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.

GG-mvs-BLEND				99.59 130	99.54 189	99.49 127	99.17 330	99.52 69		99.96 112	99.68 244	100.00 1	99.33 235	99.71 126	99.99 107	99.96 116

gg-mvs-nofinetune		96.95 233	96.10 253	99.50 142	99.41 223	99.36 142	99.07 342	99.52 69	83.69 351	99.96 112	83.60 365	100.00 1	99.20 239	99.68 135	99.99 107	99.96 116

CHOSEN 280x420		99.85 3	99.87 1	99.80 104	99.99 54	99.97 18	99.97 218	99.98 13	98.96 28	100.00 1	100.00 1	99.96 3	99.42 229	100.00 1	100.00 1	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	97.75 209	99.94 124

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	97.75 209	99.94 124

MVSTER		98.58 160	98.52 160	98.77 196	99.65 160	99.68 113	100.00 1	99.29 230	95.63 245	98.65 232	99.80 225	99.78 6	98.88 263	98.59 207	95.31 236	97.73 275

CDS-MVSNet		98.96 135	98.95 124	99.01 181	99.48 211	98.36 205	99.93 232	99.37 194	96.79 194	99.31 191	99.83 216	99.77 7	98.91 257	98.07 228	97.98 196	99.77 197
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PAPM		99.78 16	99.76 12	99.85 93	99.01 256	99.95 29	100.00 1	99.75 49	99.37 3	99.99 99	100.00 1	99.76 8	99.60 190	100.00 1	100.00 1	100.00 1

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

TESTMET0.1,1		99.08 118	98.96 120	99.44 147	99.63 167	99.38 138	100.00 1	99.45 99	95.53 249	99.48 182	100.00 1	99.71 10	99.02 246	96.84 266	99.99 107	99.91 136

test-mter		98.96 135	98.82 135	99.40 153	99.40 228	99.28 147	100.00 1	99.45 99	95.44 258	99.42 185	99.12 293	99.70 11	99.01 248	96.82 267	99.99 107	99.91 136

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
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

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

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

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

PLC	98.56 2	99.70 39	99.74 15	99.58 134	100.00 1	98.79 182	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
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	97.64 243	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

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

segment_acmp												99.55 21

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

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
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

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

DWT-MVSNet_test		99.22 112	99.22 99	99.22 171	99.56 188	97.98 228	99.89 239	99.39 189	97.20 171	99.96 112	100.00 1	99.52 26	99.82 170	99.11 179	98.34 178	99.87 169

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.
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

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

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

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

EPP-MVSNet		99.10 117	99.00 116	99.40 153	99.51 204	98.68 189	99.92 233	99.43 119	95.47 255	99.65 175	100.00 1	99.51 28	99.76 178	99.53 156	98.00 195	99.75 200

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

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

miper_enhance_ethall		98.33 175	98.27 173	98.51 208	99.66 159	99.04 168	100.00 1	99.22 260	97.53 143	98.51 243	99.38 283	99.49 35	98.75 275	98.02 230	92.61 276	97.76 234

test_post											89.05 359	99.49 35	99.59 192

HyFIR lowres test		99.32 98	99.24 94	99.58 134	99.95 103	99.26 149	100.00 1	99.99 10	96.72 200	99.29 192	99.91 205	99.49 35	99.47 221	99.74 120	98.08 194	100.00 1

PatchmatchNet		99.03 123	98.96 120	99.26 167	99.49 208	98.33 207	99.38 305	99.45 99	96.64 208	99.96 112	99.58 266	99.49 35	99.50 217	97.63 244	99.00 156	99.93 131
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
Patchmatch-test		97.83 194	97.42 205	99.06 175	99.08 247	97.66 245	98.66 348	99.21 266	93.65 298	98.25 261	99.58 266	99.47 39	99.57 196	90.25 333	98.59 163	99.95 121

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_prior2							100.00 1		98.82 43	100.00 1	100.00 1	99.47 39		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
thisisatest0530		99.37 90	99.27 90	99.69 117	99.59 177	99.41 136	100.00 1	99.46 92	96.46 217	99.90 146	100.00 1	99.44 43	99.85 164	98.97 183	99.58 146	99.80 194

tttt0517		99.34 94	99.23 97	99.67 120	99.57 185	99.38 138	100.00 1	99.46 92	96.33 225	99.89 149	100.00 1	99.44 43	99.84 166	98.93 185	99.46 149	99.78 196

thisisatest0515		99.42 85	99.31 89	99.74 113	99.59 177	99.55 121	100.00 1	99.46 92	96.65 207	99.92 143	100.00 1	99.44 43	99.85 164	99.09 180	99.63 144	99.81 184

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

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
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	99.50 158	100.00 1	100.00 1

testdata				99.66 122	99.99 54	98.97 177		99.73 54	97.96 100	100.00 1	100.00 1	99.42 49	100.00 1	99.28 171	100.00 1	100.00 1

baseline2		98.99 131	98.93 126	99.18 173	99.26 240	99.15 161	100.00 1	99.46 92	96.71 201	96.79 310	100.00 1	99.42 49	99.25 238	98.75 196	99.94 123	99.15 222

patchmatchnet-post											97.79 339	99.41 51	99.54 208

Patchmatch-RL test		93.49 304	93.63 301	93.05 331	91.78 354	83.41 354	98.21 353	96.95 358	91.58 325	91.05 346	97.64 342	99.40 52	95.83 349	94.11 305	81.95 343	99.91 136

1121		99.63 62	99.51 70	99.99 9	99.99 54	99.96 20	99.24 316	99.74 52	97.89 104	100.00 1	100.00 1	99.39 53	100.00 1	99.33 167	100.00 1	100.00 1

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

sam_mvs												99.33 55

test_post1							99.32 310				88.24 361	99.33 55	99.59 192	98.31 218

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
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

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

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

#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

test-LLR		99.03 123	98.91 128	99.40 153	99.40 228	99.28 147	100.00 1	99.45 99	96.70 202	99.42 185	99.12 293	99.31 60	99.01 248	96.82 267	99.99 107	99.91 136

test0.0.03 1		98.12 185	98.03 186	98.39 214	99.11 244	98.07 222	100.00 1	99.93 26	96.70 202	96.91 306	99.95 196	99.31 60	98.19 309	91.93 320	98.44 170	98.91 225

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

TAPA-MVS	96.40 10	97.64 200	97.37 209	98.45 211	99.94 106	95.70 286	100.00 1	99.40 176	97.65 125	99.53 179	100.00 1	99.31 60	99.66 188	80.48 354	100.00 1	100.00 1
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
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

sam_mvs1												99.29 67				99.91 136

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

CostFormer		98.84 145	98.77 140	99.04 179	99.41 223	97.58 247	99.67 277	99.35 208	94.66 271	99.96 112	99.36 284	99.28 69	99.74 181	99.41 162	97.81 206	99.81 184

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

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
RRT_test8_iter05		98.32 176	98.23 177	98.60 203	99.65 160	99.23 156	100.00 1	99.09 306	95.65 243	98.07 267	99.93 203	99.25 72	98.82 266	98.90 187	93.50 266	97.75 245

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

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

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

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
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

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

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	97.91 236	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

F-COLMAP		99.64 58	99.64 38	99.67 120	99.99 54	99.07 165	100.00 1	99.44 109	98.30 73	99.90 146	100.00 1	99.18 81	99.99 89	99.91 95	100.00 1	99.94 124

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		97.04 228	96.06 255	99.98 22	100.00 1	99.94 37	100.00 1	99.75 49	98.67 51	100.00 1	66.97 368	99.16 82	100.00 1	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

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	99.88 151	100.00 1	99.14 85	100.00 1	99.97 82	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

Test By Simon												99.10 87

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

alignmvs		99.38 88	99.21 100	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	97.12 217	99.90 145

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

EPMVS		99.25 109	99.13 109	99.60 128	99.60 174	99.20 158	99.60 286	100.00 1	96.93 186	99.92 143	99.36 284	99.05 90	99.71 185	98.77 194	98.94 157	99.90 145

RRT_MVS		98.48 168	98.41 165	98.70 199	99.64 165	99.67 114	100.00 1	99.10 300	96.26 228	98.75 227	99.96 189	99.04 93	98.75 275	98.42 212	94.51 259	97.68 295

EI-MVSNet-Vis-set		99.70 39	99.64 38	99.87 90	100.00 1	99.64 117	99.98 212	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

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

tpmrst		98.98 134	98.93 126	99.14 174	99.61 172	97.74 242	99.52 294	99.36 200	96.05 232	99.98 104	99.64 253	99.04 93	99.86 160	98.94 184	98.19 189	99.82 180

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

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

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

EI-MVSNet-UG-set		99.69 43	99.63 42	99.87 90	99.99 54	99.64 117	99.95 227	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

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

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

EPNet		99.62 66	99.69 22	99.42 150	99.99 54	98.37 203	100.00 1	99.89 32	98.83 41	100.00 1	100.00 1	98.97 102	100.00 1	99.90 96	99.61 145	99.89 150
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tpm2		98.64 157	98.58 157	98.81 195	99.42 221	97.12 264	99.69 274	99.37 194	93.63 299	99.94 137	99.67 245	98.96 105	99.47 221	98.62 205	97.95 197	99.83 175

