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				99.59 132	99.54 194	99.49 132	99.17 332	99.52 69		99.96 117	99.68 247	100.00 1	99.33 239	99.71 129	99.99 111	99.96 120

gg-mvs-nofinetune		96.95 234	96.10 254	99.50 144	99.41 227	99.36 146	99.07 344	99.52 69	83.69 355	99.96 117	83.60 368	100.00 1	99.20 243	99.68 138	99.99 111	99.96 120

CHOSEN 280x420		99.85 3	99.87 1	99.80 107	99.99 59	99.97 22	99.97 220	99.98 13	98.96 28	100.00 1	100.00 1	99.96 3	99.42 233	100.00 1	100.00 1	100.00 1

test_yl		99.51 76	99.37 83	99.95 61	99.82 123	99.90 65	100.00 1	99.47 77	97.48 152	100.00 1	100.00 1	99.80 4	100.00 1	99.98 79	97.75 213	99.94 128

DCV-MVSNet		99.51 76	99.37 83	99.95 61	99.82 123	99.90 65	100.00 1	99.47 77	97.48 152	100.00 1	100.00 1	99.80 4	100.00 1	99.98 79	97.75 213	99.94 128

MVSTER		98.58 161	98.52 161	98.77 199	99.65 165	99.68 118	100.00 1	99.29 235	95.63 249	98.65 236	99.80 228	99.78 6	98.88 267	98.59 211	95.31 240	97.73 279

CDS-MVSNet		98.96 136	98.95 125	99.01 184	99.48 215	98.36 210	99.93 234	99.37 199	96.79 198	99.31 195	99.83 219	99.77 7	98.91 261	98.07 232	97.98 200	99.77 201
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PAPM		99.78 17	99.76 12	99.85 96	99.01 261	99.95 34	100.00 1	99.75 49	99.37 3	99.99 103	100.00 1	99.76 8	99.60 194	100.00 1	100.00 1	100.00 1

DP-MVS Recon		99.76 20	99.69 23	99.98 25	100.00 1	99.95 34	100.00 1	99.52 69	97.99 96	99.99 103	100.00 1	99.72 9	100.00 1	99.96 88	100.00 1	100.00 1

TESTMET0.1,1		99.08 119	98.96 121	99.44 149	99.63 172	99.38 142	100.00 1	99.45 99	95.53 253	99.48 186	100.00 1	99.71 10	99.02 250	96.84 270	99.99 111	99.91 139

test-mter		98.96 136	98.82 136	99.40 155	99.40 232	99.28 151	100.00 1	99.45 99	95.44 262	99.42 189	99.12 296	99.70 11	99.01 252	96.82 271	99.99 111	99.91 139

MSP-MVS		99.81 11	99.77 9	99.94 76	100.00 1	99.86 89	100.00 1	99.42 127	98.87 39	100.00 1	100.00 1	99.65 12	99.96 136	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 47	100.00 1	100.00 1	99.61 13	100.00 1	100.00 1	100.00 1	100.00 1

test_one_0601					100.00 1	99.99 5		99.42 127	98.72 51	100.00 1	100.00 1	99.60 14

MSLP-MVS++		99.89 1	99.85 2	99.99 12	100.00 1	99.96 25	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 27	99.66 34	99.99 12	100.00 1	99.96 25	100.00 1	99.47 77	97.87 111	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 5	100.00 1	99.71 60	99.07 9	100.00 1	100.00 1	99.59 17	100.00 1	100.00 1	100.00 1	100.00 1

PLC	98.56 2	99.70 40	99.74 16	99.58 136	100.00 1	98.79 186	100.00 1	99.54 67	98.58 59	99.96 117	100.00 1	99.59 17	100.00 1	100.00 1	100.00 1	99.94 128
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
DPM-MVS		99.63 63	99.51 71	100.00 1	99.90 119	100.00 1	100.00 1	99.43 119	99.00 25	100.00 1	100.00 1	99.58 19	100.00 1	97.64 247	100.00 1	100.00 1

test_241102_TWO								99.42 127	99.03 19	100.00 1	100.00 1	99.56 20	100.00 1	100.00 1	100.00 1	100.00 1

APDe-MVS		99.84 6	99.78 6	99.99 12	100.00 1	99.98 17	100.00 1	99.44 109	99.06 11	100.00 1	100.00 1	99.56 20	99.99 93	100.00 1	100.00 1	100.00 1

segment_acmp												99.55 22

DVP-MVS++.		99.81 11	99.75 14	100.00 1	100.00 1	99.99 5	100.00 1	99.42 127	98.79 47	100.00 1	100.00 1	99.54 23	100.00 1	100.00 1	100.00 1	100.00 1

PC_three_1452									98.80 45	100.00 1	100.00 1	99.54 23	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 23	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 5	100.00 1	99.42 127	99.04 14	100.00 1	100.00 1	99.53 26	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 5	100.00 1	99.42 127	99.04 14	100.00 1	100.00 1	99.53 26

TEST9					100.00 1	99.95 34	100.00 1	99.42 127	97.65 129	100.00 1	100.00 1	99.53 26	99.97 122

DWT-MVSNet_test		99.22 112	99.22 99	99.22 174	99.56 193	97.98 233	99.89 241	99.39 194	97.20 175	99.96 117	100.00 1	99.52 29	99.82 174	99.11 183	98.34 182	99.87 172

SteuartSystems-ACMMP		99.78 17	99.71 20	99.98 25	99.76 147	99.95 34	100.00 1	99.42 127	98.69 52	100.00 1	100.00 1	99.52 29	99.99 93	100.00 1	100.00 1	100.00 1
Skip Steuart: Steuart Systems R&D Blog.
train_agg		99.71 36	99.63 43	99.97 34	100.00 1	99.95 34	100.00 1	99.42 127	97.70 124	100.00 1	100.00 1	99.51 31	99.97 122	100.00 1	100.00 1	100.00 1

test_8					100.00 1	99.91 57	100.00 1	99.42 127	97.70 124	100.00 1	100.00 1	99.51 31	99.98 117

agg_prior1		99.71 36	99.63 43	99.95 61	100.00 1	99.88 83	100.00 1	99.42 127	97.72 120	100.00 1	100.00 1	99.51 31	99.97 122	100.00 1	100.00 1	100.00 1

PAPR		99.76 20	99.68 26	99.99 12	100.00 1	99.96 25	100.00 1	99.47 77	98.16 81	100.00 1	100.00 1	99.51 31	100.00 1	100.00 1	100.00 1	100.00 1

EPP-MVSNet		99.10 118	99.00 117	99.40 155	99.51 209	98.68 193	99.92 235	99.43 119	95.47 259	99.65 179	100.00 1	99.51 31	99.76 182	99.53 160	98.00 199	99.75 204

SED-MVS		99.83 7	99.77 9	100.00 1	100.00 1	99.99 5	100.00 1	99.42 127	99.03 19	100.00 1	100.00 1	99.50 36	100.00 1	100.00 1	100.00 1	100.00 1

test_241102_ONE					100.00 1	99.99 5		99.42 127	99.03 19	100.00 1	100.00 1	99.50 36	100.00 1

miper_enhance_ethall		98.33 176	98.27 174	98.51 211	99.66 164	99.04 172	100.00 1	99.22 264	97.53 147	98.51 247	99.38 286	99.49 38	98.75 279	98.02 234	92.61 280	97.76 238

test_post											89.05 362	99.49 38	99.59 196

HyFIR lowres test		99.32 98	99.24 95	99.58 136	99.95 108	99.26 153	100.00 1	99.99 10	96.72 204	99.29 196	99.91 208	99.49 38	99.47 225	99.74 124	98.08 198	100.00 1

PatchmatchNet		99.03 124	98.96 121	99.26 170	99.49 213	98.33 212	99.38 307	99.45 99	96.64 212	99.96 117	99.58 269	99.49 38	99.50 221	97.63 248	99.00 160	99.93 135
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
Patchmatch-test		97.83 195	97.42 206	99.06 178	99.08 252	97.66 250	98.66 350	99.21 270	93.65 302	98.25 265	99.58 269	99.47 42	99.57 200	90.25 337	98.59 167	99.95 125

test_prior3		99.81 11	99.78 6	99.90 85	100.00 1	99.75 106	100.00 1	99.73 54	98.82 43	100.00 1	100.00 1	99.47 42	99.97 122	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 42		100.00 1	100.00 1

TSAR-MVS + MP.		99.82 9	99.77 9	99.99 12	100.00 1	99.96 25	100.00 1	99.43 119	99.05 13	100.00 1	100.00 1	99.45 45	99.99 93	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 91	99.27 91	99.69 120	99.59 182	99.41 140	100.00 1	99.46 92	96.46 221	99.90 150	100.00 1	99.44 46	99.85 168	98.97 187	99.58 149	99.80 198

tttt0517		99.34 95	99.23 98	99.67 122	99.57 190	99.38 142	100.00 1	99.46 92	96.33 229	99.89 153	100.00 1	99.44 46	99.84 170	98.93 189	99.46 153	99.78 200

thisisatest0515		99.42 86	99.31 90	99.74 116	99.59 182	99.55 126	100.00 1	99.46 92	96.65 211	99.92 147	100.00 1	99.44 46	99.85 168	99.09 184	99.63 147	99.81 188

PGM-MVS		99.69 44	99.61 48	99.95 61	99.99 59	99.85 92	100.00 1	99.58 65	97.69 126	100.00 1	100.00 1	99.44 46	100.00 1	99.79 116	100.00 1	100.00 1

SMA-MVS		99.69 44	99.59 51	99.98 25	99.99 59	99.93 47	100.00 1	99.43 119	97.50 151	100.00 1	100.00 1	99.43 50	100.00 1	100.00 1	100.00 1	100.00 1
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
原ACMM1				99.93 79	100.00 1	99.80 100		99.66 62	98.18 80	100.00 1	100.00 1	99.43 50	100.00 1	99.50 162	100.00 1	100.00 1

testdata				99.66 124	99.99 59	98.97 181		99.73 54	97.96 104	100.00 1	100.00 1	99.42 52	100.00 1	99.28 175	100.00 1	100.00 1

baseline2		98.99 132	98.93 127	99.18 176	99.26 245	99.15 165	100.00 1	99.46 92	96.71 205	96.79 314	100.00 1	99.42 52	99.25 242	98.75 200	99.94 127	99.15 226

patchmatchnet-post											97.79 342	99.41 54	99.54 212

Patchmatch-RL test		93.49 305	93.63 302	93.05 334	91.78 359	83.41 359	98.21 355	96.95 363	91.58 329	91.05 350	97.64 345	99.40 55	95.83 353	94.11 309	81.95 347	99.91 139

1121		99.63 63	99.51 71	99.99 12	99.99 59	99.96 25	99.24 318	99.74 52	97.89 108	100.00 1	100.00 1	99.39 56	100.00 1	99.33 171	100.00 1	100.00 1

