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				99.94 63		99.82 84	100.00 1	99.97 17	99.11 7	100.00 1	100.00 1	96.65 192	100.00 1	100.00 1	99.97 110	100.00 1

test_fmvsmvis_n_1920		99.46 75	99.37 76	99.73 116	98.88 290	99.18 162	100.00 1	99.26 259	98.85 49	99.79 171	100.00 1	97.70 153	100.00 1	99.98 73	99.86 132	100.00 1

test_fmvsm_n_1920		99.55 64	99.49 65	99.73 116	99.85 115	99.19 160	100.00 1	99.41 177	98.87 47	100.00 1	100.00 1	97.34 171	100.00 1	99.98 73	99.90 125	100.00 1

test_vis1_n_1920		97.77 207	97.24 230	99.34 166	99.79 141	98.04 239	100.00 1	99.25 261	98.88 44	100.00 1	100.00 1	77.52 370	100.00 1	99.88 103	99.85 135	100.00 1

mvsany_test1		99.57 62	99.48 68	99.85 85	99.86 114	99.54 115	100.00 1	99.36 205	98.94 37	100.00 1	100.00 1	97.97 140	100.00 1	99.88 103	99.28 159	100.00 1

patch_mono-2		99.04 118	99.79 6	96.81 316	99.92 103	90.47 363	100.00 1	99.41 177	98.95 35	100.00 1	100.00 1	99.78 8	100.00 1	100.00 1	100.00 1	99.95 128

DVP-MVS++		99.81 11	99.75 14	100.00 1	100.00 1	99.99 5	100.00 1	99.42 131	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		99.42 131					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		99.42 131					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	99.42 131	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								99.42 131	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		99.42 131	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 58	100.00 1	99.79 45	97.97 114	99.97 112	100.00 1	98.97 96	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	99.42 131	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	99.42 131					100.00 1	100.00 1	100.00 1	100.00 1

DPM-MVS		99.63 51	99.51 62	100.00 1	99.90 107	100.00 1	100.00 1	99.43 121	99.00 27	100.00 1	100.00 1	99.58 22	100.00 1	97.64 260	100.00 1	100.00 1

GST-MVS		99.64 48	99.53 59	99.95 51	100.00 1	99.86 77	100.00 1	99.79 45	97.72 133	99.95 140	100.00 1	98.39 131	100.00 1	99.96 85	99.99 97	100.00 1

test_yl		99.51 66	99.37 76	99.95 51	99.82 120	99.90 58	100.00 1	99.47 79	97.48 164	100.00 1	100.00 1	99.80 6	100.00 1	99.98 73	97.75 226	99.94 133

Anonymous20240529		96.93 248	96.22 265	99.05 189	99.79 141	97.30 275	99.16 360	99.47 79	88.51 369	98.69 248	100.00 1	83.50 350	100.00 1	99.83 113	97.02 237	99.83 187

Anonymous202405211		97.87 202	97.53 215	98.90 200	99.81 126	96.70 293	99.35 336	99.46 94	92.98 339	98.83 241	99.99 182	90.63 277	100.00 1	99.70 137	97.03 236	100.00 1

DCV-MVSNet		99.51 66	99.37 76	99.95 51	99.82 120	99.90 58	100.00 1	99.47 79	97.48 164	100.00 1	100.00 1	99.80 6	100.00 1	99.98 73	97.75 226	99.94 133

SMA-MVS		99.69 35	99.59 44	99.98 23	99.99 49	99.93 43	100.00 1	99.43 121	97.50 162	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	99.42 131	98.91 41	100.00 1	100.00 1	99.22 75	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		99.25 103	99.01 111	99.95 51	99.81 126	99.87 74	100.00 1	99.94 22	97.13 190	99.83 161	99.96 209	97.01 178	100.00 1	99.59 159	97.85 218	99.98 109

tfpn200view9		99.26 100	99.03 109	99.96 42	99.81 126	99.89 65	100.00 1	99.94 22	97.23 185	99.83 161	99.96 209	97.04 174	100.00 1	99.59 159	97.85 218	99.98 109

CANet_DTU		99.02 124	98.90 128	99.41 157	99.88 111	98.71 194	100.00 1	99.29 239	98.84 51	100.00 1	100.00 1	94.02 232	100.00 1	98.08 245	99.96 113	99.52 236

MP-MVS-pluss		99.61 57	99.50 63	99.97 31	99.98 84	99.92 48	100.00 1	99.42 131	97.53 157	99.77 174	100.00 1	98.77 117	100.00 1	99.99 61	100.00 1	99.99 107
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
xiu_mvs_v1_base_debu		99.35 84	99.21 95	99.79 103	99.67 163	99.71 96	99.78 277	99.36 205	98.13 100	100.00 1	100.00 1	97.00 181	100.00 1	99.83 113	99.07 163	99.66 230

ACMMP_NAP		99.67 43	99.57 49	99.97 31	99.98 84	99.92 48	100.00 1	99.42 131	97.83 125	100.00 1	100.00 1	98.89 108	100.00 1	99.98 73	100.00 1	100.00 1

xiu_mvs_v2_base		99.51 66	99.41 70	99.82 91	99.70 153	99.73 94	99.92 252	99.40 181	98.15 98	100.00 1	100.00 1	98.50 128	100.00 1	99.85 109	99.13 161	99.74 221

xiu_mvs_v1_base		99.35 84	99.21 95	99.79 103	99.67 163	99.71 96	99.78 277	99.36 205	98.13 100	100.00 1	100.00 1	97.00 181	100.00 1	99.83 113	99.07 163	99.66 230

xiu_mvs_v1_base_debi		99.35 84	99.21 95	99.79 103	99.67 163	99.71 96	99.78 277	99.36 205	98.13 100	100.00 1	100.00 1	97.00 181	100.00 1	99.83 113	99.07 163	99.66 230

MTAPA		99.68 40	99.59 44	99.97 31	99.99 49	99.91 51	100.00 1	99.42 131	98.32 88	99.94 142	100.00 1	98.65 122	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 65	100.00 1	99.76 49	97.95 118	100.00 1	100.00 1	99.31 63	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 58	100.00 1	99.74 55	97.86 124	100.00 1	100.00 1	99.19 78	100.00 1	99.99 61	100.00 1	100.00 1

PS-MVSNAJ		99.64 48	99.57 49	99.85 85	99.78 144	99.81 85	99.95 244	99.42 131	98.38 80	100.00 1	100.00 1	98.75 118	100.00 1	99.88 103	99.99 97	99.74 221

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	99.96 117	100.00 1	99.21 76	100.00 1	100.00 1	100.00 1	99.99 107

XVS		99.79 14	99.73 17	99.98 23	100.00 1	99.94 40	100.00 1	99.75 52	98.67 67	100.00 1	100.00 1	99.16 80	100.00 1	100.00 1	100.00 1	100.00 1

X-MVStestdata		97.04 243	96.06 271	99.98 23	100.00 1	99.94 40	100.00 1	99.75 52	98.67 67	100.00 1	66.97 400	99.16 80	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	99.67 148

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 77	100.00 1	97.91 253	100.00 1	100.00 1

无先验							100.00 1	99.80 43	97.98 112				100.00 1	99.33 180		100.00 1

原ACMM1				99.93 66	100.00 1	99.80 87		99.66 63	98.18 95	100.00 1	100.00 1	99.43 50	100.00 1	99.50 171	100.00 1	100.00 1

testdata2													100.00 1	97.36 272

testdata				99.66 126	99.99 49	98.97 183		99.73 56	97.96 117	100.00 1	100.00 1	99.42 52	100.00 1	99.28 185	100.00 1	100.00 1

1314		99.38 81	99.19 99	99.96 42	98.88 290	99.89 65	99.24 346	99.93 30	98.88 44	98.79 244	100.00 1	97.02 177	100.00 1	100.00 1	100.00 1	100.00 1

LFMVS		97.42 226	96.62 246	99.81 96	99.80 137	99.50 122	99.16 360	99.56 70	94.48 304	100.00 1	100.00 1	79.35 365	100.00 1	99.89 101	97.37 232	99.94 133

VDD-MVS		96.58 263	95.99 274	98.34 231	99.52 209	95.33 307	99.18 354	99.38 196	96.64 227	99.77 174	100.00 1	72.51 380	100.00 1	100.00 1	96.94 239	99.70 226

VDDNet		96.39 275	95.55 296	98.90 200	99.27 252	97.45 267	99.15 362	99.92 34	91.28 352	99.98 108	100.00 1	73.55 376	100.00 1	99.85 109	96.98 238	99.24 240

MVS		99.22 107	98.96 117	99.98 23	99.00 277	99.95 32	99.24 346	99.94 22	98.14 99	98.88 234	100.00 1	95.63 210	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	99.42 131	99.01 26	100.00 1	100.00 1	99.33 58	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		99.04 118	98.75 141	99.90 71	99.81 126	99.75 91	99.50 321	99.47 79	98.36 84	100.00 1	99.99 182	94.66 225	100.00 1	99.90 99	97.09 235	99.96 122

ACMMPR		99.74 22	99.67 27	99.96 42	100.00 1	99.89 65	100.00 1	99.76 49	97.95 118	100.00 1	100.00 1	99.29 69	100.00 1	99.99 61	100.00 1	100.00 1

thres600view7		99.24 106	99.00 113	99.95 51	99.81 126	99.87 74	100.00 1	99.94 22	97.13 190	99.83 161	99.96 209	97.01 178	100.00 1	99.54 167	97.77 225	99.97 116

MP-MVS		99.61 57	99.49 65	99.98 23	99.99 49	99.94 40	100.00 1	99.42 131	97.82 126	99.99 103	100.00 1	98.20 134	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		99.26 100	99.03 109	99.95 51	99.81 126	99.89 65	100.00 1	99.94 22	97.23 185	99.83 161	99.96 209	97.04 174	100.00 1	99.59 159	97.85 218	99.97 116

thres200		99.27 98	99.04 108	99.96 42	99.81 126	99.90 58	100.00 1	99.94 22	97.31 180	99.83 161	99.96 209	97.04 174	100.00 1	99.62 157	97.88 216	99.98 109

PGM-MVS		99.69 35	99.61 42	99.95 51	99.99 49	99.85 80	100.00 1	99.58 67	97.69 137	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 63	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 51	100.00 1	99.42 131	97.91 120	100.00 1	100.00 1	99.04 91	100.00 1	100.00 1	100.00 1	100.00 1

EPNet		99.62 55	99.69 22	99.42 156	99.99 49	98.37 212	100.00 1	99.89 37	98.83 53	100.00 1	100.00 1	98.97 96	100.00 1	99.90 99	99.61 154	99.89 161
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 56	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 82	100.00 1	99.42 131	97.77 130	100.00 1	100.00 1	99.07 87	100.00 1	100.00 1	100.00 1	100.00 1

DELS-MVS		99.62 55	99.56 54	99.82 91	99.92 103	99.45 131	100.00 1	99.78 47	98.92 39	99.73 179	100.00 1	97.70 153	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 71	99.38 73	99.85 85	100.00 1	99.54 115	100.00 1	99.42 131	97.58 152	99.98 108	100.00 1	97.43 168	100.00 1	99.99 61	100.00 1	100.00 1

UGNet		98.41 181	98.11 188	99.31 173	99.54 199	98.55 202	99.18 354	100.00 1	98.64 70	99.79 171	99.04 323	87.61 317	100.00 1	99.30 183	99.89 126	99.40 239
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 76	99.34 83	99.80 101	99.76 147	99.50 122	100.00 1	99.91 36	97.72 133	99.98 108	99.94 221	98.45 129	100.00 1	99.53 169	98.75 173	99.89 161

