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.

lecture		99.64 51	99.53 62	99.98 23	99.99 49	99.93 47	100.00 1	99.47 79	98.53 85	100.00 1	100.00 1	97.88 162	100.00 1	99.98 84	99.92 131	100.00 1

fmvsm_s_conf0.5_n_8		99.34 100	99.14 120	99.91 76	99.83 125	99.74 103	100.00 1	99.38 216	98.94 40	100.00 1	100.00 1	94.25 261	99.99 101	100.00 1	99.91 135	100.00 1

fmvsm_s_conf0.5_n_6		99.30 109	99.12 123	99.84 102	99.24 297	99.56 129	100.00 1	99.31 257	98.90 50	100.00 1	100.00 1	94.75 253	99.97 139	99.98 84	99.88 141	100.00 1

fmvsm_l_conf0.5_n_3		99.38 92	99.20 113	99.92 75	99.80 146	99.78 95	100.00 1	99.35 237	98.94 40	100.00 1	100.00 1	94.77 252	99.99 101	99.99 69	99.92 131	100.00 1

fmvsm_s_conf0.5_n_3		98.99 159	98.69 179	99.89 83	99.70 168	99.69 114	100.00 1	99.39 213	98.93 43	100.00 1	100.00 1	90.20 321	99.99 101	100.00 1	99.95 122	100.00 1

reproduce_model		99.76 18	99.69 22	99.98 23	99.96 97	99.93 47	100.00 1	99.42 147	98.81 67	100.00 1	100.00 1	98.98 107	100.00 1	100.00 1	100.00 1	100.00 1

reproduce-ours		99.76 18	99.69 22	99.98 23	99.96 97	99.94 41	100.00 1	99.42 147	98.82 63	100.00 1	100.00 1	98.99 104	100.00 1	100.00 1	100.00 1	100.00 1

our_new_method		99.76 18	99.69 22	99.98 23	99.96 97	99.94 41	100.00 1	99.42 147	98.82 63	100.00 1	100.00 1	98.99 104	100.00 1	100.00 1	100.00 1	100.00 1

fmvsm_l_conf0.5_n_a		99.63 55	99.55 59	99.86 93	99.83 125	99.58 127	100.00 1	99.36 226	98.98 30	100.00 1	100.00 1	97.85 164	99.99 101	100.00 1	99.94 126	100.00 1

fmvsm_l_conf0.5_n		99.63 55	99.56 57	99.86 93	99.81 133	99.59 125	100.00 1	99.36 226	98.98 30	100.00 1	100.00 1	97.92 159	99.99 101	100.00 1	99.95 122	100.00 1

MM		99.63 55	99.52 65	99.94 67	99.99 49	99.82 90	100.00 1	99.97 17	99.11 8	100.00 1	100.00 1	96.65 216	100.00 1	100.00 1	99.97 116	100.00 1

test_fmvsmconf_n		99.56 68	99.46 75	99.86 93	99.68 176	99.58 127	100.00 1	99.31 257	98.92 45	99.88 190	100.00 1	97.35 192	99.99 101	99.98 84	99.99 103	100.00 1

test_fmvsmvis_n_1920		99.46 81	99.37 82	99.73 134	98.88 333	99.18 187	100.00 1	99.26 295	98.85 57	99.79 210	100.00 1	97.70 173	100.00 1	99.98 84	99.86 147	100.00 1

test_fmvsm_n_1920		99.55 70	99.49 70	99.73 134	99.85 121	99.19 185	100.00 1	99.41 196	98.87 55	100.00 1	100.00 1	97.34 193	100.00 1	99.98 84	99.90 137	100.00 1

test_cas_vis1_n_1920		98.63 197	98.25 217	99.77 127	99.69 171	99.32 167	100.00 1	99.31 257	98.84 59	99.96 141	100.00 1	87.42 361	99.99 101	99.14 225	99.86 147	100.00 1

test_vis1_n_1920		97.77 250	97.24 271	99.34 200	99.79 151	98.04 275	100.00 1	99.25 299	98.88 52	100.00 1	100.00 1	77.52 414	100.00 1	99.88 116	99.85 150	100.00 1

test_vis1_n		96.69 300	95.81 324	99.32 207	99.14 301	97.98 278	99.97 267	98.98 395	98.45 91	100.00 1	100.00 1	66.44 435	99.99 101	99.78 140	99.57 178	100.00 1

test_fmvs1_n		97.43 266	96.86 279	99.15 222	99.68 176	97.48 303	99.99 235	98.98 395	98.82 63	100.00 1	100.00 1	74.85 421	99.96 156	99.67 174	99.70 165	100.00 1

mvsany_test1		99.57 67	99.48 73	99.85 97	99.86 120	99.54 134	100.00 1	99.36 226	98.94 40	100.00 1	100.00 1	97.97 156	100.00 1	99.88 116	99.28 183	100.00 1

test_fmvs1		98.37 223	98.04 236	99.34 200	99.84 122	98.07 271	100.00 1	99.00 392	98.85 57	100.00 1	100.00 1	85.11 382	99.96 156	99.69 170	99.88 141	100.00 1

DVP-MVS++		99.81 11	99.75 14	100.00 1	100.00 1	99.99 5	100.00 1	99.42 147	98.79 71	100.00 1	100.00 1	99.54 30	100.00 1	100.00 1	100.00 1	100.00 1

MSC_two_6792asdad				100.00 1	100.00 1	100.00 1		99.42 147					100.00 1	100.00 1	100.00 1	100.00 1

PC_three_1452									98.80 68	100.00 1	100.00 1	99.54 30	100.00 1	100.00 1	100.00 1	100.00 1

No_MVS				100.00 1	100.00 1	100.00 1		99.42 147					100.00 1	100.00 1	100.00 1	100.00 1

SR-MVS-dyc-post		99.63 55	99.52 65	99.97 35	99.99 49	99.91 57	100.00 1	99.42 147	97.62 163	100.00 1	100.00 1	98.65 135	99.99 101	99.99 69	100.00 1	100.00 1

RE-MVS-def			99.55 59		99.99 49	99.91 57	100.00 1	99.42 147	97.62 163	100.00 1	100.00 1	98.94 115		99.99 69	100.00 1	100.00 1

SED-MVS		99.83 7	99.77 9	100.00 1	100.00 1	99.99 5	100.00 1	99.42 147	99.03 21	100.00 1	100.00 1	99.50 41	100.00 1	100.00 1	100.00 1	100.00 1

IU-MVS					100.00 1	99.99 5		99.42 147	99.12 7	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 30	100.00 1	100.00 1	100.00 1	100.00 1

test_241102_TWO								99.42 147	99.03 21	100.00 1	100.00 1	99.56 27	100.00 1	100.00 1	100.00 1	100.00 1

SF-MVS		99.66 48	99.57 52	99.95 55	99.99 49	99.85 86	100.00 1	99.42 147	97.67 155	100.00 1	100.00 1	99.05 98	99.99 101	100.00 1	100.00 1	100.00 1

ZNCC-MVS		99.71 33	99.62 44	99.97 35	99.99 49	99.90 64	100.00 1	99.79 45	97.97 129	99.97 134	100.00 1	98.97 109	100.00 1	99.94 106	100.00 1	100.00 1

DVP-MVS		99.83 7	99.78 7	100.00 1	100.00 1	99.99 5	100.00 1	99.42 147	99.04 16	100.00 1	100.00 1	99.53 33	100.00 1	100.00 1	100.00 1	100.00 1
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test_0728_THIRD									98.79 71	100.00 1	100.00 1	99.61 20	100.00 1	100.00 1	100.00 1	100.00 1

test_0728_SECOND				100.00 1	99.99 49	99.99 5	100.00 1	99.42 147					100.00 1	100.00 1	100.00 1	100.00 1

SR-MVS		99.68 43	99.58 49	99.98 23	100.00 1	99.95 32	100.00 1	99.64 64	97.59 171	100.00 1	100.00 1	98.99 104	99.99 101	100.00 1	100.00 1	100.00 1

DPM-MVS		99.63 55	99.51 67	100.00 1	99.90 113	100.00 1	100.00 1	99.43 128	99.00 27	100.00 1	100.00 1	99.58 26	100.00 1	97.64 297	100.00 1	100.00 1

GST-MVS		99.64 51	99.53 62	99.95 55	100.00 1	99.86 83	100.00 1	99.79 45	97.72 150	99.95 170	100.00 1	98.39 147	100.00 1	99.96 98	99.99 103	100.00 1

Anonymous202405211		97.87 245	97.53 256	98.90 238	99.81 133	96.70 331	99.35 380	99.46 97	92.98 384	98.83 282	99.99 202	90.63 315	100.00 1	99.70 163	97.03 277	100.00 1

SMA-MVS		99.69 39	99.59 47	99.98 23	99.99 49	99.93 47	100.00 1	99.43 128	97.50 183	100.00 1	100.00 1	99.43 55	100.00 1	100.00 1	100.00 1	100.00 1
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
DPE-MVS		99.79 14	99.73 17	99.99 12	99.99 49	99.98 17	100.00 1	99.42 147	98.91 47	100.00 1	100.00 1	99.22 83	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
CHOSEN 280x420		99.85 3	99.87 1	99.80 115	99.99 49	99.97 21	99.97 267	99.98 16	98.96 34	100.00 1	100.00 1	99.96 4	99.42 290	100.00 1	100.00 1	100.00 1

MVS_0304		99.72 29	99.65 34	99.93 71	99.99 49	99.79 94	100.00 1	99.91 35	99.17 6	100.00 1	100.00 1	97.84 166	100.00 1	100.00 1	99.95 122	100.00 1

MSP-MVS		99.81 11	99.77 9	99.94 67	100.00 1	99.86 83	100.00 1	99.42 147	98.87 55	100.00 1	100.00 1	99.65 19	99.96 156	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
TSAR-MVS + MP.		99.82 9	99.77 9	99.99 12	100.00 1	99.96 24	100.00 1	99.43 128	99.05 15	100.00 1	100.00 1	99.45 50	99.99 101	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
ACMMP_NAP		99.67 46	99.57 52	99.97 35	99.98 87	99.92 54	100.00 1	99.42 147	97.83 140	100.00 1	100.00 1	98.89 121	100.00 1	99.98 84	100.00 1	100.00 1

MTAPA		99.68 43	99.59 47	99.97 35	99.99 49	99.91 57	100.00 1	99.42 147	98.32 103	99.94 173	100.00 1	98.65 135	100.00 1	99.96 98	100.00 1	100.00 1

test9_res														100.00 1	100.00 1	100.00 1

train_agg		99.71 33	99.63 41	99.97 35	100.00 1	99.95 32	100.00 1	99.42 147	97.70 152	100.00 1	100.00 1	99.51 37	99.97 139	100.00 1	100.00 1	100.00 1

agg_prior2														100.00 1	100.00 1	100.00 1

HFP-MVS		99.74 25	99.67 30	99.96 46	100.00 1	99.89 71	100.00 1	99.76 49	97.95 133	100.00 1	100.00 1	99.31 71	100.00 1	99.99 69	100.00 1	100.00 1

region2R		99.72 29	99.64 37	99.97 35	100.00 1	99.90 64	100.00 1	99.74 55	97.86 139	100.00 1	100.00 1	99.19 86	100.00 1	99.99 69	100.00 1	100.00 1

EI-MVSNet-UG-set		99.69 39	99.63 41	99.87 90	99.99 49	99.64 119	99.95 280	99.44 119	98.35 101	100.00 1	100.00 1	98.98 107	99.97 139	99.98 84	100.00 1	100.00 1

EI-MVSNet-Vis-set		99.70 36	99.64 37	99.87 90	100.00 1	99.64 119	99.98 261	99.44 119	98.35 101	99.99 120	100.00 1	99.04 101	99.96 156	99.98 84	100.00 1	100.00 1

XVS		99.79 14	99.73 17	99.98 23	100.00 1	99.94 41	100.00 1	99.75 52	98.67 79	100.00 1	100.00 1	99.16 88	100.00 1	100.00 1	100.00 1	100.00 1

X-MVStestdata		97.04 284	96.06 313	99.98 23	100.00 1	99.94 41	100.00 1	99.75 52	98.67 79	100.00 1	66.97 449	99.16 88	100.00 1	100.00 1	100.00 1	100.00 1

test_prior				99.90 80	100.00 1	99.75 100		99.73 56					99.97 139			100.00 1

新几何1				99.99 12	100.00 1	99.96 24		99.81 42	97.89 136	100.00 1	100.00 1	99.20 85	100.00 1	97.91 289	100.00 1	100.00 1

旧先验1					99.99 49	99.88 78		99.82 40			100.00 1	99.27 80			100.00 1	100.00 1

无先验							100.00 1	99.80 43	97.98 127				100.00 1	99.33 214		100.00 1

原ACMM1				99.93 71	100.00 1	99.80 93		99.66 63	98.18 110	100.00 1	100.00 1	99.43 55	100.00 1	99.50 203	100.00 1	100.00 1

test222					99.99 49	99.90 64	100.00 1	99.69 62	97.66 156	100.00 1	100.00 1	99.30 76			100.00 1	100.00 1

testdata				99.66 148	99.99 49	98.97 210		99.73 56	97.96 132	100.00 1	100.00 1	99.42 59	100.00 1	99.28 218	100.00 1	100.00 1

1314		99.38 92	99.19 114	99.96 46	98.88 333	99.89 71	99.24 390	99.93 30	98.88 52	98.79 285	100.00 1	97.02 199	100.00 1	100.00 1	100.00 1	100.00 1

MVS		99.22 125	98.96 143	99.98 23	99.00 320	99.95 32	99.24 390	99.94 22	98.14 114	98.88 275	100.00 1	95.63 235	100.00 1	99.85 122	100.00 1	100.00 1

