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	99.09 325	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	89.90 347	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		93.83 283	92.06 318	99.15 71	99.94 17	97.50 111	99.94 93	98.42 168	96.22 93	99.41 87	41.37 532	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	99.64 235	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	99.75 194	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	99.18 318	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	99.24 313	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	91.92 334	99.90 117	99.07 37	88.67 371	95.26 271	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	99.61 242	97.78 270	96.52 78	98.61 140	99.31 157	92.73 141	99.67 168	96.77 208	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	90.78 326	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	99.39 287	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	95.11 224	98.02 167	99.85 61	95.10 231	98.74 375	98.50 137	87.22 396	93.66 293	99.86 34	87.45 239	99.95 85	90.94 330	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	99.40 283	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	99.27 163	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	99.36 293	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	99.76 188	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	99.76 188	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	99.65 231	97.95 249	96.03 98	97.41 193	99.70 101	89.61 206	99.51 178	96.73 210	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	91.21 322	99.94 58	99.99 26

MP-MVS-pluss		98.07 78	97.64 96	99.38 49	99.74 77	98.41 69	99.74 198	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	99.77 182	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	95.11 242	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	99.73 205	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	96.87 256	99.71 83	91.74 344	99.85 147	97.95 249	93.11 219	95.72 260	99.16 182	92.35 156	99.94 94	95.32 238	99.35 136	98.92 266

reproduce-ours		98.78 27	98.67 24	99.09 80	99.70 85	97.30 119	99.74 198	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	99.74 198	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	95.59 299	99.67 88	86.91 430	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	93.92 372	99.63 90	81.76 465	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	96.72 262	99.59 92	96.99 136	99.95 75	99.10 34	94.06 172	98.27 159	95.80 367	89.00 218	99.95 85	99.12 79	87.53 356	93.24 430

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	99.08 187	89.00 218	99.95 85	99.12 79	99.25 140	99.57 162

PatchMatch-RL		96.04 199	95.40 207	97.95 170	99.59 92	95.22 225	99.52 264	99.07 37	93.96 177	96.49 230	98.35 279	82.28 321	99.82 142	90.15 346	99.22 143	98.81 273

dcpmvs_2		97.42 121	98.09 63	95.42 306	99.58 96	87.24 426	99.23 314	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	99.34 295	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	99.47 274	98.87 58	91.68 290	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	99.21 174	84.48 297	99.95 85	94.92 248	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	88.48 376	99.06 114	99.66 116	90.30 198	99.64 173	96.32 222	99.97 42	99.96 75

cl22		93.77 288	93.25 289	95.33 310	99.49 102	94.43 255	99.61 242	98.09 234	90.38 336	89.16 365	95.61 375	90.56 193	97.34 349	91.93 313	84.45 379	94.21 368

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	94.97 246	99.94 58	100.00 1

MAR-MVS		97.43 117	97.19 120	98.15 158	99.47 103	94.79 242	99.05 336	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		94.54 257	93.42 279	97.91 176	99.46 105	94.04 273	98.93 354	97.48 307	81.15 451	90.04 336	99.55 132	87.02 247	99.95 85	88.97 361	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	98.46 394	99.42 21	97.03 57	99.02 116	99.09 186	99.35 2	98.21 311	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	97.65 311

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	93.81 279	99.94 58	99.98 57

TAPA-MVS	92.12 8	94.42 265	93.60 271	96.90 255	99.33 110	91.78 343	99.78 177	98.00 243	89.89 348	94.52 278	99.47 138	91.97 168	99.18 204	69.90 474	99.52 114	99.73 120
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
reproduce_monomvs		95.38 229	95.07 226	96.32 277	99.32 112	96.60 156	99.76 188	98.85 62	96.65 74	87.83 394	96.05 364	99.52 1	98.11 316	96.58 214	81.07 408	94.25 361

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	98.38 294	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	98.59 283

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	99.64 235	99.27 27	91.43 299	97.88 177	98.99 204	95.84 46	99.84 138	98.82 101	95.32 283	99.79 112

DCV-MVSNet		97.83 92	97.37 111	99.21 60	99.18 119	97.98 86	99.64 235	99.27 27	91.43 299	97.88 177	98.99 204	95.84 46	99.84 138	98.82 101	95.32 283	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	99.66 230	98.06 237	96.37 89	94.37 284	99.49 137	83.29 314	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	99.24 170	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	98.34 298	98.38 128	99.14 145	99.54 168

Anonymous202405211		93.10 306	91.99 319	96.40 273	99.10 125	89.65 395	98.88 360	97.93 251	83.71 435	94.00 290	98.75 241	68.79 434	99.88 124	95.08 243	91.71 316	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	98.62 282

HyFIR lowres test		96.66 166	96.43 156	97.36 234	99.05 129	93.91 279	99.70 221	99.80 3	90.54 332	96.26 242	98.08 291	92.15 164	98.23 310	96.84 203	95.46 278	99.93 88

LFMVS		94.75 251	93.56 274	98.30 148	99.03 130	95.70 196	98.74 375	97.98 246	87.81 389	98.47 148	99.39 149	67.43 443	99.53 175	98.01 152	95.20 286	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	97.64 312

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	81.83 327	99.94 94	99.78 35	98.79 162	97.51 320

AllTest		92.48 323	91.64 326	95.00 319	99.01 131	88.43 413	98.94 352	96.82 416	86.50 405	88.71 370	98.47 274	74.73 408	99.88 124	85.39 407	96.18 252	96.71 326

TestCases				95.00 319	99.01 131	88.43 413		96.82 416	86.50 405	88.71 370	98.47 274	74.73 408	99.88 124	85.39 407	96.18 252	96.71 326

COLMAP_ROB	90.47 14	92.18 330	91.49 332	94.25 353	99.00 135	88.04 419	98.42 400	96.70 423	82.30 446	88.43 382	99.01 197	76.97 383	99.85 130	86.11 403	96.50 244	94.86 337
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	98.39 290

test_fmvs1		95.35 230	95.68 197	94.36 349	98.99 136	84.98 441	99.96 56	96.65 425	97.60 34	99.73 47	98.96 210	71.58 424	99.93 104	98.31 134	99.37 134	98.17 295

HY-MVS	92.50 7	97.79 99	97.17 122	99.63 19	98.98 138	99.32 10	97.49 428	99.52 14	95.69 109	98.32 157	97.41 312	93.32 121	99.77 150	98.08 149	95.75 268	99.81 109

VNet		97.21 131	96.57 149	99.13 77	98.97 139	97.82 95	99.03 339	99.21 32	94.31 159	99.18 105	98.88 222	86.26 260	99.89 118	98.93 92	94.32 296	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	98.88 222	89.87 203	99.51 178	94.26 267	94.91 288	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	96.02 251	98.87 229	89.33 210	99.50 180	93.84 276	94.57 292	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	96.02 251	98.87 229	89.33 210	99.50 180	93.84 276	94.57 292	99.16 238

sasdasda		97.09 138	96.32 160	99.39 46	98.93 143	98.95 29	99.72 209	97.35 321	94.45 147	97.88 177	99.42 142	86.71 251	99.52 176	98.48 123	93.97 302	99.72 122

Anonymous20231211		89.86 381	88.44 389	94.13 361	98.93 143	90.68 373	98.54 391	98.26 207	76.28 469	86.73 408	95.54 379	70.60 430	97.56 342	90.82 333	80.27 417	94.15 377

canonicalmvs		97.09 138	96.32 160	99.39 46	98.93 143	98.95 29	99.72 209	97.35 321	94.45 147	97.88 177	99.42 142	86.71 251	99.52 176	98.48 123	93.97 302	99.72 122

SDMVSNet		94.80 246	93.96 261	97.33 237	98.92 146	95.42 209	99.59 247	98.99 40	92.41 262	92.55 308	97.85 303	75.81 398	98.93 221	97.90 161	91.62 317	97.64 312

sd_testset		93.55 295	92.83 299	95.74 297	98.92 146	90.89 369	98.24 407	98.85 62	92.41 262	92.55 308	97.85 303	71.07 429	98.68 258	93.93 273	91.62 317	97.64 312

EPNet_dtu		95.71 218	95.39 208	96.66 264	98.92 146	93.41 297	99.57 253	98.90 50	96.19 95	97.52 187	98.56 264	92.65 144	97.36 347	77.89 455	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	95.58 277	99.78 115

CHOSEN 1792x2688		96.81 153	96.53 150	97.64 199	98.91 150	93.07 303	99.65 231	99.80 3	95.64 110	95.39 267	98.86 231	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	99.74 198	99.71 7	92.59 249	95.84 254	98.86 231	89.25 212	99.50 180	93.84 276	94.57 292	99.27 226

thres600view7		96.69 164	95.87 189	99.14 73	98.90 151	98.78 46	99.74 198	99.71 7	92.59 249	95.84 254	98.86 231	89.25 212	99.50 180	93.44 289	94.50 295	99.16 238

MSDG		94.37 267	93.36 286	97.40 230	98.88 153	93.95 278	99.37 291	97.38 316	85.75 416	90.80 327	99.17 179	84.11 303	99.88 124	86.35 399	98.43 173	98.36 292

