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.

AdaColmap		97.23 130	96.80 138	98.51 133	99.99 1	95.60 202	99.09 331	98.84 65	93.32 209	96.74 223	99.72 95	86.04 264	100.00 1	98.01 152	99.43 129	99.94 87

CNVR-MVS		99.40 1	99.26 1	99.84 7	99.98 2	99.51 7	99.98 24	98.69 82	98.20 9	99.93 3	99.98 2	96.82 26	100.00 1	99.75 41	100.00 1	99.99 26

TestfortrainingZip				99.90 5	99.97 3	99.70 5	99.97 42	98.89 52	96.02 99	99.99 1	99.96 3	97.97 5	100.00 1		99.65 96	100.00 1

MCST-MVS		99.32 3	99.14 4	99.86 6	99.97 3	99.59 6	99.97 42	98.64 91	98.47 3	99.13 107	99.92 16	96.38 36	100.00 1	99.74 43	100.00 1	100.00 1

mPP-MVS		98.39 56	98.20 54	98.97 93	99.97 3	96.92 139	99.95 75	98.38 184	95.04 124	98.61 140	99.80 59	93.39 117	100.00 1	98.64 114	100.00 1	99.98 57

CPTT-MVS		97.64 110	97.32 114	98.58 122	99.97 3	95.77 191	99.96 56	98.35 190	89.90 353	98.36 155	99.79 63	91.18 180	99.99 39	98.37 130	99.99 21	99.99 26

DP-MVS Recon		98.41 53	98.02 68	99.56 30	99.97 3	98.70 53	99.92 103	98.44 148	92.06 281	98.40 154	99.84 49	95.68 48	100.00 1	98.19 141	99.71 91	99.97 67

PAPR		98.52 43	98.16 58	99.58 29	99.97 3	98.77 47	99.95 75	98.43 156	95.35 118	98.03 170	99.75 81	94.03 102	99.98 51	98.11 146	99.83 80	99.99 26

MED-MVS test				99.60 24	99.96 9	98.79 42	99.97 42	98.88 55	96.36 90	99.07 112	99.93 12		100.00 1	99.98 9	99.96 46	99.99 26

MED-MVS		99.24 8	99.11 7	99.60 24	99.96 9	98.79 42	99.97 42	98.88 55	96.91 62	99.07 112	99.92 16	97.36 18	100.00 1	99.98 9	99.96 46	100.00 1

TestfortrainingZip a		99.01 16	98.78 21	99.69 17	99.96 9	99.09 25	99.97 42	98.74 76	96.91 62	99.86 16	99.92 16	96.29 37	99.99 39	98.32 133	99.09 149	100.00 1

HFP-MVS		98.56 39	98.37 43	99.14 73	99.96 9	97.43 115	99.95 75	98.61 99	94.77 134	99.31 95	99.85 38	94.22 95	100.00 1	98.70 109	99.98 32	99.98 57

region2R		98.54 41	98.37 43	99.05 83	99.96 9	97.18 125	99.96 56	98.55 119	94.87 131	99.45 81	99.85 38	94.07 101	100.00 1	98.67 111	100.00 1	99.98 57

ACMMPR		98.50 44	98.32 47	99.05 83	99.96 9	97.18 125	99.95 75	98.60 101	94.77 134	99.31 95	99.84 49	93.73 111	100.00 1	98.70 109	99.98 32	99.98 57

NCCC		99.37 2	99.25 2	99.71 16	99.96 9	99.15 23	99.97 42	98.62 98	98.02 22	99.90 7	99.95 4	97.33 19	100.00 1	99.54 58	100.00 1	100.00 1

CP-MVS		98.45 48	98.32 47	98.87 98	99.96 9	96.62 154	99.97 42	98.39 180	94.43 152	98.90 121	99.87 32	94.30 92	100.00 1	99.04 85	99.99 21	99.99 26

test_one_0601					99.94 17	99.30 13		98.41 173	96.63 75	99.75 42	99.93 12	97.49 11

test_0728_SECOND				99.82 8	99.94 17	99.47 8	99.95 75	98.43 156					100.00 1	99.99 5	100.00 1	100.00 1

XVS		98.70 32	98.55 31	99.15 71	99.94 17	97.50 111	99.94 93	98.42 168	96.22 93	99.41 87	99.78 67	94.34 89	99.96 76	98.92 94	99.95 53	99.99 26

X-MVStestdata		93.83 288	92.06 323	99.15 71	99.94 17	97.50 111	99.94 93	98.42 168	96.22 93	99.41 87	41.37 542	94.34 89	99.96 76	98.92 94	99.95 53	99.99 26

test_prior				99.43 41	99.94 17	98.49 66		98.65 88					99.80 143			99.99 26

MSLP-MVS++		99.13 9	99.01 12	99.49 37	99.94 17	98.46 67	99.98 24	98.86 59	97.10 53	99.80 28	99.94 5	95.92 44	100.00 1	99.51 59	100.00 1	100.00 1

APDe-MVS		99.06 13	98.91 15	99.51 34	99.94 17	98.76 50	99.91 111	98.39 180	97.20 51	99.46 80	99.85 38	95.53 52	99.79 145	99.86 27	100.00 1	99.99 26
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
MP-MVS		98.23 71	97.97 72	99.03 85	99.94 17	97.17 128	99.95 75	98.39 180	94.70 138	98.26 161	99.81 58	91.84 171	100.00 1	98.85 100	99.97 42	99.93 88
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CDPH-MVS		98.65 35	98.36 45	99.49 37	99.94 17	98.73 51	99.87 133	98.33 195	93.97 178	99.76 41	99.87 32	94.99 68	99.75 154	98.55 118	100.00 1	99.98 57

PAPM_NR		98.12 75	97.93 78	98.70 109	99.94 17	96.13 180	99.82 166	98.43 156	94.56 142	97.52 189	99.70 101	94.40 84	99.98 51	97.00 196	99.98 32	99.99 26

MG-MVS		98.91 22	98.65 27	99.68 18	99.94 17	99.07 26	99.64 239	99.44 19	97.33 44	99.00 117	99.72 95	94.03 102	99.98 51	98.73 108	100.00 1	100.00 1

ME-MVS		99.07 11	98.89 17	99.59 27	99.93 28	98.79 42	99.95 75	98.80 71	95.89 103	99.28 99	99.93 12	96.28 38	99.98 51	99.98 9	99.96 46	99.99 26

SED-MVS		99.28 5	99.11 7	99.77 9	99.93 28	99.30 13	99.96 56	98.43 156	97.27 47	99.80 28	99.94 5	96.71 29	100.00 1	100.00 1	100.00 1	100.00 1

IU-MVS					99.93 28	99.31 11		98.41 173	97.71 31	99.84 23				100.00 1	100.00 1	100.00 1

test_241102_ONE					99.93 28	99.30 13		98.43 156	97.26 49	99.80 28	99.88 29	96.71 29	100.00 1

DVP-MVS		99.30 4	99.16 3	99.73 13	99.93 28	99.29 16	99.95 75	98.32 197	97.28 45	99.83 24	99.91 19	97.22 21	100.00 1	99.99 5	100.00 1	99.89 97
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					99.93 28	99.29 16	99.96 56	98.42 168	97.28 45	99.86 16	99.94 5	97.22 21

MSP-MVS		99.09 10	99.12 5	98.98 92	99.93 28	97.24 122	99.95 75	98.42 168	97.50 38	99.52 76	99.88 29	97.43 17	99.71 160	99.50 61	99.98 32	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
agg_prior					99.93 28	98.77 47		98.43 156		99.63 59			99.85 130

FOURS1					99.92 36	97.66 105	99.95 75	98.36 188	95.58 112	99.52 76

ZD-MVS					99.92 36	98.57 61		98.52 128	92.34 269	99.31 95	99.83 51	95.06 63	99.80 143	99.70 49	99.97 42

GST-MVS		98.27 63	97.97 72	99.17 66	99.92 36	97.57 107	99.93 100	98.39 180	94.04 176	98.80 126	99.74 88	92.98 133	100.00 1	98.16 143	99.76 88	99.93 88

TEST9					99.92 36	98.92 31	99.96 56	98.43 156	93.90 184	99.71 49	99.86 34	95.88 45	99.85 130

train_agg		98.88 23	98.65 27	99.59 27	99.92 36	98.92 31	99.96 56	98.43 156	94.35 157	99.71 49	99.86 34	95.94 42	99.85 130	99.69 50	99.98 32	99.99 26

test_8					99.92 36	98.88 34	99.96 56	98.43 156	94.35 157	99.69 51	99.85 38	95.94 42	99.85 130

PGM-MVS		98.34 58	98.13 60	98.99 90	99.92 36	97.00 135	99.75 198	99.50 17	93.90 184	99.37 92	99.76 73	93.24 126	100.00 1	97.75 173	99.96 46	99.98 57

ACMMP		97.74 103	97.44 107	98.66 113	99.92 36	96.13 180	99.18 323	99.45 18	94.84 132	96.41 242	99.71 98	91.40 174	99.99 39	97.99 154	98.03 190	99.87 100
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
DVP-MVS++		99.26 6	99.09 10	99.77 9	99.91 44	99.31 11	99.95 75	98.43 156	96.48 80	99.80 28	99.93 12	97.44 15	100.00 1	99.92 16	99.98 32	100.00 1

MSC_two_6792asdad				99.93 2	99.91 44	99.80 2		98.41 173					100.00 1	99.96 12	100.00 1	100.00 1

No_MVS				99.93 2	99.91 44	99.80 2		98.41 173					100.00 1	99.96 12	100.00 1	100.00 1

HPM-MVS++		99.07 11	98.88 18	99.63 19	99.90 47	99.02 27	99.95 75	98.56 113	97.56 37	99.44 82	99.85 38	95.38 56	100.00 1	99.31 71	99.99 21	99.87 100

APD-MVS		98.62 36	98.35 46	99.41 44	99.90 47	98.51 64	99.87 133	98.36 188	94.08 171	99.74 45	99.73 92	94.08 100	99.74 156	99.42 67	99.99 21	99.99 26
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
DeepC-MVS_fast	96.59 1	98.81 26	98.54 32	99.62 22	99.90 47	98.85 37	99.24 318	98.47 140	98.14 16	99.08 110	99.91 19	93.09 130	100.00 1	99.04 85	99.99 21	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
OPU-MVS				99.93 2	99.89 50	99.80 2	99.96 56				99.80 59	97.44 15	100.00 1	100.00 1	99.98 32	100.00 1

DPE-MVS		99.26 6	99.10 9	99.74 12	99.89 50	99.24 20	99.87 133	98.44 148	97.48 39	99.64 58	99.94 5	96.68 31	99.99 39	99.99 5	100.00 1	99.99 26
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
test_part2					99.89 50	99.25 19				99.49 79

CSCG		97.10 136	97.04 126	97.27 242	99.89 50	91.92 338	99.90 117	99.07 37	88.67 377	95.26 275	99.82 54	93.17 129	99.98 51	98.15 144	99.47 124	99.90 96

ZNCC-MVS		98.31 60	98.03 67	99.17 66	99.88 54	97.59 106	99.94 93	98.44 148	94.31 160	98.50 147	99.82 54	93.06 131	99.99 39	98.30 135	99.99 21	99.93 88

SR-MVS		98.46 47	98.30 50	98.93 96	99.88 54	97.04 134	99.84 152	98.35 190	94.92 128	99.32 94	99.80 59	93.35 119	99.78 147	99.30 72	99.95 53	99.96 75

9.14			98.38 41		99.87 56		99.91 111	98.33 195	93.22 212	99.78 39	99.89 27	94.57 80	99.85 130	99.84 29	99.97 42

SMA-MVS		98.76 29	98.48 35	99.62 22	99.87 56	98.87 35	99.86 144	98.38 184	93.19 214	99.77 40	99.94 5	95.54 50	100.00 1	99.74 43	99.99 21	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
NormalMVS		97.90 85	97.85 85	98.04 166	99.86 58	95.39 212	99.61 246	97.78 270	96.52 78	98.61 140	99.31 157	92.73 141	99.67 168	96.77 212	99.48 121	99.06 253

lecture		98.67 33	98.46 36	99.28 53	99.86 58	97.88 92	99.97 42	99.25 30	96.07 97	99.79 37	99.70 101	92.53 150	99.98 51	99.51 59	99.48 121	99.97 67

PHI-MVS		98.41 53	98.21 53	99.03 85	99.86 58	97.10 132	99.98 24	98.80 71	90.78 331	99.62 62	99.78 67	95.30 57	100.00 1	99.80 32	99.93 64	99.99 26

MTAPA		98.29 62	97.96 75	99.30 52	99.85 61	97.93 90	99.39 291	98.28 204	95.76 106	97.18 204	99.88 29	92.74 140	100.00 1	98.67 111	99.88 76	99.99 26

LS3D		95.84 211	95.11 228	98.02 167	99.85 61	95.10 232	98.74 381	98.50 137	87.22 402	93.66 298	99.86 34	87.45 239	99.95 85	90.94 334	99.81 86	99.02 261

HPM-MVS		97.96 80	97.72 90	98.68 110	99.84 63	96.39 166	99.90 117	98.17 222	92.61 250	98.62 139	99.57 131	91.87 170	99.67 168	98.87 99	99.99 21	99.99 26
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
EI-MVSNet-Vis-set		98.27 63	98.11 62	98.75 106	99.83 64	96.59 158	99.40 287	98.51 131	95.29 120	98.51 146	99.76 73	93.60 115	99.71 160	98.53 121	99.52 114	99.95 83

save fliter					99.82 65	98.79 42	99.96 56	98.40 177	97.66 33

PLC	95.54 3	97.93 83	97.89 82	98.05 165	99.82 65	94.77 244	99.92 103	98.46 142	93.93 181	97.20 202	99.27 165	95.44 55	99.97 64	97.41 180	99.51 117	99.41 198
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
APD-MVS_3200maxsize		98.25 68	98.08 64	98.78 103	99.81 67	96.60 156	99.82 166	98.30 202	93.95 180	99.37 92	99.77 71	92.84 137	99.76 153	98.95 90	99.92 67	99.97 67

EI-MVSNet-UG-set		98.14 74	97.99 70	98.60 118	99.80 68	96.27 169	99.36 297	98.50 137	95.21 122	98.30 158	99.75 81	93.29 123	99.73 159	98.37 130	99.30 138	99.81 109

SR-MVS-dyc-post		98.31 60	98.17 57	98.71 108	99.79 69	96.37 167	99.76 192	98.31 199	94.43 152	99.40 89	99.75 81	93.28 124	99.78 147	98.90 97	99.92 67	99.97 67

RE-MVS-def			98.13 60		99.79 69	96.37 167	99.76 192	98.31 199	94.43 152	99.40 89	99.75 81	92.95 134		98.90 97	99.92 67	99.97 67

HPM-MVS_fast		97.80 97	97.50 103	98.68 110	99.79 69	96.42 162	99.88 130	98.16 227	91.75 293	98.94 119	99.54 134	91.82 172	99.65 172	97.62 177	99.99 21	99.99 26

SF-MVS		98.67 33	98.40 39	99.50 35	99.77 72	98.67 54	99.90 117	98.21 217	93.53 196	99.81 26	99.89 27	94.70 76	99.86 129	99.84 29	99.93 64	99.96 75

MGCNet		99.06 13	98.84 19	99.72 14	99.76 73	99.21 22	99.99 8	99.34 25	98.70 2	99.44 82	99.75 81	93.24 126	99.99 39	99.94 14	99.41 131	99.95 83

旧先验1					99.76 73	97.52 109		98.64 91			99.85 38	95.63 49			99.94 58	99.99 26

OMC-MVS		97.28 126	97.23 118	97.41 231	99.76 73	93.36 304	99.65 235	97.95 249	96.03 98	97.41 195	99.70 101	89.61 206	99.51 178	96.73 214	98.25 180	99.38 201

新几何1				99.42 43	99.75 76	98.27 71		98.63 97	92.69 245	99.55 71	99.82 54	94.40 84	100.00 1	91.21 326	99.94 58	99.99 26

MP-MVS-pluss		98.07 78	97.64 96	99.38 49	99.74 77	98.41 69	99.74 202	98.18 221	93.35 207	96.45 235	99.85 38	92.64 145	99.97 64	98.91 96	99.89 73	99.77 116
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TSAR-MVS + MP.		98.93 20	98.77 22	99.41 44	99.74 77	98.67 54	99.77 186	98.38 184	96.73 71	99.88 13	99.74 88	94.89 70	99.59 174	99.80 32	99.98 32	99.97 67
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
test12				99.43 41	99.74 77	98.56 62		98.40 177		99.65 55		94.76 73	99.75 154		99.98 32	99.99 26

原ACMM1				98.96 94	99.73 80	96.99 136		98.51 131	94.06 174	99.62 62	99.85 38	94.97 69	99.96 76	95.11 246	99.95 53	99.92 93