SD-MVS		99.81 11	99.75 14	99.99 12	99.99 49	99.96 24	100.00 1	99.42 147	99.01 26	100.00 1	100.00 1	99.33 66	100.00 1	100.00 1	100.00 1	100.00 1
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
MSLP-MVS++		99.89 1	99.85 2	99.99 12	100.00 1	99.96 24	100.00 1	99.95 19	99.11 8	100.00 1	100.00 1	99.60 21	100.00 1	100.00 1	100.00 1	100.00 1

APDe-MVS		99.84 6	99.78 7	99.99 12	100.00 1	99.98 17	100.00 1	99.44 119	99.06 13	100.00 1	100.00 1	99.56 27	99.99 101	100.00 1	100.00 1	100.00 1
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
APD-MVS_3200maxsize		99.65 49	99.55 59	99.97 35	99.99 49	99.91 57	100.00 1	99.48 78	97.54 175	100.00 1	100.00 1	98.97 109	99.99 101	99.98 84	100.00 1	100.00 1

ACMMPR		99.74 25	99.67 30	99.96 46	100.00 1	99.89 71	100.00 1	99.76 49	97.95 133	100.00 1	100.00 1	99.29 77	100.00 1	99.99 69	100.00 1	100.00 1

MP-MVS		99.61 62	99.49 70	99.98 23	99.99 49	99.94 41	100.00 1	99.42 147	97.82 142	99.99 120	100.00 1	98.20 150	100.00 1	99.99 69	100.00 1	100.00 1
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PGM-MVS		99.69 39	99.61 45	99.95 55	99.99 49	99.85 86	100.00 1	99.58 67	97.69 154	100.00 1	100.00 1	99.44 51	100.00 1	99.79 134	100.00 1	100.00 1

MCST-MVS		99.85 3	99.80 4	100.00 1	100.00 1	99.99 5	100.00 1	99.73 56	99.19 5	100.00 1	100.00 1	99.31 71	100.00 1	100.00 1	100.00 1	100.00 1

CDPH-MVS		99.73 28	99.64 37	99.99 12	100.00 1	99.97 21	100.00 1	99.42 147	98.02 123	100.00 1	100.00 1	99.32 69	99.99 101	100.00 1	100.00 1	100.00 1

test12				99.95 55	99.99 49	99.89 71		99.42 147		100.00 1		99.24 82	99.97 139		100.00 1	100.00 1

TSAR-MVS + GP.		99.61 62	99.69 22	99.35 199	99.99 49	98.06 273	100.00 1	99.36 226	99.83 2	100.00 1	100.00 1	98.95 113	99.99 101	100.00 1	99.11 188	100.00 1

mPP-MVS		99.69 39	99.60 46	99.97 35	100.00 1	99.91 57	100.00 1	99.42 147	97.91 135	100.00 1	100.00 1	99.04 101	100.00 1	100.00 1	100.00 1	100.00 1

HPM-MVS_fast		99.60 65	99.49 70	99.91 76	99.99 49	99.78 95	100.00 1	99.42 147	97.09 218	100.00 1	100.00 1	98.95 113	99.96 156	99.98 84	100.00 1	100.00 1

HPM-MVS		99.59 66	99.50 68	99.89 83	100.00 1	99.70 112	100.00 1	99.42 147	97.46 187	100.00 1	100.00 1	98.60 138	99.96 156	99.99 69	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
EPNet_dtu		98.53 209	98.23 223	99.43 185	99.92 109	99.01 203	99.96 273	99.47 79	98.80 68	99.96 141	99.96 237	98.56 140	99.30 298	87.78 418	99.68 166	100.00 1
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CHOSEN 1792x2688		99.00 155	98.91 153	99.25 217	99.90 113	97.79 293	100.00 1	99.99 13	98.79 71	98.28 318	100.00 1	93.63 268	99.95 169	99.66 178	99.95 122	100.00 1

APD-MVS		99.68 43	99.58 49	99.97 35	99.99 49	99.96 24	100.00 1	99.42 147	97.53 178	100.00 1	100.00 1	99.27 80	99.97 139	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
CNVR-MVS		99.85 3	99.80 4	100.00 1	100.00 1	99.99 5	100.00 1	99.77 48	99.07 11	100.00 1	100.00 1	99.39 64	100.00 1	100.00 1	100.00 1	100.00 1

NCCC		99.86 2	99.82 3	100.00 1	100.00 1	99.99 5	100.00 1	99.71 61	99.07 11	100.00 1	100.00 1	99.59 24	100.00 1	100.00 1	100.00 1	100.00 1

114514_t		99.39 89	99.25 100	99.81 110	99.97 91	99.48 151	100.00 1	99.42 147	95.53 314	100.00 1	100.00 1	98.37 148	99.95 169	99.97 96	100.00 1	100.00 1

CP-MVS		99.67 46	99.58 49	99.95 55	100.00 1	99.84 88	100.00 1	99.42 147	97.77 147	100.00 1	100.00 1	99.07 95	100.00 1	100.00 1	100.00 1	100.00 1

SteuartSystems-ACMMP		99.78 16	99.71 20	99.98 23	99.76 159	99.95 32	100.00 1	99.42 147	98.69 77	100.00 1	100.00 1	99.52 36	99.99 101	100.00 1	100.00 1	100.00 1
Skip Steuart: Steuart Systems R&D Blog.
DELS-MVS		99.62 60	99.56 57	99.82 105	99.92 109	99.45 153	100.00 1	99.78 47	98.92 45	99.73 220	100.00 1	97.70 173	100.00 1	99.93 108	100.00 1	100.00 1
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
CPTT-MVS		99.49 77	99.38 79	99.85 97	100.00 1	99.54 134	100.00 1	99.42 147	97.58 172	99.98 128	100.00 1	97.43 190	100.00 1	99.99 69	100.00 1	100.00 1

DP-MVS Recon		99.76 18	99.69 22	99.98 23	100.00 1	99.95 32	100.00 1	99.52 72	97.99 125	99.99 120	100.00 1	99.72 14	100.00 1	99.96 98	100.00 1	100.00 1

MVS_111021_LR		99.70 36	99.65 34	99.88 88	99.96 97	99.70 112	100.00 1	99.97 17	98.96 34	100.00 1	100.00 1	97.93 158	99.95 169	99.99 69	100.00 1	100.00 1

DP-MVS		98.86 178	98.54 194	99.81 110	99.97 91	99.45 153	99.52 363	99.40 200	94.35 350	98.36 310	100.00 1	96.13 225	99.97 139	99.12 228	100.00 1	100.00 1

QAPM		98.99 159	98.66 181	99.96 46	99.01 316	99.87 80	99.88 305	99.93 30	97.99 125	98.68 290	100.00 1	93.17 276	100.00 1	99.32 215	100.00 1	100.00 1

HyFIR lowres test		99.32 105	99.24 104	99.58 164	99.95 101	99.26 175	100.00 1	99.99 13	96.72 252	99.29 250	99.91 258	99.49 43	99.47 281	99.74 149	98.08 238	100.00 1

PAPM_NR		99.74 25	99.66 33	99.99 12	100.00 1	99.96 24	100.00 1	99.47 79	97.87 138	100.00 1	100.00 1	99.60 21	100.00 1	100.00 1	100.00 1	100.00 1

PAPR		99.76 18	99.68 28	99.99 12	100.00 1	99.96 24	100.00 1	99.47 79	98.16 111	100.00 1	100.00 1	99.51 37	100.00 1	100.00 1	100.00 1	100.00 1

IB-MVS	96.24 12	97.54 261	96.95 276	99.33 205	99.67 184	98.10 269	100.00 1	99.47 79	97.42 193	99.26 251	99.69 300	98.83 126	99.89 194	99.43 205	78.77 430	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
MVS_111021_HR		99.71 33	99.63 41	99.93 71	99.95 101	99.83 89	100.00 1	100.00 1	98.89 51	100.00 1	100.00 1	97.85 164	99.95 169	100.00 1	100.00 1	100.00 1

CSCG		99.28 114	99.35 87	99.05 227	99.99 49	97.15 319	100.00 1	99.47 79	97.44 191	99.42 238	100.00 1	97.83 168	100.00 1	99.99 69	100.00 1	100.00 1

API-MVS		99.72 29	99.70 21	99.79 119	99.97 91	99.37 163	99.96 273	99.94 22	98.48 89	100.00 1	100.00 1	98.92 118	100.00 1	100.00 1	100.00 1	100.00 1

PAPM		99.78 16	99.76 12	99.85 97	99.01 316	99.95 32	100.00 1	99.75 52	99.37 3	99.99 120	100.00 1	99.76 12	99.60 248	100.00 1	100.00 1	100.00 1

ACMMP		99.65 49	99.57 52	99.89 83	99.99 49	99.66 117	99.75 330	99.73 56	98.16 111	99.75 216	100.00 1	98.90 120	100.00 1	99.96 98	99.88 141	100.00 1
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
CNLPA		99.72 29	99.65 34	99.91 76	99.97 91	99.72 107	100.00 1	99.47 79	98.43 92	99.88 190	100.00 1	99.14 91	100.00 1	99.97 96	100.00 1	100.00 1

PHI-MVS		99.50 75	99.39 78	99.82 105	100.00 1	99.45 153	100.00 1	99.94 22	96.38 281	100.00 1	100.00 1	98.18 151	100.00 1	100.00 1	100.00 1	100.00 1

PVSNet	94.91 18	99.30 109	99.25 100	99.44 182	100.00 1	98.32 254	100.00 1	99.86 38	98.04 122	100.00 1	100.00 1	96.10 226	100.00 1	99.55 196	99.73 162	100.00 1

PVSNet_0	93.57 19	96.41 313	95.74 330	98.41 268	99.84 122	95.22 352	100.00 1	100.00 1	98.08 120	97.55 356	99.78 288	84.40 385	100.00 1	100.00 1	81.99 422	100.00 1

DeepPCF-MVS	98.03 4	98.54 208	99.72 19	94.98 387	99.99 49	84.94 426	100.00 1	99.42 147	99.98 1	100.00 1	100.00 1	98.11 153	100.00 1	100.00 1	100.00 1	100.00 1

DeepC-MVS_fast	98.92 1	99.75 23	99.67 30	99.99 12	99.99 49	99.96 24	99.73 336	99.52 72	99.06 13	100.00 1	100.00 1	98.80 129	100.00 1	99.95 104	100.00 1	100.00 1
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MG-MVS		99.75 23	99.68 28	99.97 35	100.00 1	99.91 57	99.98 261	99.47 79	99.09 10	100.00 1	100.00 1	98.59 139	100.00 1	99.95 104	100.00 1	100.00 1

AdaColmap		99.44 84	99.26 98	99.95 55	100.00 1	99.86 83	99.70 341	99.99 13	98.53 85	99.90 185	100.00 1	95.34 238	100.00 1	99.92 109	100.00 1	100.00 1

MAR-MVS		99.49 77	99.36 85	99.89 83	99.97 91	99.66 117	99.74 331	99.95 19	97.89 136	100.00 1	100.00 1	96.71 215	100.00 1	100.00 1	100.00 1	100.00 1
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
3Dnovator+	95.58 15	99.03 146	98.71 175	99.96 46	98.99 323	99.89 71	100.00 1	99.51 76	98.96 34	98.32 315	100.00 1	92.78 282	100.00 1	99.87 119	100.00 1	100.00 1

3Dnovator	95.63 14	99.06 141	98.76 167	99.96 46	98.86 338	99.90 64	99.98 261	99.93 30	98.95 37	98.49 305	100.00 1	92.91 280	100.00 1	99.71 159	100.00 1	100.00 1

TAPA-MVS	96.40 10	97.64 254	97.37 263	98.45 265	99.94 104	95.70 346	100.00 1	99.40 200	97.65 158	99.53 229	100.00 1	99.31 71	99.66 245	80.48 433	100.00 1	100.00 1
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
fmvsm_s_conf0.5_n_5		99.00 155	98.70 177	99.88 88	99.81 133	99.64 119	100.00 1	99.26 295	98.78 74	99.97 134	100.00 1	90.65 313	99.99 101	100.00 1	99.89 138	99.99 115

MP-MVS-pluss		99.61 62	99.50 68	99.97 35	99.98 87	99.92 54	100.00 1	99.42 147	97.53 178	99.77 213	100.00 1	98.77 130	100.00 1	99.99 69	100.00 1	99.99 115
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HPM-MVS++		99.82 9	99.76 12	99.99 12	99.99 49	99.98 17	100.00 1	99.83 39	98.88 52	99.96 141	100.00 1	99.21 84	100.00 1	100.00 1	100.00 1	99.99 115

fmvsm_s_conf0.5_n_a		99.32 105	99.15 119	99.81 110	99.80 146	99.47 152	100.00 1	99.35 237	98.22 106	100.00 1	100.00 1	95.21 243	99.99 101	99.96 98	99.86 147	99.98 118

fmvsm_s_conf0.5_n		99.21 126	99.01 134	99.83 103	99.84 122	99.53 136	100.00 1	99.38 216	98.29 105	100.00 1	100.00 1	93.62 269	99.99 101	99.99 69	99.93 129	99.98 118

thres100view900		99.25 121	99.01 134	99.95 55	99.81 133	99.87 80	100.00 1	99.94 22	97.13 215	99.83 197	99.96 237	97.01 200	100.00 1	99.59 191	97.85 253	99.98 118

tfpn200view9		99.26 117	99.03 132	99.96 46	99.81 133	99.89 71	100.00 1	99.94 22	97.23 209	99.83 197	99.96 237	97.04 196	100.00 1	99.59 191	97.85 253	99.98 118

thres200		99.27 115	99.04 131	99.96 46	99.81 133	99.90 64	100.00 1	99.94 22	97.31 204	99.83 197	99.96 237	97.04 196	100.00 1	99.62 185	97.88 251	99.98 118

LCM-MVSNet-Re		96.52 306	97.21 273	94.44 391	99.27 294	85.80 424	99.85 309	96.61 441	95.98 298	92.75 414	98.48 401	93.97 265	97.55 410	99.58 194	98.43 210	99.98 118

