	99.99 49	99.82 86	100.00 1	99.97 17	99.11 7	100.00 1	100.00 1	96.65 197	100.00 1	100.00 1	99.97 111	100.00 1

test_fmvsmvis_n_1920		99.46 76	99.37 77	99.73 118	98.88 301	99.18 166	100.00 1	99.26 268	98.85 49	99.79 180	100.00 1	97.70 158	100.00 1	99.98 73	99.86 133	100.00 1

test_fmvsm_n_1920		99.55 65	99.49 66	99.73 118	99.85 116	99.19 164	100.00 1	99.41 184	98.87 47	100.00 1	100.00 1	97.34 176	100.00 1	99.98 73	99.90 126	100.00 1

test_vis1_n_1920		97.77 216	97.24 238	99.34 176	99.79 142	98.04 247	100.00 1	99.25 270	98.88 44	100.00 1	100.00 1	77.52 379	100.00 1	99.88 103	99.85 136	100.00 1

mvsany_test1		99.57 63	99.48 69	99.85 87	99.86 115	99.54 118	100.00 1	99.36 212	98.94 37	100.00 1	100.00 1	97.97 145	100.00 1	99.88 103	99.28 160	100.00 1

patch_mono-2		99.04 125	99.79 6	96.81 324	99.92 104	90.47 372	100.00 1	99.41 184	98.95 35	100.00 1	100.00 1	99.78 8	100.00 1	100.00 1	100.00 1	99.95 130

DVP-MVS++		99.81 11	99.75 14	100.00 1	100.00 1	99.99 5	100.00 1	99.42 138	98.79 60	100.00 1	100.00 1	99.54 26	100.00 1	100.00 1	100.00 1	100.00 1

MSC_two_6792asdad				100.00 1	100.00 1	100.00 1		99.42 138					100.00 1	100.00 1	100.00 1	100.00 1

PC_three_1452									98.80 57	100.00 1	100.00 1	99.54 26	100.00 1	100.00 1	100.00 1	100.00 1

No_MVS				100.00 1	100.00 1	100.00 1		99.42 138					100.00 1	100.00 1	100.00 1	100.00 1

SED-MVS		99.83 7	99.77 9	100.00 1	100.00 1	99.99 5	100.00 1	99.42 138	99.03 20	100.00 1	100.00 1	99.50 37	100.00 1	100.00 1	100.00 1	100.00 1

OPU-MVS				100.00 1	100.00 1	100.00 1	100.00 1				100.00 1	99.54 26	100.00 1	100.00 1	100.00 1	100.00 1

test_241102_TWO								99.42 138	99.03 20	100.00 1	100.00 1	99.56 23	100.00 1	100.00 1	100.00 1	100.00 1

test_241102_ONE					100.00 1	99.99 5		99.42 138	99.03 20	100.00 1	100.00 1	99.50 37	100.00 1

ZNCC-MVS		99.71 29	99.62 41	99.97 31	99.99 49	99.90 60	100.00 1	99.79 45	97.97 114	99.97 116	100.00 1	98.97 99	100.00 1	99.94 93	100.00 1	100.00 1

DVP-MVS		99.83 7	99.78 7	100.00 1	100.00 1	99.99 5	100.00 1	99.42 138	99.04 15	100.00 1	100.00 1	99.53 29	100.00 1	100.00 1	100.00 1	100.00 1
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test_0728_THIRD									98.79 60	100.00 1	100.00 1	99.61 16	100.00 1	100.00 1	100.00 1	100.00 1

test_0728_SECOND				100.00 1	99.99 49	99.99 5	100.00 1	99.42 138					100.00 1	100.00 1	100.00 1	100.00 1

DPM-MVS		99.63 51	99.51 63	100.00 1	99.90 108	100.00 1	100.00 1	99.43 122	99.00 27	100.00 1	100.00 1	99.58 22	100.00 1	97.64 268	100.00 1	100.00 1

GST-MVS		99.64 48	99.53 59	99.95 51	100.00 1	99.86 79	100.00 1	99.79 45	97.72 135	99.95 145	100.00 1	98.39 136	100.00 1	99.96 85	99.99 98	100.00 1

test_yl		99.51 67	99.37 77	99.95 51	99.82 121	99.90 60	100.00 1	99.47 79	97.48 168	100.00 1	100.00 1	99.80 5	100.00 1	99.98 73	97.75 230	99.94 135

Anonymous20240529		96.93 258	96.22 275	99.05 199	99.79 142	97.30 284	99.16 369	99.47 79	88.51 378	98.69 257	100.00 1	83.50 358	100.00 1	99.83 113	97.02 245	99.83 192

Anonymous202405211		97.87 211	97.53 223	98.90 210	99.81 127	96.70 302	99.35 345	99.46 94	92.98 348	98.83 251	99.99 186	90.63 284	100.00 1	99.70 141	97.03 244	100.00 1

DCV-MVSNet		99.51 67	99.37 77	99.95 51	99.82 121	99.90 60	100.00 1	99.47 79	97.48 168	100.00 1	100.00 1	99.80 5	100.00 1	99.98 73	97.75 230	99.94 135

SMA-MVS		99.69 35	99.59 44	99.98 23	99.99 49	99.93 45	100.00 1	99.43 122	97.50 166	100.00 1	100.00 1	99.43 50	100.00 1	100.00 1	100.00 1	100.00 1
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
DPE-MVS		99.79 14	99.73 17	99.99 12	99.99 49	99.98 17	100.00 1	99.42 138	98.91 41	100.00 1	100.00 1	99.22 76	100.00 1	100.00 1	100.00 1	100.00 1
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
thres100view900		99.25 105	99.01 116	99.95 51	99.81 127	99.87 76	100.00 1	99.94 22	97.13 194	99.83 169	99.96 211	97.01 183	100.00 1	99.59 166	97.85 221	99.98 111

tfpn200view9		99.26 101	99.03 114	99.96 42	99.81 127	99.89 67	100.00 1	99.94 22	97.23 189	99.83 169	99.96 211	97.04 179	100.00 1	99.59 166	97.85 221	99.98 111

CANet_DTU		99.02 132	98.90 135	99.41 165	99.88 112	98.71 200	100.00 1	99.29 247	98.84 51	100.00 1	100.00 1	94.02 239	100.00 1	98.08 252	99.96 114	99.52 246

MP-MVS-pluss		99.61 58	99.50 64	99.97 31	99.98 85	99.92 50	100.00 1	99.42 138	97.53 161	99.77 183	100.00 1	98.77 120	100.00 1	99.99 61	100.00 1	99.99 109
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
xiu_mvs_v1_base_debu		99.35 85	99.21 96	99.79 105	99.67 166	99.71 98	99.78 286	99.36 212	98.13 100	100.00 1	100.00 1	97.00 186	100.00 1	99.83 113	99.07 164	99.66 240

ACMMP_NAP		99.67 43	99.57 49	99.97 31	99.98 85	99.92 50	100.00 1	99.42 138	97.83 127	100.00 1	100.00 1	98.89 111	100.00 1	99.98 73	100.00 1	100.00 1

xiu_mvs_v2_base		99.51 67	99.41 71	99.82 93	99.70 156	99.73 96	99.92 261	99.40 188	98.15 98	100.00 1	100.00 1	98.50 133	100.00 1	99.85 109	99.13 162	99.74 231

xiu_mvs_v1_base		99.35 85	99.21 96	99.79 105	99.67 166	99.71 98	99.78 286	99.36 212	98.13 100	100.00 1	100.00 1	97.00 186	100.00 1	99.83 113	99.07 164	99.66 240

xiu_mvs_v1_base_debi		99.35 85	99.21 96	99.79 105	99.67 166	99.71 98	99.78 286	99.36 212	98.13 100	100.00 1	100.00 1	97.00 186	100.00 1	99.83 113	99.07 164	99.66 240

MTAPA		99.68 40	99.59 44	99.97 31	99.99 49	99.91 53	100.00 1	99.42 138	98.32 88	99.94 147	100.00 1	98.65 125	100.00 1	99.96 85	100.00 1	100.00 1

HFP-MVS		99.74 22	99.67 27	99.96 42	100.00 1	99.89 67	100.00 1	99.76 49	97.95 118	100.00 1	100.00 1	99.31 64	100.00 1	99.99 61	100.00 1	100.00 1

region2R		99.72 26	99.64 33	99.97 31	100.00 1	99.90 60	100.00 1	99.74 55	97.86 124	100.00 1	100.00 1	99.19 79	100.00 1	99.99 61	100.00 1	100.00 1

PS-MVSNAJ		99.64 48	99.57 49	99.85 87	99.78 145	99.81 87	99.95 254	99.42 138	98.38 80	100.00 1	100.00 1	98.75 121	100.00 1	99.88 103	99.99 98	99.74 231

HPM-MVS++		99.82 9	99.76 12	99.99 12	99.99 49	99.98 17	100.00 1	99.83 39	98.88 44	99.96 121	100.00 1	99.21 77	100.00 1	100.00 1	100.00 1	99.99 109

XVS		99.79 14	99.73 17	99.98 23	100.00 1	99.94 41	100.00 1	99.75 52	98.67 67	100.00 1	100.00 1	99.16 81	100.00 1	100.00 1	100.00 1	100.00 1

X-MVStestdata		97.04 252	96.06 281	99.98 23	100.00 1	99.94 41	100.00 1	99.75 52	98.67 67	100.00 1	66.97 409	99.16 81	100.00 1	100.00 1	100.00 1	100.00 1

旧先验2							100.00 1		98.11 104	100.00 1			100.00 1	99.67 152

新几何1				99.99 12	100.00 1	99.96 24		99.81 42	97.89 121	100.00 1	100.00 1	99.20 78	100.00 1	97.91 260	100.00 1	100.00 1

无先验							100.00 1	99.80 43	97.98 112				100.00 1	99.33 187		100.00 1

原ACMM1				99.93 67	100.00 1	99.80 89		99.66 63	98.18 95	100.00 1	100.00 1	99.43 50	100.00 1	99.50 178	100.00 1	100.00 1

testdata2													100.00 1	97.36 280

testdata				99.66 130	99.99 49	98.97 187		99.73 56	97.96 117	100.00 1	100.00 1	99.42 53	100.00 1	99.28 192	100.00 1	100.00 1

1314		99.38 82	99.19 100	99.96 42	98.88 301	99.89 67	99.24 355	99.93 30	98.88 44	98.79 254	100.00 1	97.02 182	100.00 1	100.00 1	100.00 1	100.00 1

LFMVS		97.42 235	96.62 256	99.81 98	99.80 138	99.50 126	99.16 369	99.56 70	94.48 313	100.00 1	100.00 1	79.35 374	100.00 1	99.89 101	97.37 239	99.94 135

VDD-MVS		96.58 273	95.99 284	98.34 240	99.52 219	95.33 316	99.18 363	99.38 203	96.64 236	99.77 183	100.00 1	72.51 389	100.00 1	100.00 1	96.94 247	99.70 236

VDDNet		96.39 285	95.55 306	98.90 210	99.27 263	97.45 275	99.15 371	99.92 34	91.28 361	99.98 111	100.00 1	73.55 385	100.00 1	99.85 109	96.98 246	99.24 250

MVS		99.22 109	98.96 123	99.98 23	99.00 288	99.95 32	99.24 355	99.94 22	98.14 99	98.88 244	100.00 1	95.63 215	100.00 1	99.85 109	100.00 1	100.00 1

SD-MVS		99.81 11	99.75 14	99.99 12	99.99 49	99.96 24	100.00 1	99.42 138	99.01 26	100.00 1	100.00 1	99.33 59	100.00 1	100.00 1	100.00 1	100.00 1
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
MSLP-MVS++		99.89 1	99.85 2	99.99 12	100.00 1	99.96 24	100.00 1	99.95 19	99.11 7	100.00 1	100.00 1	99.60 17	100.00 1	100.00 1	100.00 1	100.00 1

VNet		99.04 125	98.75 147	99.90 73	99.81 127	99.75 93	99.50 330	99.47 79	98.36 84	100.00 1	99.99 186	94.66 232	100.00 1	99.90 99	97.09 243	99.96 124

ACMMPR		99.74 22	99.67 27	99.96 42	100.00 1	99.89 67	100.00 1	99.76 49	97.95 118	100.00 1	100.00 1	99.29 70	100.00 1	99.99 61	100.00 1	100.00 1

thres600view7		99.24 108	99.00 118	99.95 51	99.81 127	99.87 76	100.00 1	99.94 22	97.13 194	99.83 169	99.96 211	97.01 183	100.00 1	99.54 174	97.77 229	99.97 118