MGCFI-Net		97.00 143	96.22 165	99.34 51	98.86 154	98.80 41	99.67 229	97.30 333	94.31 159	97.77 183	99.41 146	86.36 258	99.50 180	98.38 128	93.90 304	99.72 122

h-mvs33		94.92 243	94.36 246	96.59 266	98.85 155	91.29 361	98.93 354	98.94 44	95.90 101	98.77 129	98.42 277	90.89 188	99.77 150	97.80 165	70.76 460	98.72 279

Anonymous20240529		92.10 331	90.65 343	96.47 268	98.82 156	90.61 375	98.72 377	98.67 87	75.54 473	93.90 292	98.58 262	66.23 447	99.90 113	94.70 257	90.67 320	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	98.49 270	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	99.54 262	98.17 222	97.34 42	99.85 20	99.85 38	91.20 177	99.89 118	99.41 68	99.67 94	98.69 280

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	81.36 333	99.98 51	92.77 301	98.89 156	98.28 294

mvsany_test1		97.82 95	97.90 80	97.55 210	98.77 160	93.04 306	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	94.26 298	99.67 133

SymmetryMVS		97.64 110	97.46 104	98.17 154	98.74 162	95.39 212	99.61 242	99.26 29	96.52 78	98.61 140	99.31 157	92.73 141	99.67 168	96.77 208	95.63 275	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	99.52 264	98.08 236	97.05 56	99.86 16	99.86 34	90.65 190	99.71 160	99.39 70	98.63 166	98.69 280

miper_enhance_ethall		94.36 269	93.98 260	95.49 300	98.68 165	95.24 223	99.73 205	97.29 341	93.28 208	89.86 341	95.97 365	94.37 88	97.05 370	92.20 305	84.45 379	94.19 369

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	91.10 312	98.17 166	98.59 259	93.86 108	98.19 312	95.64 235	95.24 285	99.28 224

test2506		97.53 114	97.19 120	98.58 122	98.66 168	96.90 140	98.81 369	99.77 5	94.93 126	97.95 172	98.96 210	92.51 151	99.20 202	94.93 247	98.15 183	99.64 139

ECVR-MVS		95.66 221	95.05 227	97.51 215	98.66 168	93.71 283	98.85 366	98.45 143	94.93 126	96.86 214	98.96 210	75.22 404	99.20 202	95.34 237	98.15 183	99.64 139

BridgeMVS		98.27 63	97.99 70	99.11 78	98.64 170	98.43 68	99.47 274	97.79 266	94.56 142	99.74 45	98.35 279	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	99.58 249	97.74 275	90.34 339	99.26 101	98.32 282	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	99.41 282	97.56 296	93.53 194	99.42 86	97.89 302	83.33 313	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	98.84 235	93.36 118	98.30 302	95.84 231	94.30 297	99.05 252

test1111		95.57 224	94.98 230	97.37 232	98.56 174	93.37 300	98.86 364	98.45 143	94.95 125	96.63 222	98.95 215	75.21 405	99.11 208	95.02 244	98.14 185	99.64 139

MVSTER		95.53 225	95.22 219	96.45 271	98.56 174	97.72 99	99.91 111	97.67 280	92.38 265	91.39 318	97.14 319	97.24 20	97.30 354	94.80 253	87.85 349	94.34 356

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	98.73 243	95.50 53	99.69 164	98.53 121	94.63 290	98.99 260

VDD-MVS		93.77 288	92.94 297	96.27 278	98.55 177	90.22 384	98.77 374	97.79 266	90.85 318	96.82 217	99.42 142	61.18 467	99.77 150	98.95 90	94.13 299	98.82 272

tpmvs		94.28 271	93.57 273	96.40 273	98.55 177	91.50 359	95.70 467	98.55 119	87.47 391	92.15 311	94.26 432	91.42 173	98.95 220	88.15 378	95.85 263	98.76 275

UGNet		95.33 231	94.57 242	97.62 203	98.55 177	94.85 237	98.67 383	99.32 26	95.75 107	96.80 219	96.27 354	72.18 421	99.96 76	94.58 260	99.05 152	98.04 300
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	95.18 234	94.10 254	98.43 140	98.55 177	95.99 184	97.91 421	97.31 332	90.35 338	89.48 354	99.22 171	85.19 281	99.89 118	90.40 343	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	94.34 350	98.51 182	89.99 389	99.39 287	98.57 107	93.14 216	97.33 196	98.31 284	93.44 116	94.68 458	93.69 286	95.98 257	98.34 293

UWE-MVS		96.79 154	96.72 142	97.00 250	98.51 182	93.70 284	99.71 214	98.60 101	92.96 224	97.09 204	98.34 281	96.67 33	98.85 227	92.11 311	96.50 244	98.44 288

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	98.75 241	95.20 58	99.48 186	98.93 92	96.40 247	99.29 222

test_vis1_n_1920		95.44 227	95.31 215	95.82 294	98.50 184	88.74 407	99.98 24	97.30 333	97.84 28	99.85 20	99.19 177	66.82 445	99.97 64	98.82 101	99.46 126	98.76 275

BH-w/o		95.71 218	95.38 213	96.68 263	98.49 186	92.28 325	99.84 152	97.50 305	92.12 275	92.06 314	98.79 239	84.69 293	98.67 260	95.29 239	99.66 95	99.09 246

baseline1		95.78 214	94.86 233	98.54 128	98.47 187	98.07 80	99.06 332	97.99 244	92.68 243	94.13 289	98.62 256	93.28 124	98.69 257	93.79 281	85.76 366	98.84 271

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	97.66 310

EPMVS		96.53 173	96.01 173	98.09 162	98.43 189	96.12 182	96.36 454	99.43 20	93.53 194	97.64 185	95.04 407	94.41 83	98.38 294	91.13 324	98.11 186	99.75 118

kuosan		93.17 303	92.60 305	94.86 326	98.40 190	89.54 397	98.44 396	98.53 126	84.46 430	88.49 377	97.92 299	90.57 192	97.05 370	83.10 424	93.49 307	97.99 301

WBMVS		94.52 260	94.03 258	95.98 284	98.38 191	96.68 151	99.92 103	97.63 284	90.75 327	89.64 349	95.25 400	96.77 27	96.90 383	94.35 265	83.57 386	94.35 354

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	98.42 277	96.77 27	99.17 205	98.54 119	96.20 251	99.11 245

sss		97.57 113	97.03 127	99.18 63	98.37 193	98.04 83	99.73 205	99.38 22	93.46 199	98.76 132	99.06 191	91.21 176	99.89 118	96.33 221	97.01 230	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	98.70 246	95.48 54	99.22 198	97.85 163	95.05 287	99.07 249

BH-untuned		95.18 234	94.83 234	96.22 279	98.36 194	91.22 362	99.80 172	97.32 331	90.91 316	91.08 321	98.67 248	83.51 306	98.54 276	94.23 268	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	98.72 244	94.81 72	99.21 199	96.98 195	94.63 290	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	98.72 244	94.83 71	99.21 199	97.00 193	94.64 289	98.95 262

ET-MVSNet_ETH3D		94.37 267	93.28 288	97.64 199	98.30 198	97.99 85	99.99 8	97.61 290	94.35 156	71.57 481	99.45 141	96.23 39	95.34 448	96.91 201	85.14 373	99.59 154

AUN-MVS		93.28 300	92.60 305	95.34 309	98.29 199	90.09 387	99.31 301	98.56 113	91.80 288	96.35 241	98.00 294	89.38 209	98.28 305	92.46 302	69.22 466	97.64 312

FMVSNet3		92.69 318	91.58 328	95.99 283	98.29 199	97.42 116	99.26 312	97.62 287	89.80 349	89.68 345	95.32 394	81.62 331	96.27 424	87.01 395	85.65 367	94.29 358

PMMVS		96.76 157	96.76 139	96.76 260	98.28 201	92.10 329	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		94.38 266	94.08 257	95.31 311	98.27 202	90.02 388	99.29 308	98.56 113	95.90 101	98.77 129	98.00 294	90.89 188	98.26 309	97.80 165	69.20 467	97.64 312

PVSNet_0	88.03 19	91.80 338	90.27 352	96.38 275	98.27 202	90.46 379	99.94 93	99.61 13	93.99 175	86.26 418	97.39 314	71.13 428	99.89 118	98.77 105	67.05 473	98.79 274

UA-Net		96.54 172	95.96 180	98.27 150	98.23 204	95.71 195	98.00 419	98.45 143	93.72 190	98.41 152	99.27 163	88.71 223	99.66 171	91.19 323	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	99.08 187	77.10 378	99.97 64	97.64 173	99.45 127	98.74 277

FE-MVS		95.70 220	95.01 229	97.79 184	98.21 206	94.57 248	95.03 468	98.69 82	88.90 365	97.50 189	96.19 356	92.60 147	99.49 185	89.99 348	97.94 192	99.31 217

GG-mvs-BLEND				98.54 128	98.21 206	98.01 84	93.87 473	98.52 128		97.92 173	97.92 299	99.02 3	97.94 329	98.17 142	99.58 109	99.67 133

mvs_anonymous		95.65 222	95.03 228	97.53 212	98.19 208	95.74 193	99.33 296	97.49 306	90.87 317	90.47 330	97.10 321	88.23 226	97.16 361	95.92 229	97.66 198	99.68 131