TSAR-MVS + GP.		99.61 68	99.69 22	99.35 158	99.99 54	98.06 223	100.00 1	99.36 200	99.83 2	100.00 1	100.00 1	98.95 106	99.99 89	100.00 1	99.11 152	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	97.09 178	100.00 1	100.00 1	98.95 106	99.96 132	99.98 75	100.00 1	100.00 1

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

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	99.84 154	100.00 1	98.94 108	99.98 113	99.86 101	98.21 187	99.94 124

HY-MVS	96.53 9	99.50 78	99.35 86	99.96 46	99.81 122	99.93 42	99.64 279	100.00 1	97.97 96	99.84 154	99.85 213	98.94 108	99.99 89	99.86 101	98.23 186	99.95 121

MDTV_nov1_ep13			98.94 125		99.53 192	98.36 205	99.39 304	99.46 92	96.54 213	99.99 99	99.63 257	98.92 111	99.86 160	98.30 220	98.71 161

API-MVS		99.72 32	99.70 20	99.79 106	99.97 96	99.37 141	99.96 222	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

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

ACMMP		99.65 56	99.57 57	99.89 85	99.99 54	99.66 115	99.75 262	99.73 54	98.16 77	99.75 170	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
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

MDTV_nov1_ep13_2view						99.24 153	99.56 289		96.31 226	99.96 112		98.86 116		98.92 186		99.89 150

KD-MVS_2432*1600		94.15 299	93.08 306	97.35 275	99.53 192	97.83 239	99.63 281	99.19 270	92.88 315	96.29 317	97.68 340	98.84 117	96.70 338	89.73 335	63.92 357	97.53 320

miper_refine_blended		94.15 299	93.08 306	97.35 275	99.53 192	97.83 239	99.63 281	99.19 270	92.88 315	96.29 317	97.68 340	98.84 117	96.70 338	89.73 335	63.92 357	97.53 320

dp		98.72 151	98.61 153	99.03 180	99.53 192	97.39 252	99.45 298	99.39 189	95.62 246	99.94 137	99.52 274	98.83 119	99.82 170	96.77 272	98.42 172	99.89 150

TAMVS		98.76 149	98.73 143	98.86 191	99.44 220	97.69 243	99.57 288	99.34 213	96.57 211	99.12 202	99.81 222	98.83 119	99.16 240	97.97 235	97.91 199	99.73 205

IB-MVS	96.24 12	97.54 207	96.95 220	99.33 160	99.67 155	98.10 221	100.00 1	99.47 77	97.42 154	99.26 193	99.69 240	98.83 119	99.89 156	99.43 160	78.77 348	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
DeepC-MVS_fast	98.92 1	99.75 21	99.67 30	99.99 9	99.99 54	99.96 20	99.73 267	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
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	99.77 167	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
pcd_1.5k_mvsjas		8.24 340	10.99 343	0.00 352	0.00 373	0.00 374	0.00 364	0.00 374	0.00 369	0.00 370	0.14 370	98.75 124	0.00 370	0.00 368	0.00 368	0.00 366

PS-MVSNAJss		98.03 188	98.06 185	97.94 256	97.63 318	97.33 257	99.89 239	99.23 257	96.27 227	98.03 269	99.59 265	98.75 124	98.78 270	98.52 209	94.61 257	97.70 289

PS-MVSNAJ		99.64 58	99.57 57	99.85 93	99.78 137	99.81 93	99.95 227	99.42 127	98.38 66	100.00 1	100.00 1	98.75 124	100.00 1	99.88 99	99.99 107	99.74 201

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

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	99.94 137	100.00 1	98.65 127	100.00 1	99.96 84	100.00 1	100.00 1

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	99.94 137	100.00 1	98.65 127	100.00 1	99.96 84	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
MG-MVS		99.75 21	99.68 25	99.97 31	100.00 1	99.91 52	99.98 212	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

EPNet_dtu		98.53 165	98.23 177	99.43 149	99.92 111	99.01 172	99.96 222	99.47 77	98.80 45	99.96 112	99.96 189	98.56 133	99.30 236	87.78 342	99.68 140	100.00 1
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
JIA-IIPM		97.09 224	96.34 245	99.36 157	98.88 271	98.59 193	99.81 249	99.43 119	84.81 349	99.96 112	90.34 358	98.55 134	99.52 214	97.00 261	98.28 184	99.98 108

xiu_mvs_v2_base		99.51 75	99.41 76	99.82 97	99.70 148	99.73 105	99.92 233	99.40 176	98.15 79	100.00 1	100.00 1	98.50 135	100.00 1	99.85 103	99.13 151	99.74 201

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	99.94 200	98.45 136	100.00 1	99.53 156	98.75 160	99.89 150

IS-MVSNet		99.08 118	98.91 128	99.59 130	99.65 160	99.38 138	99.78 254	99.24 253	96.70 202	99.51 180	100.00 1	98.44 137	99.52 214	98.47 211	98.39 175	99.88 161

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	99.95 135	100.00 1	98.39 138	100.00 1	99.96 84	99.99 107	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	95.53 249	100.00 1	100.00 1	98.37 139	99.95 138	99.97 82	100.00 1	100.00 1

baseline1		98.91 140	98.61 153	99.81 100	99.71 146	99.77 97	99.78 254	99.44 109	97.51 146	98.81 224	99.99 169	98.25 140	99.76 178	98.60 206	95.41 232	99.89 150

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.
miper_ehance_all_eth		97.81 195	97.66 199	98.23 225	99.49 208	98.37 203	99.99 194	99.11 298	94.78 266	98.25 261	99.21 290	98.18 142	98.57 290	97.35 254	92.61 276	97.76 234

PHI-MVS		99.50 78	99.39 79	99.82 97	100.00 1	99.45 132	100.00 1	99.94 18	96.38 222	100.00 1	100.00 1	98.18 142	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

Vis-MVSNet (Re-imp)		98.99 131	98.89 132	99.29 163	99.64 165	98.89 179	99.98 212	99.31 222	96.74 197	99.48 182	100.00 1	98.11 145	99.10 242	98.39 214	98.34 178	99.89 150

DeepPCF-MVS	98.03 4	98.54 164	99.72 18	94.98 317	99.99 54	84.94 352	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

cl-mvsnet2		98.23 183	98.11 181	98.58 207	99.82 118	99.01 172	100.00 1	99.28 235	96.92 188	98.33 253	99.21 290	98.09 147	98.97 253	98.72 197	92.61 276	97.76 234

PatchT		95.90 282	94.95 294	98.75 197	99.03 254	98.39 200	99.08 340	99.32 217	85.52 347	99.96 112	94.99 352	97.94 148	98.05 325	80.20 355	98.47 169	99.81 184

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

cl_fuxian		97.58 204	97.42 205	98.06 243	99.48 211	98.16 216	99.96 222	99.10 300	94.54 275	98.13 265	99.20 292	97.87 150	98.25 308	97.28 255	91.20 299	97.75 245

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

CSCG		99.28 102	99.35 86	99.05 177	99.99 54	97.15 263	100.00 1	99.47 77	97.44 152	99.42 185	100.00 1	97.83 152	100.00 1	99.99 58	100.00 1	100.00 1

ET-MVSNet_ETH3D		96.41 255	95.48 284	99.20 172	99.81 122	99.75 101	100.00 1	99.02 325	97.30 166	78.33 358	100.00 1	97.73 153	97.94 329	99.70 128	87.41 329	99.92 133

CS-MVS-test		99.34 94	99.23 97	99.68 119	99.49 208	99.42 135	100.00 1	99.23 257	97.16 172	99.96 112	100.00 1	97.71 154	99.71 185	99.72 123	99.78 136	99.92 133

tpm cat1		98.05 187	97.76 194	98.92 187	99.50 207	97.10 266	99.77 259	99.30 225	90.20 334	99.72 172	98.71 321	97.71 154	99.86 160	96.75 273	98.20 188	99.81 184

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	99.73 171	100.00 1	97.70 156	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
cl-mvsnet1		97.52 209	97.35 210	98.05 247	99.46 216	98.11 219	100.00 1	99.10 300	94.21 286	97.62 287	99.63 257	97.65 157	98.29 306	96.47 274	91.98 287	97.76 234

tpmvs		98.59 159	98.38 168	99.23 169	99.69 149	97.90 233	99.31 311	99.47 77	94.52 276	99.68 174	99.28 288	97.64 158	99.89 156	97.71 241	98.17 191	99.89 150

tpm		98.24 182	98.22 179	98.32 220	99.13 243	95.79 285	99.53 293	99.12 297	95.20 259	99.96 112	99.36 284	97.58 159	99.28 237	97.41 251	96.67 224	99.88 161

ADS-MVSNet2		98.28 180	98.51 161	97.62 267	99.51 204	95.03 294	99.24 316	99.41 174	95.52 251	99.96 112	99.70 238	97.57 160	97.94 329	97.11 258	98.54 164	99.88 161

