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.

SED-MVS		99.83 7	99.77 9	100.00 1	100.00 1	99.99 3	100.00 1	99.42 127	99.03 19	100.00 1	100.00 1	99.50 33	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 22	100.00 1	100.00 1	100.00 1	100.00 1

test_241102_TWO								99.42 127	99.03 19	100.00 1	100.00 1	99.56 19	100.00 1	100.00 1	100.00 1	100.00 1

test_241102_ONE					100.00 1	99.99 3		99.42 127	99.03 19	100.00 1	100.00 1	99.50 33	100.00 1

ZNCC-MVS		99.71 35	99.62 46	99.97 31	99.99 54	99.90 60	100.00 1	99.79 41	97.97 96	99.97 108	100.00 1	98.97 102	100.00 1	99.94 92	100.00 1	100.00 1

testtj		99.70 39	99.61 47	99.99 9	100.00 1	99.98 13	100.00 1	99.53 68	97.71 119	100.00 1	100.00 1	99.23 74	100.00 1	100.00 1	100.00 1	100.00 1

DVP-MVS		99.83 7	99.78 6	100.00 1	100.00 1	99.99 3	100.00 1	99.42 127	99.04 14	100.00 1	100.00 1	99.53 23	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 46	100.00 1	100.00 1	99.61 13	100.00 1	100.00 1	100.00 1	100.00 1

test_0728_SECOND				100.00 1	99.99 54	99.99 3	100.00 1	99.42 127					100.00 1	100.00 1	100.00 1	100.00 1

DPM-MVS		99.63 62	99.51 70	100.00 1	99.90 114	100.00 1	100.00 1	99.43 119	99.00 25	100.00 1	100.00 1	99.58 18	100.00 1	97.64 241	100.00 1	100.00 1

GST-MVS		99.64 58	99.53 66	99.95 58	100.00 1	99.86 84	100.00 1	99.79 41	97.72 116	99.95 133	100.00 1	98.39 138	100.00 1	99.96 84	99.99 107	100.00 1

test_yl		99.51 75	99.37 82	99.95 58	99.82 118	99.90 60	100.00 1	99.47 77	97.48 148	100.00 1	100.00 1	99.80 4	100.00 1	99.98 75	97.75 207	99.94 124

Anonymous20240529		96.93 232	96.22 247	99.05 175	99.79 135	97.30 256	99.16 329	99.47 77	88.51 338	98.69 228	100.00 1	83.50 329	100.00 1	99.83 106	97.02 218	99.83 174

Anonymous202405211		97.87 190	97.53 201	98.90 186	99.81 122	96.70 272	99.35 306	99.46 92	92.98 311	98.83 221	99.99 167	90.63 265	100.00 1	99.70 127	97.03 217	100.00 1

DCV-MVSNet		99.51 75	99.37 82	99.95 58	99.82 118	99.90 60	100.00 1	99.47 77	97.48 148	100.00 1	100.00 1	99.80 4	100.00 1	99.98 75	97.75 207	99.94 124

SMA-MVS		99.69 43	99.59 50	99.98 22	99.99 54	99.93 42	100.00 1	99.43 119	97.50 147	100.00 1	100.00 1	99.43 47	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 16	99.99 9	99.99 54	99.98 13	100.00 1	99.42 127	98.91 35	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		99.25 107	99.01 113	99.95 58	99.81 122	99.87 81	100.00 1	99.94 18	97.13 174	99.83 154	99.96 187	97.01 177	100.00 1	99.59 144	97.85 200	99.98 108

tfpn200view9		99.26 104	99.03 111	99.96 46	99.81 122	99.89 68	100.00 1	99.94 18	97.23 167	99.83 154	99.96 187	97.04 173	100.00 1	99.59 144	97.85 200	99.98 108

CANet_DTU		99.02 125	98.90 129	99.41 149	99.88 116	98.71 185	100.00 1	99.29 230	98.84 40	100.00 1	100.00 1	94.02 227	100.00 1	98.08 225	99.96 120	99.52 214

MP-MVS-pluss		99.61 68	99.50 72	99.97 31	99.98 92	99.92 49	100.00 1	99.42 127	97.53 143	99.77 165	100.00 1	98.77 123	100.00 1	99.99 58	100.00 1	99.99 106
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
xiu_mvs_v1_base_debu		99.35 91	99.21 99	99.79 106	99.67 155	99.71 107	99.78 252	99.36 200	98.13 81	100.00 1	100.00 1	97.00 180	100.00 1	99.83 106	99.07 151	99.66 208

ACMMP_NAP		99.67 52	99.57 57	99.97 31	99.98 92	99.92 49	100.00 1	99.42 127	97.83 109	100.00 1	100.00 1	98.89 115	100.00 1	99.98 75	100.00 1	100.00 1

zzz-MVS		99.68 47	99.59 50	99.97 31	99.99 54	99.91 52	100.00 1	99.42 127	98.32 71	99.94 135	100.00 1	98.65 127	100.00 1	99.96 84	100.00 1	100.00 1

xiu_mvs_v2_base		99.51 75	99.41 76	99.82 97	99.70 148	99.73 105	99.92 231	99.40 176	98.15 79	100.00 1	100.00 1	98.50 135	100.00 1	99.85 103	99.13 149	99.74 199

xiu_mvs_v1_base		99.35 91	99.21 99	99.79 106	99.67 155	99.71 107	99.78 252	99.36 200	98.13 81	100.00 1	100.00 1	97.00 180	100.00 1	99.83 106	99.07 151	99.66 208

xiu_mvs_v1_base_debi		99.35 91	99.21 99	99.79 106	99.67 155	99.71 107	99.78 252	99.36 200	98.13 81	100.00 1	100.00 1	97.00 180	100.00 1	99.83 106	99.07 151	99.66 208

MTAPA		99.68 47	99.59 50	99.97 31	99.99 54	99.91 52	100.00 1	99.42 127	98.32 71	99.94 135	100.00 1	98.65 127	100.00 1	99.96 84	100.00 1	100.00 1

HFP-MVS		99.74 26	99.67 30	99.96 46	100.00 1	99.89 68	100.00 1	99.76 45	97.95 101	100.00 1	100.00 1	99.31 60	100.00 1	99.99 58	100.00 1	100.00 1

region2R		99.72 32	99.64 38	99.97 31	100.00 1	99.90 60	100.00 1	99.74 52	97.86 108	100.00 1	100.00 1	99.19 80	100.00 1	99.99 58	100.00 1	100.00 1

PS-MVSNAJ		99.64 58	99.57 57	99.85 93	99.78 137	99.81 93	99.95 225	99.42 127	98.38 66	100.00 1	100.00 1	98.75 124	100.00 1	99.88 99	99.99 107	99.74 199

#test#		99.75 21	99.68 25	99.96 46	100.00 1	99.89 68	100.00 1	99.76 45	98.07 87	100.00 1	100.00 1	99.31 60	100.00 1	99.99 58	100.00 1	100.00 1

HPM-MVS++		99.82 9	99.76 12	99.99 9	99.99 54	99.98 13	100.00 1	99.83 35	98.88 37	99.96 112	100.00 1	99.21 78	100.00 1	100.00 1	100.00 1	99.99 106

XVS		99.79 14	99.73 16	99.98 22	100.00 1	99.94 37	100.00 1	99.75 49	98.67 51	100.00 1	100.00 1	99.16 82	100.00 1	100.00 1	100.00 1	100.00 1

X-MVStestdata		97.04 226	96.06 253	99.98 22	100.00 1	99.94 37	100.00 1	99.75 49	98.67 51	100.00 1	66.97 366	99.16 82	100.00 1	100.00 1	100.00 1	100.00 1

旧先验2							100.00 1		98.11 85	100.00 1			100.00 1	99.67 136

新几何1				99.99 9	100.00 1	99.96 20		99.81 38	97.89 104	100.00 1	100.00 1	99.20 79	100.00 1	97.91 234	100.00 1	100.00 1

无先验							100.00 1	99.80 39	97.98 94				100.00 1	99.33 165		100.00 1

原ACMM1				99.93 76	100.00 1	99.80 95		99.66 62	98.18 76	100.00 1	100.00 1	99.43 47	100.00 1	99.50 156	100.00 1	100.00 1

testdata2													100.00 1	97.36 251

testdata				99.66 121	99.99 54	98.97 175		99.73 54	97.96 100	100.00 1	100.00 1	99.42 49	100.00 1	99.28 169	100.00 1	100.00 1

1314		99.38 88	99.19 103	99.96 46	98.88 269	99.89 68	99.24 314	99.93 26	98.88 37	98.79 224	100.00 1	97.02 176	100.00 1	100.00 1	100.00 1	100.00 1

1121		99.63 62	99.51 70	99.99 9	99.99 54	99.96 20	99.24 314	99.74 52	97.89 104	100.00 1	100.00 1	99.39 53	100.00 1	99.33 165	100.00 1	100.00 1

LFMVS		97.42 209	96.62 229	99.81 100	99.80 132	99.50 124	99.16 329	99.56 66	94.48 277	100.00 1	100.00 1	79.35 343	100.00 1	99.89 98	97.37 213	99.94 124

VDD-MVS		96.58 246	95.99 256	98.34 216	99.52 199	95.33 287	99.18 323	99.38 192	96.64 206	99.77 165	100.00 1	72.51 353	100.00 1	100.00 1	96.94 220	99.70 204

VDDNet		96.39 257	95.55 277	98.90 186	99.27 236	97.45 248	99.15 331	99.92 30	91.28 324	99.98 104	100.00 1	73.55 351	100.00 1	99.85 103	96.98 219	99.24 218

MVS		99.22 111	98.96 118	99.98 22	99.00 258	99.95 29	99.24 314	99.94 18	98.14 80	98.88 214	100.00 1	95.63 207	100.00 1	99.85 103	100.00 1	100.00 1

SD-MVS		99.81 11	99.75 14	99.99 9	99.99 54	99.96 20	100.00 1	99.42 127	99.01 24	100.00 1	100.00 1	99.33 55	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 9	100.00 1	99.96 20	100.00 1	99.95 15	99.11 7	100.00 1	100.00 1	99.60 14	100.00 1	100.00 1	100.00 1	100.00 1

VNet		99.04 120	98.75 140	99.90 82	99.81 122	99.75 101	99.50 294	99.47 77	98.36 68	100.00 1	99.99 167	94.66 220	100.00 1	99.90 96	97.09 216	99.96 116

ACMMPR		99.74 26	99.67 30	99.96 46	100.00 1	99.89 68	100.00 1	99.76 45	97.95 101	100.00 1	100.00 1	99.29 67	100.00 1	99.99 58	100.00 1	100.00 1

thres600view7		99.24 109	99.00 114	99.95 58	99.81 122	99.87 81	100.00 1	99.94 18	97.13 174	99.83 154	99.96 187	97.01 177	100.00 1	99.54 152	97.77 206	99.97 113