CNVR-MVS		99.85 3	99.80 4	100.00 1	100.00 1	99.99 5	100.00 1	99.77 44	99.07 9	100.00 1	100.00 1	99.39 56	100.00 1	100.00 1	100.00 1	100.00 1

sam_mvs												99.33 58

test_post1							99.32 312				88.24 364	99.33 58	99.59 196	98.31 222

SD-MVS		99.81 11	99.75 14	99.99 12	99.99 59	99.96 25	100.00 1	99.42 127	99.01 24	100.00 1	100.00 1	99.33 58	100.00 1	100.00 1	100.00 1	100.00 1
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
ZD-MVS					100.00 1	99.98 17		99.80 39	97.31 168	100.00 1	100.00 1	99.32 61	99.99 93	100.00 1	100.00 1

CDPH-MVS		99.73 30	99.64 39	99.99 12	100.00 1	99.97 22	100.00 1	99.42 127	98.02 94	100.00 1	100.00 1	99.32 61	99.99 93	100.00 1	100.00 1	100.00 1

HFP-MVS		99.74 27	99.67 31	99.96 49	100.00 1	99.89 73	100.00 1	99.76 45	97.95 105	100.00 1	100.00 1	99.31 63	100.00 1	99.99 62	100.00 1	100.00 1

#test#		99.75 22	99.68 26	99.96 49	100.00 1	99.89 73	100.00 1	99.76 45	98.07 91	100.00 1	100.00 1	99.31 63	100.00 1	99.99 62	100.00 1	100.00 1

test-LLR		99.03 124	98.91 129	99.40 155	99.40 232	99.28 151	100.00 1	99.45 99	96.70 206	99.42 189	99.12 296	99.31 63	99.01 252	96.82 271	99.99 111	99.91 139

test0.0.03 1		98.12 186	98.03 187	98.39 217	99.11 249	98.07 227	100.00 1	99.93 26	96.70 206	96.91 310	99.95 199	99.31 63	98.19 313	91.93 324	98.44 174	98.91 229

MCST-MVS		99.85 3	99.80 4	100.00 1	100.00 1	99.99 5	100.00 1	99.73 54	99.19 5	100.00 1	100.00 1	99.31 63	100.00 1	100.00 1	100.00 1	100.00 1

TAPA-MVS	96.40 10	97.64 201	97.37 210	98.45 214	99.94 111	95.70 291	100.00 1	99.40 181	97.65 129	99.53 183	100.00 1	99.31 63	99.66 192	80.48 358	100.00 1	100.00 1
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
test222					99.99 59	99.90 65	100.00 1	99.69 61	97.66 128	100.00 1	100.00 1	99.30 69			100.00 1	100.00 1

sam_mvs1												99.29 70				99.91 139

ACMMPR		99.74 27	99.67 31	99.96 49	100.00 1	99.89 73	100.00 1	99.76 45	97.95 105	100.00 1	100.00 1	99.29 70	100.00 1	99.99 62	100.00 1	100.00 1

CostFormer		98.84 146	98.77 141	99.04 182	99.41 227	97.58 252	99.67 279	99.35 213	94.66 275	99.96 117	99.36 287	99.28 72	99.74 185	99.41 166	97.81 210	99.81 188

旧先验1					99.99 59	99.88 83		99.82 36			100.00 1	99.27 73			100.00 1	100.00 1

APD-MVS		99.68 48	99.58 55	99.97 34	99.99 59	99.96 25	100.00 1	99.42 127	97.53 147	100.00 1	100.00 1	99.27 73	99.97 122	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		98.32 177	98.23 178	98.60 206	99.65 165	99.23 160	100.00 1	99.09 311	95.65 247	98.07 271	99.93 206	99.25 75	98.82 270	98.90 191	93.50 270	97.75 249

test12				99.95 61	99.99 59	99.89 73		99.42 127		100.00 1		99.24 76	99.97 122		100.00 1	100.00 1

testtj		99.70 40	99.61 48	99.99 12	100.00 1	99.98 17	100.00 1	99.53 68	97.71 123	100.00 1	100.00 1	99.23 77	100.00 1	100.00 1	100.00 1	100.00 1

Regformer-1		99.75 22	99.68 26	99.98 25	100.00 1	99.94 42	100.00 1	99.44 109	98.43 64	100.00 1	100.00 1	99.23 77	99.99 93	99.99 62	100.00 1	100.00 1

DPE-MVS		99.79 15	99.73 17	99.99 12	99.99 59	99.98 17	100.00 1	99.42 127	98.91 35	100.00 1	100.00 1	99.22 79	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 22	99.68 26	99.97 34	100.00 1	99.94 42	100.00 1	99.44 109	98.42 66	100.00 1	100.00 1	99.22 79	99.99 93	99.99 62	100.00 1	100.00 1

HPM-MVS++		99.82 9	99.76 12	99.99 12	99.99 59	99.98 17	100.00 1	99.83 35	98.88 37	99.96 117	100.00 1	99.21 81	100.00 1	100.00 1	100.00 1	99.99 110

新几何1				99.99 12	100.00 1	99.96 25		99.81 38	97.89 108	100.00 1	100.00 1	99.20 82	100.00 1	97.91 240	100.00 1	100.00 1

region2R		99.72 33	99.64 39	99.97 34	100.00 1	99.90 65	100.00 1	99.74 52	97.86 112	100.00 1	100.00 1	99.19 83	100.00 1	99.99 62	100.00 1	100.00 1

F-COLMAP		99.64 59	99.64 39	99.67 122	99.99 59	99.07 168	100.00 1	99.44 109	98.30 77	99.90 150	100.00 1	99.18 84	99.99 93	99.91 99	100.00 1	99.94 128

XVS		99.79 15	99.73 17	99.98 25	100.00 1	99.94 42	100.00 1	99.75 49	98.67 54	100.00 1	100.00 1	99.16 85	100.00 1	100.00 1	100.00 1	100.00 1

X-MVStestdata		97.04 229	96.06 256	99.98 25	100.00 1	99.94 42	100.00 1	99.75 49	98.67 54	100.00 1	66.97 371	99.16 85	100.00 1	100.00 1	100.00 1	100.00 1

9.14			99.57 58		99.99 59		100.00 1	99.42 127	97.54 145	100.00 1	100.00 1	99.15 87	99.99 93	100.00 1	100.00 1

CNLPA		99.72 33	99.65 35	99.91 82	99.97 101	99.72 111	100.00 1	99.47 77	98.43 64	99.88 155	100.00 1	99.14 88	100.00 1	99.97 86	100.00 1	100.00 1

ETH3D-3000-0.1		99.66 55	99.57 58	99.95 61	99.99 59	99.85 92	100.00 1	99.42 127	97.58 141	100.00 1	100.00 1	99.12 89	99.99 93	100.00 1	100.00 1	100.00 1

Test By Simon												99.10 90

CP-MVS		99.67 53	99.58 55	99.95 61	100.00 1	99.84 95	100.00 1	99.42 127	97.77 117	100.00 1	100.00 1	99.07 91	100.00 1	100.00 1	100.00 1	100.00 1

alignmvs		99.38 89	99.21 100	99.91 82	99.73 150	99.92 54	100.00 1	99.51 73	97.61 135	100.00 1	100.00 1	99.06 92	99.93 155	99.83 110	97.12 221	99.90 148

xxxxxxxxxxxxxcwj		99.77 19	99.70 21	99.97 34	99.99 59	99.93 47	100.00 1	99.31 227	98.93 32	100.00 1	100.00 1	99.05 93	99.99 93	100.00 1	100.00 1	100.00 1

SF-MVS		99.66 55	99.57 58	99.95 61	99.99 59	99.85 92	100.00 1	99.42 127	97.67 127	100.00 1	100.00 1	99.05 93	99.99 93	100.00 1	100.00 1	100.00 1

EPMVS		99.25 109	99.13 109	99.60 130	99.60 179	99.20 162	99.60 288	100.00 1	96.93 190	99.92 147	99.36 287	99.05 93	99.71 189	98.77 198	98.94 161	99.90 148

RRT_MVS		98.48 169	98.41 166	98.70 202	99.64 170	99.67 119	100.00 1	99.10 305	96.26 232	98.75 231	99.96 192	99.04 96	98.75 279	98.42 216	94.51 263	97.68 299

EI-MVSNet-Vis-set		99.70 40	99.64 39	99.87 93	100.00 1	99.64 122	99.98 214	99.44 109	98.35 73	99.99 103	100.00 1	99.04 96	99.96 136	99.98 79	100.00 1	100.00 1

mPP-MVS		99.69 44	99.60 50	99.97 34	100.00 1	99.91 57	100.00 1	99.42 127	97.91 107	100.00 1	100.00 1	99.04 96	100.00 1	100.00 1	100.00 1	100.00 1

tpmrst		98.98 135	98.93 127	99.14 177	99.61 177	97.74 247	99.52 296	99.36 205	96.05 236	99.98 108	99.64 256	99.04 96	99.86 164	98.94 188	98.19 193	99.82 184

Regformer-3		99.73 30	99.65 35	99.96 49	100.00 1	99.89 73	100.00 1	99.44 109	98.40 67	100.00 1	100.00 1	99.03 100	99.97 122	99.99 62	100.00 1	100.00 1

Regformer-4		99.73 30	99.65 35	99.95 61	100.00 1	99.89 73	100.00 1	99.44 109	98.40 67	100.00 1	100.00 1	99.03 100	99.97 122	99.99 62	100.00 1	100.00 1

SR-MVS		99.68 48	99.58 55	99.98 25	100.00 1	99.95 34	100.00 1	99.64 63	97.59 139	100.00 1	100.00 1	98.99 102	99.99 93	100.00 1	100.00 1	100.00 1

ETH3D cwj APD-0.16		99.63 63	99.53 67	99.94 76	99.97 101	99.82 97	100.00 1	99.42 127	97.39 159	100.00 1	100.00 1	98.98 103	99.99 93	100.00 1	100.00 1	100.00 1

EI-MVSNet-UG-set		99.69 44	99.63 43	99.87 93	99.99 59	99.64 122	99.95 229	99.44 109	98.35 73	100.00 1	100.00 1	98.98 103	99.97 122	99.98 79	100.00 1	100.00 1

ZNCC-MVS		99.71 36	99.62 47	99.97 34	99.99 59	99.90 65	100.00 1	99.79 41	97.97 100	99.97 112	100.00 1	98.97 105	100.00 1	99.94 96	100.00 1	100.00 1

APD-MVS_3200maxsize		99.65 57	99.55 65	99.97 34	99.99 59	99.91 57	100.00 1	99.48 76	97.54 145	100.00 1	100.00 1	98.97 105	99.99 93	99.98 79	100.00 1	100.00 1

EPNet		99.62 67	99.69 23	99.42 152	99.99 59	98.37 208	100.00 1	99.89 32	98.83 41	100.00 1	100.00 1	98.97 105	100.00 1	99.90 100	99.61 148	99.89 153
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tpm2		98.64 158	98.58 158	98.81 198	99.42 225	97.12 269	99.69 276	99.37 199	93.63 303	99.94 141	99.67 248	98.96 108	99.47 225	98.62 209	97.95 201	99.83 179

TSAR-MVS + GP.		99.61 69	99.69 23	99.35 161	99.99 59	98.06 228	100.00 1	99.36 205	99.83 2	100.00 1	100.00 1	98.95 109	99.99 93	100.00 1	99.11 156	100.00 1

