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	99.09 326	98.84 65	93.32 206	96.74 220	99.72 95	86.04 263	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	89.90 348	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	92.06 278	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	94.43 151	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		93.83 284	92.06 319	99.15 71	99.94 17	97.50 111	99.94 93	98.42 168	96.22 93	99.41 87	41.37 533	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	93.97 176	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	99.82 165	98.43 156	94.56 142	97.52 187	99.70 101	94.40 84	99.98 51	97.00 193	99.98 32	99.99 26

MG-MVS		98.91 22	98.65 27	99.68 18	99.94 17	99.07 26	99.64 236	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	92.34 266	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	94.04 174	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	93.90 182	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	94.35 156	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	94.35 156	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	99.75 195	99.50 17	93.90 182	99.37 92	99.76 73	93.24 126	100.00 1	97.75 172	99.96 46	99.98 57

ACMMP		97.74 103	97.44 107	98.66 113	99.92 36	96.13 180	99.18 319	99.45 18	94.84 132	96.41 239	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	94.08 169	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	99.24 314	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	97.27 239	99.89 50	91.92 335	99.90 117	99.07 37	88.67 372	95.26 272	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	94.31 159	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	93.22 209	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	93.19 211	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	99.61 243	97.78 270	96.52 78	98.61 140	99.31 157	92.73 141	99.67 168	96.77 209	99.48 121	99.06 250

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	90.78 327	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	99.39 288	98.28 204	95.76 106	97.18 202	99.88 29	92.74 140	100.00 1	98.67 111	99.88 76	99.99 26

LS3D		95.84 208	95.11 225	98.02 167	99.85 61	95.10 231	98.74 376	98.50 137	87.22 397	93.66 294	99.86 34	87.45 239	99.95 85	90.94 331	99.81 86	99.02 258

HPM-MVS		97.96 80	97.72 90	98.68 110	99.84 63	96.39 166	99.90 117	98.17 222	92.61 247	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	99.40 284	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	94.77 243	99.92 103	98.46 142	93.93 179	97.20 200	99.27 164	95.44 55	99.97 64	97.41 178	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	99.82 165	98.30 202	93.95 178	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	99.36 294	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	99.76 189	98.31 199	94.43 151	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	99.76 189	98.31 199	94.43 151	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	91.75 289	98.94 119	99.54 134	91.82 172	99.65 172	97.62 175	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	93.53 194	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	97.41 229	99.76 73	93.36 301	99.65 232	97.95 249	96.03 98	97.41 193	99.70 101	89.61 206	99.51 178	96.73 211	98.25 180	99.38 200

新几何1				99.42 43	99.75 76	98.27 71		98.63 97	92.69 242	99.55 71	99.82 54	94.40 84	100.00 1	91.21 323	99.94 58	99.99 26

MP-MVS-pluss		98.07 78	97.64 96	99.38 49	99.74 77	98.41 69	99.74 199	98.18 221	93.35 204	96.45 232	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	99.77 183	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	94.06 172	99.62 62	99.85 38	94.97 69	99.96 76	95.11 243	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	99.73 206	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	96.87 257	99.71 83	91.74 345	99.85 147	97.95 249	93.11 219	95.72 261	99.16 183	92.35 156	99.94 94	95.32 239	99.35 136	98.92 266

reproduce-ours		98.78 27	98.67 24	99.09 80	99.70 85	97.30 119	99.74 199	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	99.74 199	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	95.59 300	99.67 88	86.91 431	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	93.92 373	99.63 90	81.76 466	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		96.05 198	95.82 190	96.72 263	99.59 92	96.99 136	99.95 75	99.10 34	94.06 172	98.27 159	95.80 368	89.00 218	99.95 85	99.12 79	87.53 357	93.24 431

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	99.08 188	89.00 218	99.95 85	99.12 79	99.25 140	99.57 162

PatchMatch-RL		96.04 199	95.40 208	97.95 170	99.59 92	95.22 225	99.52 265	99.07 37	93.96 177	96.49 230	98.35 280	82.28 322	99.82 142	90.15 347	99.22 143	98.81 274

dcpmvs_2		97.42 121	98.09 63	95.42 307	99.58 96	87.24 427	99.23 315	96.95 402	94.28 162	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	99.34 296	98.55 119	91.86 284	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	93.78 186	96.55 228	99.69 105	92.28 158	99.98 51	97.13 188	99.44 128	99.93 88

API-MVS		97.86 88	97.66 94	98.47 135	99.52 99	95.41 210	99.47 275	98.87 58	91.68 291	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	99.21 175	84.48 297	99.95 85	94.92 249	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	88.48 377	99.06 114	99.66 116	90.30 198	99.64 173	96.32 223	99.97 42	99.96 75

cl22		93.77 289	93.25 290	95.33 311	99.49 102	94.43 255	99.61 243	98.09 234	90.38 337	89.16 366	95.61 376	90.56 193	97.34 350	91.93 314	84.45 380	94.21 369

testdata				98.42 142	99.47 103	95.33 216		98.56 113	93.78 186	99.79 37	99.85 38	93.64 114	99.94 94	94.97 247	99.94 58	100.00 1

MAR-MVS		97.43 117	97.19 120	98.15 158	99.47 103	94.79 242	99.05 337	98.76 73	92.65 245	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		94.54 258	93.42 280	97.91 176	99.46 105	94.04 273	98.93 355	97.48 307	81.15 452	90.04 337	99.55 132	87.02 247	99.95 85	88.97 362	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	98.46 395	99.42 21	97.03 57	99.02 116	99.09 187	99.35 2	98.21 312	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	86.21 261	99.95 85	99.89 21	99.68 93	97.65 312

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	93.81 280	99.94 58	99.98 57

TAPA-MVS	92.12 8	94.42 266	93.60 272	96.90 256	99.33 110	91.78 344	99.78 177	98.00 243	89.89 349	94.52 279	99.47 138	91.97 168	99.18 204	69.90 475	99.52 114	99.73 120
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
reproduce_monomvs		95.38 230	95.07 227	96.32 278	99.32 112	96.60 156	99.76 189	98.85 62	96.65 74	87.83 395	96.05 365	99.52 1	98.11 317	96.58 215	81.07 409	94.25 362

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	97.70 194	99.28 113	95.20 226	99.98 24	97.15 362	95.53 114	99.62 62	99.79 63	92.08 166	98.38 295	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	95.18 227	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	98.59 284

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	85.97 265	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	99.64 236	99.27 27	91.43 300	97.88 177	98.99 205	95.84 46	99.84 138	98.82 101	95.32 284	99.79 112

DCV-MVSNet		97.83 92	97.37 111	99.21 60	99.18 119	97.98 86	99.64 236	99.27 27	91.43 300	97.88 177	98.99 205	95.84 46	99.84 138	98.82 101	95.32 284	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	99.66 231	98.06 237	96.37 89	94.37 285	99.49 137	83.29 315	99.90 113	97.63 174	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	99.24 171	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	97.43 226	99.10 125	94.42 256	99.99 8	97.10 375	95.07 123	99.68 52	99.75 81	92.95 134	98.34 299	98.38 128	99.14 145	99.54 168

Anonymous202405211		93.10 307	91.99 320	96.40 274	99.10 125	89.65 396	98.88 361	97.93 251	83.71 436	94.00 291	98.75 242	68.79 435	99.88 124	95.08 244	91.71 317	99.68 131

fmvsm_s_conf0.5_n		97.80 97	97.85 85	97.67 195	99.06 128	94.41 257	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	98.62 283

HyFIR lowres test		96.66 166	96.43 156	97.36 234	99.05 129	93.91 279	99.70 222	99.80 3	90.54 333	96.26 242	98.08 292	92.15 164	98.23 311	96.84 203	95.46 279	99.93 88

LFMVS		94.75 252	93.56 275	98.30 148	99.03 130	95.70 196	98.74 376	97.98 246	87.81 390	98.47 148	99.39 149	67.43 444	99.53 175	98.01 152	95.20 287	99.67 133

fmvsm_s_conf0.5_n_4		97.75 102	97.86 84	97.42 227	99.01 131	94.69 246	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	97.64 313

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	81.83 328	99.94 94	99.78 35	98.79 162	97.51 321

AllTest		92.48 324	91.64 327	95.00 320	99.01 131	88.43 414	98.94 353	96.82 417	86.50 406	88.71 371	98.47 275	74.73 409	99.88 124	85.39 408	96.18 253	96.71 327

TestCases				95.00 320	99.01 131	88.43 414		96.82 417	86.50 406	88.71 371	98.47 275	74.73 409	99.88 124	85.39 408	96.18 253	96.71 327

COLMAP_ROB	90.47 14	92.18 331	91.49 333	94.25 354	99.00 135	88.04 420	98.42 401	96.70 424	82.30 447	88.43 383	99.01 198	76.97 384	99.85 130	86.11 404	96.50 245	94.86 338
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	84.15 301	99.97 64	99.76 40	99.50 119	98.39 291

test_fmvs1		95.35 231	95.68 197	94.36 350	98.99 136	84.98 442	99.96 56	96.65 426	97.60 34	99.73 47	98.96 211	71.58 425	99.93 104	98.31 134	99.37 134	98.17 296

HY-MVS	92.50 7	97.79 99	97.17 122	99.63 19	98.98 138	99.32 10	97.49 429	99.52 14	95.69 109	98.32 157	97.41 313	93.32 121	99.77 150	98.08 149	95.75 269	99.81 109

VNet		97.21 131	96.57 149	99.13 77	98.97 139	97.82 95	99.03 340	99.21 32	94.31 159	99.18 105	98.88 223	86.26 260	99.89 118	98.93 92	94.32 297	99.69 130

thres200		96.96 145	96.21 166	99.22 59	98.97 139	98.84 38	99.85 147	99.71 7	93.17 213	96.26 242	98.88 223	89.87 203	99.51 178	94.26 268	94.91 289	99.31 217

tfpn200view9		96.79 154	95.99 174	99.19 62	98.94 141	98.82 39	99.78 177	99.71 7	92.86 229	96.02 252	98.87 230	89.33 210	99.50 180	93.84 277	94.57 293	99.27 226

thres400		96.78 156	95.99 174	99.16 69	98.94 141	98.82 39	99.78 177	99.71 7	92.86 229	96.02 252	98.87 230	89.33 210	99.50 180	93.84 277	94.57 293	99.16 238

sasdasda		97.09 138	96.32 160	99.39 46	98.93 143	98.95 29	99.72 210	97.35 321	94.45 147	97.88 177	99.42 142	86.71 251	99.52 176	98.48 123	93.97 303	99.72 122

Anonymous20231211		89.86 382	88.44 390	94.13 362	98.93 143	90.68 374	98.54 392	98.26 207	76.28 470	86.73 409	95.54 380	70.60 431	97.56 343	90.82 334	80.27 418	94.15 378

canonicalmvs		97.09 138	96.32 160	99.39 46	98.93 143	98.95 29	99.72 210	97.35 321	94.45 147	97.88 177	99.42 142	86.71 251	99.52 176	98.48 123	93.97 303	99.72 122

SDMVSNet		94.80 247	93.96 262	97.33 237	98.92 146	95.42 209	99.59 248	98.99 40	92.41 262	92.55 309	97.85 304	75.81 399	98.93 221	97.90 161	91.62 318	97.64 313

sd_testset		93.55 296	92.83 300	95.74 298	98.92 146	90.89 370	98.24 408	98.85 62	92.41 262	92.55 309	97.85 304	71.07 430	98.68 259	93.93 274	91.62 318	97.64 313

EPNet_dtu		95.71 219	95.39 209	96.66 265	98.92 146	93.41 297	99.57 254	98.90 50	96.19 95	97.52 187	98.56 265	92.65 144	97.36 348	77.89 456	98.33 175	99.20 235
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	95.58 278	99.78 115

CHOSEN 1792x2688		96.81 153	96.53 150	97.64 199	98.91 150	93.07 304	99.65 232	99.80 3	95.64 110	95.39 268	98.86 232	84.35 299	99.90 113	96.98 195	99.16 144	99.95 83

thres100view900		96.74 161	95.92 186	99.18 63	98.90 151	98.77 47	99.74 199	99.71 7	92.59 249	95.84 255	98.86 232	89.25 212	99.50 180	93.84 277	94.57 293	99.27 226

thres600view7		96.69 164	95.87 189	99.14 73	98.90 151	98.78 46	99.74 199	99.71 7	92.59 249	95.84 255	98.86 232	89.25 212	99.50 180	93.44 290	94.50 296	99.16 238

MSDG		94.37 268	93.36 287	97.40 230	98.88 153	93.95 278	99.37 292	97.38 316	85.75 417	90.80 328	99.17 180	84.11 303	99.88 124	86.35 400	98.43 173	98.36 293

MGCFI-Net		97.00 143	96.22 165	99.34 51	98.86 154	98.80 41	99.67 230	97.30 333	94.31 159	97.77 183	99.41 146	86.36 258	99.50 180	98.38 128	93.90 305	99.72 122

h-mvs33		94.92 244	94.36 247	96.59 267	98.85 155	91.29 362	98.93 355	98.94 44	95.90 101	98.77 129	98.42 278	90.89 188	99.77 150	97.80 165	70.76 461	98.72 280

Anonymous20240529		92.10 332	90.65 344	96.47 269	98.82 156	90.61 376	98.72 378	98.67 87	75.54 474	93.90 293	98.58 263	66.23 448	99.90 113	94.70 258	90.67 321	98.90 269