ADS-MVSNet		98.70 153	98.51 161	99.28 165	99.51 204	98.39 200	99.24 316	99.44 109	95.52 251	99.96 112	99.70 238	97.57 160	99.58 195	97.11 258	98.54 164	99.88 161

cl-mvsnet____		97.54 207	97.32 211	98.18 229	99.47 214	98.14 218	100.00 1	99.10 300	94.16 289	97.60 289	99.63 257	97.52 162	98.65 281	96.47 274	91.97 288	97.76 234

hse-mvs3		97.03 229	96.53 233	98.51 208	99.79 135	95.90 284	99.45 298	99.45 99	98.21 74	100.00 1	99.78 228	97.49 163	99.99 89	99.72 123	74.92 350	99.65 213

hse-mvs2		96.79 237	96.38 241	98.04 249	99.68 152	95.54 288	99.81 249	99.42 127	98.21 74	100.00 1	99.80 225	97.49 163	99.46 225	99.72 123	73.27 353	99.12 223

CS-MVS		99.28 102	99.18 106	99.57 137	99.45 217	99.21 157	100.00 1	99.22 260	97.14 174	99.96 112	100.00 1	97.49 163	99.66 188	99.64 141	99.76 137	99.83 175

EIA-MVS		99.26 106	99.19 104	99.45 146	99.63 167	98.75 183	100.00 1	99.27 243	96.93 186	99.95 135	100.00 1	97.47 166	99.79 174	99.74 120	99.72 139	99.82 180

Test_1112_low_res		98.83 146	98.60 155	99.51 140	99.69 149	98.75 183	99.99 194	99.14 288	96.81 193	98.84 221	99.06 297	97.45 167	99.89 156	98.66 199	97.75 209	99.89 150

1112_ss		98.91 140	98.71 146	99.51 140	99.69 149	98.75 183	99.99 194	99.15 283	96.82 192	98.84 221	100.00 1	97.45 167	99.89 156	98.66 199	97.75 209	99.89 150

ETV-MVS		99.34 94	99.24 94	99.64 123	99.58 182	99.33 143	100.00 1	99.25 248	97.57 139	99.96 112	100.00 1	97.44 169	99.79 174	99.70 128	99.65 142	99.81 184

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	97.43 170	100.00 1	99.99 58	100.00 1	100.00 1

miper_lstm_enhance		97.40 212	97.28 213	97.75 264	99.48 211	97.52 248	100.00 1	99.07 312	94.08 290	98.01 272	99.61 263	97.38 171	97.98 327	96.44 277	91.47 296	97.76 234

EI-MVSNet		97.98 190	97.93 190	98.16 232	99.11 244	97.84 238	99.74 263	99.29 230	94.39 282	98.65 232	100.00 1	97.21 172	98.88 263	97.62 245	95.31 236	97.75 245

IterMVS-LS		97.56 205	97.44 204	97.92 259	99.38 232	97.90 233	99.89 239	99.10 300	94.41 281	98.32 254	99.54 273	97.21 172	98.11 316	97.50 247	91.62 292	97.75 245
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tfpn200view9		99.26 106	99.03 113	99.96 46	99.81 122	99.89 68	100.00 1	99.94 18	97.23 167	99.83 156	99.96 189	97.04 174	100.00 1	99.59 146	97.85 202	99.98 108

thres400		99.26 106	99.03 113	99.95 58	99.81 122	99.89 68	100.00 1	99.94 18	97.23 167	99.83 156	99.96 189	97.04 174	100.00 1	99.59 146	97.85 202	99.97 113

thres200		99.27 104	99.04 112	99.96 46	99.81 122	99.90 60	100.00 1	99.94 18	97.31 164	99.83 156	99.96 189	97.04 174	100.00 1	99.62 145	97.88 200	99.98 108

1314		99.38 88	99.19 104	99.96 46	98.88 271	99.89 68	99.24 316	99.93 26	98.88 37	98.79 226	100.00 1	97.02 177	100.00 1	100.00 1	100.00 1	100.00 1

thres100view900		99.25 109	99.01 115	99.95 58	99.81 122	99.87 81	100.00 1	99.94 18	97.13 176	99.83 156	99.96 189	97.01 178	100.00 1	99.59 146	97.85 202	99.98 108

thres600view7		99.24 111	99.00 116	99.95 58	99.81 122	99.87 81	100.00 1	99.94 18	97.13 176	99.83 156	99.96 189	97.01 178	100.00 1	99.54 154	97.77 208	99.97 113

OMC-MVS		99.27 104	99.38 80	98.96 185	99.95 103	97.06 267	100.00 1	99.40 176	98.83 41	99.88 151	100.00 1	97.01 178	99.86 160	99.47 159	99.84 131	99.97 113

xiu_mvs_v1_base_debu		99.35 91	99.21 100	99.79 106	99.67 155	99.71 107	99.78 254	99.36 200	98.13 81	100.00 1	100.00 1	97.00 181	100.00 1	99.83 106	99.07 153	99.66 210

xiu_mvs_v1_base		99.35 91	99.21 100	99.79 106	99.67 155	99.71 107	99.78 254	99.36 200	98.13 81	100.00 1	100.00 1	97.00 181	100.00 1	99.83 106	99.07 153	99.66 210

xiu_mvs_v1_base_debi		99.35 91	99.21 100	99.79 106	99.67 155	99.71 107	99.78 254	99.36 200	98.13 81	100.00 1	100.00 1	97.00 181	100.00 1	99.83 106	99.07 153	99.66 210

CR-MVSNet		98.02 189	97.71 198	98.93 186	99.31 235	98.86 180	99.13 335	99.00 328	96.53 214	99.96 112	98.98 306	96.94 184	98.10 319	91.18 324	98.40 173	99.84 172

Patchmtry		96.81 236	96.37 242	98.14 233	99.31 235	98.55 194	98.91 345	99.00 328	90.45 331	97.92 275	98.98 306	96.94 184	98.12 314	94.27 301	91.53 293	97.75 245

DROMVSNet		99.20 114	99.10 111	99.50 142	99.42 221	99.12 163	100.00 1	99.21 266	96.97 185	99.96 112	100.00 1	96.93 186	99.50 217	99.64 141	99.79 135	99.88 161

eth_miper_zixun_eth		97.47 210	97.28 213	98.06 243	99.41 223	97.94 231	99.62 284	99.08 308	94.46 280	98.19 264	99.56 270	96.91 187	98.50 295	96.78 270	91.49 294	97.74 268

LS3D		99.31 99	99.13 109	99.87 90	99.99 54	99.71 107	99.55 290	99.46 92	97.32 162	99.82 163	100.00 1	96.85 188	99.97 118	99.14 176	100.00 1	99.92 133

MVSFormer		98.94 138	98.82 135	99.28 165	99.45 217	99.49 127	100.00 1	99.13 292	95.46 256	99.97 108	100.00 1	96.76 189	98.59 287	98.63 203	100.00 1	99.74 201

lupinMVS		99.29 101	99.16 108	99.69 117	99.45 217	99.49 127	100.00 1	99.15 283	97.45 151	99.97 108	100.00 1	96.76 189	99.76 178	99.67 137	100.00 1	99.81 184

MAR-MVS		99.49 80	99.36 84	99.89 85	99.97 96	99.66 115	99.74 263	99.95 15	97.89 104	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
MSDG		98.90 142	98.63 151	99.70 116	99.92 111	99.25 151	100.00 1	99.37 194	95.71 242	99.40 189	100.00 1	96.58 192	99.95 138	96.80 269	99.94 123	99.91 136

PVSNet_BlendedMVS		98.71 152	98.62 152	98.98 184	99.98 92	99.60 119	100.00 1	100.00 1	97.23 167	100.00 1	99.03 302	96.57 193	99.99 89	100.00 1	94.75 254	97.35 329

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	96.57 193	99.99 89	100.00 1	99.88 128	99.90 145

MVS_Test		98.93 139	98.65 149	99.77 112	99.62 170	99.50 124	99.99 194	99.19 270	95.52 251	99.96 112	99.86 211	96.54 195	99.98 113	98.65 201	98.48 168	99.82 180

PMMVS		99.12 115	98.97 119	99.58 134	99.57 185	98.98 175	100.00 1	99.30 225	97.14 174	99.96 112	100.00 1	96.53 196	99.82 170	99.70 128	98.49 167	99.94 124

PVSNet_Blended_VisFu		99.33 97	99.18 106	99.78 110	99.82 118	99.49 127	100.00 1	99.95 15	97.36 157	99.63 176	100.00 1	96.45 197	99.95 138	99.79 112	99.65 142	99.89 150

mvs_anonymous		98.80 148	98.60 155	99.38 156	99.57 185	99.24 153	100.00 1	99.21 266	95.87 235	98.92 213	99.82 219	96.39 198	99.03 245	99.13 177	98.50 166	99.88 161

bset_n11_16_dypcd		97.66 199	97.54 202	98.04 249	96.88 337	98.04 225	100.00 1	99.25 248	95.72 241	98.47 246	99.73 231	96.23 199	98.34 304	98.18 224	93.37 269	97.74 268