HPM-MVS_fast		99.60 72	99.49 75	99.91 82	99.99 59	99.78 101	100.00 1	99.42 127	97.09 181	100.00 1	100.00 1	98.95 109	99.96 136	99.98 79	100.00 1	100.00 1

RE-MVS-def			99.55 65		99.99 59	99.91 57	100.00 1	99.42 127	97.62 133	100.00 1	100.00 1	98.94 111		99.99 62	100.00 1	100.00 1

WTY-MVS		99.54 74	99.40 78	99.95 61	99.81 127	99.93 47	100.00 1	100.00 1	97.98 98	99.84 158	100.00 1	98.94 111	99.98 117	99.86 105	98.21 191	99.94 128

HY-MVS	96.53 9	99.50 79	99.35 87	99.96 49	99.81 127	99.93 47	99.64 281	100.00 1	97.97 100	99.84 158	99.85 216	98.94 111	99.99 93	99.86 105	98.23 190	99.95 125

MDTV_nov1_ep13			98.94 126		99.53 197	98.36 210	99.39 306	99.46 92	96.54 217	99.99 103	99.63 260	98.92 114	99.86 164	98.30 224	98.71 165

API-MVS		99.72 33	99.70 21	99.79 109	99.97 101	99.37 145	99.96 224	99.94 18	98.48 62	100.00 1	100.00 1	98.92 114	100.00 1	100.00 1	100.00 1	100.00 1

ETH3 D test6400		99.68 48	99.59 51	99.97 34	99.99 59	99.93 47	100.00 1	99.42 127	97.97 100	100.00 1	100.00 1	98.90 116	99.99 93	100.00 1	100.00 1	100.00 1

ACMMP		99.65 57	99.57 58	99.89 88	99.99 59	99.66 120	99.75 264	99.73 54	98.16 81	99.75 174	100.00 1	98.90 116	100.00 1	99.96 88	99.88 132	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 53	99.57 58	99.97 34	99.98 97	99.92 54	100.00 1	99.42 127	97.83 113	100.00 1	100.00 1	98.89 118	100.00 1	99.98 79	100.00 1	100.00 1

MDTV_nov1_ep13_2view						99.24 157	99.56 291		96.31 230	99.96 117		98.86 119		98.92 190		99.89 153

KD-MVS_2432*1600		94.15 300	93.08 307	97.35 278	99.53 197	97.83 244	99.63 283	99.19 275	92.88 319	96.29 321	97.68 343	98.84 120	96.70 342	89.73 339	63.92 361	97.53 324

miper_refine_blended		94.15 300	93.08 307	97.35 278	99.53 197	97.83 244	99.63 283	99.19 275	92.88 319	96.29 321	97.68 343	98.84 120	96.70 342	89.73 339	63.92 361	97.53 324

dp		98.72 152	98.61 154	99.03 183	99.53 197	97.39 257	99.45 300	99.39 194	95.62 250	99.94 141	99.52 277	98.83 122	99.82 174	96.77 276	98.42 176	99.89 153

TAMVS		98.76 150	98.73 144	98.86 194	99.44 224	97.69 248	99.57 290	99.34 218	96.57 215	99.12 206	99.81 225	98.83 122	99.16 244	97.97 239	97.91 203	99.73 209

IB-MVS	96.24 12	97.54 208	96.95 221	99.33 163	99.67 160	98.10 226	100.00 1	99.47 77	97.42 158	99.26 197	99.69 243	98.83 122	99.89 160	99.43 164	78.77 352	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 22	99.67 31	99.99 12	99.99 59	99.96 25	99.73 269	99.52 69	99.06 11	100.00 1	100.00 1	98.80 125	100.00 1	99.95 94	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 69	99.50 73	99.97 34	99.98 97	99.92 54	100.00 1	99.42 127	97.53 147	99.77 171	100.00 1	98.77 126	100.00 1	99.99 62	100.00 1	99.99 110
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
pcd_1.5k_mvsjas		8.24 341	10.99 344	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.14 374	98.75 127	0.00 374	0.00 372	0.00 372	0.00 370

PS-MVSNAJss		98.03 189	98.06 186	97.94 259	97.63 323	97.33 262	99.89 241	99.23 262	96.27 231	98.03 273	99.59 268	98.75 127	98.78 274	98.52 213	94.61 261	97.70 293

PS-MVSNAJ		99.64 59	99.57 58	99.85 96	99.78 142	99.81 98	99.95 229	99.42 127	98.38 70	100.00 1	100.00 1	98.75 127	100.00 1	99.88 103	99.99 111	99.74 205

test1172		99.64 59	99.53 67	99.96 49	100.00 1	99.90 65	100.00 1	99.51 73	97.59 139	100.00 1	100.00 1	98.65 130	99.99 93	99.99 62	100.00 1	100.00 1

SR-MVS-dyc-post		99.63 63	99.52 70	99.97 34	99.99 59	99.91 57	100.00 1	99.42 127	97.62 133	100.00 1	100.00 1	98.65 130	99.99 93	99.99 62	100.00 1	100.00 1

zzz-MVS		99.68 48	99.59 51	99.97 34	99.99 59	99.91 57	100.00 1	99.42 127	98.32 75	99.94 141	100.00 1	98.65 130	100.00 1	99.96 88	100.00 1	100.00 1

MTAPA		99.68 48	99.59 51	99.97 34	99.99 59	99.91 57	100.00 1	99.42 127	98.32 75	99.94 141	100.00 1	98.65 130	100.00 1	99.96 88	100.00 1	100.00 1

HPM-MVS		99.59 73	99.50 73	99.89 88	100.00 1	99.70 116	100.00 1	99.42 127	97.46 154	100.00 1	100.00 1	98.60 134	99.96 136	99.99 62	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 22	99.68 26	99.97 34	100.00 1	99.91 57	99.98 214	99.47 77	99.09 8	100.00 1	100.00 1	98.59 135	100.00 1	99.95 94	100.00 1	100.00 1

EPNet_dtu		98.53 166	98.23 178	99.43 151	99.92 116	99.01 176	99.96 224	99.47 77	98.80 45	99.96 117	99.96 192	98.56 136	99.30 240	87.78 346	99.68 143	100.00 1
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
JIA-IIPM		97.09 225	96.34 246	99.36 160	98.88 276	98.59 198	99.81 251	99.43 119	84.81 353	99.96 117	90.34 361	98.55 137	99.52 218	97.00 265	98.28 188	99.98 112

xiu_mvs_v2_base		99.51 76	99.41 77	99.82 100	99.70 153	99.73 110	99.92 235	99.40 181	98.15 83	100.00 1	100.00 1	98.50 138	100.00 1	99.85 107	99.13 155	99.74 205

sss		99.45 84	99.34 89	99.80 107	99.76 147	99.50 129	100.00 1	99.91 31	97.72 120	99.98 108	99.94 203	98.45 139	100.00 1	99.53 160	98.75 164	99.89 153

IS-MVSNet		99.08 119	98.91 129	99.59 132	99.65 165	99.38 142	99.78 256	99.24 258	96.70 206	99.51 184	100.00 1	98.44 140	99.52 218	98.47 215	98.39 179	99.88 164

GST-MVS		99.64 59	99.53 67	99.95 61	100.00 1	99.86 89	100.00 1	99.79 41	97.72 120	99.95 139	100.00 1	98.39 141	100.00 1	99.96 88	99.99 111	100.00 1

114514_t		99.39 88	99.25 93	99.81 103	99.97 101	99.48 136	100.00 1	99.42 127	95.53 253	100.00 1	100.00 1	98.37 142	99.95 142	99.97 86	100.00 1	100.00 1

baseline1		98.91 141	98.61 154	99.81 103	99.71 151	99.77 102	99.78 256	99.44 109	97.51 150	98.81 228	99.99 172	98.25 143	99.76 182	98.60 210	95.41 236	99.89 153

MP-MVS		99.61 69	99.49 75	99.98 25	99.99 59	99.94 42	100.00 1	99.42 127	97.82 114	99.99 103	100.00 1	98.20 144	100.00 1	99.99 62	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		97.81 196	97.66 200	98.23 228	99.49 213	98.37 208	99.99 196	99.11 303	94.78 270	98.25 265	99.21 293	98.18 145	98.57 294	97.35 258	92.61 280	97.76 238

PHI-MVS		99.50 79	99.39 80	99.82 100	100.00 1	99.45 137	100.00 1	99.94 18	96.38 226	100.00 1	100.00 1	98.18 145	100.00 1	100.00 1	100.00 1	100.00 1

abl_6		99.53 75	99.40 78	99.92 81	100.00 1	99.76 104	100.00 1	99.42 127	97.21 174	100.00 1	100.00 1	98.12 147	100.00 1	99.82 115	100.00 1	100.00 1

Vis-MVSNet (Re-imp)		98.99 132	98.89 133	99.29 166	99.64 170	98.89 183	99.98 214	99.31 227	96.74 201	99.48 186	100.00 1	98.11 148	99.10 246	98.39 218	98.34 182	99.89 153

DeepPCF-MVS	98.03 4	98.54 165	99.72 19	94.98 320	99.99 59	84.94 357	100.00 1	99.42 127	99.98 1	100.00 1	100.00 1	98.11 148	100.00 1	100.00 1	100.00 1	100.00 1

cl22		98.23 184	98.11 182	98.58 210	99.82 123	99.01 176	100.00 1	99.28 240	96.92 192	98.33 257	99.21 293	98.09 150	98.97 257	98.72 201	92.61 280	97.76 238

PatchT		95.90 283	94.95 295	98.75 200	99.03 259	98.39 205	99.08 342	99.32 222	85.52 351	99.96 117	94.99 355	97.94 151	98.05 329	80.20 359	98.47 173	99.81 188

MVS_111021_LR		99.70 40	99.65 35	99.88 92	99.96 107	99.70 116	100.00 1	99.97 14	98.96 28	100.00 1	100.00 1	97.93 152	99.95 142	99.99 62	100.00 1	100.00 1

c3_l		97.58 205	97.42 206	98.06 246	99.48 215	98.16 221	99.96 224	99.10 305	94.54 279	98.13 269	99.20 295	97.87 153	98.25 312	97.28 259	91.20 303	97.75 249

MVS_111021_HR		99.71 36	99.63 43	99.93 79	99.95 108	99.83 96	100.00 1	100.00 1	98.89 36	100.00 1	100.00 1	97.85 154	99.95 142	100.00 1	100.00 1	100.00 1

CSCG		99.28 102	99.35 87	99.05 180	99.99 59	97.15 268	100.00 1	99.47 77	97.44 156	99.42 189	100.00 1	97.83 155	100.00 1	99.99 62	100.00 1	100.00 1

ET-MVSNet_ETH3D		96.41 256	95.48 285	99.20 175	99.81 127	99.75 106	100.00 1	99.02 330	97.30 170	78.33 362	100.00 1	97.73 156	97.94 333	99.70 131	87.41 333	99.92 137

tpm cat1		98.05 188	97.76 195	98.92 190	99.50 212	97.10 271	99.77 261	99.30 230	90.20 338	99.72 176	98.71 324	97.71 157	99.86 164	96.75 277	98.20 192	99.81 188