JIA-IIPM		97.09 280	96.34 302	99.36 198	98.88 333	98.59 233	99.81 315	99.43 128	84.81 425	99.96 141	90.34 438	98.55 141	99.52 273	97.00 319	98.28 226	99.98 118

fmvsm_s_conf0.1_n_a		98.71 188	98.36 212	99.78 124	99.09 306	99.42 157	100.00 1	99.26 295	97.42 193	100.00 1	100.00 1	89.78 328	99.96 156	99.82 131	99.85 150	99.97 125

fmvsm_s_conf0.1_n		98.77 183	98.42 204	99.82 105	99.47 264	99.52 140	100.00 1	99.27 288	97.53 178	100.00 1	100.00 1	89.73 330	99.96 156	99.84 125	99.93 129	99.97 125

test_fmvsmconf0.1_n		99.25 121	99.05 130	99.82 105	98.92 329	99.55 131	100.00 1	99.23 309	98.91 47	99.75 216	99.97 220	94.79 251	99.94 182	99.94 106	99.99 103	99.97 125

thres600view7		99.24 124	99.00 137	99.95 55	99.81 133	99.87 80	100.00 1	99.94 22	97.13 215	99.83 197	99.96 237	97.01 200	100.00 1	99.54 199	97.77 262	99.97 125

thres400		99.26 117	99.03 132	99.95 55	99.81 133	99.89 71	100.00 1	99.94 22	97.23 209	99.83 197	99.96 237	97.04 196	100.00 1	99.59 191	97.85 253	99.97 125

OMC-MVS		99.27 115	99.38 79	98.96 235	99.95 101	97.06 323	100.00 1	99.40 200	98.83 61	99.88 190	100.00 1	97.01 200	99.86 203	99.47 204	99.84 152	99.97 125

dmvs_re		97.54 261	97.88 242	96.54 363	99.55 232	90.35 411	99.86 307	99.46 97	97.00 225	99.41 243	100.00 1	90.78 312	99.30 298	99.60 189	95.24 305	99.96 131

CANet		99.40 88	99.24 104	99.89 83	99.99 49	99.76 99	100.00 1	99.73 56	98.40 93	99.78 212	100.00 1	95.28 239	99.96 156	100.00 1	99.99 103	99.96 131

GG-mvs-BLEND				99.59 160	99.54 234	99.49 147	99.17 403	99.52 72		99.96 141	99.68 304	100.00 1	99.33 297	99.71 159	99.99 103	99.96 131

gg-mvs-nofinetune		96.95 289	96.10 311	99.50 172	99.41 276	99.36 165	99.07 417	99.52 72	83.69 427	99.96 141	83.60 446	100.00 1	99.20 303	99.68 171	99.99 103	99.96 131

VNet		99.04 144	98.75 168	99.90 80	99.81 133	99.75 100	99.50 365	99.47 79	98.36 99	100.00 1	99.99 202	94.66 255	100.00 1	99.90 112	97.09 276	99.96 131

BH-w/o		98.82 181	98.81 163	98.88 240	99.62 209	96.71 330	100.00 1	99.28 276	97.09 218	98.81 283	100.00 1	94.91 249	99.96 156	99.54 199	100.00 1	99.96 131

patch_mono-2		99.04 144	99.79 6	96.81 358	99.92 109	90.47 410	100.00 1	99.41 196	98.95 37	100.00 1	100.00 1	99.78 9	100.00 1	100.00 1	100.00 1	99.95 137

dcpmvs_2		98.87 177	99.53 62	96.90 352	99.87 119	90.88 408	99.94 288	99.07 372	98.20 109	100.00 1	100.00 1	98.69 134	99.86 203	100.00 1	100.00 1	99.95 137

Patchmatch-test		97.83 247	97.42 259	99.06 225	99.08 307	97.66 297	98.66 426	99.21 321	93.65 366	98.25 322	99.58 327	99.47 48	99.57 255	90.25 406	98.59 202	99.95 137

HY-MVS	96.53 9	99.50 75	99.35 87	99.96 46	99.81 133	99.93 47	99.64 348	100.00 1	97.97 129	99.84 194	99.85 271	98.94 115	99.99 101	99.86 120	98.23 230	99.95 137

PatchMatch-RL		99.02 152	98.78 165	99.74 131	99.99 49	99.29 170	100.00 1	100.00 1	98.38 95	99.89 188	99.81 281	93.14 278	99.99 101	97.85 291	99.98 113	99.95 137

balanced_conf03		99.43 85	99.28 92	99.85 97	99.68 176	99.68 115	99.97 267	99.28 276	97.03 223	99.96 141	99.97 220	97.90 160	99.93 186	99.77 142	100.00 1	99.94 142

test2506		99.48 79	99.38 79	99.75 130	99.89 115	99.51 141	99.45 369	100.00 1	98.38 95	99.83 197	100.00 1	98.86 122	99.81 222	99.25 219	98.78 197	99.94 142

test1111		98.42 218	98.12 227	99.29 210	99.88 117	98.15 264	99.46 367	100.00 1	98.36 99	99.42 238	100.00 1	87.91 354	99.79 227	99.31 216	98.78 197	99.94 142

ECVR-MVS		98.43 216	98.14 226	99.32 207	99.89 115	98.21 262	99.46 367	100.00 1	98.38 95	99.47 236	100.00 1	87.91 354	99.80 226	99.35 211	98.78 197	99.94 142

test_yl		99.51 72	99.37 82	99.95 55	99.82 127	99.90 64	100.00 1	99.47 79	97.48 185	100.00 1	100.00 1	99.80 6	100.00 1	99.98 84	97.75 263	99.94 142

DCV-MVSNet		99.51 72	99.37 82	99.95 55	99.82 127	99.90 64	100.00 1	99.47 79	97.48 185	100.00 1	100.00 1	99.80 6	100.00 1	99.98 84	97.75 263	99.94 142

LFMVS		97.42 267	96.62 288	99.81 110	99.80 146	99.50 143	99.16 404	99.56 70	94.48 346	100.00 1	100.00 1	79.35 408	100.00 1	99.89 114	97.37 272	99.94 142

WTY-MVS		99.54 71	99.40 77	99.95 55	99.81 133	99.93 47	100.00 1	100.00 1	97.98 127	99.84 194	100.00 1	98.94 115	99.98 131	99.86 120	98.21 231	99.94 142

PMMVS		99.12 135	98.97 142	99.58 164	99.57 227	98.98 208	100.00 1	99.30 263	97.14 213	99.96 141	100.00 1	96.53 221	99.82 219	99.70 163	98.49 206	99.94 142

F-COLMAP		99.64 51	99.64 37	99.67 145	99.99 49	99.07 194	100.00 1	99.44 119	98.30 104	99.90 185	100.00 1	99.18 87	99.99 101	99.91 111	100.00 1	99.94 142

PLC	98.56 2	99.70 36	99.74 16	99.58 164	100.00 1	98.79 219	100.00 1	99.54 71	98.58 84	99.96 141	100.00 1	99.59 24	100.00 1	100.00 1	100.00 1	99.94 142
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
TR-MVS		98.14 235	97.74 247	99.33 205	99.59 218	98.28 257	99.27 387	99.21 321	96.42 278	99.15 258	99.94 250	88.87 344	99.79 227	98.88 240	98.29 225	99.93 153

PatchmatchNet		99.03 146	98.96 143	99.26 216	99.49 258	98.33 252	99.38 377	99.45 105	96.64 261	99.96 141	99.58 327	99.49 43	99.50 277	97.63 298	99.00 193	99.93 153
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
fmvsm_s_conf0.5_n_2		98.90 175	98.57 192	99.90 80	99.79 151	99.78 95	100.00 1	99.25 299	98.97 32	100.00 1	100.00 1	89.22 338	99.99 101	100.00 1	99.88 141	99.92 155

ET-MVSNet_ETH3D		96.41 313	95.48 344	99.20 220	99.81 133	99.75 100	100.00 1	99.02 389	97.30 206	78.33 438	100.00 1	97.73 171	97.94 400	99.70 163	87.41 401	99.92 155

LS3D		99.31 107	99.13 121	99.87 90	99.99 49	99.71 108	99.55 359	99.46 97	97.32 202	99.82 205	100.00 1	96.85 210	99.97 139	99.14 225	100.00 1	99.92 155

MGCFI-Net		99.01 154	98.70 177	99.93 71	99.74 163	99.94 41	100.00 1	99.29 270	97.60 170	100.00 1	100.00 1	95.10 245	99.96 156	99.74 149	96.85 283	99.91 158

sasdasda		99.03 146	98.73 171	99.94 67	99.75 161	99.95 32	100.00 1	99.30 263	97.64 160	100.00 1	100.00 1	95.22 241	99.97 139	99.76 144	96.90 281	99.91 158

GSMVS																99.91 158

sam_mvs1												99.29 77				99.91 158

SCA		98.30 225	97.98 240	99.23 218	99.41 276	98.25 259	99.99 235	99.45 105	96.91 234	99.76 215	99.58 327	89.65 332	99.54 267	98.31 271	98.79 196	99.91 158

Patchmatch-RL test		93.49 368	93.63 365	93.05 402	91.78 433	83.41 428	98.21 432	96.95 438	91.58 396	91.05 417	97.64 417	99.40 63	95.83 427	94.11 372	81.95 423	99.91 158

canonicalmvs		99.03 146	98.73 171	99.94 67	99.75 161	99.95 32	100.00 1	99.30 263	97.64 160	100.00 1	100.00 1	95.22 241	99.97 139	99.76 144	96.90 281	99.91 158

test-LLR		99.03 146	98.91 153	99.40 192	99.40 281	99.28 172	100.00 1	99.45 105	96.70 254	99.42 238	99.12 363	99.31 71	99.01 311	96.82 326	99.99 103	99.91 158

TESTMET0.1,1		99.08 138	98.96 143	99.44 182	99.63 202	99.38 160	100.00 1	99.45 105	95.53 314	99.48 233	100.00 1	99.71 15	99.02 310	96.84 325	99.99 103	99.91 158

test-mter		98.96 166	98.82 161	99.40 192	99.40 281	99.28 172	100.00 1	99.45 105	95.44 325	99.42 238	99.12 363	99.70 16	99.01 311	96.82 326	99.99 103	99.91 158

MSDG		98.90 175	98.63 185	99.70 140	99.92 109	99.25 177	100.00 1	99.37 220	95.71 308	99.40 244	100.00 1	96.58 217	99.95 169	96.80 328	99.94 126	99.91 158

MVSMamba_PlusPlus		99.39 89	99.25 100	99.80 115	99.68 176	99.59 125	99.99 235	99.30 263	96.66 259	99.96 141	99.97 220	97.89 161	99.92 189	99.76 144	100.00 1	99.90 169

test_fmvsmconf0.01_n		98.60 200	98.24 220	99.67 145	96.90 410	99.21 183	99.99 235	99.04 385	98.80 68	99.57 228	99.96 237	90.12 322	99.91 191	99.89 114	99.89 138	99.90 169

alignmvs		99.38 92	99.21 109	99.91 76	99.73 164	99.92 54	100.00 1	99.51 76	97.61 167	100.00 1	100.00 1	99.06 96	99.93 186	99.83 126	97.12 275	99.90 169

PVSNet_Blended		99.48 79	99.36 85	99.83 103	99.98 87	99.60 123	100.00 1	100.00 1	97.79 145	100.00 1	100.00 1	96.57 218	99.99 101	100.00 1	99.88 141	99.90 169

EPMVS		99.25 121	99.13 121	99.60 158	99.60 214	99.20 184	99.60 354	100.00 1	96.93 231	99.92 180	99.36 351	99.05 98	99.71 242	98.77 246	98.94 194	99.90 169

PCF-MVS	98.23 3	98.69 191	98.37 210	99.62 154	99.78 156	99.02 201	99.23 395	99.06 380	96.43 275	98.08 327	100.00 1	94.72 254	99.95 169	98.16 278	99.91 135	99.90 169
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
dongtai		98.29 227	98.25 217	98.42 267	99.58 223	95.86 343	100.00 1	99.44 119	93.46 373	99.69 223	99.97 220	97.53 182	99.51 275	96.28 338	98.27 228	99.89 175

kuosan		98.55 205	98.53 196	98.62 253	99.66 192	96.16 338	100.00 1	99.44 119	93.93 360	99.81 209	99.98 210	97.58 177	99.81 222	98.08 280	98.28 226	99.89 175

GA-MVS		97.72 252	97.27 269	99.06 225	99.24 297	97.93 284	100.00 1	99.24 305	95.80 307	98.99 270	99.64 313	89.77 329	99.36 293	95.12 358	97.62 271	99.89 175

baseline1		98.91 173	98.61 187	99.81 110	99.71 166	99.77 98	99.78 321	99.44 119	97.51 182	98.81 283	99.99 202	98.25 149	99.76 235	98.60 259	95.41 296	99.89 175

tpmvs		98.59 201	98.38 208	99.23 218	99.69 171	97.90 285	99.31 385	99.47 79	94.52 344	99.68 224	99.28 355	97.64 176	99.89 194	97.71 295	98.17 235	99.89 175

EPNet		99.62 60	99.69 22	99.42 187	99.99 49	98.37 247	100.00 1	99.89 37	98.83 61	100.00 1	100.00 1	98.97 109	100.00 1	99.90 112	99.61 175	99.89 175
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet_Blended_VisFu		99.33 103	99.18 117	99.78 124	99.82 127	99.49 147	100.00 1	99.95 19	97.36 196	99.63 226	100.00 1	96.45 222	99.95 169	99.79 134	99.65 171	99.89 175

dp		98.72 187	98.61 187	99.03 230	99.53 237	97.39 306	99.45 369	99.39 213	95.62 311	99.94 173	99.52 337	98.83 126	99.82 219	96.77 331	98.42 211	99.89 175

sss		99.45 82	99.34 89	99.80 115	99.76 159	99.50 143	100.00 1	99.91 35	97.72 150	99.98 128	99.94 250	98.45 144	100.00 1	99.53 201	98.75 200	99.89 175