MP-MVS		99.61 68	99.49 74	99.98 22	99.99 54	99.94 37	100.00 1	99.42 127	97.82 110	99.99 99	100.00 1	98.20 141	100.00 1	99.99 58	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		99.26 104	99.03 111	99.95 58	99.81 122	99.89 68	100.00 1	99.94 18	97.23 167	99.83 154	99.96 187	97.04 173	100.00 1	99.59 144	97.85 200	99.97 113

thres200		99.27 102	99.04 110	99.96 46	99.81 122	99.90 60	100.00 1	99.94 18	97.31 164	99.83 154	99.96 187	97.04 173	100.00 1	99.62 143	97.88 198	99.98 108

PGM-MVS		99.69 43	99.61 47	99.95 58	99.99 54	99.85 87	100.00 1	99.58 65	97.69 122	100.00 1	100.00 1	99.44 43	100.00 1	99.79 112	100.00 1	100.00 1

MCST-MVS		99.85 3	99.80 4	100.00 1	100.00 1	99.99 3	100.00 1	99.73 54	99.19 5	100.00 1	100.00 1	99.31 60	100.00 1	100.00 1	100.00 1	100.00 1

abl_6		99.53 74	99.40 77	99.92 78	100.00 1	99.76 99	100.00 1	99.42 127	97.21 170	100.00 1	100.00 1	98.12 144	100.00 1	99.82 111	100.00 1	100.00 1

mPP-MVS		99.69 43	99.60 49	99.97 31	100.00 1	99.91 52	100.00 1	99.42 127	97.91 103	100.00 1	100.00 1	99.04 93	100.00 1	100.00 1	100.00 1	100.00 1

EPNet		99.62 66	99.69 22	99.42 148	99.99 54	98.37 201	100.00 1	99.89 32	98.83 41	100.00 1	100.00 1	98.97 102	100.00 1	99.90 96	99.61 143	99.89 149
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 3	100.00 1	99.77 44	99.07 9	100.00 1	100.00 1	99.39 53	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 3	100.00 1	99.71 60	99.07 9	100.00 1	100.00 1	99.59 16	100.00 1	100.00 1	100.00 1	100.00 1

CP-MVS		99.67 52	99.58 54	99.95 58	100.00 1	99.84 90	100.00 1	99.42 127	97.77 113	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 66	99.56 63	99.82 97	99.92 111	99.45 132	100.00 1	99.78 43	98.92 34	99.73 169	100.00 1	97.70 155	100.00 1	99.93 93	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 80	99.38 80	99.85 93	100.00 1	99.54 122	100.00 1	99.42 127	97.58 137	99.98 104	100.00 1	97.43 168	100.00 1	99.99 58	100.00 1	100.00 1

UGNet		98.41 170	98.11 179	99.31 160	99.54 189	98.55 192	99.18 323	100.00 1	98.64 54	99.79 163	99.04 298	87.61 299	100.00 1	99.30 168	99.89 127	99.40 217
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 83	99.34 88	99.80 104	99.76 142	99.50 124	100.00 1	99.91 31	97.72 116	99.98 104	99.94 198	98.45 136	100.00 1	99.53 154	98.75 158	99.89 149

DP-MVS Recon		99.76 19	99.69 22	99.98 22	100.00 1	99.95 29	100.00 1	99.52 69	97.99 92	99.99 99	100.00 1	99.72 9	100.00 1	99.96 84	100.00 1	100.00 1

QAPM		98.99 129	98.66 146	99.96 46	99.01 254	99.87 81	99.88 240	99.93 26	97.99 92	98.68 229	100.00 1	93.17 237	100.00 1	99.32 167	100.00 1	100.00 1

PAPM_NR		99.74 26	99.66 33	99.99 9	100.00 1	99.96 20	100.00 1	99.47 77	97.87 107	100.00 1	100.00 1	99.60 14	100.00 1	100.00 1	100.00 1	100.00 1

PAPR		99.76 19	99.68 25	99.99 9	100.00 1	99.96 20	100.00 1	99.47 77	98.16 77	100.00 1	100.00 1	99.51 28	100.00 1	100.00 1	100.00 1	100.00 1

RPSCF		97.37 211	98.24 173	94.76 318	99.80 132	84.57 351	99.99 192	99.05 319	94.95 261	99.82 161	100.00 1	94.03 226	100.00 1	98.15 224	98.38 174	99.70 204

CSCG		99.28 101	99.35 86	99.05 175	99.99 54	97.15 261	100.00 1	99.47 77	97.44 152	99.42 183	100.00 1	97.83 152	100.00 1	99.99 58	100.00 1	100.00 1

API-MVS		99.72 32	99.70 20	99.79 106	99.97 96	99.37 140	99.96 220	99.94 18	98.48 58	100.00 1	100.00 1	98.92 111	100.00 1	100.00 1	100.00 1	100.00 1

ACMMP		99.65 56	99.57 57	99.89 85	99.99 54	99.66 115	99.75 260	99.73 54	98.16 77	99.75 168	100.00 1	98.90 113	100.00 1	99.96 84	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 32	99.65 34	99.91 79	99.97 96	99.72 106	100.00 1	99.47 77	98.43 60	99.88 149	100.00 1	99.14 85	100.00 1	99.97 82	100.00 1	100.00 1

PHI-MVS		99.50 78	99.39 79	99.82 97	100.00 1	99.45 132	100.00 1	99.94 18	96.38 220	100.00 1	100.00 1	98.18 142	100.00 1	100.00 1	100.00 1	100.00 1

PVSNet	94.91 18	99.30 99	99.25 92	99.44 145	100.00 1	98.32 207	100.00 1	99.86 34	98.04 89	100.00 1	100.00 1	96.10 199	100.00 1	99.55 149	99.73 136	100.00 1

PVSNet_0	93.57 19	96.41 253	95.74 268	98.41 211	99.84 117	95.22 289	100.00 1	100.00 1	98.08 86	97.55 289	99.78 226	84.40 321	100.00 1	100.00 1	81.99 340	100.00 1

DeepPCF-MVS	98.03 4	98.54 162	99.72 18	94.98 315	99.99 54	84.94 350	100.00 1	99.42 127	99.98 1	100.00 1	100.00 1	98.11 145	100.00 1	100.00 1	100.00 1	100.00 1

DeepC-MVS_fast	98.92 1	99.75 21	99.67 30	99.99 9	99.99 54	99.96 20	99.73 265	99.52 69	99.06 11	100.00 1	100.00 1	98.80 122	100.00 1	99.95 90	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 21	99.68 25	99.97 31	100.00 1	99.91 52	99.98 210	99.47 77	99.09 8	100.00 1	100.00 1	98.59 132	100.00 1	99.95 90	100.00 1	100.00 1

AdaColmap		99.44 84	99.26 91	99.95 58	100.00 1	99.86 84	99.70 270	99.99 10	98.53 57	99.90 144	100.00 1	95.34 209	100.00 1	99.92 94	100.00 1	100.00 1

PLC	98.56 2	99.70 39	99.74 15	99.58 133	100.00 1	98.79 180	100.00 1	99.54 67	98.58 56	99.96 112	100.00 1	99.59 16	100.00 1	100.00 1	100.00 1	99.94 124
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MAR-MVS		99.49 80	99.36 84	99.89 85	99.97 96	99.66 115	99.74 261	99.95 15	97.89 104	100.00 1	100.00 1	96.71 189	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	99.03 121	98.71 144	99.96 46	98.99 261	99.89 68	100.00 1	99.51 73	98.96 28	98.32 252	100.00 1	92.78 243	100.00 1	99.87 100	100.00 1	100.00 1

3Dnovator	95.63 14	99.06 118	98.76 139	99.96 46	98.86 272	99.90 60	99.98 210	99.93 26	98.95 31	98.49 243	100.00 1	92.91 242	100.00 1	99.71 125	100.00 1	100.00 1

OpenMVS	95.20 17	98.76 147	98.41 163	99.78 110	98.89 268	99.81 93	99.99 192	99.76 45	98.02 90	98.02 269	100.00 1	91.44 255	100.00 1	99.63 142	99.97 118	99.55 212

hse-mvs3		97.03 227	96.53 231	98.51 206	99.79 135	95.90 282	99.45 296	99.45 99	98.21 74	100.00 1	99.78 226	97.49 162	99.99 89	99.72 123	74.92 348	99.65 211

ZD-MVS					100.00 1	99.98 13		99.80 39	97.31 164	100.00 1	100.00 1	99.32 58	99.99 89	100.00 1	100.00 1

test1172		99.64 58	99.53 66	99.96 46	100.00 1	99.90 60	100.00 1	99.51 73	97.59 135	100.00 1	100.00 1	98.65 127	99.99 89	99.99 58	100.00 1	100.00 1

SR-MVS-dyc-post		99.63 62	99.52 69	99.97 31	99.99 54	99.91 52	100.00 1	99.42 127	97.62 129	100.00 1	100.00 1	98.65 127	99.99 89	99.99 58	100.00 1	100.00 1

xxxxxxxxxxxxxcwj		99.77 18	99.70 20	99.97 31	99.99 54	99.93 42	100.00 1	99.31 222	98.93 32	100.00 1	100.00 1	99.05 90	99.99 89	100.00 1	100.00 1	100.00 1

SF-MVS		99.66 54	99.57 57	99.95 58	99.99 54	99.85 87	100.00 1	99.42 127	97.67 123	100.00 1	100.00 1	99.05 90	99.99 89	100.00 1	100.00 1	100.00 1

ETH3D cwj APD-0.16		99.63 62	99.53 66	99.94 73	99.97 96	99.82 92	100.00 1	99.42 127	97.39 155	100.00 1	100.00 1	98.98 100	99.99 89	100.00 1	100.00 1	100.00 1

ETH3 D test6400		99.68 47	99.59 50	99.97 31	99.99 54	99.93 42	100.00 1	99.42 127	97.97 96	100.00 1	100.00 1	98.90 113	99.99 89	100.00 1	100.00 1	100.00 1

9.14			99.57 57		99.99 54		100.00 1	99.42 127	97.54 141	100.00 1	100.00 1	99.15 84	99.99 89	100.00 1	100.00 1

ETH3D-3000-0.1		99.66 54	99.57 57	99.95 58	99.99 54	99.85 87	100.00 1	99.42 127	97.58 137	100.00 1	100.00 1	99.12 86	99.99 89	100.00 1	100.00 1	100.00 1

SR-MVS		99.68 47	99.58 54	99.98 22	100.00 1	99.95 29	100.00 1	99.64 63	97.59 135	100.00 1	100.00 1	98.99 99	99.99 89	100.00 1	100.00 1	100.00 1

TSAR-MVS + MP.		99.82 9	99.77 9	99.99 9	100.00 1	99.96 20	100.00 1	99.43 119	99.05 13	100.00 1	100.00 1	99.45 42	99.99 89	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
Regformer-1		99.75 21	99.68 25	99.98 22	100.00 1	99.94 37	100.00 1	99.44 109	98.43 60	100.00 1	100.00 1	99.23 74	99.99 89	99.99 58	100.00 1	100.00 1