MP-MVS		99.61 58	99.49 66	99.98 23	99.99 49	99.94 41	100.00 1	99.42 138	97.82 128	99.99 105	100.00 1	98.20 139	100.00 1	99.99 61	100.00 1	100.00 1
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
thres400		99.26 101	99.03 114	99.95 51	99.81 127	99.89 67	100.00 1	99.94 22	97.23 189	99.83 169	99.96 211	97.04 179	100.00 1	99.59 166	97.85 221	99.97 118

thres200		99.27 99	99.04 113	99.96 42	99.81 127	99.90 60	100.00 1	99.94 22	97.31 184	99.83 169	99.96 211	97.04 179	100.00 1	99.62 162	97.88 219	99.98 111

PGM-MVS		99.69 35	99.61 42	99.95 51	99.99 49	99.85 82	100.00 1	99.58 67	97.69 139	100.00 1	100.00 1	99.44 46	100.00 1	99.79 119	100.00 1	100.00 1

MCST-MVS		99.85 3	99.80 4	100.00 1	100.00 1	99.99 5	100.00 1	99.73 56	99.19 5	100.00 1	100.00 1	99.31 64	100.00 1	100.00 1	100.00 1	100.00 1

mPP-MVS		99.69 35	99.60 43	99.97 31	100.00 1	99.91 53	100.00 1	99.42 138	97.91 120	100.00 1	100.00 1	99.04 94	100.00 1	100.00 1	100.00 1	100.00 1

EPNet		99.62 56	99.69 22	99.42 164	99.99 49	98.37 221	100.00 1	99.89 37	98.83 53	100.00 1	100.00 1	98.97 99	100.00 1	99.90 99	99.61 155	99.89 165
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CNVR-MVS		99.85 3	99.80 4	100.00 1	100.00 1	99.99 5	100.00 1	99.77 48	99.07 10	100.00 1	100.00 1	99.39 57	100.00 1	100.00 1	100.00 1	100.00 1

NCCC		99.86 2	99.82 3	100.00 1	100.00 1	99.99 5	100.00 1	99.71 61	99.07 10	100.00 1	100.00 1	99.59 20	100.00 1	100.00 1	100.00 1	100.00 1

CP-MVS		99.67 43	99.58 46	99.95 51	100.00 1	99.84 84	100.00 1	99.42 138	97.77 132	100.00 1	100.00 1	99.07 88	100.00 1	100.00 1	100.00 1	100.00 1

DELS-MVS		99.62 56	99.56 54	99.82 93	99.92 104	99.45 135	100.00 1	99.78 47	98.92 39	99.73 188	100.00 1	97.70 158	100.00 1	99.93 95	100.00 1	100.00 1
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
CPTT-MVS		99.49 72	99.38 74	99.85 87	100.00 1	99.54 118	100.00 1	99.42 138	97.58 156	99.98 111	100.00 1	97.43 173	100.00 1	99.99 61	100.00 1	100.00 1

UGNet		98.41 189	98.11 196	99.31 183	99.54 209	98.55 209	99.18 363	100.00 1	98.64 70	99.79 180	99.04 332	87.61 324	100.00 1	99.30 190	99.89 127	99.40 249
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		99.45 77	99.34 84	99.80 103	99.76 148	99.50 126	100.00 1	99.91 36	97.72 135	99.98 111	99.94 224	98.45 134	100.00 1	99.53 176	98.75 174	99.89 165

DP-MVS Recon		99.76 18	99.69 22	99.98 23	100.00 1	99.95 32	100.00 1	99.52 72	97.99 110	99.99 105	100.00 1	99.72 12	100.00 1	99.96 85	100.00 1	100.00 1

QAPM		98.99 138	98.66 156	99.96 42	99.01 284	99.87 76	99.88 270	99.93 30	97.99 110	98.68 258	100.00 1	93.17 251	100.00 1	99.32 188	100.00 1	100.00 1

PAPM_NR		99.74 22	99.66 30	99.99 12	100.00 1	99.96 24	100.00 1	99.47 79	97.87 123	100.00 1	100.00 1	99.60 17	100.00 1	100.00 1	100.00 1	100.00 1

PAPR		99.76 18	99.68 25	99.99 12	100.00 1	99.96 24	100.00 1	99.47 79	98.16 96	100.00 1	100.00 1	99.51 33	100.00 1	100.00 1	100.00 1	100.00 1

RPSCF		97.37 237	98.24 188	94.76 350	99.80 138	84.57 387	99.99 213	99.05 346	94.95 298	99.82 177	100.00 1	94.03 238	100.00 1	98.15 251	98.38 190	99.70 236

CSCG		99.28 98	99.35 82	99.05 199	99.99 49	97.15 289	100.00 1	99.47 79	97.44 172	99.42 204	100.00 1	97.83 154	100.00 1	99.99 61	100.00 1	100.00 1

API-MVS		99.72 26	99.70 21	99.79 105	99.97 89	99.37 145	99.96 248	99.94 22	98.48 75	100.00 1	100.00 1	98.92 108	100.00 1	100.00 1	100.00 1	100.00 1

ACMMP		99.65 46	99.57 49	99.89 75	99.99 49	99.66 105	99.75 295	99.73 56	98.16 96	99.75 186	100.00 1	98.90 110	100.00 1	99.96 85	99.88 129	100.00 1
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
CNLPA		99.72 26	99.65 31	99.91 70	99.97 89	99.72 97	100.00 1	99.47 79	98.43 78	99.88 162	100.00 1	99.14 84	100.00 1	99.97 83	100.00 1	100.00 1

PHI-MVS		99.50 70	99.39 73	99.82 93	100.00 1	99.45 135	100.00 1	99.94 22	96.38 252	100.00 1	100.00 1	98.18 140	100.00 1	100.00 1	100.00 1	100.00 1

PVSNet	94.91 18	99.30 96	99.25 90	99.44 160	100.00 1	98.32 227	100.00 1	99.86 38	98.04 107	100.00 1	100.00 1	96.10 206	100.00 1	99.55 171	99.73 145	100.00 1

PVSNet_0	93.57 19	96.41 281	95.74 297	98.41 235	99.84 117	95.22 318	100.00 1	100.00 1	98.08 105	97.55 322	99.78 259	84.40 349	100.00 1	100.00 1	81.99 383	100.00 1

DeepPCF-MVS	98.03 4	98.54 177	99.72 19	94.98 347	99.99 49	84.94 386	100.00 1	99.42 138	99.98 1	100.00 1	100.00 1	98.11 142	100.00 1	100.00 1	100.00 1	100.00 1

DeepC-MVS_fast	98.92 1	99.75 20	99.67 27	99.99 12	99.99 49	99.96 24	99.73 301	99.52 72	99.06 12	100.00 1	100.00 1	98.80 119	100.00 1	99.95 91	100.00 1	100.00 1
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MG-MVS		99.75 20	99.68 25	99.97 31	100.00 1	99.91 53	99.98 237	99.47 79	99.09 9	100.00 1	100.00 1	98.59 129	100.00 1	99.95 91	100.00 1	100.00 1

AdaColmap		99.44 78	99.26 89	99.95 51	100.00 1	99.86 79	99.70 306	99.99 13	98.53 73	99.90 157	100.00 1	95.34 217	100.00 1	99.92 96	100.00 1	100.00 1

PLC	98.56 2	99.70 32	99.74 16	99.58 145	100.00 1	98.79 194	100.00 1	99.54 71	98.58 72	99.96 121	100.00 1	99.59 20	100.00 1	100.00 1	100.00 1	99.94 135
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MAR-MVS		99.49 72	99.36 80	99.89 75	99.97 89	99.66 105	99.74 296	99.95 19	97.89 121	100.00 1	100.00 1	96.71 196	100.00 1	100.00 1	100.00 1	100.00 1
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
3Dnovator+	95.58 15	99.03 127	98.71 153	99.96 42	98.99 291	99.89 67	100.00 1	99.51 76	98.96 32	98.32 281	100.00 1	92.78 257	100.00 1	99.87 106	100.00 1	100.00 1

3Dnovator	95.63 14	99.06 123	98.76 146	99.96 42	98.86 305	99.90 60	99.98 237	99.93 30	98.95 35	98.49 273	100.00 1	92.91 255	100.00 1	99.71 138	100.00 1	100.00 1

OpenMVS	95.20 17	98.76 157	98.41 175	99.78 109	98.89 300	99.81 87	99.99 213	99.76 49	98.02 108	98.02 299	100.00 1	91.44 272	100.00 1	99.63 161	99.97 111	99.55 244

fmvsm_l_conf0.5_n_a		99.63 51	99.55 56	99.86 83	99.83 120	99.58 113	100.00 1	99.36 212	98.98 30	100.00 1	100.00 1	97.85 151	99.99 94	100.00 1	99.94 119	100.00 1

fmvsm_l_conf0.5_n		99.63 51	99.56 54	99.86 83	99.81 127	99.59 112	100.00 1	99.36 212	98.98 30	100.00 1	100.00 1	97.92 148	99.99 94	100.00 1	99.95 117	100.00 1

fmvsm_s_conf0.5_n_a		99.32 92	99.15 105	99.81 98	99.80 138	99.47 134	100.00 1	99.35 223	98.22 91	100.00 1	100.00 1	95.21 222	99.99 94	99.96 85	99.86 133	99.98 111

fmvsm_s_conf0.5_n		99.21 110	99.01 116	99.83 91	99.84 117	99.53 120	100.00 1	99.38 203	98.29 90	100.00 1	100.00 1	93.62 244	99.99 94	99.99 61	99.93 122	99.98 111

test_fmvsmconf_n		99.56 64	99.46 70	99.86 83	99.68 161	99.58 113	100.00 1	99.31 238	98.92 39	99.88 162	100.00 1	97.35 175	99.99 94	99.98 73	99.99 98	100.00 1

test_cas_vis1_n_1920		98.63 170	98.25 186	99.77 112	99.69 157	99.32 148	100.00 1	99.31 238	98.84 51	99.96 121	100.00 1	87.42 326	99.99 94	99.14 200	99.86 133	100.00 1

test_vis1_n		96.69 268	95.81 291	99.32 181	99.14 269	97.98 250	99.97 243	98.98 358	98.45 77	100.00 1	100.00 1	66.44 395	99.99 94	99.78 125	99.57 157	100.00 1

h-mvs33		97.03 253	96.53 259	98.51 230	99.79 142	95.90 311	99.45 334	99.45 102	98.21 92	100.00 1	99.78 259	97.49 167	99.99 94	99.72 133	74.92 392	99.65 243

ZD-MVS					100.00 1	99.98 17		99.80 43	97.31 184	100.00 1	100.00 1	99.32 62	99.99 94	100.00 1	100.00 1

SR-MVS-dyc-post		99.63 51	99.52 61	99.97 31	99.99 49	99.91 53	100.00 1	99.42 138	97.62 147	100.00 1	100.00 1	98.65 125	99.99 94	99.99 61	100.00 1	100.00 1

SF-MVS		99.66 45	99.57 49	99.95 51	99.99 49	99.85 82	100.00 1	99.42 138	97.67 140	100.00 1	100.00 1	99.05 91	99.99 94	100.00 1	100.00 1	100.00 1

9.14			99.57 49		99.99 49		100.00 1	99.42 138	97.54 159	100.00 1	100.00 1	99.15 83	99.99 94	100.00 1	100.00 1

SR-MVS		99.68 40	99.58 46	99.98 23	100.00 1	99.95 32	100.00 1	99.64 64	97.59 155	100.00 1	100.00 1	98.99 97	99.99 94	100.00 1	100.00 1	100.00 1

TSAR-MVS + MP.		99.82 9	99.77 9	99.99 12	100.00 1	99.96 24	100.00 1	99.43 122	99.05 14	100.00 1	100.00 1	99.45 45	99.99 94	100.00 1	100.00 1	100.00 1
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
APDe-MVS		99.84 6	99.78 7	99.99 12	100.00 1	99.98 17	100.00 1	99.44 116	99.06 12	100.00 1	100.00 1	99.56 23	99.99 94	100.00 1	100.00 1	100.00 1
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
APD-MVS_3200maxsize		99.65 46	99.55 56	99.97 31	99.99 49	99.91 53	100.00 1	99.48 78	97.54 159	100.00 1	100.00 1	98.97 99	99.99 94	99.98 73	100.00 1	100.00 1

CDPH-MVS		99.73 25	99.64 33	99.99 12	100.00 1	99.97 21	100.00 1	99.42 138	98.02 108	100.00 1	100.00 1	99.32 62	99.99 94	100.00 1	100.00 1	100.00 1

TSAR-MVS + GP.		99.61 58	99.69 22	99.35 175	99.99 49	98.06 245	100.00 1	99.36 212	99.83 2	100.00 1	100.00 1	98.95 103	99.99 94	100.00 1	99.11 163	100.00 1