DP-MVS		98.86 144	98.54 159	99.81 100	99.97 96	99.45 132	99.52 294	99.40 176	94.35 283	98.36 250	100.00 1	96.13 200	99.97 118	99.12 178	100.00 1	100.00 1

PVSNet	94.91 18	99.30 100	99.25 92	99.44 147	100.00 1	98.32 209	100.00 1	99.86 34	98.04 89	100.00 1	100.00 1	96.10 201	100.00 1	99.55 151	99.73 138	100.00 1

Effi-MVS+-dtu		98.51 167	98.86 133	97.47 271	99.77 140	94.21 317	100.00 1	98.94 330	97.61 131	99.91 145	98.75 320	95.89 202	99.51 216	99.36 164	99.48 148	98.68 227

mvs-test1		98.89 143	98.99 118	98.60 203	99.77 140	95.96 280	100.00 1	98.94 330	97.61 131	99.93 142	99.92 204	95.89 202	99.93 151	99.36 164	99.50 147	99.90 145

our_test_3		96.51 250	96.35 244	96.98 290	97.61 320	95.05 293	99.98 212	99.01 327	94.68 270	96.77 312	99.06 297	95.87 204	98.14 312	91.81 321	92.37 281	97.75 245

UA-Net		99.06 120	98.83 134	99.74 113	99.52 199	99.40 137	99.08 340	99.45 99	97.64 127	99.83 156	100.00 1	95.80 205	99.94 149	98.35 216	99.80 134	99.88 161

FMVSNet5		95.32 290	95.43 286	94.99 316	99.39 231	92.99 328	99.25 315	99.24 253	90.45 331	97.44 294	98.45 330	95.78 206	94.39 354	87.02 343	91.88 289	97.59 316

CVMVSNet		98.56 162	98.47 163	98.82 193	99.11 244	97.67 244	99.74 263	99.47 77	97.57 139	99.06 207	100.00 1	95.72 207	98.97 253	98.21 223	97.33 216	99.83 175

RPMNet		95.26 292	93.82 298	99.56 138	99.31 235	98.86 180	99.13 335	99.42 127	79.82 354	99.96 112	95.13 350	95.69 208	99.98 113	77.54 359	98.40 173	99.84 172

MVS		99.22 112	98.96 120	99.98 22	99.00 260	99.95 29	99.24 316	99.94 18	98.14 80	98.88 216	100.00 1	95.63 209	100.00 1	99.85 103	100.00 1	100.00 1

jason		99.11 116	98.96 120	99.59 130	99.17 242	99.31 145	100.00 1	99.13 292	97.38 156	99.83 156	100.00 1	95.54 210	99.72 184	99.57 150	99.97 118	99.74 201
jason: jason.
AdaColmap		99.44 84	99.26 91	99.95 58	100.00 1	99.86 84	99.70 272	99.99 10	98.53 57	99.90 146	100.00 1	95.34 211	100.00 1	99.92 94	100.00 1	100.00 1

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	99.78 166	100.00 1	95.28 212	99.96 132	100.00 1	99.99 107	99.96 116

FIs		97.95 191	97.73 197	98.62 202	98.53 285	99.24 153	100.00 1	99.43 119	96.74 197	97.87 277	99.82 219	95.27 213	98.89 260	98.78 193	93.07 271	97.74 268

canonicalmvs		99.03 123	98.73 143	99.94 73	99.75 144	99.95 29	100.00 1	99.30 225	97.64 127	100.00 1	100.00 1	95.22 214	99.97 118	99.76 118	96.90 223	99.91 136

FC-MVSNet-test		97.84 193	97.63 201	98.45 211	98.30 295	99.05 167	100.00 1	99.43 119	96.63 210	97.61 288	99.82 219	95.19 215	98.57 290	98.64 202	93.05 272	97.73 275

UniMVSNet_NR-MVSNet		97.16 221	96.80 224	98.22 226	98.38 289	98.41 197	100.00 1	99.45 99	96.14 231	97.76 279	99.64 253	95.05 216	98.50 295	97.98 232	86.84 332	97.75 245

Fast-Effi-MVS+-dtu		98.38 174	98.56 158	97.82 262	99.58 182	94.44 314	100.00 1	99.16 282	96.75 196	99.51 180	99.63 257	95.03 217	99.60 190	97.71 241	99.67 141	99.42 218

UniMVSNet (Re)		97.29 217	96.85 223	98.59 206	98.49 286	99.13 162	100.00 1	99.42 127	96.52 215	98.24 263	98.90 314	94.93 218	98.89 260	97.54 246	87.61 328	97.75 245

BH-w/o		98.82 147	98.81 137	98.88 190	99.62 170	96.71 273	100.00 1	99.28 235	97.09 178	98.81 224	100.00 1	94.91 219	99.96 132	99.54 154	100.00 1	99.96 116

AUN-MVS		96.26 266	95.67 276	98.06 243	99.68 152	95.60 287	99.82 248	99.42 127	96.78 195	99.88 151	99.80 225	94.84 220	99.47 221	97.48 248	73.29 352	99.12 223

PCF-MVS	98.23 3	98.69 154	98.37 170	99.62 125	99.78 137	99.02 170	99.23 322	99.06 319	96.43 218	98.08 266	100.00 1	94.72 221	99.95 138	98.16 225	99.91 126	99.90 145
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
VNet		99.04 122	98.75 142	99.90 82	99.81 122	99.75 101	99.50 296	99.47 77	98.36 68	100.00 1	99.99 169	94.66 222	100.00 1	99.90 96	97.09 218	99.96 116

diffmvs		98.96 135	98.73 143	99.63 124	99.54 189	99.16 160	100.00 1	99.18 276	97.33 161	99.96 112	100.00 1	94.60 223	99.91 155	99.66 139	98.33 182	99.82 180

BH-untuned		98.64 157	98.65 149	98.60 203	99.59 177	96.17 279	100.00 1	99.28 235	96.67 206	98.41 249	100.00 1	94.52 224	99.83 167	99.41 162	100.00 1	99.81 184

nrg030		97.64 200	97.27 215	98.75 197	98.34 290	99.53 123	100.00 1	99.22 260	96.21 230	98.27 259	99.95 196	94.40 225	98.98 251	99.23 173	89.78 311	97.75 245

ppachtmachnet_test		96.17 271	95.89 262	97.02 287	97.61 320	95.24 290	99.99 194	99.24 253	93.31 307	96.71 313	99.62 261	94.34 226	98.07 321	89.87 334	92.30 283	97.75 245

D2MVS		97.63 203	97.83 192	97.05 285	98.83 277	94.60 310	100.00 1	99.82 36	96.89 191	98.28 257	99.03 302	94.05 227	99.47 221	98.58 208	94.97 252	97.09 334

RPSCF		97.37 213	98.24 175	94.76 320	99.80 132	84.57 353	99.99 194	99.05 321	94.95 263	99.82 163	100.00 1	94.03 228	100.00 1	98.15 226	98.38 176	99.70 206

CANet_DTU		99.02 127	98.90 131	99.41 151	99.88 116	98.71 187	100.00 1	99.29 230	98.84 40	100.00 1	100.00 1	94.02 229	100.00 1	98.08 227	99.96 120	99.52 216

LCM-MVSNet-Re		96.52 249	97.21 217	94.44 321	99.27 238	85.80 351	99.85 244	96.61 361	95.98 233	92.75 343	98.48 329	93.97 230	97.55 334	99.58 149	98.43 171	99.98 108

Effi-MVS+		98.58 160	98.24 175	99.61 126	99.60 174	99.26 149	97.85 355	99.10 300	96.22 229	99.97 108	99.89 207	93.75 231	99.77 177	99.43 160	98.34 178	99.81 184

pmmvs4		97.17 220	96.80 224	98.27 222	97.68 317	98.64 192	100.00 1	99.18 276	94.22 285	98.55 238	99.71 235	93.67 232	98.47 298	95.66 283	92.57 279	97.71 288

CHOSEN 1792x2688		99.00 129	98.91 128	99.25 168	99.90 114	97.79 241	100.00 1	99.99 10	98.79 46	98.28 257	100.00 1	93.63 233	99.95 138	99.66 139	99.95 122	100.00 1

cdsmvs_eth3d_5k		24.41 338	32.55 340	0.00 352	0.00 373	0.00 374	0.00 364	99.39 189	0.00 369	0.00 370	100.00 1	93.55 234	0.00 370	0.00 368	0.00 368	0.00 366

AllTest		98.55 163	98.40 167	98.99 182	99.93 108	97.35 254	100.00 1	99.40 176	97.08 180	99.09 203	99.98 172	93.37 235	99.95 138	96.94 262	99.84 131	99.68 208

TestCases				98.99 182	99.93 108	97.35 254		99.40 176	97.08 180	99.09 203	99.98 172	93.37 235	99.95 138	96.94 262	99.84 131	99.68 208

FMVSNet3		97.30 216	96.95 220	98.37 216	99.65 160	99.25 151	99.71 270	99.28 235	94.23 284	98.53 240	98.91 313	93.30 237	98.11 316	95.31 289	93.60 263	97.73 275