Regformer-2		99.75 21	99.68 25	99.97 31	100.00 1	99.94 37	100.00 1	99.44 109	98.42 62	100.00 1	100.00 1	99.22 76	99.99 89	99.99 58	100.00 1	100.00 1

APDe-MVS		99.84 6	99.78 6	99.99 9	100.00 1	99.98 13	100.00 1	99.44 109	99.06 11	100.00 1	100.00 1	99.56 19	99.99 89	100.00 1	100.00 1	100.00 1

APD-MVS_3200maxsize		99.65 56	99.55 64	99.97 31	99.99 54	99.91 52	100.00 1	99.48 76	97.54 141	100.00 1	100.00 1	98.97 102	99.99 89	99.98 75	100.00 1	100.00 1

CDPH-MVS		99.73 29	99.64 38	99.99 9	100.00 1	99.97 18	100.00 1	99.42 127	98.02 90	100.00 1	100.00 1	99.32 58	99.99 89	100.00 1	100.00 1	100.00 1

TSAR-MVS + GP.		99.61 68	99.69 22	99.35 156	99.99 54	98.06 221	100.00 1	99.36 200	99.83 2	100.00 1	100.00 1	98.95 106	99.99 89	100.00 1	99.11 150	100.00 1

SteuartSystems-ACMMP		99.78 16	99.71 19	99.98 22	99.76 142	99.95 29	100.00 1	99.42 127	98.69 49	100.00 1	100.00 1	99.52 26	99.99 89	100.00 1	100.00 1	100.00 1
Skip Steuart: Steuart Systems R&D Blog.
PVSNet_BlendedMVS		98.71 150	98.62 150	98.98 182	99.98 92	99.60 119	100.00 1	100.00 1	97.23 167	100.00 1	99.03 300	96.57 191	99.99 89	100.00 1	94.75 252	97.35 327

PVSNet_Blended		99.48 82	99.36 84	99.83 96	99.98 92	99.60 119	100.00 1	100.00 1	97.79 112	100.00 1	100.00 1	96.57 191	99.99 89	100.00 1	99.88 128	99.90 144

HY-MVS	96.53 9	99.50 78	99.35 86	99.96 46	99.81 122	99.93 42	99.64 277	100.00 1	97.97 96	99.84 152	99.85 211	98.94 108	99.99 89	99.86 101	98.23 184	99.95 121

PatchMatch-RL		99.02 125	98.78 137	99.74 113	99.99 54	99.29 145	100.00 1	100.00 1	98.38 66	99.89 147	99.81 220	93.14 239	99.99 89	97.85 236	99.98 115	99.95 121

F-COLMAP		99.64 58	99.64 38	99.67 119	99.99 54	99.07 163	100.00 1	99.44 109	98.30 73	99.90 144	100.00 1	99.18 81	99.99 89	99.91 95	100.00 1	99.94 124

test_8					100.00 1	99.91 52	100.00 1	99.42 127	97.70 120	100.00 1	100.00 1	99.51 28	99.98 113

MVS_Test		98.93 137	98.65 147	99.77 112	99.62 170	99.50 124	99.99 192	99.19 268	95.52 249	99.96 112	99.86 209	96.54 193	99.98 113	98.65 199	98.48 166	99.82 178

RPMNet		95.26 290	93.82 296	99.56 136	99.31 233	98.86 178	99.13 333	99.42 127	79.82 352	99.96 112	95.13 348	95.69 206	99.98 113	77.54 357	98.40 171	99.84 171

WTY-MVS		99.54 73	99.40 77	99.95 58	99.81 122	99.93 42	100.00 1	100.00 1	97.98 94	99.84 152	100.00 1	98.94 108	99.98 113	99.86 101	98.21 185	99.94 124

ab-mvs		98.42 169	98.02 185	99.61 125	99.71 146	99.00 172	99.10 336	99.64 63	96.70 200	99.04 207	99.81 220	90.64 264	99.98 113	99.64 141	97.93 196	99.84 171

TEST9					100.00 1	99.95 29	100.00 1	99.42 127	97.65 125	100.00 1	100.00 1	99.53 23	99.97 118

train_agg		99.71 35	99.63 42	99.97 31	100.00 1	99.95 29	100.00 1	99.42 127	97.70 120	100.00 1	100.00 1	99.51 28	99.97 118	100.00 1	100.00 1	100.00 1

agg_prior1		99.71 35	99.63 42	99.95 58	100.00 1	99.88 78	100.00 1	99.42 127	97.72 116	100.00 1	100.00 1	99.51 28	99.97 118	100.00 1	100.00 1	100.00 1

agg_prior					100.00 1	99.88 78		99.42 127		100.00 1			99.97 118

canonicalmvs		99.03 121	98.73 141	99.94 73	99.75 144	99.95 29	100.00 1	99.30 225	97.64 127	100.00 1	100.00 1	95.22 212	99.97 118	99.76 118	96.90 221	99.91 135

EI-MVSNet-UG-set		99.69 43	99.63 42	99.87 90	99.99 54	99.64 117	99.95 225	99.44 109	98.35 69	100.00 1	100.00 1	98.98 100	99.97 118	99.98 75	100.00 1	100.00 1

Regformer-3		99.73 29	99.65 34	99.96 46	100.00 1	99.89 68	100.00 1	99.44 109	98.40 63	100.00 1	100.00 1	99.03 97	99.97 118	99.99 58	100.00 1	100.00 1

Regformer-4		99.73 29	99.65 34	99.95 58	100.00 1	99.89 68	100.00 1	99.44 109	98.40 63	100.00 1	100.00 1	99.03 97	99.97 118	99.99 58	100.00 1	100.00 1

test_prior3		99.81 11	99.78 6	99.90 82	100.00 1	99.75 101	100.00 1	99.73 54	98.82 43	100.00 1	100.00 1	99.47 39	99.97 118	100.00 1	100.00 1	100.00 1

test_prior				99.90 82	100.00 1	99.75 101		99.73 54					99.97 118			100.00 1

test12				99.95 58	99.99 54	99.89 68		99.42 127		100.00 1		99.24 73	99.97 118		100.00 1	100.00 1

APD-MVS		99.68 47	99.58 54	99.97 31	99.99 54	99.96 20	100.00 1	99.42 127	97.53 143	100.00 1	100.00 1	99.27 70	99.97 118	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	98.54 157	99.81 100	99.97 96	99.45 132	99.52 292	99.40 176	94.35 281	98.36 248	100.00 1	96.13 198	99.97 118	99.12 176	100.00 1	100.00 1

LS3D		99.31 98	99.13 107	99.87 90	99.99 54	99.71 107	99.55 288	99.46 92	97.32 162	99.82 161	100.00 1	96.85 186	99.97 118	99.14 174	100.00 1	99.92 133

CANet		99.40 86	99.24 94	99.89 85	99.99 54	99.76 99	100.00 1	99.73 54	98.40 63	99.78 164	100.00 1	95.28 210	99.96 132	100.00 1	99.99 107	99.96 116

MSP-MVS		99.81 11	99.77 9	99.94 73	100.00 1	99.86 84	100.00 1	99.42 127	98.87 39	100.00 1	100.00 1	99.65 12	99.96 132	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 39	99.64 38	99.87 90	100.00 1	99.64 117	99.98 210	99.44 109	98.35 69	99.99 99	100.00 1	99.04 93	99.96 132	99.98 75	100.00 1	100.00 1

HPM-MVS_fast		99.60 71	99.49 74	99.91 79	99.99 54	99.78 96	100.00 1	99.42 127	97.09 176	100.00 1	100.00 1	98.95 106	99.96 132	99.98 75	100.00 1	100.00 1

HPM-MVS		99.59 72	99.50 72	99.89 85	100.00 1	99.70 111	100.00 1	99.42 127	97.46 150	100.00 1	100.00 1	98.60 131	99.96 132	99.99 58	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	98.81 135	98.88 188	99.62 170	96.71 271	100.00 1	99.28 235	97.09 176	98.81 222	100.00 1	94.91 217	99.96 132	99.54 152	100.00 1	99.96 116

AllTest		98.55 161	98.40 165	98.99 180	99.93 108	97.35 252	100.00 1	99.40 176	97.08 178	99.09 201	99.98 170	93.37 233	99.95 138	96.94 260	99.84 131	99.68 206

TestCases				98.99 180	99.93 108	97.35 252		99.40 176	97.08 178	99.09 201	99.98 170	93.37 233	99.95 138	96.94 260	99.84 131	99.68 206

CHOSEN 1792x2688		99.00 127	98.91 126	99.25 166	99.90 114	97.79 239	100.00 1	99.99 10	98.79 46	98.28 255	100.00 1	93.63 231	99.95 138	99.66 138	99.95 122	100.00 1

114514_t		99.39 87	99.25 92	99.81 100	99.97 96	99.48 131	100.00 1	99.42 127	95.53 247	100.00 1	100.00 1	98.37 139	99.95 138	99.97 82	100.00 1	100.00 1

PVSNet_Blended_VisFu		99.33 96	99.18 105	99.78 110	99.82 118	99.49 127	100.00 1	99.95 15	97.36 157	99.63 174	100.00 1	96.45 195	99.95 138	99.79 112	99.65 140	99.89 149

MVS_111021_LR		99.70 39	99.65 34	99.88 89	99.96 102	99.70 111	100.00 1	99.97 14	98.96 28	100.00 1	100.00 1	97.93 149	99.95 138	99.99 58	100.00 1	100.00 1

MVS_111021_HR		99.71 35	99.63 42	99.93 76	99.95 103	99.83 91	100.00 1	100.00 1	98.89 36	100.00 1	100.00 1	97.85 151	99.95 138	100.00 1	100.00 1	100.00 1

DeepC-MVS	97.84 5	99.00 127	98.80 136	99.60 127	99.93 108	99.03 167	100.00 1	99.40 176	98.61 55	99.33 188	100.00 1	92.23 249	99.95 138	99.74 120	99.96 120	99.83 174
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	98.69 152	98.37 168	99.62 124	99.78 137	99.02 168	99.23 320	99.06 317	96.43 216	98.08 264	100.00 1	94.72 219	99.95 138	98.16 223	99.91 126	99.90 144
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MSDG		98.90 140	98.63 149	99.70 116	99.92 111	99.25 150	100.00 1	99.37 194	95.71 240	99.40 187	100.00 1	96.58 190	99.95 138	96.80 267	99.94 123	99.91 135

COLMAP_ROB	97.10 7	98.29 177	98.17 178	98.65 199	99.94 106	97.39 250	99.30 310	99.40 176	95.64 242	97.75 280	100.00 1	92.69 246	99.95 138	98.89 186	99.92 125	98.62 227
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
UA-Net		99.06 118	98.83 132	99.74 113	99.52 199	99.40 136	99.08 338	99.45 99	97.64 127	99.83 154	100.00 1	95.80 203	99.94 149	98.35 214	99.80 134	99.88 160