DELS-MVS		99.62 67	99.56 64	99.82 100	99.92 116	99.45 137	100.00 1	99.78 43	98.92 34	99.73 175	100.00 1	97.70 158	100.00 1	99.93 97	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
DIV-MVS_self_test		97.52 210	97.35 211	98.05 250	99.46 220	98.11 224	100.00 1	99.10 305	94.21 290	97.62 291	99.63 260	97.65 159	98.29 310	96.47 278	91.98 291	97.76 238

tpmvs		98.59 160	98.38 169	99.23 172	99.69 154	97.90 238	99.31 313	99.47 77	94.52 280	99.68 178	99.28 291	97.64 160	99.89 160	97.71 245	98.17 195	99.89 153

tpm		98.24 183	98.22 180	98.32 223	99.13 248	95.79 290	99.53 295	99.12 302	95.20 263	99.96 117	99.36 287	97.58 161	99.28 241	97.41 255	96.67 228	99.88 164

ADS-MVSNet2		98.28 181	98.51 162	97.62 270	99.51 209	95.03 299	99.24 318	99.41 179	95.52 255	99.96 117	99.70 241	97.57 162	97.94 333	97.11 262	98.54 168	99.88 164

ADS-MVSNet		98.70 154	98.51 162	99.28 168	99.51 209	98.39 205	99.24 318	99.44 109	95.52 255	99.96 117	99.70 241	97.57 162	99.58 199	97.11 262	98.54 168	99.88 164

cl____		97.54 208	97.32 212	98.18 232	99.47 218	98.14 223	100.00 1	99.10 305	94.16 293	97.60 293	99.63 260	97.52 164	98.65 285	96.47 278	91.97 292	97.76 238

h-mvs33		97.03 230	96.53 234	98.51 211	99.79 140	95.90 289	99.45 300	99.45 99	98.21 78	100.00 1	99.78 231	97.49 165	99.99 93	99.72 127	74.92 354	99.65 217

hse-mvs2		96.79 238	96.38 242	98.04 252	99.68 157	95.54 293	99.81 251	99.42 127	98.21 78	100.00 1	99.80 228	97.49 165	99.46 229	99.72 127	73.27 357	99.12 227

CS-MVS		99.28 102	99.18 106	99.57 139	99.45 221	99.21 161	100.00 1	99.22 264	97.14 177	99.96 117	100.00 1	97.49 165	99.66 192	99.64 145	99.76 140	99.83 179

EIA-MVS		99.26 106	99.19 104	99.45 148	99.63 172	98.75 187	100.00 1	99.27 248	96.93 190	99.95 139	100.00 1	97.47 168	99.79 178	99.74 124	99.72 142	99.82 184

Test_1112_low_res		98.83 147	98.60 156	99.51 142	99.69 154	98.75 187	99.99 196	99.14 293	96.81 197	98.84 225	99.06 300	97.45 169	99.89 160	98.66 203	97.75 213	99.89 153

1112_ss		98.91 141	98.71 147	99.51 142	99.69 154	98.75 187	99.99 196	99.15 288	96.82 196	98.84 225	100.00 1	97.45 169	99.89 160	98.66 203	97.75 213	99.89 153

ETV-MVS		99.34 95	99.24 95	99.64 125	99.58 187	99.33 147	100.00 1	99.25 253	97.57 143	99.96 117	100.00 1	97.44 171	99.79 178	99.70 131	99.65 145	99.81 188

CPTT-MVS		99.49 81	99.38 81	99.85 96	100.00 1	99.54 127	100.00 1	99.42 127	97.58 141	99.98 108	100.00 1	97.43 172	100.00 1	99.99 62	100.00 1	100.00 1

miper_lstm_enhance		97.40 213	97.28 214	97.75 267	99.48 215	97.52 253	100.00 1	99.07 317	94.08 294	98.01 276	99.61 266	97.38 173	97.98 331	96.44 281	91.47 300	97.76 238

EI-MVSNet		97.98 191	97.93 191	98.16 235	99.11 249	97.84 243	99.74 265	99.29 235	94.39 286	98.65 236	100.00 1	97.21 174	98.88 267	97.62 249	95.31 240	97.75 249

IterMVS-LS		97.56 206	97.44 205	97.92 262	99.38 237	97.90 238	99.89 241	99.10 305	94.41 285	98.32 258	99.54 276	97.21 174	98.11 320	97.50 251	91.62 296	97.75 249
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CS-MVS-test		99.18 115	99.11 111	99.39 158	99.40 232	98.63 197	100.00 1	99.20 274	97.06 185	99.97 112	100.00 1	97.11 176	99.68 191	99.66 142	99.57 150	99.85 174

tfpn200view9		99.26 106	99.03 114	99.96 49	99.81 127	99.89 73	100.00 1	99.94 18	97.23 171	99.83 160	99.96 192	97.04 177	100.00 1	99.59 150	97.85 206	99.98 112

thres400		99.26 106	99.03 114	99.95 61	99.81 127	99.89 73	100.00 1	99.94 18	97.23 171	99.83 160	99.96 192	97.04 177	100.00 1	99.59 150	97.85 206	99.97 117

thres200		99.27 104	99.04 113	99.96 49	99.81 127	99.90 65	100.00 1	99.94 18	97.31 168	99.83 160	99.96 192	97.04 177	100.00 1	99.62 149	97.88 204	99.98 112

1314		99.38 89	99.19 104	99.96 49	98.88 276	99.89 73	99.24 318	99.93 26	98.88 37	98.79 230	100.00 1	97.02 180	100.00 1	100.00 1	100.00 1	100.00 1

thres100view900		99.25 109	99.01 116	99.95 61	99.81 127	99.87 86	100.00 1	99.94 18	97.13 179	99.83 160	99.96 192	97.01 181	100.00 1	99.59 150	97.85 206	99.98 112

thres600view7		99.24 111	99.00 117	99.95 61	99.81 127	99.87 86	100.00 1	99.94 18	97.13 179	99.83 160	99.96 192	97.01 181	100.00 1	99.54 158	97.77 212	99.97 117

OMC-MVS		99.27 104	99.38 81	98.96 188	99.95 108	97.06 272	100.00 1	99.40 181	98.83 41	99.88 155	100.00 1	97.01 181	99.86 164	99.47 163	99.84 135	99.97 117

xiu_mvs_v1_base_debu		99.35 92	99.21 100	99.79 109	99.67 160	99.71 112	99.78 256	99.36 205	98.13 85	100.00 1	100.00 1	97.00 184	100.00 1	99.83 110	99.07 157	99.66 214

xiu_mvs_v1_base		99.35 92	99.21 100	99.79 109	99.67 160	99.71 112	99.78 256	99.36 205	98.13 85	100.00 1	100.00 1	97.00 184	100.00 1	99.83 110	99.07 157	99.66 214

xiu_mvs_v1_base_debi		99.35 92	99.21 100	99.79 109	99.67 160	99.71 112	99.78 256	99.36 205	98.13 85	100.00 1	100.00 1	97.00 184	100.00 1	99.83 110	99.07 157	99.66 214

CR-MVSNet		98.02 190	97.71 199	98.93 189	99.31 240	98.86 184	99.13 337	99.00 333	96.53 218	99.96 117	98.98 309	96.94 187	98.10 323	91.18 328	98.40 177	99.84 176

Patchmtry		96.81 237	96.37 243	98.14 236	99.31 240	98.55 199	98.91 347	99.00 333	90.45 335	97.92 279	98.98 309	96.94 187	98.12 318	94.27 305	91.53 297	97.75 249

eth_miper_zixun_eth		97.47 211	97.28 214	98.06 246	99.41 227	97.94 236	99.62 286	99.08 313	94.46 284	98.19 268	99.56 273	96.91 189	98.50 299	96.78 274	91.49 298	97.74 272

DROMVSNet		99.19 114	99.09 112	99.48 146	99.42 225	99.07 168	100.00 1	99.21 270	96.95 189	99.96 117	100.00 1	96.88 190	99.48 223	99.64 145	99.79 139	99.88 164

LS3D		99.31 99	99.13 109	99.87 93	99.99 59	99.71 112	99.55 292	99.46 92	97.32 166	99.82 167	100.00 1	96.85 191	99.97 122	99.14 180	100.00 1	99.92 137

MVSFormer		98.94 139	98.82 136	99.28 168	99.45 221	99.49 132	100.00 1	99.13 297	95.46 260	99.97 112	100.00 1	96.76 192	98.59 291	98.63 207	100.00 1	99.74 205

lupinMVS		99.29 101	99.16 108	99.69 120	99.45 221	99.49 132	100.00 1	99.15 288	97.45 155	99.97 112	100.00 1	96.76 192	99.76 182	99.67 140	100.00 1	99.81 188

MAR-MVS		99.49 81	99.36 85	99.89 88	99.97 101	99.66 120	99.74 265	99.95 15	97.89 108	100.00 1	100.00 1	96.71 194	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		98.90 143	98.63 152	99.70 119	99.92 116	99.25 155	100.00 1	99.37 199	95.71 246	99.40 193	100.00 1	96.58 195	99.95 142	96.80 273	99.94 127	99.91 139

PVSNet_BlendedMVS		98.71 153	98.62 153	98.98 187	99.98 97	99.60 124	100.00 1	100.00 1	97.23 171	100.00 1	99.03 305	96.57 196	99.99 93	100.00 1	94.75 258	97.35 333

PVSNet_Blended		99.48 83	99.36 85	99.83 99	99.98 97	99.60 124	100.00 1	100.00 1	97.79 116	100.00 1	100.00 1	96.57 196	99.99 93	100.00 1	99.88 132	99.90 148

MVS_Test		98.93 140	98.65 150	99.77 115	99.62 175	99.50 129	99.99 196	99.19 275	95.52 255	99.96 117	99.86 214	96.54 198	99.98 117	98.65 205	98.48 172	99.82 184

PMMVS		99.12 116	98.97 120	99.58 136	99.57 190	98.98 179	100.00 1	99.30 230	97.14 177	99.96 117	100.00 1	96.53 199	99.82 174	99.70 131	98.49 171	99.94 128

PVSNet_Blended_VisFu		99.33 97	99.18 106	99.78 113	99.82 123	99.49 132	100.00 1	99.95 15	97.36 161	99.63 180	100.00 1	96.45 200	99.95 142	99.79 116	99.65 145	99.89 153

mvs_anonymous		98.80 149	98.60 156	99.38 159	99.57 190	99.24 157	100.00 1	99.21 270	95.87 239	98.92 217	99.82 222	96.39 201	99.03 249	99.13 181	98.50 170	99.88 164

bset_n11_16_dypcd		97.66 200	97.54 203	98.04 252	96.88 342	98.04 230	100.00 1	99.25 253	95.72 245	98.47 250	99.73 234	96.23 202	98.34 308	98.18 228	93.37 273	97.74 272

DP-MVS		98.86 145	98.54 160	99.81 103	99.97 101	99.45 137	99.52 296	99.40 181	94.35 287	98.36 254	100.00 1	96.13 203	99.97 122	99.12 182	100.00 1	100.00 1