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	96.38 240	98.49 271	89.05 216	99.88 124	97.10 190	98.34 174	99.43 194

PS-MVSNAJ		98.44 49	98.20 54	99.16 69	98.80 158	98.92 31	99.54 263	98.17 222	97.34 42	99.85 20	99.85 38	91.20 177	99.89 118	99.41 68	99.67 94	98.69 281

CANet_DTU		96.76 157	96.15 168	98.60 118	98.78 159	97.53 108	99.84 152	97.63 284	97.25 50	99.20 102	99.64 119	81.36 334	99.98 51	92.77 302	98.89 156	98.28 295

mvsany_test1		97.82 95	97.90 80	97.55 210	98.77 160	93.04 307	99.80 172	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	94.40 155	98.41 152	99.47 138	93.65 113	99.42 190	98.57 117	94.26 299	99.67 133

SymmetryMVS		97.64 110	97.46 104	98.17 154	98.74 162	95.39 212	99.61 243	99.26 29	96.52 78	98.61 140	99.31 157	92.73 141	99.67 168	96.77 209	95.63 276	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	99.52 265	98.08 236	97.05 56	99.86 16	99.86 34	90.65 190	99.71 160	99.39 70	98.63 166	98.69 281

miper_enhance_ethall		94.36 270	93.98 261	95.49 301	98.68 165	95.24 223	99.73 206	97.29 341	93.28 208	89.86 342	95.97 366	94.37 88	97.05 371	92.20 306	84.45 380	94.19 370

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	85.99 264	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	91.10 313	98.17 166	98.59 260	93.86 108	98.19 313	95.64 236	95.24 286	99.28 224

test2506		97.53 114	97.19 120	98.58 122	98.66 168	96.90 140	98.81 370	99.77 5	94.93 126	97.95 172	98.96 211	92.51 151	99.20 202	94.93 248	98.15 183	99.64 139

ECVR-MVS		95.66 222	95.05 228	97.51 215	98.66 168	93.71 283	98.85 367	98.45 143	94.93 126	96.86 214	98.96 211	75.22 405	99.20 202	95.34 238	98.15 183	99.64 139

BridgeMVS		98.27 63	97.99 70	99.11 78	98.64 170	98.43 68	99.47 275	97.79 266	94.56 142	99.74 45	98.35 280	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	97.77 188	98.63 171	94.26 264	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	99.08 248

MVSMamba_PlusPlus		97.83 92	97.45 106	98.99 90	98.60 172	98.15 72	99.58 250	97.74 275	90.34 340	99.26 101	98.32 283	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	94.94 235	99.41 283	97.56 296	93.53 194	99.42 86	97.89 303	83.33 314	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	92.53 257	98.84 123	98.84 236	93.36 118	98.30 303	95.84 232	94.30 298	99.05 252

test1111		95.57 225	94.98 231	97.37 232	98.56 174	93.37 300	98.86 365	98.45 143	94.95 125	96.63 222	98.95 216	75.21 406	99.11 208	95.02 245	98.14 185	99.64 139

MVSTER		95.53 226	95.22 220	96.45 272	98.56 174	97.72 99	99.91 111	97.67 280	92.38 265	91.39 319	97.14 320	97.24 20	97.30 355	94.80 254	87.85 350	94.34 357

testing3-2		97.72 106	97.43 109	98.60 118	98.55 177	97.11 131	100.00 1	99.23 31	93.78 186	97.90 174	98.73 244	95.50 53	99.69 164	98.53 121	94.63 291	98.99 260

VDD-MVS		93.77 289	92.94 298	96.27 279	98.55 177	90.22 385	98.77 375	97.79 266	90.85 319	96.82 217	99.42 142	61.18 468	99.77 150	98.95 90	94.13 300	98.82 273

tpmvs		94.28 272	93.57 274	96.40 274	98.55 177	91.50 360	95.70 468	98.55 119	87.47 392	92.15 312	94.26 433	91.42 173	98.95 220	88.15 379	95.85 264	98.76 276

UGNet		95.33 232	94.57 243	97.62 203	98.55 177	94.85 237	98.67 384	99.32 26	95.75 107	96.80 219	96.27 355	72.18 422	99.96 76	94.58 261	99.05 152	98.04 301
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	95.18 235	94.10 255	98.43 140	98.55 177	95.99 184	97.91 422	97.31 332	90.35 339	89.48 355	99.22 172	85.19 281	99.89 118	90.40 344	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		95.95 202	96.49 152	94.34 351	98.51 182	89.99 390	99.39 288	98.57 107	93.14 216	97.33 196	98.31 285	93.44 116	94.68 459	93.69 287	95.98 258	98.34 294

UWE-MVS		96.79 154	96.72 142	97.00 250	98.51 182	93.70 284	99.71 215	98.60 101	92.96 224	97.09 204	98.34 282	96.67 33	98.85 227	92.11 312	96.50 245	98.44 289

myMVS_eth3d28		97.86 88	97.59 100	98.68 110	98.50 184	97.26 121	99.92 103	98.55 119	93.79 185	98.26 161	98.75 242	95.20 58	99.48 186	98.93 92	96.40 248	99.29 222

test_vis1_n_1920		95.44 228	95.31 216	95.82 295	98.50 184	88.74 408	99.98 24	97.30 333	97.84 28	99.85 20	99.19 178	66.82 446	99.97 64	98.82 101	99.46 126	98.76 276

BH-w/o		95.71 219	95.38 214	96.68 264	98.49 186	92.28 326	99.84 152	97.50 305	92.12 275	92.06 315	98.79 240	84.69 293	98.67 261	95.29 240	99.66 95	99.09 246

baseline1		95.78 215	94.86 234	98.54 128	98.47 187	98.07 80	99.06 333	97.99 244	92.68 243	94.13 290	98.62 257	93.28 124	98.69 258	93.79 282	85.76 367	98.84 272

fmvsm_s_conf0.5_n_7		97.70 108	97.74 89	97.59 208	98.44 188	95.16 229	99.97 42	98.65 88	97.95 24	99.62 62	99.78 67	86.09 262	99.94 94	99.69 50	99.50 119	97.66 311

EPMVS		96.53 173	96.01 173	98.09 162	98.43 189	96.12 182	96.36 455	99.43 20	93.53 194	97.64 185	95.04 408	94.41 83	98.38 295	91.13 325	98.11 186	99.75 118

kuosan		93.17 304	92.60 306	94.86 327	98.40 190	89.54 398	98.44 397	98.53 126	84.46 431	88.49 378	97.92 300	90.57 192	97.05 371	83.10 425	93.49 308	97.99 302

WBMVS		94.52 261	94.03 259	95.98 285	98.38 191	96.68 151	99.92 103	97.63 284	90.75 328	89.64 350	95.25 401	96.77 27	96.90 384	94.35 266	83.57 387	94.35 355

UBG		97.84 91	97.69 93	98.29 149	98.38 191	96.59 158	99.90 117	98.53 126	93.91 181	98.52 144	98.42 278	96.77 27	99.17 205	98.54 119	96.20 252	99.11 245

sss		97.57 113	97.03 127	99.18 63	98.37 193	98.04 83	99.73 206	99.38 22	93.46 199	98.76 132	99.06 192	91.21 176	99.89 118	96.33 222	97.01 231	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	93.45 201	98.15 167	98.70 247	95.48 54	99.22 198	97.85 163	95.05 288	99.07 249

BH-untuned		95.18 235	94.83 235	96.22 280	98.36 194	91.22 363	99.80 172	97.32 331	90.91 317	91.08 322	98.67 249	83.51 307	98.54 277	94.23 269	99.61 104	98.92 266

testing91		97.16 133	96.90 131	97.97 168	98.35 196	95.67 199	99.91 111	98.42 168	92.91 227	97.33 196	98.72 245	94.81 72	99.21 199	96.98 195	94.63 291	99.03 257

testing99		97.17 132	96.91 130	97.95 170	98.35 196	95.70 196	99.91 111	98.43 156	92.94 225	97.36 194	98.72 245	94.83 71	99.21 199	97.00 193	94.64 290	98.95 262

ET-MVSNet_ETH3D		94.37 268	93.28 289	97.64 199	98.30 198	97.99 85	99.99 8	97.61 290	94.35 156	71.57 482	99.45 141	96.23 39	95.34 449	96.91 201	85.14 374	99.59 154

AUN-MVS		93.28 301	92.60 306	95.34 310	98.29 199	90.09 388	99.31 302	98.56 113	91.80 288	96.35 241	98.00 295	89.38 209	98.28 306	92.46 303	69.22 467	97.64 313

FMVSNet3		92.69 319	91.58 329	95.99 284	98.29 199	97.42 116	99.26 313	97.62 287	89.80 350	89.68 346	95.32 395	81.62 332	96.27 425	87.01 396	85.65 368	94.29 359

PMMVS		96.76 157	96.76 139	96.76 261	98.28 201	92.10 330	99.91 111	97.98 246	94.12 167	99.53 74	99.39 149	86.93 249	98.73 249	96.95 198	97.73 194	99.45 190

hse-mvs2		94.38 267	94.08 258	95.31 312	98.27 202	90.02 389	99.29 309	98.56 113	95.90 101	98.77 129	98.00 295	90.89 188	98.26 310	97.80 165	69.20 468	97.64 313

PVSNet_0	88.03 19	91.80 339	90.27 353	96.38 276	98.27 202	90.46 380	99.94 93	99.61 13	93.99 175	86.26 419	97.39 315	71.13 429	99.89 118	98.77 105	67.05 474	98.79 275

UA-Net		96.54 172	95.96 180	98.27 150	98.23 204	95.71 195	98.00 420	98.45 143	93.72 190	98.41 152	99.27 164	88.71 223	99.66 171	91.19 324	97.69 195	99.44 193

test_cas_vis1_n_1920		96.59 169	96.23 163	97.65 198	98.22 205	94.23 266	99.99 8	97.25 346	97.77 29	99.58 70	99.08 188	77.10 379	99.97 64	97.64 173	99.45 127	98.74 278

FE-MVS		95.70 221	95.01 230	97.79 184	98.21 206	94.57 248	95.03 469	98.69 82	88.90 366	97.50 189	96.19 357	92.60 147	99.49 185	89.99 349	97.94 192	99.31 217

GG-mvs-BLEND				98.54 128	98.21 206	98.01 84	93.87 474	98.52 128		97.92 173	97.92 300	99.02 3	97.94 330	98.17 142	99.58 109	99.67 133

mvs_anonymous		95.65 223	95.03 229	97.53 212	98.19 208	95.74 193	99.33 297	97.49 306	90.87 318	90.47 331	97.10 322	88.23 226	97.16 362	95.92 230	97.66 198	99.68 131

MVS_Test		96.46 176	95.74 193	98.61 117	98.18 209	97.23 123	99.31 302	97.15 362	91.07 314	98.84 123	97.05 326	88.17 227	98.97 217	94.39 263	97.50 200	99.61 151

BH-RMVSNet		95.18 235	94.31 250	97.80 182	98.17 210	95.23 224	99.76 189	97.53 301	92.52 258	94.27 288	99.25 170	76.84 386	98.80 239	90.89 333	99.54 111	99.35 208

dongtai		91.55 345	91.13 338	92.82 403	98.16 211	86.35 432	99.47 275	98.51 131	83.24 439	85.07 429	97.56 309	90.33 197	94.94 455	76.09 464	91.73 316	97.18 324

RPSCF		91.80 339	92.79 302	88.83 446	98.15 212	69.87 486	98.11 416	96.60 428	83.93 434	94.33 286	99.27 164	79.60 357	99.46 189	91.99 313	93.16 313	97.18 324

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	98.82 231	99.02 89	98.54 170	99.46 185

IS-MVSNet		96.29 189	95.90 187	97.45 222	98.13 214	94.80 241	99.08 328	97.61 290	92.02 280	95.54 266	98.96 211	90.64 191	98.08 319	93.73 285	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	80.88 341	99.93 104	99.59 56	98.17 181	97.29 322

ab-mvs		94.69 253	93.42 280	98.51 133	98.07 217	96.26 170	96.49 453	98.68 84	90.31 341	94.54 278	97.00 329	76.30 394	99.71 160	95.98 229	93.38 311	99.56 163

XVG-OURS-SEG-HR		94.79 248	94.70 242	95.08 317	98.05 218	89.19 400	99.08 328	97.54 299	93.66 191	94.87 275	99.58 128	78.78 365	99.79 145	97.31 181	93.40 310	96.25 331

EIA-MVS		97.53 114	97.46 104	97.76 190	98.04 219	94.84 238	99.98 24	97.61 290	94.41 154	97.90 174	99.59 125	92.40 155	98.87 225	98.04 151	99.13 146	99.59 154

XVG-OURS		94.82 245	94.74 241	95.06 318	98.00 220	89.19 400	99.08 328	97.55 297	94.10 168	94.71 276	99.62 123	80.51 348	99.74 156	96.04 228	93.06 315	96.25 331

mvsmamba		96.94 146	96.73 141	97.55 210	97.99 221	94.37 261	99.62 239	97.70 277	93.13 217	98.42 151	97.92 300	88.02 228	98.75 247	98.78 104	99.01 153	99.52 173

dp		95.05 239	94.43 245	96.91 254	97.99 221	92.73 315	96.29 458	97.98 246	89.70 351	95.93 254	94.67 423	93.83 110	98.45 283	86.91 399	96.53 244	99.54 168

tpmrst		96.27 191	95.98 176	97.13 245	97.96 223	93.15 303	96.34 456	98.17 222	92.07 276	98.71 135	95.12 405	93.91 105	98.73 249	94.91 251	96.62 242	99.50 179