Fast-Effi-MVS+		98.40 173	98.02 187	99.55 139	99.63 167	99.06 166	100.00 1	99.15 283	95.07 260	99.42 185	99.95 196	93.26 238	99.73 183	97.44 249	98.24 185	99.87 169

baseline		98.69 154	98.45 164	99.41 151	99.52 199	98.67 190	100.00 1	99.17 281	97.03 183	99.13 201	100.00 1	93.17 239	99.74 181	99.70 128	98.34 178	99.81 184

QAPM		98.99 131	98.66 148	99.96 46	99.01 256	99.87 81	99.88 242	99.93 26	97.99 92	98.68 231	100.00 1	93.17 239	100.00 1	99.32 169	100.00 1	100.00 1

PatchMatch-RL		99.02 127	98.78 139	99.74 113	99.99 54	99.29 146	100.00 1	100.00 1	98.38 66	99.89 149	99.81 222	93.14 241	99.99 89	97.85 238	99.98 115	99.95 121

test_part1		96.98 232	96.36 243	98.85 192	99.78 137	99.77 97	99.63 281	99.30 225	92.74 318	97.05 301	99.52 274	93.10 242	99.02 246	98.02 230	88.10 324	97.76 234

WR-MVS_H		96.73 240	96.32 247	97.95 255	98.26 297	97.88 235	99.72 269	99.43 119	95.06 261	96.99 303	98.68 322	93.02 243	98.53 293	97.43 250	88.33 323	97.43 325

3Dnovator	95.63 14	99.06 120	98.76 141	99.96 46	98.86 274	99.90 60	99.98 212	99.93 26	98.95 31	98.49 245	100.00 1	92.91 244	100.00 1	99.71 126	100.00 1	100.00 1

3Dnovator+	95.58 15	99.03 123	98.71 146	99.96 46	98.99 263	99.89 68	100.00 1	99.51 73	98.96 28	98.32 254	100.00 1	92.78 245	100.00 1	99.87 100	100.00 1	100.00 1

casdiffmvs		98.65 156	98.38 168	99.46 145	99.52 199	98.74 186	100.00 1	99.15 283	96.91 189	99.05 208	100.00 1	92.75 246	99.83 167	99.70 128	98.38 176	99.81 184

NR-MVSNet		96.63 245	96.04 256	98.38 215	98.31 293	98.98 175	99.22 324	99.35 208	95.87 235	94.43 337	99.65 249	92.73 247	98.40 301	96.78 270	88.05 325	97.75 245

COLMAP_ROB	97.10 7	98.29 179	98.17 180	98.65 201	99.94 106	97.39 252	99.30 312	99.40 176	95.64 244	97.75 282	100.00 1	92.69 248	99.95 138	98.89 188	99.92 125	98.62 229
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
EU-MVSNet		96.63 245	96.53 233	96.94 292	97.59 322	96.87 270	99.76 261	99.47 77	96.35 223	96.85 308	99.78 228	92.57 249	96.27 345	95.33 288	91.08 300	97.68 295

MVP-Stereo		96.51 250	96.48 237	96.60 302	95.65 345	94.25 316	98.84 347	98.16 343	95.85 239	95.23 328	99.04 300	92.54 250	99.13 241	92.98 313	99.98 115	96.43 345
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
DeepC-MVS	97.84 5	99.00 129	98.80 138	99.60 128	99.93 108	99.03 169	100.00 1	99.40 176	98.61 55	99.33 190	100.00 1	92.23 251	99.95 138	99.74 120	99.96 120	99.83 175
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DU-MVS		96.93 234	96.49 236	98.22 226	98.31 293	98.41 197	100.00 1	99.37 194	96.41 220	97.76 279	99.65 249	92.14 252	98.50 295	97.98 232	86.84 332	97.75 245

Baseline_NR-MVSNet		96.16 272	95.70 272	97.56 270	98.28 296	96.79 272	100.00 1	97.86 354	91.93 323	97.63 285	99.47 279	92.14 252	98.35 303	97.13 257	86.83 334	97.54 319

cascas		98.43 170	98.07 184	99.50 142	99.65 160	99.02 170	100.00 1	99.22 260	94.21 286	99.72 172	99.98 172	92.03 254	99.93 151	99.68 135	98.12 192	99.54 215

test_djsdf		97.55 206	97.38 208	98.07 239	97.50 326	97.99 227	100.00 1	99.13 292	95.46 256	98.47 246	99.85 213	92.01 255	98.59 287	98.63 203	95.36 234	97.62 312

v8		96.35 261	95.73 271	98.21 228	98.11 304	98.23 214	99.94 231	99.07 312	92.66 319	98.29 256	99.00 305	91.46 256	98.77 273	94.17 302	88.83 321	97.62 312

OpenMVS	95.20 17	98.76 149	98.41 165	99.78 110	98.89 270	99.81 93	99.99 194	99.76 45	98.02 90	98.02 271	100.00 1	91.44 257	100.00 1	99.63 144	99.97 118	99.55 214

v148		96.29 264	95.84 264	97.63 265	97.74 315	96.53 277	100.00 1	99.07 312	93.52 301	98.01 272	99.42 282	91.22 258	98.60 285	96.37 278	87.22 331	97.75 245

GeoE		98.06 186	97.65 200	99.29 163	99.47 214	98.41 197	100.00 1	99.19 270	94.85 265	98.88 216	100.00 1	91.21 259	99.59 192	97.02 260	98.19 189	99.88 161

XVG-OURS		98.30 177	98.36 171	98.13 236	99.58 182	95.91 283	100.00 1	99.36 200	98.69 49	99.23 194	100.00 1	91.20 260	99.92 154	99.34 166	97.82 205	98.56 230

WR-MVS		97.09 224	96.64 229	98.46 210	98.43 287	99.09 164	99.97 218	99.33 215	95.62 246	97.76 279	99.67 245	91.17 261	98.56 292	98.49 210	89.28 316	97.74 268

V42		96.65 244	96.16 252	98.11 238	98.17 303	98.23 214	99.99 194	99.09 306	93.97 291	98.74 229	99.05 299	91.09 262	98.82 266	95.46 287	89.90 309	97.27 331

v10		96.14 274	95.50 280	98.07 239	98.19 301	97.96 229	99.83 246	99.07 312	92.10 322	98.07 267	98.94 311	91.07 263	98.61 283	92.41 319	89.82 310	97.63 310

v7n		96.06 278	95.42 287	97.99 254	97.58 323	97.35 254	99.86 243	99.11 298	92.81 317	97.91 276	99.49 277	90.99 264	98.92 256	92.51 316	88.49 322	97.70 289

v1144		96.51 250	95.97 260	98.13 236	97.98 309	98.04 225	99.99 194	99.08 308	93.51 302	98.62 235	98.98 306	90.98 265	98.62 282	93.79 308	90.79 303	97.74 268

ab-mvs		98.42 171	98.02 187	99.61 126	99.71 146	99.00 174	99.10 338	99.64 63	96.70 202	99.04 209	99.81 222	90.64 266	99.98 113	99.64 141	97.93 198	99.84 172

Anonymous202405211		97.87 192	97.53 203	98.90 188	99.81 122	96.70 274	99.35 308	99.46 92	92.98 313	98.83 223	99.99 169	90.63 267	100.00 1	99.70 128	97.03 219	100.00 1

Anonymous20231206		93.45 305	93.17 305	94.30 324	95.00 350	89.69 344	99.98 212	98.43 340	93.30 308	94.50 336	98.59 325	90.52 268	95.73 350	77.46 360	90.73 305	97.48 324

anonymousdsp		97.16 221	96.88 222	98.00 252	97.08 336	98.06 223	99.81 249	99.15 283	94.58 273	97.84 278	99.62 261	90.49 269	98.60 285	97.98 232	95.32 235	97.33 330

v2v482		96.70 243	96.18 250	98.27 222	98.04 306	98.39 200	100.00 1	99.13 292	94.19 288	98.58 237	99.08 296	90.48 270	98.67 279	95.69 282	90.44 307	97.75 245

v144192		96.40 258	95.81 265	98.17 231	97.89 312	98.11 219	99.99 194	99.06 319	93.39 304	98.75 227	99.09 295	90.43 271	98.66 280	93.10 312	90.55 306	97.75 245

Vis-MVSNet		98.52 166	98.25 174	99.34 159	99.68 152	98.55 194	99.68 276	99.41 174	97.34 160	99.94 137	100.00 1	90.38 272	99.70 187	99.03 182	98.84 158	99.76 199
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CP-MVSNet		96.73 240	96.25 248	98.18 229	98.21 300	98.67 190	99.77 259	99.32 217	95.06 261	97.20 299	99.65 249	90.10 273	98.19 309	98.06 229	88.90 319	97.66 301

TranMVSNet+NR-MVSNet		96.45 254	96.01 257	97.79 263	98.00 308	97.62 246	100.00 1	99.35 208	95.98 233	97.31 296	99.64 253	90.09 274	98.00 326	96.89 265	86.80 335	97.75 245