MVS_Test		96.46 176	95.74 193	98.61 117	98.18 209	97.23 123	99.31 301	97.15 362	91.07 313	98.84 123	97.05 325	88.17 227	98.97 217	94.39 262	97.50 200	99.61 151

BH-RMVSNet		95.18 234	94.31 249	97.80 182	98.17 210	95.23 224	99.76 188	97.53 301	92.52 258	94.27 287	99.25 169	76.84 385	98.80 239	90.89 332	99.54 111	99.35 208

dongtai		91.55 344	91.13 337	92.82 402	98.16 211	86.35 431	99.47 274	98.51 131	83.24 438	85.07 428	97.56 308	90.33 197	94.94 454	76.09 463	91.73 315	97.18 323

RPSCF		91.80 338	92.79 301	88.83 445	98.15 212	69.87 485	98.11 415	96.60 427	83.93 433	94.33 285	99.27 163	79.60 356	99.46 189	91.99 312	93.16 312	97.18 323

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	99.08 327	97.61 290	92.02 280	95.54 265	98.96 210	90.64 191	98.08 318	93.73 284	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	80.88 340	99.93 104	99.59 56	98.17 181	97.29 321

ab-mvs		94.69 252	93.42 279	98.51 133	98.07 217	96.26 170	96.49 452	98.68 84	90.31 340	94.54 277	97.00 328	76.30 393	99.71 160	95.98 228	93.38 310	99.56 163

XVG-OURS-SEG-HR		94.79 247	94.70 241	95.08 316	98.05 218	89.19 399	99.08 327	97.54 299	93.66 191	94.87 274	99.58 128	78.78 364	99.79 145	97.31 181	93.40 309	96.25 330

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		94.82 244	94.74 240	95.06 317	98.00 220	89.19 399	99.08 327	97.55 297	94.10 168	94.71 275	99.62 123	80.51 347	99.74 156	96.04 227	93.06 314	96.25 330

mvsmamba		96.94 146	96.73 141	97.55 210	97.99 221	94.37 261	99.62 238	97.70 277	93.13 217	98.42 151	97.92 299	88.02 228	98.75 247	98.78 104	99.01 153	99.52 173

dp		95.05 238	94.43 244	96.91 253	97.99 221	92.73 314	96.29 457	97.98 246	89.70 350	95.93 253	94.67 422	93.83 110	98.45 282	86.91 398	96.53 243	99.54 168

tpmrst		96.27 191	95.98 176	97.13 245	97.96 223	93.15 302	96.34 455	98.17 222	92.07 276	98.71 135	95.12 404	93.91 105	98.73 249	94.91 250	96.62 241	99.50 179

TR-MVS		94.54 257	93.56 274	97.49 220	97.96 223	94.34 262	98.71 378	97.51 304	90.30 341	94.51 279	98.69 247	75.56 399	98.77 243	92.82 300	95.99 256	99.35 208

Vis-MVSNet (Re-imp)		96.32 186	95.98 176	97.35 236	97.93 225	94.82 240	99.47 274	98.15 230	91.83 285	95.09 272	99.11 185	91.37 175	97.47 345	93.47 288	97.43 201	99.74 119

MDTV_nov1_ep13			95.69 195		97.90 226	94.15 270	95.98 463	98.44 148	93.12 218	97.98 171	95.74 369	95.10 61	98.58 269	90.02 347	96.92 232

Fast-Effi-MVS+		95.02 240	94.19 252	97.52 214	97.88 227	94.55 249	99.97 42	97.08 379	88.85 367	94.47 280	97.96 298	84.59 294	98.41 286	89.84 350	97.10 222	99.59 154

ADS-MVSNet2		93.80 287	93.88 264	93.55 385	97.87 228	85.94 435	94.24 469	96.84 413	90.07 344	96.43 237	94.48 427	90.29 199	95.37 447	87.44 385	97.23 212	99.36 204

ADS-MVSNet		94.79 247	94.02 259	97.11 247	97.87 228	93.79 280	94.24 469	98.16 227	90.07 344	96.43 237	94.48 427	90.29 199	98.19 312	87.44 385	97.23 212	99.36 204

Effi-MVS+		96.30 188	95.69 195	98.16 155	97.85 230	96.26 170	97.41 431	97.21 353	90.37 337	98.65 138	98.58 262	86.61 255	98.70 255	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	96.05 461	98.40 177	92.86 229	97.09 204	95.28 399	94.21 97	98.07 320	89.26 359	98.11 186	99.70 125
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
cascas		94.64 255	93.61 269	97.74 192	97.82 232	96.26 170	99.96 56	97.78 270	85.76 414	94.00 290	97.54 309	76.95 384	99.21 199	97.23 186	95.43 280	97.76 309

1112_ss		96.01 200	95.20 220	98.42 142	97.80 233	96.41 163	99.65 231	96.66 424	92.71 240	92.88 304	99.40 147	92.16 163	99.30 194	91.92 314	93.66 305	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		95.72 216	94.83 234	98.42 142	97.79 234	96.41 163	99.65 231	96.65 425	92.70 241	92.86 305	96.13 360	92.15 164	99.30 194	91.88 315	93.64 306	99.55 164

Effi-MVS+-dtu		94.53 259	95.30 216	92.22 410	97.77 236	82.54 458	99.59 247	97.06 388	94.92 128	95.29 269	95.37 392	85.81 266	97.89 330	94.80 253	97.07 223	96.23 332

tpm cat1		93.51 296	92.52 311	96.47 268	97.77 236	91.47 360	96.13 459	98.06 237	80.98 452	92.91 303	93.78 436	89.66 204	98.87 225	87.03 394	96.39 248	99.09 246

FA-MVS(test-final)		95.86 206	95.09 225	98.15 158	97.74 238	95.62 201	96.31 456	98.17 222	91.42 301	96.26 242	96.13 360	90.56 193	99.47 188	92.18 306	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	99.31 301	97.86 260	96.43 83	99.62 62	99.69 105	85.56 273	99.68 165	99.05 82	98.31 176	97.83 305

xiu_mvs_v1_base		97.43 117	97.06 123	98.55 124	97.74 238	98.14 74	99.31 301	97.86 260	96.43 83	99.62 62	99.69 105	85.56 273	99.68 165	99.05 82	98.31 176	97.83 305

xiu_mvs_v1_base_debi		97.43 117	97.06 123	98.55 124	97.74 238	98.14 74	99.31 301	97.86 260	96.43 83	99.62 62	99.69 105	85.56 273	99.68 165	99.05 82	98.31 176	97.83 305

EPP-MVSNet		96.69 164	96.60 147	96.96 252	97.74 238	93.05 305	99.37 291	98.56 113	88.75 369	95.83 256	99.01 197	96.01 40	98.56 272	96.92 199	97.20 214	99.25 230

gg-mvs-nofinetune		93.51 296	91.86 323	98.47 135	97.72 243	97.96 89	92.62 483	98.51 131	74.70 476	97.33 196	69.59 505	98.91 4	97.79 333	97.77 170	99.56 110	99.67 133

IB-MVS	92.85 6	94.99 241	93.94 262	98.16 155	97.72 243	95.69 198	99.99 8	98.81 67	94.28 162	92.70 306	96.90 332	95.08 62	99.17 205	96.07 226	73.88 449	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	99.04 193	97.50 10	99.10 209	94.75 255	96.37 249	99.16 238

VortexMVS		94.11 275	93.50 276	95.94 286	97.70 246	96.61 155	99.35 294	97.18 356	93.52 197	89.57 352	95.74 369	87.55 236	96.97 378	95.76 234	85.13 374	94.23 363

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	99.26 167	84.85 286	98.66 263	96.61 212	97.02 229	99.43 194

Syy-MVS		90.00 379	90.63 344	88.11 452	97.68 248	74.66 482	99.71 214	98.35 190	90.79 324	92.10 312	98.67 248	79.10 362	93.09 474	63.35 489	95.95 260	96.59 328

myMVS_eth3d		94.46 264	94.76 239	93.55 385	97.68 248	90.97 364	99.71 214	98.35 190	90.79 324	92.10 312	98.67 248	92.46 154	93.09 474	87.13 391	95.95 260	96.59 328

test_fmvs1_n		94.25 272	94.36 246	93.92 372	97.68 248	83.70 448	99.90 117	96.57 428	97.40 40	99.67 53	98.88 222	61.82 464	99.92 110	98.23 140	99.13 146	98.14 298

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	99.27 311	97.10 375	92.79 235	97.43 192	97.99 296	81.85 326	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	99.28 160	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	99.77 182	97.33 325	93.41 202	97.34 195	99.17 179	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	99.70 221	97.23 351	92.76 237	96.63 222	99.05 192	84.96 285	98.64 265	96.65 211	97.35 207	99.31 217