TR-MVS		94.54 258	93.56 275	97.49 220	97.96 223	94.34 262	98.71 379	97.51 304	90.30 342	94.51 280	98.69 248	75.56 400	98.77 243	92.82 301	95.99 257	99.35 208

Vis-MVSNet (Re-imp)		96.32 186	95.98 176	97.35 236	97.93 225	94.82 240	99.47 275	98.15 230	91.83 285	95.09 273	99.11 186	91.37 175	97.47 346	93.47 289	97.43 201	99.74 119

MDTV_nov1_ep13			95.69 195		97.90 226	94.15 270	95.98 464	98.44 148	93.12 218	97.98 171	95.74 370	95.10 61	98.58 270	90.02 348	96.92 233

Fast-Effi-MVS+		95.02 241	94.19 253	97.52 214	97.88 227	94.55 249	99.97 42	97.08 379	88.85 368	94.47 281	97.96 299	84.59 294	98.41 287	89.84 351	97.10 222	99.59 154

ADS-MVSNet2		93.80 288	93.88 265	93.55 386	97.87 228	85.94 436	94.24 470	96.84 414	90.07 345	96.43 237	94.48 428	90.29 199	95.37 448	87.44 386	97.23 212	99.36 204

ADS-MVSNet		94.79 248	94.02 260	97.11 247	97.87 228	93.79 280	94.24 470	98.16 227	90.07 345	96.43 237	94.48 428	90.29 199	98.19 313	87.44 386	97.23 212	99.36 204

Effi-MVS+		96.30 188	95.69 195	98.16 155	97.85 230	96.26 170	97.41 432	97.21 353	90.37 338	98.65 138	98.58 263	86.61 255	98.70 256	97.11 189	97.37 206	99.52 173

PatchmatchNet		95.94 203	95.45 204	97.39 231	97.83 231	94.41 257	96.05 462	98.40 177	92.86 229	97.09 204	95.28 400	94.21 97	98.07 321	89.26 360	98.11 186	99.70 125
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
cascas		94.64 256	93.61 270	97.74 192	97.82 232	96.26 170	99.96 56	97.78 270	85.76 415	94.00 291	97.54 310	76.95 385	99.21 199	97.23 186	95.43 281	97.76 310

1112_ss		96.01 200	95.20 221	98.42 142	97.80 233	96.41 163	99.65 232	96.66 425	92.71 240	92.88 305	99.40 147	92.16 163	99.30 194	91.92 315	93.66 306	99.55 164

E3new		96.75 159	96.43 156	97.71 193	97.79 234	94.83 239	99.80 172	97.33 325	93.52 197	97.49 190	99.31 157	87.73 231	98.83 228	97.52 176	97.40 205	99.48 182

Test_1112_low_res		95.72 217	94.83 235	98.42 142	97.79 234	96.41 163	99.65 232	96.65 426	92.70 241	92.86 306	96.13 361	92.15 164	99.30 194	91.88 316	93.64 307	99.55 164

Effi-MVS+-dtu		94.53 260	95.30 217	92.22 411	97.77 236	82.54 459	99.59 248	97.06 388	94.92 128	95.29 270	95.37 393	85.81 266	97.89 331	94.80 254	97.07 223	96.23 333

tpm cat1		93.51 297	92.52 312	96.47 269	97.77 236	91.47 361	96.13 460	98.06 237	80.98 453	92.91 304	93.78 437	89.66 204	98.87 225	87.03 395	96.39 249	99.09 246

FA-MVS(test-final)		95.86 206	95.09 226	98.15 158	97.74 238	95.62 201	96.31 457	98.17 222	91.42 302	96.26 242	96.13 361	90.56 193	99.47 188	92.18 307	97.07 223	99.35 208

xiu_mvs_v1_base_debu		97.43 117	97.06 123	98.55 124	97.74 238	98.14 74	99.31 302	97.86 260	96.43 83	99.62 62	99.69 105	85.56 273	99.68 165	99.05 82	98.31 176	97.83 306

xiu_mvs_v1_base		97.43 117	97.06 123	98.55 124	97.74 238	98.14 74	99.31 302	97.86 260	96.43 83	99.62 62	99.69 105	85.56 273	99.68 165	99.05 82	98.31 176	97.83 306

xiu_mvs_v1_base_debi		97.43 117	97.06 123	98.55 124	97.74 238	98.14 74	99.31 302	97.86 260	96.43 83	99.62 62	99.69 105	85.56 273	99.68 165	99.05 82	98.31 176	97.83 306

EPP-MVSNet		96.69 164	96.60 147	96.96 252	97.74 238	93.05 306	99.37 292	98.56 113	88.75 370	95.83 257	99.01 198	96.01 40	98.56 273	96.92 199	97.20 214	99.25 230

gg-mvs-nofinetune		93.51 297	91.86 324	98.47 135	97.72 243	97.96 89	92.62 484	98.51 131	74.70 477	97.33 196	69.59 506	98.91 4	97.79 334	97.77 170	99.56 110	99.67 133

IB-MVS	92.85 6	94.99 242	93.94 263	98.16 155	97.72 243	95.69 198	99.99 8	98.81 67	94.28 162	92.70 307	96.90 333	95.08 62	99.17 205	96.07 227	73.88 450	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	91.83 285	97.45 191	99.04 194	97.50 10	99.10 209	94.75 256	96.37 250	99.16 238

VortexMVS		94.11 276	93.50 277	95.94 287	97.70 246	96.61 155	99.35 295	97.18 356	93.52 197	89.57 353	95.74 370	87.55 236	96.97 379	95.76 235	85.13 375	94.23 364

viewdifsd2359ckpt09		96.21 193	95.77 191	97.53 212	97.69 247	94.50 252	99.78 177	97.23 351	92.88 228	96.58 225	99.26 168	84.85 286	98.66 264	96.61 213	97.02 229	99.43 194

Syy-MVS		90.00 380	90.63 345	88.11 453	97.68 248	74.66 483	99.71 215	98.35 190	90.79 325	92.10 313	98.67 249	79.10 363	93.09 475	63.35 490	95.95 261	96.59 329

myMVS_eth3d		94.46 265	94.76 240	93.55 386	97.68 248	90.97 365	99.71 215	98.35 190	90.79 325	92.10 313	98.67 249	92.46 154	93.09 475	87.13 392	95.95 261	96.59 329

test_fmvs1_n		94.25 273	94.36 247	93.92 373	97.68 248	83.70 449	99.90 117	96.57 429	97.40 40	99.67 53	98.88 223	61.82 465	99.92 110	98.23 140	99.13 146	98.14 299

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		96.24 192	95.68 197	97.94 173	97.65 252	94.92 236	99.27 312	97.10 375	92.79 235	97.43 192	97.99 297	81.85 327	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	99.81 167	97.19 354	94.67 140	98.95 118	99.28 161	86.43 256	98.76 245	98.37 130	97.42 203	99.33 211
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		96.59 169	96.23 163	97.66 197	97.63 254	94.70 244	99.77 183	97.33 325	93.41 202	97.34 195	99.17 180	86.72 250	98.83 228	97.40 179	97.32 209	99.46 185

viewdifsd2359ckpt13		96.19 194	95.77 191	97.45 222	97.62 255	94.40 259	99.70 222	97.23 351	92.76 237	96.63 222	99.05 193	84.96 285	98.64 266	96.65 212	97.35 207	99.31 217

Vis-MVSNet		95.72 217	95.15 224	97.45 222	97.62 255	94.28 263	99.28 310	98.24 210	94.27 164	96.84 215	98.94 218	79.39 358	98.76 245	93.25 292	98.49 171	99.30 220
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	91.11 312	97.07 206	98.97 209	97.47 13	99.03 212	93.73 285	96.09 255	98.92 266

GDP-MVS		97.88 86	97.59 100	98.75 106	97.59 258	97.81 96	99.95 75	97.37 319	94.44 150	99.08 110	99.58 128	97.13 25	99.08 210	94.99 246	98.17 181	99.37 202

miper_ehance_all_eth		93.16 305	92.60 306	94.82 328	97.57 259	93.56 292	99.50 269	97.07 387	88.75 370	88.85 370	95.52 382	90.97 184	96.74 395	90.77 335	84.45 380	94.17 372

guyue		97.15 134	96.82 136	98.15 158	97.56 260	96.25 174	99.71 215	97.84 263	95.75 107	98.13 168	98.65 252	87.58 235	98.82 231	98.29 136	97.91 193	99.36 204

viewmanbaseed2359cas		96.45 177	96.07 170	97.59 208	97.55 261	94.59 247	99.70 222	97.33 325	93.62 193	97.00 210	99.32 154	85.57 272	98.71 253	97.26 185	97.33 208	99.47 183

testing3		93.92 282	94.23 252	92.99 400	97.54 262	90.23 384	99.99 8	99.16 33	90.57 332	91.33 321	98.63 256	92.99 132	92.52 479	82.46 429	95.39 282	96.22 334

SSM_0404		95.75 216	95.16 223	97.50 217	97.53 263	95.39 212	99.11 324	97.25 346	90.81 321	95.27 271	98.83 237	84.74 290	98.67 261	95.24 241	97.69 195	98.45 288

LCM-MVSNet-Re		92.31 328	92.60 306	91.43 420	97.53 263	79.27 476	99.02 342	91.83 491	92.07 276	80.31 453	94.38 431	83.50 308	95.48 445	97.22 187	97.58 199	99.54 168

GBi-Net		90.88 356	89.82 362	94.08 364	97.53 263	91.97 331	98.43 398	96.95 402	87.05 398	89.68 346	94.72 419	71.34 426	96.11 431	87.01 396	85.65 368	94.17 372

test1		90.88 356	89.82 362	94.08 364	97.53 263	91.97 331	98.43 398	96.95 402	87.05 398	89.68 346	94.72 419	71.34 426	96.11 431	87.01 396	85.65 368	94.17 372

FMVSNet2		91.02 353	89.56 367	95.41 308	97.53 263	95.74 193	98.98 345	97.41 314	87.05 398	88.43 383	95.00 413	71.34 426	96.24 427	85.12 411	85.21 373	94.25 362

tttt0517		96.85 151	96.49 152	97.92 174	97.48 268	95.89 187	99.85 147	98.54 123	90.72 329	96.63 222	98.93 221	97.47 13	99.02 213	93.03 299	95.76 268	98.85 271

BP-MVS1		98.33 59	98.18 56	98.81 101	97.44 269	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	99.36 204

casdiffmvs_mvg		96.43 178	95.94 184	97.89 178	97.44 269	95.47 205	99.86 144	97.29 341	93.35 204	96.03 250	99.19 178	85.39 277	98.72 252	97.89 162	97.04 226	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		96.36 183	95.95 182	97.60 205	97.41 271	94.52 250	99.71 215	97.33 325	93.20 210	97.02 207	99.07 190	85.37 278	98.82 231	97.27 182	97.14 219	99.46 185

EC-MVSNet		97.38 124	97.24 117	97.80 182	97.41 271	95.64 200	99.99 8	97.06 388	94.59 141	99.63 59	99.32 154	89.20 215	98.14 315	98.76 106	99.23 142	99.62 147

viewdifsd2359ckpt07		95.83 209	95.42 206	97.07 248	97.40 273	93.04 307	99.60 246	97.24 349	92.39 264	96.09 249	99.14 185	83.07 318	98.93 221	97.02 192	96.87 234	99.23 233

c3_l		92.53 323	91.87 323	94.52 340	97.40 273	92.99 309	99.40 284	96.93 407	87.86 388	88.69 373	95.44 387	89.95 202	96.44 413	90.45 341	80.69 414	94.14 382

hybrid		96.53 173	96.15 168	97.67 195	97.39 275	95.12 230	99.80 172	97.15 362	93.38 203	98.23 164	99.16 183	85.20 280	98.70 256	97.92 158	97.15 218	99.20 235

viewmambaseed2359dif		95.92 205	95.55 202	97.04 249	97.38 276	93.41 297	99.78 177	96.97 400	91.14 311	96.58 225	99.27 164	84.85 286	98.75 247	96.87 202	97.12 221	98.97 261

fmvsm_s_conf0.1_n		97.30 125	97.21 119	97.60 205	97.38 276	94.40 259	99.90 117	98.64 91	96.47 82	99.51 78	99.65 118	84.99 284	99.93 104	99.22 76	99.09 149	98.46 287

hybridcas		96.09 197	95.62 199	97.50 217	97.37 278	94.44 253	99.84 152	97.16 360	93.16 214	96.03 250	99.21 175	84.19 300	98.65 265	96.53 217	97.07 223	99.42 197

E3		96.36 183	95.95 182	97.60 205	97.37 278	94.52 250	99.71 215	97.33 325	93.18 212	97.02 207	99.07 190	85.45 276	98.82 231	97.27 182	97.14 219	99.46 185