XVG-OURS-SEG-HR		98.27 179	98.31 170	98.14 231	99.59 177	95.92 280	100.00 1	99.36 200	98.48 58	99.21 193	100.00 1	89.27 282	99.94 149	99.76 118	99.17 148	98.56 228

mvs-test1		98.89 141	98.99 116	98.60 201	99.77 140	95.96 278	100.00 1	98.94 328	97.61 131	99.93 140	99.92 202	95.89 200	99.93 151	99.36 162	99.50 145	99.90 144

alignmvs		99.38 88	99.21 99	99.91 79	99.73 145	99.92 49	100.00 1	99.51 73	97.61 131	100.00 1	100.00 1	99.06 89	99.93 151	99.83 106	97.12 215	99.90 144

cascas		98.43 168	98.07 182	99.50 141	99.65 160	99.02 168	100.00 1	99.22 259	94.21 284	99.72 170	99.98 170	92.03 252	99.93 151	99.68 134	98.12 190	99.54 213

XVG-OURS		98.30 175	98.36 169	98.13 234	99.58 182	95.91 281	100.00 1	99.36 200	98.69 49	99.23 192	100.00 1	91.20 258	99.92 154	99.34 164	97.82 203	98.56 228

diffmvs		98.96 133	98.73 141	99.63 123	99.54 189	99.16 158	100.00 1	99.18 274	97.33 161	99.96 112	100.00 1	94.60 221	99.91 155	99.66 138	98.33 180	99.82 178

tpmvs		98.59 157	98.38 166	99.23 167	99.69 149	97.90 231	99.31 309	99.47 77	94.52 274	99.68 172	99.28 286	97.64 157	99.89 156	97.71 239	98.17 189	99.89 149

Test_1112_low_res		98.83 144	98.60 153	99.51 139	99.69 149	98.75 181	99.99 192	99.14 286	96.81 191	98.84 219	99.06 295	97.45 165	99.89 156	98.66 197	97.75 207	99.89 149

1112_ss		98.91 138	98.71 144	99.51 139	99.69 149	98.75 181	99.99 192	99.15 281	96.82 190	98.84 219	100.00 1	97.45 165	99.89 156	98.66 197	97.75 207	99.89 149

IB-MVS	96.24 12	97.54 205	96.95 218	99.33 158	99.67 155	98.10 219	100.00 1	99.47 77	97.42 154	99.26 191	99.69 238	98.83 119	99.89 156	99.43 158	78.77 346	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
tpm cat1		98.05 185	97.76 192	98.92 185	99.50 207	97.10 264	99.77 257	99.30 225	90.20 332	99.72 170	98.71 319	97.71 154	99.86 160	96.75 271	98.20 186	99.81 182

tpmrst		98.98 132	98.93 124	99.14 172	99.61 172	97.74 240	99.52 292	99.36 200	96.05 230	99.98 104	99.64 251	99.04 93	99.86 160	98.94 182	98.19 187	99.82 178

MDTV_nov1_ep13			98.94 123		99.53 192	98.36 203	99.39 302	99.46 92	96.54 211	99.99 99	99.63 255	98.92 111	99.86 160	98.30 218	98.71 159

OMC-MVS		99.27 102	99.38 80	98.96 183	99.95 103	97.06 265	100.00 1	99.40 176	98.83 41	99.88 149	100.00 1	97.01 177	99.86 160	99.47 157	99.84 131	99.97 113

thisisatest0530		99.37 90	99.27 90	99.69 117	99.59 177	99.41 135	100.00 1	99.46 92	96.46 215	99.90 144	100.00 1	99.44 43	99.85 164	98.97 181	99.58 144	99.80 192

thisisatest0515		99.42 85	99.31 89	99.74 113	99.59 177	99.55 121	100.00 1	99.46 92	96.65 205	99.92 141	100.00 1	99.44 43	99.85 164	99.09 178	99.63 142	99.81 182

tttt0517		99.34 94	99.23 97	99.67 119	99.57 185	99.38 137	100.00 1	99.46 92	96.33 223	99.89 147	100.00 1	99.44 43	99.84 166	98.93 183	99.46 147	99.78 194

casdiffmvs		98.65 154	98.38 166	99.46 143	99.52 199	98.74 184	100.00 1	99.15 281	96.91 187	99.05 206	100.00 1	92.75 244	99.83 167	99.70 127	98.38 174	99.81 182

BH-untuned		98.64 155	98.65 147	98.60 201	99.59 177	96.17 277	100.00 1	99.28 235	96.67 204	98.41 247	100.00 1	94.52 222	99.83 167	99.41 160	100.00 1	99.81 182

DeepMVS_CX				89.98 333	98.90 267	71.46 358		99.18 274	97.61 131	96.92 302	99.83 214	86.07 312	99.83 167	96.02 277	97.65 211	98.65 226

DWT-MVSNet_test		99.22 111	99.22 98	99.22 169	99.56 188	97.98 226	99.89 237	99.39 189	97.20 171	99.96 112	100.00 1	99.52 26	99.82 170	99.11 177	98.34 176	99.87 168

dp		98.72 149	98.61 151	99.03 178	99.53 192	97.39 250	99.45 296	99.39 189	95.62 244	99.94 135	99.52 272	98.83 119	99.82 170	96.77 270	98.42 170	99.89 149

BH-RMVSNet		98.46 167	98.08 181	99.59 129	99.61 172	99.19 157	100.00 1	99.28 235	97.06 180	98.95 210	100.00 1	88.99 285	99.82 170	98.83 190	100.00 1	99.77 195

PMMVS		99.12 113	98.97 117	99.58 133	99.57 185	98.98 173	100.00 1	99.30 225	97.14 173	99.96 112	100.00 1	96.53 194	99.82 170	99.70 127	98.49 165	99.94 124

EIA-MVS		99.26 104	99.19 103	99.45 144	99.63 167	98.75 181	100.00 1	99.27 243	96.93 184	99.95 133	100.00 1	97.47 164	99.79 174	99.74 120	99.72 137	99.82 178

ETV-MVS		99.34 94	99.24 94	99.64 122	99.58 182	99.33 142	100.00 1	99.25 248	97.57 139	99.96 112	100.00 1	97.44 167	99.79 174	99.70 127	99.65 140	99.81 182

TR-MVS		98.14 182	97.74 193	99.33 158	99.59 177	98.28 209	99.27 311	99.21 264	96.42 217	99.15 198	99.94 198	88.87 288	99.79 174	98.88 187	98.29 181	99.93 131

Effi-MVS+		98.58 158	98.24 173	99.61 125	99.60 174	99.26 148	97.85 353	99.10 298	96.22 227	99.97 108	99.89 205	93.75 229	99.77 177	99.43 158	98.34 176	99.81 182

baseline1		98.91 138	98.61 151	99.81 100	99.71 146	99.77 97	99.78 252	99.44 109	97.51 146	98.81 222	99.99 167	98.25 140	99.76 178	98.60 204	95.41 230	99.89 149

lupinMVS		99.29 100	99.16 106	99.69 117	99.45 216	99.49 127	100.00 1	99.15 281	97.45 151	99.97 108	100.00 1	96.76 187	99.76 178	99.67 136	100.00 1	99.81 182

EPP-MVSNet		99.10 115	99.00 114	99.40 151	99.51 204	98.68 187	99.92 231	99.43 119	95.47 253	99.65 173	100.00 1	99.51 28	99.76 178	99.53 154	98.00 193	99.75 198

baseline		98.69 152	98.45 162	99.41 149	99.52 199	98.67 188	100.00 1	99.17 279	97.03 181	99.13 199	100.00 1	93.17 237	99.74 181	99.70 127	98.34 176	99.81 182

CostFormer		98.84 143	98.77 138	99.04 177	99.41 221	97.58 245	99.67 275	99.35 208	94.66 269	99.96 112	99.36 282	99.28 69	99.74 181	99.41 160	97.81 204	99.81 182

Fast-Effi-MVS+		98.40 171	98.02 185	99.55 138	99.63 167	99.06 164	100.00 1	99.15 281	95.07 258	99.42 183	99.95 194	93.26 236	99.73 183	97.44 247	98.24 183	99.87 168

jason		99.11 114	98.96 118	99.59 129	99.17 240	99.31 144	100.00 1	99.13 290	97.38 156	99.83 154	100.00 1	95.54 208	99.72 184	99.57 148	99.97 118	99.74 199
jason: jason.
EPMVS		99.25 107	99.13 107	99.60 127	99.60 174	99.20 156	99.60 284	100.00 1	96.93 184	99.92 141	99.36 282	99.05 90	99.71 185	98.77 192	98.94 155	99.90 144

Vis-MVSNet		98.52 164	98.25 172	99.34 157	99.68 152	98.55 192	99.68 274	99.41 174	97.34 160	99.94 135	100.00 1	90.38 270	99.70 186	99.03 180	98.84 156	99.76 197
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
TAPA-MVS	96.40 10	97.64 198	97.37 207	98.45 209	99.94 106	95.70 284	100.00 1	99.40 176	97.65 125	99.53 177	100.00 1	99.31 60	99.66 187	80.48 352	100.00 1	100.00 1
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CS-MVS		99.23 110	99.12 109	99.56 136	99.42 219	99.16 158	100.00 1	99.21 264	97.03 181	99.96 112	100.00 1	97.06 172	99.63 188	99.65 140	99.79 135	99.88 160

Fast-Effi-MVS+-dtu		98.38 172	98.56 156	97.82 260	99.58 182	94.44 312	100.00 1	99.16 280	96.75 194	99.51 178	99.63 255	95.03 215	99.60 189	97.71 239	99.67 139	99.42 216

PAPM		99.78 16	99.76 12	99.85 93	99.01 254	99.95 29	100.00 1	99.75 49	99.37 3	99.99 99	100.00 1	99.76 8	99.60 189	100.00 1	100.00 1	100.00 1

GeoE		98.06 184	97.65 198	99.29 161	99.47 213	98.41 195	100.00 1	99.19 268	94.85 263	98.88 214	100.00 1	91.21 257	99.59 191	97.02 258	98.19 187	99.88 160

test_post1							99.32 308				88.24 359	99.33 55	99.59 191	98.31 216

test_post											89.05 357	99.49 35	99.59 191

ADS-MVSNet		98.70 151	98.51 159	99.28 163	99.51 204	98.39 198	99.24 314	99.44 109	95.52 249	99.96 112	99.70 236	97.57 159	99.58 194	97.11 256	98.54 162	99.88 160

Patchmatch-test		97.83 192	97.42 203	99.06 173	99.08 245	97.66 243	98.66 346	99.21 264	93.65 296	98.25 259	99.58 264	99.47 39	99.57 195	90.25 331	98.59 161	99.95 121

HQP4-MVS										99.17 194			99.57 195			97.77 230

HQP-MVS		97.73 194	97.85 189	97.39 270	99.07 246	94.82 296	100.00 1	99.40 176	99.04 14	99.17 194	99.97 178	88.61 291	99.57 195	99.79 112	95.58 224	97.77 230