DSMNet-mixed		95.18 293	95.21 289	95.08 314	96.03 343	90.21 343	99.65 278	93.64 365	92.91 314	98.34 252	97.40 343	90.05 275	95.51 351	91.02 326	97.86 201	99.51 217

N_pmnet		91.88 314	93.37 304	87.40 338	97.24 335	66.33 364	99.90 236	91.05 367	89.77 336	95.65 326	98.58 326	90.05 275	98.11 316	85.39 345	92.72 275	97.75 245

GA-MVS		97.72 197	97.27 215	99.06 175	99.24 241	97.93 232	100.00 1	99.24 253	95.80 240	98.99 211	99.64 253	89.77 277	99.36 232	95.12 293	97.62 214	99.89 150

MIMVSNet		97.06 227	96.73 227	98.05 247	99.38 232	96.64 276	98.47 351	99.35 208	93.41 303	99.48 182	98.53 327	89.66 278	97.70 333	94.16 304	98.11 193	99.80 194

IterMVS-SCA-FT		96.72 242	96.42 240	97.62 267	99.40 228	96.83 271	99.99 194	99.14 288	94.65 272	97.55 291	99.72 233	89.65 279	98.31 305	95.62 285	92.05 285	97.73 275

SCA		98.30 177	97.98 189	99.23 169	99.41 223	98.25 213	99.99 194	99.45 99	96.91 189	99.76 169	99.58 266	89.65 279	99.54 208	98.31 218	98.79 159	99.91 136

IterMVS		96.76 239	96.46 238	97.63 265	99.41 223	96.89 269	99.99 194	99.13 292	94.74 269	97.59 290	99.66 247	89.63 281	98.28 307	95.71 281	92.31 282	97.72 281
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1192		96.18 269	95.49 282	98.26 224	98.01 307	98.15 217	99.99 194	99.08 308	93.36 305	98.54 239	98.97 309	89.47 282	98.89 260	91.15 325	90.82 302	97.75 245

v1921920		96.16 272	95.50 280	98.14 233	97.88 313	97.96 229	99.99 194	99.07 312	93.33 306	98.60 236	99.24 289	89.37 283	98.71 277	91.28 323	90.74 304	97.75 245

XVG-OURS-SEG-HR		98.27 181	98.31 172	98.14 233	99.59 177	95.92 282	100.00 1	99.36 200	98.48 58	99.21 195	100.00 1	89.27 284	99.94 149	99.76 118	99.17 150	98.56 230

test20.03		93.11 307	92.85 309	93.88 329	95.19 349	91.83 335	100.00 1	98.87 335	93.68 297	92.76 342	98.88 315	89.20 285	92.71 359	77.88 358	89.19 317	97.09 334

MDA-MVSNet_test_wron		92.61 309	91.09 317	97.19 283	96.71 339	97.26 260	100.00 1	99.14 288	88.61 339	67.90 363	98.32 335	89.03 286	96.57 340	90.47 331	89.59 312	97.74 268

BH-RMVSNet		98.46 169	98.08 183	99.59 130	99.61 172	99.19 159	100.00 1	99.28 235	97.06 182	98.95 212	100.00 1	88.99 287	99.82 170	98.83 192	100.00 1	99.77 197

v1240		95.96 280	95.25 288	98.07 239	97.91 311	97.87 237	99.96 222	99.07 312	93.24 309	98.64 234	98.96 310	88.98 288	98.61 283	89.58 337	90.92 301	97.75 245

Anonymous20231211		96.29 264	95.70 272	98.07 239	99.80 132	97.49 249	99.15 333	99.40 176	89.11 337	97.75 282	99.45 280	88.93 289	98.98 251	98.26 222	89.47 314	97.73 275

TR-MVS		98.14 184	97.74 195	99.33 160	99.59 177	98.28 211	99.27 313	99.21 266	96.42 219	99.15 200	99.94 200	88.87 290	99.79 174	98.88 189	98.29 183	99.93 131

CL-MVSNet_2432*1600		91.07 318	90.35 321	93.24 330	93.27 353	89.16 346	99.55 290	99.25 248	92.34 320	95.23 328	97.05 344	88.86 291	93.59 357	80.67 353	66.95 356	96.96 337

YYNet1		92.44 310	90.92 318	97.03 286	96.20 341	97.06 267	99.99 194	99.14 288	88.21 342	67.93 362	98.43 332	88.63 292	96.28 344	90.64 327	89.08 318	97.74 268

HQP2-MVS												88.61 293

HQP-MVS		97.73 196	97.85 191	97.39 272	99.07 248	94.82 298	100.00 1	99.40 176	99.04 14	99.17 196	99.97 180	88.61 293	99.57 196	99.79 112	95.58 226	97.77 232

HQP_MVS		97.71 198	97.82 193	97.37 273	99.00 260	94.80 301	100.00 1	99.40 176	99.00 25	99.08 205	99.97 180	88.58 295	99.55 205	99.79 112	95.57 230	97.76 234

plane_prior6					99.06 252	94.80 301						88.58 295

tfpnnormal		96.36 260	95.69 275	98.37 216	98.55 283	98.71 187	99.69 274	99.45 99	93.16 311	96.69 314	99.71 235	88.44 297	98.99 250	94.17 302	91.38 297	97.41 326

TransMVSNet (Re)		94.78 295	93.72 299	97.93 258	98.34 290	97.88 235	99.23 322	97.98 351	91.60 324	94.55 334	99.71 235	87.89 298	98.36 302	89.30 339	84.92 338	97.56 318

DTE-MVSNet		95.52 287	94.99 293	97.08 284	97.49 328	96.45 278	100.00 1	99.25 248	93.82 293	96.17 320	99.57 269	87.81 299	97.18 335	94.57 297	86.26 337	97.62 312

XXY-MVS		97.14 223	96.63 230	98.67 200	98.65 279	98.92 178	99.54 292	99.29 230	95.57 248	97.63 285	99.83 216	87.79 300	99.35 234	98.39 214	92.95 273	97.75 245

UGNet		98.41 172	98.11 181	99.31 162	99.54 189	98.55 194	99.18 325	100.00 1	98.64 54	99.79 165	99.04 300	87.61 301	100.00 1	99.30 170	99.89 127	99.40 219
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
CLD-MVS		97.64 200	97.74 195	97.36 274	99.01 256	94.76 306	100.00 1	99.34 213	99.30 4	99.00 210	99.97 180	87.49 302	99.57 196	99.96 84	95.58 226	97.75 245
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
PEN-MVS		96.01 279	95.48 284	97.58 269	97.74 315	97.26 260	99.90 236	99.29 230	94.55 274	96.79 310	99.55 271	87.38 303	97.84 331	96.92 264	87.24 330	97.65 306

ACMM	97.17 6	97.37 213	97.40 207	97.29 278	99.01 256	94.64 309	100.00 1	99.25 248	98.07 87	98.44 248	99.98 172	87.38 303	99.55 205	99.25 172	95.19 243	97.69 293
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
jajsoiax		97.07 226	96.79 226	97.89 260	97.28 334	97.12 264	99.95 227	99.19 270	96.55 212	97.31 296	99.69 240	87.35 305	98.91 257	98.70 198	95.12 248	97.66 301

pmmvs5		95.94 281	95.61 277	96.95 291	97.42 331	94.66 307	100.00 1	98.08 347	93.60 300	97.05 301	99.43 281	87.02 306	98.46 299	95.76 280	92.12 284	97.72 281

VPA-MVSNet		97.03 229	96.43 239	98.82 193	98.64 280	99.32 144	99.38 305	99.47 77	96.73 199	98.91 215	98.94 311	87.00 307	99.40 230	99.23 173	89.59 312	97.76 234

PS-CasMVS		96.34 262	95.78 268	98.03 251	98.18 302	98.27 212	99.71 270	99.32 217	94.75 267	96.82 309	99.65 249	86.98 308	98.15 311	97.74 240	88.85 320	97.66 301

LPG-MVS_test		97.31 215	97.32 211	97.28 279	98.85 275	94.60 310	100.00 1	99.37 194	97.35 158	98.85 219	99.98 172	86.66 309	99.56 200	99.55 151	95.26 238	97.70 289

LGP-MVS_train				97.28 279	98.85 275	94.60 310		99.37 194	97.35 158	98.85 219	99.98 172	86.66 309	99.56 200	99.55 151	95.26 238	97.70 289

mvs_tets		97.00 231	96.69 228	97.94 256	97.41 333	97.27 259	99.60 286	99.18 276	96.51 216	97.35 295	99.69 240	86.53 311	98.91 257	98.84 190	95.09 249	97.65 306

pm-mvs1		95.76 284	95.01 292	98.00 252	98.23 299	97.45 250	99.24 316	99.04 324	93.13 312	95.93 324	99.72 233	86.28 312	98.84 265	95.62 285	87.92 326	97.72 281

DIV-MVS_2432*1600		91.16 317	90.09 322	94.35 323	94.44 352	91.27 340	99.74 263	99.08 308	90.82 330	94.53 335	94.91 353	86.11 313	94.78 353	82.67 350	68.52 355	96.99 336