SteuartSystems-ACMMP		99.78 16	99.71 20	99.98 23	99.76 148	99.95 32	100.00 1	99.42 138	98.69 65	100.00 1	100.00 1	99.52 32	99.99 94	100.00 1	100.00 1	100.00 1
Skip Steuart: Steuart Systems R&D Blog.
PVSNet_BlendedMVS		98.71 161	98.62 160	98.98 206	99.98 85	99.60 110	100.00 1	100.00 1	97.23 189	100.00 1	99.03 334	96.57 199	99.99 94	100.00 1	94.75 290	97.35 358

PVSNet_Blended		99.48 74	99.36 80	99.83 91	99.98 85	99.60 110	100.00 1	100.00 1	97.79 130	100.00 1	100.00 1	96.57 199	99.99 94	100.00 1	99.88 129	99.90 160

HY-MVS	96.53 9	99.50 70	99.35 82	99.96 42	99.81 127	99.93 45	99.64 313	100.00 1	97.97 114	99.84 166	99.85 244	98.94 105	99.99 94	99.86 107	98.23 201	99.95 130

PatchMatch-RL		99.02 132	98.78 143	99.74 115	99.99 49	99.29 151	100.00 1	100.00 1	98.38 80	99.89 160	99.81 253	93.14 253	99.99 94	97.85 262	99.98 108	99.95 130

F-COLMAP		99.64 48	99.64 33	99.67 127	99.99 49	99.07 172	100.00 1	99.44 116	98.30 89	99.90 157	100.00 1	99.18 80	99.99 94	99.91 98	100.00 1	99.94 135

testing91		99.18 112	99.10 108	99.41 165	99.60 193	98.43 213	100.00 1	99.43 122	96.76 220	99.82 177	99.92 229	99.05 91	99.98 118	99.62 162	97.67 234	99.81 206

testing99		99.18 112	99.10 108	99.41 165	99.60 193	98.43 213	100.00 1	99.43 122	96.76 220	99.84 166	99.92 229	99.06 89	99.98 118	99.62 162	97.67 234	99.81 206

test_8					100.00 1	99.91 53	100.00 1	99.42 138	97.70 137	100.00 1	100.00 1	99.51 33	99.98 118

MVS_Test		98.93 146	98.65 157	99.77 112	99.62 189	99.50 126	99.99 213	99.19 294	95.52 285	99.96 121	99.86 239	96.54 201	99.98 118	98.65 227	98.48 181	99.82 197

RPMNet		95.26 319	93.82 327	99.56 148	99.31 259	98.86 191	99.13 373	99.42 138	79.82 395	99.96 121	95.13 388	95.69 213	99.98 118	77.54 398	98.40 186	99.84 189

WTY-MVS		99.54 66	99.40 72	99.95 51	99.81 127	99.93 45	100.00 1	100.00 1	97.98 112	99.84 166	100.00 1	98.94 105	99.98 118	99.86 107	98.21 202	99.94 135

ab-mvs		98.42 187	98.02 205	99.61 137	99.71 154	99.00 183	99.10 376	99.64 64	96.70 229	99.04 234	99.81 253	90.64 283	99.98 118	99.64 158	97.93 216	99.84 189

testing11		99.26 101	99.19 100	99.46 157	99.64 182	98.61 206	100.00 1	99.43 122	96.94 205	99.92 152	99.94 224	99.43 50	99.97 125	99.67 152	97.79 228	99.82 197

sasdasda		99.03 127	98.73 149	99.94 63	99.75 150	99.95 32	100.00 1	99.30 242	97.64 144	100.00 1	100.00 1	95.22 220	99.97 125	99.76 126	96.90 248	99.91 149

FE-MVS		99.16 115	98.99 120	99.66 130	99.65 176	99.18 166	99.58 321	99.43 122	95.24 293	99.91 155	99.59 296	99.37 58	99.97 125	98.31 243	99.81 141	99.83 192

TEST9					100.00 1	99.95 32	100.00 1	99.42 138	97.65 142	100.00 1	100.00 1	99.53 29	99.97 125

train_agg		99.71 29	99.63 37	99.97 31	100.00 1	99.95 32	100.00 1	99.42 138	97.70 137	100.00 1	100.00 1	99.51 33	99.97 125	100.00 1	100.00 1	100.00 1

agg_prior					100.00 1	99.88 74		99.42 138		100.00 1			99.97 125

canonicalmvs		99.03 127	98.73 149	99.94 63	99.75 150	99.95 32	100.00 1	99.30 242	97.64 144	100.00 1	100.00 1	95.22 220	99.97 125	99.76 126	96.90 248	99.91 149

EI-MVSNet-UG-set		99.69 35	99.63 37	99.87 80	99.99 49	99.64 107	99.95 254	99.44 116	98.35 86	100.00 1	100.00 1	98.98 98	99.97 125	99.98 73	100.00 1	100.00 1

test_prior				99.90 73	100.00 1	99.75 93		99.73 56					99.97 125			100.00 1

test12				99.95 51	99.99 49	99.89 67		99.42 138		100.00 1		99.24 75	99.97 125		100.00 1	100.00 1

APD-MVS		99.68 40	99.58 46	99.97 31	99.99 49	99.96 24	100.00 1	99.42 138	97.53 161	100.00 1	100.00 1	99.27 73	99.97 125	100.00 1	100.00 1	100.00 1
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
DP-MVS		98.86 151	98.54 167	99.81 98	99.97 89	99.45 135	99.52 328	99.40 188	94.35 317	98.36 277	100.00 1	96.13 205	99.97 125	99.12 203	100.00 1	100.00 1

LS3D		99.31 94	99.13 106	99.87 80	99.99 49	99.71 98	99.55 324	99.46 94	97.32 182	99.82 177	100.00 1	96.85 193	99.97 125	99.14 200	100.00 1	99.92 147

MGCFI-Net		99.01 134	98.70 155	99.93 67	99.74 152	99.94 41	100.00 1	99.29 247	97.60 154	100.00 1	100.00 1	95.10 224	99.96 138	99.74 129	96.85 250	99.91 149

fmvsm_s_conf0.1_n_a		98.71 161	98.36 182	99.78 109	99.09 274	99.42 139	100.00 1	99.26 268	97.42 174	100.00 1	100.00 1	89.78 296	99.96 138	99.82 118	99.85 136	99.97 118

fmvsm_s_conf0.1_n		98.77 156	98.42 174	99.82 93	99.47 237	99.52 123	100.00 1	99.27 262	97.53 161	100.00 1	100.00 1	89.73 298	99.96 138	99.84 112	99.93 122	99.97 118

test_fmvs1_n		97.43 234	96.86 247	99.15 195	99.68 161	97.48 274	99.99 213	98.98 358	98.82 55	100.00 1	100.00 1	74.85 384	99.96 138	99.67 152	99.70 147	100.00 1

test_fmvs1		98.37 192	98.04 203	99.34 176	99.84 117	98.07 243	100.00 1	99.00 355	98.85 49	100.00 1	100.00 1	85.11 346	99.96 138	99.69 148	99.88 129	100.00 1

FA-MVS(test-final)		99.00 135	98.75 147	99.73 118	99.63 184	99.43 138	99.83 276	99.43 122	95.84 274	99.52 197	99.37 315	97.84 153	99.96 138	97.63 269	99.68 148	99.79 223

bld_raw_dy_0_64		97.64 221	96.98 243	99.63 134	99.67 166	98.94 188	100.00 1	97.98 385	97.85 125	98.93 239	100.00 1	83.23 360	99.96 138	99.72 133	95.41 263	100.00 1

CANet		99.40 80	99.24 92	99.89 75	99.99 49	99.76 92	100.00 1	99.73 56	98.40 79	99.78 182	100.00 1	95.28 218	99.96 138	100.00 1	99.99 98	99.96 124

MVS_0304		99.69 35	99.63 37	99.86 83	99.96 94	99.63 109	100.00 1	99.92 34	99.03 20	99.97 116	100.00 1	97.87 149	99.96 138	100.00 1	99.96 114	100.00 1

MSP-MVS		99.81 11	99.77 9	99.94 63	100.00 1	99.86 79	100.00 1	99.42 138	98.87 47	100.00 1	100.00 1	99.65 15	99.96 138	100.00 1	100.00 1	100.00 1
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
EI-MVSNet-Vis-set		99.70 32	99.64 33	99.87 80	100.00 1	99.64 107	99.98 237	99.44 116	98.35 86	99.99 105	100.00 1	99.04 94	99.96 138	99.98 73	100.00 1	100.00 1

HPM-MVS_fast		99.60 61	99.49 66	99.91 70	99.99 49	99.78 90	100.00 1	99.42 138	97.09 196	100.00 1	100.00 1	98.95 103	99.96 138	99.98 73	100.00 1	100.00 1

HPM-MVS		99.59 62	99.50 64	99.89 75	100.00 1	99.70 102	100.00 1	99.42 138	97.46 170	100.00 1	100.00 1	98.60 128	99.96 138	99.99 61	100.00 1	100.00 1
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
BH-w/o		98.82 154	98.81 141	98.88 212	99.62 189	96.71 301	100.00 1	99.28 254	97.09 196	98.81 252	100.00 1	94.91 228	99.96 138	99.54 174	100.00 1	99.96 124

test_vis1_rt		93.10 336	92.93 337	93.58 360	99.63 184	85.07 385	99.99 213	93.71 406	97.49 167	90.96 379	97.10 380	60.40 397	99.95 152	99.24 195	97.90 218	95.72 382

AllTest		98.55 176	98.40 176	98.99 204	99.93 101	97.35 280	100.00 1	99.40 188	97.08 198	99.09 228	99.98 191	93.37 247	99.95 152	96.94 289	99.84 138	99.68 238

TestCases				98.99 204	99.93 101	97.35 280		99.40 188	97.08 198	99.09 228	99.98 191	93.37 247	99.95 152	96.94 289	99.84 138	99.68 238

CHOSEN 1792x2688		99.00 135	98.91 132	99.25 190	99.90 108	97.79 265	100.00 1	99.99 13	98.79 60	98.28 284	100.00 1	93.63 243	99.95 152	99.66 156	99.95 117	100.00 1

114514_t		99.39 81	99.25 90	99.81 98	99.97 89	99.48 133	100.00 1	99.42 138	95.53 283	100.00 1	100.00 1	98.37 137	99.95 152	99.97 83	100.00 1	100.00 1

PVSNet_Blended_VisFu		99.33 90	99.18 103	99.78 109	99.82 121	99.49 129	100.00 1	99.95 19	97.36 177	99.63 193	100.00 1	96.45 203	99.95 152	99.79 119	99.65 152	99.89 165

MVS_111021_LR		99.70 32	99.65 31	99.88 79	99.96 94	99.70 102	100.00 1	99.97 17	98.96 32	100.00 1	100.00 1	97.93 147	99.95 152	99.99 61	100.00 1	100.00 1

MVS_111021_HR		99.71 29	99.63 37	99.93 67	99.95 96	99.83 85	100.00 1	100.00 1	98.89 43	100.00 1	100.00 1	97.85 151	99.95 152	100.00 1	100.00 1	100.00 1

DeepC-MVS	97.84 5	99.00 135	98.80 142	99.60 139	99.93 101	99.03 178	100.00 1	99.40 188	98.61 71	99.33 214	100.00 1	92.23 266	99.95 152	99.74 129	99.96 114	99.83 192
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PCF-MVS	98.23 3	98.69 164	98.37 180	99.62 136	99.78 145	99.02 179	99.23 360	99.06 344	96.43 246	98.08 293	100.00 1	94.72 231	99.95 152	98.16 250	99.91 125	99.90 160
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MSDG		98.90 149	98.63 159	99.70 123	99.92 104	99.25 156	100.00 1	99.37 206	95.71 277	99.40 210	100.00 1	96.58 198	99.95 152	96.80 296	99.94 119	99.91 149

COLMAP_ROB	97.10 7	98.29 196	98.17 193	98.65 222	99.94 99	97.39 277	99.30 351	99.40 188	95.64 278	97.75 313	100.00 1	92.69 262	99.95 152	98.89 212	99.92 124	98.62 260
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
UWE-MVS		99.18 112	99.06 111	99.51 150	99.67 166	98.80 193	100.00 1	99.43 122	96.80 217	99.93 151	99.86 239	99.79 7	99.94 164	97.78 264	98.33 196	99.80 220

test_fmvsmconf0.1_n		99.25 105	99.05 112	99.82 93	98.92 297	99.55 116	100.00 1	99.23 279	98.91 41	99.75 186	99.97 199	94.79 230	99.94 164	99.94 93	99.99 98	99.97 118

UA-Net		99.06 123	98.83 138	99.74 115	99.52 219	99.40 141	99.08 379	99.45 102	97.64 144	99.83 169	100.00 1	95.80 210	99.94 164	98.35 241	99.80 143	99.88 176