TSAR-MVS + GP.		98.60 37	98.51 34	98.86 99	99.73 80	96.63 153	99.97 42	97.92 254	98.07 19	98.76 132	99.55 132	95.00 67	99.94 94	99.91 19	97.68 197	99.99 26

CANet		98.27 63	97.82 87	99.63 19	99.72 82	99.10 24	99.98 24	98.51 131	97.00 59	98.52 144	99.71 98	87.80 230	99.95 85	99.75 41	99.38 133	99.83 105

reproduce_model		98.75 30	98.66 26	99.03 85	99.71 83	97.10 132	99.73 209	98.23 212	97.02 58	99.18 105	99.90 23	94.54 81	99.99 39	99.77 37	99.90 72	99.99 26

F-COLMAP		96.93 148	96.95 129	96.87 260	99.71 83	91.74 348	99.85 147	97.95 249	93.11 222	95.72 264	99.16 185	92.35 156	99.94 94	95.32 242	99.35 136	98.92 269

reproduce-ours		98.78 27	98.67 24	99.09 80	99.70 85	97.30 119	99.74 202	98.25 208	97.10 53	99.10 108	99.90 23	94.59 77	99.99 39	99.77 37	99.91 70	99.99 26

our_new_method		98.78 27	98.67 24	99.09 80	99.70 85	97.30 119	99.74 202	98.25 208	97.10 53	99.10 108	99.90 23	94.59 77	99.99 39	99.77 37	99.91 70	99.99 26

SD-MVS		98.92 21	98.70 23	99.56 30	99.70 85	98.73 51	99.94 93	98.34 194	96.38 86	99.81 26	99.76 73	94.59 77	99.98 51	99.84 29	99.96 46	99.97 67
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
patch_mono-2		98.24 69	99.12 5	95.59 304	99.67 88	86.91 435	99.95 75	98.89 52	97.60 34	99.90 7	99.76 73	96.54 34	99.98 51	99.94 14	99.82 84	99.88 98

ACMMP_NAP		98.49 45	98.14 59	99.54 32	99.66 89	98.62 60	99.85 147	98.37 187	94.68 139	99.53 74	99.83 51	92.87 136	100.00 1	98.66 113	99.84 79	99.99 26

DeepPCF-MVS	95.94 2	97.71 107	98.98 13	93.92 377	99.63 90	81.76 470	99.96 56	98.56 113	99.47 1	99.19 104	99.99 1	94.16 99	100.00 1	99.92 16	99.93 64	100.00 1

EPNet		98.49 45	98.40 39	98.77 105	99.62 91	96.80 147	99.90 117	99.51 16	97.60 34	99.20 102	99.36 152	93.71 112	99.91 111	97.99 154	98.71 165	99.61 151
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MM		98.83 24	98.53 33	99.76 11	99.59 92	99.33 9	99.99 8	99.76 6	98.39 4	99.39 91	99.80 59	90.49 195	99.96 76	99.89 21	99.43 129	99.98 57

PVSNet_BlendedMVS		96.05 201	95.82 193	96.72 266	99.59 92	96.99 136	99.95 75	99.10 34	94.06 174	98.27 159	95.80 372	89.00 218	99.95 85	99.12 79	87.53 361	93.24 435

PVSNet_Blended		97.94 82	97.64 96	98.83 100	99.59 92	96.99 136	100.00 1	99.10 34	95.38 117	98.27 159	99.08 190	89.00 218	99.95 85	99.12 79	99.25 140	99.57 162

PatchMatch-RL		96.04 202	95.40 211	97.95 170	99.59 92	95.22 226	99.52 268	99.07 37	93.96 179	96.49 233	98.35 284	82.28 325	99.82 142	90.15 350	99.22 143	98.81 277

dcpmvs_2		97.42 121	98.09 63	95.42 311	99.58 96	87.24 431	99.23 319	96.95 405	94.28 163	98.93 120	99.73 92	94.39 87	99.16 207	99.89 21	99.82 84	99.86 102

test222					99.55 97	97.41 117	99.34 299	98.55 119	91.86 287	99.27 100	99.83 51	93.84 109			99.95 53	99.99 26

CNLPA		97.76 101	97.38 110	98.92 97	99.53 98	96.84 141	99.87 133	98.14 231	93.78 188	96.55 231	99.69 105	92.28 158	99.98 51	97.13 191	99.44 128	99.93 88

API-MVS		97.86 88	97.66 94	98.47 135	99.52 99	95.41 210	99.47 278	98.87 58	91.68 295	98.84 123	99.85 38	92.34 157	99.99 39	98.44 126	99.96 46	100.00 1

PVSNet	91.05 13	97.13 135	96.69 144	98.45 138	99.52 99	95.81 189	99.95 75	99.65 12	94.73 136	99.04 115	99.21 177	84.48 300	99.95 85	94.92 252	98.74 164	99.58 160

114514_t		97.41 122	96.83 135	99.14 73	99.51 101	97.83 94	99.89 127	98.27 206	88.48 382	99.06 114	99.66 116	90.30 198	99.64 173	96.32 226	99.97 42	99.96 75

cl22		93.77 293	93.25 293	95.33 315	99.49 102	94.43 256	99.61 246	98.09 234	90.38 342	89.16 370	95.61 381	90.56 193	97.34 354	91.93 317	84.45 384	94.21 373

testdata				98.42 142	99.47 103	95.33 216		98.56 113	93.78 188	99.79 37	99.85 38	93.64 114	99.94 94	94.97 250	99.94 58	100.00 1

MAR-MVS		97.43 117	97.19 120	98.15 158	99.47 103	94.79 243	99.05 342	98.76 73	92.65 248	98.66 137	99.82 54	88.52 224	99.98 51	98.12 145	99.63 98	99.67 133
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
DP-MVS		94.54 261	93.42 283	97.91 176	99.46 105	94.04 275	98.93 360	97.48 307	81.15 459	90.04 341	99.55 132	87.02 247	99.95 85	88.97 365	98.11 186	99.73 120

MVS_111021_LR		98.42 52	98.38 41	98.53 130	99.39 106	95.79 190	99.87 133	99.86 2	96.70 72	98.78 127	99.79 63	92.03 167	99.90 113	99.17 78	99.86 78	99.88 98

CHOSEN 280x420		99.01 16	99.03 11	98.95 95	99.38 107	98.87 35	98.46 400	99.42 21	97.03 57	99.02 116	99.09 189	99.35 2	98.21 316	99.73 45	99.78 87	99.77 116

MVS_111021_HR		98.72 31	98.62 29	99.01 89	99.36 108	97.18 125	99.93 100	99.90 1	96.81 69	98.67 136	99.77 71	93.92 104	99.89 118	99.27 74	99.94 58	99.96 75

fmvsm_s_conf0.5_n_11		98.03 79	97.89 82	98.46 137	99.35 109	97.76 98	99.99 8	98.04 240	98.20 9	99.90 7	99.78 67	86.21 262	99.95 85	99.89 21	99.68 93	97.65 316

DPM-MVS		98.83 24	98.46 36	99.97 1	99.33 110	99.92 1	99.96 56	98.44 148	97.96 23	99.55 71	99.94 5	97.18 23	100.00 1	93.81 283	99.94 58	99.98 57

TAPA-MVS	92.12 8	94.42 269	93.60 275	96.90 259	99.33 110	91.78 347	99.78 180	98.00 243	89.89 354	94.52 283	99.47 138	91.97 168	99.18 204	69.90 482	99.52 114	99.73 120
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
reproduce_monomvs		95.38 233	95.07 230	96.32 281	99.32 112	96.60 156	99.76 192	98.85 62	96.65 74	87.83 399	96.05 369	99.52 1	98.11 321	96.58 218	81.07 413	94.25 366

fmvsm_s_conf0.5_n_9		98.15 73	98.02 68	98.55 124	99.28 113	95.84 188	99.99 8	98.57 107	98.17 13	99.93 3	99.74 88	87.04 246	99.97 64	99.86 27	99.59 108	99.83 105

SPE-MVS-test		97.88 86	97.94 77	97.70 195	99.28 113	95.20 227	99.98 24	97.15 364	95.53 114	99.62 62	99.79 63	92.08 166	98.38 298	98.75 107	99.28 139	99.52 173

test_fmvsm_n_1920		98.44 49	98.61 30	97.92 174	99.27 115	95.18 228	100.00 1	98.90 50	98.05 20	99.80 28	99.73 92	92.64 145	99.99 39	99.58 57	99.51 117	98.59 287

fmvsm_s_conf0.5_n_10		98.24 69	97.90 80	99.26 55	99.24 116	97.88 92	99.99 8	98.76 73	98.20 9	99.92 5	99.74 88	85.97 266	99.94 94	99.72 46	99.53 113	99.96 75

fmvsm_l_conf0.5_n_a		99.00 18	98.91 15	99.28 53	99.21 117	97.91 91	99.98 24	98.85 62	98.25 5	99.92 5	99.75 81	94.72 74	99.97 64	99.87 25	99.64 97	99.95 83

fmvsm_s_conf0.5_n_8		98.38 57	98.05 66	99.35 50	99.20 118	98.12 77	99.98 24	98.81 67	98.22 7	99.80 28	99.71 98	87.37 241	99.97 64	99.91 19	99.48 121	99.97 67

test_yl		97.83 92	97.37 111	99.21 60	99.18 119	97.98 86	99.64 239	99.27 27	91.43 304	97.88 179	98.99 207	95.84 46	99.84 138	98.82 101	95.32 288	99.79 112

DCV-MVSNet		97.83 92	97.37 111	99.21 60	99.18 119	97.98 86	99.64 239	99.27 27	91.43 304	97.88 179	98.99 207	95.84 46	99.84 138	98.82 101	95.32 288	99.79 112

fmvsm_l_conf0.5_n		98.94 19	98.84 19	99.25 56	99.17 121	97.81 96	99.98 24	98.86 59	98.25 5	99.90 7	99.76 73	94.21 97	99.97 64	99.87 25	99.52 114	99.98 57

DeepC-MVS	94.51 4	96.92 149	96.40 159	98.45 138	99.16 122	95.90 186	99.66 234	98.06 237	96.37 89	94.37 289	99.49 137	83.29 318	99.90 113	97.63 176	99.61 104	99.55 164
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DELS-MVS		98.54 41	98.22 52	99.50 35	99.15 123	98.65 58	100.00 1	98.58 105	97.70 32	98.21 165	99.24 173	92.58 148	99.94 94	98.63 116	99.94 58	99.92 93
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
fmvsm_l_conf0.5_n_3		98.41 53	98.08 64	99.39 46	99.12 124	98.29 70	99.98 24	98.64 91	98.14 16	99.86 16	99.76 73	87.99 229	99.97 64	99.72 46	99.54 111	99.91 95

fmvsm_l_conf0.5_n_9		98.55 40	98.23 51	99.49 37	99.10 125	98.50 65	99.99 8	98.70 80	98.14 16	99.94 2	99.68 112	89.02 217	99.98 51	99.89 21	99.61 104	99.99 26

CS-MVS		97.79 99	97.91 79	97.43 227	99.10 125	94.42 257	99.99 8	97.10 378	95.07 123	99.68 52	99.75 81	92.95 134	98.34 302	98.38 128	99.14 145	99.54 168

Anonymous202405211		93.10 311	91.99 324	96.40 277	99.10 125	89.65 400	98.88 366	97.93 251	83.71 442	94.00 295	98.75 245	68.79 439	99.88 124	95.08 247	91.71 321	99.68 131

fmvsm_s_conf0.5_n		97.80 97	97.85 85	97.67 196	99.06 128	94.41 258	99.98 24	98.97 43	97.34 42	99.63 59	99.69 105	87.27 242	99.97 64	99.62 55	99.06 151	98.62 286

HyFIR lowres test		96.66 166	96.43 156	97.36 236	99.05 129	93.91 281	99.70 225	99.80 3	90.54 337	96.26 245	98.08 296	92.15 164	98.23 315	96.84 206	95.46 283	99.93 88

LFMVS		94.75 255	93.56 278	98.30 148	99.03 130	95.70 196	98.74 381	97.98 246	87.81 395	98.47 148	99.39 149	67.43 448	99.53 175	98.01 152	95.20 291	99.67 133

fmvsm_s_conf0.5_n_4		97.75 102	97.86 84	97.42 228	99.01 131	94.69 247	99.97 42	98.76 73	97.91 25	99.87 14	99.76 73	86.70 253	99.93 104	99.67 52	99.12 148	97.64 317

fmvsm_s_conf0.5_n_2		97.59 112	97.28 115	98.53 130	99.01 131	98.15 72	99.98 24	98.59 103	98.17 13	99.75 42	99.63 122	81.83 331	99.94 94	99.78 35	98.79 162	97.51 325

AllTest		92.48 328	91.64 331	95.00 324	99.01 131	88.43 418	98.94 358	96.82 420	86.50 412	88.71 375	98.47 279	74.73 413	99.88 124	85.39 412	96.18 257	96.71 331

TestCases				95.00 324	99.01 131	88.43 418		96.82 420	86.50 412	88.71 375	98.47 279	74.73 413	99.88 124	85.39 412	96.18 257	96.71 331

COLMAP_ROB	90.47 14	92.18 335	91.49 337	94.25 358	99.00 135	88.04 424	98.42 406	96.70 427	82.30 454	88.43 387	99.01 200	76.97 388	99.85 130	86.11 408	96.50 248	94.86 342
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
fmvsm_s_conf0.5_n_3		97.95 81	97.66 94	98.81 101	98.99 136	98.07 80	99.98 24	98.81 67	98.18 12	99.89 11	99.70 101	84.15 304	99.97 64	99.76 40	99.50 119	98.39 294

test_fmvs1		95.35 234	95.68 200	94.36 354	98.99 136	84.98 446	99.96 56	96.65 429	97.60 34	99.73 47	98.96 213	71.58 429	99.93 104	98.31 134	99.37 134	98.17 300

HY-MVS	92.50 7	97.79 99	97.17 122	99.63 19	98.98 138	99.32 10	97.49 435	99.52 14	95.69 109	98.32 157	97.41 317	93.32 121	99.77 150	98.08 149	95.75 273	99.81 109

VNet		97.21 131	96.57 149	99.13 77	98.97 139	97.82 95	99.03 345	99.21 32	94.31 160	99.18 105	98.88 226	86.26 261	99.89 118	98.93 92	94.32 301	99.69 130

thres200		96.96 145	96.21 167	99.22 59	98.97 139	98.84 38	99.85 147	99.71 7	93.17 216	96.26 245	98.88 226	89.87 203	99.51 178	94.26 271	94.91 293	99.31 219

tfpn200view9		96.79 154	95.99 176	99.19 62	98.94 141	98.82 39	99.78 180	99.71 7	92.86 232	96.02 255	98.87 233	89.33 210	99.50 180	93.84 280	94.57 297	99.27 228

thres400		96.78 156	95.99 176	99.16 69	98.94 141	98.82 39	99.78 180	99.71 7	92.86 232	96.02 255	98.87 233	89.33 210	99.50 180	93.84 280	94.57 297	99.16 241

sasdasda		97.09 138	96.32 161	99.39 46	98.93 143	98.95 29	99.72 213	97.35 321	94.45 148	97.88 179	99.42 142	86.71 251	99.52 176	98.48 123	93.97 307	99.72 122

Anonymous20231211		89.86 386	88.44 394	94.13 366	98.93 143	90.68 378	98.54 397	98.26 207	76.28 477	86.73 413	95.54 385	70.60 435	97.56 347	90.82 337	80.27 422	94.15 382

canonicalmvs		97.09 138	96.32 161	99.39 46	98.93 143	98.95 29	99.72 213	97.35 321	94.45 148	97.88 179	99.42 142	86.71 251	99.52 176	98.48 123	93.97 307	99.72 122

SDMVSNet		94.80 250	93.96 265	97.33 239	98.92 146	95.42 209	99.59 251	98.99 40	92.41 265	92.55 313	97.85 308	75.81 403	98.93 221	97.90 161	91.62 322	97.64 317

sd_testset		93.55 300	92.83 304	95.74 302	98.92 146	90.89 374	98.24 413	98.85 62	92.41 265	92.55 313	97.85 308	71.07 434	98.68 261	93.93 277	91.62 322	97.64 317

EPNet_dtu		95.71 222	95.39 212	96.66 268	98.92 146	93.41 300	99.57 257	98.90 50	96.19 95	97.52 189	98.56 269	92.65 144	97.36 352	77.89 463	98.33 175	99.20 238
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
WTY-MVS		98.10 76	97.60 98	99.60 24	98.92 146	99.28 18	99.89 127	99.52 14	95.58 112	98.24 163	99.39 149	93.33 120	99.74 156	97.98 156	95.58 282	99.78 115