CDS-MVSNet		96.34 185	96.07 170	97.13 245	97.37 278	94.96 233	99.53 264	97.91 255	91.55 294	95.37 269	98.32 283	95.05 64	97.13 365	93.80 281	95.75 269	99.30 220
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
TESTMET0.1,1		96.74 161	96.26 162	98.16 155	97.36 281	96.48 160	99.96 56	98.29 203	91.93 281	95.77 258	98.07 293	95.54 50	98.29 304	90.55 339	98.89 156	99.70 125

miper_lstm_enhance		91.81 336	91.39 335	93.06 399	97.34 282	89.18 402	99.38 290	96.79 419	86.70 405	87.47 401	95.22 402	90.00 201	95.86 440	88.26 375	81.37 403	94.15 378

baseline		96.43 178	95.98 176	97.76 190	97.34 282	95.17 228	99.51 267	97.17 358	93.92 180	96.90 213	99.28 161	85.37 278	98.64 266	97.50 177	96.86 236	99.46 185

cl____		92.31 328	91.58 329	94.52 340	97.33 284	92.77 311	99.57 254	96.78 420	86.97 402	87.56 399	95.51 383	89.43 208	96.62 402	88.60 365	82.44 395	94.16 377

SD_0403		92.63 322	93.38 284	90.40 434	97.32 285	77.91 478	97.75 427	98.03 242	91.89 282	90.83 327	98.29 287	82.00 324	93.79 468	88.51 370	95.75 269	99.52 173

DIV-MVS_self_test		92.32 327	91.60 328	94.47 344	97.31 286	92.74 313	99.58 250	96.75 421	86.99 401	87.64 397	95.54 380	89.55 207	96.50 408	88.58 366	82.44 395	94.17 372

casdiffmvs		96.42 180	95.97 179	97.77 188	97.30 287	94.98 232	99.84 152	97.09 378	93.75 189	96.58 225	99.26 168	85.07 282	98.78 242	97.77 170	97.04 226	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		94.36 270	93.48 278	96.99 251	97.29 288	93.54 293	99.96 56	96.72 423	88.35 381	93.43 295	98.94 218	82.05 323	98.05 322	88.12 381	96.48 247	99.37 202

eth_miper_zixun_eth		92.41 326	91.93 321	93.84 377	97.28 289	90.68 374	98.83 368	96.97 400	88.57 375	89.19 365	95.73 373	89.24 214	96.69 400	89.97 350	81.55 401	94.15 378

MVSFormer		96.94 146	96.60 147	97.95 170	97.28 289	97.70 102	99.55 261	97.27 343	91.17 308	99.43 84	99.54 134	90.92 185	96.89 385	94.67 259	99.62 99	99.25 230

lupinMVS		97.85 90	97.60 98	98.62 116	97.28 289	97.70 102	99.99 8	97.55 297	95.50 116	99.43 84	99.67 114	90.92 185	98.71 253	98.40 127	99.62 99	99.45 190

dtuplus		95.79 214	95.42 206	96.93 253	97.24 292	93.16 302	99.78 177	96.93 407	91.69 290	96.18 247	99.29 160	83.80 305	98.73 249	96.83 204	97.02 229	98.89 270

diffmvs_AUTHOR		96.75 159	96.41 158	97.79 184	97.20 293	95.46 206	99.69 225	97.15 362	94.46 146	98.78 127	99.21 175	85.64 270	98.77 243	98.27 137	97.31 210	99.13 242

mamba_0408		94.98 243	94.09 256	97.64 199	97.14 294	95.31 217	93.48 480	97.08 379	90.48 334	94.40 282	98.62 257	84.49 295	98.67 261	93.99 272	97.18 215	98.93 263

SSM_04072		94.77 250	94.09 256	96.82 258	97.14 294	95.31 217	93.48 480	97.08 379	90.48 334	94.40 282	98.62 257	84.49 295	96.21 428	93.99 272	97.18 215	98.93 263

SSM_0407		95.62 224	94.95 232	97.61 204	97.14 294	95.31 217	99.00 343	97.25 346	90.81 321	94.40 282	98.83 237	84.74 290	98.58 270	95.24 241	97.18 215	98.93 263

SCA		94.69 253	93.81 267	97.33 237	97.10 297	94.44 253	98.86 365	98.32 197	93.30 207	96.17 248	95.59 378	76.48 392	97.95 328	91.06 327	97.43 201	99.59 154

viewmacassd2359aftdt		95.93 204	95.45 204	97.36 234	97.09 298	94.12 272	99.57 254	97.26 345	93.05 222	96.50 229	99.17 180	82.76 319	98.68 259	96.61 213	97.04 226	99.28 224

KinetiMVS		96.10 195	95.29 218	98.53 130	97.08 299	97.12 129	99.56 258	98.12 233	94.78 133	98.44 149	98.94 218	80.30 352	99.39 191	91.56 320	98.79 162	99.06 250

TAMVS		95.85 207	95.58 200	96.65 266	97.07 300	93.50 294	99.17 320	97.82 265	91.39 304	95.02 274	98.01 294	92.20 162	97.30 355	93.75 284	95.83 265	99.14 241

Fast-Effi-MVS+-dtu		93.72 292	93.86 266	93.29 391	97.06 301	86.16 433	99.80 172	96.83 415	92.66 244	92.58 308	97.83 306	81.39 333	97.67 339	89.75 352	96.87 234	96.05 336

E4		96.01 200	95.53 203	97.44 225	97.05 302	94.23 266	99.57 254	97.30 333	92.72 238	96.47 231	99.03 195	83.98 304	98.83 228	96.92 199	96.77 237	99.27 226

E5new		95.83 209	95.39 209	97.15 241	97.03 303	93.59 287	99.32 300	97.30 333	92.58 251	96.45 232	99.00 202	83.37 311	98.81 235	96.81 205	96.65 240	99.04 253

E5		95.83 209	95.39 209	97.15 241	97.03 303	93.59 287	99.32 300	97.30 333	92.58 251	96.45 232	99.00 202	83.37 311	98.81 235	96.81 205	96.65 240	99.04 253

CostFormer		96.10 195	95.88 188	96.78 260	97.03 303	92.55 321	97.08 441	97.83 264	90.04 347	98.72 134	94.89 417	95.01 66	98.29 304	96.54 216	95.77 267	99.50 179

test_fmvsmvis_n_1920		97.67 109	97.59 100	97.91 176	97.02 306	95.34 215	99.95 75	98.45 143	97.87 26	97.02 207	99.59 125	89.64 205	99.98 51	99.41 68	99.34 137	98.42 290

test-LLR		96.47 175	96.04 172	97.78 186	97.02 306	95.44 207	99.96 56	98.21 217	94.07 170	95.55 264	96.38 350	93.90 106	98.27 308	90.42 342	98.83 160	99.64 139

test-mter		96.39 181	95.93 185	97.78 186	97.02 306	95.44 207	99.96 56	98.21 217	91.81 287	95.55 264	96.38 350	95.17 59	98.27 308	90.42 342	98.83 160	99.64 139

casdiffseed414692147		95.07 238	94.26 251	97.50 217	97.01 309	94.70 244	99.58 250	97.02 392	91.27 306	94.66 277	98.82 239	80.79 343	98.55 276	93.39 291	95.79 266	99.27 226

E6new		95.83 209	95.39 209	97.14 243	97.00 310	93.58 289	99.31 302	97.30 333	92.57 253	96.45 232	99.01 198	83.44 309	98.81 235	96.80 207	96.66 238	99.04 253

E6		95.83 209	95.39 209	97.14 243	97.00 310	93.58 289	99.31 302	97.30 333	92.57 253	96.45 232	99.01 198	83.44 309	98.81 235	96.80 207	96.66 238	99.04 253

icg_test_0407_2		95.04 240	94.78 239	95.84 294	96.97 312	91.64 352	98.63 387	97.12 368	92.33 267	95.60 262	98.88 223	85.65 268	96.56 405	92.12 308	95.70 272	99.32 213

IMVS_0407		95.21 234	94.80 238	96.46 271	96.97 312	91.64 352	98.81 370	97.12 368	92.33 267	95.60 262	98.88 223	85.65 268	98.42 285	92.12 308	95.70 272	99.32 213

IMVS_0404		93.83 284	93.17 291	95.80 296	96.97 312	91.64 352	97.78 426	97.12 368	92.33 267	90.87 326	98.88 223	76.78 387	96.43 414	92.12 308	95.70 272	99.32 213

IMVS_0403		95.25 233	94.81 237	96.58 268	96.97 312	91.64 352	98.97 350	97.12 368	92.33 267	95.43 267	98.88 223	85.78 267	98.79 240	92.12 308	95.70 272	99.32 213

gm-plane-assit					96.97 312	93.76 282			91.47 298		98.96 211		98.79 240	94.92 249

WB-MVSnew		92.90 311	92.77 303	93.26 393	96.95 317	93.63 286	99.71 215	98.16 227	91.49 295	94.28 287	98.14 290	81.33 335	96.48 411	79.47 446	95.46 279	89.68 476

QAPM		95.40 229	94.17 254	99.10 79	96.92 318	97.71 100	99.40 284	98.68 84	89.31 354	88.94 369	98.89 222	82.48 321	99.96 76	93.12 298	99.83 80	99.62 147

KD-MVS_2432*1600		88.00 402	86.10 406	93.70 382	96.91 319	94.04 273	97.17 438	97.12 368	84.93 426	81.96 443	92.41 453	92.48 152	94.51 461	79.23 447	52.68 504	92.56 443

miper_refine_blended		88.00 402	86.10 406	93.70 382	96.91 319	94.04 273	97.17 438	97.12 368	84.93 426	81.96 443	92.41 453	92.48 152	94.51 461	79.23 447	52.68 504	92.56 443

tpm2		95.47 227	95.18 222	96.35 277	96.91 319	91.70 350	96.96 444	97.93 251	88.04 386	98.44 149	95.40 389	93.32 121	97.97 325	94.00 271	95.61 277	99.38 200

FMVSNet5		88.32 398	87.47 400	90.88 423	96.90 322	88.39 416	97.28 435	95.68 451	82.60 446	84.67 431	92.40 455	79.83 355	91.16 484	76.39 463	81.51 402	93.09 434

3Dnovator+	91.53 11	96.31 187	95.24 219	99.52 33	96.88 323	98.64 59	99.72 210	98.24 210	95.27 121	88.42 385	98.98 207	82.76 319	99.94 94	97.10 190	99.83 80	99.96 75

Patchmatch-test		92.65 321	91.50 332	96.10 283	96.85 324	90.49 379	91.50 489	97.19 354	82.76 445	90.23 332	95.59 378	95.02 65	98.00 324	77.41 458	96.98 232	99.82 107

MVS		96.60 168	95.56 201	99.72 14	96.85 324	99.22 21	98.31 404	98.94 44	91.57 293	90.90 325	99.61 124	86.66 254	99.96 76	97.36 180	99.88 76	99.99 26

3Dnovator	91.47 12	96.28 190	95.34 215	99.08 82	96.82 326	97.47 114	99.45 280	98.81 67	95.52 115	89.39 356	99.00 202	81.97 325	99.95 85	97.27 182	99.83 80	99.84 104

EI-MVSNet		93.73 291	93.40 283	94.74 329	96.80 327	92.69 316	99.06 333	97.67 280	88.96 363	91.39 319	99.02 196	88.75 222	97.30 355	91.07 326	87.85 350	94.22 367

CVMVSNet		94.68 255	94.94 233	93.89 376	96.80 327	86.92 430	99.06 333	98.98 41	94.45 147	94.23 289	99.02 196	85.60 271	95.31 450	90.91 332	95.39 282	99.43 194

IterMVS-LS		92.69 319	92.11 317	94.43 348	96.80 327	92.74 313	99.45 280	96.89 411	88.98 361	89.65 349	95.38 392	88.77 221	96.34 421	90.98 330	82.04 398	94.22 367
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
AstraMVS		96.57 171	96.46 155	96.91 254	96.79 330	92.50 322	99.90 117	97.38 316	96.02 99	97.79 182	99.32 154	86.36 258	98.99 214	98.26 138	96.33 251	99.23 233

IterMVS		90.91 355	90.17 357	93.12 396	96.78 331	90.42 382	98.89 359	97.05 391	89.03 358	86.49 414	95.42 388	76.59 390	95.02 452	87.22 391	84.09 383	93.93 405
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
1314		96.84 152	95.96 180	99.48 40	96.74 332	98.52 63	98.31 404	98.86 59	95.82 104	89.91 340	98.98 207	87.49 238	99.96 76	97.80 165	99.73 90	99.96 75

IterMVS-SCA-FT		90.85 358	90.16 358	92.93 401	96.72 333	89.96 391	98.89 359	96.99 396	88.95 364	86.63 411	95.67 374	76.48 392	95.00 453	87.04 394	84.04 386	93.84 412

MVS-HIRNet		86.22 415	83.19 428	95.31 312	96.71 334	90.29 383	92.12 486	97.33 325	62.85 493	86.82 408	70.37 504	69.37 434	97.49 345	75.12 466	97.99 191	98.15 297

viewdifsd2359ckpt11		94.09 278	93.63 269	95.46 305	96.68 335	88.92 405	99.62 239	97.12 368	93.07 220	95.73 259	99.22 172	77.05 380	98.88 224	96.52 218	87.69 355	98.58 285

viewmsd2359difaftdt		94.09 278	93.64 268	95.46 305	96.68 335	88.92 405	99.62 239	97.13 367	93.07 220	95.73 259	99.22 172	77.05 380	98.89 223	96.52 218	87.70 354	98.58 285