Vis-MVSNet		95.72 216	95.15 223	97.45 222	97.62 255	94.28 263	99.28 309	98.24 210	94.27 164	96.84 215	98.94 217	79.39 357	98.76 245	93.25 291	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	91.11 311	97.07 206	98.97 208	97.47 13	99.03 212	93.73 284	96.09 254	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	94.99 245	98.17 181	99.37 202

miper_ehance_all_eth		93.16 304	92.60 305	94.82 327	97.57 259	93.56 292	99.50 268	97.07 387	88.75 369	88.85 369	95.52 381	90.97 184	96.74 394	90.77 334	84.45 379	94.17 371

guyue		97.15 134	96.82 136	98.15 158	97.56 260	96.25 174	99.71 214	97.84 263	95.75 107	98.13 168	98.65 251	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	99.70 221	97.33 325	93.62 193	97.00 210	99.32 154	85.57 272	98.71 252	97.26 185	97.33 208	99.47 183

testing3		93.92 281	94.23 251	92.99 399	97.54 262	90.23 383	99.99 8	99.16 33	90.57 331	91.33 320	98.63 255	92.99 132	92.52 478	82.46 428	95.39 281	96.22 333

SSM_0404		95.75 215	95.16 222	97.50 217	97.53 263	95.39 212	99.11 323	97.25 346	90.81 320	95.27 270	98.83 236	84.74 290	98.67 260	95.24 240	97.69 195	98.45 287

LCM-MVSNet-Re		92.31 327	92.60 305	91.43 419	97.53 263	79.27 475	99.02 341	91.83 490	92.07 276	80.31 452	94.38 430	83.50 307	95.48 444	97.22 187	97.58 199	99.54 168

GBi-Net		90.88 355	89.82 361	94.08 363	97.53 263	91.97 330	98.43 397	96.95 402	87.05 397	89.68 345	94.72 418	71.34 425	96.11 430	87.01 395	85.65 367	94.17 371

test1		90.88 355	89.82 361	94.08 363	97.53 263	91.97 330	98.43 397	96.95 402	87.05 397	89.68 345	94.72 418	71.34 425	96.11 430	87.01 395	85.65 367	94.17 371

FMVSNet2		91.02 352	89.56 366	95.41 307	97.53 263	95.74 193	98.98 344	97.41 314	87.05 397	88.43 382	95.00 412	71.34 425	96.24 426	85.12 410	85.21 372	94.25 361

tttt0517		96.85 151	96.49 152	97.92 174	97.48 268	95.89 187	99.85 147	98.54 123	90.72 328	96.63 222	98.93 220	97.47 13	99.02 213	93.03 298	95.76 267	98.85 270

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	96.03 249	99.19 177	85.39 277	98.72 251	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	99.71 214	97.33 325	93.20 210	97.02 207	99.07 189	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	98.14 314	98.76 106	99.23 142	99.62 147

viewdifsd2359ckpt07		95.83 209	95.42 206	97.07 248	97.40 273	93.04 306	99.60 245	97.24 349	92.39 264	96.09 248	99.14 184	83.07 317	98.93 221	97.02 192	96.87 233	99.23 233

c3_l		92.53 322	91.87 322	94.52 339	97.40 273	92.99 308	99.40 283	96.93 407	87.86 387	88.69 372	95.44 386	89.95 202	96.44 412	90.45 340	80.69 413	94.14 381

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	99.16 182	85.20 280	98.70 255	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	91.14 310	96.58 225	99.27 163	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	98.46 286

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	96.03 249	99.21 174	84.19 300	98.65 264	96.53 216	97.07 223	99.42 197

E3		96.36 183	95.95 182	97.60 205	97.37 278	94.52 250	99.71 214	97.33 325	93.18 212	97.02 207	99.07 189	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	99.53 263	97.91 255	91.55 293	95.37 268	98.32 282	95.05 64	97.13 364	93.80 280	95.75 268	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	95.77 257	98.07 292	95.54 50	98.29 303	90.55 338	98.89 156	99.70 125

miper_lstm_enhance		91.81 335	91.39 334	93.06 398	97.34 282	89.18 401	99.38 289	96.79 418	86.70 404	87.47 400	95.22 401	90.00 201	95.86 439	88.26 374	81.37 402	94.15 377

baseline		96.43 178	95.98 176	97.76 190	97.34 282	95.17 228	99.51 266	97.17 358	93.92 180	96.90 213	99.28 160	85.37 278	98.64 265	97.50 177	96.86 235	99.46 185

cl____		92.31 327	91.58 328	94.52 339	97.33 284	92.77 310	99.57 253	96.78 419	86.97 401	87.56 398	95.51 382	89.43 208	96.62 401	88.60 364	82.44 394	94.16 376

SD_0403		92.63 321	93.38 283	90.40 433	97.32 285	77.91 477	97.75 426	98.03 242	91.89 282	90.83 326	98.29 286	82.00 323	93.79 467	88.51 369	95.75 268	99.52 173

DIV-MVS_self_test		92.32 326	91.60 327	94.47 343	97.31 286	92.74 312	99.58 249	96.75 420	86.99 400	87.64 396	95.54 379	89.55 207	96.50 407	88.58 365	82.44 394	94.17 371

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	99.26 167	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		94.36 269	93.48 277	96.99 251	97.29 288	93.54 293	99.96 56	96.72 422	88.35 380	93.43 294	98.94 217	82.05 322	98.05 321	88.12 380	96.48 246	99.37 202

eth_miper_zixun_eth		92.41 325	91.93 320	93.84 376	97.28 289	90.68 373	98.83 367	96.97 400	88.57 374	89.19 364	95.73 372	89.24 214	96.69 399	89.97 349	81.55 400	94.15 377

MVSFormer		96.94 146	96.60 147	97.95 170	97.28 289	97.70 102	99.55 260	97.27 343	91.17 307	99.43 84	99.54 134	90.92 185	96.89 384	94.67 258	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	98.71 252	98.40 127	99.62 99	99.45 190

diffmvs_AUTHOR		96.75 159	96.41 158	97.79 184	97.20 292	95.46 206	99.69 224	97.15 362	94.46 146	98.78 127	99.21 174	85.64 270	98.77 243	98.27 137	97.31 210	99.13 242

mamba_0408		94.98 242	94.09 255	97.64 199	97.14 293	95.31 217	93.48 479	97.08 379	90.48 333	94.40 281	98.62 256	84.49 295	98.67 260	93.99 271	97.18 215	98.93 263

SSM_04072		94.77 249	94.09 255	96.82 257	97.14 293	95.31 217	93.48 479	97.08 379	90.48 333	94.40 281	98.62 256	84.49 295	96.21 427	93.99 271	97.18 215	98.93 263

SSM_0407		95.62 223	94.95 231	97.61 204	97.14 293	95.31 217	99.00 342	97.25 346	90.81 320	94.40 281	98.83 236	84.74 290	98.58 269	95.24 240	97.18 215	98.93 263

SCA		94.69 252	93.81 266	97.33 237	97.10 296	94.44 253	98.86 364	98.32 197	93.30 207	96.17 247	95.59 377	76.48 391	97.95 327	91.06 326	97.43 201	99.59 154

viewmacassd2359aftdt		95.93 204	95.45 204	97.36 234	97.09 297	94.12 272	99.57 253	97.26 345	93.05 222	96.50 229	99.17 179	82.76 318	98.68 258	96.61 212	97.04 226	99.28 224

KinetiMVS		96.10 195	95.29 217	98.53 130	97.08 298	97.12 129	99.56 257	98.12 233	94.78 133	98.44 149	98.94 217	80.30 351	99.39 191	91.56 319	98.79 162	99.06 250

TAMVS		95.85 207	95.58 200	96.65 265	97.07 299	93.50 294	99.17 319	97.82 265	91.39 303	95.02 273	98.01 293	92.20 162	97.30 354	93.75 283	95.83 264	99.14 241

Fast-Effi-MVS+-dtu		93.72 291	93.86 265	93.29 390	97.06 300	86.16 432	99.80 172	96.83 414	92.66 244	92.58 307	97.83 305	81.39 332	97.67 338	89.75 351	96.87 233	96.05 335

E4		96.01 200	95.53 203	97.44 225	97.05 301	94.23 266	99.57 253	97.30 333	92.72 238	96.47 231	99.03 194	83.98 304	98.83 228	96.92 199	96.77 236	99.27 226

E5new		95.83 209	95.39 208	97.15 241	97.03 302	93.59 287	99.32 299	97.30 333	92.58 251	96.45 232	99.00 201	83.37 310	98.81 235	96.81 204	96.65 239	99.04 253

E5		95.83 209	95.39 208	97.15 241	97.03 302	93.59 287	99.32 299	97.30 333	92.58 251	96.45 232	99.00 201	83.37 310	98.81 235	96.81 204	96.65 239	99.04 253

CostFormer		96.10 195	95.88 188	96.78 259	97.03 302	92.55 320	97.08 440	97.83 264	90.04 346	98.72 134	94.89 416	95.01 66	98.29 303	96.54 215	95.77 266	99.50 179

test_fmvsmvis_n_1920		97.67 109	97.59 100	97.91 176	97.02 305	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	98.42 289

test-LLR		96.47 175	96.04 172	97.78 186	97.02 305	95.44 207	99.96 56	98.21 217	94.07 170	95.55 263	96.38 349	93.90 106	98.27 307	90.42 341	98.83 160	99.64 139

test-mter		96.39 181	95.93 185	97.78 186	97.02 305	95.44 207	99.96 56	98.21 217	91.81 287	95.55 263	96.38 349	95.17 59	98.27 307	90.42 341	98.83 160	99.64 139

casdiffseed414692147		95.07 237	94.26 250	97.50 217	97.01 308	94.70 244	99.58 249	97.02 392	91.27 305	94.66 276	98.82 238	80.79 342	98.55 275	93.39 290	95.79 265	99.27 226