CHOSEN 1792x2688		96.81 153	96.53 150	97.64 200	98.91 150	93.07 307	99.65 235	99.80 3	95.64 110	95.39 271	98.86 235	84.35 302	99.90 113	96.98 198	99.16 144	99.95 83

thres100view900		96.74 161	95.92 188	99.18 63	98.90 151	98.77 47	99.74 202	99.71 7	92.59 252	95.84 258	98.86 235	89.25 212	99.50 180	93.84 280	94.57 297	99.27 228

thres600view7		96.69 164	95.87 192	99.14 73	98.90 151	98.78 46	99.74 202	99.71 7	92.59 252	95.84 258	98.86 235	89.25 212	99.50 180	93.44 293	94.50 300	99.16 241

MSDG		94.37 271	93.36 290	97.40 232	98.88 153	93.95 280	99.37 295	97.38 316	85.75 423	90.80 332	99.17 182	84.11 306	99.88 124	86.35 404	98.43 173	98.36 296

MGCFI-Net		97.00 143	96.22 166	99.34 51	98.86 154	98.80 41	99.67 233	97.30 333	94.31 160	97.77 185	99.41 146	86.36 259	99.50 180	98.38 128	93.90 309	99.72 122

h-mvs33		94.92 247	94.36 250	96.59 270	98.85 155	91.29 366	98.93 360	98.94 44	95.90 101	98.77 129	98.42 282	90.89 188	99.77 150	97.80 166	70.76 467	98.72 283

Anonymous20240529		92.10 336	90.65 348	96.47 272	98.82 156	90.61 380	98.72 383	98.67 87	75.54 481	93.90 297	98.58 267	66.23 453	99.90 113	94.70 261	90.67 325	98.90 272

PVSNet_Blended_VisFu		97.27 127	96.81 137	98.66 113	98.81 157	96.67 152	99.92 103	98.64 91	94.51 144	96.38 243	98.49 275	89.05 216	99.88 124	97.10 193	98.34 174	99.43 194

PS-MVSNAJ		98.44 49	98.20 54	99.16 69	98.80 158	98.92 31	99.54 266	98.17 222	97.34 42	99.85 20	99.85 38	91.20 177	99.89 118	99.41 68	99.67 94	98.69 284

CANet_DTU		96.76 157	96.15 170	98.60 118	98.78 159	97.53 108	99.84 152	97.63 284	97.25 50	99.20 102	99.64 119	81.36 337	99.98 51	92.77 305	98.89 156	98.28 298

mvsany_test1		97.82 95	97.90 80	97.55 211	98.77 160	93.04 310	99.80 174	97.93 251	96.95 61	99.61 69	99.68 112	90.92 185	99.83 140	99.18 77	98.29 179	99.80 111

alignmvs		97.81 96	97.33 113	99.25 56	98.77 160	98.66 56	99.99 8	98.44 148	94.40 156	98.41 152	99.47 138	93.65 113	99.42 190	98.57 117	94.26 303	99.67 133

SymmetryMVS		97.64 110	97.46 104	98.17 154	98.74 162	95.39 212	99.61 246	99.26 29	96.52 78	98.61 140	99.31 157	92.73 141	99.67 168	96.77 212	95.63 280	99.45 190

SteuartSystems-ACMMP		99.02 15	98.97 14	99.18 63	98.72 163	97.71 100	99.98 24	98.44 148	96.85 64	99.80 28	99.91 19	97.57 9	99.85 130	99.44 66	99.99 21	99.99 26
Skip Steuart: Steuart Systems R&D Blog.
xiu_mvs_v2_base		98.23 71	97.97 72	99.02 88	98.69 164	98.66 56	99.52 268	98.08 236	97.05 56	99.86 16	99.86 34	90.65 190	99.71 160	99.39 70	98.63 166	98.69 284

miper_enhance_ethall		94.36 273	93.98 264	95.49 305	98.68 165	95.24 224	99.73 209	97.29 341	93.28 211	89.86 346	95.97 370	94.37 88	97.05 375	92.20 309	84.45 384	94.19 374

fmvsm_s_conf0.5_n_5		98.08 77	97.71 92	99.17 66	98.67 166	97.69 104	99.99 8	98.57 107	97.40 40	99.89 11	99.69 105	85.99 265	99.96 76	99.80 32	99.40 132	99.85 103

ETVMVS		97.03 142	96.64 145	98.20 153	98.67 166	97.12 129	99.89 127	98.57 107	91.10 317	98.17 166	98.59 264	93.86 108	98.19 317	95.64 239	95.24 290	99.28 226

test2506		97.53 114	97.19 120	98.58 122	98.66 168	96.90 140	98.81 375	99.77 5	94.93 126	97.95 173	98.96 213	92.51 151	99.20 202	94.93 251	98.15 183	99.64 139

ECVR-MVS		95.66 225	95.05 231	97.51 216	98.66 168	93.71 285	98.85 372	98.45 143	94.93 126	96.86 216	98.96 213	75.22 409	99.20 202	95.34 241	98.15 183	99.64 139

BridgeMVS		98.27 63	97.99 70	99.11 78	98.64 170	98.43 68	99.47 278	97.79 266	94.56 142	99.74 45	98.35 284	94.33 91	99.25 196	99.12 79	99.96 46	99.64 139

fmvsm_s_conf0.5_n_a		97.73 105	97.72 90	97.77 189	98.63 171	94.26 266	99.96 56	98.92 49	97.18 52	99.75 42	99.69 105	87.00 248	99.97 64	99.46 64	98.89 156	99.08 251

MVSMamba_PlusPlus		97.83 92	97.45 106	98.99 90	98.60 172	98.15 72	99.58 253	97.74 275	90.34 345	99.26 101	98.32 287	94.29 93	99.23 197	99.03 88	99.89 73	99.58 160

balanced_ft_v1		96.88 150	96.52 151	97.96 169	98.60 172	94.94 236	99.41 286	97.56 296	93.53 196	99.42 86	97.89 307	83.33 317	99.31 193	99.29 73	99.62 99	99.64 139

testing222		97.08 141	96.75 140	98.06 164	98.56 174	96.82 142	99.85 147	98.61 99	92.53 260	98.84 123	98.84 239	93.36 118	98.30 307	95.84 235	94.30 302	99.05 255

test1111		95.57 228	94.98 234	97.37 234	98.56 174	93.37 303	98.86 370	98.45 143	94.95 125	96.63 225	98.95 218	75.21 410	99.11 208	95.02 248	98.14 185	99.64 139

MVSTER		95.53 229	95.22 223	96.45 275	98.56 174	97.72 99	99.91 111	97.67 280	92.38 268	91.39 323	97.14 324	97.24 20	97.30 359	94.80 257	87.85 354	94.34 361

testing3-2		97.72 106	97.43 109	98.60 118	98.55 177	97.11 131	100.00 1	99.23 31	93.78 188	97.90 175	98.73 247	95.50 53	99.69 164	98.53 121	94.63 295	98.99 263

VDD-MVS		93.77 293	92.94 302	96.27 282	98.55 177	90.22 389	98.77 380	97.79 266	90.85 323	96.82 220	99.42 142	61.18 473	99.77 150	98.95 90	94.13 304	98.82 276

tpmvs		94.28 275	93.57 277	96.40 277	98.55 177	91.50 364	95.70 474	98.55 119	87.47 397	92.15 316	94.26 439	91.42 173	98.95 220	88.15 382	95.85 268	98.76 279

UGNet		95.33 235	94.57 246	97.62 204	98.55 177	94.85 238	98.67 389	99.32 26	95.75 107	96.80 222	96.27 359	72.18 426	99.96 76	94.58 264	99.05 152	98.04 305
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
PCF-MVS	94.20 5	95.18 238	94.10 258	98.43 140	98.55 177	95.99 184	97.91 428	97.31 332	90.35 344	89.48 359	99.22 174	85.19 282	99.89 118	90.40 347	98.47 172	99.41 198
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
UWE-MVS-28		95.95 205	96.49 152	94.34 355	98.51 182	89.99 394	99.39 291	98.57 107	93.14 219	97.33 198	98.31 289	93.44 116	94.68 464	93.69 290	95.98 262	98.34 297

UWE-MVS		96.79 154	96.72 142	97.00 253	98.51 182	93.70 286	99.71 218	98.60 101	92.96 227	97.09 206	98.34 286	96.67 33	98.85 228	92.11 315	96.50 248	98.44 292

myMVS_eth3d28		97.86 88	97.59 100	98.68 110	98.50 184	97.26 121	99.92 103	98.55 119	93.79 187	98.26 161	98.75 245	95.20 58	99.48 186	98.93 92	96.40 251	99.29 224

test_vis1_n_1920		95.44 231	95.31 219	95.82 299	98.50 184	88.74 412	99.98 24	97.30 333	97.84 28	99.85 20	99.19 180	66.82 451	99.97 64	98.82 101	99.46 126	98.76 279

BH-w/o		95.71 222	95.38 217	96.68 267	98.49 186	92.28 329	99.84 152	97.50 305	92.12 278	92.06 319	98.79 243	84.69 295	98.67 263	95.29 243	99.66 95	99.09 249

baseline1		95.78 218	94.86 237	98.54 128	98.47 187	98.07 80	99.06 338	97.99 244	92.68 246	94.13 294	98.62 261	93.28 124	98.69 260	93.79 285	85.76 371	98.84 275

fmvsm_s_conf0.5_n_7		97.70 108	97.74 89	97.59 209	98.44 188	95.16 230	99.97 42	98.65 88	97.95 24	99.62 62	99.78 67	86.09 263	99.94 94	99.69 50	99.50 119	97.66 315

EPMVS		96.53 175	96.01 175	98.09 162	98.43 189	96.12 182	96.36 461	99.43 20	93.53 196	97.64 187	95.04 413	94.41 83	98.38 298	91.13 328	98.11 186	99.75 118

kuosan		93.17 308	92.60 310	94.86 331	98.40 190	89.54 402	98.44 402	98.53 126	84.46 437	88.49 382	97.92 304	90.57 192	97.05 375	83.10 429	93.49 312	97.99 306

WBMVS		94.52 264	94.03 262	95.98 289	98.38 191	96.68 151	99.92 103	97.63 284	90.75 332	89.64 354	95.25 406	96.77 27	96.90 388	94.35 269	83.57 391	94.35 359

UBG		97.84 91	97.69 93	98.29 149	98.38 191	96.59 158	99.90 117	98.53 126	93.91 183	98.52 144	98.42 282	96.77 27	99.17 205	98.54 119	96.20 256	99.11 248

sss		97.57 113	97.03 127	99.18 63	98.37 193	98.04 83	99.73 209	99.38 22	93.46 201	98.76 132	99.06 194	91.21 176	99.89 118	96.33 225	97.01 234	99.62 147

testing11		97.48 116	97.27 116	98.10 161	98.36 194	96.02 183	99.92 103	98.45 143	93.45 203	98.15 167	98.70 251	95.48 54	99.22 198	97.85 163	95.05 292	99.07 252

BH-untuned		95.18 238	94.83 238	96.22 283	98.36 194	91.22 367	99.80 174	97.32 331	90.91 321	91.08 326	98.67 253	83.51 310	98.54 280	94.23 272	99.61 104	98.92 269

testing91		97.16 133	96.90 131	97.97 168	98.35 196	95.67 199	99.91 111	98.42 168	92.91 230	97.33 198	98.72 248	94.81 72	99.21 199	96.98 198	94.63 295	99.03 260

testing99		97.17 132	96.91 130	97.95 170	98.35 196	95.70 196	99.91 111	98.43 156	92.94 228	97.36 196	98.72 248	94.83 71	99.21 199	97.00 196	94.64 294	98.95 265

ET-MVSNet_ETH3D		94.37 271	93.28 292	97.64 200	98.30 198	97.99 85	99.99 8	97.61 290	94.35 157	71.57 489	99.45 141	96.23 39	95.34 454	96.91 204	85.14 378	99.59 154

AUN-MVS		93.28 305	92.60 310	95.34 314	98.29 199	90.09 392	99.31 305	98.56 113	91.80 291	96.35 244	98.00 299	89.38 209	98.28 310	92.46 306	69.22 474	97.64 317

FMVSNet3		92.69 323	91.58 333	95.99 288	98.29 199	97.42 116	99.26 317	97.62 287	89.80 355	89.68 350	95.32 400	81.62 335	96.27 429	87.01 400	85.65 372	94.29 363

PMMVS		96.76 157	96.76 139	96.76 264	98.28 201	92.10 333	99.91 111	97.98 246	94.12 169	99.53 74	99.39 149	86.93 249	98.73 251	96.95 201	97.73 194	99.45 190

hse-mvs2		94.38 270	94.08 261	95.31 316	98.27 202	90.02 393	99.29 312	98.56 113	95.90 101	98.77 129	98.00 299	90.89 188	98.26 314	97.80 166	69.20 475	97.64 317

PVSNet_0	88.03 19	91.80 343	90.27 357	96.38 279	98.27 202	90.46 384	99.94 93	99.61 13	93.99 177	86.26 423	97.39 319	71.13 433	99.89 118	98.77 105	67.05 481	98.79 278

UA-Net		96.54 174	95.96 182	98.27 150	98.23 204	95.71 195	98.00 425	98.45 143	93.72 192	98.41 152	99.27 165	88.71 223	99.66 171	91.19 327	97.69 195	99.44 193

test_cas_vis1_n_1920		96.59 170	96.23 164	97.65 199	98.22 205	94.23 268	99.99 8	97.25 346	97.77 29	99.58 70	99.08 190	77.10 383	99.97 64	97.64 175	99.45 127	98.74 281

FE-MVS		95.70 224	95.01 233	97.79 185	98.21 206	94.57 249	95.03 475	98.69 82	88.90 371	97.50 191	96.19 361	92.60 147	99.49 185	89.99 352	97.94 192	99.31 219

GG-mvs-BLEND				98.54 128	98.21 206	98.01 84	93.87 480	98.52 128		97.92 174	97.92 304	99.02 3	97.94 334	98.17 142	99.58 109	99.67 133

mvs_anonymous		95.65 226	95.03 232	97.53 213	98.19 208	95.74 193	99.33 300	97.49 306	90.87 322	90.47 335	97.10 326	88.23 226	97.16 366	95.92 233	97.66 198	99.68 131

MVS_Test		96.46 178	95.74 196	98.61 117	98.18 209	97.23 123	99.31 305	97.15 364	91.07 318	98.84 123	97.05 330	88.17 227	98.97 217	94.39 266	97.50 200	99.61 151

BH-RMVSNet		95.18 238	94.31 253	97.80 183	98.17 210	95.23 225	99.76 192	97.53 301	92.52 261	94.27 292	99.25 172	76.84 390	98.80 240	90.89 336	99.54 111	99.35 209

dongtai		91.55 349	91.13 342	92.82 407	98.16 211	86.35 436	99.47 278	98.51 131	83.24 445	85.07 434	97.56 313	90.33 197	94.94 460	76.09 471	91.73 320	97.18 328

RPSCF		91.80 343	92.79 306	88.83 450	98.15 212	69.87 493	98.11 421	96.60 431	83.93 440	94.33 290	99.27 165	79.60 361	99.46 189	91.99 316	93.16 317	97.18 328

ETV-MVS		97.92 84	97.80 88	98.25 151	98.14 213	96.48 160	99.98 24	97.63 284	95.61 111	99.29 98	99.46 140	92.55 149	98.82 232	99.02 89	98.54 170	99.46 185

IS-MVSNet		96.29 191	95.90 189	97.45 223	98.13 214	94.80 242	99.08 333	97.61 290	92.02 283	95.54 269	98.96 213	90.64 191	98.08 323	93.73 288	97.41 204	99.47 183

test_fmvsmconf_n		98.43 51	98.32 47	98.78 103	98.12 215	96.41 163	99.99 8	98.83 66	98.22 7	99.67 53	99.64 119	91.11 181	99.94 94	99.67 52	99.62 99	99.98 57

fmvsm_s_conf0.1_n_2		97.25 128	96.85 134	98.43 140	98.08 216	98.08 79	99.92 103	97.76 274	98.05 20	99.65 55	99.58 128	80.88 345	99.93 104	99.59 56	98.17 181	97.29 326

ab-mvs		94.69 256	93.42 283	98.51 133	98.07 217	96.26 170	96.49 459	98.68 84	90.31 346	94.54 282	97.00 333	76.30 398	99.71 160	95.98 232	93.38 315	99.56 163

XVG-OURS-SEG-HR		94.79 251	94.70 245	95.08 321	98.05 218	89.19 404	99.08 333	97.54 299	93.66 193	94.87 278	99.58 128	78.78 369	99.79 145	97.31 183	93.40 314	96.25 335

EIA-MVS		97.53 114	97.46 104	97.76 191	98.04 219	94.84 239	99.98 24	97.61 290	94.41 155	97.90 175	99.59 125	92.40 155	98.87 225	98.04 151	99.13 146	99.59 154