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	99.99 103	100.00 1	99.72 12	100.00 1	99.96 85	100.00 1	100.00 1

QAPM		98.99 129	98.66 148	99.96 42	99.01 273	99.87 74	99.88 261	99.93 30	97.99 110	98.68 249	100.00 1	93.17 244	100.00 1	99.32 181	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		97.37 228	98.24 180	94.76 341	99.80 137	84.57 378	99.99 203	99.05 339	94.95 289	99.82 169	100.00 1	94.03 231	100.00 1	98.15 244	98.38 188	99.70 226

CSCG		99.28 97	99.35 81	99.05 189	99.99 49	97.15 280	100.00 1	99.47 79	97.44 168	99.42 195	100.00 1	97.83 149	100.00 1	99.99 61	100.00 1	100.00 1

API-MVS		99.72 26	99.70 21	99.79 103	99.97 88	99.37 141	99.96 238	99.94 22	98.48 75	100.00 1	100.00 1	98.92 105	100.00 1	100.00 1	100.00 1	100.00 1

ACMMP		99.65 46	99.57 49	99.89 73	99.99 49	99.66 103	99.75 286	99.73 56	98.16 96	99.75 177	100.00 1	98.90 107	100.00 1	99.96 85	99.88 128	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 68	99.97 88	99.72 95	100.00 1	99.47 79	98.43 78	99.88 155	100.00 1	99.14 83	100.00 1	99.97 83	100.00 1	100.00 1

PHI-MVS		99.50 69	99.39 72	99.82 91	100.00 1	99.45 131	100.00 1	99.94 22	96.38 244	100.00 1	100.00 1	98.18 135	100.00 1	100.00 1	100.00 1	100.00 1

PVSNet	94.91 18	99.30 95	99.25 89	99.44 152	100.00 1	98.32 218	100.00 1	99.86 38	98.04 107	100.00 1	100.00 1	96.10 201	100.00 1	99.55 164	99.73 144	100.00 1

PVSNet_0	93.57 19	96.41 271	95.74 287	98.41 226	99.84 116	95.22 309	100.00 1	100.00 1	98.08 105	97.55 313	99.78 250	84.40 342	100.00 1	100.00 1	81.99 374	100.00 1

DeepPCF-MVS	98.03 4	98.54 169	99.72 19	94.98 338	99.99 49	84.94 377	100.00 1	99.42 131	99.98 1	100.00 1	100.00 1	98.11 137	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	99.73 292	99.52 72	99.06 12	100.00 1	100.00 1	98.80 116	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 51	99.98 227	99.47 79	99.09 9	100.00 1	100.00 1	98.59 125	100.00 1	99.95 91	100.00 1	100.00 1

AdaColmap		99.44 77	99.26 88	99.95 51	100.00 1	99.86 77	99.70 297	99.99 13	98.53 73	99.90 150	100.00 1	95.34 212	100.00 1	99.92 96	100.00 1	100.00 1

PLC	98.56 2	99.70 32	99.74 16	99.58 139	100.00 1	98.79 188	100.00 1	99.54 71	98.58 72	99.96 117	100.00 1	99.59 20	100.00 1	100.00 1	100.00 1	99.94 133
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MAR-MVS		99.49 71	99.36 79	99.89 73	99.97 88	99.66 103	99.74 287	99.95 19	97.89 121	100.00 1	100.00 1	96.71 191	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	99.03 120	98.71 146	99.96 42	98.99 280	99.89 65	100.00 1	99.51 76	98.96 32	98.32 272	100.00 1	92.78 250	100.00 1	99.87 106	100.00 1	100.00 1

3Dnovator	95.63 14	99.06 116	98.76 139	99.96 42	98.86 294	99.90 58	99.98 227	99.93 30	98.95 35	98.49 264	100.00 1	92.91 248	100.00 1	99.71 134	100.00 1	100.00 1

OpenMVS	95.20 17	98.76 148	98.41 167	99.78 107	98.89 289	99.81 85	99.99 203	99.76 49	98.02 108	98.02 290	100.00 1	91.44 265	100.00 1	99.63 156	99.97 110	99.55 234

fmvsm_l_conf0.5_n_a		99.63 51	99.55 56	99.86 81	99.83 119	99.58 111	100.00 1	99.36 205	98.98 30	100.00 1	100.00 1	97.85 146	99.99 94	100.00 1	99.94 118	100.00 1

fmvsm_l_conf0.5_n		99.63 51	99.56 54	99.86 81	99.81 126	99.59 110	100.00 1	99.36 205	98.98 30	100.00 1	100.00 1	97.92 143	99.99 94	100.00 1	99.95 116	100.00 1

fmvsm_s_conf0.5_n_a		99.32 91	99.15 103	99.81 96	99.80 137	99.47 130	100.00 1	99.35 216	98.22 91	100.00 1	100.00 1	95.21 216	99.99 94	99.96 85	99.86 132	99.98 109

fmvsm_s_conf0.5_n		99.21 108	99.01 111	99.83 89	99.84 116	99.53 117	100.00 1	99.38 196	98.29 90	100.00 1	100.00 1	93.62 237	99.99 94	99.99 61	99.93 121	99.98 109

test_fmvsmconf_n		99.56 63	99.46 69	99.86 81	99.68 158	99.58 111	100.00 1	99.31 231	98.92 39	99.88 155	100.00 1	97.35 170	99.99 94	99.98 73	99.99 97	100.00 1

test_cas_vis1_n_1920		98.63 162	98.25 178	99.77 110	99.69 154	99.32 144	100.00 1	99.31 231	98.84 51	99.96 117	100.00 1	87.42 319	99.99 94	99.14 194	99.86 132	100.00 1

test_vis1_n		96.69 258	95.81 281	99.32 171	99.14 258	97.98 242	99.97 233	98.98 351	98.45 77	100.00 1	100.00 1	66.44 386	99.99 94	99.78 125	99.57 156	100.00 1

h-mvs33		97.03 244	96.53 249	98.51 221	99.79 141	95.90 302	99.45 325	99.45 102	98.21 92	100.00 1	99.78 250	97.49 162	99.99 94	99.72 131	74.92 383	99.65 233

ZD-MVS					100.00 1	99.98 17		99.80 43	97.31 180	100.00 1	100.00 1	99.32 61	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 51	100.00 1	99.42 131	97.62 144	100.00 1	100.00 1	98.65 122	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 80	100.00 1	99.42 131	97.67 138	100.00 1	100.00 1	99.05 89	99.99 94	100.00 1	100.00 1	100.00 1

9.14			99.57 49		99.99 49		100.00 1	99.42 131	97.54 155	100.00 1	100.00 1	99.15 82	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	97.59 151	100.00 1	100.00 1	98.99 94	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	99.43 121	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	99.44 115	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 51	100.00 1	99.48 78	97.54 155	100.00 1	100.00 1	98.97 96	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	99.42 131	98.02 108	100.00 1	100.00 1	99.32 61	99.99 94	100.00 1	100.00 1	100.00 1

TSAR-MVS + GP.		99.61 57	99.69 22	99.35 165	99.99 49	98.06 237	100.00 1	99.36 205	99.83 2	100.00 1	100.00 1	98.95 100	99.99 94	100.00 1	99.11 162	100.00 1

SteuartSystems-ACMMP		99.78 16	99.71 20	99.98 23	99.76 147	99.95 32	100.00 1	99.42 131	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		98.71 153	98.62 152	98.98 196	99.98 84	99.60 108	100.00 1	100.00 1	97.23 185	100.00 1	99.03 325	96.57 194	99.99 94	100.00 1	94.75 280	97.35 350

PVSNet_Blended		99.48 73	99.36 79	99.83 89	99.98 84	99.60 108	100.00 1	100.00 1	97.79 128	100.00 1	100.00 1	96.57 194	99.99 94	100.00 1	99.88 128	99.90 156

HY-MVS	96.53 9	99.50 69	99.35 81	99.96 42	99.81 126	99.93 43	99.64 304	100.00 1	97.97 114	99.84 159	99.85 235	98.94 102	99.99 94	99.86 107	98.23 198	99.95 128

PatchMatch-RL		99.02 124	98.78 136	99.74 113	99.99 49	99.29 147	100.00 1	100.00 1	98.38 80	99.89 153	99.81 244	93.14 246	99.99 94	97.85 255	99.98 107	99.95 128

F-COLMAP		99.64 48	99.64 33	99.67 123	99.99 49	99.07 169	100.00 1	99.44 115	98.30 89	99.90 150	100.00 1	99.18 79	99.99 94	99.91 98	100.00 1	99.94 133

test_8					100.00 1	99.91 51	100.00 1	99.42 131	97.70 135	100.00 1	100.00 1	99.51 33	99.98 118

MVS_Test		98.93 137	98.65 149	99.77 110	99.62 181	99.50 122	99.99 203	99.19 286	95.52 276	99.96 117	99.86 231	96.54 196	99.98 118	98.65 219	98.48 180	99.82 192

RPMNet		95.26 309	93.82 317	99.56 142	99.31 248	98.86 186	99.13 364	99.42 131	79.82 386	99.96 117	95.13 379	95.69 208	99.98 118	77.54 389	98.40 185	99.84 184

WTY-MVS		99.54 65	99.40 71	99.95 51	99.81 126	99.93 43	100.00 1	100.00 1	97.98 112	99.84 159	100.00 1	98.94 102	99.98 118	99.86 107	98.21 199	99.94 133

ab-mvs		98.42 179	98.02 197	99.61 131	99.71 151	99.00 179	99.10 367	99.64 64	96.70 220	99.04 224	99.81 244	90.64 276	99.98 118	99.64 153	97.93 213	99.84 184

FE-MVS		99.16 110	98.99 115	99.66 126	99.65 168	99.18 162	99.58 312	99.43 121	95.24 284	99.91 148	99.59 287	99.37 57	99.97 123	98.31 236	99.81 140	99.83 187

TEST9					100.00 1	99.95 32	100.00 1	99.42 131	97.65 140	100.00 1	100.00 1	99.53 29	99.97 123

train_agg		99.71 29	99.63 37	99.97 31	100.00 1	99.95 32	100.00 1	99.42 131	97.70 135	100.00 1	100.00 1	99.51 33	99.97 123	100.00 1	100.00 1	100.00 1

agg_prior					100.00 1	99.88 72		99.42 131		100.00 1			99.97 123

canonicalmvs		99.03 120	98.73 143	99.94 63	99.75 149	99.95 32	100.00 1	99.30 235	97.64 142	100.00 1	100.00 1	95.22 215	99.97 123	99.76 126	96.90 240	99.91 147

EI-MVSNet-UG-set		99.69 35	99.63 37	99.87 78	99.99 49	99.64 105	99.95 244	99.44 115	98.35 86	100.00 1	100.00 1	98.98 95	99.97 123	99.98 73	100.00 1	100.00 1

test_prior				99.90 71	100.00 1	99.75 91		99.73 56					99.97 123			100.00 1

test12				99.95 51	99.99 49	99.89 65		99.42 131		100.00 1		99.24 74	99.97 123		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	99.42 131	97.53 157	100.00 1	100.00 1	99.27 72	99.97 123	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		98.86 142	98.54 159	99.81 96	99.97 88	99.45 131	99.52 319	99.40 181	94.35 308	98.36 268	100.00 1	96.13 200	99.97 123	99.12 197	100.00 1	100.00 1