CLD-MVS		97.64 198	97.74 193	97.36 272	99.01 254	94.76 304	100.00 1	99.34 213	99.30 4	99.00 208	99.97 178	87.49 300	99.57 195	99.96 84	95.58 224	97.75 243
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
OPM-MVS		97.21 216	97.18 216	97.32 275	98.08 303	94.66 305	100.00 1	99.28 235	98.65 53	98.92 211	99.98 170	86.03 314	99.56 199	98.28 219	95.41 230	97.72 279

LPG-MVS_test		97.31 213	97.32 209	97.28 277	98.85 273	94.60 308	100.00 1	99.37 194	97.35 158	98.85 217	99.98 170	86.66 307	99.56 199	99.55 149	95.26 236	97.70 287

LGP-MVS_train				97.28 277	98.85 273	94.60 308		99.37 194	97.35 158	98.85 217	99.98 170	86.66 307	99.56 199	99.55 149	95.26 236	97.70 287

ITE_SJBPF				96.84 295	98.96 264	93.49 320		98.12 343	98.12 84	98.35 249	99.97 178	84.45 320	99.56 199	95.63 282	95.25 238	97.49 320

ACMP	97.00 8	97.19 217	97.16 217	97.27 279	98.97 263	94.58 311	100.00 1	99.32 217	97.97 96	97.45 291	99.98 170	85.79 316	99.56 199	99.70 127	95.24 239	97.67 298
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
HQP_MVS		97.71 196	97.82 191	97.37 271	99.00 258	94.80 299	100.00 1	99.40 176	99.00 25	99.08 203	99.97 178	88.58 293	99.55 204	99.79 112	95.57 228	97.76 232

plane_prior5								99.40 176					99.55 204	99.79 112	95.57 228	97.76 232

ACMM	97.17 6	97.37 211	97.40 205	97.29 276	99.01 254	94.64 307	100.00 1	99.25 248	98.07 87	98.44 246	99.98 170	87.38 301	99.55 204	99.25 170	95.19 241	97.69 291
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
patchmatchnet-post											97.79 337	99.41 51	99.54 207

SCA		98.30 175	97.98 187	99.23 167	99.41 221	98.25 211	99.99 192	99.45 99	96.91 187	99.76 167	99.58 264	89.65 277	99.54 207	98.31 216	98.79 157	99.91 135

XVG-ACMP-BASELINE		96.60 245	96.52 233	96.84 295	98.41 286	93.29 323	99.99 192	99.32 217	97.76 115	98.51 241	99.29 285	81.95 335	99.54 207	98.40 211	95.03 248	97.68 293

ACMH+	96.20 13	96.49 251	96.33 244	97.00 286	99.06 250	93.80 318	99.81 247	99.31 222	97.32 162	95.89 323	99.97 178	82.62 333	99.54 207	98.34 215	94.63 254	97.65 304

ACMH	96.25 11	96.77 236	96.62 229	97.21 280	98.96 264	94.43 313	99.64 277	99.33 215	97.43 153	96.55 313	99.97 178	83.52 328	99.54 207	99.07 179	95.13 245	97.66 299
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
TDRefinement		91.93 310	90.48 318	96.27 305	81.60 361	92.65 329	99.10 336	97.61 354	93.96 290	93.77 337	99.85 211	80.03 340	99.53 212	97.82 237	70.59 352	96.63 341

JIA-IIPM		97.09 222	96.34 243	99.36 155	98.88 269	98.59 191	99.81 247	99.43 119	84.81 347	99.96 112	90.34 356	98.55 134	99.52 213	97.00 259	98.28 182	99.98 108

IS-MVSNet		99.08 116	98.91 126	99.59 129	99.65 160	99.38 137	99.78 252	99.24 253	96.70 200	99.51 178	100.00 1	98.44 137	99.52 213	98.47 209	98.39 173	99.88 160

Effi-MVS+-dtu		98.51 165	98.86 131	97.47 269	99.77 140	94.21 315	100.00 1	98.94 328	97.61 131	99.91 143	98.75 318	95.89 200	99.51 215	99.36 162	99.48 146	98.68 225

PatchmatchNet		99.03 121	98.96 118	99.26 165	99.49 208	98.33 205	99.38 303	99.45 99	96.64 206	99.96 112	99.58 264	99.49 35	99.50 216	97.63 242	99.00 154	99.93 131
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
TinyColmap		95.50 286	95.12 289	96.64 298	98.69 276	93.00 325	99.40 301	97.75 353	96.40 219	96.14 319	99.87 207	79.47 342	99.50 216	93.62 307	94.72 253	97.40 325

LTVRE_ROB	95.29 16	96.32 261	96.10 251	96.99 287	98.55 281	93.88 317	99.45 296	99.28 235	94.50 276	96.46 314	99.52 272	84.86 319	99.48 218	97.26 254	95.03 248	97.59 314
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		96.26 264	95.67 274	98.06 241	99.68 152	95.60 285	99.82 246	99.42 127	96.78 193	99.88 149	99.80 223	94.84 218	99.47 219	97.48 246	73.29 350	99.12 221

D2MVS		97.63 201	97.83 190	97.05 283	98.83 275	94.60 308	100.00 1	99.82 36	96.89 189	98.28 255	99.03 300	94.05 225	99.47 219	98.58 206	94.97 250	97.09 332

tpm2		98.64 155	98.58 155	98.81 193	99.42 219	97.12 262	99.69 272	99.37 194	93.63 297	99.94 135	99.67 243	98.96 105	99.47 219	98.62 203	97.95 195	99.83 174

HyFIR lowres test		99.32 97	99.24 94	99.58 133	99.95 103	99.26 148	100.00 1	99.99 10	96.72 198	99.29 190	99.91 203	99.49 35	99.47 219	99.74 120	98.08 192	100.00 1

hse-mvs2		96.79 235	96.38 239	98.04 247	99.68 152	95.54 286	99.81 247	99.42 127	98.21 74	100.00 1	99.80 223	97.49 162	99.46 223	99.72 123	73.27 351	99.12 221

USDC		95.90 280	95.70 270	96.50 301	98.60 280	92.56 330	100.00 1	98.30 339	97.77 113	96.92 302	99.94 198	81.25 339	99.45 224	93.54 308	94.96 251	97.49 320

gm-plane-assit					99.52 199	97.26 258			95.86 235		100.00 1		99.43 225	98.76 193

MS-PatchMatch		95.66 284	95.87 261	95.05 313	97.80 312	89.25 343	98.88 344	99.30 225	96.35 221	96.86 305	99.01 302	81.35 338	99.43 225	93.30 309	99.98 115	96.46 342

CHOSEN 280x420		99.85 3	99.87 1	99.80 104	99.99 54	99.97 18	99.97 216	99.98 13	98.96 28	100.00 1	100.00 1	99.96 3	99.42 227	100.00 1	100.00 1	100.00 1

VPA-MVSNet		97.03 227	96.43 237	98.82 191	98.64 278	99.32 143	99.38 303	99.47 77	96.73 197	98.91 213	98.94 309	87.00 305	99.40 228	99.23 171	89.59 310	97.76 232

LF4IMVS		96.19 266	96.18 248	96.23 306	98.26 295	92.09 332	100.00 1	97.89 351	97.82 110	97.94 272	99.87 207	82.71 332	99.38 229	97.41 249	93.71 260	97.20 330

UniMVSNet_ETH3D		95.28 289	94.41 294	97.89 258	98.91 266	95.14 290	99.13 333	99.35 208	92.11 319	97.17 298	99.66 245	70.28 356	99.36 230	97.88 235	95.18 242	99.16 219

GA-MVS		97.72 195	97.27 213	99.06 173	99.24 239	97.93 230	100.00 1	99.24 253	95.80 238	98.99 209	99.64 251	89.77 275	99.36 230	95.12 291	97.62 212	99.89 149

XXY-MVS		97.14 221	96.63 228	98.67 198	98.65 277	98.92 176	99.54 290	99.29 230	95.57 246	97.63 283	99.83 214	87.79 298	99.35 232	98.39 212	92.95 271	97.75 243

GG-mvs-BLEND				99.59 129	99.54 189	99.49 127	99.17 328	99.52 69		99.96 112	99.68 242	100.00 1	99.33 233	99.71 125	99.99 107	99.96 116

EPNet_dtu		98.53 163	98.23 175	99.43 147	99.92 111	99.01 170	99.96 220	99.47 77	98.80 45	99.96 112	99.96 187	98.56 133	99.30 234	87.78 340	99.68 138	100.00 1
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tpm		98.24 180	98.22 177	98.32 218	99.13 241	95.79 283	99.53 291	99.12 295	95.20 257	99.96 112	99.36 282	97.58 158	99.28 235	97.41 249	96.67 222	99.88 160

baseline2		98.99 129	98.93 124	99.18 171	99.26 238	99.15 160	100.00 1	99.46 92	96.71 199	96.79 308	100.00 1	99.42 49	99.25 236	98.75 194	99.94 123	99.15 220

gg-mvs-nofinetune		96.95 231	96.10 251	99.50 141	99.41 221	99.36 141	99.07 340	99.52 69	83.69 349	99.96 112	83.60 363	100.00 1	99.20 237	99.68 134	99.99 107	99.96 116

TAMVS		98.76 147	98.73 141	98.86 189	99.44 218	97.69 241	99.57 286	99.34 213	96.57 209	99.12 200	99.81 220	98.83 119	99.16 238	97.97 233	97.91 197	99.73 203

MVP-Stereo		96.51 248	96.48 235	96.60 300	95.65 343	94.25 314	98.84 345	98.16 341	95.85 237	95.23 326	99.04 298	92.54 248	99.13 239	92.98 311	99.98 115	96.43 343
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
Vis-MVSNet (Re-imp)		98.99 129	98.89 130	99.29 161	99.64 165	98.89 177	99.98 210	99.31 222	96.74 195	99.48 180	100.00 1	98.11 145	99.10 240	98.39 212	98.34 176	99.89 149

EG-PatchMatch MVS		92.94 306	92.49 310	94.29 323	95.87 342	87.07 348	99.07 340	98.11 344	93.19 308	88.98 348	98.66 321	70.89 354	99.08 241	92.43 316	95.21 240	96.72 339

MVS-HIRNet		94.12 299	92.73 309	98.29 219	99.33 232	95.95 279	99.38 303	99.19 268	74.54 355	98.26 258	86.34 360	86.07 312	99.06 242	91.60 320	99.87 130	99.85 170

mvs_anonymous		98.80 146	98.60 153	99.38 154	99.57 185	99.24 152	100.00 1	99.21 264	95.87 233	98.92 211	99.82 217	96.39 196	99.03 243	99.13 175	98.50 164	99.88 160

test_part1		96.98 230	96.36 241	98.85 190	99.78 137	99.77 97	99.63 279	99.30 225	92.74 316	97.05 299	99.52 272	93.10 240	99.02 244	98.02 228	88.10 322	97.76 232