Test_1112_low_res		98.83 180	98.60 189	99.51 169	99.69 171	98.75 221	99.99 235	99.14 345	96.81 241	98.84 280	99.06 367	97.45 187	99.89 194	98.66 251	97.75 263	99.89 175

1112_ss		98.91 173	98.71 175	99.51 169	99.69 171	98.75 221	99.99 235	99.15 340	96.82 240	98.84 280	100.00 1	97.45 187	99.89 194	98.66 251	97.75 263	99.89 175

MDTV_nov1_ep13_2view						99.24 179	99.56 358		96.31 287	99.96 141		98.86 122		98.92 238		99.89 175

Vis-MVSNet (Re-imp)		98.99 159	98.89 157	99.29 210	99.64 200	98.89 214	99.98 261	99.31 257	96.74 249	99.48 233	100.00 1	98.11 153	99.10 306	98.39 267	98.34 219	99.89 175

myMVS_eth3d28		99.41 87	99.28 92	99.80 115	99.69 171	99.53 136	100.00 1	99.43 128	97.12 217	99.98 128	99.97 220	99.41 61	100.00 1	99.81 133	98.07 239	99.88 188

UBG		99.36 96	99.27 94	99.63 152	99.63 202	99.01 203	100.00 1	99.43 128	96.99 226	100.00 1	99.92 255	99.69 17	99.99 101	99.74 149	98.06 240	99.88 188

ETVMVS		99.16 132	98.98 140	99.69 141	99.67 184	99.56 129	100.00 1	99.45 105	96.36 283	99.98 128	99.95 244	98.65 135	99.64 246	99.11 229	97.63 270	99.88 188

GeoE		98.06 239	97.65 254	99.29 210	99.47 264	98.41 241	100.00 1	99.19 325	94.85 333	98.88 275	100.00 1	91.21 301	99.59 250	97.02 318	98.19 233	99.88 188

UA-Net		99.06 141	98.83 160	99.74 131	99.52 245	99.40 159	99.08 415	99.45 105	97.64 160	99.83 197	100.00 1	95.80 231	99.94 182	98.35 269	99.80 160	99.88 188

ADS-MVSNet2		98.28 229	98.51 199	97.62 322	99.51 250	95.03 355	99.24 390	99.41 196	95.52 316	99.96 141	99.70 297	97.57 179	97.94 400	97.11 316	98.54 203	99.88 188

ADS-MVSNet		98.70 190	98.51 199	99.28 213	99.51 250	98.39 244	99.24 390	99.44 119	95.52 316	99.96 141	99.70 297	97.57 179	99.58 254	97.11 316	98.54 203	99.88 188

mvs_anonymous		98.80 182	98.60 189	99.38 196	99.57 227	99.24 179	100.00 1	99.21 321	95.87 301	98.92 272	99.82 278	96.39 223	99.03 309	99.13 227	98.50 205	99.88 188

tpm		98.24 231	98.22 224	98.32 275	99.13 302	95.79 344	99.53 362	99.12 356	95.20 327	99.96 141	99.36 351	97.58 177	99.28 300	97.41 307	96.67 284	99.88 188

EC-MVSNet		99.19 128	99.09 127	99.48 176	99.42 274	99.07 194	100.00 1	99.21 321	96.95 229	99.96 141	100.00 1	96.88 209	99.48 279	99.64 180	99.79 161	99.88 188

IS-MVSNet		99.08 138	98.91 153	99.59 160	99.65 194	99.38 160	99.78 321	99.24 305	96.70 254	99.51 231	100.00 1	98.44 145	99.52 273	98.47 264	98.39 214	99.88 188

GDP-MVS		99.39 89	99.26 98	99.77 127	99.53 237	99.55 131	100.00 1	99.11 358	97.14 213	99.96 141	100.00 1	99.83 5	99.89 194	98.47 264	99.26 184	99.87 199

BP-MVS1		99.56 68	99.48 73	99.79 119	99.48 260	99.61 122	100.00 1	99.32 249	97.34 199	99.94 173	100.00 1	99.74 13	99.89 194	99.75 148	99.72 163	99.87 199

CS-MVS		99.33 103	99.27 94	99.50 172	99.99 49	99.00 206	100.00 1	99.13 350	97.26 207	99.96 141	100.00 1	97.79 169	99.64 246	99.64 180	99.67 168	99.87 199

Fast-Effi-MVS+		98.40 221	98.02 238	99.55 168	99.63 202	99.06 196	100.00 1	99.15 340	95.07 328	99.42 238	99.95 244	93.26 275	99.73 240	97.44 305	98.24 229	99.87 199

dmvs_testset		93.27 371	95.48 344	86.65 414	98.74 344	68.42 443	99.92 294	98.91 400	96.19 294	93.28 411	100.00 1	91.06 306	91.67 439	89.64 410	91.54 361	99.86 203

testing3-2		99.45 82	99.31 90	99.86 93	99.70 168	99.73 105	100.00 1	99.47 79	97.46 187	99.97 134	99.97 220	99.48 47	100.00 1	99.78 140	97.99 242	99.85 204

MVS-HIRNet		94.12 364	92.73 378	98.29 276	99.33 287	95.95 339	99.38 377	99.19 325	74.54 438	98.26 321	86.34 442	86.07 374	99.06 308	91.60 394	99.87 146	99.85 204

mamv4		98.95 169	99.11 124	98.46 263	99.68 176	95.67 347	99.14 408	99.27 288	96.43 275	99.94 173	99.97 220	97.79 169	99.88 201	99.77 142	100.00 1	99.84 206

CR-MVSNet		98.02 242	97.71 252	98.93 236	99.31 288	98.86 215	99.13 409	99.00 392	96.53 269	99.96 141	98.98 377	96.94 206	98.10 389	91.18 396	98.40 212	99.84 206

RPMNet		95.26 353	93.82 362	99.56 167	99.31 288	98.86 215	99.13 409	99.42 147	79.82 432	99.96 141	95.13 428	95.69 234	99.98 131	77.54 438	98.40 212	99.84 206

ab-mvs		98.42 218	98.02 238	99.61 156	99.71 166	99.00 206	99.10 412	99.64 64	96.70 254	99.04 268	99.81 281	90.64 314	99.98 131	99.64 180	97.93 248	99.84 206

SymmetryMVS		99.30 109	99.25 100	99.45 180	99.79 151	98.55 235	99.94 288	99.47 79	98.39 94	100.00 1	100.00 1	98.44 145	99.98 131	99.36 209	97.83 256	99.83 210

FE-MVS		99.16 132	98.99 139	99.66 148	99.65 194	99.18 187	99.58 356	99.43 128	95.24 326	99.91 183	99.59 325	99.37 65	99.97 139	98.31 271	99.81 157	99.83 210

Anonymous20240529		96.93 290	96.22 307	99.05 227	99.79 151	97.30 313	99.16 404	99.47 79	88.51 414	98.69 288	100.00 1	83.50 393	100.00 1	99.83 126	97.02 278	99.83 210

CVMVSNet		98.56 204	98.47 202	98.82 242	99.11 303	97.67 296	99.74 331	99.47 79	97.57 173	99.06 266	100.00 1	95.72 233	98.97 317	98.21 277	97.33 273	99.83 210

tpm2		98.64 194	98.58 191	98.81 245	99.42 274	97.12 320	99.69 343	99.37 220	93.63 367	99.94 173	99.67 305	98.96 112	99.47 281	98.62 258	97.95 247	99.83 210

DeepC-MVS	97.84 5	99.00 155	98.80 164	99.60 158	99.93 106	99.03 199	100.00 1	99.40 200	98.61 83	99.33 248	100.00 1	92.23 292	99.95 169	99.74 149	99.96 120	99.83 210
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
testing11		99.26 117	99.19 114	99.46 178	99.64 200	98.61 231	100.00 1	99.43 128	96.94 230	99.92 180	99.94 250	99.43 55	99.97 139	99.67 174	97.79 261	99.82 216

Syy-MVS		96.17 330	96.57 290	95.00 385	99.50 254	87.37 422	100.00 1	99.57 68	96.23 289	98.07 328	100.00 1	92.41 291	97.81 403	85.34 423	97.96 245	99.82 216

myMVS_eth3d		98.52 210	98.51 199	98.53 259	99.50 254	97.98 278	100.00 1	99.57 68	96.23 289	98.07 328	100.00 1	99.09 94	97.81 403	96.17 339	97.96 245	99.82 216

testing3		98.44 215	98.37 210	98.65 251	99.51 250	98.32 254	100.00 1	99.62 66	96.43 275	97.93 337	99.99 202	99.11 92	97.81 403	94.88 361	97.80 259	99.82 216

EIA-MVS		99.26 117	99.19 114	99.45 180	99.63 202	98.75 221	100.00 1	99.27 288	96.93 231	99.95 170	100.00 1	97.47 186	99.79 227	99.74 149	99.72 163	99.82 216

SPE-MVS-test		99.31 107	99.27 94	99.43 185	99.99 49	98.77 220	100.00 1	99.19 325	97.24 208	99.96 141	100.00 1	97.56 181	99.70 243	99.68 171	99.81 157	99.82 216

MVS_Test		98.93 172	98.65 182	99.77 127	99.62 209	99.50 143	99.99 235	99.19 325	95.52 316	99.96 141	99.86 266	96.54 220	99.98 131	98.65 253	98.48 207	99.82 216

diffmvs		98.96 166	98.73 171	99.63 152	99.54 234	99.16 189	100.00 1	99.18 332	97.33 201	99.96 141	100.00 1	94.60 256	99.91 191	99.66 178	98.33 222	99.82 216
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
tpmrst		98.98 163	98.93 150	99.14 223	99.61 211	97.74 294	99.52 363	99.36 226	96.05 297	99.98 128	99.64 313	99.04 101	99.86 203	98.94 236	98.19 233	99.82 216

testing91		99.18 129	99.10 125	99.41 188	99.60 214	98.43 239	100.00 1	99.43 128	96.76 245	99.82 205	99.92 255	99.05 98	99.98 131	99.62 185	97.67 267	99.81 225

testing99		99.18 129	99.10 125	99.41 188	99.60 214	98.43 239	100.00 1	99.43 128	96.76 245	99.84 194	99.92 255	99.06 96	99.98 131	99.62 185	97.67 267	99.81 225

testing222		99.14 134	98.94 148	99.73 134	99.67 184	99.51 141	100.00 1	99.43 128	96.90 236	99.99 120	99.90 260	98.55 141	99.86 203	98.85 241	97.18 274	99.81 225

MonoMVSNet		98.55 205	98.64 184	98.26 279	98.21 369	95.76 345	99.94 288	99.16 338	96.23 289	99.47 236	99.24 357	96.75 213	99.22 302	99.61 188	99.17 185	99.81 225

ETV-MVS		99.34 100	99.24 104	99.64 151	99.58 223	99.33 166	100.00 1	99.25 299	97.57 173	99.96 141	100.00 1	97.44 189	99.79 227	99.70 163	99.65 171	99.81 225

thisisatest0515		99.42 86	99.31 90	99.74 131	99.59 218	99.55 131	100.00 1	99.46 97	96.65 260	99.92 180	100.00 1	99.44 51	99.85 210	99.09 230	99.63 174	99.81 225

Effi-MVS+		98.58 202	98.24 220	99.61 156	99.60 214	99.26 175	97.85 434	99.10 361	96.22 292	99.97 134	99.89 261	93.75 266	99.77 232	99.43 205	98.34 219	99.81 225

RRT-MVS		98.75 186	98.52 197	99.44 182	99.65 194	98.57 234	99.90 299	99.08 367	96.51 271	99.96 141	99.95 244	92.59 288	99.96 156	99.60 189	99.45 181	99.81 225

casdiffmvs		98.65 193	98.38 208	99.46 178	99.52 245	98.74 224	100.00 1	99.15 340	96.91 234	99.05 267	100.00 1	92.75 283	99.83 216	99.70 163	98.38 216	99.81 225
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
mvsmamba		99.05 143	98.98 140	99.27 215	99.57 227	98.10 269	100.00 1	99.28 276	95.92 300	99.96 141	99.97 220	96.73 214	99.89 194	99.72 155	99.65 171	99.81 225

lupinMVS		99.29 112	99.16 118	99.69 141	99.45 270	99.49 147	100.00 1	99.15 340	97.45 189	99.97 134	100.00 1	96.76 211	99.76 235	99.67 174	100.00 1	99.81 225

casdiffmvs_mvg		98.64 194	98.39 207	99.40 192	99.50 254	98.60 232	100.00 1	99.22 314	96.85 238	99.10 261	100.00 1	92.75 283	99.78 231	99.71 159	98.35 218	99.81 225
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
baseline		98.69 191	98.45 203	99.41 188	99.52 245	98.67 228	100.00 1	99.17 337	97.03 223	99.13 259	100.00 1	93.17 276	99.74 238	99.70 163	98.34 219	99.81 225

tpm cat1		98.05 240	97.76 246	98.92 237	99.50 254	97.10 322	99.77 326	99.30 263	90.20 408	99.72 221	98.71 392	97.71 172	99.86 203	96.75 332	98.20 232	99.81 225

CostFormer		98.84 179	98.77 166	99.04 229	99.41 276	97.58 300	99.67 346	99.35 237	94.66 339	99.96 141	99.36 351	99.28 79	99.74 238	99.41 207	97.81 258	99.81 225

PatchT		95.90 342	94.95 357	98.75 248	99.03 314	98.39 244	99.08 415	99.32 249	85.52 423	99.96 141	94.99 430	97.94 157	98.05 395	80.20 434	98.47 208	99.81 225

BH-untuned		98.64 194	98.65 182	98.60 255	99.59 218	96.17 337	100.00 1	99.28 276	96.67 258	98.41 308	100.00 1	94.52 257	99.83 216	99.41 207	100.00 1	99.81 225

fmvsm_s_conf0.5_n_7		98.98 163	98.85 159	99.37 197	99.67 184	98.34 251	100.00 1	99.31 257	98.97 32	100.00 1	100.00 1	91.70 297	99.97 139	99.99 69	99.97 116	99.80 242