VDDNet		93.12 306	91.91 322	96.76 261	96.67 337	92.65 319	98.69 382	98.21 217	82.81 444	97.75 184	99.28 161	61.57 466	99.48 186	98.09 148	94.09 301	98.15 297

dmvs_re		93.20 303	93.15 292	93.34 389	96.54 338	83.81 448	98.71 379	98.51 131	91.39 304	92.37 311	98.56 265	78.66 367	97.83 333	93.89 275	89.74 322	98.38 292

Elysia		94.50 262	93.38 284	97.85 180	96.49 339	96.70 148	98.98 345	97.78 270	90.81 321	96.19 245	98.55 267	73.63 417	98.98 215	89.41 353	98.56 168	97.88 304

StellarMVS		94.50 262	93.38 284	97.85 180	96.49 339	96.70 148	98.98 345	97.78 270	90.81 321	96.19 245	98.55 267	73.63 417	98.98 215	89.41 353	98.56 168	97.88 304

MIMVSNet		90.30 371	88.67 386	95.17 316	96.45 341	91.64 352	92.39 485	97.15 362	85.99 412	90.50 330	93.19 446	66.95 445	94.86 457	82.01 433	93.43 309	99.01 259

CR-MVSNet		93.45 300	92.62 305	95.94 287	96.29 342	92.66 317	92.01 487	96.23 437	92.62 246	96.94 211	93.31 443	91.04 182	96.03 436	79.23 447	95.96 259	99.13 242

RPMNet		89.76 384	87.28 401	97.19 240	96.29 342	92.66 317	92.01 487	98.31 199	70.19 484	96.94 211	85.87 491	87.25 243	99.78 147	62.69 491	95.96 259	99.13 242

tt0805		91.28 348	90.18 356	94.60 335	96.26 344	87.55 423	98.39 402	98.72 78	89.00 360	89.22 362	98.47 275	62.98 461	98.96 219	90.57 338	88.00 349	97.28 323

Patchmtry		89.70 385	88.49 389	93.33 390	96.24 345	89.94 394	91.37 490	96.23 437	78.22 467	87.69 396	93.31 443	91.04 182	96.03 436	80.18 445	82.10 397	94.02 395

test_vis1_rt		86.87 412	86.05 409	89.34 442	96.12 346	78.07 477	99.87 133	83.54 507	92.03 279	78.21 464	89.51 473	45.80 487	99.91 111	96.25 224	93.11 314	90.03 472

JIA-IIPM		91.76 342	90.70 343	94.94 322	96.11 347	87.51 424	93.16 482	98.13 232	75.79 473	97.58 186	77.68 499	92.84 137	97.97 325	88.47 371	96.54 243	99.33 211

OpenMVS	90.15 15	94.77 250	93.59 273	98.33 146	96.07 348	97.48 113	99.56 258	98.57 107	90.46 336	86.51 413	98.95 216	78.57 368	99.94 94	93.86 276	99.74 89	97.57 318

PAPM		98.60 37	98.42 38	99.14 73	96.05 349	98.96 28	99.90 117	99.35 24	96.68 73	98.35 156	99.66 116	96.45 35	98.51 278	99.45 65	99.89 73	99.96 75

CLD-MVS		94.06 281	93.90 264	94.55 339	96.02 350	90.69 373	99.98 24	97.72 276	96.62 77	91.05 324	98.85 235	77.21 378	98.47 279	98.11 146	89.51 328	94.48 343
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
PatchT		90.38 368	88.75 385	95.25 314	95.99 351	90.16 386	91.22 491	97.54 299	76.80 469	97.26 199	86.01 490	91.88 169	96.07 435	66.16 484	95.91 263	99.51 177

ACMH+	89.98 16	90.35 369	89.54 368	92.78 405	95.99 351	86.12 434	98.81 370	97.18 356	89.38 353	83.14 439	97.76 307	68.42 439	98.43 284	89.11 361	86.05 366	93.78 415

DeepMVS_CX				82.92 467	95.98 353	58.66 500		96.01 443	92.72 238	78.34 463	95.51 383	58.29 473	98.08 319	82.57 428	85.29 371	92.03 452

ACMP	92.05 9	92.74 317	92.42 314	93.73 378	95.91 354	88.72 409	99.81 167	97.53 301	94.13 166	87.00 407	98.23 288	74.07 413	98.47 279	96.22 225	88.86 335	93.99 400
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test_vis1_n		93.61 295	93.03 295	95.35 309	95.86 355	86.94 429	99.87 133	96.36 435	96.85 64	99.54 73	98.79 240	52.41 481	99.83 140	98.64 114	98.97 154	99.29 222

HQP-NCC					95.78 356		99.87 133		96.82 66	93.37 296

ACMP_Plane					95.78 356		99.87 133		96.82 66	93.37 296

HQP-MVS		94.61 257	94.50 244	94.92 323	95.78 356	91.85 338	99.87 133	97.89 256	96.82 66	93.37 296	98.65 252	80.65 346	98.39 291	97.92 158	89.60 323	94.53 339

NP-MVS					95.77 359	91.79 342					98.65 252

test_fmvsmconf0.1_n		97.74 103	97.44 107	98.64 115	95.76 360	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					95.76 360	91.72 349						80.47 350

ACMM	91.95 10	92.88 312	92.52 312	93.98 372	95.75 362	89.08 404	99.77 183	97.52 303	93.00 223	89.95 339	97.99 297	76.17 396	98.46 282	93.63 288	88.87 334	94.39 351
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GA-MVS		93.83 284	92.84 299	96.80 259	95.73 363	93.57 291	99.88 130	97.24 349	92.57 253	92.92 303	96.66 342	78.73 366	97.67 339	87.75 384	94.06 302	99.17 237

plane_prior1					95.73 363

jason		97.24 129	96.86 133	98.38 145	95.73 363	97.32 118	99.97 42	97.40 315	95.34 119	98.60 143	99.54 134	87.70 232	98.56 273	97.94 157	99.47 124	99.25 230
jason: jason.
mmtdpeth		88.52 396	87.75 398	90.85 425	95.71 366	83.47 454	98.94 353	94.85 467	88.78 369	97.19 201	89.58 472	63.29 459	98.97 217	98.54 119	62.86 483	90.10 471

HQP_MVS		94.49 264	94.36 247	94.87 324	95.71 366	91.74 345	99.84 152	97.87 258	96.38 86	93.01 301	98.59 260	80.47 350	98.37 297	97.79 168	89.55 326	94.52 341

plane_prior7					95.71 366	91.59 358

ITE_SJBPF				92.38 408	95.69 369	85.14 440		95.71 450	92.81 232	89.33 359	98.11 291	70.23 432	98.42 285	85.91 406	88.16 347	93.59 423

fmvsm_s_conf0.1_n_a		97.09 138	96.90 131	97.63 202	95.65 370	94.21 268	99.83 160	98.50 137	96.27 92	99.65 55	99.64 119	84.72 292	99.93 104	99.04 85	98.84 159	98.74 278

ACMH	89.72 17	90.64 362	89.63 365	93.66 384	95.64 371	88.64 412	98.55 390	97.45 308	89.03 358	81.62 446	97.61 308	69.75 433	98.41 287	89.37 355	87.62 356	93.92 406
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
baseline2		96.71 163	96.49 152	97.37 232	95.63 372	95.96 185	99.74 199	98.88 55	92.94 225	91.61 317	98.97 209	97.72 7	98.62 268	94.83 253	98.08 189	97.53 320

FMVSNet1		88.50 397	86.64 404	94.08 364	95.62 373	91.97 331	98.43 398	96.95 402	83.00 442	86.08 421	94.72 419	59.09 472	96.11 431	81.82 435	84.07 384	94.17 372

LuminaMVS		96.63 167	96.21 166	97.87 179	95.58 374	96.82 142	99.12 322	97.67 280	94.47 145	97.88 177	98.31 285	87.50 237	98.71 253	98.07 150	97.29 211	98.10 300

0.3-1-1-0.015		94.22 274	93.13 294	97.49 220	95.50 375	94.17 269	100.00 1	98.22 213	88.44 379	97.14 203	97.04 328	92.73 141	98.59 269	96.45 220	72.65 455	99.70 125

0.4-1-1-0.2		94.14 275	93.02 296	97.51 215	95.45 376	94.25 265	100.00 1	98.22 213	88.53 376	96.83 216	96.95 331	92.25 160	98.57 272	96.34 221	72.65 455	99.70 125

LPG-MVS_test		92.96 309	92.71 304	93.71 380	95.43 377	88.67 410	99.75 195	97.62 287	92.81 232	90.05 335	98.49 271	75.24 403	98.40 289	95.84 232	89.12 330	94.07 391

LGP-MVS_train				93.71 380	95.43 377	88.67 410		97.62 287	92.81 232	90.05 335	98.49 271	75.24 403	98.40 289	95.84 232	89.12 330	94.07 391

tpm		93.70 293	93.41 282	94.58 337	95.36 379	87.41 425	97.01 442	96.90 410	90.85 319	96.72 221	94.14 434	90.40 196	96.84 389	90.75 336	88.54 342	99.51 177

0.4-1-1-0.1		94.07 280	92.95 297	97.42 227	95.24 380	94.00 276	100.00 1	98.22 213	88.27 383	96.81 218	96.93 332	92.27 159	98.56 273	96.21 226	72.63 457	99.70 125

D2MVS		92.76 316	92.59 310	93.27 392	95.13 381	89.54 398	99.69 225	99.38 22	92.26 272	87.59 398	94.61 425	85.05 283	97.79 334	91.59 319	88.01 348	92.47 447

VPA-MVSNet		92.70 318	91.55 331	96.16 281	95.09 382	96.20 176	98.88 361	99.00 39	91.02 316	91.82 316	95.29 399	76.05 398	97.96 327	95.62 237	81.19 404	94.30 358

LTVRE_ROB	88.28 18	90.29 372	89.05 379	94.02 367	95.08 383	90.15 387	97.19 437	97.43 310	84.91 428	83.99 435	97.06 325	74.00 414	98.28 306	84.08 417	87.71 352	93.62 422
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		87.87 404	86.51 405	91.94 414	95.05 384	85.57 438	97.65 428	94.08 477	84.40 432	81.82 445	96.85 337	62.14 464	98.33 300	80.25 444	86.37 363	91.91 454

test0.0.03 1		93.86 283	93.61 270	94.64 333	95.02 385	92.18 329	99.93 100	98.58 105	94.07 170	87.96 393	98.50 270	93.90 106	94.96 454	81.33 436	93.17 312	96.78 326

UniMVSNet (Re)		93.07 308	92.13 316	95.88 291	94.84 386	96.24 175	99.88 130	98.98 41	92.49 260	89.25 360	95.40 389	87.09 245	97.14 364	93.13 297	78.16 428	94.26 360

USDC		90.00 380	88.96 380	93.10 398	94.81 387	88.16 418	98.71 379	95.54 455	93.66 191	83.75 437	97.20 319	65.58 450	98.31 302	83.96 420	87.49 358	92.85 440

VPNet		91.81 336	90.46 347	95.85 293	94.74 388	95.54 204	98.98 345	98.59 103	92.14 274	90.77 329	97.44 312	68.73 437	97.54 344	94.89 252	77.89 430	94.46 344

FIs		94.10 277	93.43 279	96.11 282	94.70 389	96.82 142	99.58 250	98.93 48	92.54 256	89.34 358	97.31 316	87.62 234	97.10 368	94.22 270	86.58 361	94.40 350

UniMVSNet_ETH3D		90.06 379	88.58 388	94.49 343	94.67 390	88.09 419	97.81 425	97.57 295	83.91 435	88.44 380	97.41 313	57.44 474	97.62 341	91.41 321	88.59 341	97.77 309

UniMVSNet_NR-MVSNet		92.95 310	92.11 317	95.49 301	94.61 391	95.28 221	99.83 160	99.08 36	91.49 295	89.21 363	96.86 336	87.14 244	96.73 396	93.20 293	77.52 433	94.46 344

test_fmvs2		89.47 389	89.70 364	88.77 449	94.54 392	75.74 479	99.83 160	94.70 473	94.71 137	91.08 322	96.82 341	54.46 477	97.78 336	92.87 300	88.27 345	92.80 441

MonoMVSNet		94.82 245	94.43 245	95.98 285	94.54 392	90.73 372	99.03 340	97.06 388	93.16 214	93.15 300	95.47 386	88.29 225	97.57 342	97.85 163	91.33 320	99.62 147

WR-MVS		92.31 328	91.25 336	95.48 304	94.45 394	95.29 220	99.60 246	98.68 84	90.10 344	88.07 392	96.89 334	80.68 345	96.80 393	93.14 296	79.67 421	94.36 352

nrg030		93.51 297	92.53 311	96.45 272	94.36 395	97.20 124	99.81 167	97.16 360	91.60 292	89.86 342	97.46 311	86.37 257	97.68 338	95.88 231	80.31 417	94.46 344

tfpnnormal		89.29 392	87.61 399	94.34 351	94.35 396	94.13 271	98.95 352	98.94 44	83.94 433	84.47 432	95.51 383	74.84 408	97.39 347	77.05 461	80.41 415	91.48 457