LS3D		99.31 93	99.13 104	99.87 78	99.99 49	99.71 96	99.55 315	99.46 94	97.32 178	99.82 169	100.00 1	96.85 188	99.97 123	99.14 194	100.00 1	99.92 145

fmvsm_s_conf0.1_n_a		98.71 153	98.36 174	99.78 107	99.09 263	99.42 135	100.00 1	99.26 259	97.42 170	100.00 1	100.00 1	89.78 289	99.96 134	99.82 118	99.85 135	99.97 116

fmvsm_s_conf0.1_n		98.77 147	98.42 166	99.82 91	99.47 227	99.52 120	100.00 1	99.27 253	97.53 157	100.00 1	100.00 1	89.73 291	99.96 134	99.84 112	99.93 121	99.97 116

test_fmvs1_n		97.43 225	96.86 237	99.15 185	99.68 158	97.48 266	99.99 203	98.98 351	98.82 55	100.00 1	100.00 1	74.85 375	99.96 134	99.67 148	99.70 146	100.00 1

test_fmvs1		98.37 184	98.04 195	99.34 166	99.84 116	98.07 235	100.00 1	99.00 348	98.85 49	100.00 1	100.00 1	85.11 339	99.96 134	99.69 144	99.88 128	100.00 1

FA-MVS(test-final)		99.00 126	98.75 141	99.73 116	99.63 176	99.43 134	99.83 267	99.43 121	95.84 265	99.52 188	99.37 306	97.84 148	99.96 134	97.63 261	99.68 147	99.79 213

CANet		99.40 79	99.24 91	99.89 73	99.99 49	99.76 90	100.00 1	99.73 56	98.40 79	99.78 173	100.00 1	95.28 213	99.96 134	100.00 1	99.99 97	99.96 122

MVS_0304		99.69 35	99.63 37	99.86 81	99.96 93	99.63 107	100.00 1	99.92 34	99.03 20	99.97 112	100.00 1	97.87 144	99.96 134	100.00 1	99.96 113	100.00 1

MSP-MVS		99.81 11	99.77 9	99.94 63	100.00 1	99.86 77	100.00 1	99.42 131	98.87 47	100.00 1	100.00 1	99.65 15	99.96 134	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 78	100.00 1	99.64 105	99.98 227	99.44 115	98.35 86	99.99 103	100.00 1	99.04 91	99.96 134	99.98 73	100.00 1	100.00 1

HPM-MVS_fast		99.60 60	99.49 65	99.91 68	99.99 49	99.78 88	100.00 1	99.42 131	97.09 192	100.00 1	100.00 1	98.95 100	99.96 134	99.98 73	100.00 1	100.00 1

HPM-MVS		99.59 61	99.50 63	99.89 73	100.00 1	99.70 100	100.00 1	99.42 131	97.46 166	100.00 1	100.00 1	98.60 124	99.96 134	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		98.82 145	98.81 134	98.88 202	99.62 181	96.71 292	100.00 1	99.28 245	97.09 192	98.81 242	100.00 1	94.91 221	99.96 134	99.54 167	100.00 1	99.96 122

test_vis1_rt		93.10 326	92.93 327	93.58 351	99.63 176	85.07 376	99.99 203	93.71 397	97.49 163	90.96 370	97.10 371	60.40 388	99.95 146	99.24 188	97.90 215	95.72 374

AllTest		98.55 168	98.40 168	98.99 194	99.93 100	97.35 271	100.00 1	99.40 181	97.08 194	99.09 218	99.98 187	93.37 239	99.95 146	96.94 281	99.84 137	99.68 228

TestCases				98.99 194	99.93 100	97.35 271		99.40 181	97.08 194	99.09 218	99.98 187	93.37 239	99.95 146	96.94 281	99.84 137	99.68 228

CHOSEN 1792x2688		99.00 126	98.91 125	99.25 180	99.90 107	97.79 257	100.00 1	99.99 13	98.79 60	98.28 275	100.00 1	93.63 236	99.95 146	99.66 151	99.95 116	100.00 1

114514_t		99.39 80	99.25 89	99.81 96	99.97 88	99.48 129	100.00 1	99.42 131	95.53 274	100.00 1	100.00 1	98.37 132	99.95 146	99.97 83	100.00 1	100.00 1

PVSNet_Blended_VisFu		99.33 89	99.18 101	99.78 107	99.82 120	99.49 125	100.00 1	99.95 19	97.36 173	99.63 184	100.00 1	96.45 198	99.95 146	99.79 119	99.65 151	99.89 161

MVS_111021_LR		99.70 32	99.65 31	99.88 77	99.96 93	99.70 100	100.00 1	99.97 17	98.96 32	100.00 1	100.00 1	97.93 142	99.95 146	99.99 61	100.00 1	100.00 1

MVS_111021_HR		99.71 29	99.63 37	99.93 66	99.95 95	99.83 83	100.00 1	100.00 1	98.89 43	100.00 1	100.00 1	97.85 146	99.95 146	100.00 1	100.00 1	100.00 1

DeepC-MVS	97.84 5	99.00 126	98.80 135	99.60 133	99.93 100	99.03 174	100.00 1	99.40 181	98.61 71	99.33 204	100.00 1	92.23 259	99.95 146	99.74 128	99.96 113	99.83 187
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	98.69 156	98.37 172	99.62 130	99.78 144	99.02 175	99.23 351	99.06 337	96.43 238	98.08 284	100.00 1	94.72 224	99.95 146	98.16 243	99.91 124	99.90 156
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MSDG		98.90 140	98.63 151	99.70 120	99.92 103	99.25 152	100.00 1	99.37 199	95.71 268	99.40 201	100.00 1	96.58 193	99.95 146	96.80 288	99.94 118	99.91 147

COLMAP_ROB	97.10 7	98.29 188	98.17 185	98.65 212	99.94 98	97.39 269	99.30 342	99.40 181	95.64 269	97.75 304	100.00 1	92.69 255	99.95 146	98.89 205	99.92 123	98.62 250
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
test_fmvsmconf0.1_n		99.25 103	99.05 107	99.82 91	98.92 286	99.55 113	100.00 1	99.23 270	98.91 41	99.75 177	99.97 195	94.79 223	99.94 158	99.94 93	99.99 97	99.97 116

UA-Net		99.06 116	98.83 131	99.74 113	99.52 209	99.40 137	99.08 370	99.45 102	97.64 142	99.83 161	100.00 1	95.80 205	99.94 158	98.35 234	99.80 142	99.88 172

XVG-OURS-SEG-HR		98.27 190	98.31 176	98.14 248	99.59 187	95.92 300	100.00 1	99.36 205	98.48 75	99.21 209	100.00 1	89.27 298	99.94 158	99.76 126	99.17 160	98.56 251

alignmvs		99.38 81	99.21 95	99.91 68	99.73 150	99.92 48	100.00 1	99.51 76	97.61 148	100.00 1	100.00 1	99.06 88	99.93 161	99.83 113	97.12 234	99.90 156

cascas		98.43 177	98.07 192	99.50 146	99.65 168	99.02 175	100.00 1	99.22 273	94.21 311	99.72 180	99.98 187	92.03 262	99.93 161	99.68 145	98.12 204	99.54 235

XVG-OURS		98.30 186	98.36 174	98.13 251	99.58 192	95.91 301	100.00 1	99.36 205	98.69 65	99.23 208	100.00 1	91.20 268	99.92 163	99.34 179	97.82 221	98.56 251

test_fmvsmconf0.01_n		98.60 163	98.24 180	99.67 123	96.90 361	99.21 158	99.99 203	99.04 342	98.80 57	99.57 186	99.96 209	90.12 283	99.91 164	99.89 101	99.89 126	99.90 156

APD_test1		93.07 327	94.14 315	89.85 359	99.18 256	72.49 387	99.76 284	98.90 358	92.86 343	96.35 338	99.94 221	75.56 373	99.91 164	86.73 371	97.98 208	97.15 355

diffmvs		98.96 133	98.73 143	99.63 129	99.54 199	99.16 165	100.00 1	99.18 293	97.33 177	99.96 117	100.00 1	94.60 226	99.91 164	99.66 151	98.33 194	99.82 192
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		98.59 164	98.38 170	99.23 181	99.69 154	97.90 249	99.31 341	99.47 79	94.52 302	99.68 182	99.28 311	97.64 156	99.89 167	97.71 258	98.17 203	99.89 161

Test_1112_low_res		98.83 144	98.60 155	99.51 144	99.69 154	98.75 190	99.99 203	99.14 305	96.81 210	98.84 239	99.06 320	97.45 165	99.89 167	98.66 217	97.75 226	99.89 161

1112_ss		98.91 138	98.71 146	99.51 144	99.69 154	98.75 190	99.99 203	99.15 300	96.82 209	98.84 239	100.00 1	97.45 165	99.89 167	98.66 217	97.75 226	99.89 161

IB-MVS	96.24 12	97.54 220	96.95 234	99.33 169	99.67 163	98.10 234	100.00 1	99.47 79	97.42 170	99.26 207	99.69 262	98.83 113	99.89 167	99.43 173	78.77 381	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		98.49 174	98.08 190	99.73 116	99.82 120	99.53 117	99.99 203	99.45 102	97.62 144	99.38 202	99.86 231	90.06 286	99.88 171	99.92 96	96.61 243	99.79 213

dcpmvs_2		98.87 141	99.53 59	96.90 310	99.87 113	90.88 362	99.94 249	99.07 329	98.20 94	100.00 1	100.00 1	98.69 121	99.86 172	100.00 1	100.00 1	99.95 128

tpm cat1		98.05 197	97.76 205	98.92 199	99.50 218	97.10 283	99.77 282	99.30 235	90.20 363	99.72 180	98.71 344	97.71 152	99.86 172	96.75 292	98.20 200	99.81 200

tpmrst		98.98 132	98.93 123	99.14 186	99.61 183	97.74 258	99.52 319	99.36 205	96.05 256	99.98 108	99.64 275	99.04 91	99.86 172	98.94 202	98.19 201	99.82 192

MDTV_nov1_ep13			98.94 122		99.53 202	98.36 214	99.39 332	99.46 94	96.54 233	99.99 103	99.63 279	98.92 105	99.86 172	98.30 239	98.71 174

OMC-MVS		99.27 98	99.38 73	98.96 197	99.95 95	97.06 284	100.00 1	99.40 181	98.83 53	99.88 155	100.00 1	97.01 178	99.86 172	99.47 172	99.84 137	99.97 116

thisisatest0530		99.37 83	99.27 85	99.69 121	99.59 187	99.41 136	100.00 1	99.46 94	96.46 237	99.90 150	100.00 1	99.44 46	99.85 177	98.97 201	99.58 155	99.80 211

thisisatest0515		99.42 78	99.31 84	99.74 113	99.59 187	99.55 113	100.00 1	99.46 94	96.65 226	99.92 146	100.00 1	99.44 46	99.85 177	99.09 198	99.63 153	99.81 200

tttt0517		99.34 87	99.23 94	99.67 123	99.57 195	99.38 138	100.00 1	99.46 94	96.33 247	99.89 153	100.00 1	99.44 46	99.84 179	98.93 203	99.46 158	99.78 216

casdiffmvs		98.65 158	98.38 170	99.46 150	99.52 209	98.74 193	100.00 1	99.15 300	96.91 204	99.05 223	100.00 1	92.75 251	99.83 180	99.70 137	98.38 188	99.81 200
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		98.64 159	98.65 149	98.60 215	99.59 187	96.17 298	100.00 1	99.28 245	96.67 225	98.41 267	100.00 1	94.52 227	99.83 180	99.41 175	100.00 1	99.81 200