UWE-MVS-28		99.29 112	99.23 107	99.48 176	99.73 164	98.86 215	100.00 1	99.43 128	96.97 228	99.99 120	99.83 274	99.43 55	99.77 232	99.35 211	98.31 224	99.80 242

UWE-MVS		99.18 129	99.06 129	99.51 169	99.67 184	98.80 218	100.00 1	99.43 128	96.80 242	99.93 179	99.86 266	99.79 8	99.94 182	97.78 293	98.33 222	99.80 242

thisisatest0530		99.37 95	99.27 94	99.69 141	99.59 218	99.41 158	100.00 1	99.46 97	96.46 274	99.90 185	100.00 1	99.44 51	99.85 210	98.97 235	99.58 176	99.80 242

MIMVSNet		97.06 283	96.73 284	98.05 303	99.38 285	96.64 333	98.47 430	99.35 237	93.41 374	99.48 233	98.53 399	89.66 331	97.70 409	94.16 371	98.11 237	99.80 242

SDMVSNet		98.49 213	98.08 232	99.73 134	99.82 127	99.53 136	99.99 235	99.45 105	97.62 163	99.38 245	99.86 266	90.06 325	99.88 201	99.92 109	96.61 286	99.79 247

sd_testset		97.81 248	97.48 257	98.79 246	99.82 127	96.80 328	99.32 382	99.45 105	97.62 163	99.38 245	99.86 266	85.56 380	99.77 232	99.72 155	96.61 286	99.79 247

FA-MVS(test-final)		99.00 155	98.75 168	99.73 134	99.63 202	99.43 156	99.83 311	99.43 128	95.84 306	99.52 230	99.37 350	97.84 166	99.96 156	97.63 298	99.68 166	99.79 247

tttt0517		99.34 100	99.23 107	99.67 145	99.57 227	99.38 160	100.00 1	99.46 97	96.33 286	99.89 188	100.00 1	99.44 51	99.84 214	98.93 237	99.46 180	99.78 250

BH-RMVSNet		98.46 214	98.08 232	99.59 160	99.61 211	99.19 185	100.00 1	99.28 276	97.06 222	98.95 271	100.00 1	88.99 341	99.82 219	98.83 244	100.00 1	99.77 251

CDS-MVSNet		98.96 166	98.95 147	99.01 231	99.48 260	98.36 249	99.93 292	99.37 220	96.79 243	99.31 249	99.83 274	99.77 11	98.91 323	98.07 282	97.98 243	99.77 251
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Vis-MVSNet		98.52 210	98.25 217	99.34 200	99.68 176	98.55 235	99.68 345	99.41 196	97.34 199	99.94 173	100.00 1	90.38 320	99.70 243	99.03 232	98.84 195	99.76 253
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
KinetiMVS		98.61 198	98.26 216	99.65 150	99.46 267	99.24 179	99.96 273	99.44 119	97.54 175	99.99 120	99.99 202	90.83 311	99.95 169	97.18 314	99.92 131	99.75 254

guyue		99.21 126	99.07 128	99.62 154	99.55 232	99.29 170	100.00 1	99.32 249	97.66 156	99.96 141	100.00 1	95.84 230	99.84 214	99.63 183	99.67 168	99.75 254

EPP-MVSNet		99.10 137	99.00 137	99.40 192	99.51 250	98.68 227	99.92 294	99.43 128	95.47 320	99.65 225	100.00 1	99.51 37	99.76 235	99.53 201	98.00 241	99.75 254

AstraMVS		99.03 146	99.01 134	99.09 224	99.46 267	97.66 297	100.00 1	99.23 309	97.83 140	99.95 170	100.00 1	95.52 237	99.86 203	99.74 149	99.39 182	99.74 257

xiu_mvs_v2_base		99.51 72	99.41 76	99.82 105	99.70 168	99.73 105	99.92 294	99.40 200	98.15 113	100.00 1	100.00 1	98.50 143	100.00 1	99.85 122	99.13 187	99.74 257

PS-MVSNAJ		99.64 51	99.57 52	99.85 97	99.78 156	99.81 91	99.95 280	99.42 147	98.38 95	100.00 1	100.00 1	98.75 131	100.00 1	99.88 116	99.99 103	99.74 257

MVSFormer		98.94 171	98.82 161	99.28 213	99.45 270	99.49 147	100.00 1	99.13 350	95.46 321	99.97 134	100.00 1	96.76 211	98.59 354	98.63 256	100.00 1	99.74 257

jason		99.11 136	98.96 143	99.59 160	99.17 300	99.31 169	100.00 1	99.13 350	97.38 195	99.83 197	100.00 1	95.54 236	99.72 241	99.57 195	99.97 116	99.74 257
jason: jason.
TAMVS		98.76 184	98.73 171	98.86 241	99.44 272	97.69 295	99.57 357	99.34 244	96.57 266	99.12 260	99.81 281	98.83 126	99.16 304	97.97 288	97.91 249	99.73 262

VDD-MVS		96.58 305	95.99 316	98.34 273	99.52 245	95.33 350	99.18 398	99.38 216	96.64 261	99.77 213	100.00 1	72.51 426	100.00 1	100.00 1	96.94 280	99.70 263

RPSCF		97.37 269	98.24 220	94.76 390	99.80 146	84.57 427	99.99 235	99.05 382	94.95 331	99.82 205	100.00 1	94.03 263	100.00 1	98.15 279	98.38 216	99.70 263

AllTest		98.55 205	98.40 206	98.99 232	99.93 106	97.35 309	100.00 1	99.40 200	97.08 220	99.09 262	99.98 210	93.37 272	99.95 169	96.94 320	99.84 152	99.68 265

TestCases				98.99 232	99.93 106	97.35 309		99.40 200	97.08 220	99.09 262	99.98 210	93.37 272	99.95 169	96.94 320	99.84 152	99.68 265

xiu_mvs_v1_base_debu		99.35 97	99.21 109	99.79 119	99.67 184	99.71 108	99.78 321	99.36 226	98.13 115	100.00 1	100.00 1	97.00 203	100.00 1	99.83 126	99.07 189	99.66 267

xiu_mvs_v1_base		99.35 97	99.21 109	99.79 119	99.67 184	99.71 108	99.78 321	99.36 226	98.13 115	100.00 1	100.00 1	97.00 203	100.00 1	99.83 126	99.07 189	99.66 267

xiu_mvs_v1_base_debi		99.35 97	99.21 109	99.79 119	99.67 184	99.71 108	99.78 321	99.36 226	98.13 115	100.00 1	100.00 1	97.00 203	100.00 1	99.83 126	99.07 189	99.66 267

h-mvs33		97.03 285	96.53 291	98.51 260	99.79 151	95.90 342	99.45 369	99.45 105	98.21 107	100.00 1	99.78 288	97.49 184	99.99 101	99.72 155	74.92 432	99.65 270

fmvsm_s_conf0.5_n_4		98.98 163	98.74 170	99.68 144	99.81 133	99.50 143	100.00 1	99.26 295	98.91 47	100.00 1	100.00 1	90.87 310	99.97 139	99.99 69	99.81 157	99.57 271

OpenMVS	95.20 17	98.76 184	98.41 205	99.78 124	98.89 332	99.81 91	99.99 235	99.76 49	98.02 123	98.02 333	100.00 1	91.44 299	100.00 1	99.63 183	99.97 116	99.55 272

cascas		98.43 216	98.07 234	99.50 172	99.65 194	99.02 201	100.00 1	99.22 314	94.21 353	99.72 221	99.98 210	92.03 295	99.93 186	99.68 171	98.12 236	99.54 273

LuminaMVS		99.07 140	98.92 152	99.50 172	98.87 336	99.12 192	99.92 294	99.22 314	97.45 189	99.82 205	99.98 210	96.29 224	99.85 210	99.71 159	99.05 192	99.52 274

CANet_DTU		99.02 152	98.90 156	99.41 188	99.88 117	98.71 225	100.00 1	99.29 270	98.84 59	100.00 1	100.00 1	94.02 264	100.00 1	98.08 280	99.96 120	99.52 274

DSMNet-mixed		95.18 354	95.21 351	95.08 382	96.03 416	90.21 412	99.65 347	93.64 447	92.91 385	98.34 313	97.40 418	90.05 326	95.51 429	91.02 398	97.86 252	99.51 276

fmvsm_s_conf0.1_n_2		98.95 169	98.69 179	99.73 134	99.61 211	99.74 103	100.00 1	99.23 309	98.95 37	99.97 134	100.00 1	90.92 309	99.97 139	100.00 1	99.58 176	99.47 277

sc_t1		92.52 377	91.34 381	96.09 373	97.80 385	89.86 414	98.61 427	99.12 356	77.73 433	96.09 387	99.79 287	68.64 432	98.94 320	96.94 320	87.31 402	99.46 278

Fast-Effi-MVS+-dtu		98.38 222	98.56 193	97.82 317	99.58 223	94.44 375	100.00 1	99.16 338	96.75 247	99.51 231	99.63 317	95.03 247	99.60 248	97.71 295	99.67 168	99.42 279

UGNet		98.41 220	98.11 228	99.31 209	99.54 234	98.55 235	99.18 398	100.00 1	98.64 82	99.79 210	99.04 370	87.61 359	100.00 1	99.30 217	99.89 138	99.40 280
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
Elysia		98.12 236	97.72 250	99.34 200	99.30 291	98.96 211	99.95 280	99.28 276	96.64 261	99.75 216	99.99 202	88.71 346	99.81 222	95.99 341	99.84 152	99.26 281

StellarMVS		98.12 236	97.72 250	99.34 200	99.30 291	98.96 211	99.95 280	99.28 276	96.64 261	99.75 216	99.99 202	88.71 346	99.81 222	95.99 341	99.84 152	99.26 281

VDDNet		96.39 317	95.55 339	98.90 238	99.27 294	97.45 304	99.15 406	99.92 34	91.28 397	99.98 128	100.00 1	73.55 422	100.00 1	99.85 122	96.98 279	99.24 283

UniMVSNet_ETH3D		95.28 352	94.41 358	97.89 315	98.91 330	95.14 353	99.13 409	99.35 237	92.11 392	97.17 365	99.66 307	70.28 430	99.36 293	97.88 290	95.18 309	99.16 284

baseline2		98.99 159	98.93 150	99.18 221	99.26 296	99.15 190	100.00 1	99.46 97	96.71 253	96.79 374	100.00 1	99.42 59	99.25 301	98.75 248	99.94 126	99.15 285

hse-mvs2		96.79 293	96.38 299	98.04 305	99.68 176	95.54 349	99.81 315	99.42 147	98.21 107	100.00 1	99.80 284	97.49 184	99.46 285	99.72 155	73.27 435	99.12 286

AUN-MVS		96.26 324	95.67 336	98.06 299	99.68 176	95.60 348	99.82 314	99.42 147	96.78 244	99.88 190	99.80 284	94.84 250	99.47 281	97.48 304	73.29 434	99.12 286

tt0805		96.52 306	96.23 306	97.40 327	99.30 291	93.55 383	99.32 382	99.45 105	96.75 247	97.88 340	99.99 202	79.99 406	99.59 250	97.39 309	95.98 289	99.06 288

test0.0.03 1		98.12 236	98.03 237	98.39 269	99.11 303	98.07 271	100.00 1	99.93 30	96.70 254	96.91 370	99.95 244	99.31 71	98.19 379	91.93 391	98.44 209	98.91 289

testgi		96.18 328	95.93 319	96.93 351	98.98 324	94.20 379	100.00 1	99.07 372	97.16 212	96.06 389	99.86 266	84.08 390	97.79 406	90.38 405	97.80 259	98.81 290

Effi-MVS+-dtu		98.51 212	98.86 158	97.47 326	99.77 158	94.21 378	100.00 1	98.94 397	97.61 167	99.91 183	98.75 391	95.89 228	99.51 275	99.36 209	99.48 179	98.68 291

DeepMVS_CX				89.98 407	98.90 331	71.46 438		99.18 332	97.61 167	96.92 368	99.83 274	86.07 374	99.83 216	96.02 340	97.65 269	98.65 292

COLMAP_ROB	97.10 7	98.29 227	98.17 225	98.65 251	99.94 104	97.39 306	99.30 386	99.40 200	95.64 309	97.75 347	100.00 1	92.69 287	99.95 169	98.89 239	99.92 131	98.62 293
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
XVG-OURS-SEG-HR		98.27 230	98.31 214	98.14 289	99.59 218	95.92 340	100.00 1	99.36 226	98.48 89	99.21 253	100.00 1	89.27 337	99.94 182	99.76 144	99.17 185	98.56 294

XVG-OURS		98.30 225	98.36 212	98.13 292	99.58 223	95.91 341	100.00 1	99.36 226	98.69 77	99.23 252	100.00 1	91.20 302	99.92 189	99.34 213	97.82 257	98.56 294

HQP4-MVS										99.17 254			99.57 255			97.77 296

HQP-MVS		97.73 251	97.85 243	97.39 328	99.07 308	94.82 359	100.00 1	99.40 200	99.04 16	99.17 254	99.97 220	88.61 349	99.57 255	99.79 134	95.58 290	97.77 296

WBMVS		98.19 234	98.10 231	98.47 262	99.63 202	99.03 199	100.00 1	99.32 249	95.46 321	98.39 309	99.40 348	99.69 17	98.61 349	98.64 254	92.39 346	97.76 298

cl22		98.23 232	98.11 228	98.58 258	99.82 127	99.01 203	100.00 1	99.28 276	96.92 233	98.33 314	99.21 360	98.09 155	98.97 317	98.72 249	92.61 341	97.76 298

miper_ehance_all_eth		97.81 248	97.66 253	98.23 281	99.49 258	98.37 247	99.99 235	99.11 358	94.78 334	98.25 322	99.21 360	98.18 151	98.57 357	97.35 311	92.61 341	97.76 298

miper_enhance_ethall		98.33 224	98.27 215	98.51 260	99.66 192	99.04 198	100.00 1	99.22 314	97.53 178	98.51 303	99.38 349	99.49 43	98.75 339	98.02 284	92.61 341	97.76 298

cl____		97.54 261	97.32 265	98.18 285	99.47 264	98.14 266	100.00 1	99.10 361	94.16 356	97.60 354	99.63 317	97.52 183	98.65 346	96.47 333	91.97 354	97.76 298