E6new		95.83 209	95.39 208	97.14 243	97.00 309	93.58 289	99.31 301	97.30 333	92.57 253	96.45 232	99.01 197	83.44 308	98.81 235	96.80 206	96.66 237	99.04 253

E6		95.83 209	95.39 208	97.14 243	97.00 309	93.58 289	99.31 301	97.30 333	92.57 253	96.45 232	99.01 197	83.44 308	98.81 235	96.80 206	96.66 237	99.04 253

icg_test_0407_2		95.04 239	94.78 238	95.84 293	96.97 311	91.64 351	98.63 386	97.12 368	92.33 267	95.60 261	98.88 222	85.65 268	96.56 404	92.12 307	95.70 271	99.32 213

IMVS_0407		95.21 233	94.80 237	96.46 270	96.97 311	91.64 351	98.81 369	97.12 368	92.33 267	95.60 261	98.88 222	85.65 268	98.42 284	92.12 307	95.70 271	99.32 213

IMVS_0404		93.83 283	93.17 290	95.80 295	96.97 311	91.64 351	97.78 425	97.12 368	92.33 267	90.87 325	98.88 222	76.78 386	96.43 413	92.12 307	95.70 271	99.32 213

IMVS_0403		95.25 232	94.81 236	96.58 267	96.97 311	91.64 351	98.97 349	97.12 368	92.33 267	95.43 266	98.88 222	85.78 267	98.79 240	92.12 307	95.70 271	99.32 213

gm-plane-assit					96.97 311	93.76 282			91.47 297		98.96 210		98.79 240	94.92 248

WB-MVSnew		92.90 310	92.77 302	93.26 392	96.95 316	93.63 286	99.71 214	98.16 227	91.49 294	94.28 286	98.14 289	81.33 334	96.48 410	79.47 445	95.46 278	89.68 475

QAPM		95.40 228	94.17 253	99.10 79	96.92 317	97.71 100	99.40 283	98.68 84	89.31 353	88.94 368	98.89 221	82.48 320	99.96 76	93.12 297	99.83 80	99.62 147

KD-MVS_2432*1600		88.00 401	86.10 405	93.70 381	96.91 318	94.04 273	97.17 437	97.12 368	84.93 425	81.96 442	92.41 452	92.48 152	94.51 460	79.23 446	52.68 503	92.56 442

miper_refine_blended		88.00 401	86.10 405	93.70 381	96.91 318	94.04 273	97.17 437	97.12 368	84.93 425	81.96 442	92.41 452	92.48 152	94.51 460	79.23 446	52.68 503	92.56 442

tpm2		95.47 226	95.18 221	96.35 276	96.91 318	91.70 349	96.96 443	97.93 251	88.04 385	98.44 149	95.40 388	93.32 121	97.97 324	94.00 270	95.61 276	99.38 200

FMVSNet5		88.32 397	87.47 399	90.88 422	96.90 321	88.39 415	97.28 434	95.68 450	82.60 445	84.67 430	92.40 454	79.83 354	91.16 483	76.39 462	81.51 401	93.09 433

3Dnovator+	91.53 11	96.31 187	95.24 218	99.52 33	96.88 322	98.64 59	99.72 209	98.24 210	95.27 121	88.42 384	98.98 206	82.76 318	99.94 94	97.10 190	99.83 80	99.96 75

Patchmatch-test		92.65 320	91.50 331	96.10 282	96.85 323	90.49 378	91.50 488	97.19 354	82.76 444	90.23 331	95.59 377	95.02 65	98.00 323	77.41 457	96.98 231	99.82 107

MVS		96.60 168	95.56 201	99.72 14	96.85 323	99.22 21	98.31 403	98.94 44	91.57 292	90.90 324	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	95.34 214	99.08 82	96.82 325	97.47 114	99.45 279	98.81 67	95.52 115	89.39 355	99.00 201	81.97 324	99.95 85	97.27 182	99.83 80	99.84 104

EI-MVSNet		93.73 290	93.40 282	94.74 328	96.80 326	92.69 315	99.06 332	97.67 280	88.96 362	91.39 318	99.02 195	88.75 222	97.30 354	91.07 325	87.85 349	94.22 366

CVMVSNet		94.68 254	94.94 232	93.89 375	96.80 326	86.92 429	99.06 332	98.98 41	94.45 147	94.23 288	99.02 195	85.60 271	95.31 449	90.91 331	95.39 281	99.43 194

IterMVS-LS		92.69 318	92.11 316	94.43 347	96.80 326	92.74 312	99.45 279	96.89 410	88.98 360	89.65 348	95.38 391	88.77 221	96.34 420	90.98 329	82.04 397	94.22 366
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
AstraMVS		96.57 171	96.46 155	96.91 253	96.79 329	92.50 321	99.90 117	97.38 316	96.02 99	97.79 182	99.32 154	86.36 258	98.99 214	98.26 138	96.33 250	99.23 233

IterMVS		90.91 354	90.17 356	93.12 395	96.78 330	90.42 381	98.89 358	97.05 391	89.03 357	86.49 413	95.42 387	76.59 389	95.02 451	87.22 390	84.09 382	93.93 404
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	96.74 331	98.52 63	98.31 403	98.86 59	95.82 104	89.91 339	98.98 206	87.49 238	99.96 76	97.80 165	99.73 90	99.96 75

IterMVS-SCA-FT		90.85 357	90.16 357	92.93 400	96.72 332	89.96 390	98.89 358	96.99 396	88.95 363	86.63 410	95.67 373	76.48 391	95.00 452	87.04 393	84.04 385	93.84 411

MVS-HIRNet		86.22 414	83.19 427	95.31 311	96.71 333	90.29 382	92.12 485	97.33 325	62.85 492	86.82 407	70.37 503	69.37 433	97.49 344	75.12 465	97.99 191	98.15 296

viewdifsd2359ckpt11		94.09 277	93.63 268	95.46 304	96.68 334	88.92 404	99.62 238	97.12 368	93.07 220	95.73 258	99.22 171	77.05 379	98.88 224	96.52 217	87.69 354	98.58 284

viewmsd2359difaftdt		94.09 277	93.64 267	95.46 304	96.68 334	88.92 404	99.62 238	97.13 367	93.07 220	95.73 258	99.22 171	77.05 379	98.89 223	96.52 217	87.70 353	98.58 284

VDDNet		93.12 305	91.91 321	96.76 260	96.67 336	92.65 318	98.69 381	98.21 217	82.81 443	97.75 184	99.28 160	61.57 465	99.48 186	98.09 148	94.09 300	98.15 296

dmvs_re		93.20 302	93.15 291	93.34 388	96.54 337	83.81 447	98.71 378	98.51 131	91.39 303	92.37 310	98.56 264	78.66 366	97.83 332	93.89 274	89.74 321	98.38 291

Elysia		94.50 261	93.38 283	97.85 180	96.49 338	96.70 148	98.98 344	97.78 270	90.81 320	96.19 245	98.55 266	73.63 416	98.98 215	89.41 352	98.56 168	97.88 303

StellarMVS		94.50 261	93.38 283	97.85 180	96.49 338	96.70 148	98.98 344	97.78 270	90.81 320	96.19 245	98.55 266	73.63 416	98.98 215	89.41 352	98.56 168	97.88 303

MIMVSNet		90.30 370	88.67 385	95.17 315	96.45 340	91.64 351	92.39 484	97.15 362	85.99 411	90.50 329	93.19 445	66.95 444	94.86 456	82.01 432	93.43 308	99.01 259

CR-MVSNet		93.45 299	92.62 304	95.94 286	96.29 341	92.66 316	92.01 486	96.23 436	92.62 246	96.94 211	93.31 442	91.04 182	96.03 435	79.23 446	95.96 258	99.13 242

RPMNet		89.76 383	87.28 400	97.19 240	96.29 341	92.66 316	92.01 486	98.31 199	70.19 483	96.94 211	85.87 490	87.25 243	99.78 147	62.69 490	95.96 258	99.13 242

tt0805		91.28 347	90.18 355	94.60 334	96.26 343	87.55 422	98.39 401	98.72 78	89.00 359	89.22 361	98.47 274	62.98 460	98.96 219	90.57 337	88.00 348	97.28 322

Patchmtry		89.70 384	88.49 388	93.33 389	96.24 344	89.94 393	91.37 489	96.23 436	78.22 466	87.69 395	93.31 442	91.04 182	96.03 435	80.18 444	82.10 396	94.02 394

test_vis1_rt		86.87 411	86.05 408	89.34 441	96.12 345	78.07 476	99.87 133	83.54 506	92.03 279	78.21 463	89.51 472	45.80 486	99.91 111	96.25 223	93.11 313	90.03 471

JIA-IIPM		91.76 341	90.70 342	94.94 321	96.11 346	87.51 423	93.16 481	98.13 232	75.79 472	97.58 186	77.68 498	92.84 137	97.97 324	88.47 370	96.54 242	99.33 211

OpenMVS	90.15 15	94.77 249	93.59 272	98.33 146	96.07 347	97.48 113	99.56 257	98.57 107	90.46 335	86.51 412	98.95 215	78.57 367	99.94 94	93.86 275	99.74 89	97.57 317