XVG-OURS		94.82 248	94.74 244	95.06 322	98.00 220	89.19 404	99.08 333	97.55 297	94.10 170	94.71 280	99.62 123	80.51 352	99.74 156	96.04 231	93.06 319	96.25 335

mvsmamba		96.94 146	96.73 141	97.55 211	97.99 221	94.37 262	99.62 242	97.70 277	93.13 220	98.42 151	97.92 304	88.02 228	98.75 249	98.78 104	99.01 153	99.52 173

dp		95.05 242	94.43 248	96.91 257	97.99 221	92.73 318	96.29 464	97.98 246	89.70 356	95.93 257	94.67 428	93.83 110	98.45 286	86.91 403	96.53 247	99.54 168

tpmrst		96.27 193	95.98 178	97.13 248	97.96 223	93.15 306	96.34 462	98.17 222	92.07 279	98.71 135	95.12 410	93.91 105	98.73 251	94.91 254	96.62 245	99.50 179

TR-MVS		94.54 261	93.56 278	97.49 221	97.96 223	94.34 264	98.71 384	97.51 304	90.30 347	94.51 284	98.69 252	75.56 404	98.77 245	92.82 304	95.99 261	99.35 209

Vis-MVSNet (Re-imp)		96.32 188	95.98 178	97.35 238	97.93 225	94.82 241	99.47 278	98.15 230	91.83 288	95.09 276	99.11 188	91.37 175	97.47 350	93.47 292	97.43 201	99.74 119

MDTV_nov1_ep13			95.69 198		97.90 226	94.15 272	95.98 470	98.44 148	93.12 221	97.98 172	95.74 374	95.10 61	98.58 273	90.02 351	96.92 236

Fast-Effi-MVS+		95.02 244	94.19 256	97.52 215	97.88 227	94.55 250	99.97 42	97.08 382	88.85 373	94.47 285	97.96 303	84.59 297	98.41 290	89.84 354	97.10 224	99.59 154

ADS-MVSNet2		93.80 292	93.88 268	93.55 390	97.87 228	85.94 440	94.24 476	96.84 417	90.07 350	96.43 240	94.48 433	90.29 199	95.37 453	87.44 389	97.23 212	99.36 205

ADS-MVSNet		94.79 251	94.02 263	97.11 250	97.87 228	93.79 282	94.24 476	98.16 227	90.07 350	96.43 240	94.48 433	90.29 199	98.19 317	87.44 389	97.23 212	99.36 205

Effi-MVS+		96.30 190	95.69 198	98.16 155	97.85 230	96.26 170	97.41 438	97.21 354	90.37 343	98.65 138	98.58 267	86.61 255	98.70 258	97.11 192	97.37 206	99.52 173

PatchmatchNet		95.94 206	95.45 207	97.39 233	97.83 231	94.41 258	96.05 468	98.40 177	92.86 232	97.09 206	95.28 405	94.21 97	98.07 325	89.26 363	98.11 186	99.70 125
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
cascas		94.64 259	93.61 273	97.74 193	97.82 232	96.26 170	99.96 56	97.78 270	85.76 421	94.00 295	97.54 314	76.95 389	99.21 199	97.23 188	95.43 285	97.76 314

1112_ss		96.01 203	95.20 224	98.42 142	97.80 233	96.41 163	99.65 235	96.66 428	92.71 243	92.88 309	99.40 147	92.16 163	99.30 194	91.92 318	93.66 310	99.55 164

E3new		96.75 159	96.43 156	97.71 194	97.79 234	94.83 240	99.80 174	97.33 325	93.52 199	97.49 192	99.31 157	87.73 231	98.83 229	97.52 178	97.40 205	99.48 182

Test_1112_low_res		95.72 220	94.83 238	98.42 142	97.79 234	96.41 163	99.65 235	96.65 429	92.70 244	92.86 310	96.13 365	92.15 164	99.30 194	91.88 319	93.64 311	99.55 164

Effi-MVS+-dtu		94.53 263	95.30 220	92.22 415	97.77 236	82.54 463	99.59 251	97.06 391	94.92 128	95.29 273	95.37 398	85.81 267	97.89 335	94.80 257	97.07 225	96.23 337

tpm cat1		93.51 301	92.52 316	96.47 272	97.77 236	91.47 365	96.13 466	98.06 237	80.98 460	92.91 308	93.78 444	89.66 204	98.87 225	87.03 399	96.39 252	99.09 249

FA-MVS(test-final)		95.86 209	95.09 229	98.15 158	97.74 238	95.62 201	96.31 463	98.17 222	91.42 306	96.26 245	96.13 365	90.56 193	99.47 188	92.18 310	97.07 225	99.35 209

xiu_mvs_v1_base_debu		97.43 117	97.06 123	98.55 124	97.74 238	98.14 74	99.31 305	97.86 260	96.43 83	99.62 62	99.69 105	85.56 274	99.68 165	99.05 82	98.31 176	97.83 310

xiu_mvs_v1_base		97.43 117	97.06 123	98.55 124	97.74 238	98.14 74	99.31 305	97.86 260	96.43 83	99.62 62	99.69 105	85.56 274	99.68 165	99.05 82	98.31 176	97.83 310

xiu_mvs_v1_base_debi		97.43 117	97.06 123	98.55 124	97.74 238	98.14 74	99.31 305	97.86 260	96.43 83	99.62 62	99.69 105	85.56 274	99.68 165	99.05 82	98.31 176	97.83 310

EPP-MVSNet		96.69 164	96.60 147	96.96 255	97.74 238	93.05 309	99.37 295	98.56 113	88.75 375	95.83 260	99.01 200	96.01 40	98.56 276	96.92 202	97.20 214	99.25 232

gg-mvs-nofinetune		93.51 301	91.86 328	98.47 135	97.72 243	97.96 89	92.62 491	98.51 131	74.70 484	97.33 198	69.59 515	98.91 4	97.79 338	97.77 171	99.56 110	99.67 133

IB-MVS	92.85 6	94.99 245	93.94 266	98.16 155	97.72 243	95.69 198	99.99 8	98.81 67	94.28 163	92.70 311	96.90 337	95.08 62	99.17 205	96.07 230	73.88 455	99.60 153
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
thisisatest0515		97.41 122	97.02 128	98.59 121	97.71 245	97.52 109	99.97 42	98.54 123	91.83 288	97.45 193	99.04 196	97.50 10	99.10 209	94.75 259	96.37 253	99.16 241

VortexMVS		94.11 279	93.50 280	95.94 291	97.70 246	96.61 155	99.35 298	97.18 357	93.52 199	89.57 357	95.74 374	87.55 236	96.97 383	95.76 238	85.13 379	94.23 368

viewdifsd2359ckpt09		96.21 196	95.77 194	97.53 213	97.69 247	94.50 253	99.78 180	97.23 351	92.88 231	96.58 228	99.26 169	84.85 288	98.66 266	96.61 216	97.02 232	99.43 194

Syy-MVS		90.00 384	90.63 349	88.11 458	97.68 248	74.66 489	99.71 218	98.35 190	90.79 329	92.10 317	98.67 253	79.10 367	93.09 480	63.35 498	95.95 265	96.59 333

myMVS_eth3d		94.46 268	94.76 243	93.55 390	97.68 248	90.97 369	99.71 218	98.35 190	90.79 329	92.10 317	98.67 253	92.46 154	93.09 480	87.13 396	95.95 265	96.59 333

test_fmvs1_n		94.25 276	94.36 250	93.92 377	97.68 248	83.70 453	99.90 117	96.57 432	97.40 40	99.67 53	98.88 226	61.82 470	99.92 110	98.23 140	99.13 146	98.14 303

fmvsm_s_conf0.5_n_6		98.27 63	97.96 75	99.23 58	97.66 251	98.11 78	99.98 24	98.64 91	97.85 27	99.87 14	99.72 95	88.86 220	99.93 104	99.64 54	99.36 135	99.63 146

RRT-MVS		96.24 195	95.68 200	97.94 173	97.65 252	94.92 237	99.27 315	97.10 378	92.79 238	97.43 194	97.99 301	81.85 330	99.37 192	98.46 125	98.57 167	99.53 172

diffmvs		97.00 143	96.64 145	98.09 162	97.64 253	96.17 179	99.81 168	97.19 355	94.67 140	98.95 118	99.28 161	86.43 256	98.76 247	98.37 130	97.42 203	99.33 212
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
viewcassd2359sk11		96.59 170	96.23 164	97.66 198	97.63 254	94.70 245	99.77 186	97.33 325	93.41 204	97.34 197	99.17 182	86.72 250	98.83 229	97.40 181	97.32 209	99.46 185

viewdifsd2359ckpt13		96.19 197	95.77 194	97.45 223	97.62 255	94.40 260	99.70 225	97.23 351	92.76 240	96.63 225	99.05 195	84.96 287	98.64 269	96.65 215	97.35 207	99.31 219

Vis-MVSNet		95.72 220	95.15 227	97.45 223	97.62 255	94.28 265	99.28 313	98.24 210	94.27 165	96.84 218	98.94 220	79.39 362	98.76 247	93.25 295	98.49 171	99.30 222
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
thisisatest0530		97.10 136	96.72 142	98.22 152	97.60 257	96.70 148	99.92 103	98.54 123	91.11 316	97.07 208	98.97 211	97.47 13	99.03 212	93.73 288	96.09 259	98.92 269

GDP-MVS		97.88 86	97.59 100	98.75 106	97.59 258	97.81 96	99.95 75	97.37 319	94.44 151	99.08 110	99.58 128	97.13 25	99.08 210	94.99 249	98.17 181	99.37 203

miper_ehance_all_eth		93.16 309	92.60 310	94.82 332	97.57 259	93.56 295	99.50 272	97.07 390	88.75 375	88.85 374	95.52 387	90.97 184	96.74 399	90.77 338	84.45 384	94.17 376

guyue		97.15 134	96.82 136	98.15 158	97.56 260	96.25 174	99.71 218	97.84 263	95.75 107	98.13 168	98.65 256	87.58 235	98.82 232	98.29 136	97.91 193	99.36 205

viewmanbaseed2359cas		96.45 179	96.07 172	97.59 209	97.55 261	94.59 248	99.70 225	97.33 325	93.62 195	97.00 212	99.32 154	85.57 273	98.71 255	97.26 187	97.33 208	99.47 183

testing3		93.92 285	94.23 255	92.99 404	97.54 262	90.23 388	99.99 8	99.16 33	90.57 336	91.33 325	98.63 260	92.99 132	92.52 484	82.46 433	95.39 286	96.22 338

SSM_0404		95.75 219	95.16 226	97.50 218	97.53 263	95.39 212	99.11 329	97.25 346	90.81 325	95.27 274	98.83 240	84.74 292	98.67 263	95.24 244	97.69 195	98.45 291

LCM-MVSNet-Re		92.31 332	92.60 310	91.43 424	97.53 263	79.27 481	99.02 347	91.83 498	92.07 279	80.31 459	94.38 437	83.50 311	95.48 450	97.22 189	97.58 199	99.54 168

GBi-Net		90.88 360	89.82 366	94.08 368	97.53 263	91.97 334	98.43 403	96.95 405	87.05 403	89.68 350	94.72 424	71.34 430	96.11 435	87.01 400	85.65 372	94.17 376

test1		90.88 360	89.82 366	94.08 368	97.53 263	91.97 334	98.43 403	96.95 405	87.05 403	89.68 350	94.72 424	71.34 430	96.11 435	87.01 400	85.65 372	94.17 376

FMVSNet2		91.02 357	89.56 371	95.41 312	97.53 263	95.74 193	98.98 350	97.41 314	87.05 403	88.43 387	95.00 418	71.34 430	96.24 431	85.12 415	85.21 377	94.25 366

tttt0517		96.85 151	96.49 152	97.92 174	97.48 268	95.89 187	99.85 147	98.54 123	90.72 333	96.63 225	98.93 223	97.47 13	99.02 213	93.03 302	95.76 272	98.85 274

cashybrid2		96.25 194	95.89 190	97.32 241	97.45 269	93.68 288	99.80 174	97.22 353	93.38 205	96.86 216	99.28 161	84.64 296	98.87 225	97.18 190	97.19 215	99.41 198

BP-MVS1		98.33 59	98.18 56	98.81 101	97.44 270	97.98 86	99.96 56	98.17 222	94.88 130	98.77 129	99.59 125	97.59 8	99.08 210	98.24 139	98.93 155	99.36 205

casdiffmvs_mvg		96.43 180	95.94 186	97.89 178	97.44 270	95.47 205	99.86 144	97.29 341	93.35 207	96.03 253	99.19 180	85.39 278	98.72 254	97.89 162	97.04 229	99.49 181
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
E2		96.36 185	95.95 184	97.60 206	97.41 272	94.52 251	99.71 218	97.33 325	93.20 213	97.02 209	99.07 192	85.37 279	98.82 232	97.27 184	97.14 221	99.46 185

EC-MVSNet		97.38 124	97.24 117	97.80 183	97.41 272	95.64 200	99.99 8	97.06 391	94.59 141	99.63 59	99.32 154	89.20 215	98.14 319	98.76 106	99.23 142	99.62 147

viewdifsd2359ckpt07		95.83 212	95.42 209	97.07 251	97.40 274	93.04 310	99.60 249	97.24 349	92.39 267	96.09 252	99.14 187	83.07 321	98.93 221	97.02 195	96.87 237	99.23 235

c3_l		92.53 327	91.87 327	94.52 344	97.40 274	92.99 312	99.40 287	96.93 410	87.86 393	88.69 377	95.44 392	89.95 202	96.44 417	90.45 344	80.69 418	94.14 386

hybrid		96.53 175	96.15 170	97.67 196	97.39 276	95.12 231	99.80 174	97.15 364	93.38 205	98.23 164	99.16 185	85.20 281	98.70 258	97.92 158	97.15 220	99.20 238

viewmambaseed2359dif		95.92 208	95.55 205	97.04 252	97.38 277	93.41 300	99.78 180	96.97 403	91.14 315	96.58 228	99.27 165	84.85 288	98.75 249	96.87 205	97.12 223	98.97 264

fmvsm_s_conf0.1_n		97.30 125	97.21 119	97.60 206	97.38 277	94.40 260	99.90 117	98.64 91	96.47 82	99.51 78	99.65 118	84.99 286	99.93 104	99.22 76	99.09 149	98.46 290

hybridcas		96.09 200	95.62 202	97.50 218	97.37 279	94.44 254	99.84 152	97.16 361	93.16 217	96.03 253	99.21 177	84.19 303	98.65 268	96.53 220	97.07 225	99.42 197

E3		96.36 185	95.95 184	97.60 206	97.37 279	94.52 251	99.71 218	97.33 325	93.18 215	97.02 209	99.07 192	85.45 277	98.82 232	97.27 184	97.14 221	99.46 185

CDS-MVSNet		96.34 187	96.07 172	97.13 248	97.37 279	94.96 234	99.53 267	97.91 255	91.55 298	95.37 272	98.32 287	95.05 64	97.13 369	93.80 284	95.75 273	99.30 222
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
hybridnocas07		96.57 172	96.16 169	97.81 182	97.36 282	95.32 217	99.81 168	97.12 370	94.17 167	98.02 171	98.90 224	85.05 284	98.80 240	97.85 163	97.18 216	99.32 214

TESTMET0.1,1		96.74 161	96.26 163	98.16 155	97.36 282	96.48 160	99.96 56	98.29 203	91.93 284	95.77 261	98.07 297	95.54 50	98.29 308	90.55 342	98.89 156	99.70 125

miper_lstm_enhance		91.81 340	91.39 339	93.06 403	97.34 284	89.18 406	99.38 293	96.79 422	86.70 411	87.47 405	95.22 407	90.00 201	95.86 444	88.26 378	81.37 407	94.15 382

baseline		96.43 180	95.98 178	97.76 191	97.34 284	95.17 229	99.51 270	97.17 359	93.92 182	96.90 215	99.28 161	85.37 279	98.64 269	97.50 179	96.86 239	99.46 185

cl____		92.31 332	91.58 333	94.52 344	97.33 286	92.77 314	99.57 257	96.78 423	86.97 407	87.56 403	95.51 388	89.43 208	96.62 406	88.60 368	82.44 399	94.16 381

SD_0403		92.63 326	93.38 287	90.40 438	97.32 287	77.91 483	97.75 433	98.03 242	91.89 285	90.83 331	98.29 291	82.00 327	93.79 473	88.51 373	95.75 273	99.52 173