DIV-MVS_self_test		97.52 264	97.35 264	98.05 303	99.46 267	98.11 267	100.00 1	99.10 361	94.21 353	97.62 352	99.63 317	97.65 175	98.29 374	96.47 333	91.98 353	97.76 298

miper_lstm_enhance		97.40 268	97.28 267	97.75 319	99.48 260	97.52 301	100.00 1	99.07 372	94.08 357	98.01 334	99.61 323	97.38 191	97.98 398	96.44 336	91.47 365	97.76 298

VPNet		96.41 313	95.76 329	98.33 274	98.61 348	98.30 256	99.48 366	99.45 105	96.98 227	98.87 277	99.88 263	81.57 400	98.93 321	99.22 224	87.82 398	97.76 298

VPA-MVSNet		97.03 285	96.43 297	98.82 242	98.64 347	99.32 167	99.38 377	99.47 79	96.73 251	98.91 274	98.94 382	87.00 366	99.40 291	99.23 222	89.59 381	97.76 298

HQP_MVS		97.71 253	97.82 245	97.37 329	99.00 320	94.80 362	100.00 1	99.40 200	99.00 27	99.08 264	99.97 220	88.58 351	99.55 264	99.79 134	95.57 294	97.76 298

plane_prior5								99.40 200					99.55 264	99.79 134	95.57 294	97.76 298

our_test_3		96.51 308	96.35 301	96.98 348	97.61 393	95.05 354	99.98 261	99.01 391	94.68 338	96.77 376	99.06 367	95.87 229	98.14 382	91.81 392	92.37 347	97.75 309

ppachtmachnet_test		96.17 330	95.89 320	97.02 345	97.61 393	95.24 351	99.99 235	99.24 305	93.31 378	96.71 377	99.62 321	94.34 259	98.07 391	89.87 407	92.30 349	97.75 309

c3_l		97.58 258	97.42 259	98.06 299	99.48 260	98.16 263	99.96 273	99.10 361	94.54 343	98.13 326	99.20 362	97.87 163	98.25 377	97.28 312	91.20 368	97.75 309

nrg030		97.64 254	97.27 269	98.75 248	98.34 357	99.53 136	100.00 1	99.22 314	96.21 293	98.27 320	99.95 244	94.40 258	98.98 315	99.23 222	89.78 380	97.75 309

v144192		96.40 316	95.81 324	98.17 287	97.89 383	98.11 267	99.99 235	99.06 380	93.39 375	98.75 286	99.09 365	90.43 319	98.66 344	93.10 383	90.55 375	97.75 309

v1921920		96.16 332	95.50 340	98.14 289	97.88 384	97.96 281	99.99 235	99.07 372	93.33 377	98.60 295	99.24 357	89.37 336	98.71 341	91.28 395	90.74 373	97.75 309

v1192		96.18 328	95.49 342	98.26 279	98.01 378	98.15 264	99.99 235	99.08 367	93.36 376	98.54 299	98.97 380	89.47 335	98.89 326	91.15 397	90.82 371	97.75 309

v148		96.29 322	95.84 323	97.63 320	97.74 388	96.53 335	100.00 1	99.07 372	93.52 370	98.01 334	99.42 346	91.22 300	98.60 352	96.37 337	87.22 404	97.75 309

v1240		95.96 340	95.25 349	98.07 295	97.91 382	97.87 289	99.96 273	99.07 372	93.24 380	98.64 293	98.96 381	88.98 342	98.61 349	89.58 411	90.92 370	97.75 309

v2v482		96.70 299	96.18 308	98.27 277	98.04 376	98.39 244	100.00 1	99.13 350	94.19 355	98.58 296	99.08 366	90.48 318	98.67 343	95.69 347	90.44 376	97.75 309

EI-MVSNet		97.98 243	97.93 241	98.16 288	99.11 303	97.84 290	99.74 331	99.29 270	94.39 349	98.65 291	100.00 1	97.21 194	98.88 329	97.62 301	95.31 300	97.75 309

MDA-MVSNet-bldmvs		91.65 386	89.94 394	96.79 359	96.72 411	96.70 331	99.42 374	98.94 397	88.89 412	66.97 446	98.37 405	81.43 401	95.91 426	89.24 414	89.46 384	97.75 309

UniMVSNet_NR-MVSNet		97.16 277	96.80 281	98.22 282	98.38 356	98.41 241	100.00 1	99.45 105	96.14 295	97.76 344	99.64 313	95.05 246	98.50 362	97.98 285	86.84 405	97.75 309

DU-MVS		96.93 290	96.49 294	98.22 282	98.31 360	98.41 241	100.00 1	99.37 220	96.41 279	97.76 344	99.65 309	92.14 293	98.50 362	97.98 285	86.84 405	97.75 309

UniMVSNet (Re)		97.29 273	96.85 280	98.59 256	98.49 353	99.13 191	100.00 1	99.42 147	96.52 270	98.24 324	98.90 385	94.93 248	98.89 326	97.54 302	87.61 399	97.75 309

NR-MVSNet		96.63 302	96.04 314	98.38 270	98.31 360	98.98 208	99.22 397	99.35 237	95.87 301	94.43 405	99.65 309	92.73 285	98.40 369	96.78 329	88.05 396	97.75 309

TranMVSNet+NR-MVSNet		96.45 312	96.01 315	97.79 318	98.00 379	97.62 299	100.00 1	99.35 237	95.98 298	97.31 361	99.64 313	90.09 324	98.00 396	96.89 324	86.80 408	97.75 309

Patchmtry		96.81 292	96.37 300	98.14 289	99.31 288	98.55 235	98.91 420	99.00 392	90.45 404	97.92 338	98.98 377	96.94 206	98.12 384	94.27 368	91.53 362	97.75 309

N_pmnet		91.88 383	93.37 368	87.40 413	97.24 408	66.33 446	99.90 299	91.05 449	89.77 410	95.65 393	98.58 398	90.05 326	98.11 386	85.39 422	92.72 340	97.75 309

XXY-MVS		97.14 279	96.63 287	98.67 250	98.65 346	98.92 213	99.54 361	99.29 270	95.57 313	97.63 350	99.83 274	87.79 358	99.35 295	98.39 267	92.95 337	97.75 309

IterMVS-LS		97.56 259	97.44 258	97.92 314	99.38 285	97.90 285	99.89 303	99.10 361	94.41 348	98.32 315	99.54 336	97.21 194	98.11 386	97.50 303	91.62 360	97.75 309
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CLD-MVS		97.64 254	97.74 247	97.36 330	99.01 316	94.76 367	100.00 1	99.34 244	99.30 4	99.00 269	99.97 220	87.49 360	99.57 255	99.96 98	95.58 290	97.75 309
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
reproduce_monomvs		98.61 198	98.54 194	98.82 242	99.97 91	99.28 172	100.00 1	99.33 246	98.51 88	97.87 341	99.24 357	99.98 3	99.45 286	99.02 233	92.93 338	97.74 331

eth_miper_zixun_eth		97.47 265	97.28 267	98.06 299	99.41 276	97.94 283	99.62 352	99.08 367	94.46 347	98.19 325	99.56 332	96.91 208	98.50 362	96.78 329	91.49 363	97.74 331

FIs		97.95 244	97.73 249	98.62 253	98.53 352	99.24 179	100.00 1	99.43 128	96.74 249	97.87 341	99.82 278	95.27 240	98.89 326	98.78 245	93.07 335	97.74 331

v1144		96.51 308	95.97 318	98.13 292	97.98 380	98.04 275	99.99 235	99.08 367	93.51 371	98.62 294	98.98 377	90.98 308	98.62 348	93.79 375	90.79 372	97.74 331

YYNet1		92.44 378	90.92 387	97.03 344	96.20 414	97.06 323	99.99 235	99.14 345	88.21 416	67.93 443	98.43 404	88.63 348	96.28 422	90.64 399	89.08 388	97.74 331

MDA-MVSNet_test_wron		92.61 376	91.09 386	97.19 340	96.71 412	97.26 315	100.00 1	99.14 345	88.61 413	67.90 444	98.32 407	89.03 340	96.57 418	90.47 404	89.59 381	97.74 331

WR-MVS		97.09 280	96.64 286	98.46 263	98.43 354	99.09 193	99.97 267	99.33 246	95.62 311	97.76 344	99.67 305	91.17 303	98.56 359	98.49 263	89.28 386	97.74 331

VortexMVS		98.23 232	98.11 228	98.59 256	99.56 231	99.37 163	99.95 280	99.03 388	96.47 273	98.69 288	99.55 333	95.91 227	98.66 344	99.01 234	94.80 320	97.73 338

IterMVS-SCA-FT		96.72 298	96.42 298	97.62 322	99.40 281	96.83 327	99.99 235	99.14 345	94.65 340	97.55 356	99.72 292	89.65 332	98.31 373	95.62 350	92.05 351	97.73 338

Anonymous20231211		96.29 322	95.70 332	98.07 295	99.80 146	97.49 302	99.15 406	99.40 200	89.11 411	97.75 347	99.45 344	88.93 343	98.98 315	98.26 276	89.47 383	97.73 338

FC-MVSNet-test		97.84 246	97.63 255	98.45 265	98.30 362	99.05 197	100.00 1	99.43 128	96.63 265	97.61 353	99.82 278	95.19 244	98.57 357	98.64 254	93.05 336	97.73 338

MVSTER		98.58 202	98.52 197	98.77 247	99.65 194	99.68 115	100.00 1	99.29 270	95.63 310	98.65 291	99.80 284	99.78 9	98.88 329	98.59 260	95.31 300	97.73 338

FMVSNet3		97.30 272	96.95 276	98.37 271	99.65 194	99.25 177	99.71 339	99.28 276	94.23 351	98.53 300	98.91 384	93.30 274	98.11 386	95.31 354	93.60 329	97.73 338

FMVSNet2		96.22 326	95.60 338	98.06 299	99.53 237	98.33 252	99.45 369	99.27 288	93.71 362	98.03 331	98.84 387	84.23 387	98.10 389	93.97 373	93.40 332	97.73 338

SSC-MVS3.2		95.32 350	94.97 356	96.37 367	98.29 364	92.75 393	100.00 1	99.30 263	95.46 321	98.36 310	99.42 346	78.92 410	98.63 347	93.28 382	91.72 359	97.72 345

OPM-MVS		97.21 274	97.18 274	97.32 333	98.08 375	94.66 368	100.00 1	99.28 276	98.65 81	98.92 272	99.98 210	86.03 376	99.56 259	98.28 275	95.41 296	97.72 345
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
pmmvs5		95.94 341	95.61 337	96.95 349	97.42 404	94.66 368	100.00 1	98.08 421	93.60 368	97.05 366	99.43 345	87.02 365	98.46 366	95.76 344	92.12 350	97.72 345

pm-mvs1		95.76 344	95.01 354	98.00 307	98.23 368	97.45 304	99.24 390	99.04 385	93.13 383	95.93 391	99.72 292	86.28 372	98.84 331	95.62 350	87.92 397	97.72 345

GBi-Net		96.07 336	95.80 326	96.89 353	99.53 237	94.87 356	99.18 398	99.27 288	93.71 362	98.53 300	98.81 388	84.23 387	98.07 391	95.31 354	93.60 329	97.72 345

test1		96.07 336	95.80 326	96.89 353	99.53 237	94.87 356	99.18 398	99.27 288	93.71 362	98.53 300	98.81 388	84.23 387	98.07 391	95.31 354	93.60 329	97.72 345

FMVSNet1		94.45 358	93.63 365	96.89 353	98.87 336	94.87 356	99.18 398	99.27 288	90.95 401	97.31 361	98.81 388	72.89 425	98.07 391	92.61 385	92.81 339	97.72 345

IterMVS		96.76 295	96.46 296	97.63 320	99.41 276	96.89 325	99.99 235	99.13 350	94.74 337	97.59 355	99.66 307	89.63 334	98.28 375	95.71 346	92.31 348	97.72 345
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
pmmvs4		97.17 276	96.80 281	98.27 277	97.68 390	98.64 230	100.00 1	99.18 332	94.22 352	98.55 298	99.71 294	93.67 267	98.47 365	95.66 348	92.57 344	97.71 353

tt0320		92.36 379	91.28 382	95.58 379	98.30 362	90.65 409	98.69 425	99.14 345	76.73 434	96.07 388	99.50 340	72.28 427	98.39 370	93.29 381	87.56 400	97.70 354

v7n		96.06 338	95.42 348	97.99 309	97.58 396	97.35 309	99.86 307	99.11 358	92.81 389	97.91 339	99.49 341	90.99 307	98.92 322	92.51 387	88.49 394	97.70 354

PS-MVSNAJss		98.03 241	98.06 235	97.94 311	97.63 391	97.33 312	99.89 303	99.23 309	96.27 288	98.03 331	99.59 325	98.75 131	98.78 334	98.52 262	94.61 324	97.70 354

LPG-MVS_test		97.31 271	97.32 265	97.28 336	98.85 339	94.60 371	100.00 1	99.37 220	97.35 197	98.85 278	99.98 210	86.66 368	99.56 259	99.55 196	95.26 302	97.70 354

LGP-MVS_train				97.28 336	98.85 339	94.60 371		99.37 220	97.35 197	98.85 278	99.98 210	86.66 368	99.56 259	99.55 196	95.26 302	97.70 354