DeepMVS_CX				89.98 358	98.90 288	71.46 389		99.18 293	97.61 148	96.92 325	99.83 238	86.07 331	99.83 180	96.02 299	97.65 230	98.65 249

dp		98.72 151	98.61 153	99.03 192	99.53 202	97.39 269	99.45 325	99.39 194	95.62 271	99.94 142	99.52 297	98.83 113	99.82 183	96.77 291	98.42 184	99.89 161

BH-RMVSNet		98.46 175	98.08 190	99.59 135	99.61 183	99.19 160	100.00 1	99.28 245	97.06 196	98.95 229	100.00 1	88.99 301	99.82 183	98.83 210	100.00 1	99.77 217

PMMVS		99.12 111	98.97 116	99.58 139	99.57 195	98.98 181	100.00 1	99.30 235	97.14 189	99.96 117	100.00 1	96.53 197	99.82 183	99.70 137	98.49 179	99.94 133

test2506		99.48 73	99.38 73	99.75 112	99.89 109	99.51 121	99.45 325	100.00 1	98.38 80	99.83 161	100.00 1	98.86 109	99.81 186	99.25 186	98.78 170	99.94 133

ECVR-MVS		98.43 177	98.14 186	99.32 171	99.89 109	98.21 226	99.46 323	100.00 1	98.38 80	99.47 194	100.00 1	87.91 312	99.80 187	99.35 178	98.78 170	99.94 133

test1111		98.42 179	98.12 187	99.29 174	99.88 111	98.15 229	99.46 323	100.00 1	98.36 84	99.42 195	100.00 1	87.91 312	99.79 188	99.31 182	98.78 170	99.94 133

EIA-MVS		99.26 100	99.19 99	99.45 151	99.63 176	98.75 190	100.00 1	99.27 253	96.93 201	99.95 140	100.00 1	97.47 164	99.79 188	99.74 128	99.72 145	99.82 192

ETV-MVS		99.34 87	99.24 91	99.64 128	99.58 192	99.33 143	100.00 1	99.25 261	97.57 153	99.96 117	100.00 1	97.44 167	99.79 188	99.70 137	99.65 151	99.81 200

TR-MVS		98.14 193	97.74 207	99.33 169	99.59 187	98.28 221	99.27 343	99.21 282	96.42 240	99.15 214	99.94 221	88.87 304	99.79 188	98.88 206	98.29 195	99.93 143

casdiffmvs_mvg		98.64 159	98.39 169	99.40 159	99.50 218	98.60 200	100.00 1	99.22 273	96.85 207	99.10 217	100.00 1	92.75 251	99.78 192	99.71 134	98.35 190	99.81 200
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		97.81 205	97.48 216	98.79 207	99.82 120	96.80 290	99.32 338	99.45 102	97.62 144	99.38 202	99.86 231	85.56 337	99.77 193	99.72 131	96.61 243	99.79 213

Effi-MVS+		98.58 165	98.24 180	99.61 131	99.60 185	99.26 150	97.85 386	99.10 319	96.22 252	99.97 112	99.89 227	93.75 234	99.77 193	99.43 173	98.34 191	99.81 200

baseline1		98.91 138	98.61 153	99.81 96	99.71 151	99.77 89	99.78 277	99.44 115	97.51 161	98.81 242	99.99 182	98.25 133	99.76 195	98.60 225	95.41 253	99.89 161

lupinMVS		99.29 96	99.16 102	99.69 121	99.45 231	99.49 125	100.00 1	99.15 300	97.45 167	99.97 112	100.00 1	96.76 189	99.76 195	99.67 148	100.00 1	99.81 200

EPP-MVSNet		99.10 113	99.00 113	99.40 159	99.51 214	98.68 196	99.92 252	99.43 121	95.47 280	99.65 183	100.00 1	99.51 33	99.76 195	99.53 169	98.00 207	99.75 220

baseline		98.69 156	98.45 165	99.41 157	99.52 209	98.67 197	100.00 1	99.17 298	97.03 197	99.13 215	100.00 1	93.17 244	99.74 198	99.70 137	98.34 191	99.81 200

CostFormer		98.84 143	98.77 137	99.04 191	99.41 236	97.58 263	99.67 302	99.35 216	94.66 297	99.96 117	99.36 307	99.28 71	99.74 198	99.41 175	97.81 222	99.81 200

Fast-Effi-MVS+		98.40 182	98.02 197	99.55 143	99.63 176	99.06 171	100.00 1	99.15 300	95.07 286	99.42 195	99.95 217	93.26 242	99.73 200	97.44 267	98.24 197	99.87 180

jason		99.11 112	98.96 117	99.59 135	99.17 257	99.31 146	100.00 1	99.13 309	97.38 172	99.83 161	100.00 1	95.54 211	99.72 201	99.57 163	99.97 110	99.74 221
jason: jason.
EPMVS		99.25 103	99.13 104	99.60 133	99.60 185	99.20 159	99.60 310	100.00 1	96.93 201	99.92 146	99.36 307	99.05 89	99.71 202	98.77 212	98.94 167	99.90 156

CS-MVS-test		99.31 93	99.27 85	99.43 154	99.99 49	98.77 189	100.00 1	99.19 286	97.24 184	99.96 117	100.00 1	97.56 160	99.70 203	99.68 145	99.81 140	99.82 192

Vis-MVSNet		98.52 171	98.25 178	99.34 166	99.68 158	98.55 202	99.68 301	99.41 177	97.34 176	99.94 142	100.00 1	90.38 282	99.70 203	99.03 200	98.84 168	99.76 219
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
TAPA-MVS	96.40 10	97.64 213	97.37 222	98.45 224	99.94 98	95.70 304	100.00 1	99.40 181	97.65 140	99.53 187	100.00 1	99.31 63	99.66 205	80.48 384	100.00 1	100.00 1
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CS-MVS		99.33 89	99.27 85	99.50 146	99.99 49	99.00 179	100.00 1	99.13 309	97.26 183	99.96 117	100.00 1	97.79 150	99.64 206	99.64 153	99.67 149	99.87 180

Fast-Effi-MVS+-dtu		98.38 183	98.56 158	97.82 276	99.58 192	94.44 332	100.00 1	99.16 299	96.75 213	99.51 189	99.63 279	95.03 219	99.60 207	97.71 258	99.67 149	99.42 238

PAPM		99.78 16	99.76 12	99.85 85	99.01 273	99.95 32	100.00 1	99.75 52	99.37 3	99.99 103	100.00 1	99.76 11	99.60 207	100.00 1	100.00 1	100.00 1

tt0805		96.52 264	96.23 264	97.40 287	99.30 251	93.55 340	99.32 338	99.45 102	96.75 213	97.88 298	99.99 182	79.99 363	99.59 209	97.39 271	95.98 246	99.06 245

GeoE		98.06 196	97.65 212	99.29 174	99.47 227	98.41 206	100.00 1	99.19 286	94.85 291	98.88 234	100.00 1	91.21 267	99.59 209	97.02 279	98.19 201	99.88 172

test_post1							99.32 338				88.24 392	99.33 58	99.59 209	98.31 236

test_post											89.05 390	99.49 39	99.59 209

ADS-MVSNet		98.70 155	98.51 161	99.28 177	99.51 214	98.39 209	99.24 346	99.44 115	95.52 276	99.96 117	99.70 259	97.57 158	99.58 213	97.11 277	98.54 176	99.88 172

Patchmatch-test		97.83 204	97.42 218	99.06 187	99.08 264	97.66 261	98.66 380	99.21 282	93.65 323	98.25 279	99.58 289	99.47 43	99.57 214	90.25 358	98.59 175	99.95 128

HQP4-MVS										99.17 210			99.57 214			97.77 253

HQP-MVS		97.73 209	97.85 202	97.39 288	99.07 265	94.82 316	100.00 1	99.40 181	99.04 15	99.17 210	99.97 195	88.61 307	99.57 214	99.79 119	95.58 247	97.77 253

CLD-MVS		97.64 213	97.74 207	97.36 290	99.01 273	94.76 324	100.00 1	99.34 222	99.30 4	99.00 226	99.97 195	87.49 318	99.57 214	99.96 85	95.58 247	97.75 266
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
OPM-MVS		97.21 233	97.18 232	97.32 293	98.08 327	94.66 325	100.00 1	99.28 245	98.65 69	98.92 230	99.98 187	86.03 333	99.56 218	98.28 240	95.41 253	97.72 302
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
LPG-MVS_test		97.31 230	97.32 224	97.28 295	98.85 295	94.60 328	100.00 1	99.37 199	97.35 174	98.85 237	99.98 187	86.66 326	99.56 218	99.55 164	95.26 259	97.70 311

LGP-MVS_train				97.28 295	98.85 295	94.60 328		99.37 199	97.35 174	98.85 237	99.98 187	86.66 326	99.56 218	99.55 164	95.26 259	97.70 311

ITE_SJBPF				96.84 314	98.96 283	93.49 341		98.12 371	98.12 103	98.35 269	99.97 195	84.45 341	99.56 218	95.63 306	95.25 261	97.49 343

ACMP	97.00 8	97.19 234	97.16 233	97.27 297	98.97 282	94.58 331	100.00 1	99.32 226	97.97 114	97.45 315	99.98 187	85.79 335	99.56 218	99.70 137	95.24 262	97.67 321
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
HQP_MVS		97.71 211	97.82 204	97.37 289	99.00 277	94.80 319	100.00 1	99.40 181	99.00 27	99.08 220	99.97 195	88.58 309	99.55 223	99.79 119	95.57 251	97.76 255

plane_prior5								99.40 181					99.55 223	99.79 119	95.57 251	97.76 255

ACMM	97.17 6	97.37 228	97.40 220	97.29 294	99.01 273	94.64 327	100.00 1	99.25 261	98.07 106	98.44 266	99.98 187	87.38 320	99.55 223	99.25 186	95.19 265	97.69 315
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
patchmatchnet-post											97.79 366	99.41 54	99.54 226

SCA		98.30 186	97.98 199	99.23 181	99.41 236	98.25 223	99.99 203	99.45 102	96.91 204	99.76 176	99.58 289	89.65 293	99.54 226	98.31 236	98.79 169	99.91 147

XVG-ACMP-BASELINE		96.60 262	96.52 251	96.84 314	98.41 309	93.29 344	99.99 203	99.32 226	97.76 132	98.51 262	99.29 310	81.95 356	99.54 226	98.40 231	95.03 274	97.68 317

ACMH+	96.20 13	96.49 269	96.33 261	97.00 304	99.06 269	93.80 338	99.81 271	99.31 231	97.32 178	95.89 347	99.97 195	82.62 354	99.54 226	98.34 235	94.63 283	97.65 327

ACMH	96.25 11	96.77 252	96.62 246	97.21 298	98.96 283	94.43 333	99.64 304	99.33 224	97.43 169	96.55 336	99.97 195	83.52 349	99.54 226	99.07 199	95.13 270	97.66 322
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
TDRefinement		91.93 332	90.48 340	96.27 326	81.60 396	92.65 351	99.10 367	97.61 385	93.96 317	93.77 361	99.85 235	80.03 361	99.53 231	97.82 256	70.59 387	96.63 366

JIA-IIPM		97.09 239	96.34 260	99.36 164	98.88 290	98.59 201	99.81 271	99.43 121	84.81 379	99.96 117	90.34 389	98.55 127	99.52 232	97.00 280	98.28 196	99.98 109