XVG-OURS-SEG-HR		98.27 198	98.31 184	98.14 256	99.59 197	95.92 309	100.00 1	99.36 212	98.48 75	99.21 219	100.00 1	89.27 305	99.94 164	99.76 126	99.17 161	98.56 261

alignmvs		99.38 82	99.21 96	99.91 70	99.73 153	99.92 50	100.00 1	99.51 76	97.61 151	100.00 1	100.00 1	99.06 89	99.93 168	99.83 113	97.12 242	99.90 160

cascas		98.43 185	98.07 200	99.50 153	99.65 176	99.02 179	100.00 1	99.22 282	94.21 320	99.72 189	99.98 191	92.03 269	99.93 168	99.68 149	98.12 207	99.54 245

XVG-OURS		98.30 194	98.36 182	98.13 259	99.58 202	95.91 310	100.00 1	99.36 212	98.69 65	99.23 218	100.00 1	91.20 275	99.92 170	99.34 186	97.82 224	98.56 261

test_fmvsmconf0.01_n		98.60 171	98.24 188	99.67 127	96.90 371	99.21 162	99.99 213	99.04 349	98.80 57	99.57 195	99.96 211	90.12 290	99.91 171	99.89 101	99.89 127	99.90 160

APD_test1		93.07 337	94.14 325	89.85 368	99.18 267	72.49 396	99.76 293	98.90 365	92.86 352	96.35 347	99.94 224	75.56 382	99.91 171	86.73 380	97.98 211	97.15 363

diffmvs		98.96 142	98.73 149	99.63 134	99.54 209	99.16 168	100.00 1	99.18 301	97.33 181	99.96 121	100.00 1	94.60 233	99.91 171	99.66 156	98.33 196	99.82 197
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
tpmvs		98.59 172	98.38 178	99.23 191	99.69 157	97.90 257	99.31 350	99.47 79	94.52 311	99.68 191	99.28 320	97.64 161	99.89 174	97.71 266	98.17 206	99.89 165

Test_1112_low_res		98.83 153	98.60 163	99.51 150	99.69 157	98.75 196	99.99 213	99.14 313	96.81 216	98.84 249	99.06 329	97.45 170	99.89 174	98.66 225	97.75 230	99.89 165

1112_ss		98.91 147	98.71 153	99.51 150	99.69 157	98.75 196	99.99 213	99.15 308	96.82 215	98.84 249	100.00 1	97.45 170	99.89 174	98.66 225	97.75 230	99.89 165

IB-MVS	96.24 12	97.54 229	96.95 244	99.33 179	99.67 166	98.10 242	100.00 1	99.47 79	97.42 174	99.26 217	99.69 271	98.83 116	99.89 174	99.43 180	78.77 390	100.00 1
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
SDMVSNet		98.49 182	98.08 198	99.73 118	99.82 121	99.53 120	99.99 213	99.45 102	97.62 147	99.38 211	99.86 239	90.06 293	99.88 178	99.92 96	96.61 253	99.79 223

iter_conf05_11		98.21 201	97.74 215	99.65 132	99.67 166	99.06 174	100.00 1	98.87 366	97.84 126	99.96 121	100.00 1	83.57 356	99.88 178	99.72 133	100.00 1	100.00 1

testing222		99.14 117	98.94 128	99.73 118	99.67 166	99.51 124	100.00 1	99.43 122	96.90 211	99.99 105	99.90 233	98.55 131	99.86 180	98.85 215	97.18 241	99.81 206

dcpmvs_2		98.87 150	99.53 59	96.90 318	99.87 114	90.88 371	99.94 258	99.07 336	98.20 94	100.00 1	100.00 1	98.69 124	99.86 180	100.00 1	100.00 1	99.95 130

tpm cat1		98.05 206	97.76 213	98.92 209	99.50 228	97.10 292	99.77 291	99.30 242	90.20 372	99.72 189	98.71 353	97.71 157	99.86 180	96.75 300	98.20 203	99.81 206

tpmrst		98.98 141	98.93 130	99.14 196	99.61 191	97.74 266	99.52 328	99.36 212	96.05 266	99.98 111	99.64 284	99.04 94	99.86 180	98.94 209	98.19 204	99.82 197

MDTV_nov1_ep13			98.94 128		99.53 212	98.36 223	99.39 341	99.46 94	96.54 241	99.99 105	99.63 288	98.92 108	99.86 180	98.30 246	98.71 175

OMC-MVS		99.27 99	99.38 74	98.96 207	99.95 96	97.06 293	100.00 1	99.40 188	98.83 53	99.88 162	100.00 1	97.01 183	99.86 180	99.47 179	99.84 138	99.97 118

thisisatest0530		99.37 84	99.27 86	99.69 124	99.59 197	99.41 140	100.00 1	99.46 94	96.46 245	99.90 157	100.00 1	99.44 46	99.85 186	98.97 208	99.58 156	99.80 220

thisisatest0515		99.42 79	99.31 85	99.74 115	99.59 197	99.55 116	100.00 1	99.46 94	96.65 235	99.92 152	100.00 1	99.44 46	99.85 186	99.09 205	99.63 154	99.81 206

tttt0517		99.34 88	99.23 95	99.67 127	99.57 205	99.38 142	100.00 1	99.46 94	96.33 256	99.89 160	100.00 1	99.44 46	99.84 188	98.93 210	99.46 159	99.78 226

casdiffmvs		98.65 166	98.38 178	99.46 157	99.52 219	98.74 199	100.00 1	99.15 308	96.91 209	99.05 233	100.00 1	92.75 258	99.83 189	99.70 141	98.38 190	99.81 206
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
BH-untuned		98.64 167	98.65 157	98.60 225	99.59 197	96.17 307	100.00 1	99.28 254	96.67 234	98.41 276	100.00 1	94.52 234	99.83 189	99.41 182	100.00 1	99.81 206

DeepMVS_CX				89.98 367	98.90 299	71.46 398		99.18 301	97.61 151	96.92 334	99.83 247	86.07 338	99.83 189	96.02 307	97.65 236	98.65 259

dp		98.72 160	98.61 161	99.03 202	99.53 212	97.39 277	99.45 334	99.39 201	95.62 280	99.94 147	99.52 306	98.83 116	99.82 192	96.77 299	98.42 185	99.89 165

BH-RMVSNet		98.46 183	98.08 198	99.59 141	99.61 191	99.19 164	100.00 1	99.28 254	97.06 200	98.95 238	100.00 1	88.99 308	99.82 192	98.83 218	100.00 1	99.77 227

PMMVS		99.12 118	98.97 122	99.58 145	99.57 205	98.98 185	100.00 1	99.30 242	97.14 193	99.96 121	100.00 1	96.53 202	99.82 192	99.70 141	98.49 180	99.94 135

test2506		99.48 74	99.38 74	99.75 114	99.89 110	99.51 124	99.45 334	100.00 1	98.38 80	99.83 169	100.00 1	98.86 112	99.81 195	99.25 193	98.78 171	99.94 135

ECVR-MVS		98.43 185	98.14 194	99.32 181	99.89 110	98.21 235	99.46 332	100.00 1	98.38 80	99.47 203	100.00 1	87.91 319	99.80 196	99.35 185	98.78 171	99.94 135

test1111		98.42 187	98.12 195	99.29 184	99.88 112	98.15 237	99.46 332	100.00 1	98.36 84	99.42 204	100.00 1	87.91 319	99.79 197	99.31 189	98.78 171	99.94 135

EIA-MVS		99.26 101	99.19 100	99.45 159	99.63 184	98.75 196	100.00 1	99.27 262	96.93 206	99.95 145	100.00 1	97.47 169	99.79 197	99.74 129	99.72 146	99.82 197

ETV-MVS		99.34 88	99.24 92	99.64 133	99.58 202	99.33 147	100.00 1	99.25 270	97.57 157	99.96 121	100.00 1	97.44 172	99.79 197	99.70 141	99.65 152	99.81 206

TR-MVS		98.14 202	97.74 215	99.33 179	99.59 197	98.28 230	99.27 352	99.21 290	96.42 248	99.15 224	99.94 224	88.87 311	99.79 197	98.88 213	98.29 198	99.93 145

casdiffmvs_mvg		98.64 167	98.39 177	99.40 169	99.50 228	98.60 207	100.00 1	99.22 282	96.85 213	99.10 227	100.00 1	92.75 258	99.78 201	99.71 138	98.35 192	99.81 206
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
sd_testset		97.81 214	97.48 224	98.79 217	99.82 121	96.80 299	99.32 347	99.45 102	97.62 147	99.38 211	99.86 239	85.56 344	99.77 202	99.72 133	96.61 253	99.79 223

Effi-MVS+		98.58 173	98.24 188	99.61 137	99.60 193	99.26 154	97.85 395	99.10 326	96.22 261	99.97 116	99.89 234	93.75 241	99.77 202	99.43 180	98.34 193	99.81 206

baseline1		98.91 147	98.61 161	99.81 98	99.71 154	99.77 91	99.78 286	99.44 116	97.51 165	98.81 252	99.99 186	98.25 138	99.76 204	98.60 232	95.41 263	99.89 165

lupinMVS		99.29 97	99.16 104	99.69 124	99.45 241	99.49 129	100.00 1	99.15 308	97.45 171	99.97 116	100.00 1	96.76 194	99.76 204	99.67 152	100.00 1	99.81 206

EPP-MVSNet		99.10 120	99.00 118	99.40 169	99.51 224	98.68 202	99.92 261	99.43 122	95.47 289	99.65 192	100.00 1	99.51 33	99.76 204	99.53 176	98.00 210	99.75 230

baseline		98.69 164	98.45 173	99.41 165	99.52 219	98.67 203	100.00 1	99.17 306	97.03 201	99.13 225	100.00 1	93.17 251	99.74 207	99.70 141	98.34 193	99.81 206

CostFormer		98.84 152	98.77 144	99.04 201	99.41 247	97.58 271	99.67 311	99.35 223	94.66 306	99.96 121	99.36 316	99.28 72	99.74 207	99.41 182	97.81 225	99.81 206

Fast-Effi-MVS+		98.40 190	98.02 205	99.55 149	99.63 184	99.06 174	100.00 1	99.15 308	95.07 295	99.42 204	99.95 219	93.26 250	99.73 209	97.44 275	98.24 200	99.87 185

jason		99.11 119	98.96 123	99.59 141	99.17 268	99.31 150	100.00 1	99.13 317	97.38 176	99.83 169	100.00 1	95.54 216	99.72 210	99.57 170	99.97 111	99.74 231
jason: jason.
EPMVS		99.25 105	99.13 106	99.60 139	99.60 193	99.20 163	99.60 319	100.00 1	96.93 206	99.92 152	99.36 316	99.05 91	99.71 211	98.77 220	98.94 168	99.90 160

CS-MVS-test		99.31 94	99.27 86	99.43 162	99.99 49	98.77 195	100.00 1	99.19 294	97.24 188	99.96 121	100.00 1	97.56 165	99.70 212	99.68 149	99.81 141	99.82 197

Vis-MVSNet		98.52 179	98.25 186	99.34 176	99.68 161	98.55 209	99.68 310	99.41 184	97.34 180	99.94 147	100.00 1	90.38 289	99.70 212	99.03 207	98.84 169	99.76 229
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
TAPA-MVS	96.40 10	97.64 221	97.37 230	98.45 233	99.94 99	95.70 313	100.00 1	99.40 188	97.65 142	99.53 196	100.00 1	99.31 64	99.66 214	80.48 393	100.00 1	100.00 1
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
ETVMVS		99.16 115	98.98 121	99.69 124	99.67 166	99.56 115	100.00 1	99.45 102	96.36 253	99.98 111	99.95 219	98.65 125	99.64 215	99.11 204	97.63 237	99.88 176

CS-MVS		99.33 90	99.27 86	99.50 153	99.99 49	99.00 183	100.00 1	99.13 317	97.26 187	99.96 121	100.00 1	97.79 155	99.64 215	99.64 158	99.67 150	99.87 185

Fast-Effi-MVS+-dtu		98.38 191	98.56 166	97.82 284	99.58 202	94.44 341	100.00 1	99.16 307	96.75 222	99.51 198	99.63 288	95.03 226	99.60 217	97.71 266	99.67 150	99.42 248

PAPM		99.78 16	99.76 12	99.85 87	99.01 284	99.95 32	100.00 1	99.75 52	99.37 3	99.99 105	100.00 1	99.76 11	99.60 217	100.00 1	100.00 1	100.00 1

tt0805		96.52 274	96.23 274	97.40 295	99.30 262	93.55 349	99.32 347	99.45 102	96.75 222	97.88 307	99.99 186	79.99 372	99.59 219	97.39 279	95.98 256	99.06 255