PAPM		98.60 37	98.42 38	99.14 73	96.05 348	98.96 28	99.90 117	99.35 24	96.68 73	98.35 156	99.66 116	96.45 35	98.51 277	99.45 65	99.89 73	99.96 75

CLD-MVS		94.06 280	93.90 263	94.55 338	96.02 349	90.69 372	99.98 24	97.72 276	96.62 77	91.05 323	98.85 234	77.21 377	98.47 278	98.11 146	89.51 327	94.48 342
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
PatchT		90.38 367	88.75 384	95.25 313	95.99 350	90.16 385	91.22 490	97.54 299	76.80 468	97.26 199	86.01 489	91.88 169	96.07 434	66.16 483	95.91 262	99.51 177

ACMH+	89.98 16	90.35 368	89.54 367	92.78 404	95.99 350	86.12 433	98.81 369	97.18 356	89.38 352	83.14 438	97.76 306	68.42 438	98.43 283	89.11 360	86.05 365	93.78 414

DeepMVS_CX				82.92 466	95.98 352	58.66 499		96.01 442	92.72 238	78.34 462	95.51 382	58.29 472	98.08 318	82.57 427	85.29 370	92.03 451

ACMP	92.05 9	92.74 316	92.42 313	93.73 377	95.91 353	88.72 408	99.81 167	97.53 301	94.13 166	87.00 406	98.23 287	74.07 412	98.47 278	96.22 224	88.86 334	93.99 399
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test_vis1_n		93.61 294	93.03 294	95.35 308	95.86 354	86.94 428	99.87 133	96.36 434	96.85 64	99.54 73	98.79 239	52.41 480	99.83 140	98.64 114	98.97 154	99.29 222

HQP-NCC					95.78 355		99.87 133		96.82 66	93.37 295

ACMP_Plane					95.78 355		99.87 133		96.82 66	93.37 295

HQP-MVS		94.61 256	94.50 243	94.92 322	95.78 355	91.85 337	99.87 133	97.89 256	96.82 66	93.37 295	98.65 251	80.65 345	98.39 290	97.92 158	89.60 322	94.53 338

NP-MVS					95.77 358	91.79 341					98.65 251

test_fmvsmconf0.1_n		97.74 103	97.44 107	98.64 115	95.76 359	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					95.76 359	91.72 348						80.47 349

ACMM	91.95 10	92.88 311	92.52 311	93.98 371	95.75 361	89.08 403	99.77 182	97.52 303	93.00 223	89.95 338	97.99 296	76.17 395	98.46 281	93.63 287	88.87 333	94.39 350
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GA-MVS		93.83 283	92.84 298	96.80 258	95.73 362	93.57 291	99.88 130	97.24 349	92.57 253	92.92 302	96.66 341	78.73 365	97.67 338	87.75 383	94.06 301	99.17 237

plane_prior1					95.73 362

jason		97.24 129	96.86 133	98.38 145	95.73 362	97.32 118	99.97 42	97.40 315	95.34 119	98.60 143	99.54 134	87.70 232	98.56 272	97.94 157	99.47 124	99.25 230
jason: jason.
mmtdpeth		88.52 395	87.75 397	90.85 424	95.71 365	83.47 453	98.94 352	94.85 466	88.78 368	97.19 201	89.58 471	63.29 458	98.97 217	98.54 119	62.86 482	90.10 470

HQP_MVS		94.49 263	94.36 246	94.87 323	95.71 365	91.74 344	99.84 152	97.87 258	96.38 86	93.01 300	98.59 259	80.47 349	98.37 296	97.79 168	89.55 325	94.52 340

plane_prior7					95.71 365	91.59 357

ITE_SJBPF				92.38 407	95.69 368	85.14 439		95.71 449	92.81 232	89.33 358	98.11 290	70.23 431	98.42 284	85.91 405	88.16 346	93.59 422

fmvsm_s_conf0.1_n_a		97.09 138	96.90 131	97.63 202	95.65 369	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	98.74 277

ACMH	89.72 17	90.64 361	89.63 364	93.66 383	95.64 370	88.64 411	98.55 389	97.45 308	89.03 357	81.62 445	97.61 307	69.75 432	98.41 286	89.37 354	87.62 355	93.92 405
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	95.63 371	95.96 185	99.74 198	98.88 55	92.94 225	91.61 316	98.97 208	97.72 7	98.62 267	94.83 252	98.08 189	97.53 319

FMVSNet1		88.50 396	86.64 403	94.08 363	95.62 372	91.97 330	98.43 397	96.95 402	83.00 441	86.08 420	94.72 418	59.09 471	96.11 430	81.82 434	84.07 383	94.17 371

LuminaMVS		96.63 167	96.21 166	97.87 179	95.58 373	96.82 142	99.12 321	97.67 280	94.47 145	97.88 177	98.31 284	87.50 237	98.71 252	98.07 150	97.29 211	98.10 299

0.3-1-1-0.015		94.22 273	93.13 293	97.49 220	95.50 374	94.17 269	100.00 1	98.22 213	88.44 378	97.14 203	97.04 327	92.73 141	98.59 268	96.45 219	72.65 454	99.70 125

0.4-1-1-0.2		94.14 274	93.02 295	97.51 215	95.45 375	94.25 265	100.00 1	98.22 213	88.53 375	96.83 216	96.95 330	92.25 160	98.57 271	96.34 220	72.65 454	99.70 125

LPG-MVS_test		92.96 308	92.71 303	93.71 379	95.43 376	88.67 409	99.75 194	97.62 287	92.81 232	90.05 334	98.49 270	75.24 402	98.40 288	95.84 231	89.12 329	94.07 390

LGP-MVS_train				93.71 379	95.43 376	88.67 409		97.62 287	92.81 232	90.05 334	98.49 270	75.24 402	98.40 288	95.84 231	89.12 329	94.07 390

tpm		93.70 292	93.41 281	94.58 336	95.36 378	87.41 424	97.01 441	96.90 409	90.85 318	96.72 221	94.14 433	90.40 196	96.84 388	90.75 335	88.54 341	99.51 177

0.4-1-1-0.1		94.07 279	92.95 296	97.42 227	95.24 379	94.00 276	100.00 1	98.22 213	88.27 382	96.81 218	96.93 331	92.27 159	98.56 272	96.21 225	72.63 456	99.70 125

D2MVS		92.76 315	92.59 309	93.27 391	95.13 380	89.54 397	99.69 224	99.38 22	92.26 272	87.59 397	94.61 424	85.05 283	97.79 333	91.59 318	88.01 347	92.47 446

VPA-MVSNet		92.70 317	91.55 330	96.16 280	95.09 381	96.20 176	98.88 360	99.00 39	91.02 315	91.82 315	95.29 398	76.05 397	97.96 326	95.62 236	81.19 403	94.30 357

LTVRE_ROB	88.28 18	90.29 371	89.05 378	94.02 366	95.08 382	90.15 386	97.19 436	97.43 310	84.91 427	83.99 434	97.06 324	74.00 413	98.28 305	84.08 416	87.71 351	93.62 421
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		87.87 403	86.51 404	91.94 413	95.05 383	85.57 437	97.65 427	94.08 476	84.40 431	81.82 444	96.85 336	62.14 463	98.33 299	80.25 443	86.37 362	91.91 453

test0.0.03 1		93.86 282	93.61 269	94.64 332	95.02 384	92.18 328	99.93 100	98.58 105	94.07 170	87.96 392	98.50 269	93.90 106	94.96 453	81.33 435	93.17 311	96.78 325

UniMVSNet (Re)		93.07 307	92.13 315	95.88 290	94.84 385	96.24 175	99.88 130	98.98 41	92.49 260	89.25 359	95.40 388	87.09 245	97.14 363	93.13 296	78.16 427	94.26 359

USDC		90.00 379	88.96 379	93.10 397	94.81 386	88.16 417	98.71 378	95.54 454	93.66 191	83.75 436	97.20 318	65.58 449	98.31 301	83.96 419	87.49 357	92.85 439

VPNet		91.81 335	90.46 346	95.85 292	94.74 387	95.54 204	98.98 344	98.59 103	92.14 274	90.77 328	97.44 311	68.73 436	97.54 343	94.89 251	77.89 429	94.46 343

FIs		94.10 276	93.43 278	96.11 281	94.70 388	96.82 142	99.58 249	98.93 48	92.54 256	89.34 357	97.31 315	87.62 234	97.10 367	94.22 269	86.58 360	94.40 349

UniMVSNet_ETH3D		90.06 378	88.58 387	94.49 342	94.67 389	88.09 418	97.81 424	97.57 295	83.91 434	88.44 379	97.41 312	57.44 473	97.62 340	91.41 320	88.59 340	97.77 308

UniMVSNet_NR-MVSNet		92.95 309	92.11 316	95.49 300	94.61 390	95.28 221	99.83 160	99.08 36	91.49 294	89.21 362	96.86 335	87.14 244	96.73 395	93.20 292	77.52 432	94.46 343

test_fmvs2		89.47 388	89.70 363	88.77 448	94.54 391	75.74 478	99.83 160	94.70 472	94.71 137	91.08 321	96.82 340	54.46 476	97.78 335	92.87 299	88.27 344	92.80 440