DIV-MVS_self_test		92.32 331	91.60 332	94.47 348	97.31 288	92.74 316	99.58 253	96.75 424	86.99 406	87.64 401	95.54 385	89.55 207	96.50 412	88.58 369	82.44 399	94.17 376

casdiffmvs		96.42 182	95.97 181	97.77 189	97.30 289	94.98 233	99.84 152	97.09 381	93.75 191	96.58 228	99.26 169	85.07 283	98.78 244	97.77 171	97.04 229	99.54 168
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
GeoE		94.36 273	93.48 281	96.99 254	97.29 290	93.54 296	99.96 56	96.72 426	88.35 386	93.43 299	98.94 220	82.05 326	98.05 326	88.12 384	96.48 250	99.37 203

eth_miper_zixun_eth		92.41 330	91.93 325	93.84 381	97.28 291	90.68 378	98.83 373	96.97 403	88.57 380	89.19 369	95.73 377	89.24 214	96.69 404	89.97 353	81.55 405	94.15 382

MVSFormer		96.94 146	96.60 147	97.95 170	97.28 291	97.70 102	99.55 264	97.27 343	91.17 312	99.43 84	99.54 134	90.92 185	96.89 389	94.67 262	99.62 99	99.25 232

lupinMVS		97.85 90	97.60 98	98.62 116	97.28 291	97.70 102	99.99 8	97.55 297	95.50 116	99.43 84	99.67 114	90.92 185	98.71 255	98.40 127	99.62 99	99.45 190

nocashy02		96.61 168	96.34 160	97.42 228	97.26 294	94.37 262	99.83 160	97.16 361	94.51 144	97.89 177	99.26 169	86.38 257	98.66 266	97.70 174	97.06 228	99.23 235

dtuplus		95.79 217	95.42 209	96.93 256	97.24 295	93.16 305	99.78 180	96.93 410	91.69 294	96.18 250	99.29 160	83.80 308	98.73 251	96.83 207	97.02 232	98.89 273

diffmvs_AUTHOR		96.75 159	96.41 158	97.79 185	97.20 296	95.46 206	99.69 228	97.15 364	94.46 147	98.78 127	99.21 177	85.64 271	98.77 245	98.27 137	97.31 210	99.13 245

mamba_0408		94.98 246	94.09 259	97.64 200	97.14 297	95.31 218	93.48 486	97.08 382	90.48 339	94.40 286	98.62 261	84.49 298	98.67 263	93.99 275	97.18 216	98.93 266

SSM_04072		94.77 253	94.09 259	96.82 261	97.14 297	95.31 218	93.48 486	97.08 382	90.48 339	94.40 286	98.62 261	84.49 298	96.21 432	93.99 275	97.18 216	98.93 266

SSM_0407		95.62 227	94.95 235	97.61 205	97.14 297	95.31 218	99.00 348	97.25 346	90.81 325	94.40 286	98.83 240	84.74 292	98.58 273	95.24 244	97.18 216	98.93 266

SCA		94.69 256	93.81 270	97.33 239	97.10 300	94.44 254	98.86 370	98.32 197	93.30 210	96.17 251	95.59 383	76.48 396	97.95 332	91.06 330	97.43 201	99.59 154

viewmacassd2359aftdt		95.93 207	95.45 207	97.36 236	97.09 301	94.12 274	99.57 257	97.26 345	93.05 225	96.50 232	99.17 182	82.76 322	98.68 261	96.61 216	97.04 229	99.28 226

KinetiMVS		96.10 198	95.29 221	98.53 130	97.08 302	97.12 129	99.56 261	98.12 233	94.78 133	98.44 149	98.94 220	80.30 356	99.39 191	91.56 323	98.79 162	99.06 253

TAMVS		95.85 210	95.58 203	96.65 269	97.07 303	93.50 297	99.17 324	97.82 265	91.39 308	95.02 277	98.01 298	92.20 162	97.30 359	93.75 287	95.83 269	99.14 244

Fast-Effi-MVS+-dtu		93.72 296	93.86 269	93.29 395	97.06 304	86.16 437	99.80 174	96.83 418	92.66 247	92.58 312	97.83 310	81.39 336	97.67 343	89.75 355	96.87 237	96.05 340

E4		96.01 203	95.53 206	97.44 226	97.05 305	94.23 268	99.57 257	97.30 333	92.72 241	96.47 234	99.03 197	83.98 307	98.83 229	96.92 202	96.77 240	99.27 228

E5new		95.83 212	95.39 212	97.15 244	97.03 306	93.59 290	99.32 303	97.30 333	92.58 254	96.45 235	99.00 204	83.37 314	98.81 236	96.81 208	96.65 243	99.04 256

E5		95.83 212	95.39 212	97.15 244	97.03 306	93.59 290	99.32 303	97.30 333	92.58 254	96.45 235	99.00 204	83.37 314	98.81 236	96.81 208	96.65 243	99.04 256

CostFormer		96.10 198	95.88 191	96.78 263	97.03 306	92.55 324	97.08 447	97.83 264	90.04 352	98.72 134	94.89 422	95.01 66	98.29 308	96.54 219	95.77 271	99.50 179

test_fmvsmvis_n_1920		97.67 109	97.59 100	97.91 176	97.02 309	95.34 215	99.95 75	98.45 143	97.87 26	97.02 209	99.59 125	89.64 205	99.98 51	99.41 68	99.34 137	98.42 293

test-LLR		96.47 177	96.04 174	97.78 187	97.02 309	95.44 207	99.96 56	98.21 217	94.07 172	95.55 267	96.38 354	93.90 106	98.27 312	90.42 345	98.83 160	99.64 139

test-mter		96.39 183	95.93 187	97.78 187	97.02 309	95.44 207	99.96 56	98.21 217	91.81 290	95.55 267	96.38 354	95.17 59	98.27 312	90.42 345	98.83 160	99.64 139

casdiffseed414692147		95.07 241	94.26 254	97.50 218	97.01 312	94.70 245	99.58 253	97.02 395	91.27 310	94.66 281	98.82 242	80.79 347	98.55 279	93.39 294	95.79 270	99.27 228

E6new		95.83 212	95.39 212	97.14 246	97.00 313	93.58 292	99.31 305	97.30 333	92.57 256	96.45 235	99.01 200	83.44 312	98.81 236	96.80 210	96.66 241	99.04 256

E6		95.83 212	95.39 212	97.14 246	97.00 313	93.58 292	99.31 305	97.30 333	92.57 256	96.45 235	99.01 200	83.44 312	98.81 236	96.80 210	96.66 241	99.04 256

icg_test_0407_2		95.04 243	94.78 242	95.84 298	96.97 315	91.64 356	98.63 392	97.12 370	92.33 270	95.60 265	98.88 226	85.65 269	96.56 409	92.12 311	95.70 276	99.32 214

IMVS_0407		95.21 237	94.80 241	96.46 274	96.97 315	91.64 356	98.81 375	97.12 370	92.33 270	95.60 265	98.88 226	85.65 269	98.42 288	92.12 311	95.70 276	99.32 214

IMVS_0404		93.83 288	93.17 294	95.80 300	96.97 315	91.64 356	97.78 432	97.12 370	92.33 270	90.87 330	98.88 226	76.78 391	96.43 418	92.12 311	95.70 276	99.32 214

IMVS_0403		95.25 236	94.81 240	96.58 271	96.97 315	91.64 356	98.97 355	97.12 370	92.33 270	95.43 270	98.88 226	85.78 268	98.79 242	92.12 311	95.70 276	99.32 214

gm-plane-assit					96.97 315	93.76 284			91.47 302		98.96 213		98.79 242	94.92 252

WB-MVSnew		92.90 315	92.77 307	93.26 397	96.95 320	93.63 289	99.71 218	98.16 227	91.49 299	94.28 291	98.14 294	81.33 338	96.48 415	79.47 452	95.46 283	89.68 482

QAPM		95.40 232	94.17 257	99.10 79	96.92 321	97.71 100	99.40 287	98.68 84	89.31 359	88.94 373	98.89 225	82.48 324	99.96 76	93.12 301	99.83 80	99.62 147

KD-MVS_2432*1600		88.00 406	86.10 410	93.70 386	96.91 322	94.04 275	97.17 444	97.12 370	84.93 432	81.96 448	92.41 460	92.48 152	94.51 466	79.23 454	52.68 512	92.56 447

miper_refine_blended		88.00 406	86.10 410	93.70 386	96.91 322	94.04 275	97.17 444	97.12 370	84.93 432	81.96 448	92.41 460	92.48 152	94.51 466	79.23 454	52.68 512	92.56 447

tpm2		95.47 230	95.18 225	96.35 280	96.91 322	91.70 353	96.96 450	97.93 251	88.04 391	98.44 149	95.40 394	93.32 121	97.97 329	94.00 274	95.61 281	99.38 201

FMVSNet5		88.32 402	87.47 404	90.88 427	96.90 325	88.39 420	97.28 441	95.68 454	82.60 453	84.67 436	92.40 462	79.83 359	91.16 490	76.39 470	81.51 406	93.09 438

3Dnovator+	91.53 11	96.31 189	95.24 222	99.52 33	96.88 326	98.64 59	99.72 213	98.24 210	95.27 121	88.42 389	98.98 209	82.76 322	99.94 94	97.10 193	99.83 80	99.96 75

Patchmatch-test		92.65 325	91.50 336	96.10 286	96.85 327	90.49 383	91.50 497	97.19 355	82.76 452	90.23 336	95.59 383	95.02 65	98.00 328	77.41 465	96.98 235	99.82 107

MVS		96.60 169	95.56 204	99.72 14	96.85 327	99.22 21	98.31 409	98.94 44	91.57 297	90.90 329	99.61 124	86.66 254	99.96 76	97.36 182	99.88 76	99.99 26

3Dnovator	91.47 12	96.28 192	95.34 218	99.08 82	96.82 329	97.47 114	99.45 283	98.81 67	95.52 115	89.39 360	99.00 204	81.97 328	99.95 85	97.27 184	99.83 80	99.84 104

EI-MVSNet		93.73 295	93.40 286	94.74 333	96.80 330	92.69 319	99.06 338	97.67 280	88.96 368	91.39 323	99.02 198	88.75 222	97.30 359	91.07 329	87.85 354	94.22 371

CVMVSNet		94.68 258	94.94 236	93.89 380	96.80 330	86.92 434	99.06 338	98.98 41	94.45 148	94.23 293	99.02 198	85.60 272	95.31 455	90.91 335	95.39 286	99.43 194

IterMVS-LS		92.69 323	92.11 321	94.43 352	96.80 330	92.74 316	99.45 283	96.89 414	88.98 366	89.65 353	95.38 397	88.77 221	96.34 425	90.98 333	82.04 402	94.22 371
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
AstraMVS		96.57 172	96.46 155	96.91 257	96.79 333	92.50 325	99.90 117	97.38 316	96.02 99	97.79 184	99.32 154	86.36 259	98.99 214	98.26 138	96.33 254	99.23 235

IterMVS		90.91 359	90.17 361	93.12 400	96.78 334	90.42 386	98.89 364	97.05 394	89.03 363	86.49 418	95.42 393	76.59 394	95.02 457	87.22 395	84.09 387	93.93 409
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
1314		96.84 152	95.96 182	99.48 40	96.74 335	98.52 63	98.31 409	98.86 59	95.82 104	89.91 344	98.98 209	87.49 238	99.96 76	97.80 166	99.73 90	99.96 75

IterMVS-SCA-FT		90.85 362	90.16 362	92.93 405	96.72 336	89.96 395	98.89 364	96.99 399	88.95 369	86.63 415	95.67 378	76.48 396	95.00 458	87.04 398	84.04 390	93.84 416

MVS-HIRNet		86.22 419	83.19 434	95.31 316	96.71 337	90.29 387	92.12 493	97.33 325	62.85 501	86.82 412	70.37 513	69.37 438	97.49 349	75.12 473	97.99 191	98.15 301

viewdifsd2359ckpt11		94.09 281	93.63 272	95.46 309	96.68 338	88.92 409	99.62 242	97.12 370	93.07 223	95.73 262	99.22 174	77.05 384	98.88 224	96.52 221	87.69 359	98.58 288

viewmsd2359difaftdt		94.09 281	93.64 271	95.46 309	96.68 338	88.92 409	99.62 242	97.13 369	93.07 223	95.73 262	99.22 174	77.05 384	98.89 223	96.52 221	87.70 358	98.58 288

VDDNet		93.12 310	91.91 326	96.76 264	96.67 340	92.65 322	98.69 387	98.21 217	82.81 451	97.75 186	99.28 161	61.57 471	99.48 186	98.09 148	94.09 305	98.15 301

dmvs_re		93.20 307	93.15 296	93.34 393	96.54 341	83.81 452	98.71 384	98.51 131	91.39 308	92.37 315	98.56 269	78.66 371	97.83 337	93.89 278	89.74 326	98.38 295

Elysia		94.50 265	93.38 287	97.85 180	96.49 342	96.70 148	98.98 350	97.78 270	90.81 325	96.19 248	98.55 271	73.63 421	98.98 215	89.41 356	98.56 168	97.88 308

StellarMVS		94.50 265	93.38 287	97.85 180	96.49 342	96.70 148	98.98 350	97.78 270	90.81 325	96.19 248	98.55 271	73.63 421	98.98 215	89.41 356	98.56 168	97.88 308

MIMVSNet		90.30 375	88.67 390	95.17 320	96.45 344	91.64 356	92.39 492	97.15 364	85.99 418	90.50 334	93.19 453	66.95 449	94.86 462	82.01 437	93.43 313	99.01 262

CR-MVSNet		93.45 304	92.62 309	95.94 291	96.29 345	92.66 320	92.01 494	96.23 440	92.62 249	96.94 213	93.31 450	91.04 182	96.03 440	79.23 454	95.96 263	99.13 245

RPMNet		89.76 388	87.28 405	97.19 243	96.29 345	92.66 320	92.01 494	98.31 199	70.19 492	96.94 213	85.87 499	87.25 243	99.78 147	62.69 499	95.96 263	99.13 245

tt0805		91.28 352	90.18 360	94.60 339	96.26 347	87.55 427	98.39 407	98.72 78	89.00 365	89.22 366	98.47 279	62.98 466	98.96 219	90.57 341	88.00 353	97.28 327

Patchmtry		89.70 389	88.49 393	93.33 394	96.24 348	89.94 398	91.37 498	96.23 440	78.22 474	87.69 400	93.31 450	91.04 182	96.03 440	80.18 451	82.10 401	94.02 399

test_vis1_rt		86.87 416	86.05 413	89.34 446	96.12 349	78.07 482	99.87 133	83.54 515	92.03 282	78.21 471	89.51 481	45.80 493	99.91 111	96.25 227	93.11 318	90.03 478

JIA-IIPM		91.76 346	90.70 347	94.94 326	96.11 350	87.51 428	93.16 489	98.13 232	75.79 480	97.58 188	77.68 508	92.84 137	97.97 329	88.47 374	96.54 246	99.33 212

OpenMVS	90.15 15	94.77 253	93.59 276	98.33 146	96.07 351	97.48 113	99.56 261	98.57 107	90.46 341	86.51 417	98.95 218	78.57 372	99.94 94	93.86 279	99.74 89	97.57 322

PAPM		98.60 37	98.42 38	99.14 73	96.05 352	98.96 28	99.90 117	99.35 24	96.68 73	98.35 156	99.66 116	96.45 35	98.51 281	99.45 65	99.89 73	99.96 75

CLD-MVS		94.06 284	93.90 267	94.55 343	96.02 353	90.69 377	99.98 24	97.72 276	96.62 77	91.05 328	98.85 238	77.21 382	98.47 282	98.11 146	89.51 332	94.48 347
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
PatchT		90.38 372	88.75 389	95.25 318	95.99 354	90.16 390	91.22 499	97.54 299	76.80 476	97.26 201	86.01 498	91.88 169	96.07 439	66.16 492	95.91 267	99.51 177

ACMH+	89.98 16	90.35 373	89.54 372	92.78 409	95.99 354	86.12 438	98.81 375	97.18 357	89.38 358	83.14 444	97.76 311	68.42 443	98.43 287	89.11 364	86.05 370	93.78 419

DeepMVS_CX				82.92 474	95.98 356	58.66 507		96.01 446	92.72 241	78.34 470	95.51 388	58.29 478	98.08 323	82.57 432	85.29 375	92.03 457

ACMP	92.05 9	92.74 321	92.42 318	93.73 382	95.91 357	88.72 413	99.81 168	97.53 301	94.13 168	87.00 411	98.23 292	74.07 417	98.47 282	96.22 228	88.86 339	93.99 404
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test_vis1_n		93.61 299	93.03 299	95.35 313	95.86 358	86.94 433	99.87 133	96.36 438	96.85 64	99.54 73	98.79 243	52.41 486	99.83 140	98.64 114	98.97 154	99.29 224

HQP-NCC					95.78 359		99.87 133		96.82 66	93.37 300

ACMP_Plane					95.78 359		99.87 133		96.82 66	93.37 300

HQP-MVS		94.61 260	94.50 247	94.92 327	95.78 359	91.85 341	99.87 133	97.89 256	96.82 66	93.37 300	98.65 256	80.65 350	98.39 294	97.92 158	89.60 327	94.53 343