SixPastTwentyTwo		95.71 345	95.49 342	96.38 366	97.42 404	93.01 389	99.84 310	98.23 416	94.75 335	95.98 390	99.97 220	85.35 381	98.43 367	94.71 362	93.17 334	97.69 359

ACMM	97.17 6	97.37 269	97.40 261	97.29 335	99.01 316	94.64 370	100.00 1	99.25 299	98.07 121	98.44 307	99.98 210	87.38 362	99.55 264	99.25 219	95.19 308	97.69 359
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EU-MVSNet		96.63 302	96.53 291	96.94 350	97.59 395	96.87 326	99.76 328	99.47 79	96.35 284	96.85 372	99.78 288	92.57 289	96.27 423	95.33 353	91.08 369	97.68 361

K. test v3		95.46 349	95.14 352	96.40 365	97.53 398	93.40 386	99.99 235	99.23 309	95.49 319	92.70 415	99.73 291	84.26 386	98.12 384	93.94 374	93.38 333	97.68 361

lessismore_v0				96.05 374	97.55 397	91.80 401		99.22 314		91.87 416	99.91 258	83.50 393	98.68 342	92.48 388	90.42 377	97.68 361

XVG-ACMP-BASELINE		96.60 304	96.52 293	96.84 356	98.41 355	93.29 388	99.99 235	99.32 249	97.76 149	98.51 303	99.29 354	81.95 399	99.54 267	98.40 266	95.03 315	97.68 361

ACMP	97.00 8	97.19 275	97.16 275	97.27 338	98.97 325	94.58 374	100.00 1	99.32 249	97.97 129	97.45 358	99.98 210	85.79 378	99.56 259	99.70 163	95.24 305	97.67 365
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
tt0320-xc		91.69 385	90.50 389	95.26 381	98.04 376	90.12 413	98.60 428	98.70 410	76.63 436	94.66 401	99.52 337	68.57 433	97.99 397	94.61 363	85.18 412	97.66 366

jajsoiax		97.07 282	96.79 283	97.89 315	97.28 407	97.12 320	99.95 280	99.19 325	96.55 267	97.31 361	99.69 300	87.35 364	98.91 323	98.70 250	95.12 313	97.66 366

PS-CasMVS		96.34 320	95.78 328	98.03 306	98.18 372	98.27 258	99.71 339	99.32 249	94.75 335	96.82 373	99.65 309	86.98 367	98.15 381	97.74 294	88.85 391	97.66 366

CP-MVSNet		96.73 296	96.25 305	98.18 285	98.21 369	98.67 228	99.77 326	99.32 249	95.06 329	97.20 364	99.65 309	90.10 323	98.19 379	98.06 283	88.90 390	97.66 366

ACMH	96.25 11	96.77 294	96.62 288	97.21 339	98.96 326	94.43 376	99.64 348	99.33 246	97.43 192	96.55 379	99.97 220	83.52 392	99.54 267	99.07 231	95.13 312	97.66 366
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
mvs_tets		97.00 288	96.69 285	97.94 311	97.41 406	97.27 314	99.60 354	99.18 332	96.51 271	97.35 360	99.69 300	86.53 370	98.91 323	98.84 242	95.09 314	97.65 371

PEN-MVS		96.01 339	95.48 344	97.58 324	97.74 388	97.26 315	99.90 299	99.29 270	94.55 342	96.79 374	99.55 333	87.38 362	97.84 402	96.92 323	87.24 403	97.65 371

ACMH+	96.20 13	96.49 311	96.33 303	97.00 346	99.06 312	93.80 381	99.81 315	99.31 257	97.32 202	95.89 392	99.97 220	82.62 397	99.54 267	98.34 270	94.63 323	97.65 371

OurMVSNet-221017-0		96.14 334	95.98 317	96.62 361	97.49 401	93.44 385	99.92 294	98.16 417	95.86 303	97.65 349	99.95 244	85.71 379	98.78 334	94.93 360	94.18 327	97.64 374

pmmvs6		93.64 367	92.87 375	95.94 376	97.47 403	91.41 404	98.92 419	99.02 389	87.84 418	95.01 397	99.61 323	77.24 416	98.77 337	94.33 367	86.41 410	97.63 375

v10		96.14 334	95.50 340	98.07 295	98.19 371	97.96 281	99.83 311	99.07 372	92.10 393	98.07 328	98.94 382	91.07 305	98.61 349	92.41 390	89.82 379	97.63 375

v8		96.35 319	95.73 331	98.21 284	98.11 374	98.23 260	99.94 288	99.07 372	92.66 390	98.29 317	99.00 376	91.46 298	98.77 337	94.17 369	88.83 392	97.62 377

DTE-MVSNet		95.52 347	94.99 355	97.08 342	97.49 401	96.45 336	100.00 1	99.25 299	93.82 361	96.17 385	99.57 331	87.81 357	97.18 411	94.57 364	86.26 411	97.62 377

test_djsdf		97.55 260	97.38 262	98.07 295	97.50 399	97.99 277	100.00 1	99.13 350	95.46 321	98.47 306	99.85 271	92.01 296	98.59 354	98.63 256	95.36 298	97.62 377

MIMVSNet1		91.96 380	91.20 383	94.23 396	94.94 427	91.69 402	99.34 381	99.22 314	88.23 415	94.18 406	98.45 402	75.52 420	93.41 436	79.37 435	91.49 363	97.60 380

FMVSNet5		95.32 350	95.43 347	94.99 386	99.39 284	92.99 391	99.25 389	99.24 305	90.45 404	97.44 359	98.45 402	95.78 232	94.39 432	87.02 419	91.88 355	97.59 381

LTVRE_ROB	95.29 16	96.32 321	96.10 311	96.99 347	98.55 350	93.88 380	99.45 369	99.28 276	94.50 345	96.46 380	99.52 337	84.86 383	99.48 279	97.26 313	95.03 315	97.59 381
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
TransMVSNet (Re)		94.78 356	93.72 363	97.93 313	98.34 357	97.88 287	99.23 395	97.98 426	91.60 395	94.55 402	99.71 294	87.89 356	98.36 371	89.30 413	84.92 413	97.56 383

Baseline_NR-MVSNet		96.16 332	95.70 332	97.56 325	98.28 365	96.79 329	100.00 1	97.86 429	91.93 394	97.63 350	99.47 343	92.14 293	98.35 372	97.13 315	86.83 407	97.54 384

KD-MVS_2432*1600		94.15 362	93.08 371	97.35 331	99.53 237	97.83 291	99.63 350	99.19 325	92.88 386	96.29 382	97.68 415	98.84 124	96.70 415	89.73 408	63.92 439	97.53 385

miper_refine_blended		94.15 362	93.08 371	97.35 331	99.53 237	97.83 291	99.63 350	99.19 325	92.88 386	96.29 382	97.68 415	98.84 124	96.70 415	89.73 408	63.92 439	97.53 385

USDC		95.90 342	95.70 332	96.50 364	98.60 349	92.56 397	100.00 1	98.30 415	97.77 147	96.92 368	99.94 250	81.25 403	99.45 286	93.54 378	94.96 319	97.49 387

ITE_SJBPF				96.84 356	98.96 326	93.49 384		98.12 419	98.12 118	98.35 312	99.97 220	84.45 384	99.56 259	95.63 349	95.25 304	97.49 387

Anonymous20231206		93.45 369	93.17 370	94.30 394	95.00 426	89.69 415	99.98 261	98.43 414	93.30 379	94.50 404	98.59 397	90.52 316	95.73 428	77.46 439	90.73 374	97.48 389

WR-MVS_H		96.73 296	96.32 304	97.95 310	98.26 366	97.88 287	99.72 338	99.43 128	95.06 329	96.99 367	98.68 394	93.02 279	98.53 360	97.43 306	88.33 395	97.43 390

tfpnnormal		96.36 318	95.69 335	98.37 271	98.55 350	98.71 225	99.69 343	99.45 105	93.16 382	96.69 378	99.71 294	88.44 353	98.99 314	94.17 369	91.38 366	97.41 391

TinyColmap		95.50 348	95.12 353	96.64 360	98.69 345	93.00 390	99.40 375	97.75 431	96.40 280	96.14 386	99.87 264	79.47 407	99.50 277	93.62 377	94.72 322	97.40 392

UnsupCasMVSNet_eth		94.25 361	93.89 361	95.34 380	97.63 391	92.13 398	99.73 336	99.36 226	94.88 332	92.78 412	98.63 396	82.72 395	96.53 419	94.57 364	84.73 414	97.36 393

PVSNet_BlendedMVS		98.71 188	98.62 186	98.98 234	99.98 87	99.60 123	100.00 1	100.00 1	97.23 209	100.00 1	99.03 373	96.57 218	99.99 101	100.00 1	94.75 321	97.35 394

anonymousdsp		97.16 277	96.88 278	98.00 307	97.08 409	98.06 273	99.81 315	99.15 340	94.58 341	97.84 343	99.62 321	90.49 317	98.60 352	97.98 285	95.32 299	97.33 395

V42		96.65 301	96.16 310	98.11 294	98.17 373	98.23 260	99.99 235	99.09 366	93.97 358	98.74 287	99.05 369	91.09 304	98.82 332	95.46 352	89.90 378	97.27 396

LF4IMVS		96.19 327	96.18 308	96.23 371	98.26 366	92.09 399	100.00 1	97.89 428	97.82 142	97.94 336	99.87 264	82.71 396	99.38 292	97.41 307	93.71 328	97.20 397

new_pmnet		94.11 365	93.47 367	96.04 375	96.60 413	92.82 392	99.97 267	98.91 400	90.21 407	95.26 394	98.05 413	85.89 377	98.14 382	84.28 425	92.01 352	97.16 398

APD_test1		93.07 374	94.14 360	89.85 408	99.18 299	72.49 436	99.76 328	98.90 402	92.86 388	96.35 381	99.94 250	75.56 419	99.91 191	86.73 420	97.98 243	97.15 399

D2MVS		97.63 257	97.83 244	97.05 343	98.83 341	94.60 371	100.00 1	99.82 40	96.89 237	98.28 318	99.03 373	94.05 262	99.47 281	98.58 261	94.97 318	97.09 400

test20.03		93.11 372	92.85 376	93.88 399	95.19 425	91.83 400	100.00 1	98.87 403	93.68 365	92.76 413	98.88 386	89.20 339	92.71 437	77.88 437	89.19 387	97.09 400

KD-MVS_self_test		91.16 387	90.09 392	94.35 393	94.44 428	91.27 405	99.74 331	99.08 367	90.82 402	94.53 403	94.91 431	86.11 373	94.78 431	82.67 428	68.52 437	96.99 402

CL-MVSNet_self_test		91.07 388	90.35 391	93.24 401	93.27 430	89.16 417	99.55 359	99.25 299	92.34 391	95.23 395	97.05 421	88.86 345	93.59 435	80.67 432	66.95 438	96.96 403

test_method		91.04 389	91.10 385	90.85 405	98.34 357	77.63 432	100.00 1	98.93 399	76.69 435	96.25 384	98.52 400	70.44 429	97.98 398	89.02 416	91.74 357	96.92 404

pmmvs3		90.62 391	89.36 397	94.40 392	90.53 440	91.49 403	100.00 1	96.73 439	84.21 426	93.65 409	96.65 423	82.56 398	94.83 430	82.28 429	77.62 431	96.89 405

test_0402		94.35 359	93.70 364	96.32 369	97.92 381	93.60 382	99.61 353	98.85 404	88.19 417	94.68 400	99.48 342	80.01 405	98.58 356	89.39 412	95.15 311	96.77 406

EG-PatchMatch MVS		92.94 375	92.49 379	94.29 395	95.87 418	87.07 423	99.07 417	98.11 420	93.19 381	88.98 424	98.66 395	70.89 428	99.08 307	92.43 389	95.21 307	96.72 407

test_fmvs2		95.17 355	95.23 350	95.01 384	98.95 328	88.99 418	99.99 235	97.77 430	97.79 145	98.58 296	99.70 297	73.36 423	99.34 296	95.88 343	95.03 315	96.70 408

Anonymous20240521		93.29 370	92.76 377	94.90 389	95.64 422	91.27 405	99.97 267	98.82 405	87.04 419	94.71 399	98.19 408	83.86 391	96.80 414	84.04 426	92.56 345	96.64 409

TDRefinement		91.93 381	90.48 390	96.27 370	81.60 446	92.65 396	99.10 412	97.61 434	93.96 359	93.77 408	99.85 271	80.03 404	99.53 272	97.82 292	70.59 436	96.63 410

mmtdpeth		94.58 357	94.18 359	95.81 377	98.82 343	91.09 407	99.99 235	98.61 412	96.38 281	100.00 1	97.23 419	76.52 417	99.85 210	99.82 131	80.22 426	96.48 411

MS-PatchMatch		95.66 346	95.87 322	95.05 383	97.80 385	89.25 416	98.88 421	99.30 263	96.35 284	96.86 371	99.01 375	81.35 402	99.43 288	93.30 380	99.98 113	96.46 412

MVP-Stereo		96.51 308	96.48 295	96.60 362	95.65 421	94.25 377	98.84 422	98.16 417	95.85 305	95.23 395	99.04 370	92.54 290	99.13 305	92.98 384	99.98 113	96.43 413
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
mvs5depth		93.81 366	93.00 373	96.23 371	94.25 429	93.33 387	97.43 436	98.07 422	93.47 372	94.15 407	99.58 327	77.52 414	98.97 317	93.64 376	88.92 389	96.39 414

ttmdpeth		96.24 325	95.88 321	97.32 333	97.80 385	96.61 334	99.95 280	98.77 408	97.80 144	93.42 410	99.28 355	86.42 371	99.01 311	97.63 298	91.84 356	96.33 415

UnsupCasMVSNet_bld		89.50 393	88.00 399	93.99 398	95.30 424	88.86 419	98.52 429	99.28 276	85.50 424	87.80 428	94.11 432	61.63 436	96.96 413	90.63 400	79.26 427	96.15 416