PVSNet	94.91 18	99.30 100	99.25 93	99.44 149	100.00 1	98.32 214	100.00 1	99.86 34	98.04 93	100.00 1	100.00 1	96.10 204	100.00 1	99.55 155	99.73 141	100.00 1

Effi-MVS+-dtu		98.51 168	98.86 134	97.47 274	99.77 145	94.21 322	100.00 1	98.94 335	97.61 135	99.91 149	98.75 323	95.89 205	99.51 220	99.36 168	99.48 152	98.68 231

mvs-test1		98.89 144	98.99 119	98.60 206	99.77 145	95.96 285	100.00 1	98.94 335	97.61 135	99.93 146	99.92 207	95.89 205	99.93 155	99.36 168	99.50 151	99.90 148

our_test_3		96.51 251	96.35 245	96.98 293	97.61 325	95.05 298	99.98 214	99.01 332	94.68 274	96.77 316	99.06 300	95.87 207	98.14 316	91.81 325	92.37 285	97.75 249

UA-Net		99.06 121	98.83 135	99.74 116	99.52 204	99.40 141	99.08 342	99.45 99	97.64 131	99.83 160	100.00 1	95.80 208	99.94 153	98.35 220	99.80 138	99.88 164

FMVSNet5		95.32 291	95.43 287	94.99 319	99.39 236	92.99 333	99.25 317	99.24 258	90.45 335	97.44 298	98.45 333	95.78 209	94.39 358	87.02 347	91.88 293	97.59 320

CVMVSNet		98.56 163	98.47 164	98.82 196	99.11 249	97.67 249	99.74 265	99.47 77	97.57 143	99.06 211	100.00 1	95.72 210	98.97 257	98.21 227	97.33 220	99.83 179

RPMNet		95.26 293	93.82 299	99.56 140	99.31 240	98.86 184	99.13 337	99.42 127	79.82 358	99.96 117	95.13 353	95.69 211	99.98 117	77.54 363	98.40 177	99.84 176

MVS		99.22 112	98.96 121	99.98 25	99.00 265	99.95 34	99.24 318	99.94 18	98.14 84	98.88 220	100.00 1	95.63 212	100.00 1	99.85 107	100.00 1	100.00 1

jason		99.11 117	98.96 121	99.59 132	99.17 247	99.31 149	100.00 1	99.13 297	97.38 160	99.83 160	100.00 1	95.54 213	99.72 188	99.57 154	99.97 122	99.74 205
jason: jason.
AdaColmap		99.44 85	99.26 92	99.95 61	100.00 1	99.86 89	99.70 274	99.99 10	98.53 60	99.90 150	100.00 1	95.34 214	100.00 1	99.92 98	100.00 1	100.00 1

CANet		99.40 87	99.24 95	99.89 88	99.99 59	99.76 104	100.00 1	99.73 54	98.40 67	99.78 170	100.00 1	95.28 215	99.96 136	100.00 1	99.99 111	99.96 120

FIs		97.95 192	97.73 198	98.62 205	98.53 290	99.24 157	100.00 1	99.43 119	96.74 201	97.87 281	99.82 222	95.27 216	98.89 264	98.78 197	93.07 275	97.74 272

canonicalmvs		99.03 124	98.73 144	99.94 76	99.75 149	99.95 34	100.00 1	99.30 230	97.64 131	100.00 1	100.00 1	95.22 217	99.97 122	99.76 122	96.90 227	99.91 139

FC-MVSNet-test		97.84 194	97.63 202	98.45 214	98.30 300	99.05 171	100.00 1	99.43 119	96.63 214	97.61 292	99.82 222	95.19 218	98.57 294	98.64 206	93.05 276	97.73 279

UniMVSNet_NR-MVSNet		97.16 222	96.80 225	98.22 229	98.38 294	98.41 202	100.00 1	99.45 99	96.14 235	97.76 283	99.64 256	95.05 219	98.50 299	97.98 236	86.84 336	97.75 249

Fast-Effi-MVS+-dtu		98.38 175	98.56 159	97.82 265	99.58 187	94.44 319	100.00 1	99.16 287	96.75 200	99.51 184	99.63 260	95.03 220	99.60 194	97.71 245	99.67 144	99.42 222

UniMVSNet (Re)		97.29 218	96.85 224	98.59 209	98.49 291	99.13 166	100.00 1	99.42 127	96.52 219	98.24 267	98.90 317	94.93 221	98.89 264	97.54 250	87.61 332	97.75 249

BH-w/o		98.82 148	98.81 138	98.88 193	99.62 175	96.71 278	100.00 1	99.28 240	97.09 181	98.81 228	100.00 1	94.91 222	99.96 136	99.54 158	100.00 1	99.96 120

AUN-MVS		96.26 267	95.67 277	98.06 246	99.68 157	95.60 292	99.82 250	99.42 127	96.78 199	99.88 155	99.80 228	94.84 223	99.47 225	97.48 252	73.29 356	99.12 227

PCF-MVS	98.23 3	98.69 155	98.37 171	99.62 127	99.78 142	99.02 174	99.23 324	99.06 324	96.43 222	98.08 270	100.00 1	94.72 224	99.95 142	98.16 229	99.91 130	99.90 148
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
VNet		99.04 123	98.75 143	99.90 85	99.81 127	99.75 106	99.50 298	99.47 77	98.36 72	100.00 1	99.99 172	94.66 225	100.00 1	99.90 100	97.09 222	99.96 120

diffmvs		98.96 136	98.73 144	99.63 126	99.54 194	99.16 164	100.00 1	99.18 281	97.33 165	99.96 117	100.00 1	94.60 226	99.91 159	99.66 142	98.33 186	99.82 184

BH-untuned		98.64 158	98.65 150	98.60 206	99.59 182	96.17 284	100.00 1	99.28 240	96.67 210	98.41 253	100.00 1	94.52 227	99.83 171	99.41 166	100.00 1	99.81 188

nrg030		97.64 201	97.27 216	98.75 200	98.34 295	99.53 128	100.00 1	99.22 264	96.21 234	98.27 263	99.95 199	94.40 228	98.98 255	99.23 177	89.78 315	97.75 249

ppachtmachnet_test		96.17 272	95.89 263	97.02 290	97.61 325	95.24 295	99.99 196	99.24 258	93.31 311	96.71 317	99.62 264	94.34 229	98.07 325	89.87 338	92.30 287	97.75 249

D2MVS		97.63 204	97.83 193	97.05 288	98.83 282	94.60 315	100.00 1	99.82 36	96.89 195	98.28 261	99.03 305	94.05 230	99.47 225	98.58 212	94.97 256	97.09 338

RPSCF		97.37 214	98.24 176	94.76 323	99.80 137	84.57 358	99.99 196	99.05 326	94.95 267	99.82 167	100.00 1	94.03 231	100.00 1	98.15 230	98.38 180	99.70 210

CANet_DTU		99.02 128	98.90 132	99.41 153	99.88 121	98.71 191	100.00 1	99.29 235	98.84 40	100.00 1	100.00 1	94.02 232	100.00 1	98.08 231	99.96 124	99.52 220

LCM-MVSNet-Re		96.52 250	97.21 218	94.44 324	99.27 243	85.80 356	99.85 246	96.61 366	95.98 237	92.75 347	98.48 332	93.97 233	97.55 338	99.58 153	98.43 175	99.98 112

Effi-MVS+		98.58 161	98.24 176	99.61 128	99.60 179	99.26 153	97.85 357	99.10 305	96.22 233	99.97 112	99.89 210	93.75 234	99.77 181	99.43 164	98.34 182	99.81 188

pmmvs4		97.17 221	96.80 225	98.27 225	97.68 322	98.64 196	100.00 1	99.18 281	94.22 289	98.55 242	99.71 238	93.67 235	98.47 302	95.66 287	92.57 283	97.71 292

CHOSEN 1792x2688		99.00 130	98.91 129	99.25 171	99.90 119	97.79 246	100.00 1	99.99 10	98.79 47	98.28 261	100.00 1	93.63 236	99.95 142	99.66 142	99.95 126	100.00 1

cdsmvs_eth3d_5k		24.41 339	32.55 341	0.00 355	0.00 378	0.00 379	0.00 366	99.39 194	0.00 373	0.00 374	100.00 1	93.55 237	0.00 374	0.00 372	0.00 372	0.00 370

AllTest		98.55 164	98.40 168	98.99 185	99.93 113	97.35 259	100.00 1	99.40 181	97.08 183	99.09 207	99.98 175	93.37 238	99.95 142	96.94 266	99.84 135	99.68 212

TestCases				98.99 185	99.93 113	97.35 259		99.40 181	97.08 183	99.09 207	99.98 175	93.37 238	99.95 142	96.94 266	99.84 135	99.68 212

FMVSNet3		97.30 217	96.95 221	98.37 219	99.65 165	99.25 155	99.71 272	99.28 240	94.23 288	98.53 244	98.91 316	93.30 240	98.11 320	95.31 293	93.60 267	97.73 279

Fast-Effi-MVS+		98.40 174	98.02 188	99.55 141	99.63 172	99.06 170	100.00 1	99.15 288	95.07 264	99.42 189	99.95 199	93.26 241	99.73 187	97.44 253	98.24 189	99.87 172

baseline		98.69 155	98.45 165	99.41 153	99.52 204	98.67 194	100.00 1	99.17 286	97.03 187	99.13 205	100.00 1	93.17 242	99.74 185	99.70 131	98.34 182	99.81 188

QAPM		98.99 132	98.66 149	99.96 49	99.01 261	99.87 86	99.88 244	99.93 26	97.99 96	98.68 235	100.00 1	93.17 242	100.00 1	99.32 173	100.00 1	100.00 1

PatchMatch-RL		99.02 128	98.78 140	99.74 116	99.99 59	99.29 150	100.00 1	100.00 1	98.38 70	99.89 153	99.81 225	93.14 244	99.99 93	97.85 242	99.98 119	99.95 125

test_part1		96.98 233	96.36 244	98.85 195	99.78 142	99.77 102	99.63 283	99.30 230	92.74 322	97.05 305	99.52 277	93.10 245	99.02 250	98.02 234	88.10 328	97.76 238

WR-MVS_H		96.73 241	96.32 248	97.95 258	98.26 302	97.88 240	99.72 271	99.43 119	95.06 265	96.99 307	98.68 325	93.02 246	98.53 297	97.43 254	88.33 327	97.43 329

3Dnovator	95.63 14	99.06 121	98.76 142	99.96 49	98.86 279	99.90 65	99.98 214	99.93 26	98.95 31	98.49 249	100.00 1	92.91 247	100.00 1	99.71 129	100.00 1	100.00 1

3Dnovator+	95.58 15	99.03 124	98.71 147	99.96 49	98.99 268	99.89 73	100.00 1	99.51 73	98.96 28	98.32 258	100.00 1	92.78 248	100.00 1	99.87 104	100.00 1	100.00 1

casdiffmvs		98.65 157	98.38 169	99.46 147	99.52 204	98.74 190	100.00 1	99.15 288	96.91 193	99.05 212	100.00 1	92.75 249	99.83 171	99.70 131	98.38 180	99.81 188