GeoE		98.06 205	97.65 221	99.29 184	99.47 237	98.41 215	100.00 1	99.19 294	94.85 300	98.88 244	100.00 1	91.21 274	99.59 219	97.02 287	98.19 204	99.88 176

test_post1							99.32 347				88.24 401	99.33 59	99.59 219	98.31 243

test_post											89.05 399	99.49 39	99.59 219

ADS-MVSNet		98.70 163	98.51 169	99.28 187	99.51 224	98.39 218	99.24 355	99.44 116	95.52 285	99.96 121	99.70 268	97.57 163	99.58 223	97.11 285	98.54 177	99.88 176

Patchmatch-test		97.83 213	97.42 226	99.06 197	99.08 275	97.66 269	98.66 389	99.21 290	93.65 332	98.25 288	99.58 298	99.47 43	99.57 224	90.25 366	98.59 176	99.95 130

HQP4-MVS										99.17 220			99.57 224			97.77 263

HQP-MVS		97.73 218	97.85 210	97.39 296	99.07 276	94.82 325	100.00 1	99.40 188	99.04 15	99.17 220	99.97 199	88.61 314	99.57 224	99.79 119	95.58 257	97.77 263

CLD-MVS		97.64 221	97.74 215	97.36 298	99.01 284	94.76 333	100.00 1	99.34 229	99.30 4	99.00 235	99.97 199	87.49 325	99.57 224	99.96 85	95.58 257	97.75 275
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
OPM-MVS		97.21 242	97.18 241	97.32 301	98.08 338	94.66 334	100.00 1	99.28 254	98.65 69	98.92 240	99.98 191	86.03 340	99.56 228	98.28 247	95.41 263	97.72 311
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
LPG-MVS_test		97.31 239	97.32 232	97.28 303	98.85 306	94.60 337	100.00 1	99.37 206	97.35 178	98.85 247	99.98 191	86.66 333	99.56 228	99.55 171	95.26 270	97.70 320

LGP-MVS_train				97.28 303	98.85 306	94.60 337		99.37 206	97.35 178	98.85 247	99.98 191	86.66 333	99.56 228	99.55 171	95.26 270	97.70 320

ITE_SJBPF				96.84 322	98.96 294	93.49 350		98.12 379	98.12 103	98.35 278	99.97 199	84.45 348	99.56 228	95.63 314	95.25 272	97.49 351

ACMP	97.00 8	97.19 243	97.16 242	97.27 305	98.97 293	94.58 340	100.00 1	99.32 233	97.97 114	97.45 324	99.98 191	85.79 342	99.56 228	99.70 141	95.24 273	97.67 330
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
HQP_MVS		97.71 220	97.82 212	97.37 297	99.00 288	94.80 328	100.00 1	99.40 188	99.00 27	99.08 230	99.97 199	88.58 316	99.55 233	99.79 119	95.57 261	97.76 265

plane_prior5								99.40 188					99.55 233	99.79 119	95.57 261	97.76 265

ACMM	97.17 6	97.37 237	97.40 228	97.29 302	99.01 284	94.64 336	100.00 1	99.25 270	98.07 106	98.44 275	99.98 191	87.38 327	99.55 233	99.25 193	95.19 276	97.69 324
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
patchmatchnet-post											97.79 375	99.41 55	99.54 236

SCA		98.30 194	97.98 207	99.23 191	99.41 247	98.25 232	99.99 213	99.45 102	96.91 209	99.76 185	99.58 298	89.65 300	99.54 236	98.31 243	98.79 170	99.91 149

XVG-ACMP-BASELINE		96.60 272	96.52 261	96.84 322	98.41 320	93.29 353	99.99 213	99.32 233	97.76 134	98.51 271	99.29 319	81.95 365	99.54 236	98.40 238	95.03 284	97.68 326

ACMH+	96.20 13	96.49 279	96.33 271	97.00 312	99.06 280	93.80 347	99.81 280	99.31 238	97.32 182	95.89 356	99.97 199	82.62 363	99.54 236	98.34 242	94.63 292	97.65 335

ACMH	96.25 11	96.77 262	96.62 256	97.21 306	98.96 294	94.43 342	99.64 313	99.33 231	97.43 173	96.55 345	99.97 199	83.52 357	99.54 236	99.07 206	95.13 280	97.66 331
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
TDRefinement		91.93 342	90.48 350	96.27 335	81.60 406	92.65 360	99.10 376	97.61 394	93.96 326	93.77 370	99.85 244	80.03 370	99.53 241	97.82 263	70.59 396	96.63 374

JIA-IIPM		97.09 248	96.34 270	99.36 174	98.88 301	98.59 208	99.81 280	99.43 122	84.81 388	99.96 121	90.34 398	98.55 131	99.52 242	97.00 288	98.28 199	99.98 111

IS-MVSNet		99.08 121	98.91 132	99.59 141	99.65 176	99.38 142	99.78 286	99.24 275	96.70 229	99.51 198	100.00 1	98.44 135	99.52 242	98.47 237	98.39 188	99.88 176

Effi-MVS+-dtu		98.51 181	98.86 137	97.47 294	99.77 147	94.21 344	100.00 1	98.94 360	97.61 151	99.91 155	98.75 352	95.89 208	99.51 244	99.36 184	99.48 158	98.68 258

PatchmatchNet		99.03 127	98.96 123	99.26 189	99.49 232	98.33 225	99.38 342	99.45 102	96.64 236	99.96 121	99.58 298	99.49 39	99.50 245	97.63 269	99.00 167	99.93 145
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
TinyColmap		95.50 315	95.12 320	96.64 326	98.69 310	93.00 355	99.40 340	97.75 391	96.40 251	96.14 352	99.87 237	79.47 373	99.50 245	93.62 340	94.72 291	97.40 356

EC-MVSNet		99.19 111	99.09 110	99.48 156	99.42 245	99.07 172	100.00 1	99.21 290	96.95 204	99.96 121	100.00 1	96.88 192	99.48 247	99.64 158	99.79 144	99.88 176

LTVRE_ROB	95.29 16	96.32 289	96.10 279	96.99 313	98.55 315	93.88 346	99.45 334	99.28 254	94.50 312	96.46 346	99.52 306	84.86 347	99.48 247	97.26 283	95.03 284	97.59 345
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
AUN-MVS		96.26 292	95.67 303	98.06 266	99.68 161	95.60 314	99.82 279	99.42 138	96.78 219	99.88 162	99.80 256	94.84 229	99.47 249	97.48 274	73.29 394	99.12 253

D2MVS		97.63 225	97.83 211	97.05 309	98.83 308	94.60 337	100.00 1	99.82 40	96.89 212	98.28 284	99.03 334	94.05 237	99.47 249	98.58 234	94.97 287	97.09 364

tpm2		98.64 167	98.58 165	98.81 216	99.42 245	97.12 290	99.69 308	99.37 206	93.63 333	99.94 147	99.67 276	98.96 102	99.47 249	98.62 231	97.95 215	99.83 192

HyFIR lowres test		99.32 92	99.24 92	99.58 145	99.95 96	99.26 154	100.00 1	99.99 13	96.72 227	99.29 216	99.91 231	99.49 39	99.47 249	99.74 129	98.08 209	100.00 1

hse-mvs2		96.79 261	96.38 267	98.04 272	99.68 161	95.54 315	99.81 280	99.42 138	98.21 92	100.00 1	99.80 256	97.49 167	99.46 253	99.72 133	73.27 395	99.12 253

USDC		95.90 309	95.70 299	96.50 330	98.60 314	92.56 361	100.00 1	98.30 375	97.77 132	96.92 334	99.94 224	81.25 369	99.45 254	93.54 341	94.96 288	97.49 351

gm-plane-assit					99.52 219	97.26 286			95.86 271		100.00 1		99.43 255	98.76 221

MS-PatchMatch		95.66 313	95.87 289	95.05 343	97.80 348	89.25 376	98.88 385	99.30 242	96.35 254	96.86 337	99.01 336	81.35 368	99.43 255	93.30 343	99.98 108	96.46 375

CHOSEN 280x420		99.85 3	99.87 1	99.80 103	99.99 49	99.97 21	99.97 243	99.98 16	98.96 32	100.00 1	100.00 1	99.96 4	99.42 257	100.00 1	100.00 1	100.00 1

VPA-MVSNet		97.03 253	96.43 265	98.82 214	98.64 312	99.32 148	99.38 342	99.47 79	96.73 226	98.91 242	98.94 343	87.00 331	99.40 258	99.23 196	89.59 347	97.76 265

LF4IMVS		96.19 294	96.18 276	96.23 336	98.26 329	92.09 363	100.00 1	97.89 388	97.82 128	97.94 303	99.87 237	82.71 362	99.38 259	97.41 277	93.71 298	97.20 361

UniMVSNet_ETH3D		95.28 318	94.41 324	97.89 282	98.91 298	95.14 319	99.13 373	99.35 223	92.11 356	97.17 331	99.66 278	70.28 392	99.36 260	97.88 261	95.18 277	99.16 251

GA-MVS		97.72 219	97.27 236	99.06 197	99.24 266	97.93 256	100.00 1	99.24 275	95.80 275	98.99 236	99.64 284	89.77 297	99.36 260	95.12 323	97.62 238	99.89 165

XXY-MVS		97.14 247	96.63 255	98.67 221	98.65 311	98.92 189	99.54 326	99.29 247	95.57 282	97.63 316	99.83 247	87.79 323	99.35 262	98.39 239	92.95 307	97.75 275

test_fmvs2		95.17 321	95.23 317	95.01 344	98.95 296	88.99 378	99.99 213	97.77 390	97.79 130	98.58 264	99.70 268	73.36 386	99.34 263	95.88 308	95.03 284	96.70 372

GG-mvs-BLEND				99.59 141	99.54 209	99.49 129	99.17 368	99.52 72		99.96 121	99.68 275	100.00 1	99.33 264	99.71 138	99.99 98	99.96 124

dmvs_re		97.54 229	97.88 209	96.54 329	99.55 208	90.35 373	99.86 272	99.46 94	97.00 202	99.41 209	100.00 1	90.78 282	99.30 265	99.60 165	95.24 273	99.96 124

EPNet_dtu		98.53 178	98.23 191	99.43 162	99.92 104	99.01 181	99.96 248	99.47 79	98.80 57	99.96 121	99.96 211	98.56 130	99.30 265	87.78 378	99.68 148	100.00 1
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tpm		98.24 199	98.22 192	98.32 242	99.13 270	95.79 312	99.53 327	99.12 323	95.20 294	99.96 121	99.36 316	97.58 162	99.28 267	97.41 277	96.67 251	99.88 176

baseline2		98.99 138	98.93 130	99.18 194	99.26 265	99.15 169	100.00 1	99.46 94	96.71 228	96.79 340	100.00 1	99.42 53	99.25 268	98.75 222	99.94 119	99.15 252

gg-mvs-nofinetune		96.95 257	96.10 279	99.50 153	99.41 247	99.36 146	99.07 381	99.52 72	83.69 390	99.96 121	83.60 406	100.00 1	99.20 269	99.68 149	99.99 98	99.96 124

TAMVS		98.76 157	98.73 149	98.86 213	99.44 243	97.69 267	99.57 322	99.34 229	96.57 239	99.12 226	99.81 253	98.83 116	99.16 270	97.97 259	97.91 217	99.73 235

MVP-Stereo		96.51 276	96.48 263	96.60 328	95.65 382	94.25 343	98.84 386	98.16 377	95.85 273	95.23 359	99.04 332	92.54 264	99.13 271	92.98 345	99.98 108	96.43 376
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
Vis-MVSNet (Re-imp)		98.99 138	98.89 136	99.29 184	99.64 182	98.89 190	99.98 237	99.31 238	96.74 224	99.48 200	100.00 1	98.11 142	99.10 272	98.39 239	98.34 193	99.89 165

EG-PatchMatch MVS		92.94 338	92.49 342	94.29 355	95.87 379	87.07 383	99.07 381	98.11 380	93.19 345	88.98 385	98.66 356	70.89 390	99.08 273	92.43 350	95.21 275	96.72 371

MVS-HIRNet		94.12 328	92.73 341	98.29 244	99.33 258	95.95 308	99.38 342	99.19 294	74.54 398	98.26 287	86.34 402	86.07 338	99.06 274	91.60 355	99.87 132	99.85 188

mvs_anonymous		98.80 155	98.60 163	99.38 173	99.57 205	99.24 158	100.00 1	99.21 290	95.87 269	98.92 240	99.82 250	96.39 204	99.03 275	99.13 202	98.50 179	99.88 176