FC-MVSNet-test		93.81 287	93.15 292	95.80 296	94.30 397	96.20 176	99.42 282	98.89 52	92.33 267	89.03 368	97.27 318	87.39 240	96.83 391	93.20 293	86.48 362	94.36 352

SSC-MVS3.2		89.59 387	88.66 387	92.38 408	94.29 398	86.12 434	99.49 271	97.66 283	90.28 343	88.63 376	95.18 403	64.46 455	96.88 387	85.30 410	82.66 392	94.14 382

MS-PatchMatch		90.65 361	90.30 352	91.71 419	94.22 399	85.50 439	98.24 408	97.70 277	88.67 372	86.42 416	96.37 352	67.82 442	98.03 323	83.62 422	99.62 99	91.60 455

WR-MVS_H		91.30 346	90.35 350	94.15 358	94.17 400	92.62 320	99.17 320	98.94 44	88.87 367	86.48 415	94.46 430	84.36 298	96.61 403	88.19 377	78.51 426	93.21 432

DU-MVS		92.46 325	91.45 334	95.49 301	94.05 401	95.28 221	99.81 167	98.74 76	92.25 273	89.21 363	96.64 344	81.66 330	96.73 396	93.20 293	77.52 433	94.46 344

NR-MVSNet		91.56 344	90.22 354	95.60 299	94.05 401	95.76 192	98.25 407	98.70 80	91.16 310	80.78 452	96.64 344	83.23 316	96.57 404	91.41 321	77.73 432	94.46 344

CP-MVSNet		91.23 350	90.22 354	94.26 353	93.96 403	92.39 325	99.09 326	98.57 107	88.95 364	86.42 416	96.57 347	79.19 361	96.37 419	90.29 345	78.95 423	94.02 395

XXY-MVS		91.82 335	90.46 347	95.88 291	93.91 404	95.40 211	98.87 364	97.69 279	88.63 374	87.87 394	97.08 323	74.38 412	97.89 331	91.66 318	84.07 384	94.35 355

PS-CasMVS		90.63 363	89.51 370	93.99 370	93.83 405	91.70 350	98.98 345	98.52 128	88.48 377	86.15 420	96.53 349	75.46 401	96.31 424	88.83 363	78.86 425	93.95 403

test_0402		85.58 417	83.94 422	90.50 431	93.81 406	85.04 441	98.55 390	95.20 464	76.01 471	79.72 458	95.13 404	64.15 457	96.26 426	66.04 486	86.88 360	90.21 468

XVG-ACMP-BASELINE		91.22 351	90.75 342	92.63 407	93.73 407	85.61 437	98.52 394	97.44 309	92.77 236	89.90 341	96.85 337	66.64 447	98.39 291	92.29 305	88.61 339	93.89 408

TranMVSNet+NR-MVSNet		91.68 343	90.61 346	94.87 324	93.69 408	93.98 277	99.69 225	98.65 88	91.03 315	88.44 380	96.83 340	80.05 354	96.18 429	90.26 346	76.89 441	94.45 349

TransMVSNet (Re)		87.25 410	85.28 417	93.16 395	93.56 409	91.03 364	98.54 392	94.05 479	83.69 437	81.09 450	96.16 358	75.32 402	96.40 418	76.69 462	68.41 470	92.06 451

v10		90.25 373	88.82 382	94.57 338	93.53 410	93.43 296	99.08 328	96.87 413	85.00 425	87.34 405	94.51 426	80.93 340	97.02 378	82.85 427	79.23 422	93.26 430

testgi		89.01 394	88.04 395	91.90 415	93.49 411	84.89 443	99.73 206	95.66 452	93.89 184	85.14 427	98.17 289	59.68 470	94.66 460	77.73 457	88.88 333	96.16 335

v8		90.54 365	89.17 375	94.66 332	93.43 412	93.40 299	99.20 317	96.94 406	85.76 415	87.56 399	94.51 426	81.96 326	97.19 361	84.94 413	78.25 427	93.38 428

V42		91.28 348	90.12 359	94.74 329	93.42 413	93.46 295	99.68 228	97.02 392	87.36 394	89.85 344	95.05 407	81.31 336	97.34 350	87.34 389	80.07 419	93.40 426

pm-mvs1		89.36 391	87.81 397	94.01 368	93.40 414	91.93 334	98.62 388	96.48 433	86.25 410	83.86 436	96.14 360	73.68 416	97.04 374	86.16 403	75.73 446	93.04 436

v1144		91.09 352	89.83 361	94.87 324	93.25 415	93.69 285	99.62 239	96.98 398	86.83 404	89.64 350	94.99 414	80.94 339	97.05 371	85.08 412	81.16 405	93.87 410

v1192		90.62 364	89.25 374	94.72 331	93.13 416	93.07 304	99.50 269	97.02 392	86.33 409	89.56 354	95.01 411	79.22 360	97.09 370	82.34 431	81.16 405	94.01 397

v2v482		91.30 346	90.07 360	95.01 319	93.13 416	93.79 280	99.77 183	97.02 392	88.05 385	89.25 360	95.37 393	80.73 344	97.15 363	87.28 390	80.04 420	94.09 390

OPM-MVS		93.21 302	92.80 301	94.44 346	93.12 418	90.85 371	99.77 183	97.61 290	96.19 95	91.56 318	98.65 252	75.16 407	98.47 279	93.78 283	89.39 329	93.99 400
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
v144192		90.79 359	89.52 369	94.59 336	93.11 419	92.77 311	99.56 258	96.99 396	86.38 408	89.82 345	94.95 416	80.50 349	97.10 368	83.98 419	80.41 415	93.90 407

PEN-MVS		90.19 375	89.06 378	93.57 385	93.06 420	90.90 369	99.06 333	98.47 140	88.11 384	85.91 422	96.30 354	76.67 388	95.94 439	87.07 393	76.91 440	93.89 408

v1240		90.20 374	88.79 383	94.44 346	93.05 421	92.27 327	99.38 290	96.92 409	85.89 413	89.36 357	94.87 418	77.89 375	97.03 376	80.66 440	81.08 408	94.01 397

usedtu_dtu_shiyan1		92.78 314	91.73 325	95.92 289	93.03 422	96.82 142	99.83 160	97.79 266	90.58 330	90.09 333	95.04 408	84.75 288	96.72 398	88.19 377	86.23 364	94.23 364

FE-MVSNET3		92.78 314	91.73 325	95.92 289	93.03 422	96.82 142	99.83 160	97.79 266	90.58 330	90.09 333	95.04 408	84.75 288	96.72 398	88.20 376	86.23 364	94.23 364

v148		90.70 360	89.63 365	93.92 373	92.97 424	90.97 365	99.75 195	96.89 411	87.51 391	88.27 389	95.01 411	81.67 329	97.04 374	87.40 388	77.17 438	93.75 416

v1921920		90.46 366	89.12 376	94.50 342	92.96 425	92.46 323	99.49 271	96.98 398	86.10 411	89.61 352	95.30 396	78.55 369	97.03 376	82.17 432	80.89 413	94.01 397

MVStest1		85.03 423	82.76 432	91.83 416	92.95 426	89.16 403	98.57 389	94.82 468	71.68 482	68.54 487	95.11 406	83.17 317	95.66 443	74.69 467	65.32 477	90.65 464

tt0320-xc		82.94 438	80.35 445	90.72 429	92.90 427	83.54 452	96.85 447	94.73 471	63.12 492	79.85 457	93.77 438	49.43 485	95.46 446	80.98 439	71.54 459	93.16 433

Baseline_NR-MVSNet		90.33 370	89.51 370	92.81 404	92.84 428	89.95 392	99.77 183	93.94 480	84.69 430	89.04 367	95.66 375	81.66 330	96.52 407	90.99 329	76.98 439	91.97 453

test_method		80.79 444	79.70 447	84.08 463	92.83 429	67.06 490	99.51 267	95.42 457	54.34 500	81.07 451	93.53 440	44.48 488	92.22 481	78.90 452	77.23 437	92.94 438

pmmvs4		92.10 332	91.07 340	95.18 315	92.82 430	94.96 233	99.48 274	96.83 415	87.45 393	88.66 375	96.56 348	83.78 306	96.83 391	89.29 358	84.77 378	93.75 416

LF4IMVS		89.25 393	88.85 381	90.45 433	92.81 431	81.19 469	98.12 415	94.79 469	91.44 299	86.29 418	97.11 321	65.30 453	98.11 317	88.53 368	85.25 372	92.07 450

tt0320		83.56 437	81.15 440	90.77 427	92.77 432	83.58 451	96.83 448	95.52 456	63.26 491	81.36 448	92.54 450	53.26 479	95.77 441	80.45 441	74.38 449	92.96 437

DTE-MVSNet		89.40 390	88.24 393	92.88 402	92.66 433	89.95 392	99.10 325	98.22 213	87.29 395	85.12 428	96.22 356	76.27 395	95.30 451	83.56 423	75.74 445	93.41 425

EU-MVSNet		90.14 377	90.34 351	89.54 441	92.55 434	81.06 470	98.69 382	98.04 240	91.41 303	86.59 412	96.84 339	80.83 342	93.31 473	86.20 402	81.91 399	94.26 360

APD_test1		81.15 442	80.92 442	81.86 468	92.45 435	59.76 499	96.04 463	93.61 484	73.29 480	77.06 467	96.64 344	44.28 489	96.16 430	72.35 471	82.52 393	89.67 477

sc_t1		85.01 424	82.46 434	92.67 406	92.44 436	83.09 455	97.39 433	95.72 449	65.06 489	85.64 425	96.16 358	49.50 484	97.34 350	84.86 414	75.39 447	97.57 318

our_test_3		90.39 367	89.48 372	93.12 396	92.40 437	89.57 397	99.33 297	96.35 436	87.84 389	85.30 426	94.99 414	84.14 302	96.09 434	80.38 442	84.56 379	93.71 421

ppachtmachnet_test		89.58 388	88.35 391	93.25 394	92.40 437	90.44 381	99.33 297	96.73 422	85.49 420	85.90 423	95.77 369	81.09 338	96.00 438	76.00 465	82.49 394	93.30 429

v7n		89.65 386	88.29 392	93.72 379	92.22 439	90.56 378	99.07 332	97.10 375	85.42 422	86.73 409	94.72 419	80.06 353	97.13 365	81.14 437	78.12 429	93.49 424

dmvs_testset		83.79 433	86.07 408	76.94 474	92.14 440	48.60 511	96.75 449	90.27 495	89.48 352	78.65 461	98.55 267	79.25 359	86.65 497	66.85 482	82.69 391	95.57 337

PS-MVSNAJss		93.64 294	93.31 288	94.61 334	92.11 441	92.19 328	99.12 322	97.38 316	92.51 259	88.45 379	96.99 330	91.20 177	97.29 358	94.36 264	87.71 352	94.36 352

pmmvs5		90.17 376	89.09 377	93.40 388	92.10 442	89.77 395	99.74 199	95.58 454	85.88 414	87.24 406	95.74 370	73.41 419	96.48 411	88.54 367	83.56 388	93.95 403

N_pmnet		80.06 447	80.78 443	77.89 473	91.94 443	45.28 515	98.80 373	56.82 518	78.10 468	80.08 455	93.33 441	77.03 382	95.76 442	68.14 479	82.81 390	92.64 442

test_djsdf		92.83 313	92.29 315	94.47 344	91.90 444	92.46 323	99.55 261	97.27 343	91.17 308	89.96 338	96.07 364	81.10 337	96.89 385	94.67 259	88.91 332	94.05 394

SixPastTwentyTwo		88.73 395	88.01 396	90.88 423	91.85 445	82.24 461	98.22 412	95.18 465	88.97 362	82.26 442	96.89 334	71.75 424	96.67 401	84.00 418	82.98 389	93.72 420

K. test v3		88.05 401	87.24 402	90.47 432	91.82 446	82.23 462	98.96 351	97.42 312	89.05 357	76.93 469	95.60 377	68.49 438	95.42 447	85.87 407	81.01 411	93.75 416

OurMVSNet-221017-0		89.81 383	89.48 372	90.83 426	91.64 447	81.21 468	98.17 414	95.38 459	91.48 297	85.65 424	97.31 316	72.66 420	97.29 358	88.15 379	84.83 377	93.97 402

mvs_tets		91.81 336	91.08 339	94.00 369	91.63 448	90.58 377	98.67 384	97.43 310	92.43 261	87.37 404	97.05 326	71.76 423	97.32 353	94.75 256	88.68 338	94.11 389

Gipuma		66.95 465	65.00 465	72.79 480	91.52 449	67.96 487	66.16 515	95.15 466	47.89 502	58.54 495	67.99 511	29.74 496	87.54 496	50.20 502	77.83 431	62.87 510
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_fmvsmconf0.01_n		96.39 181	95.74 193	98.32 147	91.47 450	95.56 203	99.84 152	97.30 333	97.74 30	97.89 176	99.35 153	79.62 356	99.85 130	99.25 75	99.24 141	99.55 164

jajsoiax		91.92 334	91.18 337	94.15 358	91.35 451	90.95 368	99.00 343	97.42 312	92.61 247	87.38 403	97.08 323	72.46 421	97.36 348	94.53 262	88.77 336	94.13 387

MDA-MVSNet-bldmvs		84.09 431	81.52 438	91.81 417	91.32 452	88.00 421	98.67 384	95.92 445	80.22 456	55.60 498	93.32 442	68.29 440	93.60 471	73.76 468	76.61 442	93.82 414