TESTMET0.1,1		99.08 116	98.96 118	99.44 145	99.63 167	99.38 137	100.00 1	99.45 99	95.53 247	99.48 180	100.00 1	99.71 10	99.02 244	96.84 264	99.99 107	99.91 135

test-LLR		99.03 121	98.91 126	99.40 151	99.40 226	99.28 146	100.00 1	99.45 99	96.70 200	99.42 183	99.12 291	99.31 60	99.01 246	96.82 265	99.99 107	99.91 135

test-mter		98.96 133	98.82 133	99.40 151	99.40 226	99.28 146	100.00 1	99.45 99	95.44 256	99.42 183	99.12 291	99.70 11	99.01 246	96.82 265	99.99 107	99.91 135

tfpnnormal		96.36 258	95.69 273	98.37 214	98.55 281	98.71 185	99.69 272	99.45 99	93.16 309	96.69 312	99.71 233	88.44 295	98.99 248	94.17 300	91.38 295	97.41 324

Anonymous20231211		96.29 262	95.70 270	98.07 237	99.80 132	97.49 247	99.15 331	99.40 176	89.11 335	97.75 280	99.45 278	88.93 287	98.98 249	98.26 220	89.47 312	97.73 273

nrg030		97.64 198	97.27 213	98.75 195	98.34 288	99.53 123	100.00 1	99.22 259	96.21 228	98.27 257	99.95 194	94.40 223	98.98 249	99.23 171	89.78 309	97.75 243

cl-mvsnet2		98.23 181	98.11 179	98.58 205	99.82 118	99.01 170	100.00 1	99.28 235	96.92 186	98.33 251	99.21 288	98.09 147	98.97 251	98.72 195	92.61 274	97.76 232

CVMVSNet		98.56 160	98.47 161	98.82 191	99.11 242	97.67 242	99.74 261	99.47 77	97.57 139	99.06 205	100.00 1	95.72 205	98.97 251	98.21 221	97.33 214	99.83 174

VPNet		96.41 253	95.76 267	98.33 217	98.61 279	98.30 208	99.48 295	99.45 99	96.98 183	98.87 216	99.88 206	81.57 336	98.93 253	99.22 173	87.82 325	97.76 232

v7n		96.06 276	95.42 285	97.99 252	97.58 321	97.35 252	99.86 241	99.11 296	92.81 315	97.91 274	99.49 275	90.99 262	98.92 254	92.51 314	88.49 320	97.70 287

jajsoiax		97.07 224	96.79 224	97.89 258	97.28 332	97.12 262	99.95 225	99.19 268	96.55 210	97.31 294	99.69 238	87.35 303	98.91 255	98.70 196	95.12 246	97.66 299

mvs_tets		97.00 229	96.69 226	97.94 254	97.41 331	97.27 257	99.60 284	99.18 274	96.51 214	97.35 293	99.69 238	86.53 309	98.91 255	98.84 188	95.09 247	97.65 304

CDS-MVSNet		98.96 133	98.95 122	99.01 179	99.48 210	98.36 203	99.93 230	99.37 194	96.79 192	99.31 189	99.83 214	99.77 7	98.91 255	98.07 226	97.98 194	99.77 195
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
FIs		97.95 189	97.73 195	98.62 200	98.53 283	99.24 152	100.00 1	99.43 119	96.74 195	97.87 275	99.82 217	95.27 211	98.89 258	98.78 191	93.07 269	97.74 266

v1192		96.18 267	95.49 280	98.26 222	98.01 305	98.15 215	99.99 192	99.08 306	93.36 303	98.54 237	98.97 307	89.47 280	98.89 258	91.15 323	90.82 300	97.75 243

UniMVSNet (Re)		97.29 215	96.85 221	98.59 204	98.49 284	99.13 161	100.00 1	99.42 127	96.52 213	98.24 261	98.90 312	94.93 216	98.89 258	97.54 244	87.61 326	97.75 243

EI-MVSNet		97.98 188	97.93 188	98.16 230	99.11 242	97.84 236	99.74 261	99.29 230	94.39 280	98.65 230	100.00 1	97.21 170	98.88 261	97.62 243	95.31 234	97.75 243

MVSTER		98.58 158	98.52 158	98.77 194	99.65 160	99.68 113	100.00 1	99.29 230	95.63 243	98.65 230	99.80 223	99.78 6	98.88 261	98.59 205	95.31 234	97.73 273

pm-mvs1		95.76 282	95.01 290	98.00 250	98.23 297	97.45 248	99.24 314	99.04 322	93.13 310	95.93 322	99.72 231	86.28 310	98.84 263	95.62 283	87.92 324	97.72 279

MVS_0304		95.11 292	94.84 293	95.91 310	99.60 174	91.24 340	98.64 347	99.88 33	94.51 275	99.62 175	98.31 334	69.19 358	98.82 264	95.22 290	98.60 160	97.66 299

V42		96.65 242	96.16 250	98.11 236	98.17 301	98.23 212	99.99 192	99.09 304	93.97 289	98.74 227	99.05 297	91.09 260	98.82 264	95.46 285	89.90 307	97.27 329

RRT_test8_iter05		98.32 174	98.23 175	98.60 201	99.65 160	99.23 155	100.00 1	99.09 304	95.65 241	98.07 265	99.93 201	99.25 72	98.82 264	98.90 185	93.50 264	97.75 243

CMPMVS	66.12 22	90.65 318	92.04 311	86.46 337	96.18 340	66.87 361	98.03 352	99.38 192	83.38 350	85.49 353	99.55 269	77.59 347	98.80 267	94.44 297	94.31 258	93.72 352
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
PS-MVSNAJss		98.03 186	98.06 183	97.94 254	97.63 316	97.33 255	99.89 237	99.23 257	96.27 225	98.03 267	99.59 263	98.75 124	98.78 268	98.52 207	94.61 255	97.70 287

OurMVSNet-221017-0		96.14 272	95.98 257	96.62 299	97.49 326	93.44 321	99.92 231	98.16 341	95.86 235	97.65 282	99.95 194	85.71 317	98.78 268	94.93 293	94.18 259	97.64 307

OpenMVS_ROB	88.34 20	91.89 311	91.12 313	94.19 325	95.55 345	87.63 347	99.26 312	98.03 346	86.61 344	90.65 347	96.82 343	70.14 357	98.78 268	86.54 342	96.50 223	96.15 344

pmmvs6		93.64 301	92.87 306	95.94 309	97.47 328	91.41 337	98.92 342	99.02 323	87.84 342	95.01 328	99.61 261	77.24 348	98.77 271	94.33 298	86.41 334	97.63 308

v8		96.35 259	95.73 269	98.21 226	98.11 302	98.23 212	99.94 229	99.07 310	92.66 317	98.29 254	99.00 303	91.46 254	98.77 271	94.17 300	88.83 319	97.62 310

miper_enhance_ethall		98.33 173	98.27 171	98.51 206	99.66 159	99.04 166	100.00 1	99.22 259	97.53 143	98.51 241	99.38 281	99.49 35	98.75 273	98.02 228	92.61 274	97.76 232

RRT_MVS		98.48 166	98.41 163	98.70 197	99.64 165	99.67 114	100.00 1	99.10 298	96.26 226	98.75 225	99.96 187	99.04 93	98.75 273	98.42 210	94.51 257	97.68 293

v1921920		96.16 270	95.50 278	98.14 231	97.88 311	97.96 227	99.99 192	99.07 310	93.33 304	98.60 234	99.24 287	89.37 281	98.71 275	91.28 321	90.74 302	97.75 243

lessismore_v0				96.05 307	97.55 322	91.80 334		99.22 259		91.87 343	99.91 203	83.50 329	98.68 276	92.48 315	90.42 306	97.68 293

v2v482		96.70 241	96.18 248	98.27 220	98.04 304	98.39 198	100.00 1	99.13 290	94.19 286	98.58 235	99.08 294	90.48 268	98.67 277	95.69 280	90.44 305	97.75 243

v144192		96.40 256	95.81 263	98.17 229	97.89 310	98.11 217	99.99 192	99.06 317	93.39 302	98.75 225	99.09 293	90.43 269	98.66 278	93.10 310	90.55 304	97.75 243

cl-mvsnet____		97.54 205	97.32 209	98.18 227	99.47 213	98.14 216	100.00 1	99.10 298	94.16 287	97.60 287	99.63 255	97.52 161	98.65 279	96.47 272	91.97 286	97.76 232

v1144		96.51 248	95.97 258	98.13 234	97.98 307	98.04 223	99.99 192	99.08 306	93.51 300	98.62 233	98.98 304	90.98 263	98.62 280	93.79 306	90.79 301	97.74 266

v1240		95.96 278	95.25 286	98.07 237	97.91 309	97.87 235	99.96 220	99.07 310	93.24 307	98.64 232	98.96 308	88.98 286	98.61 281	89.58 335	90.92 299	97.75 243

v10		96.14 272	95.50 278	98.07 237	98.19 299	97.96 227	99.83 244	99.07 310	92.10 320	98.07 265	98.94 309	91.07 261	98.61 281	92.41 317	89.82 308	97.63 308

anonymousdsp		97.16 219	96.88 220	98.00 250	97.08 334	98.06 221	99.81 247	99.15 281	94.58 271	97.84 276	99.62 259	90.49 267	98.60 283	97.98 230	95.32 233	97.33 328

v148		96.29 262	95.84 262	97.63 263	97.74 313	96.53 275	100.00 1	99.07 310	93.52 299	98.01 270	99.42 280	91.22 256	98.60 283	96.37 276	87.22 329	97.75 243

MVSFormer		98.94 136	98.82 133	99.28 163	99.45 216	99.49 127	100.00 1	99.13 290	95.46 254	99.97 108	100.00 1	96.76 187	98.59 285	98.63 201	100.00 1	99.74 199

test_djsdf		97.55 204	97.38 206	98.07 237	97.50 324	97.99 225	100.00 1	99.13 290	95.46 254	98.47 244	99.85 211	92.01 253	98.59 285	98.63 201	95.36 232	97.62 310

test_0402		94.35 295	93.70 298	96.32 304	97.92 308	93.60 319	99.61 283	98.85 334	88.19 341	94.68 331	99.48 276	80.01 341	98.58 287	89.39 336	95.15 244	96.77 338

miper_ehance_all_eth		97.81 193	97.66 197	98.23 223	99.49 208	98.37 201	99.99 192	99.11 296	94.78 264	98.25 259	99.21 288	98.18 142	98.57 288	97.35 252	92.61 274	97.76 232

FC-MVSNet-test		97.84 191	97.63 199	98.45 209	98.30 293	99.05 165	100.00 1	99.43 119	96.63 208	97.61 286	99.82 217	95.19 213	98.57 288	98.64 200	93.05 270	97.73 273

WR-MVS		97.09 222	96.64 227	98.46 208	98.43 285	99.09 162	99.97 216	99.33 215	95.62 244	97.76 277	99.67 243	91.17 259	98.56 290	98.49 208	89.28 314	97.74 266