NP-MVS					95.77 362	91.79 345					98.65 256

test_fmvsmconf0.1_n		97.74 103	97.44 107	98.64 115	95.76 363	96.20 176	99.94 93	98.05 239	98.17 13	98.89 122	99.42 142	87.65 233	99.90 113	99.50 61	99.60 107	99.82 107

plane_prior6					95.76 363	91.72 352						80.47 354

ACMM	91.95 10	92.88 316	92.52 316	93.98 376	95.75 365	89.08 408	99.77 186	97.52 303	93.00 226	89.95 343	97.99 301	76.17 400	98.46 285	93.63 291	88.87 338	94.39 355
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GA-MVS		93.83 288	92.84 303	96.80 262	95.73 366	93.57 294	99.88 130	97.24 349	92.57 256	92.92 307	96.66 346	78.73 370	97.67 343	87.75 387	94.06 306	99.17 240

plane_prior1					95.73 366

jason		97.24 129	96.86 133	98.38 145	95.73 366	97.32 118	99.97 42	97.40 315	95.34 119	98.60 143	99.54 134	87.70 232	98.56 276	97.94 157	99.47 124	99.25 232
jason: jason.
mmtdpeth		88.52 400	87.75 402	90.85 429	95.71 369	83.47 458	98.94 358	94.85 472	88.78 374	97.19 203	89.58 480	63.29 464	98.97 217	98.54 119	62.86 490	90.10 477

HQP_MVS		94.49 267	94.36 250	94.87 328	95.71 369	91.74 348	99.84 152	97.87 258	96.38 86	93.01 305	98.59 264	80.47 354	98.37 300	97.79 169	89.55 330	94.52 345

plane_prior7					95.71 369	91.59 362

ITE_SJBPF				92.38 412	95.69 372	85.14 444		95.71 453	92.81 235	89.33 363	98.11 295	70.23 436	98.42 288	85.91 410	88.16 351	93.59 427

fmvsm_s_conf0.1_n_a		97.09 138	96.90 131	97.63 203	95.65 373	94.21 270	99.83 160	98.50 137	96.27 92	99.65 55	99.64 119	84.72 294	99.93 104	99.04 85	98.84 159	98.74 281

ACMH	89.72 17	90.64 366	89.63 369	93.66 388	95.64 374	88.64 416	98.55 395	97.45 308	89.03 363	81.62 451	97.61 312	69.75 437	98.41 290	89.37 358	87.62 360	93.92 410
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
baseline2		96.71 163	96.49 152	97.37 234	95.63 375	95.96 185	99.74 202	98.88 55	92.94 228	91.61 321	98.97 211	97.72 7	98.62 271	94.83 256	98.08 189	97.53 324

FMVSNet1		88.50 401	86.64 408	94.08 368	95.62 376	91.97 334	98.43 403	96.95 405	83.00 449	86.08 425	94.72 424	59.09 477	96.11 435	81.82 439	84.07 388	94.17 376

LuminaMVS		96.63 167	96.21 167	97.87 179	95.58 377	96.82 142	99.12 327	97.67 280	94.47 146	97.88 179	98.31 289	87.50 237	98.71 255	98.07 150	97.29 211	98.10 304

0.3-1-1-0.015		94.22 277	93.13 298	97.49 221	95.50 378	94.17 271	100.00 1	98.22 213	88.44 384	97.14 205	97.04 332	92.73 141	98.59 272	96.45 223	72.65 461	99.70 125

0.4-1-1-0.2		94.14 278	93.02 300	97.51 216	95.45 379	94.25 267	100.00 1	98.22 213	88.53 381	96.83 219	96.95 335	92.25 160	98.57 275	96.34 224	72.65 461	99.70 125

LPG-MVS_test		92.96 313	92.71 308	93.71 384	95.43 380	88.67 414	99.75 198	97.62 287	92.81 235	90.05 339	98.49 275	75.24 407	98.40 292	95.84 235	89.12 334	94.07 395

LGP-MVS_train				93.71 384	95.43 380	88.67 414		97.62 287	92.81 235	90.05 339	98.49 275	75.24 407	98.40 292	95.84 235	89.12 334	94.07 395

tpm		93.70 297	93.41 285	94.58 341	95.36 382	87.41 429	97.01 448	96.90 413	90.85 323	96.72 224	94.14 441	90.40 196	96.84 393	90.75 339	88.54 346	99.51 177

0.4-1-1-0.1		94.07 283	92.95 301	97.42 228	95.24 383	94.00 278	100.00 1	98.22 213	88.27 388	96.81 221	96.93 336	92.27 159	98.56 276	96.21 229	72.63 463	99.70 125

D2MVS		92.76 320	92.59 314	93.27 396	95.13 384	89.54 402	99.69 228	99.38 22	92.26 275	87.59 402	94.61 430	85.05 284	97.79 338	91.59 322	88.01 352	92.47 451

VPA-MVSNet		92.70 322	91.55 335	96.16 284	95.09 385	96.20 176	98.88 366	99.00 39	91.02 320	91.82 320	95.29 404	76.05 402	97.96 331	95.62 240	81.19 408	94.30 362

LTVRE_ROB	88.28 18	90.29 376	89.05 383	94.02 371	95.08 386	90.15 391	97.19 443	97.43 310	84.91 434	83.99 440	97.06 329	74.00 418	98.28 310	84.08 421	87.71 356	93.62 426
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
TinyColmap		87.87 408	86.51 409	91.94 418	95.05 387	85.57 442	97.65 434	94.08 483	84.40 438	81.82 450	96.85 341	62.14 469	98.33 303	80.25 450	86.37 367	91.91 459

test0.0.03 1		93.86 287	93.61 273	94.64 337	95.02 388	92.18 332	99.93 100	98.58 105	94.07 172	87.96 397	98.50 274	93.90 106	94.96 459	81.33 440	93.17 316	96.78 330

UniMVSNet (Re)		93.07 312	92.13 320	95.88 295	94.84 389	96.24 175	99.88 130	98.98 41	92.49 263	89.25 364	95.40 394	87.09 245	97.14 368	93.13 300	78.16 433	94.26 364

USDC		90.00 384	88.96 384	93.10 402	94.81 390	88.16 422	98.71 384	95.54 458	93.66 193	83.75 442	97.20 323	65.58 455	98.31 305	83.96 424	87.49 362	92.85 444

VPNet		91.81 340	90.46 351	95.85 297	94.74 391	95.54 204	98.98 350	98.59 103	92.14 277	90.77 333	97.44 316	68.73 441	97.54 348	94.89 255	77.89 435	94.46 348

FIs		94.10 280	93.43 282	96.11 285	94.70 392	96.82 142	99.58 253	98.93 48	92.54 259	89.34 362	97.31 320	87.62 234	97.10 372	94.22 273	86.58 365	94.40 354

UniMVSNet_ETH3D		90.06 383	88.58 392	94.49 347	94.67 393	88.09 423	97.81 431	97.57 295	83.91 441	88.44 384	97.41 317	57.44 479	97.62 345	91.41 324	88.59 345	97.77 313

UniMVSNet_NR-MVSNet		92.95 314	92.11 321	95.49 305	94.61 394	95.28 222	99.83 160	99.08 36	91.49 299	89.21 367	96.86 340	87.14 244	96.73 400	93.20 296	77.52 438	94.46 348

test_fmvs2		89.47 393	89.70 368	88.77 453	94.54 395	75.74 485	99.83 160	94.70 478	94.71 137	91.08 326	96.82 345	54.46 482	97.78 340	92.87 303	88.27 349	92.80 445

MonoMVSNet		94.82 248	94.43 248	95.98 289	94.54 395	90.73 376	99.03 345	97.06 391	93.16 217	93.15 304	95.47 391	88.29 225	97.57 346	97.85 163	91.33 324	99.62 147

WR-MVS		92.31 332	91.25 340	95.48 308	94.45 397	95.29 221	99.60 249	98.68 84	90.10 349	88.07 396	96.89 338	80.68 349	96.80 397	93.14 299	79.67 425	94.36 356

dtuonly		93.89 286	93.16 295	96.08 287	94.37 398	91.67 355	99.15 326	95.04 470	91.79 292	94.74 279	98.72 248	81.01 342	98.31 305	87.29 393	96.33 254	98.27 299

nrg030		93.51 301	92.53 315	96.45 275	94.36 399	97.20 124	99.81 168	97.16 361	91.60 296	89.86 346	97.46 315	86.37 258	97.68 342	95.88 234	80.31 421	94.46 348

tfpnnormal		89.29 396	87.61 403	94.34 355	94.35 400	94.13 273	98.95 357	98.94 44	83.94 439	84.47 437	95.51 388	74.84 412	97.39 351	77.05 468	80.41 419	91.48 462

FC-MVSNet-test		93.81 291	93.15 296	95.80 300	94.30 401	96.20 176	99.42 285	98.89 52	92.33 270	89.03 372	97.27 322	87.39 240	96.83 395	93.20 296	86.48 366	94.36 356

SSC-MVS3.2		89.59 391	88.66 391	92.38 412	94.29 402	86.12 438	99.49 274	97.66 283	90.28 348	88.63 380	95.18 408	64.46 460	96.88 391	85.30 414	82.66 396	94.14 386

MS-PatchMatch		90.65 365	90.30 356	91.71 423	94.22 403	85.50 443	98.24 413	97.70 277	88.67 377	86.42 420	96.37 356	67.82 446	98.03 327	83.62 426	99.62 99	91.60 460

WR-MVS_H		91.30 350	90.35 354	94.15 362	94.17 404	92.62 323	99.17 324	98.94 44	88.87 372	86.48 419	94.46 435	84.36 301	96.61 407	88.19 380	78.51 430	93.21 436

DU-MVS		92.46 329	91.45 338	95.49 305	94.05 405	95.28 222	99.81 168	98.74 76	92.25 276	89.21 367	96.64 348	81.66 333	96.73 400	93.20 296	77.52 438	94.46 348

NR-MVSNet		91.56 348	90.22 358	95.60 303	94.05 405	95.76 192	98.25 412	98.70 80	91.16 314	80.78 458	96.64 348	83.23 319	96.57 408	91.41 324	77.73 437	94.46 348

CP-MVSNet		91.23 354	90.22 358	94.26 357	93.96 407	92.39 328	99.09 331	98.57 107	88.95 369	86.42 420	96.57 351	79.19 365	96.37 423	90.29 348	78.95 427	94.02 399

XXY-MVS		91.82 339	90.46 351	95.88 295	93.91 408	95.40 211	98.87 369	97.69 279	88.63 379	87.87 398	97.08 327	74.38 416	97.89 335	91.66 321	84.07 388	94.35 359

PS-CasMVS		90.63 367	89.51 374	93.99 374	93.83 409	91.70 353	98.98 350	98.52 128	88.48 382	86.15 424	96.53 353	75.46 405	96.31 428	88.83 366	78.86 429	93.95 407

test_0402		85.58 422	83.94 428	90.50 435	93.81 410	85.04 445	98.55 395	95.20 467	76.01 478	79.72 464	95.13 409	64.15 462	96.26 430	66.04 494	86.88 364	90.21 474

XVG-ACMP-BASELINE		91.22 355	90.75 346	92.63 411	93.73 411	85.61 441	98.52 399	97.44 309	92.77 239	89.90 345	96.85 341	66.64 452	98.39 294	92.29 308	88.61 343	93.89 412

TranMVSNet+NR-MVSNet		91.68 347	90.61 350	94.87 328	93.69 412	93.98 279	99.69 228	98.65 88	91.03 319	88.44 384	96.83 344	80.05 358	96.18 433	90.26 349	76.89 446	94.45 353

TransMVSNet (Re)		87.25 414	85.28 422	93.16 399	93.56 413	91.03 368	98.54 397	94.05 485	83.69 443	81.09 455	96.16 362	75.32 406	96.40 422	76.69 469	68.41 477	92.06 456

v10		90.25 377	88.82 386	94.57 342	93.53 414	93.43 299	99.08 333	96.87 416	85.00 431	87.34 409	94.51 431	80.93 344	97.02 382	82.85 431	79.23 426	93.26 434

testgi		89.01 398	88.04 399	91.90 419	93.49 415	84.89 447	99.73 209	95.66 455	93.89 186	85.14 431	98.17 293	59.68 475	94.66 465	77.73 464	88.88 337	96.16 339

v8		90.54 369	89.17 379	94.66 336	93.43 416	93.40 302	99.20 321	96.94 409	85.76 421	87.56 403	94.51 431	81.96 329	97.19 365	84.94 417	78.25 432	93.38 432

V42		91.28 352	90.12 363	94.74 333	93.42 417	93.46 298	99.68 231	97.02 395	87.36 399	89.85 348	95.05 412	81.31 339	97.34 354	87.34 392	80.07 423	93.40 430

pm-mvs1		89.36 395	87.81 401	94.01 372	93.40 418	91.93 337	98.62 393	96.48 436	86.25 416	83.86 441	96.14 364	73.68 420	97.04 378	86.16 407	75.73 451	93.04 440

v1144		91.09 356	89.83 365	94.87 328	93.25 419	93.69 287	99.62 242	96.98 401	86.83 409	89.64 354	94.99 419	80.94 343	97.05 375	85.08 416	81.16 409	93.87 414

v1192		90.62 368	89.25 378	94.72 335	93.13 420	93.07 307	99.50 272	97.02 395	86.33 415	89.56 358	95.01 416	79.22 364	97.09 374	82.34 435	81.16 409	94.01 401

v2v482		91.30 350	90.07 364	95.01 323	93.13 420	93.79 282	99.77 186	97.02 395	88.05 390	89.25 364	95.37 398	80.73 348	97.15 367	87.28 394	80.04 424	94.09 394

OPM-MVS		93.21 306	92.80 305	94.44 350	93.12 422	90.85 375	99.77 186	97.61 290	96.19 95	91.56 322	98.65 256	75.16 411	98.47 282	93.78 286	89.39 333	93.99 404
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
v144192		90.79 363	89.52 373	94.59 340	93.11 423	92.77 314	99.56 261	96.99 399	86.38 414	89.82 349	94.95 421	80.50 353	97.10 372	83.98 423	80.41 419	93.90 411

PEN-MVS		90.19 379	89.06 382	93.57 389	93.06 424	90.90 373	99.06 338	98.47 140	88.11 389	85.91 426	96.30 358	76.67 392	95.94 443	87.07 397	76.91 445	93.89 412

v1240		90.20 378	88.79 387	94.44 350	93.05 425	92.27 330	99.38 293	96.92 412	85.89 419	89.36 361	94.87 423	77.89 379	97.03 380	80.66 445	81.08 412	94.01 401

usedtu_dtu_shiyan1		92.78 318	91.73 329	95.92 293	93.03 426	96.82 142	99.83 160	97.79 266	90.58 334	90.09 337	95.04 413	84.75 290	96.72 402	88.19 380	86.23 368	94.23 368

FE-MVSNET3		92.78 318	91.73 329	95.92 293	93.03 426	96.82 142	99.83 160	97.79 266	90.58 334	90.09 337	95.04 413	84.75 290	96.72 402	88.20 379	86.23 368	94.23 368

ArgMatch-SfM		85.25 427	84.17 425	88.48 455	92.99 428	77.23 484	97.92 426	94.24 481	90.50 338	85.08 433	95.65 380	49.84 489	95.83 445	81.06 443	70.22 468	92.39 453

v148		90.70 364	89.63 369	93.92 377	92.97 429	90.97 369	99.75 198	96.89 414	87.51 396	88.27 393	95.01 416	81.67 332	97.04 378	87.40 391	77.17 443	93.75 420

v1921920		90.46 370	89.12 380	94.50 346	92.96 430	92.46 326	99.49 274	96.98 401	86.10 417	89.61 356	95.30 401	78.55 373	97.03 380	82.17 436	80.89 417	94.01 401

MVStest1		85.03 429	82.76 438	91.83 420	92.95 431	89.16 407	98.57 394	94.82 473	71.68 489	68.54 494	95.11 411	83.17 320	95.66 448	74.69 474	65.32 484	90.65 469

tt0320-xc		82.94 444	80.35 451	90.72 433	92.90 432	83.54 456	96.85 453	94.73 476	63.12 500	79.85 463	93.77 445	49.43 491	95.46 451	80.98 444	71.54 465	93.16 437

Baseline_NR-MVSNet		90.33 374	89.51 374	92.81 408	92.84 433	89.95 396	99.77 186	93.94 486	84.69 436	89.04 371	95.66 379	81.66 333	96.52 411	90.99 332	76.98 444	91.97 458

test_method		80.79 450	79.70 453	84.08 469	92.83 434	67.06 497	99.51 270	95.42 460	54.34 509	81.07 456	93.53 447	44.48 494	92.22 487	78.90 459	77.23 442	92.94 442

pmmvs4		92.10 336	91.07 344	95.18 319	92.82 435	94.96 234	99.48 277	96.83 418	87.45 398	88.66 379	96.56 352	83.78 309	96.83 395	89.29 361	84.77 382	93.75 420