TESTMET0.1,1		99.08 121	98.96 123	99.44 160	99.63 184	99.38 142	100.00 1	99.45 102	95.53 283	99.48 200	100.00 1	99.71 13	99.02 276	96.84 293	99.99 98	99.91 149

test-LLR		99.03 127	98.91 132	99.40 169	99.40 252	99.28 152	100.00 1	99.45 102	96.70 229	99.42 204	99.12 325	99.31 64	99.01 277	96.82 294	99.99 98	99.91 149

test-mter		98.96 142	98.82 139	99.40 169	99.40 252	99.28 152	100.00 1	99.45 102	95.44 292	99.42 204	99.12 325	99.70 14	99.01 277	96.82 294	99.99 98	99.91 149

tfpnnormal		96.36 286	95.69 302	98.37 238	98.55 315	98.71 200	99.69 308	99.45 102	93.16 346	96.69 344	99.71 265	88.44 318	98.99 279	94.17 333	91.38 332	97.41 355

Anonymous20231211		96.29 290	95.70 299	98.07 262	99.80 138	97.49 273	99.15 371	99.40 188	89.11 375	97.75 313	99.45 311	88.93 310	98.98 280	98.26 248	89.47 349	97.73 304

nrg030		97.64 221	97.27 236	98.75 219	98.34 322	99.53 120	100.00 1	99.22 282	96.21 262	98.27 286	99.95 219	94.40 235	98.98 280	99.23 196	89.78 346	97.75 275

cl22		98.23 200	98.11 196	98.58 228	99.82 121	99.01 181	100.00 1	99.28 254	96.92 208	98.33 280	99.21 322	98.09 144	98.97 282	98.72 223	92.61 310	97.76 265

CVMVSNet		98.56 175	98.47 172	98.82 214	99.11 271	97.67 268	99.74 296	99.47 79	97.57 157	99.06 232	100.00 1	95.72 212	98.97 282	98.21 249	97.33 240	99.83 192

VPNet		96.41 281	95.76 296	98.33 241	98.61 313	98.30 229	99.48 331	99.45 102	96.98 203	98.87 246	99.88 236	81.57 366	98.93 284	99.22 198	87.82 363	97.76 265

v7n		96.06 305	95.42 315	97.99 276	97.58 357	97.35 280	99.86 272	99.11 324	92.81 353	97.91 306	99.49 308	90.99 280	98.92 285	92.51 348	88.49 359	97.70 320

jajsoiax		97.07 250	96.79 251	97.89 282	97.28 368	97.12 290	99.95 254	99.19 294	96.55 240	97.31 327	99.69 271	87.35 329	98.91 286	98.70 224	95.12 281	97.66 331

mvs_tets		97.00 256	96.69 253	97.94 278	97.41 367	97.27 285	99.60 319	99.18 301	96.51 244	97.35 326	99.69 271	86.53 335	98.91 286	98.84 216	95.09 283	97.65 335

CDS-MVSNet		98.96 142	98.95 127	99.01 203	99.48 234	98.36 223	99.93 260	99.37 206	96.79 218	99.31 215	99.83 247	99.77 10	98.91 286	98.07 253	97.98 211	99.77 227
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
FIs		97.95 210	97.73 218	98.62 224	98.53 317	99.24 158	100.00 1	99.43 122	96.74 224	97.87 308	99.82 250	95.27 219	98.89 289	98.78 219	93.07 305	97.74 298

v1192		96.18 295	95.49 309	98.26 247	98.01 340	98.15 237	99.99 213	99.08 332	93.36 340	98.54 267	98.97 341	89.47 303	98.89 289	91.15 358	90.82 337	97.75 275

UniMVSNet (Re)		97.29 241	96.85 248	98.59 227	98.49 318	99.13 170	100.00 1	99.42 138	96.52 243	98.24 290	98.90 346	94.93 227	98.89 289	97.54 272	87.61 364	97.75 275

EI-MVSNet		97.98 209	97.93 208	98.16 255	99.11 271	97.84 262	99.74 296	99.29 247	94.39 316	98.65 259	100.00 1	97.21 177	98.88 292	97.62 271	95.31 268	97.75 275

MVSTER		98.58 173	98.52 168	98.77 218	99.65 176	99.68 104	100.00 1	99.29 247	95.63 279	98.65 259	99.80 256	99.78 8	98.88 292	98.59 233	95.31 268	97.73 304

iter_conf05		98.73 159	98.77 144	98.60 225	99.65 176	99.22 161	100.00 1	99.22 282	96.68 233	98.98 237	99.97 199	99.99 3	98.84 294	99.29 191	95.11 282	97.75 275

pm-mvs1		95.76 311	95.01 321	98.00 274	98.23 331	97.45 275	99.24 355	99.04 349	93.13 347	95.93 355	99.72 263	86.28 336	98.84 294	95.62 315	87.92 362	97.72 311

V42		96.65 269	96.16 278	98.11 261	98.17 336	98.23 233	99.99 213	99.09 331	93.97 325	98.74 256	99.05 331	91.09 277	98.82 296	95.46 317	89.90 344	97.27 360

CMPMVS	66.12 22	90.65 350	92.04 343	86.46 375	96.18 376	66.87 405	98.03 394	99.38 203	83.38 391	85.49 393	99.55 303	77.59 378	98.80 297	94.44 330	94.31 295	93.72 390
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
PS-MVSNAJss		98.03 207	98.06 201	97.94 278	97.63 352	97.33 283	99.89 268	99.23 279	96.27 258	98.03 297	99.59 296	98.75 121	98.78 298	98.52 235	94.61 293	97.70 320

OurMVSNet-221017-0		96.14 301	95.98 285	96.62 327	97.49 362	93.44 351	99.92 261	98.16 377	95.86 271	97.65 315	99.95 219	85.71 343	98.78 298	94.93 325	94.18 296	97.64 338

OpenMVS_ROB	88.34 20	91.89 343	91.12 345	94.19 357	95.55 384	87.63 381	99.26 353	98.03 382	86.61 385	90.65 383	96.82 382	70.14 393	98.78 298	86.54 381	96.50 255	96.15 377

pmmvs6		93.64 330	92.87 338	95.94 339	97.47 364	91.41 368	98.92 383	99.02 352	87.84 382	95.01 361	99.61 294	77.24 380	98.77 301	94.33 331	86.41 372	97.63 339

v8		96.35 287	95.73 298	98.21 251	98.11 337	98.23 233	99.94 258	99.07 336	92.66 354	98.29 283	99.00 337	91.46 271	98.77 301	94.17 333	88.83 357	97.62 341

miper_enhance_ethall		98.33 193	98.27 185	98.51 230	99.66 175	99.04 177	100.00 1	99.22 282	97.53 161	98.51 271	99.38 314	99.49 39	98.75 303	98.02 255	92.61 310	97.76 265

EGC-MVSNET		79.46 365	74.04 373	95.72 340	96.00 378	92.73 358	99.09 378	99.04 349	5.08 410	16.72 410	98.71 353	73.03 387	98.74 304	82.05 390	96.64 252	95.69 383

mvsmamba		98.13 203	98.06 201	98.32 242	98.22 332	98.50 212	100.00 1	99.22 282	96.41 249	98.91 242	99.96 211	95.69 213	98.73 305	99.19 199	94.95 289	97.73 304

v1921920		96.16 299	95.50 307	98.14 256	97.88 347	97.96 253	99.99 213	99.07 336	93.33 341	98.60 263	99.24 321	89.37 304	98.71 306	91.28 356	90.74 339	97.75 275

lessismore_v0				96.05 337	97.55 358	91.80 365		99.22 282		91.87 377	99.91 231	83.50 358	98.68 307	92.48 349	90.42 343	97.68 326

v2v482		96.70 267	96.18 276	98.27 245	98.04 339	98.39 218	100.00 1	99.13 317	94.19 322	98.58 264	99.08 328	90.48 287	98.67 308	95.69 312	90.44 342	97.75 275

v144192		96.40 284	95.81 291	98.17 254	97.89 345	98.11 240	99.99 213	99.06 344	93.39 339	98.75 255	99.09 327	90.43 288	98.66 309	93.10 344	90.55 341	97.75 275

cl____		97.54 229	97.32 232	98.18 252	99.47 237	98.14 239	100.00 1	99.10 326	94.16 323	97.60 320	99.63 288	97.52 166	98.65 310	96.47 301	91.97 322	97.76 265

v1144		96.51 276	95.97 286	98.13 259	97.98 342	98.04 247	99.99 213	99.08 332	93.51 337	98.62 262	98.98 338	90.98 281	98.62 311	93.79 339	90.79 338	97.74 298

v1240		95.96 307	95.25 316	98.07 262	97.91 344	97.87 261	99.96 248	99.07 336	93.24 344	98.64 261	98.96 342	88.98 309	98.61 312	89.58 371	90.92 336	97.75 275

v10		96.14 301	95.50 307	98.07 262	98.19 334	97.96 253	99.83 276	99.07 336	92.10 357	98.07 294	98.94 343	91.07 278	98.61 312	92.41 351	89.82 345	97.63 339

anonymousdsp		97.16 245	96.88 246	98.00 274	97.08 370	98.06 245	99.81 280	99.15 308	94.58 308	97.84 309	99.62 292	90.49 286	98.60 314	97.98 256	95.32 267	97.33 359

v148		96.29 290	95.84 290	97.63 288	97.74 349	96.53 305	100.00 1	99.07 336	93.52 336	98.01 300	99.42 313	91.22 273	98.60 314	96.37 305	87.22 367	97.75 275

MVSFormer		98.94 145	98.82 139	99.28 187	99.45 241	99.49 129	100.00 1	99.13 317	95.46 290	99.97 116	100.00 1	96.76 194	98.59 316	98.63 229	100.00 1	99.74 231

test_djsdf		97.55 228	97.38 229	98.07 262	97.50 360	97.99 249	100.00 1	99.13 317	95.46 290	98.47 274	99.85 244	92.01 270	98.59 316	98.63 229	95.36 266	97.62 341

test_0402		94.35 324	93.70 329	96.32 334	97.92 343	93.60 348	99.61 318	98.85 368	88.19 381	94.68 364	99.48 309	80.01 371	98.58 318	89.39 372	95.15 279	96.77 370

miper_ehance_all_eth		97.81 214	97.66 220	98.23 248	99.49 232	98.37 221	99.99 213	99.11 324	94.78 301	98.25 288	99.21 322	98.18 140	98.57 319	97.35 281	92.61 310	97.76 265

FC-MVSNet-test		97.84 212	97.63 222	98.45 233	98.30 327	99.05 176	100.00 1	99.43 122	96.63 238	97.61 319	99.82 250	95.19 223	98.57 319	98.64 228	93.05 306	97.73 304

WR-MVS		97.09 248	96.64 254	98.46 232	98.43 319	99.09 171	99.97 243	99.33 231	95.62 280	97.76 310	99.67 276	91.17 276	98.56 321	98.49 236	89.28 352	97.74 298

RRT_MVS		97.77 216	97.76 213	97.78 286	97.89 345	97.06 293	100.00 1	99.29 247	95.74 276	98.00 302	99.97 199	95.94 207	98.55 322	98.87 214	94.18 296	97.72 311

WR-MVS_H		96.73 264	96.32 272	97.95 277	98.26 329	97.88 259	99.72 303	99.43 122	95.06 296	96.99 333	98.68 355	93.02 254	98.53 323	97.43 276	88.33 360	97.43 354

ambc				88.45 370	86.84 402	70.76 399	97.79 396	98.02 384		90.91 380	95.14 387	38.69 405	98.51 324	94.97 324	84.23 376	96.09 379

eth_miper_zixun_eth		97.47 233	97.28 234	98.06 266	99.41 247	97.94 255	99.62 317	99.08 332	94.46 314	98.19 291	99.56 302	96.91 191	98.50 325	96.78 297	91.49 329	97.74 298

UniMVSNet_NR-MVSNet		97.16 245	96.80 249	98.22 249	98.38 321	98.41 215	100.00 1	99.45 102	96.14 264	97.76 310	99.64 284	95.05 225	98.50 325	97.98 256	86.84 368	97.75 275

DU-MVS		96.93 258	96.49 262	98.22 249	98.31 325	98.41 215	100.00 1	99.37 206	96.41 249	97.76 310	99.65 280	92.14 267	98.50 325	97.98 256	86.84 368	97.75 275

pmmvs4		97.17 244	96.80 249	98.27 245	97.68 351	98.64 205	100.00 1	99.18 301	94.22 319	98.55 266	99.71 265	93.67 242	98.47 328	95.66 313	92.57 313	97.71 319

pmmvs5		95.94 308	95.61 304	96.95 315	97.42 365	94.66 334	100.00 1	98.08 381	93.60 334	97.05 332	99.43 312	87.02 330	98.46 329	95.76 309	92.12 318	97.72 311