MonoMVSNet		94.82 244	94.43 244	95.98 284	94.54 391	90.73 371	99.03 339	97.06 388	93.16 214	93.15 299	95.47 385	88.29 225	97.57 341	97.85 163	91.33 319	99.62 147

WR-MVS		92.31 327	91.25 335	95.48 303	94.45 393	95.29 220	99.60 245	98.68 84	90.10 343	88.07 391	96.89 333	80.68 344	96.80 392	93.14 295	79.67 420	94.36 351

nrg030		93.51 296	92.53 310	96.45 271	94.36 394	97.20 124	99.81 167	97.16 360	91.60 291	89.86 341	97.46 310	86.37 257	97.68 337	95.88 230	80.31 416	94.46 343

tfpnnormal		89.29 391	87.61 398	94.34 350	94.35 395	94.13 271	98.95 351	98.94 44	83.94 432	84.47 431	95.51 382	74.84 407	97.39 346	77.05 460	80.41 414	91.48 456

FC-MVSNet-test		93.81 286	93.15 291	95.80 295	94.30 396	96.20 176	99.42 281	98.89 52	92.33 267	89.03 367	97.27 317	87.39 240	96.83 390	93.20 292	86.48 361	94.36 351

SSC-MVS3.2		89.59 386	88.66 386	92.38 407	94.29 397	86.12 433	99.49 270	97.66 283	90.28 342	88.63 375	95.18 402	64.46 454	96.88 386	85.30 409	82.66 391	94.14 381

MS-PatchMatch		90.65 360	90.30 351	91.71 418	94.22 398	85.50 438	98.24 407	97.70 277	88.67 371	86.42 415	96.37 351	67.82 441	98.03 322	83.62 421	99.62 99	91.60 454

WR-MVS_H		91.30 345	90.35 349	94.15 357	94.17 399	92.62 319	99.17 319	98.94 44	88.87 366	86.48 414	94.46 429	84.36 298	96.61 402	88.19 376	78.51 425	93.21 431

DU-MVS		92.46 324	91.45 333	95.49 300	94.05 400	95.28 221	99.81 167	98.74 76	92.25 273	89.21 362	96.64 343	81.66 329	96.73 395	93.20 292	77.52 432	94.46 343

NR-MVSNet		91.56 343	90.22 353	95.60 298	94.05 400	95.76 192	98.25 406	98.70 80	91.16 309	80.78 451	96.64 343	83.23 315	96.57 403	91.41 320	77.73 431	94.46 343

CP-MVSNet		91.23 349	90.22 353	94.26 352	93.96 402	92.39 324	99.09 325	98.57 107	88.95 363	86.42 415	96.57 346	79.19 360	96.37 418	90.29 344	78.95 422	94.02 394

XXY-MVS		91.82 334	90.46 346	95.88 290	93.91 403	95.40 211	98.87 363	97.69 279	88.63 373	87.87 393	97.08 322	74.38 411	97.89 330	91.66 317	84.07 383	94.35 354

PS-CasMVS		90.63 362	89.51 369	93.99 369	93.83 404	91.70 349	98.98 344	98.52 128	88.48 376	86.15 419	96.53 348	75.46 400	96.31 423	88.83 362	78.86 424	93.95 402

test_0402		85.58 416	83.94 421	90.50 430	93.81 405	85.04 440	98.55 389	95.20 463	76.01 470	79.72 457	95.13 403	64.15 456	96.26 425	66.04 485	86.88 359	90.21 467

XVG-ACMP-BASELINE		91.22 350	90.75 341	92.63 406	93.73 406	85.61 436	98.52 393	97.44 309	92.77 236	89.90 340	96.85 336	66.64 446	98.39 290	92.29 304	88.61 338	93.89 407

TranMVSNet+NR-MVSNet		91.68 342	90.61 345	94.87 323	93.69 407	93.98 277	99.69 224	98.65 88	91.03 314	88.44 379	96.83 339	80.05 353	96.18 428	90.26 345	76.89 440	94.45 348

TransMVSNet (Re)		87.25 409	85.28 416	93.16 394	93.56 408	91.03 363	98.54 391	94.05 478	83.69 436	81.09 449	96.16 357	75.32 401	96.40 417	76.69 461	68.41 469	92.06 450

v10		90.25 372	88.82 381	94.57 337	93.53 409	93.43 296	99.08 327	96.87 412	85.00 424	87.34 404	94.51 425	80.93 339	97.02 377	82.85 426	79.23 421	93.26 429

testgi		89.01 393	88.04 394	91.90 414	93.49 410	84.89 442	99.73 205	95.66 451	93.89 184	85.14 426	98.17 288	59.68 469	94.66 459	77.73 456	88.88 332	96.16 334

v8		90.54 364	89.17 374	94.66 331	93.43 411	93.40 299	99.20 316	96.94 406	85.76 414	87.56 398	94.51 425	81.96 325	97.19 360	84.94 412	78.25 426	93.38 427

V42		91.28 347	90.12 358	94.74 328	93.42 412	93.46 295	99.68 227	97.02 392	87.36 393	89.85 343	95.05 406	81.31 335	97.34 349	87.34 388	80.07 418	93.40 425

pm-mvs1		89.36 390	87.81 396	94.01 367	93.40 413	91.93 333	98.62 387	96.48 432	86.25 409	83.86 435	96.14 359	73.68 415	97.04 373	86.16 402	75.73 445	93.04 435

v1144		91.09 351	89.83 360	94.87 323	93.25 414	93.69 285	99.62 238	96.98 398	86.83 403	89.64 349	94.99 413	80.94 338	97.05 370	85.08 411	81.16 404	93.87 409

v1192		90.62 363	89.25 373	94.72 330	93.13 415	93.07 303	99.50 268	97.02 392	86.33 408	89.56 353	95.01 410	79.22 359	97.09 369	82.34 430	81.16 404	94.01 396

v2v482		91.30 345	90.07 359	95.01 318	93.13 415	93.79 280	99.77 182	97.02 392	88.05 384	89.25 359	95.37 392	80.73 343	97.15 362	87.28 389	80.04 419	94.09 389

OPM-MVS		93.21 301	92.80 300	94.44 345	93.12 417	90.85 370	99.77 182	97.61 290	96.19 95	91.56 317	98.65 251	75.16 406	98.47 278	93.78 282	89.39 328	93.99 399
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
v144192		90.79 358	89.52 368	94.59 335	93.11 418	92.77 310	99.56 257	96.99 396	86.38 407	89.82 344	94.95 415	80.50 348	97.10 367	83.98 418	80.41 414	93.90 406

PEN-MVS		90.19 374	89.06 377	93.57 384	93.06 419	90.90 368	99.06 332	98.47 140	88.11 383	85.91 421	96.30 353	76.67 387	95.94 438	87.07 392	76.91 439	93.89 407

v1240		90.20 373	88.79 382	94.44 345	93.05 420	92.27 326	99.38 289	96.92 408	85.89 412	89.36 356	94.87 417	77.89 374	97.03 375	80.66 439	81.08 407	94.01 396

usedtu_dtu_shiyan1		92.78 313	91.73 324	95.92 288	93.03 421	96.82 142	99.83 160	97.79 266	90.58 329	90.09 332	95.04 407	84.75 288	96.72 397	88.19 376	86.23 363	94.23 363

FE-MVSNET3		92.78 313	91.73 324	95.92 288	93.03 421	96.82 142	99.83 160	97.79 266	90.58 329	90.09 332	95.04 407	84.75 288	96.72 397	88.20 375	86.23 363	94.23 363

v148		90.70 359	89.63 364	93.92 372	92.97 423	90.97 364	99.75 194	96.89 410	87.51 390	88.27 388	95.01 410	81.67 328	97.04 373	87.40 387	77.17 437	93.75 415

v1921920		90.46 365	89.12 375	94.50 341	92.96 424	92.46 322	99.49 270	96.98 398	86.10 410	89.61 351	95.30 395	78.55 368	97.03 375	82.17 431	80.89 412	94.01 396

MVStest1		85.03 422	82.76 431	91.83 415	92.95 425	89.16 402	98.57 388	94.82 467	71.68 481	68.54 486	95.11 405	83.17 316	95.66 442	74.69 466	65.32 476	90.65 463

tt0320-xc		82.94 437	80.35 444	90.72 428	92.90 426	83.54 451	96.85 446	94.73 470	63.12 491	79.85 456	93.77 437	49.43 484	95.46 445	80.98 438	71.54 458	93.16 432

Baseline_NR-MVSNet		90.33 369	89.51 369	92.81 403	92.84 427	89.95 391	99.77 182	93.94 479	84.69 429	89.04 366	95.66 374	81.66 329	96.52 406	90.99 328	76.98 438	91.97 452

test_method		80.79 443	79.70 446	84.08 462	92.83 428	67.06 489	99.51 266	95.42 456	54.34 499	81.07 450	93.53 439	44.48 487	92.22 480	78.90 451	77.23 436	92.94 437

pmmvs4		92.10 331	91.07 339	95.18 314	92.82 429	94.96 233	99.48 273	96.83 414	87.45 392	88.66 374	96.56 347	83.78 305	96.83 390	89.29 357	84.77 377	93.75 415