LF4IMVS		89.25 397	88.85 385	90.45 437	92.81 436	81.19 473	98.12 420	94.79 474	91.44 303	86.29 422	97.11 325	65.30 458	98.11 321	88.53 371	85.25 376	92.07 455

tt0320		83.56 443	81.15 446	90.77 431	92.77 437	83.58 455	96.83 454	95.52 459	63.26 499	81.36 453	92.54 457	53.26 484	95.77 446	80.45 446	74.38 454	92.96 441

DTE-MVSNet		89.40 394	88.24 397	92.88 406	92.66 438	89.95 396	99.10 330	98.22 213	87.29 400	85.12 432	96.22 360	76.27 399	95.30 456	83.56 427	75.74 450	93.41 429

EU-MVSNet		90.14 381	90.34 355	89.54 445	92.55 439	81.06 474	98.69 387	98.04 240	91.41 307	86.59 416	96.84 343	80.83 346	93.31 478	86.20 406	81.91 403	94.26 364

APD_test1		81.15 448	80.92 448	81.86 475	92.45 440	59.76 506	96.04 469	93.61 490	73.29 487	77.06 474	96.64 348	44.28 495	96.16 434	72.35 478	82.52 397	89.67 483

sc_t1		85.01 430	82.46 440	92.67 410	92.44 441	83.09 459	97.39 439	95.72 452	65.06 497	85.64 429	96.16 362	49.50 490	97.34 354	84.86 418	75.39 452	97.57 322

our_test_3		90.39 371	89.48 376	93.12 400	92.40 442	89.57 401	99.33 300	96.35 439	87.84 394	85.30 430	94.99 419	84.14 305	96.09 438	80.38 448	84.56 383	93.71 425

ppachtmachnet_test		89.58 392	88.35 395	93.25 398	92.40 442	90.44 385	99.33 300	96.73 425	85.49 426	85.90 427	95.77 373	81.09 341	96.00 442	76.00 472	82.49 398	93.30 433

v7n		89.65 390	88.29 396	93.72 383	92.22 444	90.56 382	99.07 337	97.10 378	85.42 428	86.73 413	94.72 424	80.06 357	97.13 369	81.14 441	78.12 434	93.49 428

dmvs_testset		83.79 439	86.07 412	76.94 482	92.14 445	48.60 520	96.75 455	90.27 502	89.48 357	78.65 468	98.55 271	79.25 363	86.65 505	66.85 490	82.69 395	95.57 341

PS-MVSNAJss		93.64 298	93.31 291	94.61 338	92.11 446	92.19 331	99.12 327	97.38 316	92.51 262	88.45 383	96.99 334	91.20 177	97.29 362	94.36 267	87.71 356	94.36 356

pmmvs5		90.17 380	89.09 381	93.40 392	92.10 447	89.77 399	99.74 202	95.58 457	85.88 420	87.24 410	95.74 374	73.41 423	96.48 415	88.54 370	83.56 392	93.95 407

N_pmnet		80.06 453	80.78 449	77.89 480	91.94 448	45.28 524	98.80 378	56.82 527	78.10 475	80.08 461	93.33 448	77.03 386	95.76 447	68.14 487	82.81 394	92.64 446

test_djsdf		92.83 317	92.29 319	94.47 348	91.90 449	92.46 326	99.55 264	97.27 343	91.17 312	89.96 342	96.07 368	81.10 340	96.89 389	94.67 262	88.91 336	94.05 398

SixPastTwentyTwo		88.73 399	88.01 400	90.88 427	91.85 450	82.24 465	98.22 417	95.18 468	88.97 367	82.26 447	96.89 338	71.75 428	96.67 405	84.00 422	82.98 393	93.72 424

dtuonlycased		86.10 420	85.82 415	86.95 461	91.84 451	79.57 480	99.27 315	94.89 471	86.79 410	79.46 465	94.46 435	66.85 450	90.93 493	80.41 447	78.44 431	90.34 471

K. test v3		88.05 405	87.24 406	90.47 436	91.82 452	82.23 466	98.96 356	97.42 312	89.05 362	76.93 476	95.60 382	68.49 442	95.42 452	85.87 411	81.01 415	93.75 420

OurMVSNet-221017-0		89.81 387	89.48 376	90.83 430	91.64 453	81.21 472	98.17 419	95.38 462	91.48 301	85.65 428	97.31 320	72.66 424	97.29 362	88.15 382	84.83 381	93.97 406

mvs_tets		91.81 340	91.08 343	94.00 373	91.63 454	90.58 381	98.67 389	97.43 310	92.43 264	87.37 408	97.05 330	71.76 427	97.32 357	94.75 259	88.68 342	94.11 393

Gipuma		66.95 473	65.00 473	72.79 488	91.52 455	67.96 494	66.16 524	95.15 469	47.89 511	58.54 503	67.99 520	29.74 504	87.54 504	50.20 511	77.83 436	62.87 519
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_fmvsmconf0.01_n		96.39 183	95.74 196	98.32 147	91.47 456	95.56 203	99.84 152	97.30 333	97.74 30	97.89 177	99.35 153	79.62 360	99.85 130	99.25 75	99.24 141	99.55 164

jajsoiax		91.92 338	91.18 341	94.15 362	91.35 457	90.95 372	99.00 348	97.42 312	92.61 250	87.38 407	97.08 327	72.46 425	97.36 352	94.53 265	88.77 340	94.13 391

MDA-MVSNet-bldmvs		84.09 437	81.52 444	91.81 421	91.32 458	88.00 425	98.67 389	95.92 448	80.22 463	55.60 506	93.32 449	68.29 444	93.60 476	73.76 475	76.61 447	93.82 418

MVP-Stereo		90.93 358	90.45 353	92.37 414	91.25 459	88.76 411	98.05 424	96.17 442	87.27 401	84.04 438	95.30 401	78.46 374	97.27 364	83.78 425	99.70 92	91.09 463
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MDA-MVSNet_test_wron		85.51 424	83.32 433	92.10 416	90.96 460	88.58 417	99.20 321	96.52 434	79.70 465	57.12 505	92.69 456	79.11 366	93.86 472	77.10 467	77.46 440	93.86 415

YYNet1		85.50 425	83.33 432	92.00 417	90.89 461	88.38 421	99.22 320	96.55 433	79.60 466	57.26 504	92.72 455	79.09 368	93.78 474	77.25 466	77.37 441	93.84 416

ALIKED-NN		54.48 483	52.67 485	59.89 505	90.79 462	45.45 522	81.25 517	55.75 531	34.99 520	44.87 517	71.98 511	25.50 512	74.36 519	21.88 529	47.04 514	59.85 521

anonymousdsp		91.79 345	90.92 345	94.41 353	90.76 463	92.93 313	98.93 360	97.17 359	89.08 361	87.46 406	95.30 401	78.43 375	96.92 386	92.38 307	88.73 341	93.39 431

lessismore_v0				90.53 434	90.58 464	80.90 475		95.80 449		77.01 475	95.84 371	66.15 454	96.95 384	83.03 430	75.05 453	93.74 423

EG-PatchMatch MVS		85.35 426	83.81 430	89.99 443	90.39 465	81.89 468	98.21 418	96.09 444	81.78 456	74.73 482	93.72 446	51.56 488	97.12 371	79.16 457	88.61 343	90.96 466

EGC-MVSNET		69.38 466	63.76 476	86.26 465	90.32 466	81.66 471	96.24 465	93.85 487	0.99 546	3.22 547	92.33 467	52.44 485	92.92 482	59.53 505	84.90 380	84.21 501

CMPMVS	61.59 21	84.75 433	85.14 423	83.57 470	90.32 466	62.54 502	96.98 449	97.59 294	74.33 485	69.95 491	96.66 346	64.17 461	98.32 304	87.88 386	88.41 348	89.84 480
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
ALIKED-MNN		52.51 487	50.15 492	59.60 507	90.05 468	44.33 526	81.60 516	54.93 532	32.36 523	40.96 524	68.77 517	20.90 521	75.30 517	20.00 530	41.78 519	59.18 522

new_pmnet		84.49 436	82.92 436	89.21 447	90.03 469	82.60 462	96.89 452	95.62 456	80.59 461	75.77 481	89.17 482	65.04 459	94.79 463	72.12 479	81.02 414	90.23 473

pmmvs6		85.69 421	83.84 429	91.26 426	90.00 470	84.41 450	97.82 430	96.15 443	75.86 479	81.29 454	95.39 396	61.21 472	96.87 392	83.52 428	73.29 457	92.50 450

ttmdpeth		88.23 404	87.06 407	91.75 422	89.91 471	87.35 430	98.92 363	95.73 451	87.92 392	84.02 439	96.31 357	68.23 445	96.84 393	86.33 405	76.12 448	91.06 464

DSMNet-mixed		88.28 403	88.24 397	88.42 456	89.64 472	75.38 488	98.06 423	89.86 503	85.59 425	88.20 395	92.14 469	76.15 401	91.95 488	78.46 461	96.05 260	97.92 307

DenseAffine		75.91 459	73.39 463	83.47 471	89.52 473	71.86 491	93.39 488	89.29 508	71.44 490	66.83 495	90.32 477	30.65 501	89.67 497	68.20 486	60.88 499	88.88 491

UnsupCasMVSNet_eth		85.52 423	83.99 426	90.10 441	89.36 474	83.51 457	96.65 456	97.99 244	89.14 360	75.89 480	93.83 443	63.25 465	93.92 470	81.92 438	67.90 480	92.88 443

Anonymous20231206		86.32 418	85.42 421	89.02 449	89.11 475	80.53 478	99.05 342	95.28 463	85.43 427	82.82 445	93.92 442	74.40 415	93.44 477	66.99 489	81.83 404	93.08 439

ALIKED-LG		54.29 484	52.28 486	60.32 501	88.90 476	45.51 521	81.66 515	56.33 528	38.60 513	42.62 522	70.81 512	25.00 514	75.20 518	19.87 531	46.76 516	60.24 520

Anonymous20240521		85.15 428	83.81 430	89.16 448	88.32 477	82.69 461	98.80 378	95.74 450	79.72 464	81.53 452	90.99 472	65.38 457	94.16 468	72.69 477	81.11 411	90.63 470

OpenMVS_ROB	79.82 20	83.77 440	81.68 443	90.03 442	88.30 478	82.82 460	98.46 400	95.22 466	73.92 486	76.00 479	91.29 471	55.00 481	96.94 385	68.40 485	88.51 347	90.34 471

test20.03		84.72 434	83.99 426	86.91 462	88.19 479	80.62 477	98.88 366	95.94 447	88.36 385	78.87 466	94.62 429	68.75 440	89.11 499	66.52 491	75.82 449	91.00 465

RoMa-SfM		74.91 462	72.77 464	81.35 476	88.00 480	67.35 496	93.55 485	86.23 513	68.27 495	66.79 496	92.92 454	30.40 502	87.68 501	66.14 493	62.62 491	89.02 489

gbinet_0.2-2-1-0.02		87.63 413	85.51 420	93.99 374	87.22 481	91.56 363	99.81 168	97.36 320	79.54 467	88.60 381	93.29 452	73.76 419	96.34 425	89.27 362	60.78 500	94.06 397

blend_shiyan4		90.13 382	88.79 387	94.17 359	87.12 482	91.83 343	99.75 198	97.08 382	79.27 472	88.69 377	92.53 458	92.25 160	96.50 412	89.35 359	73.04 459	94.18 375

KD-MVS_self_test		83.59 441	82.06 441	88.20 457	86.93 483	80.70 476	97.21 442	96.38 437	82.87 450	82.49 446	88.97 483	67.63 447	92.32 485	73.75 476	62.30 493	91.58 461

DKM		72.18 464	69.80 467	79.34 479	86.79 484	65.15 498	92.70 490	84.00 514	67.67 496	61.97 499	89.63 479	23.69 517	85.17 507	67.39 488	54.35 510	87.70 495

MIMVSNet1		82.58 445	80.51 450	88.78 451	86.68 485	84.20 451	96.65 456	95.41 461	78.75 473	78.59 469	92.44 459	51.88 487	89.76 496	65.26 495	78.95 427	92.38 454

wanda-best-256-512		87.82 409	85.71 416	94.15 362	86.66 486	91.88 339	99.76 192	97.08 382	79.46 468	88.37 390	92.36 463	78.01 376	96.43 418	88.39 375	61.26 495	94.14 386

FE-blended-shiyan7		87.82 409	85.71 416	94.15 362	86.66 486	91.88 339	99.76 192	97.08 382	79.46 468	88.37 390	92.36 463	78.01 376	96.43 418	88.39 375	61.26 495	94.14 386

usedtu_blend_shiyan5		86.75 417	84.29 424	94.16 360	86.66 486	91.83 343	97.42 436	95.23 465	69.94 493	88.37 390	92.36 463	78.01 376	96.50 412	89.35 359	61.26 495	94.14 386

SP-NN		55.28 482	53.59 484	60.34 500	86.63 489	39.01 531	86.70 508	56.31 529	31.08 525	43.77 520	68.45 518	23.39 518	60.24 524	29.19 524	56.76 507	81.77 506

LoFTR		74.41 463	70.88 466	84.99 468	86.56 490	67.85 495	93.74 481	89.63 505	69.46 494	54.95 507	87.39 493	30.76 500	96.92 386	61.37 501	64.06 487	90.19 475

blended_shiyan8		87.82 409	85.71 416	94.16 360	86.54 491	91.79 345	99.72 213	97.08 382	79.32 470	88.44 384	92.35 466	77.88 380	96.56 409	88.53 371	61.51 494	94.15 382

blended_shiyan6		87.74 412	85.62 419	94.09 367	86.53 492	91.73 351	99.72 213	97.08 382	79.32 470	88.22 394	92.31 468	77.82 381	96.43 418	88.31 377	61.26 495	94.13 391

CL-MVSNet_self_test		84.50 435	83.15 435	88.53 454	86.00 493	81.79 469	98.82 374	97.35 321	85.12 430	83.62 443	90.91 474	76.66 393	91.40 489	69.53 483	60.36 501	92.40 452

MatchFormer		70.84 465	66.72 471	83.19 473	85.99 494	64.61 499	93.58 484	88.62 509	59.32 504	50.64 510	82.31 505	28.00 507	96.79 398	52.52 510	59.50 503	88.18 492

UnsupCasMVSNet_bld		79.97 455	77.03 461	88.78 451	85.62 495	81.98 467	93.66 482	97.35 321	75.51 482	70.79 490	83.05 502	48.70 492	94.91 461	78.31 462	60.29 502	89.46 486

mvs5depth		84.87 431	82.90 437	90.77 431	85.59 496	84.84 448	91.10 500	93.29 492	83.14 447	85.07 434	94.33 438	62.17 468	97.32 357	78.83 460	72.59 464	90.14 476

SP-LightGlue		55.29 480	53.65 483	60.20 502	85.58 497	39.12 530	86.36 511	57.52 526	32.34 524	44.34 519	67.75 521	24.36 515	59.32 527	29.62 522	54.98 508	82.17 504

SP-SuperGlue		55.29 480	53.71 482	60.00 504	85.11 498	38.86 532	86.96 507	57.95 525	32.77 522	44.54 518	68.00 519	23.90 516	59.51 526	29.61 523	54.59 509	81.63 507

SP-MNN		53.97 485	52.04 489	59.73 506	84.72 499	38.63 533	86.51 509	55.94 530	29.25 526	40.20 525	67.48 522	22.18 520	59.59 525	27.79 525	54.33 511	80.98 508

Patchmatch-RL test		86.90 415	85.98 414	89.67 444	84.45 500	75.59 486	89.71 503	92.43 494	86.89 408	77.83 473	90.94 473	94.22 95	93.63 475	87.75 387	69.61 471	99.79 112

MASt3R-SfM		78.94 456	79.57 454	77.07 481	84.15 501	50.74 516	91.56 496	92.34 495	83.22 446	80.84 457	94.16 440	36.67 498	92.30 486	79.45 453	73.71 456	88.16 493

pmmvs-eth3d		84.03 438	81.97 442	90.20 439	84.15 501	87.09 432	98.10 422	94.73 476	83.05 448	74.10 486	87.77 490	65.56 456	94.01 469	81.08 442	69.24 473	89.49 485

test_fmvs3		79.99 454	80.17 452	79.45 478	84.02 503	62.83 500	99.05 342	93.49 491	88.29 387	80.06 462	86.65 496	28.09 506	88.00 500	88.63 367	73.27 458	87.54 497

PM-MVS		80.47 451	78.88 456	85.26 466	83.79 504	72.22 490	95.89 472	91.08 500	85.71 424	76.56 478	88.30 486	36.64 499	93.90 471	82.39 434	69.57 472	89.66 484

new-patchmatchnet		81.19 447	79.34 455	86.76 463	82.86 505	80.36 479	97.92 426	95.27 464	82.09 455	72.02 488	86.87 495	62.81 467	90.74 494	71.10 480	63.08 489	89.19 488