MVS-HIRNet		94.12 301	92.73 311	98.29 221	99.33 234	95.95 281	99.38 305	99.19 270	74.54 357	98.26 260	86.34 362	86.07 314	99.06 244	91.60 322	99.87 130	99.85 171

DeepMVS_CX				89.98 335	98.90 269	71.46 360		99.18 276	97.61 131	96.92 304	99.83 216	86.07 314	99.83 167	96.02 279	97.65 213	98.65 228

OPM-MVS		97.21 218	97.18 218	97.32 277	98.08 305	94.66 307	100.00 1	99.28 235	98.65 53	98.92 213	99.98 172	86.03 316	99.56 200	98.28 221	95.41 232	97.72 281

new_pmnet		94.11 302	93.47 303	96.04 310	96.60 340	92.82 329	99.97 218	98.91 334	90.21 333	95.26 327	98.05 338	85.89 317	98.14 312	84.28 347	92.01 286	97.16 333

ACMP	97.00 8	97.19 219	97.16 219	97.27 281	98.97 265	94.58 313	100.00 1	99.32 217	97.97 96	97.45 293	99.98 172	85.79 318	99.56 200	99.70 128	95.24 241	97.67 300
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
OurMVSNet-221017-0		96.14 274	95.98 259	96.62 301	97.49 328	93.44 323	99.92 233	98.16 343	95.86 237	97.65 284	99.95 196	85.71 319	98.78 270	94.93 295	94.18 261	97.64 309

SixPastTwentyTwo		95.71 285	95.49 282	96.38 305	97.42 331	93.01 326	99.84 245	98.23 342	94.75 267	95.98 323	99.97 180	85.35 320	98.43 300	94.71 296	93.17 270	97.69 293

LTVRE_ROB	95.29 16	96.32 263	96.10 253	96.99 289	98.55 283	93.88 319	99.45 298	99.28 235	94.50 278	96.46 316	99.52 274	84.86 321	99.48 220	97.26 256	95.03 250	97.59 316
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
ITE_SJBPF				96.84 297	98.96 266	93.49 322		98.12 345	98.12 84	98.35 251	99.97 180	84.45 322	99.56 200	95.63 284	95.25 240	97.49 322

PVSNet_0	93.57 19	96.41 255	95.74 270	98.41 213	99.84 117	95.22 291	100.00 1	100.00 1	98.08 86	97.55 291	99.78 228	84.40 323	100.00 1	100.00 1	81.99 342	100.00 1

K. test v3		95.46 289	95.14 290	96.40 304	97.53 325	93.40 324	99.99 194	99.23 257	95.49 254	92.70 344	99.73 231	84.26 324	98.12 314	93.94 307	93.38 268	97.68 295

GBi-Net		96.07 276	95.80 266	96.89 294	99.53 192	94.87 295	99.18 325	99.27 243	93.71 294	98.53 240	98.81 317	84.23 325	98.07 321	95.31 289	93.60 263	97.72 281

test1		96.07 276	95.80 266	96.89 294	99.53 192	94.87 295	99.18 325	99.27 243	93.71 294	98.53 240	98.81 317	84.23 325	98.07 321	95.31 289	93.60 263	97.72 281

FMVSNet2		96.22 267	95.60 278	98.06 243	99.53 192	98.33 207	99.45 298	99.27 243	93.71 294	98.03 269	98.84 316	84.23 325	98.10 319	93.97 306	93.40 267	97.73 275

testgi		96.18 269	95.93 261	96.93 293	98.98 264	94.20 318	100.00 1	99.07 312	97.16 172	96.06 322	99.86 211	84.08 328	97.79 332	90.38 332	97.80 207	98.81 226

Anonymous20240521		93.29 306	92.76 310	94.90 319	95.64 346	91.27 340	99.97 218	98.82 337	87.04 345	94.71 332	98.19 337	83.86 329	96.80 337	84.04 348	92.56 280	96.64 342

ACMH	96.25 11	96.77 238	96.62 231	97.21 282	98.96 266	94.43 315	99.64 279	99.33 215	97.43 153	96.55 315	99.97 180	83.52 330	99.54 208	99.07 181	95.13 247	97.66 301
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Anonymous20240529		96.93 234	96.22 249	99.05 177	99.79 135	97.30 258	99.16 331	99.47 77	88.51 340	98.69 230	100.00 1	83.50 331	100.00 1	99.83 106	97.02 220	99.83 175

lessismore_v0				96.05 309	97.55 324	91.80 336		99.22 260		91.87 345	99.91 205	83.50 331	98.68 278	92.48 317	90.42 308	97.68 295

UnsupCasMVSNet_eth		94.25 298	93.89 297	95.34 313	97.63 318	92.13 333	99.73 267	99.36 200	94.88 264	92.78 341	98.63 324	82.72 333	96.53 341	94.57 297	84.73 339	97.36 328

LF4IMVS		96.19 268	96.18 250	96.23 308	98.26 297	92.09 334	100.00 1	97.89 353	97.82 110	97.94 274	99.87 209	82.71 334	99.38 231	97.41 251	93.71 262	97.20 332

ACMH+	96.20 13	96.49 253	96.33 246	97.00 288	99.06 252	93.80 320	99.81 249	99.31 222	97.32 162	95.89 325	99.97 180	82.62 335	99.54 208	98.34 217	94.63 256	97.65 306

pmmvs3		90.62 321	89.36 325	94.40 322	90.53 358	91.49 338	100.00 1	96.73 359	84.21 350	93.65 340	96.65 346	82.56 336	94.83 352	82.28 351	77.62 349	96.89 339

XVG-ACMP-BASELINE		96.60 247	96.52 235	96.84 297	98.41 288	93.29 325	99.99 194	99.32 217	97.76 115	98.51 243	99.29 287	81.95 337	99.54 208	98.40 213	95.03 250	97.68 295

VPNet		96.41 255	95.76 269	98.33 219	98.61 281	98.30 210	99.48 297	99.45 99	96.98 184	98.87 218	99.88 208	81.57 338	98.93 255	99.22 175	87.82 327	97.76 234

MDA-MVSNet-bldmvs		91.65 316	89.94 323	96.79 299	96.72 338	96.70 274	99.42 302	98.94 330	88.89 338	66.97 365	98.37 333	81.43 339	95.91 348	89.24 340	89.46 315	97.75 245

MS-PatchMatch		95.66 286	95.87 263	95.05 315	97.80 314	89.25 345	98.88 346	99.30 225	96.35 223	96.86 307	99.01 304	81.35 340	99.43 227	93.30 311	99.98 115	96.46 344

USDC		95.90 282	95.70 272	96.50 303	98.60 282	92.56 332	100.00 1	98.30 341	97.77 113	96.92 304	99.94 200	81.25 341	99.45 226	93.54 310	94.96 253	97.49 322

TDRefinement		91.93 312	90.48 320	96.27 307	81.60 363	92.65 331	99.10 338	97.61 356	93.96 292	93.77 339	99.85 213	80.03 342	99.53 213	97.82 239	70.59 354	96.63 343

test_0402		94.35 297	93.70 300	96.32 306	97.92 310	93.60 321	99.61 285	98.85 336	88.19 343	94.68 333	99.48 278	80.01 343	98.58 289	89.39 338	95.15 246	96.77 340

TinyColmap		95.50 288	95.12 291	96.64 300	98.69 278	93.00 327	99.40 303	97.75 355	96.40 221	96.14 321	99.87 209	79.47 344	99.50 217	93.62 309	94.72 255	97.40 327

LFMVS		97.42 211	96.62 231	99.81 100	99.80 132	99.50 124	99.16 331	99.56 66	94.48 279	100.00 1	100.00 1	79.35 345	100.00 1	99.89 98	97.37 215	99.94 124

pmmvs-eth3d		91.73 315	90.67 319	94.92 318	91.63 356	92.71 330	99.90 236	98.54 339	91.19 327	88.08 351	95.50 348	79.31 346	96.13 346	90.55 330	81.32 345	95.91 350

new-patchmatchnet		90.30 322	89.46 324	92.84 332	90.77 357	88.55 348	99.83 246	98.80 338	90.07 335	87.86 352	95.00 351	78.77 347	94.30 355	84.86 346	79.15 347	95.68 351

test123		79.44 327	79.23 331	80.05 343	80.03 364	71.72 359	100.00 1	77.93 372	62.52 360	94.81 331	99.69 240	78.21 348	74.53 366	92.57 315	27.33 366	93.90 352

CMPMVS	66.12 22	90.65 320	92.04 313	86.46 339	96.18 342	66.87 363	98.03 354	99.38 192	83.38 352	85.49 355	99.55 271	77.59 349	98.80 269	94.44 299	94.31 260	93.72 354
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs6		93.64 303	92.87 308	95.94 311	97.47 330	91.41 339	98.92 344	99.02 325	87.84 344	95.01 330	99.61 263	77.24 350	98.77 273	94.33 300	86.41 336	97.63 310

tmp_tt		75.80 331	74.26 334	80.43 342	52.91 372	53.67 370	87.42 362	97.98 351	61.80 361	67.04 364	100.00 1	76.43 351	96.40 342	96.47 274	28.26 365	91.23 356