MVP-Stereo		90.93 354	90.45 349	92.37 410	91.25 453	88.76 407	98.05 419	96.17 439	87.27 396	84.04 433	95.30 396	78.46 370	97.27 360	83.78 421	99.70 92	91.09 458
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MDA-MVSNet_test_wron		85.51 419	83.32 427	92.10 412	90.96 454	88.58 413	99.20 317	96.52 431	79.70 458	57.12 497	92.69 449	79.11 362	93.86 467	77.10 460	77.46 435	93.86 411

YYNet1		85.50 420	83.33 426	92.00 413	90.89 455	88.38 417	99.22 316	96.55 430	79.60 459	57.26 496	92.72 448	79.09 364	93.78 469	77.25 459	77.37 436	93.84 412

ALIKED-NN		54.48 474	52.67 476	59.89 496	90.79 456	45.45 513	81.25 508	55.75 522	34.99 511	44.87 508	71.98 502	25.50 504	74.36 510	21.88 520	47.04 506	59.85 512

anonymousdsp		91.79 341	90.92 341	94.41 349	90.76 457	92.93 310	98.93 355	97.17 358	89.08 356	87.46 402	95.30 396	78.43 371	96.92 382	92.38 304	88.73 337	93.39 427

lessismore_v0				90.53 430	90.58 458	80.90 471		95.80 446		77.01 468	95.84 367	66.15 449	96.95 380	83.03 426	75.05 448	93.74 419

EG-PatchMatch MVS		85.35 421	83.81 424	89.99 439	90.39 459	81.89 464	98.21 413	96.09 441	81.78 449	74.73 475	93.72 439	51.56 483	97.12 367	79.16 450	88.61 339	90.96 461

EGC-MVSNET		69.38 458	63.76 468	86.26 459	90.32 460	81.66 467	96.24 459	93.85 481	0.99 537	3.22 538	92.33 460	52.44 480	92.92 477	59.53 496	84.90 376	84.21 492

CMPMVS	61.59 21	84.75 427	85.14 418	83.57 464	90.32 460	62.54 495	96.98 443	97.59 294	74.33 478	69.95 484	96.66 342	64.17 456	98.32 301	87.88 383	88.41 344	89.84 474
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
ALIKED-MNN		52.51 478	50.15 483	59.60 498	90.05 462	44.33 517	81.60 507	54.93 523	32.36 514	40.96 515	68.77 508	20.90 513	75.30 508	20.00 521	41.78 510	59.18 513

new_pmnet		84.49 430	82.92 430	89.21 443	90.03 463	82.60 458	96.89 446	95.62 453	80.59 454	75.77 474	89.17 474	65.04 454	94.79 458	72.12 472	81.02 410	90.23 467

pmmvs6		85.69 416	83.84 423	91.26 422	90.00 464	84.41 446	97.82 424	96.15 440	75.86 472	81.29 449	95.39 391	61.21 467	96.87 388	83.52 424	73.29 451	92.50 446

ttmdpeth		88.23 400	87.06 403	91.75 418	89.91 465	87.35 426	98.92 358	95.73 448	87.92 387	84.02 434	96.31 353	68.23 441	96.84 389	86.33 401	76.12 443	91.06 459

DSMNet-mixed		88.28 399	88.24 393	88.42 451	89.64 466	75.38 482	98.06 418	89.86 496	85.59 419	88.20 391	92.14 462	76.15 397	91.95 482	78.46 454	96.05 256	97.92 303

UnsupCasMVSNet_eth		85.52 418	83.99 420	90.10 437	89.36 467	83.51 453	96.65 450	97.99 244	89.14 355	75.89 473	93.83 436	63.25 460	93.92 465	81.92 434	67.90 473	92.88 439

Anonymous20231206		86.32 414	85.42 416	89.02 445	89.11 468	80.53 474	99.05 337	95.28 460	85.43 421	82.82 440	93.92 435	74.40 411	93.44 472	66.99 481	81.83 400	93.08 435

ALIKED-LG		54.29 475	52.28 477	60.32 492	88.90 469	45.51 512	81.66 506	56.33 519	38.60 504	42.62 513	70.81 503	25.00 506	75.20 509	19.87 522	46.76 508	60.24 511

Anonymous20240521		85.15 422	83.81 424	89.16 444	88.32 470	82.69 457	98.80 373	95.74 447	79.72 457	81.53 447	90.99 465	65.38 452	94.16 463	72.69 470	81.11 407	90.63 465

OpenMVS_ROB	79.82 20	83.77 434	81.68 437	90.03 438	88.30 471	82.82 456	98.46 395	95.22 463	73.92 479	76.00 472	91.29 464	55.00 476	96.94 381	68.40 478	88.51 343	90.34 466

test20.03		84.72 428	83.99 420	86.91 456	88.19 472	80.62 473	98.88 361	95.94 444	88.36 380	78.87 459	94.62 424	68.75 436	89.11 491	66.52 483	75.82 444	91.00 460

RoMa-SfM		74.91 454	72.77 456	81.35 469	88.00 473	67.35 489	93.55 479	86.23 505	68.27 487	66.79 488	92.92 447	30.40 494	87.68 493	66.14 485	62.62 484	89.02 483

gbinet_0.2-2-1-0.02		87.63 409	85.51 415	93.99 370	87.22 474	91.56 359	99.81 167	97.36 320	79.54 460	88.60 377	93.29 445	73.76 415	96.34 421	89.27 359	60.78 492	94.06 393

blend_shiyan4		90.13 378	88.79 383	94.17 355	87.12 475	91.83 340	99.75 195	97.08 379	79.27 465	88.69 373	92.53 451	92.25 160	96.50 408	89.35 356	73.04 453	94.18 371

KD-MVS_self_test		83.59 435	82.06 435	88.20 452	86.93 476	80.70 472	97.21 436	96.38 434	82.87 443	82.49 441	88.97 475	67.63 443	92.32 480	73.75 469	62.30 486	91.58 456

DKM		72.18 456	69.80 459	79.34 472	86.79 477	65.15 491	92.70 483	84.00 506	67.67 488	61.97 491	89.63 471	23.69 509	85.17 499	67.39 480	54.35 502	87.70 487

MIMVSNet1		82.58 439	80.51 444	88.78 447	86.68 478	84.20 447	96.65 450	95.41 458	78.75 466	78.59 462	92.44 452	51.88 482	89.76 489	65.26 487	78.95 423	92.38 449

wanda-best-256-512		87.82 405	85.71 411	94.15 358	86.66 479	91.88 336	99.76 189	97.08 379	79.46 461	88.37 386	92.36 456	78.01 372	96.43 414	88.39 372	61.26 488	94.14 382

FE-blended-shiyan7		87.82 405	85.71 411	94.15 358	86.66 479	91.88 336	99.76 189	97.08 379	79.46 461	88.37 386	92.36 456	78.01 372	96.43 414	88.39 372	61.26 488	94.14 382

usedtu_blend_shiyan5		86.75 413	84.29 419	94.16 356	86.66 479	91.83 340	97.42 430	95.23 462	69.94 485	88.37 386	92.36 456	78.01 372	96.50 408	89.35 356	61.26 488	94.14 382

SP-NN		55.28 473	53.59 475	60.34 491	86.63 482	39.01 522	86.70 500	56.31 520	31.08 516	43.77 511	68.45 509	23.39 510	60.24 515	29.19 515	56.76 499	81.77 497

LoFTR		74.41 455	70.88 458	84.99 462	86.56 483	67.85 488	93.74 475	89.63 498	69.46 486	54.95 499	87.39 485	30.76 493	96.92 382	61.37 493	64.06 480	90.19 469

blended_shiyan8		87.82 405	85.71 411	94.16 356	86.54 484	91.79 342	99.72 210	97.08 379	79.32 463	88.44 380	92.35 459	77.88 376	96.56 405	88.53 368	61.51 487	94.15 378

blended_shiyan6		87.74 408	85.62 414	94.09 363	86.53 485	91.73 348	99.72 210	97.08 379	79.32 463	88.22 390	92.31 461	77.82 377	96.43 414	88.31 374	61.26 488	94.13 387

CL-MVSNet_self_test		84.50 429	83.15 429	88.53 450	86.00 486	81.79 465	98.82 369	97.35 321	85.12 424	83.62 438	90.91 467	76.66 389	91.40 483	69.53 476	60.36 493	92.40 448

MatchFormer		70.84 457	66.72 463	83.19 466	85.99 487	64.61 492	93.58 478	88.62 501	59.32 496	50.64 502	82.31 496	28.00 499	96.79 394	52.52 501	59.50 495	88.18 485

UnsupCasMVSNet_bld		79.97 449	77.03 454	88.78 447	85.62 488	81.98 463	93.66 476	97.35 321	75.51 475	70.79 483	83.05 493	48.70 486	94.91 456	78.31 455	60.29 494	89.46 480

mvs5depth		84.87 425	82.90 431	90.77 427	85.59 489	84.84 444	91.10 492	93.29 486	83.14 440	85.07 429	94.33 432	62.17 463	97.32 353	78.83 453	72.59 458	90.14 470

SP-LightGlue		55.29 471	53.65 474	60.20 493	85.58 490	39.12 521	86.36 503	57.52 517	32.34 515	44.34 510	67.75 512	24.36 507	59.32 518	29.62 513	54.98 500	82.17 495

SP-SuperGlue		55.29 471	53.71 473	60.00 495	85.11 491	38.86 523	86.96 499	57.95 516	32.77 513	44.54 509	68.00 510	23.90 508	59.51 517	29.61 514	54.59 501	81.63 498

SP-MNN		53.97 476	52.04 480	59.73 497	84.72 492	38.63 524	86.51 501	55.94 521	29.25 517	40.20 516	67.48 513	22.18 512	59.59 516	27.79 516	54.33 503	80.98 499

Patchmatch-RL test		86.90 411	85.98 410	89.67 440	84.45 493	75.59 480	89.71 495	92.43 488	86.89 403	77.83 466	90.94 466	94.22 95	93.63 470	87.75 384	69.61 464	99.79 112

pmmvs-eth3d		84.03 432	81.97 436	90.20 435	84.15 494	87.09 428	98.10 417	94.73 471	83.05 441	74.10 479	87.77 482	65.56 451	94.01 464	81.08 438	69.24 466	89.49 479

test_fmvs3		79.99 448	80.17 446	79.45 471	84.02 495	62.83 493	99.05 337	93.49 485	88.29 382	80.06 456	86.65 488	28.09 498	88.00 492	88.63 364	73.27 452	87.54 489

PM-MVS		80.47 445	78.88 449	85.26 460	83.79 496	72.22 484	95.89 466	91.08 493	85.71 418	76.56 471	88.30 478	36.64 492	93.90 466	82.39 430	69.57 465	89.66 478

new-patchmatchnet		81.19 441	79.34 448	86.76 457	82.86 497	80.36 475	97.92 421	95.27 461	82.09 448	72.02 481	86.87 487	62.81 462	90.74 487	71.10 473	63.08 482	89.19 482

FE-MVSNET2		83.57 436	81.36 439	90.20 435	82.83 498	87.59 422	98.28 406	96.04 442	85.33 423	74.13 478	87.45 483	59.16 471	93.26 474	79.12 451	69.91 462	89.77 475

FE-MVSNET		81.05 443	78.81 450	87.79 454	81.98 499	83.70 449	98.23 410	91.78 492	81.27 451	74.29 477	87.44 484	60.92 469	90.67 488	64.92 488	68.43 469	89.01 484

mvsany_test3		82.12 440	81.14 441	85.06 461	81.87 500	70.41 485	97.09 440	92.14 489	91.27 306	77.84 465	88.73 476	39.31 490	95.49 444	90.75 336	71.24 460	89.29 481

WB-MVS		76.28 451	77.28 453	73.29 479	81.18 501	54.68 504	97.87 423	94.19 476	81.30 450	69.43 485	90.70 468	77.02 383	82.06 502	35.71 509	68.11 472	83.13 493

test_f		78.40 450	77.59 452	80.81 470	80.82 502	62.48 496	96.96 444	93.08 487	83.44 438	74.57 476	84.57 492	27.95 500	92.63 478	84.15 416	72.79 454	87.32 490

SSC-MVS		75.42 453	76.40 455	72.49 483	80.68 503	53.62 505	97.42 430	94.06 478	80.42 455	68.75 486	90.14 470	76.54 391	81.66 503	33.25 510	66.34 476	82.19 494

pmmvs3		80.27 446	77.77 451	87.76 455	80.32 504	82.43 460	98.23 410	91.97 490	72.74 481	78.75 460	87.97 481	57.30 475	90.99 486	70.31 474	62.37 485	89.87 473

testf1		68.38 461	66.92 461	72.78 481	78.80 505	50.36 508	90.95 493	87.35 503	55.47 498	58.95 493	88.14 479	20.64 515	87.60 494	57.28 497	64.69 478	80.39 501

APD_test2		68.38 461	66.92 461	72.78 481	78.80 505	50.36 508	90.95 493	87.35 503	55.47 498	58.95 493	88.14 479	20.64 515	87.60 494	57.28 497	64.69 478	80.39 501

ambc				83.23 465	77.17 507	62.61 494	87.38 497	94.55 475		76.72 470	86.65 488	30.16 495	96.36 420	84.85 415	69.86 463	90.73 463

test_vis3_rt		68.82 459	66.69 464	75.21 478	76.24 508	60.41 498	96.44 454	68.71 513	75.13 476	50.54 503	69.52 507	16.42 521	96.32 423	80.27 443	66.92 475	68.89 507