NR-MVSNet		96.63 246	96.04 257	98.38 218	98.31 298	98.98 179	99.22 326	99.35 213	95.87 239	94.43 341	99.65 252	92.73 250	98.40 305	96.78 274	88.05 329	97.75 249

COLMAP_ROB	97.10 7	98.29 180	98.17 181	98.65 204	99.94 111	97.39 257	99.30 314	99.40 181	95.64 248	97.75 286	100.00 1	92.69 251	99.95 142	98.89 192	99.92 129	98.62 233
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
EU-MVSNet		96.63 246	96.53 234	96.94 295	97.59 327	96.87 275	99.76 263	99.47 77	96.35 227	96.85 312	99.78 231	92.57 252	96.27 349	95.33 292	91.08 304	97.68 299

MVP-Stereo		96.51 251	96.48 238	96.60 305	95.65 350	94.25 321	98.84 349	98.16 348	95.85 243	95.23 332	99.04 303	92.54 253	99.13 245	92.98 317	99.98 119	96.43 349
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
DeepC-MVS	97.84 5	99.00 130	98.80 139	99.60 130	99.93 113	99.03 173	100.00 1	99.40 181	98.61 58	99.33 194	100.00 1	92.23 254	99.95 142	99.74 124	99.96 124	99.83 179
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DU-MVS		96.93 235	96.49 237	98.22 229	98.31 298	98.41 202	100.00 1	99.37 199	96.41 224	97.76 283	99.65 252	92.14 255	98.50 299	97.98 236	86.84 336	97.75 249

Baseline_NR-MVSNet		96.16 273	95.70 273	97.56 273	98.28 301	96.79 277	100.00 1	97.86 359	91.93 327	97.63 289	99.47 282	92.14 255	98.35 307	97.13 261	86.83 338	97.54 323

cascas		98.43 171	98.07 185	99.50 144	99.65 165	99.02 174	100.00 1	99.22 264	94.21 290	99.72 176	99.98 175	92.03 257	99.93 155	99.68 138	98.12 196	99.54 219

test_djsdf		97.55 207	97.38 209	98.07 242	97.50 331	97.99 232	100.00 1	99.13 297	95.46 260	98.47 250	99.85 216	92.01 258	98.59 291	98.63 207	95.36 238	97.62 316

v8		96.35 262	95.73 272	98.21 231	98.11 309	98.23 219	99.94 233	99.07 317	92.66 323	98.29 260	99.00 308	91.46 259	98.77 277	94.17 306	88.83 325	97.62 316

OpenMVS	95.20 17	98.76 150	98.41 166	99.78 113	98.89 275	99.81 98	99.99 196	99.76 45	98.02 94	98.02 275	100.00 1	91.44 260	100.00 1	99.63 148	99.97 122	99.55 218

v148		96.29 265	95.84 265	97.63 268	97.74 320	96.53 282	100.00 1	99.07 317	93.52 305	98.01 276	99.42 285	91.22 261	98.60 289	96.37 282	87.22 335	97.75 249

GeoE		98.06 187	97.65 201	99.29 166	99.47 218	98.41 202	100.00 1	99.19 275	94.85 269	98.88 220	100.00 1	91.21 262	99.59 196	97.02 264	98.19 193	99.88 164

XVG-OURS		98.30 178	98.36 172	98.13 239	99.58 187	95.91 288	100.00 1	99.36 205	98.69 52	99.23 198	100.00 1	91.20 263	99.92 158	99.34 170	97.82 209	98.56 234

WR-MVS		97.09 225	96.64 230	98.46 213	98.43 292	99.09 167	99.97 220	99.33 220	95.62 250	97.76 283	99.67 248	91.17 264	98.56 296	98.49 214	89.28 320	97.74 272

V42		96.65 245	96.16 253	98.11 241	98.17 308	98.23 219	99.99 196	99.09 311	93.97 295	98.74 233	99.05 302	91.09 265	98.82 270	95.46 291	89.90 313	97.27 335

v10		96.14 275	95.50 281	98.07 242	98.19 306	97.96 234	99.83 248	99.07 317	92.10 326	98.07 271	98.94 314	91.07 266	98.61 287	92.41 323	89.82 314	97.63 314

v7n		96.06 279	95.42 288	97.99 257	97.58 328	97.35 259	99.86 245	99.11 303	92.81 321	97.91 280	99.49 280	90.99 267	98.92 260	92.51 320	88.49 326	97.70 293

v1144		96.51 251	95.97 261	98.13 239	97.98 314	98.04 230	99.99 196	99.08 313	93.51 306	98.62 239	98.98 309	90.98 268	98.62 286	93.79 312	90.79 307	97.74 272

ab-mvs		98.42 172	98.02 188	99.61 128	99.71 151	99.00 178	99.10 340	99.64 63	96.70 206	99.04 213	99.81 225	90.64 269	99.98 117	99.64 145	97.93 202	99.84 176

Anonymous202405211		97.87 193	97.53 204	98.90 191	99.81 127	96.70 279	99.35 310	99.46 92	92.98 317	98.83 227	99.99 172	90.63 270	100.00 1	99.70 131	97.03 223	100.00 1

Anonymous20231206		93.45 306	93.17 306	94.30 327	95.00 355	89.69 349	99.98 214	98.43 345	93.30 312	94.50 340	98.59 328	90.52 271	95.73 354	77.46 364	90.73 309	97.48 328

anonymousdsp		97.16 222	96.88 223	98.00 255	97.08 341	98.06 228	99.81 251	99.15 288	94.58 277	97.84 282	99.62 264	90.49 272	98.60 289	97.98 236	95.32 239	97.33 334

v2v482		96.70 244	96.18 251	98.27 225	98.04 311	98.39 205	100.00 1	99.13 297	94.19 292	98.58 241	99.08 299	90.48 273	98.67 283	95.69 286	90.44 311	97.75 249

v144192		96.40 259	95.81 266	98.17 234	97.89 317	98.11 224	99.99 196	99.06 324	93.39 308	98.75 231	99.09 298	90.43 274	98.66 284	93.10 316	90.55 310	97.75 249

Vis-MVSNet		98.52 167	98.25 175	99.34 162	99.68 157	98.55 199	99.68 278	99.41 179	97.34 164	99.94 141	100.00 1	90.38 275	99.70 190	99.03 186	98.84 162	99.76 203
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CP-MVSNet		96.73 241	96.25 249	98.18 232	98.21 305	98.67 194	99.77 261	99.32 222	95.06 265	97.20 303	99.65 252	90.10 276	98.19 313	98.06 233	88.90 323	97.66 305

TranMVSNet+NR-MVSNet		96.45 255	96.01 258	97.79 266	98.00 313	97.62 251	100.00 1	99.35 213	95.98 237	97.31 300	99.64 256	90.09 277	98.00 330	96.89 269	86.80 339	97.75 249

DSMNet-mixed		95.18 294	95.21 290	95.08 317	96.03 348	90.21 348	99.65 280	93.64 370	92.91 318	98.34 256	97.40 346	90.05 278	95.51 355	91.02 330	97.86 205	99.51 221

N_pmnet		91.88 315	93.37 305	87.40 341	97.24 340	66.33 369	99.90 238	91.05 372	89.77 340	95.65 330	98.58 329	90.05 278	98.11 320	85.39 349	92.72 279	97.75 249

GA-MVS		97.72 198	97.27 216	99.06 178	99.24 246	97.93 237	100.00 1	99.24 258	95.80 244	98.99 215	99.64 256	89.77 280	99.36 236	95.12 297	97.62 218	99.89 153

MIMVSNet		97.06 228	96.73 228	98.05 250	99.38 237	96.64 281	98.47 353	99.35 213	93.41 307	99.48 186	98.53 330	89.66 281	97.70 337	94.16 308	98.11 197	99.80 198

IterMVS-SCA-FT		96.72 243	96.42 241	97.62 270	99.40 232	96.83 276	99.99 196	99.14 293	94.65 276	97.55 295	99.72 236	89.65 282	98.31 309	95.62 289	92.05 289	97.73 279

SCA		98.30 178	97.98 190	99.23 172	99.41 227	98.25 218	99.99 196	99.45 99	96.91 193	99.76 173	99.58 269	89.65 282	99.54 212	98.31 222	98.79 163	99.91 139

IterMVS		96.76 240	96.46 239	97.63 268	99.41 227	96.89 274	99.99 196	99.13 297	94.74 273	97.59 294	99.66 250	89.63 284	98.28 311	95.71 285	92.31 286	97.72 285
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1192		96.18 270	95.49 283	98.26 227	98.01 312	98.15 222	99.99 196	99.08 313	93.36 309	98.54 243	98.97 312	89.47 285	98.89 264	91.15 329	90.82 306	97.75 249

v1921920		96.16 273	95.50 281	98.14 236	97.88 318	97.96 234	99.99 196	99.07 317	93.33 310	98.60 240	99.24 292	89.37 286	98.71 281	91.28 327	90.74 308	97.75 249

XVG-OURS-SEG-HR		98.27 182	98.31 173	98.14 236	99.59 182	95.92 287	100.00 1	99.36 205	98.48 62	99.21 199	100.00 1	89.27 287	99.94 153	99.76 122	99.17 154	98.56 234

test20.03		93.11 308	92.85 310	93.88 332	95.19 354	91.83 340	100.00 1	98.87 340	93.68 301	92.76 346	98.88 318	89.20 288	92.71 363	77.88 362	89.19 321	97.09 338

MDA-MVSNet_test_wron		92.61 310	91.09 318	97.19 286	96.71 344	97.26 265	100.00 1	99.14 293	88.61 343	67.90 367	98.32 338	89.03 289	96.57 344	90.47 335	89.59 316	97.74 272

BH-RMVSNet		98.46 170	98.08 184	99.59 132	99.61 177	99.19 163	100.00 1	99.28 240	97.06 185	98.95 216	100.00 1	88.99 290	99.82 174	98.83 196	100.00 1	99.77 201

v1240		95.96 281	95.25 289	98.07 242	97.91 316	97.87 242	99.96 224	99.07 317	93.24 313	98.64 238	98.96 313	88.98 291	98.61 287	89.58 341	90.92 305	97.75 249

Anonymous20231211		96.29 265	95.70 273	98.07 242	99.80 137	97.49 254	99.15 335	99.40 181	89.11 341	97.75 286	99.45 283	88.93 292	98.98 255	98.26 226	89.47 318	97.73 279

TR-MVS		98.14 185	97.74 196	99.33 163	99.59 182	98.28 216	99.27 315	99.21 270	96.42 223	99.15 204	99.94 203	88.87 293	99.79 178	98.88 193	98.29 187	99.93 135

CL-MVSNet_self_test		91.07 319	90.35 322	93.24 333	93.27 358	89.16 351	99.55 292	99.25 253	92.34 324	95.23 332	97.05 347	88.86 294	93.59 361	80.67 357	66.95 360	96.96 341

YYNet1		92.44 311	90.92 319	97.03 289	96.20 346	97.06 272	99.99 196	99.14 293	88.21 346	67.93 366	98.43 335	88.63 295	96.28 348	90.64 331	89.08 322	97.74 272