IS-MVSNet		99.08 114	98.91 125	99.59 135	99.65 168	99.38 138	99.78 277	99.24 266	96.70 220	99.51 189	100.00 1	98.44 130	99.52 232	98.47 230	98.39 187	99.88 172

Effi-MVS+-dtu		98.51 173	98.86 130	97.47 286	99.77 146	94.21 335	100.00 1	98.94 353	97.61 148	99.91 148	98.75 343	95.89 203	99.51 234	99.36 177	99.48 157	98.68 248

PatchmatchNet		99.03 120	98.96 117	99.26 179	99.49 222	98.33 216	99.38 333	99.45 102	96.64 227	99.96 117	99.58 289	99.49 39	99.50 235	97.63 261	99.00 166	99.93 143
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
TinyColmap		95.50 305	95.12 310	96.64 318	98.69 299	93.00 346	99.40 331	97.75 382	96.40 243	96.14 343	99.87 229	79.47 364	99.50 235	93.62 332	94.72 281	97.40 348

EC-MVSNet		99.19 109	99.09 106	99.48 149	99.42 234	99.07 169	100.00 1	99.21 282	96.95 200	99.96 117	100.00 1	96.88 187	99.48 237	99.64 153	99.79 143	99.88 172

LTVRE_ROB	95.29 16	96.32 279	96.10 269	96.99 305	98.55 304	93.88 337	99.45 325	99.28 245	94.50 303	96.46 337	99.52 297	84.86 340	99.48 237	97.26 275	95.03 274	97.59 337
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		96.26 282	95.67 293	98.06 258	99.68 158	95.60 305	99.82 270	99.42 131	96.78 212	99.88 155	99.80 247	94.84 222	99.47 239	97.48 266	73.29 385	99.12 243

D2MVS		97.63 216	97.83 203	97.05 301	98.83 297	94.60 328	100.00 1	99.82 40	96.89 206	98.28 275	99.03 325	94.05 230	99.47 239	98.58 227	94.97 277	97.09 356

tpm2		98.64 159	98.58 157	98.81 206	99.42 234	97.12 281	99.69 299	99.37 199	93.63 324	99.94 142	99.67 267	98.96 99	99.47 239	98.62 224	97.95 212	99.83 187

HyFIR lowres test		99.32 91	99.24 91	99.58 139	99.95 95	99.26 150	100.00 1	99.99 13	96.72 218	99.29 206	99.91 225	99.49 39	99.47 239	99.74 128	98.08 206	100.00 1

hse-mvs2		96.79 251	96.38 257	98.04 264	99.68 158	95.54 306	99.81 271	99.42 131	98.21 92	100.00 1	99.80 247	97.49 162	99.46 243	99.72 131	73.27 386	99.12 243

USDC		95.90 299	95.70 289	96.50 322	98.60 303	92.56 352	100.00 1	98.30 367	97.77 130	96.92 325	99.94 221	81.25 360	99.45 244	93.54 333	94.96 278	97.49 343

gm-plane-assit					99.52 209	97.26 277			95.86 262		100.00 1		99.43 245	98.76 213

MS-PatchMatch		95.66 303	95.87 279	95.05 334	97.80 338	89.25 367	98.88 376	99.30 235	96.35 245	96.86 328	99.01 327	81.35 359	99.43 245	93.30 335	99.98 107	96.46 367

CHOSEN 280x420		99.85 3	99.87 1	99.80 101	99.99 49	99.97 21	99.97 233	99.98 16	98.96 32	100.00 1	100.00 1	99.96 5	99.42 247	100.00 1	100.00 1	100.00 1

VPA-MVSNet		97.03 244	96.43 255	98.82 204	98.64 301	99.32 144	99.38 333	99.47 79	96.73 217	98.91 232	98.94 334	87.00 324	99.40 248	99.23 189	89.59 338	97.76 255

LF4IMVS		96.19 284	96.18 266	96.23 327	98.26 318	92.09 354	100.00 1	97.89 379	97.82 126	97.94 294	99.87 229	82.71 353	99.38 249	97.41 269	93.71 289	97.20 353

UniMVSNet_ETH3D		95.28 308	94.41 314	97.89 274	98.91 287	95.14 310	99.13 364	99.35 216	92.11 347	97.17 322	99.66 269	70.28 383	99.36 250	97.88 254	95.18 266	99.16 241

GA-MVS		97.72 210	97.27 228	99.06 187	99.24 255	97.93 248	100.00 1	99.24 266	95.80 266	98.99 227	99.64 275	89.77 290	99.36 250	95.12 315	97.62 231	99.89 161

XXY-MVS		97.14 238	96.63 245	98.67 211	98.65 300	98.92 184	99.54 317	99.29 239	95.57 273	97.63 307	99.83 238	87.79 316	99.35 252	98.39 232	92.95 298	97.75 266

test_fmvs2		95.17 311	95.23 307	95.01 335	98.95 285	88.99 369	99.99 203	97.77 381	97.79 128	98.58 255	99.70 259	73.36 377	99.34 253	95.88 300	95.03 274	96.70 364

GG-mvs-BLEND				99.59 135	99.54 199	99.49 125	99.17 359	99.52 72		99.96 117	99.68 266	100.00 1	99.33 254	99.71 134	99.99 97	99.96 122

dmvs_re		97.54 220	97.88 201	96.54 321	99.55 198	90.35 364	99.86 263	99.46 94	97.00 198	99.41 200	100.00 1	90.78 275	99.30 255	99.60 158	95.24 262	99.96 122

EPNet_dtu		98.53 170	98.23 183	99.43 154	99.92 103	99.01 177	99.96 238	99.47 79	98.80 57	99.96 117	99.96 209	98.56 126	99.30 255	87.78 369	99.68 147	100.00 1
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tpm		98.24 191	98.22 184	98.32 233	99.13 259	95.79 303	99.53 318	99.12 315	95.20 285	99.96 117	99.36 307	97.58 157	99.28 257	97.41 269	96.67 241	99.88 172

baseline2		98.99 129	98.93 123	99.18 184	99.26 254	99.15 166	100.00 1	99.46 94	96.71 219	96.79 331	100.00 1	99.42 52	99.25 258	98.75 214	99.94 118	99.15 242

gg-mvs-nofinetune		96.95 247	96.10 269	99.50 146	99.41 236	99.36 142	99.07 372	99.52 72	83.69 381	99.96 117	83.60 397	100.00 1	99.20 259	99.68 145	99.99 97	99.96 122

TAMVS		98.76 148	98.73 143	98.86 203	99.44 233	97.69 259	99.57 313	99.34 222	96.57 231	99.12 216	99.81 244	98.83 113	99.16 260	97.97 252	97.91 214	99.73 225

MVP-Stereo		96.51 266	96.48 253	96.60 320	95.65 372	94.25 334	98.84 377	98.16 369	95.85 264	95.23 350	99.04 323	92.54 257	99.13 261	92.98 337	99.98 107	96.43 368
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
Vis-MVSNet (Re-imp)		98.99 129	98.89 129	99.29 174	99.64 174	98.89 185	99.98 227	99.31 231	96.74 215	99.48 191	100.00 1	98.11 137	99.10 262	98.39 232	98.34 191	99.89 161

EG-PatchMatch MVS		92.94 328	92.49 332	94.29 346	95.87 369	87.07 374	99.07 372	98.11 372	93.19 336	88.98 376	98.66 347	70.89 381	99.08 263	92.43 342	95.21 264	96.72 363

MVS-HIRNet		94.12 318	92.73 331	98.29 235	99.33 247	95.95 299	99.38 333	99.19 286	74.54 389	98.26 278	86.34 393	86.07 331	99.06 264	91.60 347	99.87 131	99.85 183

mvs_anonymous		98.80 146	98.60 155	99.38 163	99.57 195	99.24 154	100.00 1	99.21 282	95.87 260	98.92 230	99.82 241	96.39 199	99.03 265	99.13 196	98.50 178	99.88 172

TESTMET0.1,1		99.08 114	98.96 117	99.44 152	99.63 176	99.38 138	100.00 1	99.45 102	95.53 274	99.48 191	100.00 1	99.71 13	99.02 266	96.84 285	99.99 97	99.91 147

test-LLR		99.03 120	98.91 125	99.40 159	99.40 241	99.28 148	100.00 1	99.45 102	96.70 220	99.42 195	99.12 316	99.31 63	99.01 267	96.82 286	99.99 97	99.91 147

test-mter		98.96 133	98.82 132	99.40 159	99.40 241	99.28 148	100.00 1	99.45 102	95.44 283	99.42 195	99.12 316	99.70 14	99.01 267	96.82 286	99.99 97	99.91 147

tfpnnormal		96.36 276	95.69 292	98.37 229	98.55 304	98.71 194	99.69 299	99.45 102	93.16 337	96.69 335	99.71 256	88.44 311	98.99 269	94.17 325	91.38 323	97.41 347

Anonymous20231211		96.29 280	95.70 289	98.07 254	99.80 137	97.49 265	99.15 362	99.40 181	89.11 366	97.75 304	99.45 302	88.93 303	98.98 270	98.26 241	89.47 340	97.73 295

nrg030		97.64 213	97.27 228	98.75 209	98.34 311	99.53 117	100.00 1	99.22 273	96.21 253	98.27 277	99.95 217	94.40 228	98.98 270	99.23 189	89.78 337	97.75 266

cl22		98.23 192	98.11 188	98.58 219	99.82 120	99.01 177	100.00 1	99.28 245	96.92 203	98.33 271	99.21 313	98.09 139	98.97 272	98.72 215	92.61 301	97.76 255

CVMVSNet		98.56 167	98.47 164	98.82 204	99.11 260	97.67 260	99.74 287	99.47 79	97.57 153	99.06 222	100.00 1	95.72 207	98.97 272	98.21 242	97.33 233	99.83 187

VPNet		96.41 271	95.76 286	98.33 232	98.61 302	98.30 220	99.48 322	99.45 102	96.98 199	98.87 236	99.88 228	81.57 357	98.93 274	99.22 191	87.82 354	97.76 255

v7n		96.06 295	95.42 305	97.99 268	97.58 347	97.35 271	99.86 263	99.11 317	92.81 344	97.91 297	99.49 299	90.99 273	98.92 275	92.51 340	88.49 350	97.70 311

jajsoiax		97.07 241	96.79 241	97.89 274	97.28 358	97.12 281	99.95 244	99.19 286	96.55 232	97.31 318	99.69 262	87.35 322	98.91 276	98.70 216	95.12 271	97.66 322

mvs_tets		97.00 246	96.69 243	97.94 270	97.41 357	97.27 276	99.60 310	99.18 293	96.51 236	97.35 317	99.69 262	86.53 328	98.91 276	98.84 208	95.09 273	97.65 327

CDS-MVSNet		98.96 133	98.95 121	99.01 193	99.48 224	98.36 214	99.93 251	99.37 199	96.79 211	99.31 205	99.83 238	99.77 10	98.91 276	98.07 246	97.98 208	99.77 217
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
FIs		97.95 201	97.73 209	98.62 214	98.53 306	99.24 154	100.00 1	99.43 121	96.74 215	97.87 299	99.82 241	95.27 214	98.89 279	98.78 211	93.07 296	97.74 289

v1192		96.18 285	95.49 299	98.26 238	98.01 329	98.15 229	99.99 203	99.08 325	93.36 331	98.54 258	98.97 332	89.47 296	98.89 279	91.15 350	90.82 328	97.75 266

UniMVSNet (Re)		97.29 232	96.85 238	98.59 217	98.49 307	99.13 167	100.00 1	99.42 131	96.52 235	98.24 281	98.90 337	94.93 220	98.89 279	97.54 264	87.61 355	97.75 266