PDCNetPlus		59.83 468	57.26 471	67.55 487	76.18 509	56.71 502	87.01 498	45.27 526	59.54 495	48.80 505	83.01 494	26.63 502	76.54 507	62.12 492	26.78 518	69.40 506

usedtu_dtu_shiyan2		75.87 452	72.37 457	86.39 458	76.18 509	75.49 481	96.53 452	93.82 482	64.74 490	72.53 480	88.48 477	37.67 491	91.12 485	64.13 489	57.22 497	92.56 443

TDRefinement		84.76 426	82.56 433	91.38 421	74.58 511	84.80 445	97.36 434	94.56 474	84.73 429	80.21 454	96.12 363	63.56 458	98.39 291	87.92 382	63.97 481	90.95 462

SIFT-NN		35.94 487	36.54 490	34.16 503	73.93 512	29.52 526	62.74 516	37.28 527	19.65 521	27.91 523	49.19 522	11.66 524	46.35 522	9.19 523	37.30 511	26.61 520

ELoFTR		64.32 467	60.56 470	75.60 477	73.46 513	53.20 506	86.50 502	80.09 509	60.74 494	45.95 507	82.48 495	16.05 522	89.20 490	56.48 500	43.34 509	84.38 491

E-PMN		52.30 479	52.18 479	52.67 499	71.51 514	45.40 514	93.62 477	76.60 511	36.01 508	43.50 512	64.13 516	27.11 501	67.31 513	31.06 511	26.06 519	45.30 519

EMVS		51.44 481	51.22 482	52.11 500	70.71 515	44.97 516	94.04 472	75.66 512	35.34 510	42.40 514	61.56 520	28.93 497	65.87 514	27.64 517	24.73 520	45.49 517

PMMVS2		67.15 464	64.15 467	76.14 476	70.56 516	62.07 497	93.89 473	87.52 502	58.09 497	60.02 492	78.32 498	22.38 511	84.54 500	59.56 495	47.03 507	81.80 496

SIFT-MNN		34.10 488	34.41 491	33.17 505	68.99 517	28.51 527	60.22 518	36.81 528	19.08 524	24.04 525	47.28 525	10.06 528	45.04 523	8.72 524	34.47 513	25.97 523

SIFT-NCM-Cal		31.73 490	31.67 493	31.91 508	67.18 518	27.55 533	58.36 520	33.09 532	18.38 527	14.93 532	45.16 531	8.60 531	43.82 525	7.62 533	31.68 516	24.36 526

SIFT-NN-NCMNet		33.88 489	34.14 492	33.10 506	66.88 519	28.42 528	60.42 517	36.72 529	19.15 522	24.06 524	47.14 526	10.24 526	44.77 524	8.72 524	33.94 515	26.10 522

FPMVS		68.72 460	68.72 460	68.71 485	65.95 520	44.27 518	95.97 465	94.74 470	51.13 501	53.26 500	90.50 469	25.11 505	83.00 501	60.80 494	80.97 412	78.87 503

SP-DiffGlue		56.84 469	55.72 472	60.19 494	65.70 521	40.86 519	81.89 505	60.28 515	34.62 512	50.39 504	76.88 500	26.61 503	58.81 519	48.21 503	56.94 498	80.90 500

wuyk23d		20.37 502	20.84 505	18.99 519	65.34 522	27.73 531	50.43 528	7.67 543	9.50 536	8.01 537	6.34 537	6.13 539	26.24 536	23.40 519	10.69 534	2.99 534

SIFT-ConvMatch		30.09 493	29.76 497	31.09 510	65.16 523	27.56 532	54.13 524	31.17 533	18.55 526	17.88 528	45.89 528	8.40 532	42.26 529	8.11 529	18.51 525	23.46 528

SIFT-CM-Cal		28.34 496	27.90 500	29.63 512	63.75 524	25.98 537	50.66 527	26.18 537	18.12 530	16.88 530	44.64 532	8.08 534	39.70 530	7.65 532	15.19 530	23.22 529

LCM-MVSNet		67.77 463	64.73 466	76.87 475	62.95 525	56.25 503	89.37 496	93.74 483	44.53 503	61.99 490	80.74 497	20.42 517	86.53 498	69.37 477	59.50 495	87.84 486

SIFT-NN-CMatch		31.71 491	31.56 494	32.16 507	62.58 526	27.53 534	56.45 521	33.28 531	19.00 525	23.65 526	47.34 523	10.05 529	42.72 527	8.71 526	22.96 523	26.24 521

SIFT-UM-Cal		27.47 497	27.02 501	28.83 515	62.12 527	24.58 539	53.60 525	23.46 538	18.14 529	12.85 534	45.56 529	7.49 535	39.45 531	7.68 531	12.30 531	22.45 530

SIFT-UMatch		29.40 495	28.87 499	30.98 511	62.08 528	26.57 536	56.09 522	29.45 535	18.31 528	15.86 531	46.00 527	8.23 533	42.54 528	7.99 530	15.81 528	23.85 527

GLUNet-SfM		51.10 482	46.61 485	64.56 488	61.54 529	39.88 520	79.38 511	65.13 514	36.09 507	33.36 520	69.94 505	14.50 523	78.76 505	42.46 507	17.10 527	75.02 505

SIFT-NN-UMatch		31.23 492	31.05 496	31.79 509	60.08 530	27.23 535	58.49 519	33.65 530	19.14 523	17.30 529	47.31 524	10.12 527	42.88 526	8.67 527	24.67 521	25.27 524

XFeat-NN		42.54 483	42.87 487	41.54 502	59.73 531	27.86 530	69.53 513	45.34 525	24.36 518	37.16 517	64.79 514	20.84 514	51.40 521	30.01 512	34.12 514	45.36 518

MVE	53.74 22	51.54 480	47.86 484	62.60 489	59.56 532	50.93 507	79.41 510	77.69 510	35.69 509	36.27 518	61.76 519	5.79 540	69.63 511	37.97 508	36.61 512	67.24 508
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
SIFT-NN-PointCN		29.63 494	29.72 498	29.36 513	57.55 533	23.55 540	56.07 523	30.57 534	17.99 531	20.99 527	45.21 530	9.94 530	39.33 532	8.40 528	20.81 524	25.20 525

SIFT-PointCN		25.49 498	25.71 502	24.84 516	56.17 534	18.65 541	51.37 526	26.53 536	16.31 532	12.78 535	39.87 535	6.41 538	34.09 534	6.51 535	15.42 529	21.77 531

SIFT-PCN-Cal		24.67 499	24.81 503	24.24 517	56.13 535	18.04 542	49.05 529	23.39 539	16.07 533	12.99 533	40.17 534	6.97 537	34.68 533	6.71 534	11.81 532	19.99 532

XFeat-MNN		41.51 484	41.24 488	42.32 501	55.40 536	28.19 529	69.39 514	46.53 524	23.57 519	34.47 519	63.21 518	20.04 518	52.41 520	27.43 518	31.08 517	46.37 516

SIFT-NCMNet		21.21 501	21.22 504	21.17 518	52.99 537	16.41 543	42.12 530	14.05 541	15.89 534	10.70 536	35.85 536	5.14 541	29.82 535	5.80 536	8.44 535	17.28 533

ANet_high		56.10 470	52.24 478	67.66 486	49.27 538	56.82 501	83.94 504	82.02 508	70.47 483	33.28 521	64.54 515	17.23 520	69.16 512	45.59 505	23.85 522	77.02 504

tmp_tt		65.23 466	62.94 469	72.13 484	44.90 539	50.03 510	81.05 509	89.42 500	38.45 505	48.51 506	99.90 23	54.09 478	78.70 506	91.84 317	18.26 526	87.64 488

PMVS	49.05 23	53.75 477	51.34 481	60.97 490	40.80 540	34.68 525	74.82 512	89.62 499	37.55 506	28.67 522	72.12 501	7.09 536	81.63 504	43.17 506	68.21 471	66.59 509
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test123		37.68 486	39.14 489	33.31 504	19.94 541	24.83 538	98.36 403	9.75 542	15.53 535	51.31 501	87.14 486	19.62 519	17.74 537	47.10 504	3.47 536	57.36 514

testmvs		40.60 485	44.45 486	29.05 514	19.49 542	14.11 544	99.68 228	18.47 540	20.74 520	64.59 489	98.48 274	10.95 525	17.09 538	56.66 499	11.01 533	55.94 515

mmdepth		0.00 505	0.00 508	0.00 520	0.00 543	0.00 545	0.00 531	0.00 544	0.00 538	0.00 539	0.00 539	0.00 542	0.00 539	0.00 537	0.00 537	0.00 535

monomultidepth		0.00 505	0.00 508	0.00 520	0.00 543	0.00 545	0.00 531	0.00 544	0.00 538	0.00 539	0.00 539	0.00 542	0.00 539	0.00 537	0.00 537	0.00 535

test_blank		0.00 505	0.00 508	0.00 520	0.00 543	0.00 545	0.00 531	0.00 544	0.00 538	0.00 539	0.02 538	0.00 542	0.00 539	0.00 537	0.00 537	0.00 535

eth-test2					0.00 543

eth-test					0.00 543

uanet_test		0.00 505	0.00 508	0.00 520	0.00 543	0.00 545	0.00 531	0.00 544	0.00 538	0.00 539	0.00 539	0.00 542	0.00 539	0.00 537	0.00 537	0.00 535

DCPMVS		0.00 505	0.00 508	0.00 520	0.00 543	0.00 545	0.00 531	0.00 544	0.00 538	0.00 539	0.00 539	0.00 542	0.00 539	0.00 537	0.00 537	0.00 535

cdsmvs_eth3d_5k		23.43 500	31.24 495	0.00 520	0.00 543	0.00 545	0.00 531	98.09 234	0.00 538	0.00 539	99.67 114	83.37 311	0.00 539	0.00 537	0.00 537	0.00 535

pcd_1.5k_mvsjas		7.60 504	10.13 507	0.00 520	0.00 543	0.00 545	0.00 531	0.00 544	0.00 538	0.00 539	0.00 539	91.20 177	0.00 539	0.00 537	0.00 537	0.00 535

sosnet-low-res		0.00 505	0.00 508	0.00 520	0.00 543	0.00 545	0.00 531	0.00 544	0.00 538	0.00 539	0.00 539	0.00 542	0.00 539	0.00 537	0.00 537	0.00 535

sosnet		0.00 505	0.00 508	0.00 520	0.00 543	0.00 545	0.00 531	0.00 544	0.00 538	0.00 539	0.00 539	0.00 542	0.00 539	0.00 537	0.00 537	0.00 535

uncertanet		0.00 505	0.00 508	0.00 520	0.00 543	0.00 545	0.00 531	0.00 544	0.00 538	0.00 539	0.00 539	0.00 542	0.00 539	0.00 537	0.00 537	0.00 535

Regformer		0.00 505	0.00 508	0.00 520	0.00 543	0.00 545	0.00 531	0.00 544	0.00 538	0.00 539	0.00 539	0.00 542	0.00 539	0.00 537	0.00 537	0.00 535

ab-mvs-re		8.28 503	11.04 506	0.00 520	0.00 543	0.00 545	0.00 531	0.00 544	0.00 538	0.00 539	99.40 147	0.00 542	0.00 539	0.00 537	0.00 537	0.00 535

uanet		0.00 505	0.00 508	0.00 520	0.00 543	0.00 545	0.00 531	0.00 544	0.00 538	0.00 539	0.00 539	0.00 542	0.00 539	0.00 537	0.00 537	0.00 535

WAC-MVS						90.97 365								86.10 405

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							95.78 467				59.23 521	93.20 128	97.74 337	91.06 327

test_post											63.35 517	94.43 82	98.13 316

patchmatchnet-post											91.70 463	95.12 60	97.95 328

MTMP							99.87 133	96.49 432

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							99.46 279		94.21 165	99.85 20			99.95 85	96.96 197

新几何2							99.40 284

无先验							99.49 271	98.71 79	93.46 199				100.00 1	94.36 264		99.99 26

原ACMM2							99.90 117

testdata2													99.99 39	90.54 340

segment_acmp												96.68 31

testdata1							99.28 310		96.35 91

plane_prior5								97.87 258					98.37 297	97.79 168	89.55 326	94.52 341

plane_prior4											98.59 260

plane_prior3						91.64 352			96.63 75	93.01 301

plane_prior2							99.84 152		96.38 86

plane_prior						91.74 345	99.86 144		96.76 70						89.59 325

n2								0.00 544

nn								0.00 544

door-mid								89.69 497

test11								98.44 148

door								90.31 494

HQP5-MVS						91.85 338

BP-MVS														97.92 158

HQP4-MVS										93.37 296			98.39 291			94.53 339

HQP3-MVS								97.89 256							89.60 323

HQP2-MVS												80.65 346

MDTV_nov1_ep13_2view						96.26 170	96.11 461		91.89 282	98.06 169		94.40 84		94.30 267		99.67 133

ACMMP++_ref															87.04 359

ACMMP++															88.23 346

Test By Simon												92.82 139

High-resolution multi-view benchmark