WR-MVS_H		96.73 238	96.32 245	97.95 253	98.26 295	97.88 233	99.72 267	99.43 119	95.06 259	96.99 301	98.68 320	93.02 241	98.53 291	97.43 248	88.33 321	97.43 323

ambc				88.45 334	86.84 357	70.76 359	97.79 354	98.02 348		90.91 345	95.14 347	38.69 365	98.51 292	94.97 292	84.23 338	96.09 346

eth_miper_zixun_eth		97.47 208	97.28 211	98.06 241	99.41 221	97.94 229	99.62 282	99.08 306	94.46 278	98.19 262	99.56 268	96.91 185	98.50 293	96.78 268	91.49 292	97.74 266

UniMVSNet_NR-MVSNet		97.16 219	96.80 222	98.22 224	98.38 287	98.41 195	100.00 1	99.45 99	96.14 229	97.76 277	99.64 251	95.05 214	98.50 293	97.98 230	86.84 330	97.75 243

DU-MVS		96.93 232	96.49 234	98.22 224	98.31 291	98.41 195	100.00 1	99.37 194	96.41 218	97.76 277	99.65 247	92.14 250	98.50 293	97.98 230	86.84 330	97.75 243

pmmvs4		97.17 218	96.80 222	98.27 220	97.68 315	98.64 190	100.00 1	99.18 274	94.22 283	98.55 236	99.71 233	93.67 230	98.47 296	95.66 281	92.57 277	97.71 286

pmmvs5		95.94 279	95.61 275	96.95 289	97.42 329	94.66 305	100.00 1	98.08 345	93.60 298	97.05 299	99.43 279	87.02 304	98.46 297	95.76 278	92.12 282	97.72 279

SixPastTwentyTwo		95.71 283	95.49 280	96.38 303	97.42 329	93.01 324	99.84 243	98.23 340	94.75 265	95.98 321	99.97 178	85.35 318	98.43 298	94.71 294	93.17 268	97.69 291

NR-MVSNet		96.63 243	96.04 254	98.38 213	98.31 291	98.98 173	99.22 322	99.35 208	95.87 233	94.43 335	99.65 247	92.73 245	98.40 299	96.78 268	88.05 323	97.75 243

TransMVSNet (Re)		94.78 293	93.72 297	97.93 256	98.34 288	97.88 233	99.23 320	97.98 349	91.60 322	94.55 332	99.71 233	87.89 296	98.36 300	89.30 337	84.92 336	97.56 316

Baseline_NR-MVSNet		96.16 270	95.70 270	97.56 268	98.28 294	96.79 270	100.00 1	97.86 352	91.93 321	97.63 283	99.47 277	92.14 250	98.35 301	97.13 255	86.83 332	97.54 317

bset_n11_16_dypcd		97.66 197	97.54 200	98.04 247	96.88 335	98.04 223	100.00 1	99.25 248	95.72 239	98.47 244	99.73 229	96.23 197	98.34 302	98.18 222	93.37 267	97.74 266

IterMVS-SCA-FT		96.72 240	96.42 238	97.62 265	99.40 226	96.83 269	99.99 192	99.14 286	94.65 270	97.55 289	99.72 231	89.65 277	98.31 303	95.62 283	92.05 283	97.73 273

cl-mvsnet1		97.52 207	97.35 208	98.05 245	99.46 215	98.11 217	100.00 1	99.10 298	94.21 284	97.62 285	99.63 255	97.65 156	98.29 304	96.47 272	91.98 285	97.76 232

IterMVS		96.76 237	96.46 236	97.63 263	99.41 221	96.89 267	99.99 192	99.13 290	94.74 267	97.59 288	99.66 245	89.63 279	98.28 305	95.71 279	92.31 280	97.72 279
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
cl_fuxian		97.58 202	97.42 203	98.06 241	99.48 210	98.16 214	99.96 220	99.10 298	94.54 273	98.13 263	99.20 290	97.87 150	98.25 306	97.28 253	91.20 297	97.75 243

test0.0.03 1		98.12 183	98.03 184	98.39 212	99.11 242	98.07 220	100.00 1	99.93 26	96.70 200	96.91 304	99.95 194	99.31 60	98.19 307	91.93 318	98.44 168	98.91 223

CP-MVSNet		96.73 238	96.25 246	98.18 227	98.21 298	98.67 188	99.77 257	99.32 217	95.06 259	97.20 297	99.65 247	90.10 271	98.19 307	98.06 227	88.90 317	97.66 299

PS-CasMVS		96.34 260	95.78 266	98.03 249	98.18 300	98.27 210	99.71 268	99.32 217	94.75 265	96.82 307	99.65 247	86.98 306	98.15 309	97.74 238	88.85 318	97.66 299

our_test_3		96.51 248	96.35 242	96.98 288	97.61 318	95.05 291	99.98 210	99.01 325	94.68 268	96.77 310	99.06 295	95.87 202	98.14 310	91.81 319	92.37 279	97.75 243

new_pmnet		94.11 300	93.47 301	96.04 308	96.60 338	92.82 327	99.97 216	98.91 332	90.21 331	95.26 325	98.05 336	85.89 315	98.14 310	84.28 345	92.01 284	97.16 331

K. test v3		95.46 287	95.14 288	96.40 302	97.53 323	93.40 322	99.99 192	99.23 257	95.49 252	92.70 342	99.73 229	84.26 322	98.12 312	93.94 305	93.38 266	97.68 293

Patchmtry		96.81 234	96.37 240	98.14 231	99.31 233	98.55 192	98.91 343	99.00 326	90.45 329	97.92 273	98.98 304	96.94 183	98.12 312	94.27 299	91.53 291	97.75 243

FMVSNet3		97.30 214	96.95 218	98.37 214	99.65 160	99.25 150	99.71 268	99.28 235	94.23 282	98.53 238	98.91 311	93.30 235	98.11 314	95.31 287	93.60 261	97.73 273

N_pmnet		91.88 312	93.37 302	87.40 336	97.24 333	66.33 362	99.90 234	91.05 365	89.77 334	95.65 324	98.58 324	90.05 273	98.11 314	85.39 343	92.72 273	97.75 243

IterMVS-LS		97.56 203	97.44 202	97.92 257	99.38 230	97.90 231	99.89 237	99.10 298	94.41 279	98.32 252	99.54 271	97.21 170	98.11 314	97.50 245	91.62 290	97.75 243
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CR-MVSNet		98.02 187	97.71 196	98.93 184	99.31 233	98.86 178	99.13 333	99.00 326	96.53 212	99.96 112	98.98 304	96.94 183	98.10 317	91.18 322	98.40 171	99.84 171

FMVSNet2		96.22 265	95.60 276	98.06 241	99.53 192	98.33 205	99.45 296	99.27 243	93.71 292	98.03 267	98.84 314	84.23 323	98.10 317	93.97 304	93.40 265	97.73 273

ppachtmachnet_test		96.17 269	95.89 260	97.02 285	97.61 318	95.24 288	99.99 192	99.24 253	93.31 305	96.71 311	99.62 259	94.34 224	98.07 319	89.87 332	92.30 281	97.75 243

GBi-Net		96.07 274	95.80 264	96.89 292	99.53 192	94.87 293	99.18 323	99.27 243	93.71 292	98.53 238	98.81 315	84.23 323	98.07 319	95.31 287	93.60 261	97.72 279

test1		96.07 274	95.80 264	96.89 292	99.53 192	94.87 293	99.18 323	99.27 243	93.71 292	98.53 238	98.81 315	84.23 323	98.07 319	95.31 287	93.60 261	97.72 279

FMVSNet1		94.45 294	93.63 299	96.89 292	98.87 271	94.87 293	99.18 323	99.27 243	90.95 327	97.31 294	98.81 315	72.89 352	98.07 319	92.61 312	92.81 272	97.72 279

PatchT		95.90 280	94.95 292	98.75 195	99.03 252	98.39 198	99.08 338	99.32 217	85.52 345	99.96 112	94.99 350	97.94 148	98.05 323	80.20 353	98.47 167	99.81 182

TranMVSNet+NR-MVSNet		96.45 252	96.01 255	97.79 261	98.00 306	97.62 244	100.00 1	99.35 208	95.98 231	97.31 294	99.64 251	90.09 272	98.00 324	96.89 263	86.80 333	97.75 243

test_method		91.04 317	91.10 314	90.85 332	98.34 288	77.63 355	100.00 1	98.93 331	76.69 353	96.25 317	98.52 326	70.44 355	97.98 325	89.02 339	91.74 288	96.92 336

miper_lstm_enhance		97.40 210	97.28 211	97.75 262	99.48 210	97.52 246	100.00 1	99.07 310	94.08 288	98.01 270	99.61 261	97.38 169	97.98 325	96.44 275	91.47 294	97.76 232

ET-MVSNet_ETH3D		96.41 253	95.48 282	99.20 170	99.81 122	99.75 101	100.00 1	99.02 323	97.30 166	78.33 356	100.00 1	97.73 153	97.94 327	99.70 127	87.41 327	99.92 133

ADS-MVSNet2		98.28 178	98.51 159	97.62 265	99.51 204	95.03 292	99.24 314	99.41 174	95.52 249	99.96 112	99.70 236	97.57 159	97.94 327	97.11 256	98.54 162	99.88 160

PEN-MVS		96.01 277	95.48 282	97.58 267	97.74 313	97.26 258	99.90 234	99.29 230	94.55 272	96.79 308	99.55 269	87.38 301	97.84 329	96.92 262	87.24 328	97.65 304

testgi		96.18 267	95.93 259	96.93 291	98.98 262	94.20 316	100.00 1	99.07 310	97.16 172	96.06 320	99.86 209	84.08 326	97.79 330	90.38 330	97.80 205	98.81 224

MIMVSNet		97.06 225	96.73 225	98.05 245	99.38 230	96.64 274	98.47 349	99.35 208	93.41 301	99.48 180	98.53 325	89.66 276	97.70 331	94.16 302	98.11 191	99.80 192

LCM-MVSNet-Re		96.52 247	97.21 215	94.44 319	99.27 236	85.80 349	99.85 242	96.61 359	95.98 231	92.75 341	98.48 327	93.97 228	97.55 332	99.58 147	98.43 169	99.98 108

DTE-MVSNet		95.52 285	94.99 291	97.08 282	97.49 326	96.45 276	100.00 1	99.25 248	93.82 291	96.17 318	99.57 267	87.81 297	97.18 333	94.57 295	86.26 335	97.62 310

UnsupCasMVSNet_bld		89.50 321	88.00 324	93.99 326	95.30 346	88.86 345	98.52 348	99.28 235	85.50 346	87.80 351	94.11 352	61.63 360	96.96 334	90.63 326	79.26 344	96.15 344

Anonymous20240521		93.29 304	92.76 308	94.90 317	95.64 344	91.27 338	99.97 216	98.82 335	87.04 343	94.71 330	98.19 335	83.86 327	96.80 335	84.04 346	92.56 278	96.64 340