SixPastTwentyTwo		95.71 312	95.49 309	96.38 332	97.42 365	93.01 354	99.84 275	98.23 376	94.75 302	95.98 354	99.97 199	85.35 345	98.43 330	94.71 327	93.17 304	97.69 324

WB-MVSnew		97.02 255	97.24 238	96.37 333	99.44 243	97.36 279	100.00 1	99.43 122	96.12 265	99.35 213	99.89 234	93.60 245	98.42 331	88.91 377	98.39 188	93.33 392

NR-MVSNet		96.63 270	96.04 282	98.38 237	98.31 325	98.98 185	99.22 362	99.35 223	95.87 269	94.43 368	99.65 280	92.73 260	98.40 332	96.78 297	88.05 361	97.75 275

TransMVSNet (Re)		94.78 322	93.72 328	97.93 280	98.34 322	97.88 259	99.23 360	97.98 385	91.60 359	94.55 365	99.71 265	87.89 321	98.36 333	89.30 373	84.92 374	97.56 347

Baseline_NR-MVSNet		96.16 299	95.70 299	97.56 293	98.28 328	96.79 300	100.00 1	97.86 389	91.93 358	97.63 316	99.47 310	92.14 267	98.35 334	97.13 284	86.83 370	97.54 348

IterMVS-SCA-FT		96.72 266	96.42 266	97.62 290	99.40 252	96.83 298	99.99 213	99.14 313	94.65 307	97.55 322	99.72 263	89.65 300	98.31 335	95.62 315	92.05 319	97.73 304

DIV-MVS_self_test		97.52 232	97.35 231	98.05 270	99.46 240	98.11 240	100.00 1	99.10 326	94.21 320	97.62 318	99.63 288	97.65 160	98.29 336	96.47 301	91.98 321	97.76 265

IterMVS		96.76 263	96.46 264	97.63 288	99.41 247	96.89 296	99.99 213	99.13 317	94.74 304	97.59 321	99.66 278	89.63 302	98.28 337	95.71 311	92.31 316	97.72 311
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
test_vis3_rt		79.61 364	78.19 369	83.86 378	88.68 401	69.56 400	99.81 280	82.19 414	86.78 384	68.57 402	84.51 405	25.06 411	98.26 338	89.18 375	78.94 389	83.75 402

c3_l		97.58 226	97.42 226	98.06 266	99.48 234	98.16 236	99.96 248	99.10 326	94.54 310	98.13 292	99.20 324	97.87 149	98.25 339	97.28 282	91.20 334	97.75 275

test0.0.03 1		98.12 204	98.03 204	98.39 236	99.11 271	98.07 243	100.00 1	99.93 30	96.70 229	96.91 336	99.95 219	99.31 64	98.19 340	91.93 352	98.44 183	98.91 256

CP-MVSNet		96.73 264	96.25 273	98.18 252	98.21 333	98.67 203	99.77 291	99.32 233	95.06 296	97.20 330	99.65 280	90.10 291	98.19 340	98.06 254	88.90 355	97.66 331

PS-CasMVS		96.34 288	95.78 295	98.03 273	98.18 335	98.27 231	99.71 304	99.32 233	94.75 302	96.82 339	99.65 280	86.98 332	98.15 342	97.74 265	88.85 356	97.66 331

our_test_3		96.51 276	96.35 269	96.98 314	97.61 354	95.05 320	99.98 237	99.01 354	94.68 305	96.77 342	99.06 329	95.87 209	98.14 343	91.81 353	92.37 315	97.75 275

new_pmnet		94.11 329	93.47 332	96.04 338	96.60 374	92.82 357	99.97 243	98.91 363	90.21 371	95.26 358	98.05 374	85.89 341	98.14 343	84.28 385	92.01 320	97.16 362

K. test v3		95.46 316	95.14 319	96.40 331	97.53 359	93.40 352	99.99 213	99.23 279	95.49 288	92.70 376	99.73 262	84.26 350	98.12 345	93.94 338	93.38 303	97.68 326

Patchmtry		96.81 260	96.37 268	98.14 256	99.31 259	98.55 209	98.91 384	99.00 355	90.45 368	97.92 305	98.98 338	96.94 189	98.12 345	94.27 332	91.53 328	97.75 275

FMVSNet3		97.30 240	96.95 244	98.37 238	99.65 176	99.25 156	99.71 304	99.28 254	94.23 318	98.53 268	98.91 345	93.30 249	98.11 347	95.31 319	93.60 299	97.73 304

N_pmnet		91.88 344	93.37 333	87.40 373	97.24 369	66.33 406	99.90 265	91.05 409	89.77 374	95.65 357	98.58 359	90.05 294	98.11 347	85.39 382	92.72 309	97.75 275

IterMVS-LS		97.56 227	97.44 225	97.92 281	99.38 256	97.90 257	99.89 268	99.10 326	94.41 315	98.32 281	99.54 305	97.21 177	98.11 347	97.50 273	91.62 326	97.75 275
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CR-MVSNet		98.02 208	97.71 219	98.93 208	99.31 259	98.86 191	99.13 373	99.00 355	96.53 242	99.96 121	98.98 338	96.94 189	98.10 350	91.18 357	98.40 186	99.84 189

FMVSNet2		96.22 293	95.60 305	98.06 266	99.53 212	98.33 225	99.45 334	99.27 262	93.71 328	98.03 297	98.84 348	84.23 351	98.10 350	93.97 337	93.40 302	97.73 304

ppachtmachnet_test		96.17 297	95.89 288	97.02 311	97.61 354	95.24 317	99.99 213	99.24 275	93.31 342	96.71 343	99.62 292	94.34 236	98.07 352	89.87 367	92.30 317	97.75 275

GBi-Net		96.07 303	95.80 293	96.89 319	99.53 212	94.87 322	99.18 363	99.27 262	93.71 328	98.53 268	98.81 349	84.23 351	98.07 352	95.31 319	93.60 299	97.72 311

test1		96.07 303	95.80 293	96.89 319	99.53 212	94.87 322	99.18 363	99.27 262	93.71 328	98.53 268	98.81 349	84.23 351	98.07 352	95.31 319	93.60 299	97.72 311

FMVSNet1		94.45 323	93.63 330	96.89 319	98.87 304	94.87 322	99.18 363	99.27 262	90.95 365	97.31 327	98.81 349	72.89 388	98.07 352	92.61 346	92.81 308	97.72 311

PatchT		95.90 309	94.95 323	98.75 219	99.03 282	98.39 218	99.08 379	99.32 233	85.52 386	99.96 121	94.99 390	97.94 146	98.05 356	80.20 394	98.47 182	99.81 206

TranMVSNet+NR-MVSNet		96.45 280	96.01 283	97.79 285	98.00 341	97.62 270	100.00 1	99.35 223	95.98 267	97.31 327	99.64 284	90.09 292	98.00 357	96.89 292	86.80 371	97.75 275

test_method		91.04 349	91.10 346	90.85 365	98.34 322	77.63 392	100.00 1	98.93 362	76.69 396	96.25 350	98.52 361	70.44 391	97.98 358	89.02 376	91.74 324	96.92 368

miper_lstm_enhance		97.40 236	97.28 234	97.75 287	99.48 234	97.52 272	100.00 1	99.07 336	94.08 324	98.01 300	99.61 294	97.38 174	97.98 358	96.44 304	91.47 331	97.76 265

ET-MVSNet_ETH3D		96.41 281	95.48 311	99.20 193	99.81 127	99.75 93	100.00 1	99.02 352	97.30 186	78.33 398	100.00 1	97.73 156	97.94 360	99.70 141	87.41 365	99.92 147

ADS-MVSNet2		98.28 197	98.51 169	97.62 290	99.51 224	95.03 321	99.24 355	99.41 184	95.52 285	99.96 121	99.70 268	97.57 163	97.94 360	97.11 285	98.54 177	99.88 176

PEN-MVS		96.01 306	95.48 311	97.58 292	97.74 349	97.26 286	99.90 265	99.29 247	94.55 309	96.79 340	99.55 303	87.38 327	97.84 362	96.92 291	87.24 366	97.65 335

Syy-MVS		96.17 297	96.57 258	95.00 345	99.50 228	87.37 382	100.00 1	99.57 68	96.23 259	98.07 294	100.00 1	92.41 265	97.81 363	85.34 383	97.96 213	99.82 197

myMVS_eth3d		98.52 179	98.51 169	98.53 229	99.50 228	97.98 250	100.00 1	99.57 68	96.23 259	98.07 294	100.00 1	99.09 87	97.81 363	96.17 306	97.96 213	99.82 197

testing3		98.44 184	98.37 180	98.65 222	99.51 224	98.32 227	100.00 1	99.62 66	96.43 246	97.93 304	99.99 186	99.11 85	97.81 363	94.88 326	97.80 226	99.82 197

testgi		96.18 295	95.93 287	96.93 317	98.98 292	94.20 345	100.00 1	99.07 336	97.16 192	96.06 353	99.86 239	84.08 354	97.79 366	90.38 365	97.80 226	98.81 257

testf1		84.40 361	84.79 363	83.23 379	95.71 380	58.71 412	98.79 387	97.75 391	81.58 392	84.94 394	98.07 372	45.33 403	97.73 367	77.09 400	83.85 377	93.24 393

APD_test2		84.40 361	84.79 363	83.23 379	95.71 380	58.71 412	98.79 387	97.75 391	81.58 392	84.94 394	98.07 372	45.33 403	97.73 367	77.09 400	83.85 377	93.24 393

MIMVSNet		97.06 251	96.73 252	98.05 270	99.38 256	96.64 304	98.47 391	99.35 223	93.41 338	99.48 200	98.53 360	89.66 299	97.70 369	94.16 335	98.11 208	99.80 220

LCM-MVSNet-Re		96.52 274	97.21 240	94.44 351	99.27 263	85.80 384	99.85 274	96.61 401	95.98 267	92.75 375	98.48 362	93.97 240	97.55 370	99.58 169	98.43 184	99.98 111

DTE-MVSNet		95.52 314	94.99 322	97.08 308	97.49 362	96.45 306	100.00 1	99.25 270	93.82 327	96.17 351	99.57 301	87.81 322	97.18 371	94.57 328	86.26 373	97.62 341

mvsany_test3		89.36 354	88.96 358	90.56 366	91.95 392	78.97 391	99.74 296	96.59 402	96.84 214	89.25 384	96.07 384	52.59 399	97.11 372	95.17 322	82.44 382	95.58 385

UnsupCasMVSNet_bld		89.50 353	88.00 359	93.99 358	95.30 385	88.86 379	98.52 390	99.28 254	85.50 387	87.80 389	94.11 392	61.63 396	96.96 373	90.63 361	79.26 387	96.15 377

Anonymous20240521		93.29 333	92.76 340	94.90 349	95.64 383	91.27 369	99.97 243	98.82 369	87.04 383	94.71 363	98.19 369	83.86 355	96.80 374	84.04 386	92.56 314	96.64 373

KD-MVS_2432*1600		94.15 326	93.08 335	97.35 299	99.53 212	97.83 263	99.63 315	99.19 294	92.88 350	96.29 348	97.68 376	98.84 114	96.70 375	89.73 368	63.92 399	97.53 349

miper_refine_blended		94.15 326	93.08 335	97.35 299	99.53 212	97.83 263	99.63 315	99.19 294	92.88 350	96.29 348	97.68 376	98.84 114	96.70 375	89.73 368	63.92 399	97.53 349

test_f		86.87 359	86.06 362	89.28 369	91.45 397	76.37 394	99.87 271	97.11 396	91.10 363	88.46 386	93.05 395	38.31 406	96.66 377	91.77 354	83.46 380	94.82 386

MDA-MVSNet_test_wron		92.61 339	91.09 347	97.19 307	96.71 373	97.26 286	100.00 1	99.14 313	88.61 377	67.90 404	98.32 368	89.03 307	96.57 378	90.47 364	89.59 347	97.74 298

UnsupCasMVSNet_eth		94.25 325	93.89 326	95.34 341	97.63 352	92.13 362	99.73 301	99.36 212	94.88 299	92.78 373	98.63 357	82.72 361	96.53 379	94.57 328	84.73 375	97.36 357

tmp_tt		75.80 370	74.26 372	80.43 383	52.91 415	53.67 414	87.42 402	97.98 385	61.80 402	67.04 405	100.00 1	76.43 381	96.40 380	96.47 301	28.26 407	91.23 397

Gipuma		84.73 360	83.50 365	88.40 371	97.50 360	82.21 389	88.87 400	99.05 346	65.81 400	85.71 392	90.49 397	53.70 398	96.31 381	78.64 396	91.74 324	86.67 399
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
YYNet1		92.44 340	90.92 348	97.03 310	96.20 375	97.06 293	99.99 213	99.14 313	88.21 380	67.93 403	98.43 365	88.63 313	96.28 382	90.64 360	89.08 354	97.74 298