FE-MVSNET2		83.57 442	81.36 445	90.20 439	82.83 506	87.59 426	98.28 411	96.04 445	85.33 429	74.13 485	87.45 491	59.16 476	93.26 479	79.12 458	69.91 469	89.77 481

FE-MVSNET		81.05 449	78.81 457	87.79 459	81.98 507	83.70 453	98.23 415	91.78 499	81.27 458	74.29 484	87.44 492	60.92 474	90.67 495	64.92 496	68.43 476	89.01 490

mvsany_test3		82.12 446	81.14 447	85.06 467	81.87 508	70.41 492	97.09 446	92.14 496	91.27 310	77.84 472	88.73 484	39.31 496	95.49 449	90.75 339	71.24 466	89.29 487

WB-MVS		76.28 458	77.28 460	73.29 487	81.18 509	54.68 511	97.87 429	94.19 482	81.30 457	69.43 492	90.70 475	77.02 387	82.06 510	35.71 518	68.11 479	83.13 502

test_f		78.40 457	77.59 459	80.81 477	80.82 510	62.48 503	96.96 450	93.08 493	83.44 444	74.57 483	84.57 501	27.95 508	92.63 483	84.15 420	72.79 460	87.32 498

SSC-MVS		75.42 461	76.40 462	72.49 492	80.68 511	53.62 512	97.42 436	94.06 484	80.42 462	68.75 493	90.14 478	76.54 395	81.66 511	33.25 519	66.34 483	82.19 503

pmmvs3		80.27 452	77.77 458	87.76 460	80.32 512	82.43 464	98.23 415	91.97 497	72.74 488	78.75 467	87.97 489	57.30 480	90.99 492	70.31 481	62.37 492	89.87 479

testf1		68.38 469	66.92 469	72.78 489	78.80 513	50.36 517	90.95 501	87.35 511	55.47 507	58.95 501	88.14 487	20.64 523	87.60 502	57.28 506	64.69 485	80.39 510

APD_test2		68.38 469	66.92 469	72.78 489	78.80 513	50.36 517	90.95 501	87.35 511	55.47 507	58.95 501	88.14 487	20.64 523	87.60 502	57.28 506	64.69 485	80.39 510

ambc				83.23 472	77.17 515	62.61 501	87.38 505	94.55 480		76.72 477	86.65 496	30.16 503	96.36 424	84.85 419	69.86 470	90.73 468

test_vis3_rt		68.82 467	66.69 472	75.21 486	76.24 516	60.41 505	96.44 460	68.71 521	75.13 483	50.54 511	69.52 516	16.42 529	96.32 427	80.27 449	66.92 482	68.89 516

PDCNetPlus		59.83 477	57.26 480	67.55 496	76.18 517	56.71 509	87.01 506	45.27 535	59.54 503	48.80 513	83.01 503	26.63 510	76.54 516	62.12 500	26.78 527	69.40 515

usedtu_dtu_shiyan2		75.87 460	72.37 465	86.39 464	76.18 517	75.49 487	96.53 458	93.82 488	64.74 498	72.53 487	88.48 485	37.67 497	91.12 491	64.13 497	57.22 505	92.56 447

TDRefinement		84.76 432	82.56 439	91.38 425	74.58 519	84.80 449	97.36 440	94.56 479	84.73 435	80.21 460	96.12 367	63.56 463	98.39 294	87.92 385	63.97 488	90.95 467

PMatch-SfM		62.12 476	58.57 479	72.76 491	74.34 520	52.97 514	84.95 512	65.57 522	56.89 506	46.61 515	85.70 500	9.51 539	80.54 513	60.53 503	43.03 518	84.77 499

SIFT-NN		35.94 496	36.54 499	34.16 512	73.93 521	29.52 535	62.74 525	37.28 536	19.65 530	27.91 532	49.19 531	11.66 532	46.35 531	9.19 532	37.30 520	26.61 529

ELoFTR		64.32 475	60.56 478	75.60 485	73.46 522	53.20 513	86.50 510	80.09 517	60.74 502	45.95 516	82.48 504	16.05 530	89.20 498	56.48 509	43.34 517	84.38 500

E-PMN		52.30 488	52.18 488	52.67 508	71.51 523	45.40 523	93.62 483	76.60 519	36.01 517	43.50 521	64.13 525	27.11 509	67.31 522	31.06 520	26.06 528	45.30 528

EMVS		51.44 490	51.22 491	52.11 509	70.71 524	44.97 525	94.04 478	75.66 520	35.34 519	42.40 523	61.56 529	28.93 505	65.87 523	27.64 526	24.73 529	45.49 526

PMMVS2		67.15 472	64.15 475	76.14 484	70.56 525	62.07 504	93.89 479	87.52 510	58.09 505	60.02 500	78.32 507	22.38 519	84.54 508	59.56 504	47.03 515	81.80 505

SIFT-MNN		34.10 497	34.41 500	33.17 514	68.99 526	28.51 536	60.22 527	36.81 537	19.08 533	24.04 534	47.28 534	10.06 536	45.04 532	8.72 533	34.47 522	25.97 532

SIFT-NCM-Cal		31.73 499	31.67 502	31.91 517	67.18 527	27.55 542	58.36 529	33.09 541	18.38 536	14.93 541	45.16 540	8.60 540	43.82 534	7.62 542	31.68 525	24.36 535

SIFT-NN-NCMNet		33.88 498	34.14 501	33.10 515	66.88 528	28.42 537	60.42 526	36.72 538	19.15 531	24.06 533	47.14 535	10.24 534	44.77 533	8.72 533	33.94 524	26.10 531

FPMVS		68.72 468	68.72 468	68.71 494	65.95 529	44.27 527	95.97 471	94.74 475	51.13 510	53.26 508	90.50 476	25.11 513	83.00 509	60.80 502	80.97 416	78.87 512

SP-DiffGlue		56.84 478	55.72 481	60.19 503	65.70 530	40.86 528	81.89 514	60.28 524	34.62 521	50.39 512	76.88 509	26.61 511	58.81 528	48.21 512	56.94 506	80.90 509

wuyk23d		20.37 511	20.84 514	18.99 528	65.34 531	27.73 540	50.43 537	7.67 552	9.50 545	8.01 546	6.34 546	6.13 548	26.24 545	23.40 528	10.69 543	2.99 543

SIFT-ConvMatch		30.09 502	29.76 506	31.09 519	65.16 532	27.56 541	54.13 533	31.17 542	18.55 535	17.88 537	45.89 537	8.40 541	42.26 538	8.11 538	18.51 534	23.46 537

SIFT-CM-Cal		28.34 505	27.90 509	29.63 521	63.75 533	25.98 546	50.66 536	26.18 546	18.12 539	16.88 539	44.64 541	8.08 543	39.70 539	7.65 541	15.19 539	23.22 538

LCM-MVSNet		67.77 471	64.73 474	76.87 483	62.95 534	56.25 510	89.37 504	93.74 489	44.53 512	61.99 498	80.74 506	20.42 525	86.53 506	69.37 484	59.50 503	87.84 494

SIFT-NN-CMatch		31.71 500	31.56 503	32.16 516	62.58 535	27.53 543	56.45 530	33.28 540	19.00 534	23.65 535	47.34 532	10.05 537	42.72 536	8.71 535	22.96 532	26.24 530

SIFT-UM-Cal		27.47 506	27.02 510	28.83 524	62.12 536	24.58 548	53.60 534	23.46 547	18.14 538	12.85 543	45.56 538	7.49 544	39.45 540	7.68 540	12.30 540	22.45 539

SIFT-UMatch		29.40 504	28.87 508	30.98 520	62.08 537	26.57 545	56.09 531	29.45 544	18.31 537	15.86 540	46.00 536	8.23 542	42.54 537	7.99 539	15.81 537	23.85 536

GLUNet-SfM		51.10 491	46.61 494	64.56 497	61.54 538	39.88 529	79.38 520	65.13 523	36.09 516	33.36 529	69.94 514	14.50 531	78.76 514	42.46 516	17.10 536	75.02 514

SIFT-NN-UMatch		31.23 501	31.05 505	31.79 518	60.08 539	27.23 544	58.49 528	33.65 539	19.14 532	17.30 538	47.31 533	10.12 535	42.88 535	8.67 536	24.67 530	25.27 533

XFeat-NN		42.54 492	42.87 496	41.54 511	59.73 540	27.86 539	69.53 522	45.34 534	24.36 527	37.16 526	64.79 523	20.84 522	51.40 530	30.01 521	34.12 523	45.36 527

MVE	53.74 22	51.54 489	47.86 493	62.60 498	59.56 541	50.93 515	79.41 519	77.69 518	35.69 518	36.27 527	61.76 528	5.79 549	69.63 520	37.97 517	36.61 521	67.24 517
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
SIFT-NN-PointCN		29.63 503	29.72 507	29.36 522	57.55 542	23.55 549	56.07 532	30.57 543	17.99 540	20.99 536	45.21 539	9.94 538	39.33 541	8.40 537	20.81 533	25.20 534

SIFT-PointCN		25.49 507	25.71 511	24.84 525	56.17 543	18.65 550	51.37 535	26.53 545	16.31 541	12.78 544	39.87 544	6.41 547	34.09 543	6.51 544	15.42 538	21.77 540

SIFT-PCN-Cal		24.67 508	24.81 512	24.24 526	56.13 544	18.04 551	49.05 538	23.39 548	16.07 542	12.99 542	40.17 543	6.97 546	34.68 542	6.71 543	11.81 541	19.99 541

XFeat-MNN		41.51 493	41.24 497	42.32 510	55.40 545	28.19 538	69.39 523	46.53 533	23.57 528	34.47 528	63.21 527	20.04 526	52.41 529	27.43 527	31.08 526	46.37 525

SIFT-NCMNet		21.21 510	21.22 513	21.17 527	52.99 546	16.41 552	42.12 539	14.05 550	15.89 543	10.70 545	35.85 545	5.14 550	29.82 544	5.80 545	8.44 544	17.28 542

ANet_high		56.10 479	52.24 487	67.66 495	49.27 547	56.82 508	83.94 513	82.02 516	70.47 491	33.28 530	64.54 524	17.23 528	69.16 521	45.59 514	23.85 531	77.02 513

tmp_tt		65.23 474	62.94 477	72.13 493	44.90 548	50.03 519	81.05 518	89.42 507	38.45 514	48.51 514	99.90 23	54.09 483	78.70 515	91.84 320	18.26 535	87.64 496

PMVS	49.05 23	53.75 486	51.34 490	60.97 499	40.80 549	34.68 534	74.82 521	89.62 506	37.55 515	28.67 531	72.12 510	7.09 545	81.63 512	43.17 515	68.21 478	66.59 518
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test123		37.68 495	39.14 498	33.31 513	19.94 550	24.83 547	98.36 408	9.75 551	15.53 544	51.31 509	87.14 494	19.62 527	17.74 546	47.10 513	3.47 545	57.36 523

testmvs		40.60 494	44.45 495	29.05 523	19.49 551	14.11 553	99.68 231	18.47 549	20.74 529	64.59 497	98.48 278	10.95 533	17.09 547	56.66 508	11.01 542	55.94 524

mmdepth		0.00 514	0.00 517	0.00 529	0.00 552	0.00 554	0.00 540	0.00 553	0.00 547	0.00 548	0.00 548	0.00 551	0.00 548	0.00 546	0.00 546	0.00 544

monomultidepth		0.00 514	0.00 517	0.00 529	0.00 552	0.00 554	0.00 540	0.00 553	0.00 547	0.00 548	0.00 548	0.00 551	0.00 548	0.00 546	0.00 546	0.00 544

test_blank		0.00 514	0.00 517	0.00 529	0.00 552	0.00 554	0.00 540	0.00 553	0.00 547	0.00 548	0.02 547	0.00 551	0.00 548	0.00 546	0.00 546	0.00 544

eth-test2					0.00 552

eth-test					0.00 552

uanet_test		0.00 514	0.00 517	0.00 529	0.00 552	0.00 554	0.00 540	0.00 553	0.00 547	0.00 548	0.00 548	0.00 551	0.00 548	0.00 546	0.00 546	0.00 544

DCPMVS		0.00 514	0.00 517	0.00 529	0.00 552	0.00 554	0.00 540	0.00 553	0.00 547	0.00 548	0.00 548	0.00 551	0.00 548	0.00 546	0.00 546	0.00 544

cdsmvs_eth3d_5k		23.43 509	31.24 504	0.00 529	0.00 552	0.00 554	0.00 540	98.09 234	0.00 547	0.00 548	99.67 114	83.37 314	0.00 548	0.00 546	0.00 546	0.00 544

pcd_1.5k_mvsjas		7.60 513	10.13 516	0.00 529	0.00 552	0.00 554	0.00 540	0.00 553	0.00 547	0.00 548	0.00 548	91.20 177	0.00 548	0.00 546	0.00 546	0.00 544

sosnet-low-res		0.00 514	0.00 517	0.00 529	0.00 552	0.00 554	0.00 540	0.00 553	0.00 547	0.00 548	0.00 548	0.00 551	0.00 548	0.00 546	0.00 546	0.00 544

sosnet		0.00 514	0.00 517	0.00 529	0.00 552	0.00 554	0.00 540	0.00 553	0.00 547	0.00 548	0.00 548	0.00 551	0.00 548	0.00 546	0.00 546	0.00 544

uncertanet		0.00 514	0.00 517	0.00 529	0.00 552	0.00 554	0.00 540	0.00 553	0.00 547	0.00 548	0.00 548	0.00 551	0.00 548	0.00 546	0.00 546	0.00 544

Regformer		0.00 514	0.00 517	0.00 529	0.00 552	0.00 554	0.00 540	0.00 553	0.00 547	0.00 548	0.00 548	0.00 551	0.00 548	0.00 546	0.00 546	0.00 544

ab-mvs-re		8.28 512	11.04 515	0.00 529	0.00 552	0.00 554	0.00 540	0.00 553	0.00 547	0.00 548	99.40 147	0.00 551	0.00 548	0.00 546	0.00 546	0.00 544

uanet		0.00 514	0.00 517	0.00 529	0.00 552	0.00 554	0.00 540	0.00 553	0.00 547	0.00 548	0.00 548	0.00 551	0.00 548	0.00 546	0.00 546	0.00 544

WAC-MVS						90.97 369								86.10 409

PC_three_1452									96.96 60	99.80 28	99.79 63	97.49 11	100.00 1	99.99 5	99.98 32	100.00 1

test_241102_TWO								98.43 156	97.27 47	99.80 28	99.94 5	97.18 23	100.00 1	100.00 1	100.00 1	100.00 1

test_0728_THIRD									96.48 80	99.83 24	99.91 19	97.87 6	100.00 1	99.92 16	100.00 1	100.00 1

GSMVS																99.59 154

sam_mvs1												94.72 74				99.59 154

sam_mvs												94.25 94

MTGPA								98.28 204

test_post1							95.78 473				59.23 530	93.20 128	97.74 341	91.06 330

test_post											63.35 526	94.43 82	98.13 320

patchmatchnet-post											91.70 470	95.12 60	97.95 332

MTMP							99.87 133	96.49 435

test9_res														99.71 48	99.99 21	100.00 1

agg_prior2														99.48 63	100.00 1	100.00 1

test_prior4						98.05 82	99.94 93

test_prior2							99.95 75		95.78 105	99.73 47	99.76 73	96.00 41		99.78 35	100.00 1

旧先验2							99.46 282		94.21 166	99.85 20			99.95 85	96.96 200

新几何2							99.40 287

无先验							99.49 274	98.71 79	93.46 201				100.00 1	94.36 267		99.99 26

原ACMM2							99.90 117

testdata2													99.99 39	90.54 343

segment_acmp												96.68 31

testdata1							99.28 313		96.35 91

plane_prior5								97.87 258					98.37 300	97.79 169	89.55 330	94.52 345

plane_prior4											98.59 264

plane_prior3						91.64 356			96.63 75	93.01 305

plane_prior2							99.84 152		96.38 86

plane_prior						91.74 348	99.86 144		96.76 70						89.59 329

n2								0.00 553

nn								0.00 553

door-mid								89.69 504

test11								98.44 148

door								90.31 501

HQP5-MVS						91.85 341

BP-MVS														97.92 158

HQP4-MVS										93.37 300			98.39 294			94.53 343

HQP3-MVS								97.89 256							89.60 327

HQP2-MVS												80.65 350

MDTV_nov1_ep13_2view						96.26 170	96.11 467		91.89 285	98.06 169		94.40 84		94.30 270		99.67 133

ACMMP++_ref															87.04 363

ACMMP++															88.23 350

Test By Simon												92.82 139

High-resolution multi-view benchmark