OpenMVS_ROB	88.34 20	91.89 382	91.12 384	94.19 397	95.55 423	87.63 421	99.26 388	98.03 423	86.61 422	90.65 422	96.82 422	70.14 431	98.78 334	86.54 421	96.50 288	96.15 416

MVStest1		94.27 360	93.30 369	97.19 340	98.83 341	97.18 318	99.93 292	98.79 407	86.80 420	84.88 435	99.04 370	94.32 260	98.25 377	90.55 402	86.57 409	96.12 418

ambc				88.45 410	86.84 442	70.76 439	97.79 435	98.02 425		90.91 419	95.14 427	38.69 445	98.51 361	94.97 359	84.23 415	96.09 419

PM-MVS		88.39 395	87.41 400	91.31 404	91.73 434	82.02 430	99.79 320	96.62 440	91.06 400	90.71 421	95.73 425	48.60 441	95.96 425	90.56 401	81.91 424	95.97 420

pmmvs-eth3d		91.73 384	90.67 388	94.92 388	91.63 435	92.71 395	99.90 299	98.54 413	91.19 398	88.08 426	95.50 426	79.31 409	96.13 424	90.55 402	81.32 425	95.91 421

test_vis1_rt		93.10 373	92.93 374	93.58 400	99.63 202	85.07 425	99.99 235	93.71 446	97.49 184	90.96 418	97.10 420	60.40 437	99.95 169	99.24 221	97.90 250	95.72 422

EGC-MVSNET		79.46 405	74.04 413	95.72 378	96.00 417	92.73 394	99.09 414	99.04 385	5.08 450	16.72 450	98.71 392	73.03 424	98.74 340	82.05 430	96.64 285	95.69 423

new-patchmatchnet		90.30 392	89.46 396	92.84 403	90.77 438	88.55 420	99.83 311	98.80 406	90.07 409	87.86 427	95.00 429	78.77 411	94.30 433	84.86 424	79.15 428	95.68 424

mvsany_test3		89.36 394	88.96 398	90.56 406	91.95 432	78.97 431	99.74 331	96.59 442	96.84 239	89.25 423	96.07 424	52.59 439	97.11 412	95.17 357	82.44 421	95.58 425

test_f		86.87 399	86.06 402	89.28 409	91.45 437	76.37 434	99.87 306	97.11 436	91.10 399	88.46 425	93.05 435	38.31 446	96.66 417	91.77 393	83.46 419	94.82 426

test_fmvs3		87.19 398	87.02 401	87.71 412	92.69 431	76.64 433	99.96 273	97.27 435	93.55 369	90.82 420	94.03 433	38.00 447	92.19 438	93.49 379	83.35 420	94.32 427

test123		79.44 406	79.23 408	80.05 424	80.03 447	71.72 437	100.00 1	77.93 455	62.52 441	94.81 398	99.69 300	78.21 412	74.53 448	92.57 386	27.33 448	93.90 428

LCM-MVSNet		79.01 408	76.93 411	85.27 416	78.28 448	68.01 444	96.57 437	98.03 423	55.10 444	82.03 437	93.27 434	31.99 450	93.95 434	82.72 427	74.37 433	93.84 429

CMPMVS	66.12 22	90.65 390	92.04 380	86.46 415	96.18 415	66.87 445	98.03 433	99.38 216	83.38 428	85.49 432	99.55 333	77.59 413	98.80 333	94.44 366	94.31 326	93.72 430
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
WB-MVS		88.24 396	90.09 392	82.68 421	91.56 436	69.51 441	100.00 1	98.73 409	90.72 403	87.29 429	98.12 409	92.87 281	85.01 443	62.19 444	89.34 385	93.54 431

WB-MVSnew		97.02 287	97.24 271	96.37 367	99.44 272	97.36 308	100.00 1	99.43 128	96.12 296	99.35 247	99.89 261	93.60 270	98.42 368	88.91 417	98.39 214	93.33 432

testf1		84.40 401	84.79 403	83.23 419	95.71 419	58.71 452	98.79 423	97.75 431	81.58 429	84.94 433	98.07 411	45.33 443	97.73 407	77.09 440	83.85 416	93.24 433

APD_test2		84.40 401	84.79 403	83.23 419	95.71 419	58.71 452	98.79 423	97.75 431	81.58 429	84.94 433	98.07 411	45.33 443	97.73 407	77.09 440	83.85 416	93.24 433

SSC-MVS		87.61 397	89.47 395	82.04 422	90.63 439	68.77 442	99.99 235	98.66 411	90.34 406	86.70 430	98.08 410	92.72 286	84.12 444	59.41 447	88.71 393	93.22 435

PMMVS2		79.15 407	77.28 410	84.76 417	82.34 445	72.66 435	99.70 341	95.11 445	71.68 439	84.78 436	90.87 436	32.05 449	89.99 440	75.53 442	63.45 441	91.64 436

tmp_tt		75.80 410	74.26 412	80.43 423	52.91 455	53.67 454	87.42 442	97.98 426	61.80 442	67.04 445	100.00 1	76.43 418	96.40 420	96.47 333	28.26 447	91.23 437

testmvs		80.17 403	81.95 406	74.80 426	58.54 453	59.58 451	100.00 1	87.14 452	76.09 437	99.61 227	100.00 1	67.06 434	74.19 449	98.84 242	50.30 443	90.64 438

Gipuma		84.73 400	83.50 405	88.40 411	97.50 399	82.21 429	88.87 440	99.05 382	65.81 440	85.71 431	90.49 437	53.70 438	96.31 421	78.64 436	91.74 357	86.67 439
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
FPMVS		77.92 409	79.45 407	73.34 428	76.87 449	46.81 455	98.24 431	99.05 382	59.89 443	73.55 439	98.34 406	36.81 448	86.55 441	80.96 431	91.35 367	86.65 440

ANet_high		66.05 414	63.44 418	73.88 427	61.14 452	63.45 449	95.68 439	87.18 451	79.93 431	47.35 448	80.68 448	22.35 452	72.33 450	61.24 445	35.42 446	85.88 441

test_vis3_rt		79.61 404	78.19 409	83.86 418	88.68 441	69.56 440	99.81 315	82.19 454	86.78 421	68.57 442	84.51 445	25.06 451	98.26 376	89.18 415	78.94 429	83.75 442

MVE	68.59 21	67.22 413	64.68 417	74.84 425	74.67 451	62.32 450	95.84 438	90.87 450	50.98 445	58.72 447	81.05 447	12.20 455	78.95 445	61.06 446	56.75 442	83.24 443
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMVS	60.66 23	65.98 415	65.05 416	68.75 431	55.06 454	38.40 456	88.19 441	96.98 437	48.30 448	44.82 449	88.52 440	12.22 454	86.49 442	67.58 443	83.79 418	81.35 444
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
EMVS		69.88 412	69.09 415	72.24 430	84.70 443	65.82 447	99.96 273	87.08 453	49.82 447	71.51 441	84.74 444	49.30 440	75.32 447	50.97 449	43.71 445	75.59 445

E-PMN		70.72 411	70.06 414	72.69 429	83.92 444	65.48 448	99.95 280	92.72 448	49.88 446	72.30 440	86.26 443	47.17 442	77.43 446	53.83 448	44.49 444	75.17 446

wuyk23d		28.28 416	29.73 420	23.92 432	75.89 450	32.61 457	66.50 443	12.88 456	16.09 449	14.59 451	16.59 450	12.35 453	32.36 451	39.36 450	13.36 449	6.79 447

mmdepth		0.01 421	0.02 424	0.00 433	0.00 456	0.00 458	0.00 444	0.00 457	0.00 451	0.00 452	0.14 452	0.00 456	0.00 452	0.00 451	0.00 450	0.00 448

monomultidepth		0.01 421	0.02 424	0.00 433	0.00 456	0.00 458	0.00 444	0.00 457	0.00 451	0.00 452	0.14 452	0.00 456	0.00 452	0.00 451	0.00 450	0.00 448

test_blank		0.07 420	0.09 423	0.00 433	0.00 456	0.00 458	0.00 444	0.00 457	0.00 451	0.00 452	0.79 451	0.00 456	0.00 452	0.00 451	0.00 450	0.00 448

uanet_test		0.01 421	0.02 424	0.00 433	0.00 456	0.00 458	0.00 444	0.00 457	0.00 451	0.00 452	0.14 452	0.00 456	0.00 452	0.00 451	0.00 450	0.00 448

DCPMVS		0.01 421	0.02 424	0.00 433	0.00 456	0.00 458	0.00 444	0.00 457	0.00 451	0.00 452	0.14 452	0.00 456	0.00 452	0.00 451	0.00 450	0.00 448

cdsmvs_eth3d_5k		24.41 417	32.55 419	0.00 433	0.00 456	0.00 458	0.00 444	99.39 213	0.00 451	0.00 452	100.00 1	93.55 271	0.00 452	0.00 451	0.00 450	0.00 448

pcd_1.5k_mvsjas		8.24 419	10.99 422	0.00 433	0.00 456	0.00 458	0.00 444	0.00 457	0.00 451	0.00 452	0.14 452	98.75 131	0.00 452	0.00 451	0.00 450	0.00 448

sosnet-low-res		0.01 421	0.02 424	0.00 433	0.00 456	0.00 458	0.00 444	0.00 457	0.00 451	0.00 452	0.14 452	0.00 456	0.00 452	0.00 451	0.00 450	0.00 448

sosnet		0.01 421	0.02 424	0.00 433	0.00 456	0.00 458	0.00 444	0.00 457	0.00 451	0.00 452	0.14 452	0.00 456	0.00 452	0.00 451	0.00 450	0.00 448

uncertanet		0.01 421	0.02 424	0.00 433	0.00 456	0.00 458	0.00 444	0.00 457	0.00 451	0.00 452	0.14 452	0.00 456	0.00 452	0.00 451	0.00 450	0.00 448

Regformer		0.01 421	0.02 424	0.00 433	0.00 456	0.00 458	0.00 444	0.00 457	0.00 451	0.00 452	0.14 452	0.00 456	0.00 452	0.00 451	0.00 450	0.00 448

ab-mvs-re		8.33 418	11.11 421	0.00 433	0.00 456	0.00 458	0.00 444	0.00 457	0.00 451	0.00 452	100.00 1	0.00 456	0.00 452	0.00 451	0.00 450	0.00 448

uanet		0.01 421	0.02 424	0.00 433	0.00 456	0.00 458	0.00 444	0.00 457	0.00 451	0.00 452	0.14 452	0.00 456	0.00 452	0.00 451	0.00 450	0.00 448

WAC-MVS						97.98 278								95.74 345

FOURS1					100.00 1	99.97 21	100.00 1	99.42 147	98.52 87	100.00 1

test_one_0601					100.00 1	99.99 5		99.42 147	98.72 76	100.00 1	100.00 1	99.60 21

eth-test2					0.00 456

eth-test					0.00 456

ZD-MVS					100.00 1	99.98 17		99.80 43	97.31 204	100.00 1	100.00 1	99.32 69	99.99 101	100.00 1	100.00 1

test_241102_ONE					100.00 1	99.99 5		99.42 147	99.03 21	100.00 1	100.00 1	99.50 41	100.00 1

9.14			99.57 52		99.99 49		100.00 1	99.42 147	97.54 175	100.00 1	100.00 1	99.15 90	99.99 101	100.00 1	100.00 1

save fliter					99.99 49	99.93 47	100.00 1	99.42 147	98.93 43

test0726					100.00 1	99.99 5	100.00 1	99.42 147	99.04 16	100.00 1	100.00 1	99.53 33

test_part2					100.00 1	99.99 5				100.00 1

sam_mvs												99.33 66

MTGPA								99.42 147

test_post1							99.32 382				88.24 441	99.33 66	99.59 250	98.31 271

test_post											89.05 439	99.49 43	99.59 250

patchmatchnet-post											97.79 414	99.41 61	99.54 267

MTMP							100.00 1	99.18 332

gm-plane-assit					99.52 245	97.26 315			95.86 303		100.00 1		99.43 288	98.76 247

TEST9					100.00 1	99.95 32	100.00 1	99.42 147	97.65 158	100.00 1	100.00 1	99.53 33	99.97 139

test_8					100.00 1	99.91 57	100.00 1	99.42 147	97.70 152	100.00 1	100.00 1	99.51 37	99.98 131

agg_prior					100.00 1	99.88 78		99.42 147		100.00 1			99.97 139

test_prior4						99.93 47	100.00 1

test_prior2							100.00 1		98.82 63	100.00 1	100.00 1	99.47 48		100.00 1	100.00 1

旧先验2							100.00 1		98.11 119	100.00 1			100.00 1	99.67 174

新几何2							100.00 1

原ACMM2							100.00 1

testdata2													100.00 1	97.36 310

segment_acmp												99.55 29

testdata1							100.00 1		98.77 75

plane_prior7					99.00 320	94.78 366

plane_prior6					99.06 312	94.80 362						88.58 351

plane_prior4											99.97 220

plane_prior3						94.79 365			99.03 21	99.08 264

plane_prior2							100.00 1		99.00 27

plane_prior1					99.02 315

plane_prior						94.80 362	100.00 1		99.03 21						95.58 290

n2								0.00 457

nn								0.00 457

door-mid								96.32 443

test11								99.42 147

door								96.13 444

HQP5-MVS						94.82 359

HQP-NCC					99.07 308		100.00 1		99.04 16	99.17 254

ACMP_Plane					99.07 308		100.00 1		99.04 16	99.17 254

BP-MVS														99.79 134

HQP3-MVS								99.40 200							95.58 290

HQP2-MVS												88.61 349

NP-MVS					99.07 308	94.81 361					99.97 220

MDTV_nov1_ep13			98.94 148		99.53 237	98.36 249	99.39 376	99.46 97	96.54 268	99.99 120	99.63 317	98.92 118	99.86 203	98.30 274	98.71 201

ACMMP++_ref															94.58 325

ACMMP++															95.17 310

Test By Simon												99.10 93

High-resolution multi-view benchmark