EI-MVSNet		97.98 200	97.93 200	98.16 246	99.11 260	97.84 254	99.74 287	99.29 239	94.39 307	98.65 250	100.00 1	97.21 172	98.88 282	97.62 263	95.31 257	97.75 266

MVSTER		98.58 165	98.52 160	98.77 208	99.65 168	99.68 102	100.00 1	99.29 239	95.63 270	98.65 250	99.80 247	99.78 8	98.88 282	98.59 226	95.31 257	97.73 295

iter_conf_final		98.72 151	98.76 139	98.59 217	99.64 174	99.17 164	100.00 1	99.22 273	96.63 229	99.02 225	99.97 195	99.98 4	98.84 284	99.22 191	95.18 266	97.76 255

iter_conf05		98.73 150	98.77 137	98.60 215	99.65 168	99.22 157	100.00 1	99.22 273	96.68 224	98.98 228	99.97 195	99.99 3	98.84 284	99.29 184	95.11 272	97.75 266

pm-mvs1		95.76 301	95.01 311	98.00 266	98.23 320	97.45 267	99.24 346	99.04 342	93.13 338	95.93 346	99.72 254	86.28 329	98.84 284	95.62 307	87.92 353	97.72 302

V42		96.65 259	96.16 268	98.11 253	98.17 325	98.23 224	99.99 203	99.09 324	93.97 316	98.74 246	99.05 322	91.09 270	98.82 287	95.46 309	89.90 335	97.27 352

CMPMVS	66.12 22	90.65 340	92.04 333	86.46 366	96.18 366	66.87 396	98.03 385	99.38 196	83.38 382	85.49 384	99.55 294	77.59 369	98.80 288	94.44 322	94.31 286	93.72 382
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
PS-MVSNAJss		98.03 198	98.06 193	97.94 270	97.63 342	97.33 274	99.89 259	99.23 270	96.27 249	98.03 288	99.59 287	98.75 118	98.78 289	98.52 228	94.61 284	97.70 311

OurMVSNet-221017-0		96.14 291	95.98 275	96.62 319	97.49 352	93.44 342	99.92 252	98.16 369	95.86 262	97.65 306	99.95 217	85.71 336	98.78 289	94.93 317	94.18 287	97.64 330

OpenMVS_ROB	88.34 20	91.89 333	91.12 335	94.19 348	95.55 374	87.63 372	99.26 344	98.03 374	86.61 376	90.65 374	96.82 373	70.14 384	98.78 289	86.54 372	96.50 245	96.15 369

pmmvs6		93.64 320	92.87 328	95.94 330	97.47 354	91.41 359	98.92 374	99.02 345	87.84 373	95.01 352	99.61 285	77.24 371	98.77 292	94.33 323	86.41 363	97.63 331

v8		96.35 277	95.73 288	98.21 242	98.11 326	98.23 224	99.94 249	99.07 329	92.66 345	98.29 274	99.00 328	91.46 264	98.77 292	94.17 325	88.83 348	97.62 333

miper_enhance_ethall		98.33 185	98.27 177	98.51 221	99.66 167	99.04 173	100.00 1	99.22 273	97.53 157	98.51 262	99.38 305	99.49 39	98.75 294	98.02 248	92.61 301	97.76 255

EGC-MVSNET		79.46 355	74.04 363	95.72 331	96.00 368	92.73 349	99.09 369	99.04 342	5.08 401	16.72 401	98.71 344	73.03 378	98.74 295	82.05 381	96.64 242	95.69 375

mvsmamba		98.13 194	98.06 193	98.32 233	98.22 321	98.50 205	100.00 1	99.22 273	96.41 241	98.91 232	99.96 209	95.69 208	98.73 296	99.19 193	94.95 279	97.73 295

v1921920		96.16 289	95.50 297	98.14 248	97.88 336	97.96 245	99.99 203	99.07 329	93.33 332	98.60 254	99.24 312	89.37 297	98.71 297	91.28 348	90.74 330	97.75 266

lessismore_v0				96.05 328	97.55 348	91.80 356		99.22 273		91.87 368	99.91 225	83.50 350	98.68 298	92.48 341	90.42 334	97.68 317

v2v482		96.70 257	96.18 266	98.27 236	98.04 328	98.39 209	100.00 1	99.13 309	94.19 313	98.58 255	99.08 319	90.48 280	98.67 299	95.69 304	90.44 333	97.75 266

v144192		96.40 274	95.81 281	98.17 245	97.89 334	98.11 232	99.99 203	99.06 337	93.39 330	98.75 245	99.09 318	90.43 281	98.66 300	93.10 336	90.55 332	97.75 266

cl____		97.54 220	97.32 224	98.18 243	99.47 227	98.14 231	100.00 1	99.10 319	94.16 314	97.60 311	99.63 279	97.52 161	98.65 301	96.47 293	91.97 313	97.76 255

bld_raw_dy_0_64		97.71 211	97.56 214	98.15 247	97.83 337	98.16 227	99.95 244	99.12 315	95.95 259	98.73 247	99.97 195	93.19 243	98.63 302	98.64 220	94.69 282	97.66 322

v1144		96.51 266	95.97 276	98.13 251	97.98 331	98.04 239	99.99 203	99.08 325	93.51 328	98.62 253	98.98 329	90.98 274	98.62 303	93.79 331	90.79 329	97.74 289

v1240		95.96 297	95.25 306	98.07 254	97.91 333	97.87 253	99.96 238	99.07 329	93.24 335	98.64 252	98.96 333	88.98 302	98.61 304	89.58 363	90.92 327	97.75 266

v10		96.14 291	95.50 297	98.07 254	98.19 323	97.96 245	99.83 267	99.07 329	92.10 348	98.07 285	98.94 334	91.07 271	98.61 304	92.41 343	89.82 336	97.63 331

anonymousdsp		97.16 236	96.88 236	98.00 266	97.08 360	98.06 237	99.81 271	99.15 300	94.58 299	97.84 300	99.62 283	90.49 279	98.60 306	97.98 249	95.32 256	97.33 351

v148		96.29 280	95.84 280	97.63 280	97.74 339	96.53 296	100.00 1	99.07 329	93.52 327	98.01 291	99.42 304	91.22 266	98.60 306	96.37 297	87.22 358	97.75 266

MVSFormer		98.94 136	98.82 132	99.28 177	99.45 231	99.49 125	100.00 1	99.13 309	95.46 281	99.97 112	100.00 1	96.76 189	98.59 308	98.63 222	100.00 1	99.74 221

test_djsdf		97.55 219	97.38 221	98.07 254	97.50 350	97.99 241	100.00 1	99.13 309	95.46 281	98.47 265	99.85 235	92.01 263	98.59 308	98.63 222	95.36 255	97.62 333

test_0402		94.35 314	93.70 319	96.32 325	97.92 332	93.60 339	99.61 309	98.85 360	88.19 372	94.68 355	99.48 300	80.01 362	98.58 310	89.39 364	95.15 269	96.77 362

miper_ehance_all_eth		97.81 205	97.66 211	98.23 239	99.49 222	98.37 212	99.99 203	99.11 317	94.78 292	98.25 279	99.21 313	98.18 135	98.57 311	97.35 273	92.61 301	97.76 255

FC-MVSNet-test		97.84 203	97.63 213	98.45 224	98.30 316	99.05 172	100.00 1	99.43 121	96.63 229	97.61 310	99.82 241	95.19 217	98.57 311	98.64 220	93.05 297	97.73 295

WR-MVS		97.09 239	96.64 244	98.46 223	98.43 308	99.09 168	99.97 233	99.33 224	95.62 271	97.76 301	99.67 267	91.17 269	98.56 313	98.49 229	89.28 343	97.74 289

RRT_MVS		97.77 207	97.76 205	97.78 278	97.89 334	97.06 284	100.00 1	99.29 239	95.74 267	98.00 293	99.97 195	95.94 202	98.55 314	98.87 207	94.18 287	97.72 302

WR-MVS_H		96.73 254	96.32 262	97.95 269	98.26 318	97.88 251	99.72 294	99.43 121	95.06 287	96.99 324	98.68 346	93.02 247	98.53 315	97.43 268	88.33 351	97.43 346

ambc				88.45 361	86.84 392	70.76 390	97.79 387	98.02 376		90.91 371	95.14 378	38.69 396	98.51 316	94.97 316	84.23 367	96.09 371

eth_miper_zixun_eth		97.47 224	97.28 226	98.06 258	99.41 236	97.94 247	99.62 308	99.08 325	94.46 305	98.19 282	99.56 293	96.91 186	98.50 317	96.78 289	91.49 320	97.74 289

UniMVSNet_NR-MVSNet		97.16 236	96.80 239	98.22 240	98.38 310	98.41 206	100.00 1	99.45 102	96.14 255	97.76 301	99.64 275	95.05 218	98.50 317	97.98 249	86.84 359	97.75 266

DU-MVS		96.93 248	96.49 252	98.22 240	98.31 314	98.41 206	100.00 1	99.37 199	96.41 241	97.76 301	99.65 271	92.14 260	98.50 317	97.98 249	86.84 359	97.75 266

pmmvs4		97.17 235	96.80 239	98.27 236	97.68 341	98.64 199	100.00 1	99.18 293	94.22 310	98.55 257	99.71 256	93.67 235	98.47 320	95.66 305	92.57 304	97.71 310

pmmvs5		95.94 298	95.61 294	96.95 307	97.42 355	94.66 325	100.00 1	98.08 373	93.60 325	97.05 323	99.43 303	87.02 323	98.46 321	95.76 301	92.12 309	97.72 302

SixPastTwentyTwo		95.71 302	95.49 299	96.38 324	97.42 355	93.01 345	99.84 266	98.23 368	94.75 293	95.98 345	99.97 195	85.35 338	98.43 322	94.71 319	93.17 295	97.69 315

NR-MVSNet		96.63 260	96.04 272	98.38 228	98.31 314	98.98 181	99.22 353	99.35 216	95.87 260	94.43 359	99.65 271	92.73 253	98.40 323	96.78 289	88.05 352	97.75 266

TransMVSNet (Re)		94.78 312	93.72 318	97.93 272	98.34 311	97.88 251	99.23 351	97.98 377	91.60 350	94.55 356	99.71 256	87.89 314	98.36 324	89.30 365	84.92 365	97.56 339

Baseline_NR-MVSNet		96.16 289	95.70 289	97.56 285	98.28 317	96.79 291	100.00 1	97.86 380	91.93 349	97.63 307	99.47 301	92.14 260	98.35 325	97.13 276	86.83 361	97.54 340

IterMVS-SCA-FT		96.72 256	96.42 256	97.62 282	99.40 241	96.83 289	99.99 203	99.14 305	94.65 298	97.55 313	99.72 254	89.65 293	98.31 326	95.62 307	92.05 310	97.73 295

DIV-MVS_self_test		97.52 223	97.35 223	98.05 262	99.46 230	98.11 232	100.00 1	99.10 319	94.21 311	97.62 309	99.63 279	97.65 155	98.29 327	96.47 293	91.98 312	97.76 255