HQP2-MVS												88.61 296

HQP-MVS		97.73 197	97.85 192	97.39 275	99.07 253	94.82 303	100.00 1	99.40 181	99.04 14	99.17 200	99.97 183	88.61 296	99.57 200	99.79 116	95.58 230	97.77 236

HQP_MVS		97.71 199	97.82 194	97.37 276	99.00 265	94.80 306	100.00 1	99.40 181	99.00 25	99.08 209	99.97 183	88.58 298	99.55 209	99.79 116	95.57 234	97.76 238

plane_prior6					99.06 257	94.80 306						88.58 298

tfpnnormal		96.36 261	95.69 276	98.37 219	98.55 288	98.71 191	99.69 276	99.45 99	93.16 315	96.69 318	99.71 238	88.44 300	98.99 254	94.17 306	91.38 301	97.41 330

TransMVSNet (Re)		94.78 296	93.72 300	97.93 261	98.34 295	97.88 240	99.23 324	97.98 356	91.60 328	94.55 338	99.71 238	87.89 301	98.36 306	89.30 343	84.92 342	97.56 322

DTE-MVSNet		95.52 288	94.99 294	97.08 287	97.49 333	96.45 283	100.00 1	99.25 253	93.82 297	96.17 324	99.57 272	87.81 302	97.18 339	94.57 301	86.26 341	97.62 316

XXY-MVS		97.14 224	96.63 231	98.67 203	98.65 284	98.92 182	99.54 294	99.29 235	95.57 252	97.63 289	99.83 219	87.79 303	99.35 238	98.39 218	92.95 277	97.75 249

UGNet		98.41 173	98.11 182	99.31 165	99.54 194	98.55 199	99.18 327	100.00 1	98.64 57	99.79 169	99.04 303	87.61 304	100.00 1	99.30 174	99.89 131	99.40 223
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		97.64 201	97.74 196	97.36 277	99.01 261	94.76 311	100.00 1	99.34 218	99.30 4	99.00 214	99.97 183	87.49 305	99.57 200	99.96 88	95.58 230	97.75 249
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
PEN-MVS		96.01 280	95.48 285	97.58 272	97.74 320	97.26 265	99.90 238	99.29 235	94.55 278	96.79 314	99.55 274	87.38 306	97.84 335	96.92 268	87.24 334	97.65 310

ACMM	97.17 6	97.37 214	97.40 208	97.29 281	99.01 261	94.64 314	100.00 1	99.25 253	98.07 91	98.44 252	99.98 175	87.38 306	99.55 209	99.25 176	95.19 247	97.69 297
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
jajsoiax		97.07 227	96.79 227	97.89 263	97.28 339	97.12 269	99.95 229	99.19 275	96.55 216	97.31 300	99.69 243	87.35 308	98.91 261	98.70 202	95.12 252	97.66 305

pmmvs5		95.94 282	95.61 278	96.95 294	97.42 336	94.66 312	100.00 1	98.08 352	93.60 304	97.05 305	99.43 284	87.02 309	98.46 303	95.76 284	92.12 288	97.72 285

VPA-MVSNet		97.03 230	96.43 240	98.82 196	98.64 285	99.32 148	99.38 307	99.47 77	96.73 203	98.91 219	98.94 314	87.00 310	99.40 234	99.23 177	89.59 316	97.76 238

PS-CasMVS		96.34 263	95.78 269	98.03 254	98.18 307	98.27 217	99.71 272	99.32 222	94.75 271	96.82 313	99.65 252	86.98 311	98.15 315	97.74 244	88.85 324	97.66 305

LPG-MVS_test		97.31 216	97.32 212	97.28 282	98.85 280	94.60 315	100.00 1	99.37 199	97.35 162	98.85 223	99.98 175	86.66 312	99.56 204	99.55 155	95.26 242	97.70 293

LGP-MVS_train				97.28 282	98.85 280	94.60 315		99.37 199	97.35 162	98.85 223	99.98 175	86.66 312	99.56 204	99.55 155	95.26 242	97.70 293

mvs_tets		97.00 232	96.69 229	97.94 259	97.41 338	97.27 264	99.60 288	99.18 281	96.51 220	97.35 299	99.69 243	86.53 314	98.91 261	98.84 194	95.09 253	97.65 310

pm-mvs1		95.76 285	95.01 293	98.00 255	98.23 304	97.45 255	99.24 318	99.04 329	93.13 316	95.93 328	99.72 236	86.28 315	98.84 269	95.62 289	87.92 330	97.72 285

KD-MVS_self_test		91.16 318	90.09 323	94.35 326	94.44 357	91.27 345	99.74 265	99.08 313	90.82 334	94.53 339	94.91 356	86.11 316	94.78 357	82.67 354	68.52 359	96.99 340

MVS-HIRNet		94.12 302	92.73 312	98.29 224	99.33 239	95.95 286	99.38 307	99.19 275	74.54 361	98.26 264	86.34 365	86.07 317	99.06 248	91.60 326	99.87 134	99.85 174

DeepMVS_CX				89.98 338	98.90 274	71.46 365		99.18 281	97.61 135	96.92 308	99.83 219	86.07 317	99.83 171	96.02 283	97.65 217	98.65 232

OPM-MVS		97.21 219	97.18 219	97.32 280	98.08 310	94.66 312	100.00 1	99.28 240	98.65 56	98.92 217	99.98 175	86.03 319	99.56 204	98.28 225	95.41 236	97.72 285
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
new_pmnet		94.11 303	93.47 304	96.04 313	96.60 345	92.82 334	99.97 220	98.91 339	90.21 337	95.26 331	98.05 341	85.89 320	98.14 316	84.28 351	92.01 290	97.16 337

ACMP	97.00 8	97.19 220	97.16 220	97.27 284	98.97 270	94.58 318	100.00 1	99.32 222	97.97 100	97.45 297	99.98 175	85.79 321	99.56 204	99.70 131	95.24 245	97.67 304
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
OurMVSNet-221017-0		96.14 275	95.98 260	96.62 304	97.49 333	93.44 328	99.92 235	98.16 348	95.86 241	97.65 288	99.95 199	85.71 322	98.78 274	94.93 299	94.18 265	97.64 313

SixPastTwentyTwo		95.71 286	95.49 283	96.38 308	97.42 336	93.01 331	99.84 247	98.23 347	94.75 271	95.98 327	99.97 183	85.35 323	98.43 304	94.71 300	93.17 274	97.69 297

LTVRE_ROB	95.29 16	96.32 264	96.10 254	96.99 292	98.55 288	93.88 324	99.45 300	99.28 240	94.50 282	96.46 320	99.52 277	84.86 324	99.48 223	97.26 260	95.03 254	97.59 320
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				96.84 300	98.96 271	93.49 327		98.12 350	98.12 88	98.35 255	99.97 183	84.45 325	99.56 204	95.63 288	95.25 244	97.49 326

PVSNet_0	93.57 19	96.41 256	95.74 271	98.41 216	99.84 122	95.22 296	100.00 1	100.00 1	98.08 90	97.55 295	99.78 231	84.40 326	100.00 1	100.00 1	81.99 346	100.00 1

K. test v3		95.46 290	95.14 291	96.40 307	97.53 330	93.40 329	99.99 196	99.23 262	95.49 258	92.70 348	99.73 234	84.26 327	98.12 318	93.94 311	93.38 272	97.68 299

GBi-Net		96.07 277	95.80 267	96.89 297	99.53 197	94.87 300	99.18 327	99.27 248	93.71 298	98.53 244	98.81 320	84.23 328	98.07 325	95.31 293	93.60 267	97.72 285

test1		96.07 277	95.80 267	96.89 297	99.53 197	94.87 300	99.18 327	99.27 248	93.71 298	98.53 244	98.81 320	84.23 328	98.07 325	95.31 293	93.60 267	97.72 285

FMVSNet2		96.22 268	95.60 279	98.06 246	99.53 197	98.33 212	99.45 300	99.27 248	93.71 298	98.03 273	98.84 319	84.23 328	98.10 323	93.97 310	93.40 271	97.73 279

testgi		96.18 270	95.93 262	96.93 296	98.98 269	94.20 323	100.00 1	99.07 317	97.16 176	96.06 326	99.86 214	84.08 331	97.79 336	90.38 336	97.80 211	98.81 230

Anonymous20240521		93.29 307	92.76 311	94.90 322	95.64 351	91.27 345	99.97 220	98.82 342	87.04 349	94.71 336	98.19 340	83.86 332	96.80 341	84.04 352	92.56 284	96.64 346

ACMH	96.25 11	96.77 239	96.62 232	97.21 285	98.96 271	94.43 320	99.64 281	99.33 220	97.43 157	96.55 319	99.97 183	83.52 333	99.54 212	99.07 185	95.13 251	97.66 305
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Anonymous20240529		96.93 235	96.22 250	99.05 180	99.79 140	97.30 263	99.16 333	99.47 77	88.51 344	98.69 234	100.00 1	83.50 334	100.00 1	99.83 110	97.02 224	99.83 179

lessismore_v0				96.05 312	97.55 329	91.80 341		99.22 264		91.87 349	99.91 208	83.50 334	98.68 282	92.48 321	90.42 312	97.68 299

UnsupCasMVSNet_eth		94.25 299	93.89 298	95.34 316	97.63 323	92.13 338	99.73 269	99.36 205	94.88 268	92.78 345	98.63 327	82.72 336	96.53 345	94.57 301	84.73 343	97.36 332

LF4IMVS		96.19 269	96.18 251	96.23 311	98.26 302	92.09 339	100.00 1	97.89 358	97.82 114	97.94 278	99.87 212	82.71 337	99.38 235	97.41 255	93.71 266	97.20 336

ACMH+	96.20 13	96.49 254	96.33 247	97.00 291	99.06 257	93.80 325	99.81 251	99.31 227	97.32 166	95.89 329	99.97 183	82.62 338	99.54 212	98.34 221	94.63 260	97.65 310

pmmvs3		90.62 322	89.36 326	94.40 325	90.53 363	91.49 343	100.00 1	96.73 364	84.21 354	93.65 344	96.65 349	82.56 339	94.83 356	82.28 355	77.62 353	96.89 343

XVG-ACMP-BASELINE		96.60 248	96.52 236	96.84 300	98.41 293	93.29 330	99.99 196	99.32 222	97.76 119	98.51 247	99.29 290	81.95 340	99.54 212	98.40 217	95.03 254	97.68 299

VPNet		96.41 256	95.76 270	98.33 222	98.61 286	98.30 215	99.48 299	99.45 99	96.98 188	98.87 222	99.88 211	81.57 341	98.93 259	99.22 179	87.82 331	97.76 238

MDA-MVSNet-bldmvs		91.65 317	89.94 324	96.79 302	96.72 343	96.70 279	99.42 304	98.94 335	88.89 342	66.97 369	98.37 336	81.43 342	95.91 352	89.24 344	89.46 319	97.75 249

MS-PatchMatch		95.66 287	95.87 264	95.05 318	97.80 319	89.25 350	98.88 348	99.30 230	96.35 227	96.86 311	99.01 307	81.35 343	99.43 231	93.30 315	99.98 119	96.46 348

USDC		95.90 283	95.70 273	96.50 306	98.60 287	92.56 337	100.00 1	98.30 346	97.77 117	96.92 308	99.94 203	81.25 344	99.45 230	93.54 314	94.96 257	97.49 326