MIMVSNet1		91.96 311	91.20 314	94.23 326	94.94 351	91.69 337	99.34 309	99.22 260	88.23 341	94.18 338	98.45 330	75.52 352	93.41 358	79.37 356	91.49 294	97.60 315

VDDNet		96.39 259	95.55 279	98.90 188	99.27 238	97.45 250	99.15 333	99.92 30	91.28 326	99.98 104	100.00 1	73.55 353	100.00 1	99.85 103	96.98 221	99.24 220

FMVSNet1		94.45 296	93.63 301	96.89 294	98.87 273	94.87 295	99.18 325	99.27 243	90.95 329	97.31 296	98.81 317	72.89 354	98.07 321	92.61 314	92.81 274	97.72 281

VDD-MVS		96.58 248	95.99 258	98.34 218	99.52 199	95.33 289	99.18 325	99.38 192	96.64 208	99.77 167	100.00 1	72.51 355	100.00 1	100.00 1	96.94 222	99.70 206

EG-PatchMatch MVS		92.94 308	92.49 312	94.29 325	95.87 344	87.07 350	99.07 342	98.11 346	93.19 310	88.98 350	98.66 323	70.89 356	99.08 243	92.43 318	95.21 242	96.72 341

test_method		91.04 319	91.10 316	90.85 334	98.34 290	77.63 357	100.00 1	98.93 333	76.69 355	96.25 319	98.52 328	70.44 357	97.98 327	89.02 341	91.74 290	96.92 338

UniMVSNet_ETH3D		95.28 291	94.41 296	97.89 260	98.91 268	95.14 292	99.13 335	99.35 208	92.11 321	97.17 300	99.66 247	70.28 358	99.36 232	97.88 237	95.18 244	99.16 221

OpenMVS_ROB	88.34 20	91.89 313	91.12 315	94.19 327	95.55 347	87.63 349	99.26 314	98.03 348	86.61 346	90.65 349	96.82 345	70.14 359	98.78 270	86.54 344	96.50 225	96.15 346

MVS_0304		95.11 294	94.84 295	95.91 312	99.60 174	91.24 342	98.64 349	99.88 33	94.51 277	99.62 177	98.31 336	69.19 360	98.82 266	95.22 292	98.60 162	97.66 301

testmvs		80.17 326	81.95 329	74.80 345	58.54 370	59.58 369	100.00 1	87.14 370	76.09 356	99.61 178	100.00 1	67.06 361	74.19 367	98.84 190	50.30 361	90.64 357

UnsupCasMVSNet_bld		89.50 323	88.00 326	93.99 328	95.30 348	88.86 347	98.52 350	99.28 235	85.50 348	87.80 353	94.11 354	61.63 362	96.96 336	90.63 328	79.26 346	96.15 346

Gipuma		84.73 325	83.50 328	88.40 337	97.50 326	82.21 355	88.87 360	99.05 321	65.81 359	85.71 354	90.49 357	53.70 363	96.31 343	78.64 357	91.74 290	86.67 358
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
EMVS		69.88 333	69.09 336	72.24 349	84.70 360	65.82 365	99.96 222	87.08 371	49.82 366	71.51 361	84.74 364	49.30 364	75.32 365	50.97 366	43.71 363	75.59 363

PM-MVS		88.39 324	87.41 327	91.31 333	91.73 355	82.02 356	99.79 253	96.62 360	91.06 328	90.71 348	95.73 347	48.60 365	95.96 347	90.56 329	81.91 344	95.97 349

E-PMN		70.72 332	70.06 335	72.69 348	83.92 361	65.48 366	99.95 227	92.72 366	49.88 365	72.30 360	86.26 363	47.17 366	77.43 364	53.83 365	44.49 362	75.17 364

ambc				88.45 336	86.84 359	70.76 361	97.79 356	98.02 350		90.91 347	95.14 349	38.69 367	98.51 294	94.97 294	84.23 340	96.09 348

FPMVS		77.92 330	79.45 330	73.34 347	76.87 366	46.81 371	98.24 352	99.05 321	59.89 362	73.55 359	98.34 334	36.81 368	86.55 361	80.96 352	91.35 298	86.65 359

PMMVS2		79.15 328	77.28 332	84.76 341	82.34 362	72.66 358	99.70 272	95.11 364	71.68 358	84.78 356	90.87 356	32.05 369	89.99 360	75.53 361	63.45 359	91.64 355

LCM-MVSNet		79.01 329	76.93 333	85.27 340	78.28 365	68.01 362	96.57 357	98.03 348	55.10 363	82.03 357	93.27 355	31.99 370	93.95 356	82.72 349	74.37 351	93.84 353

ANet_high		66.05 335	63.44 339	73.88 346	61.14 369	63.45 367	95.68 359	87.18 369	79.93 353	47.35 367	80.68 367	22.35 371	72.33 368	61.24 363	35.42 364	85.88 360

wuyk23d		28.28 337	29.73 341	23.92 351	75.89 367	32.61 373	66.50 363	12.88 373	16.09 368	14.59 369	16.59 369	12.35 372	32.36 369	39.36 367	13.36 367	6.79 365

PMVS	60.66 23	65.98 336	65.05 337	68.75 350	55.06 371	38.40 372	88.19 361	96.98 357	48.30 367	44.82 368	88.52 360	12.22 373	86.49 362	67.58 362	83.79 341	81.35 362
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	68.59 21	67.22 334	64.68 338	74.84 344	74.67 368	62.32 368	95.84 358	90.87 368	50.98 364	58.72 366	81.05 366	12.20 374	78.95 363	61.06 364	56.75 360	83.24 361
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
uanet_test		0.01 341	0.02 344	0.00 352	0.00 373	0.00 374	0.00 364	0.00 374	0.00 369	0.00 370	0.14 370	0.00 375	0.00 370	0.00 368	0.00 368	0.00 366

sosnet-low-res		0.01 341	0.02 344	0.00 352	0.00 373	0.00 374	0.00 364	0.00 374	0.00 369	0.00 370	0.14 370	0.00 375	0.00 370	0.00 368	0.00 368	0.00 366

sosnet		0.01 341	0.02 344	0.00 352	0.00 373	0.00 374	0.00 364	0.00 374	0.00 369	0.00 370	0.14 370	0.00 375	0.00 370	0.00 368	0.00 368	0.00 366

uncertanet		0.01 341	0.02 344	0.00 352	0.00 373	0.00 374	0.00 364	0.00 374	0.00 369	0.00 370	0.14 370	0.00 375	0.00 370	0.00 368	0.00 368	0.00 366

Regformer		0.01 341	0.02 344	0.00 352	0.00 373	0.00 374	0.00 364	0.00 374	0.00 369	0.00 370	0.14 370	0.00 375	0.00 370	0.00 368	0.00 368	0.00 366

ab-mvs-re		8.33 339	11.11 342	0.00 352	0.00 373	0.00 374	0.00 364	0.00 374	0.00 369	0.00 370	100.00 1	0.00 375	0.00 370	0.00 368	0.00 368	0.00 366

uanet		0.01 341	0.02 344	0.00 352	0.00 373	0.00 374	0.00 364	0.00 374	0.00 369	0.00 370	0.14 370	0.00 375	0.00 370	0.00 368	0.00 368	0.00 366

eth-test2					0.00 373

eth-test					0.00 373

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

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

GSMVS																99.91 136

test_part2					100.00 1	99.99 3				100.00 1

MTGPA								99.42 127

MTMP							100.00 1	99.18 276

gm-plane-assit					99.52 199	97.26 260			95.86 237		100.00 1		99.43 227	98.76 195

test9_res														100.00 1	100.00 1	100.00 1

agg_prior2														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

test_prior4						99.93 42	100.00 1

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

旧先验2							100.00 1		98.11 85	100.00 1			100.00 1	99.67 137

新几何2							100.00 1

无先验							100.00 1	99.80 39	97.98 94				100.00 1	99.33 167		100.00 1

原ACMM2							100.00 1

testdata2													100.00 1	97.36 253

testdata1							100.00 1		98.77 48

plane_prior7					99.00 260	94.78 305

plane_prior5								99.40 176					99.55 205	99.79 112	95.57 230	97.76 234

plane_prior4											99.97 180

plane_prior3						94.79 304			99.03 19	99.08 205

plane_prior2							100.00 1		99.00 25

plane_prior1					99.02 255

plane_prior						94.80 301	100.00 1		99.03 19						95.58 226

n2								0.00 374

nn								0.00 374

door-mid								96.32 362

test11								99.42 127

door								96.13 363

HQP5-MVS						94.82 298

HQP-NCC					99.07 248		100.00 1		99.04 14	99.17 196

ACMP_Plane					99.07 248		100.00 1		99.04 14	99.17 196

BP-MVS														99.79 112

HQP4-MVS										99.17 196			99.57 196			97.77 232

HQP3-MVS								99.40 176							95.58 226

NP-MVS					99.07 248	94.81 300					99.97 180

ACMMP++_ref															94.58 258

ACMMP++															95.17 245

High-resolution multi-view benchmark