IterMVS		96.76 253	96.46 254	97.63 280	99.41 236	96.89 287	99.99 203	99.13 309	94.74 295	97.59 312	99.66 269	89.63 295	98.28 328	95.71 303	92.31 307	97.72 302
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
test_vis3_rt		79.61 354	78.19 359	83.86 369	88.68 391	69.56 391	99.81 271	82.19 405	86.78 375	68.57 393	84.51 396	25.06 402	98.26 329	89.18 367	78.94 380	83.75 393

c3_l		97.58 217	97.42 218	98.06 258	99.48 224	98.16 227	99.96 238	99.10 319	94.54 301	98.13 283	99.20 315	97.87 144	98.25 330	97.28 274	91.20 325	97.75 266

test0.0.03 1		98.12 195	98.03 196	98.39 227	99.11 260	98.07 235	100.00 1	99.93 30	96.70 220	96.91 327	99.95 217	99.31 63	98.19 331	91.93 344	98.44 182	98.91 246

CP-MVSNet		96.73 254	96.25 263	98.18 243	98.21 322	98.67 197	99.77 282	99.32 226	95.06 287	97.20 321	99.65 271	90.10 284	98.19 331	98.06 247	88.90 346	97.66 322

PS-CasMVS		96.34 278	95.78 285	98.03 265	98.18 324	98.27 222	99.71 295	99.32 226	94.75 293	96.82 330	99.65 271	86.98 325	98.15 333	97.74 257	88.85 347	97.66 322

our_test_3		96.51 266	96.35 259	96.98 306	97.61 344	95.05 311	99.98 227	99.01 347	94.68 296	96.77 333	99.06 320	95.87 204	98.14 334	91.81 345	92.37 306	97.75 266

new_pmnet		94.11 319	93.47 322	96.04 329	96.60 364	92.82 348	99.97 233	98.91 356	90.21 362	95.26 349	98.05 365	85.89 334	98.14 334	84.28 376	92.01 311	97.16 354

K. test v3		95.46 306	95.14 309	96.40 323	97.53 349	93.40 343	99.99 203	99.23 270	95.49 279	92.70 367	99.73 253	84.26 343	98.12 336	93.94 330	93.38 294	97.68 317

Patchmtry		96.81 250	96.37 258	98.14 248	99.31 248	98.55 202	98.91 375	99.00 348	90.45 359	97.92 296	98.98 329	96.94 184	98.12 336	94.27 324	91.53 319	97.75 266

FMVSNet3		97.30 231	96.95 234	98.37 229	99.65 168	99.25 152	99.71 295	99.28 245	94.23 309	98.53 259	98.91 336	93.30 241	98.11 338	95.31 311	93.60 290	97.73 295

N_pmnet		91.88 334	93.37 323	87.40 364	97.24 359	66.33 397	99.90 256	91.05 400	89.77 365	95.65 348	98.58 350	90.05 287	98.11 338	85.39 373	92.72 300	97.75 266

IterMVS-LS		97.56 218	97.44 217	97.92 273	99.38 245	97.90 249	99.89 259	99.10 319	94.41 306	98.32 272	99.54 296	97.21 172	98.11 338	97.50 265	91.62 317	97.75 266
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CR-MVSNet		98.02 199	97.71 210	98.93 198	99.31 248	98.86 186	99.13 364	99.00 348	96.53 234	99.96 117	98.98 329	96.94 184	98.10 341	91.18 349	98.40 185	99.84 184

FMVSNet2		96.22 283	95.60 295	98.06 258	99.53 202	98.33 216	99.45 325	99.27 253	93.71 319	98.03 288	98.84 339	84.23 344	98.10 341	93.97 329	93.40 293	97.73 295

ppachtmachnet_test		96.17 287	95.89 278	97.02 303	97.61 344	95.24 308	99.99 203	99.24 266	93.31 333	96.71 334	99.62 283	94.34 229	98.07 343	89.87 359	92.30 308	97.75 266

GBi-Net		96.07 293	95.80 283	96.89 311	99.53 202	94.87 313	99.18 354	99.27 253	93.71 319	98.53 259	98.81 340	84.23 344	98.07 343	95.31 311	93.60 290	97.72 302

test1		96.07 293	95.80 283	96.89 311	99.53 202	94.87 313	99.18 354	99.27 253	93.71 319	98.53 259	98.81 340	84.23 344	98.07 343	95.31 311	93.60 290	97.72 302

FMVSNet1		94.45 313	93.63 320	96.89 311	98.87 293	94.87 313	99.18 354	99.27 253	90.95 356	97.31 318	98.81 340	72.89 379	98.07 343	92.61 338	92.81 299	97.72 302

PatchT		95.90 299	94.95 313	98.75 209	99.03 271	98.39 209	99.08 370	99.32 226	85.52 377	99.96 117	94.99 381	97.94 141	98.05 347	80.20 385	98.47 181	99.81 200

TranMVSNet+NR-MVSNet		96.45 270	96.01 273	97.79 277	98.00 330	97.62 262	100.00 1	99.35 216	95.98 257	97.31 318	99.64 275	90.09 285	98.00 348	96.89 284	86.80 362	97.75 266

test_method		91.04 339	91.10 336	90.85 356	98.34 311	77.63 383	100.00 1	98.93 355	76.69 387	96.25 341	98.52 352	70.44 382	97.98 349	89.02 368	91.74 315	96.92 360

miper_lstm_enhance		97.40 227	97.28 226	97.75 279	99.48 224	97.52 264	100.00 1	99.07 329	94.08 315	98.01 291	99.61 285	97.38 169	97.98 349	96.44 296	91.47 322	97.76 255

ET-MVSNet_ETH3D		96.41 271	95.48 301	99.20 183	99.81 126	99.75 91	100.00 1	99.02 345	97.30 182	78.33 389	100.00 1	97.73 151	97.94 351	99.70 137	87.41 356	99.92 145

ADS-MVSNet2		98.28 189	98.51 161	97.62 282	99.51 214	95.03 312	99.24 346	99.41 177	95.52 276	99.96 117	99.70 259	97.57 158	97.94 351	97.11 277	98.54 176	99.88 172

PEN-MVS		96.01 296	95.48 301	97.58 284	97.74 339	97.26 277	99.90 256	99.29 239	94.55 300	96.79 331	99.55 294	87.38 320	97.84 353	96.92 283	87.24 357	97.65 327

Syy-MVS		96.17 287	96.57 248	95.00 336	99.50 218	87.37 373	100.00 1	99.57 68	96.23 250	98.07 285	100.00 1	92.41 258	97.81 354	85.34 374	97.96 210	99.82 192

myMVS_eth3d		98.52 171	98.51 161	98.53 220	99.50 218	97.98 242	100.00 1	99.57 68	96.23 250	98.07 285	100.00 1	99.09 86	97.81 354	96.17 298	97.96 210	99.82 192

testing3		98.44 176	98.37 172	98.65 212	99.51 214	98.32 218	100.00 1	99.62 66	96.43 238	97.93 295	99.99 182	99.11 84	97.81 354	94.88 318	97.80 223	99.82 192

testgi		96.18 285	95.93 277	96.93 309	98.98 281	94.20 336	100.00 1	99.07 329	97.16 188	96.06 344	99.86 231	84.08 347	97.79 357	90.38 357	97.80 223	98.81 247

testf1		84.40 351	84.79 353	83.23 370	95.71 370	58.71 403	98.79 378	97.75 382	81.58 383	84.94 385	98.07 363	45.33 394	97.73 358	77.09 391	83.85 368	93.24 384

APD_test2		84.40 351	84.79 353	83.23 370	95.71 370	58.71 403	98.79 378	97.75 382	81.58 383	84.94 385	98.07 363	45.33 394	97.73 358	77.09 391	83.85 368	93.24 384

MIMVSNet		97.06 242	96.73 242	98.05 262	99.38 245	96.64 295	98.47 382	99.35 216	93.41 329	99.48 191	98.53 351	89.66 292	97.70 360	94.16 327	98.11 205	99.80 211

LCM-MVSNet-Re		96.52 264	97.21 231	94.44 342	99.27 252	85.80 375	99.85 265	96.61 392	95.98 257	92.75 366	98.48 353	93.97 233	97.55 361	99.58 162	98.43 183	99.98 109

DTE-MVSNet		95.52 304	94.99 312	97.08 300	97.49 352	96.45 297	100.00 1	99.25 261	93.82 318	96.17 342	99.57 292	87.81 315	97.18 362	94.57 320	86.26 364	97.62 333

mvsany_test3		89.36 344	88.96 348	90.56 357	91.95 382	78.97 382	99.74 287	96.59 393	96.84 208	89.25 375	96.07 375	52.59 390	97.11 363	95.17 314	82.44 373	95.58 377

UnsupCasMVSNet_bld		89.50 343	88.00 349	93.99 349	95.30 375	88.86 370	98.52 381	99.28 245	85.50 378	87.80 380	94.11 383	61.63 387	96.96 364	90.63 353	79.26 378	96.15 369

Anonymous20240521		93.29 323	92.76 330	94.90 340	95.64 373	91.27 360	99.97 233	98.82 361	87.04 374	94.71 354	98.19 360	83.86 348	96.80 365	84.04 377	92.56 305	96.64 365

KD-MVS_2432*1600		94.15 316	93.08 325	97.35 291	99.53 202	97.83 255	99.63 306	99.19 286	92.88 341	96.29 339	97.68 367	98.84 111	96.70 366	89.73 360	63.92 390	97.53 341

miper_refine_blended		94.15 316	93.08 325	97.35 291	99.53 202	97.83 255	99.63 306	99.19 286	92.88 341	96.29 339	97.68 367	98.84 111	96.70 366	89.73 360	63.92 390	97.53 341

test_f		86.87 349	86.06 352	89.28 360	91.45 387	76.37 385	99.87 262	97.11 387	91.10 354	88.46 377	93.05 386	38.31 397	96.66 368	91.77 346	83.46 371	94.82 378

MDA-MVSNet_test_wron		92.61 329	91.09 337	97.19 299	96.71 363	97.26 277	100.00 1	99.14 305	88.61 368	67.90 395	98.32 359	89.03 300	96.57 369	90.47 356	89.59 338	97.74 289

UnsupCasMVSNet_eth		94.25 315	93.89 316	95.34 332	97.63 342	92.13 353	99.73 292	99.36 205	94.88 290	92.78 364	98.63 348	82.72 352	96.53 370	94.57 320	84.73 366	97.36 349

tmp_tt		75.80 360	74.26 362	80.43 374	52.91 405	53.67 405	87.42 393	97.98 377	61.80 393	67.04 396	100.00 1	76.43 372	96.40 371	96.47 293	28.26 398	91.23 388

Gipuma		84.73 350	83.50 355	88.40 362	97.50 350	82.21 380	88.87 391	99.05 339	65.81 391	85.71 383	90.49 388	53.70 389	96.31 372	78.64 387	91.74 315	86.67 390
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
YYNet1		92.44 330	90.92 338	97.03 302	96.20 365	97.06 284	99.99 203	99.14 305	88.21 371	67.93 394	98.43 356	88.63 306	96.28 373	90.64 352	89.08 345	97.74 289

EU-MVSNet		96.63 260	96.53 249	96.94 308	97.59 346	96.87 288	99.76 284	99.47 79	96.35 245	96.85 329	99.78 250	92.57 256	96.27 374	95.33 310	91.08 326	97.68 317

pmmvs-eth3d		91.73 335	90.67 339	94.92 339	91.63 385	92.71 350	99.90 256	98.54 365	91.19 353	88.08 378	95.50 377	79.31 366	96.13 375	90.55 355	81.32 377	95.91 373

PM-MVS		88.39 345	87.41 350	91.31 355	91.73 384	82.02 381	99.79 276	96.62 391	91.06 355	90.71 373	95.73 376	48.60 392	95.96 376	90.56 354	81.91 376	95.97 372