EU-MVSNet		96.63 270	96.53 259	96.94 316	97.59 356	96.87 297	99.76 293	99.47 79	96.35 254	96.85 338	99.78 259	92.57 263	96.27 383	95.33 318	91.08 335	97.68 326

pmmvs-eth3d		91.73 345	90.67 349	94.92 348	91.63 395	92.71 359	99.90 265	98.54 373	91.19 362	88.08 387	95.50 386	79.31 375	96.13 384	90.55 363	81.32 386	95.91 381

PM-MVS		88.39 355	87.41 360	91.31 364	91.73 394	82.02 390	99.79 285	96.62 400	91.06 364	90.71 382	95.73 385	48.60 401	95.96 385	90.56 362	81.91 385	95.97 380

MDA-MVSNet-bldmvs		91.65 346	89.94 354	96.79 325	96.72 372	96.70 302	99.42 339	98.94 360	88.89 376	66.97 406	98.37 366	81.43 367	95.91 386	89.24 374	89.46 350	97.75 275

Patchmatch-RL test		93.49 331	93.63 330	93.05 362	91.78 393	83.41 388	98.21 393	96.95 398	91.58 360	91.05 378	97.64 378	99.40 56	95.83 387	94.11 336	81.95 384	99.91 149

Anonymous20231206		93.45 332	93.17 334	94.30 354	95.00 387	89.69 375	99.98 237	98.43 374	93.30 343	94.50 367	98.59 358	90.52 285	95.73 388	77.46 399	90.73 340	97.48 353

DSMNet-mixed		95.18 320	95.21 318	95.08 342	96.03 377	90.21 374	99.65 312	93.64 407	92.91 349	98.34 279	97.40 379	90.05 294	95.51 389	91.02 359	97.86 220	99.51 247

pmmvs3		90.62 351	89.36 357	94.40 352	90.53 400	91.49 367	100.00 1	96.73 399	84.21 389	93.65 371	96.65 383	82.56 364	94.83 390	82.28 389	77.62 391	96.89 369

KD-MVS_self_test		91.16 347	90.09 352	94.35 353	94.44 389	91.27 369	99.74 296	99.08 332	90.82 366	94.53 366	94.91 391	86.11 337	94.78 391	82.67 388	68.52 397	96.99 366

FMVSNet5		95.32 317	95.43 314	94.99 346	99.39 255	92.99 356	99.25 354	99.24 275	90.45 368	97.44 325	98.45 363	95.78 211	94.39 392	87.02 379	91.88 323	97.59 345

new-patchmatchnet		90.30 352	89.46 356	92.84 363	90.77 398	88.55 380	99.83 276	98.80 370	90.07 373	87.86 388	95.00 389	78.77 376	94.30 393	84.86 384	79.15 388	95.68 384

LCM-MVSNet		79.01 368	76.93 371	85.27 376	78.28 408	68.01 404	96.57 397	98.03 382	55.10 404	82.03 397	93.27 394	31.99 410	93.95 394	82.72 387	74.37 393	93.84 389

CL-MVSNet_self_test		91.07 348	90.35 351	93.24 361	93.27 390	89.16 377	99.55 324	99.25 270	92.34 355	95.23 359	97.05 381	88.86 312	93.59 395	80.67 392	66.95 398	96.96 367

MIMVSNet1		91.96 341	91.20 344	94.23 356	94.94 388	91.69 366	99.34 346	99.22 282	88.23 379	94.18 369	98.45 363	75.52 383	93.41 396	79.37 395	91.49 329	97.60 344

test20.03		93.11 335	92.85 339	93.88 359	95.19 386	91.83 364	100.00 1	98.87 366	93.68 331	92.76 374	98.88 347	89.20 306	92.71 397	77.88 397	89.19 353	97.09 364

test_fmvs3		87.19 358	87.02 361	87.71 372	92.69 391	76.64 393	99.96 248	97.27 395	93.55 335	90.82 381	94.03 393	38.00 407	92.19 398	93.49 342	83.35 381	94.32 387

dmvs_testset		93.27 334	95.48 311	86.65 374	98.74 309	68.42 403	99.92 261	98.91 363	96.19 263	93.28 372	100.00 1	91.06 279	91.67 399	89.64 370	91.54 327	99.86 187

PMMVS2		79.15 367	77.28 370	84.76 377	82.34 405	72.66 395	99.70 306	95.11 405	71.68 399	84.78 396	90.87 396	32.05 409	89.99 400	75.53 402	63.45 401	91.64 396

FPMVS		77.92 369	79.45 367	73.34 388	76.87 409	46.81 415	98.24 392	99.05 346	59.89 403	73.55 399	98.34 367	36.81 408	86.55 401	80.96 391	91.35 333	86.65 400

PMVS	60.66 23	65.98 375	65.05 376	68.75 391	55.06 414	38.40 416	88.19 401	96.98 397	48.30 408	44.82 409	88.52 400	12.22 414	86.49 402	67.58 403	83.79 379	81.35 404
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
WB-MVS		88.24 356	90.09 352	82.68 381	91.56 396	69.51 401	100.00 1	98.73 371	90.72 367	87.29 390	98.12 370	92.87 256	85.01 403	62.19 404	89.34 351	93.54 391

SSC-MVS		87.61 357	89.47 355	82.04 382	90.63 399	68.77 402	99.99 213	98.66 372	90.34 370	86.70 391	98.08 371	92.72 261	84.12 404	59.41 407	88.71 358	93.22 395

MVE	68.59 21	67.22 373	64.68 377	74.84 385	74.67 411	62.32 410	95.84 398	90.87 410	50.98 405	58.72 407	81.05 407	12.20 415	78.95 405	61.06 406	56.75 402	83.24 403
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		70.72 371	70.06 374	72.69 389	83.92 404	65.48 408	99.95 254	92.72 408	49.88 406	72.30 400	86.26 403	47.17 402	77.43 406	53.83 408	44.49 404	75.17 406

EMVS		69.88 372	69.09 375	72.24 390	84.70 403	65.82 407	99.96 248	87.08 413	49.82 407	71.51 401	84.74 404	49.30 400	75.32 407	50.97 409	43.71 405	75.59 405

test123		79.44 366	79.23 368	80.05 384	80.03 407	71.72 397	100.00 1	77.93 415	62.52 401	94.81 362	99.69 271	78.21 377	74.53 408	92.57 347	27.33 408	93.90 388

testmvs		80.17 363	81.95 366	74.80 386	58.54 413	59.58 411	100.00 1	87.14 412	76.09 397	99.61 194	100.00 1	67.06 394	74.19 409	98.84 216	50.30 403	90.64 398

ANet_high		66.05 374	63.44 378	73.88 387	61.14 412	63.45 409	95.68 399	87.18 411	79.93 394	47.35 408	80.68 408	22.35 412	72.33 410	61.24 405	35.42 406	85.88 401

wuyk23d		28.28 376	29.73 380	23.92 392	75.89 410	32.61 417	66.50 403	12.88 416	16.09 409	14.59 411	16.59 410	12.35 413	32.36 411	39.36 410	13.36 409	6.79 407

test_blank		0.07 380	0.09 383	0.00 393	0.00 416	0.00 418	0.00 404	0.00 417	0.00 411	0.00 412	0.79 411	0.00 416	0.00 412	0.00 411	0.00 410	0.00 408

uanet_test		0.01 381	0.02 384	0.00 393	0.00 416	0.00 418	0.00 404	0.00 417	0.00 411	0.00 412	0.14 412	0.00 416	0.00 412	0.00 411	0.00 410	0.00 408

DCPMVS		0.01 381	0.02 384	0.00 393	0.00 416	0.00 418	0.00 404	0.00 417	0.00 411	0.00 412	0.14 412	0.00 416	0.00 412	0.00 411	0.00 410	0.00 408

cdsmvs_eth3d_5k		24.41 377	32.55 379	0.00 393	0.00 416	0.00 418	0.00 404	99.39 201	0.00 411	0.00 412	100.00 1	93.55 246	0.00 412	0.00 411	0.00 410	0.00 408

pcd_1.5k_mvsjas		8.24 379	10.99 382	0.00 393	0.00 416	0.00 418	0.00 404	0.00 417	0.00 411	0.00 412	0.14 412	98.75 121	0.00 412	0.00 411	0.00 410	0.00 408

sosnet-low-res		0.01 381	0.02 384	0.00 393	0.00 416	0.00 418	0.00 404	0.00 417	0.00 411	0.00 412	0.14 412	0.00 416	0.00 412	0.00 411	0.00 410	0.00 408

sosnet		0.01 381	0.02 384	0.00 393	0.00 416	0.00 418	0.00 404	0.00 417	0.00 411	0.00 412	0.14 412	0.00 416	0.00 412	0.00 411	0.00 410	0.00 408

uncertanet		0.01 381	0.02 384	0.00 393	0.00 416	0.00 418	0.00 404	0.00 417	0.00 411	0.00 412	0.14 412	0.00 416	0.00 412	0.00 411	0.00 410	0.00 408

Regformer		0.01 381	0.02 384	0.00 393	0.00 416	0.00 418	0.00 404	0.00 417	0.00 411	0.00 412	0.14 412	0.00 416	0.00 412	0.00 411	0.00 410	0.00 408

ab-mvs-re		8.33 378	11.11 381	0.00 393	0.00 416	0.00 418	0.00 404	0.00 417	0.00 411	0.00 412	100.00 1	0.00 416	0.00 412	0.00 411	0.00 410	0.00 408

uanet		0.01 381	0.02 384	0.00 393	0.00 416	0.00 418	0.00 404	0.00 417	0.00 411	0.00 412	0.14 412	0.00 416	0.00 412	0.00 411	0.00 410	0.00 408

WAC-MVS						97.98 250								95.74 310

FOURS1					100.00 1	99.97 21	100.00 1	99.42 138	98.52 74	100.00 1

test_one_0601					100.00 1	99.99 5		99.42 138	98.72 64	100.00 1	100.00 1	99.60 17

eth-test2					0.00 416

eth-test					0.00 416

RE-MVS-def			99.55 56		99.99 49	99.91 53	100.00 1	99.42 138	97.62 147	100.00 1	100.00 1	98.94 105		99.99 61	100.00 1	100.00 1

IU-MVS					100.00 1	99.99 5		99.42 138	99.12 6	100.00 1				100.00 1	100.00 1	100.00 1

save fliter					99.99 49	99.93 45	100.00 1	99.42 138	98.93 38

test0726					100.00 1	99.99 5	100.00 1	99.42 138	99.04 15	100.00 1	100.00 1	99.53 29

GSMVS																99.91 149

test_part2					100.00 1	99.99 5				100.00 1

sam_mvs1												99.29 70				99.91 149

sam_mvs												99.33 59

MTGPA								99.42 138

MTMP							100.00 1	99.18 301

test9_res														100.00 1	100.00 1	100.00 1

agg_prior2														100.00 1	100.00 1	100.00 1

test_prior4						99.93 45	100.00 1

test_prior2							100.00 1		98.82 55	100.00 1	100.00 1	99.47 43		100.00 1	100.00 1

新几何2							100.00 1

旧先验1					99.99 49	99.88 74		99.82 40			100.00 1	99.27 73			100.00 1	100.00 1

原ACMM2							100.00 1

test222					99.99 49	99.90 60	100.00 1	99.69 62	97.66 141	100.00 1	100.00 1	99.30 69			100.00 1	100.00 1

segment_acmp												99.55 25

testdata1							100.00 1		98.77 63

plane_prior7					99.00 288	94.78 332

plane_prior6					99.06 280	94.80 328						88.58 316

plane_prior4											99.97 199

plane_prior3						94.79 331			99.03 20	99.08 230

plane_prior2							100.00 1		99.00 27

plane_prior1					99.02 283

plane_prior						94.80 328	100.00 1		99.03 20						95.58 257

n2								0.00 417

nn								0.00 417

door-mid								96.32 403

test11								99.42 138

door								96.13 404

HQP5-MVS						94.82 325

HQP-NCC					99.07 276		100.00 1		99.04 15	99.17 220

ACMP_Plane					99.07 276		100.00 1		99.04 15	99.17 220

BP-MVS														99.79 119

HQP3-MVS								99.40 188							95.58 257

HQP2-MVS												88.61 314

NP-MVS					99.07 276	94.81 327					99.97 199

MDTV_nov1_ep13_2view						99.24 158	99.56 323		96.31 257	99.96 121		98.86 112		98.92 211		99.89 165

ACMMP++_ref															94.58 294

ACMMP++															95.17 278

Test By Simon												99.10 86

High-resolution multi-view benchmark