TDRefinement		91.93 313	90.48 321	96.27 310	81.60 368	92.65 336	99.10 340	97.61 361	93.96 296	93.77 343	99.85 216	80.03 345	99.53 217	97.82 243	70.59 358	96.63 347

test_0402		94.35 298	93.70 301	96.32 309	97.92 315	93.60 326	99.61 287	98.85 341	88.19 347	94.68 337	99.48 281	80.01 346	98.58 293	89.39 342	95.15 250	96.77 344

TinyColmap		95.50 289	95.12 292	96.64 303	98.69 283	93.00 332	99.40 305	97.75 360	96.40 225	96.14 325	99.87 212	79.47 347	99.50 221	93.62 313	94.72 259	97.40 331

LFMVS		97.42 212	96.62 232	99.81 103	99.80 137	99.50 129	99.16 333	99.56 66	94.48 283	100.00 1	100.00 1	79.35 348	100.00 1	99.89 102	97.37 219	99.94 128

pmmvs-eth3d		91.73 316	90.67 320	94.92 321	91.63 361	92.71 335	99.90 238	98.54 344	91.19 331	88.08 355	95.50 351	79.31 349	96.13 350	90.55 334	81.32 349	95.91 354

new-patchmatchnet		90.30 323	89.46 325	92.84 335	90.77 362	88.55 353	99.83 248	98.80 343	90.07 339	87.86 356	95.00 354	78.77 350	94.30 359	84.86 350	79.15 351	95.68 355

test123		79.44 328	79.23 332	80.05 346	80.03 369	71.72 364	100.00 1	77.93 377	62.52 364	94.81 335	99.69 243	78.21 351	74.53 370	92.57 319	27.33 370	93.90 356

CMPMVS	66.12 22	90.65 321	92.04 314	86.46 342	96.18 347	66.87 368	98.03 356	99.38 197	83.38 356	85.49 359	99.55 274	77.59 352	98.80 273	94.44 303	94.31 264	93.72 358
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs6		93.64 304	92.87 309	95.94 314	97.47 335	91.41 344	98.92 346	99.02 330	87.84 348	95.01 334	99.61 266	77.24 353	98.77 277	94.33 304	86.41 340	97.63 314

tmp_tt		75.80 332	74.26 335	80.43 345	52.91 377	53.67 375	87.42 364	97.98 356	61.80 365	67.04 368	100.00 1	76.43 354	96.40 346	96.47 278	28.26 369	91.23 360

MIMVSNet1		91.96 312	91.20 315	94.23 329	94.94 356	91.69 342	99.34 311	99.22 264	88.23 345	94.18 342	98.45 333	75.52 355	93.41 362	79.37 360	91.49 298	97.60 319

VDDNet		96.39 260	95.55 280	98.90 191	99.27 243	97.45 255	99.15 335	99.92 30	91.28 330	99.98 108	100.00 1	73.55 356	100.00 1	99.85 107	96.98 225	99.24 224

FMVSNet1		94.45 297	93.63 302	96.89 297	98.87 278	94.87 300	99.18 327	99.27 248	90.95 333	97.31 300	98.81 320	72.89 357	98.07 325	92.61 318	92.81 278	97.72 285

VDD-MVS		96.58 249	95.99 259	98.34 221	99.52 204	95.33 294	99.18 327	99.38 197	96.64 212	99.77 171	100.00 1	72.51 358	100.00 1	100.00 1	96.94 226	99.70 210

EG-PatchMatch MVS		92.94 309	92.49 313	94.29 328	95.87 349	87.07 355	99.07 344	98.11 351	93.19 314	88.98 354	98.66 326	70.89 359	99.08 247	92.43 322	95.21 246	96.72 345

test_method		91.04 320	91.10 317	90.85 337	98.34 295	77.63 362	100.00 1	98.93 338	76.69 359	96.25 323	98.52 331	70.44 360	97.98 331	89.02 345	91.74 294	96.92 342

UniMVSNet_ETH3D		95.28 292	94.41 297	97.89 263	98.91 273	95.14 297	99.13 337	99.35 213	92.11 325	97.17 304	99.66 250	70.28 361	99.36 236	97.88 241	95.18 248	99.16 225

OpenMVS_ROB	88.34 20	91.89 314	91.12 316	94.19 330	95.55 352	87.63 354	99.26 316	98.03 353	86.61 350	90.65 353	96.82 348	70.14 362	98.78 274	86.54 348	96.50 229	96.15 350

MVS_0304		95.11 295	94.84 296	95.91 315	99.60 179	91.24 347	98.64 351	99.88 33	94.51 281	99.62 181	98.31 339	69.19 363	98.82 270	95.22 296	98.60 166	97.66 305

testmvs		80.17 327	81.95 330	74.80 348	58.54 375	59.58 374	100.00 1	87.14 375	76.09 360	99.61 182	100.00 1	67.06 364	74.19 371	98.84 194	50.30 365	90.64 361

UnsupCasMVSNet_bld		89.50 324	88.00 327	93.99 331	95.30 353	88.86 352	98.52 352	99.28 240	85.50 352	87.80 357	94.11 357	61.63 365	96.96 340	90.63 332	79.26 350	96.15 350

Gipuma		84.73 326	83.50 329	88.40 340	97.50 331	82.21 360	88.87 362	99.05 326	65.81 363	85.71 358	90.49 360	53.70 366	96.31 347	78.64 361	91.74 294	86.67 362
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
EMVS		69.88 334	69.09 337	72.24 352	84.70 365	65.82 370	99.96 224	87.08 376	49.82 370	71.51 365	84.74 367	49.30 367	75.32 369	50.97 370	43.71 367	75.59 367

PM-MVS		88.39 325	87.41 328	91.31 336	91.73 360	82.02 361	99.79 255	96.62 365	91.06 332	90.71 352	95.73 350	48.60 368	95.96 351	90.56 333	81.91 348	95.97 353

E-PMN		70.72 333	70.06 336	72.69 351	83.92 366	65.48 371	99.95 229	92.72 371	49.88 369	72.30 364	86.26 366	47.17 369	77.43 368	53.83 369	44.49 366	75.17 368

ambc				88.45 339	86.84 364	70.76 366	97.79 358	98.02 355		90.91 351	95.14 352	38.69 370	98.51 298	94.97 298	84.23 344	96.09 352

FPMVS		77.92 331	79.45 331	73.34 350	76.87 371	46.81 376	98.24 354	99.05 326	59.89 366	73.55 363	98.34 337	36.81 371	86.55 365	80.96 356	91.35 302	86.65 363

PMMVS2		79.15 329	77.28 333	84.76 344	82.34 367	72.66 363	99.70 274	95.11 369	71.68 362	84.78 360	90.87 359	32.05 372	89.99 364	75.53 365	63.45 363	91.64 359

LCM-MVSNet		79.01 330	76.93 334	85.27 343	78.28 370	68.01 367	96.57 359	98.03 353	55.10 367	82.03 361	93.27 358	31.99 373	93.95 360	82.72 353	74.37 355	93.84 357

ANet_high		66.05 336	63.44 340	73.88 349	61.14 374	63.45 372	95.68 361	87.18 374	79.93 357	47.35 371	80.68 370	22.35 374	72.33 372	61.24 367	35.42 368	85.88 364

wuyk23d		28.28 338	29.73 342	23.92 354	75.89 372	32.61 378	66.50 365	12.88 378	16.09 372	14.59 373	16.59 372	12.35 375	32.36 373	39.36 371	13.36 371	6.79 369

PMVS	60.66 23	65.98 337	65.05 338	68.75 353	55.06 376	38.40 377	88.19 363	96.98 362	48.30 371	44.82 372	88.52 363	12.22 376	86.49 366	67.58 366	83.79 345	81.35 366
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	68.59 21	67.22 335	64.68 339	74.84 347	74.67 373	62.32 373	95.84 360	90.87 373	50.98 368	58.72 370	81.05 369	12.20 377	78.95 367	61.06 368	56.75 364	83.24 365
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_blank		0.07 342	0.09 345	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.79 373	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

uanet_test		0.01 343	0.02 346	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.14 374	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

sosnet-low-res		0.01 343	0.02 346	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.14 374	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

sosnet		0.01 343	0.02 346	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.14 374	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

uncertanet		0.01 343	0.02 346	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.14 374	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

Regformer		0.01 343	0.02 346	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.14 374	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

ab-mvs-re		8.33 340	11.11 343	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	100.00 1	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

uanet		0.01 343	0.02 346	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.14 374	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

FOURS1					100.00 1	99.97 22	100.00 1	99.42 127	98.52 61	100.00 1

MSC_two_6792asdad				100.00 1	100.00 1	100.00 1		99.42 127					100.00 1	100.00 1	100.00 1	100.00 1

No_MVS				100.00 1	100.00 1	100.00 1		99.42 127					100.00 1	100.00 1	100.00 1	100.00 1

eth-test2					0.00 378

eth-test					0.00 378

IU-MVS					100.00 1	99.99 5		99.42 127	99.12 6	100.00 1				100.00 1	100.00 1	100.00 1

save fliter					99.99 59	99.93 47	100.00 1	99.42 127	98.93 32

test_0728_SECOND				100.00 1	99.99 59	99.99 5	100.00 1	99.42 127					100.00 1	100.00 1	100.00 1	100.00 1

GSMVS																99.91 139

test_part2					100.00 1	99.99 5				100.00 1

MTGPA								99.42 127

MTMP							100.00 1	99.18 281

gm-plane-assit					99.52 204	97.26 265			95.86 241		100.00 1		99.43 231	98.76 199

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 83		99.42 127		100.00 1			99.97 122

test_prior4						99.93 47	100.00 1

test_prior				99.90 85	100.00 1	99.75 106		99.73 54					99.97 122			100.00 1

旧先验2							100.00 1		98.11 89	100.00 1			100.00 1	99.67 140

新几何2							100.00 1

无先验							100.00 1	99.80 39	97.98 98				100.00 1	99.33 171		100.00 1

原ACMM2							100.00 1

testdata2													100.00 1	97.36 257

testdata1							100.00 1		98.77 50

plane_prior7					99.00 265	94.78 310

plane_prior5								99.40 181					99.55 209	99.79 116	95.57 234	97.76 238

plane_prior4											99.97 183

plane_prior3						94.79 309			99.03 19	99.08 209

plane_prior2							100.00 1		99.00 25

plane_prior1					99.02 260

plane_prior						94.80 306	100.00 1		99.03 19						95.58 230

n2								0.00 379

nn								0.00 379

door-mid								96.32 367

test11								99.42 127

door								96.13 368

HQP5-MVS						94.82 303

HQP-NCC					99.07 253		100.00 1		99.04 14	99.17 200

ACMP_Plane					99.07 253		100.00 1		99.04 14	99.17 200

BP-MVS														99.79 116

HQP4-MVS										99.17 200			99.57 200			97.77 236

HQP3-MVS								99.40 181							95.58 230

NP-MVS					99.07 253	94.81 305					99.97 183

ACMMP++_ref															94.58 262

ACMMP++															95.17 249

High-resolution multi-view benchmark