MDA-MVSNet-bldmvs		91.65 336	89.94 344	96.79 317	96.72 362	96.70 293	99.42 330	98.94 353	88.89 367	66.97 397	98.37 357	81.43 358	95.91 377	89.24 366	89.46 341	97.75 266

Patchmatch-RL test		93.49 321	93.63 320	93.05 353	91.78 383	83.41 379	98.21 384	96.95 389	91.58 351	91.05 369	97.64 369	99.40 55	95.83 378	94.11 328	81.95 375	99.91 147

Anonymous20231206		93.45 322	93.17 324	94.30 345	95.00 377	89.69 366	99.98 227	98.43 366	93.30 334	94.50 358	98.59 349	90.52 278	95.73 379	77.46 390	90.73 331	97.48 345

DSMNet-mixed		95.18 310	95.21 308	95.08 333	96.03 367	90.21 365	99.65 303	93.64 398	92.91 340	98.34 270	97.40 370	90.05 287	95.51 380	91.02 351	97.86 217	99.51 237

pmmvs3		90.62 341	89.36 347	94.40 343	90.53 390	91.49 358	100.00 1	96.73 390	84.21 380	93.65 362	96.65 374	82.56 355	94.83 381	82.28 380	77.62 382	96.89 361

KD-MVS_self_test		91.16 337	90.09 342	94.35 344	94.44 379	91.27 360	99.74 287	99.08 325	90.82 357	94.53 357	94.91 382	86.11 330	94.78 382	82.67 379	68.52 388	96.99 358

FMVSNet5		95.32 307	95.43 304	94.99 337	99.39 244	92.99 347	99.25 345	99.24 266	90.45 359	97.44 316	98.45 354	95.78 206	94.39 383	87.02 370	91.88 314	97.59 337

new-patchmatchnet		90.30 342	89.46 346	92.84 354	90.77 388	88.55 371	99.83 267	98.80 362	90.07 364	87.86 379	95.00 380	78.77 367	94.30 384	84.86 375	79.15 379	95.68 376

LCM-MVSNet		79.01 358	76.93 361	85.27 367	78.28 398	68.01 395	96.57 388	98.03 374	55.10 395	82.03 388	93.27 385	31.99 401	93.95 385	82.72 378	74.37 384	93.84 381

CL-MVSNet_self_test		91.07 338	90.35 341	93.24 352	93.27 380	89.16 368	99.55 315	99.25 261	92.34 346	95.23 350	97.05 372	88.86 305	93.59 386	80.67 383	66.95 389	96.96 359

MIMVSNet1		91.96 331	91.20 334	94.23 347	94.94 378	91.69 357	99.34 337	99.22 273	88.23 370	94.18 360	98.45 354	75.52 374	93.41 387	79.37 386	91.49 320	97.60 336

test20.03		93.11 325	92.85 329	93.88 350	95.19 376	91.83 355	100.00 1	98.87 359	93.68 322	92.76 365	98.88 338	89.20 299	92.71 388	77.88 388	89.19 344	97.09 356

test_fmvs3		87.19 348	87.02 351	87.71 363	92.69 381	76.64 384	99.96 238	97.27 386	93.55 326	90.82 372	94.03 384	38.00 398	92.19 389	93.49 334	83.35 372	94.32 379

dmvs_testset		93.27 324	95.48 301	86.65 365	98.74 298	68.42 394	99.92 252	98.91 356	96.19 254	93.28 363	100.00 1	91.06 272	91.67 390	89.64 362	91.54 318	99.86 182

PMMVS2		79.15 357	77.28 360	84.76 368	82.34 395	72.66 386	99.70 297	95.11 396	71.68 390	84.78 387	90.87 387	32.05 400	89.99 391	75.53 393	63.45 392	91.64 387

FPMVS		77.92 359	79.45 357	73.34 379	76.87 399	46.81 406	98.24 383	99.05 339	59.89 394	73.55 390	98.34 358	36.81 399	86.55 392	80.96 382	91.35 324	86.65 391

PMVS	60.66 23	65.98 365	65.05 366	68.75 382	55.06 404	38.40 407	88.19 392	96.98 388	48.30 399	44.82 400	88.52 391	12.22 405	86.49 393	67.58 394	83.79 370	81.35 395
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
WB-MVS		88.24 346	90.09 342	82.68 372	91.56 386	69.51 392	100.00 1	98.73 363	90.72 358	87.29 381	98.12 361	92.87 249	85.01 394	62.19 395	89.34 342	93.54 383

SSC-MVS		87.61 347	89.47 345	82.04 373	90.63 389	68.77 393	99.99 203	98.66 364	90.34 361	86.70 382	98.08 362	92.72 254	84.12 395	59.41 398	88.71 349	93.22 386

MVE	68.59 21	67.22 363	64.68 367	74.84 376	74.67 401	62.32 401	95.84 389	90.87 401	50.98 396	58.72 398	81.05 398	12.20 406	78.95 396	61.06 397	56.75 393	83.24 394
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		70.72 361	70.06 364	72.69 380	83.92 394	65.48 399	99.95 244	92.72 399	49.88 397	72.30 391	86.26 394	47.17 393	77.43 397	53.83 399	44.49 395	75.17 397

EMVS		69.88 362	69.09 365	72.24 381	84.70 393	65.82 398	99.96 238	87.08 404	49.82 398	71.51 392	84.74 395	49.30 391	75.32 398	50.97 400	43.71 396	75.59 396

test123		79.44 356	79.23 358	80.05 375	80.03 397	71.72 388	100.00 1	77.93 406	62.52 392	94.81 353	99.69 262	78.21 368	74.53 399	92.57 339	27.33 399	93.90 380

testmvs		80.17 353	81.95 356	74.80 377	58.54 403	59.58 402	100.00 1	87.14 403	76.09 388	99.61 185	100.00 1	67.06 385	74.19 400	98.84 208	50.30 394	90.64 389

ANet_high		66.05 364	63.44 368	73.88 378	61.14 402	63.45 400	95.68 390	87.18 402	79.93 385	47.35 399	80.68 399	22.35 403	72.33 401	61.24 396	35.42 397	85.88 392

wuyk23d		28.28 366	29.73 370	23.92 383	75.89 400	32.61 408	66.50 394	12.88 407	16.09 400	14.59 402	16.59 401	12.35 404	32.36 402	39.36 401	13.36 400	6.79 398

test_blank		0.07 370	0.09 373	0.00 384	0.00 406	0.00 409	0.00 395	0.00 408	0.00 402	0.00 403	0.79 402	0.00 407	0.00 403	0.00 402	0.00 401	0.00 399

uanet_test		0.01 371	0.02 374	0.00 384	0.00 406	0.00 409	0.00 395	0.00 408	0.00 402	0.00 403	0.14 403	0.00 407	0.00 403	0.00 402	0.00 401	0.00 399

DCPMVS		0.01 371	0.02 374	0.00 384	0.00 406	0.00 409	0.00 395	0.00 408	0.00 402	0.00 403	0.14 403	0.00 407	0.00 403	0.00 402	0.00 401	0.00 399

cdsmvs_eth3d_5k		24.41 367	32.55 369	0.00 384	0.00 406	0.00 409	0.00 395	99.39 194	0.00 402	0.00 403	100.00 1	93.55 238	0.00 403	0.00 402	0.00 401	0.00 399

pcd_1.5k_mvsjas		8.24 369	10.99 372	0.00 384	0.00 406	0.00 409	0.00 395	0.00 408	0.00 402	0.00 403	0.14 403	98.75 118	0.00 403	0.00 402	0.00 401	0.00 399

sosnet-low-res		0.01 371	0.02 374	0.00 384	0.00 406	0.00 409	0.00 395	0.00 408	0.00 402	0.00 403	0.14 403	0.00 407	0.00 403	0.00 402	0.00 401	0.00 399

sosnet		0.01 371	0.02 374	0.00 384	0.00 406	0.00 409	0.00 395	0.00 408	0.00 402	0.00 403	0.14 403	0.00 407	0.00 403	0.00 402	0.00 401	0.00 399

uncertanet		0.01 371	0.02 374	0.00 384	0.00 406	0.00 409	0.00 395	0.00 408	0.00 402	0.00 403	0.14 403	0.00 407	0.00 403	0.00 402	0.00 401	0.00 399

Regformer		0.01 371	0.02 374	0.00 384	0.00 406	0.00 409	0.00 395	0.00 408	0.00 402	0.00 403	0.14 403	0.00 407	0.00 403	0.00 402	0.00 401	0.00 399

ab-mvs-re		8.33 368	11.11 371	0.00 384	0.00 406	0.00 409	0.00 395	0.00 408	0.00 402	0.00 403	100.00 1	0.00 407	0.00 403	0.00 402	0.00 401	0.00 399

uanet		0.01 371	0.02 374	0.00 384	0.00 406	0.00 409	0.00 395	0.00 408	0.00 402	0.00 403	0.14 403	0.00 407	0.00 403	0.00 402	0.00 401	0.00 399

WAC-MVS						97.98 242								95.74 302

FOURS1					100.00 1	99.97 21	100.00 1	99.42 131	98.52 74	100.00 1

test_one_0601					100.00 1	99.99 5		99.42 131	98.72 64	100.00 1	100.00 1	99.60 17

eth-test2					0.00 406

eth-test					0.00 406

RE-MVS-def			99.55 56		99.99 49	99.91 51	100.00 1	99.42 131	97.62 144	100.00 1	100.00 1	98.94 102		99.99 61	100.00 1	100.00 1

IU-MVS					100.00 1	99.99 5		99.42 131	99.12 6	100.00 1				100.00 1	100.00 1	100.00 1

save fliter					99.99 49	99.93 43	100.00 1	99.42 131	98.93 38

test0726					100.00 1	99.99 5	100.00 1	99.42 131	99.04 15	100.00 1	100.00 1	99.53 29

GSMVS																99.91 147

test_part2					100.00 1	99.99 5				100.00 1

sam_mvs1												99.29 69				99.91 147

sam_mvs												99.33 58

MTGPA								99.42 131

MTMP							100.00 1	99.18 293

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 43	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 72		99.82 40			100.00 1	99.27 72			100.00 1	100.00 1

原ACMM2							100.00 1

test222					99.99 49	99.90 58	100.00 1	99.69 62	97.66 139	100.00 1	100.00 1	99.30 68			100.00 1	100.00 1

segment_acmp												99.55 25

testdata1							100.00 1		98.77 63

plane_prior7					99.00 277	94.78 323

plane_prior6					99.06 269	94.80 319						88.58 309

plane_prior4											99.97 195

plane_prior3						94.79 322			99.03 20	99.08 220

plane_prior2							100.00 1		99.00 27

plane_prior1					99.02 272

plane_prior						94.80 319	100.00 1		99.03 20						95.58 247

n2								0.00 408

nn								0.00 408

door-mid								96.32 394

test11								99.42 131

door								96.13 395

HQP5-MVS						94.82 316

HQP-NCC					99.07 265		100.00 1		99.04 15	99.17 210

ACMP_Plane					99.07 265		100.00 1		99.04 15	99.17 210

BP-MVS														99.79 119

HQP3-MVS								99.40 181							95.58 247

HQP2-MVS												88.61 307

NP-MVS					99.07 265	94.81 318					99.97 195

MDTV_nov1_ep13_2view						99.24 154	99.56 314		96.31 248	99.96 117		98.86 109		98.92 204		99.89 161

ACMMP++_ref															94.58 285

ACMMP++															95.17 268

Test By Simon												99.10 85

High-resolution multi-view benchmark