KD-MVS_2432*1600		94.15 297	93.08 304	97.35 273	99.53 192	97.83 237	99.63 279	99.19 268	92.88 313	96.29 315	97.68 338	98.84 117	96.70 336	89.73 333	63.92 355	97.53 318

miper_refine_blended		94.15 297	93.08 304	97.35 273	99.53 192	97.83 237	99.63 279	99.19 268	92.88 313	96.29 315	97.68 338	98.84 117	96.70 336	89.73 333	63.92 355	97.53 318

MDA-MVSNet_test_wron		92.61 307	91.09 315	97.19 281	96.71 337	97.26 258	100.00 1	99.14 286	88.61 337	67.90 361	98.32 333	89.03 284	96.57 338	90.47 329	89.59 310	97.74 266

UnsupCasMVSNet_eth		94.25 296	93.89 295	95.34 311	97.63 316	92.13 331	99.73 265	99.36 200	94.88 262	92.78 339	98.63 322	82.72 331	96.53 339	94.57 295	84.73 337	97.36 326

tmp_tt		75.80 329	74.26 332	80.43 340	52.91 370	53.67 368	87.42 360	97.98 349	61.80 359	67.04 362	100.00 1	76.43 349	96.40 340	96.47 272	28.26 363	91.23 354

Gipuma		84.73 323	83.50 326	88.40 335	97.50 324	82.21 353	88.87 358	99.05 319	65.81 357	85.71 352	90.49 355	53.70 361	96.31 341	78.64 355	91.74 288	86.67 356
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
YYNet1		92.44 308	90.92 316	97.03 284	96.20 339	97.06 265	99.99 192	99.14 286	88.21 340	67.93 360	98.43 330	88.63 290	96.28 342	90.64 325	89.08 316	97.74 266

EU-MVSNet		96.63 243	96.53 231	96.94 290	97.59 320	96.87 268	99.76 259	99.47 77	96.35 221	96.85 306	99.78 226	92.57 247	96.27 343	95.33 286	91.08 298	97.68 293

pmmvs-eth3d		91.73 313	90.67 317	94.92 316	91.63 354	92.71 328	99.90 234	98.54 337	91.19 325	88.08 349	95.50 346	79.31 344	96.13 344	90.55 328	81.32 343	95.91 348

PM-MVS		88.39 322	87.41 325	91.31 331	91.73 353	82.02 354	99.79 251	96.62 358	91.06 326	90.71 346	95.73 345	48.60 363	95.96 345	90.56 327	81.91 342	95.97 347

MDA-MVSNet-bldmvs		91.65 314	89.94 321	96.79 297	96.72 336	96.70 272	99.42 300	98.94 328	88.89 336	66.97 363	98.37 331	81.43 337	95.91 346	89.24 338	89.46 313	97.75 243

Patchmatch-RL test		93.49 302	93.63 299	93.05 329	91.78 352	83.41 352	98.21 351	96.95 356	91.58 323	91.05 344	97.64 340	99.40 52	95.83 347	94.11 303	81.95 341	99.91 135

Anonymous20231206		93.45 303	93.17 303	94.30 322	95.00 348	89.69 342	99.98 210	98.43 338	93.30 306	94.50 334	98.59 323	90.52 266	95.73 348	77.46 358	90.73 303	97.48 322

DSMNet-mixed		95.18 291	95.21 287	95.08 312	96.03 341	90.21 341	99.65 276	93.64 363	92.91 312	98.34 250	97.40 341	90.05 273	95.51 349	91.02 324	97.86 199	99.51 215

pmmvs3		90.62 319	89.36 323	94.40 320	90.53 356	91.49 336	100.00 1	96.73 357	84.21 348	93.65 338	96.65 344	82.56 334	94.83 350	82.28 349	77.62 347	96.89 337

DIV-MVS_2432*1600		91.16 315	90.09 320	94.35 321	94.44 350	91.27 338	99.74 261	99.08 306	90.82 328	94.53 333	94.91 351	86.11 311	94.78 351	82.67 348	68.52 353	96.99 334

FMVSNet5		95.32 288	95.43 284	94.99 314	99.39 229	92.99 326	99.25 313	99.24 253	90.45 329	97.44 292	98.45 328	95.78 204	94.39 352	87.02 341	91.88 287	97.59 314

new-patchmatchnet		90.30 320	89.46 322	92.84 330	90.77 355	88.55 346	99.83 244	98.80 336	90.07 333	87.86 350	95.00 349	78.77 345	94.30 353	84.86 344	79.15 345	95.68 349

LCM-MVSNet		79.01 327	76.93 331	85.27 338	78.28 363	68.01 360	96.57 355	98.03 346	55.10 361	82.03 355	93.27 353	31.99 368	93.95 354	82.72 347	74.37 349	93.84 351

CL-MVSNet_2432*1600		91.07 316	90.35 319	93.24 328	93.27 351	89.16 344	99.55 288	99.25 248	92.34 318	95.23 326	97.05 342	88.86 289	93.59 355	80.67 351	66.95 354	96.96 335

MIMVSNet1		91.96 309	91.20 312	94.23 324	94.94 349	91.69 335	99.34 307	99.22 259	88.23 339	94.18 336	98.45 328	75.52 350	93.41 356	79.37 354	91.49 292	97.60 313

test20.03		93.11 305	92.85 307	93.88 327	95.19 347	91.83 333	100.00 1	98.87 333	93.68 295	92.76 340	98.88 313	89.20 283	92.71 357	77.88 356	89.19 315	97.09 332

PMMVS2		79.15 326	77.28 330	84.76 339	82.34 360	72.66 356	99.70 270	95.11 362	71.68 356	84.78 354	90.87 354	32.05 367	89.99 358	75.53 359	63.45 357	91.64 353

FPMVS		77.92 328	79.45 328	73.34 345	76.87 364	46.81 369	98.24 350	99.05 319	59.89 360	73.55 357	98.34 332	36.81 366	86.55 359	80.96 350	91.35 296	86.65 357

PMVS	60.66 23	65.98 334	65.05 335	68.75 348	55.06 369	38.40 370	88.19 359	96.98 355	48.30 365	44.82 366	88.52 358	12.22 371	86.49 360	67.58 360	83.79 339	81.35 360
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	68.59 21	67.22 332	64.68 336	74.84 342	74.67 366	62.32 366	95.84 356	90.87 366	50.98 362	58.72 364	81.05 364	12.20 372	78.95 361	61.06 362	56.75 358	83.24 359
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		70.72 330	70.06 333	72.69 346	83.92 359	65.48 364	99.95 225	92.72 364	49.88 363	72.30 358	86.26 361	47.17 364	77.43 362	53.83 363	44.49 360	75.17 362

EMVS		69.88 331	69.09 334	72.24 347	84.70 358	65.82 363	99.96 220	87.08 369	49.82 364	71.51 359	84.74 362	49.30 362	75.32 363	50.97 364	43.71 361	75.59 361

test123		79.44 325	79.23 329	80.05 341	80.03 362	71.72 357	100.00 1	77.93 370	62.52 358	94.81 329	99.69 238	78.21 346	74.53 364	92.57 313	27.33 364	93.90 350

testmvs		80.17 324	81.95 327	74.80 343	58.54 368	59.58 367	100.00 1	87.14 368	76.09 354	99.61 176	100.00 1	67.06 359	74.19 365	98.84 188	50.30 359	90.64 355

ANet_high		66.05 333	63.44 337	73.88 344	61.14 367	63.45 365	95.68 357	87.18 367	79.93 351	47.35 365	80.68 365	22.35 369	72.33 366	61.24 361	35.42 362	85.88 358

wuyk23d		28.28 335	29.73 339	23.92 349	75.89 365	32.61 371	66.50 361	12.88 371	16.09 366	14.59 367	16.59 367	12.35 370	32.36 367	39.36 365	13.36 365	6.79 363

uanet_test		0.01 339	0.02 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.14 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

cdsmvs_eth3d_5k		24.41 336	32.55 338	0.00 350	0.00 371	0.00 372	0.00 362	99.39 189	0.00 367	0.00 368	100.00 1	93.55 232	0.00 368	0.00 366	0.00 366	0.00 364

pcd_1.5k_mvsjas		8.24 338	10.99 341	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.14 368	98.75 124	0.00 368	0.00 366	0.00 366	0.00 364

sosnet-low-res		0.01 339	0.02 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.14 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

sosnet		0.01 339	0.02 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.14 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

uncertanet		0.01 339	0.02 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.14 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

Regformer		0.01 339	0.02 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.14 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

ab-mvs-re		8.33 337	11.11 340	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	100.00 1	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

uanet		0.01 339	0.02 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.14 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

RE-MVS-def			99.55 64		99.99 54	99.91 52	100.00 1	99.42 127	97.62 129	100.00 1	100.00 1	98.94 108		99.99 58	100.00 1	100.00 1

IU-MVS					100.00 1	99.99 3		99.42 127	99.12 6	100.00 1				100.00 1	100.00 1	100.00 1

save fliter					99.99 54	99.93 42	100.00 1	99.42 127	98.93 32

test0726					100.00 1	99.99 3	100.00 1	99.42 127	99.04 14	100.00 1	100.00 1	99.53 23

GSMVS																99.91 135

test_part2					100.00 1	99.99 3				100.00 1

sam_mvs1												99.29 67				99.91 135

sam_mvs												99.33 55

MTGPA								99.42 127

MTMP							100.00 1	99.18 274

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 42	100.00 1

test_prior2							100.00 1		98.82 43	100.00 1	100.00 1	99.47 39		100.00 1	100.00 1

新几何2							100.00 1

旧先验1					99.99 54	99.88 78		99.82 36			100.00 1	99.27 70			100.00 1	100.00 1

原ACMM2							100.00 1

test222					99.99 54	99.90 60	100.00 1	99.69 61	97.66 124	100.00 1	100.00 1	99.30 66			100.00 1	100.00 1

segment_acmp												99.55 21

testdata1							100.00 1		98.77 48

plane_prior7					99.00 258	94.78 303

plane_prior6					99.06 250	94.80 299						88.58 293

plane_prior4											99.97 178

plane_prior3						94.79 302			99.03 19	99.08 203

plane_prior2							100.00 1		99.00 25

plane_prior1					99.02 253

plane_prior						94.80 299	100.00 1		99.03 19						95.58 224

n2								0.00 372

nn								0.00 372

door-mid								96.32 360

test11								99.42 127

door								96.13 361

HQP5-MVS						94.82 296

HQP-NCC					99.07 246		100.00 1		99.04 14	99.17 194

ACMP_Plane					99.07 246		100.00 1		99.04 14	99.17 194

BP-MVS														99.79 112

HQP3-MVS								99.40 176							95.58 224

HQP2-MVS												88.61 291

NP-MVS					99.07 246	94.81 298					99.97 178

MDTV_nov1_ep13_2view						99.24 152	99.56 287		96.31 224	99.96 112		98.86 116		98.92 184		99.89 149

ACMMP++_ref															94.58 256

ACMMP++															95.17 243

Test By Simon												99.10 87

High-resolution multi-view benchmark