LF4IMVS		89.25 392	88.85 380	90.45 432	92.81 430	81.19 468	98.12 414	94.79 468	91.44 298	86.29 417	97.11 320	65.30 452	98.11 316	88.53 367	85.25 371	92.07 449

tt0320		83.56 436	81.15 439	90.77 426	92.77 431	83.58 450	96.83 447	95.52 455	63.26 490	81.36 447	92.54 449	53.26 478	95.77 440	80.45 440	74.38 448	92.96 436

DTE-MVSNet		89.40 389	88.24 392	92.88 401	92.66 432	89.95 391	99.10 324	98.22 213	87.29 394	85.12 427	96.22 355	76.27 394	95.30 450	83.56 422	75.74 444	93.41 424

EU-MVSNet		90.14 376	90.34 350	89.54 440	92.55 433	81.06 469	98.69 381	98.04 240	91.41 302	86.59 411	96.84 338	80.83 341	93.31 472	86.20 401	81.91 398	94.26 359

APD_test1		81.15 441	80.92 441	81.86 467	92.45 434	59.76 498	96.04 462	93.61 483	73.29 479	77.06 466	96.64 343	44.28 488	96.16 429	72.35 470	82.52 392	89.67 476

sc_t1		85.01 423	82.46 433	92.67 405	92.44 435	83.09 454	97.39 432	95.72 448	65.06 488	85.64 424	96.16 357	49.50 483	97.34 349	84.86 413	75.39 446	97.57 317

our_test_3		90.39 366	89.48 371	93.12 395	92.40 436	89.57 396	99.33 296	96.35 435	87.84 388	85.30 425	94.99 413	84.14 302	96.09 433	80.38 441	84.56 378	93.71 420

ppachtmachnet_test		89.58 387	88.35 390	93.25 393	92.40 436	90.44 380	99.33 296	96.73 421	85.49 419	85.90 422	95.77 368	81.09 337	96.00 437	76.00 464	82.49 393	93.30 428

v7n		89.65 385	88.29 391	93.72 378	92.22 438	90.56 377	99.07 331	97.10 375	85.42 421	86.73 408	94.72 418	80.06 352	97.13 364	81.14 436	78.12 428	93.49 423

dmvs_testset		83.79 432	86.07 407	76.94 473	92.14 439	48.60 510	96.75 448	90.27 494	89.48 351	78.65 460	98.55 266	79.25 358	86.65 496	66.85 481	82.69 390	95.57 336

PS-MVSNAJss		93.64 293	93.31 287	94.61 333	92.11 440	92.19 327	99.12 321	97.38 316	92.51 259	88.45 378	96.99 329	91.20 177	97.29 357	94.36 263	87.71 351	94.36 351

pmmvs5		90.17 375	89.09 376	93.40 387	92.10 441	89.77 394	99.74 198	95.58 453	85.88 413	87.24 405	95.74 369	73.41 418	96.48 410	88.54 366	83.56 387	93.95 402

N_pmnet		80.06 446	80.78 442	77.89 472	91.94 442	45.28 514	98.80 372	56.82 517	78.10 467	80.08 454	93.33 440	77.03 381	95.76 441	68.14 478	82.81 389	92.64 441

test_djsdf		92.83 312	92.29 314	94.47 343	91.90 443	92.46 322	99.55 260	97.27 343	91.17 307	89.96 337	96.07 363	81.10 336	96.89 384	94.67 258	88.91 331	94.05 393

SixPastTwentyTwo		88.73 394	88.01 395	90.88 422	91.85 444	82.24 460	98.22 411	95.18 464	88.97 361	82.26 441	96.89 333	71.75 423	96.67 400	84.00 417	82.98 388	93.72 419

K. test v3		88.05 400	87.24 401	90.47 431	91.82 445	82.23 461	98.96 350	97.42 312	89.05 356	76.93 468	95.60 376	68.49 437	95.42 446	85.87 406	81.01 410	93.75 415

OurMVSNet-221017-0		89.81 382	89.48 371	90.83 425	91.64 446	81.21 467	98.17 413	95.38 458	91.48 296	85.65 423	97.31 315	72.66 419	97.29 357	88.15 378	84.83 376	93.97 401

mvs_tets		91.81 335	91.08 338	94.00 368	91.63 447	90.58 376	98.67 383	97.43 310	92.43 261	87.37 403	97.05 325	71.76 422	97.32 352	94.75 255	88.68 337	94.11 388

Gipuma		66.95 464	65.00 464	72.79 479	91.52 448	67.96 486	66.16 514	95.15 465	47.89 501	58.54 494	67.99 510	29.74 495	87.54 495	50.20 501	77.83 430	62.87 509
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	91.47 449	95.56 203	99.84 152	97.30 333	97.74 30	97.89 176	99.35 153	79.62 355	99.85 130	99.25 75	99.24 141	99.55 164

jajsoiax		91.92 333	91.18 336	94.15 357	91.35 450	90.95 367	99.00 342	97.42 312	92.61 247	87.38 402	97.08 322	72.46 420	97.36 347	94.53 261	88.77 335	94.13 386

MDA-MVSNet-bldmvs		84.09 430	81.52 437	91.81 416	91.32 451	88.00 420	98.67 383	95.92 444	80.22 455	55.60 497	93.32 441	68.29 439	93.60 470	73.76 467	76.61 441	93.82 413

MVP-Stereo		90.93 353	90.45 348	92.37 409	91.25 452	88.76 406	98.05 418	96.17 438	87.27 395	84.04 432	95.30 395	78.46 369	97.27 359	83.78 420	99.70 92	91.09 457
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MDA-MVSNet_test_wron		85.51 418	83.32 426	92.10 411	90.96 453	88.58 412	99.20 316	96.52 430	79.70 457	57.12 496	92.69 448	79.11 361	93.86 466	77.10 459	77.46 434	93.86 410

YYNet1		85.50 419	83.33 425	92.00 412	90.89 454	88.38 416	99.22 315	96.55 429	79.60 458	57.26 495	92.72 447	79.09 363	93.78 468	77.25 458	77.37 435	93.84 411

ALIKED-NN		54.48 473	52.67 475	59.89 495	90.79 455	45.45 512	81.25 507	55.75 521	34.99 510	44.87 507	71.98 501	25.50 503	74.36 509	21.88 519	47.04 505	59.85 511

anonymousdsp		91.79 340	90.92 340	94.41 348	90.76 456	92.93 309	98.93 354	97.17 358	89.08 355	87.46 401	95.30 395	78.43 370	96.92 381	92.38 303	88.73 336	93.39 426

lessismore_v0				90.53 429	90.58 457	80.90 470		95.80 445		77.01 467	95.84 366	66.15 448	96.95 379	83.03 425	75.05 447	93.74 418

EG-PatchMatch MVS		85.35 420	83.81 423	89.99 438	90.39 458	81.89 463	98.21 412	96.09 440	81.78 448	74.73 474	93.72 438	51.56 482	97.12 366	79.16 449	88.61 338	90.96 460

EGC-MVSNET		69.38 457	63.76 467	86.26 458	90.32 459	81.66 466	96.24 458	93.85 480	0.99 536	3.22 537	92.33 459	52.44 479	92.92 476	59.53 495	84.90 375	84.21 491

CMPMVS	61.59 21	84.75 426	85.14 417	83.57 463	90.32 459	62.54 494	96.98 442	97.59 294	74.33 477	69.95 483	96.66 341	64.17 455	98.32 300	87.88 382	88.41 343	89.84 473
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
ALIKED-MNN		52.51 477	50.15 482	59.60 497	90.05 461	44.33 516	81.60 506	54.93 522	32.36 513	40.96 514	68.77 507	20.90 512	75.30 507	20.00 520	41.78 509	59.18 512

new_pmnet		84.49 429	82.92 429	89.21 442	90.03 462	82.60 457	96.89 445	95.62 452	80.59 453	75.77 473	89.17 473	65.04 453	94.79 457	72.12 471	81.02 409	90.23 466

pmmvs6		85.69 415	83.84 422	91.26 421	90.00 463	84.41 445	97.82 423	96.15 439	75.86 471	81.29 448	95.39 390	61.21 466	96.87 387	83.52 423	73.29 450	92.50 445

ttmdpeth		88.23 399	87.06 402	91.75 417	89.91 464	87.35 425	98.92 357	95.73 447	87.92 386	84.02 433	96.31 352	68.23 440	96.84 388	86.33 400	76.12 442	91.06 458

DSMNet-mixed		88.28 398	88.24 392	88.42 450	89.64 465	75.38 481	98.06 417	89.86 495	85.59 418	88.20 390	92.14 461	76.15 396	91.95 481	78.46 453	96.05 255	97.92 302

UnsupCasMVSNet_eth		85.52 417	83.99 419	90.10 436	89.36 466	83.51 452	96.65 449	97.99 244	89.14 354	75.89 472	93.83 435	63.25 459	93.92 464	81.92 433	67.90 472	92.88 438

Anonymous20231206		86.32 413	85.42 415	89.02 444	89.11 467	80.53 473	99.05 336	95.28 459	85.43 420	82.82 439	93.92 434	74.40 410	93.44 471	66.99 480	81.83 399	93.08 434

ALIKED-LG		54.29 474	52.28 476	60.32 491	88.90 468	45.51 511	81.66 505	56.33 518	38.60 503	42.62 512	70.81 502	25.00 505	75.20 508	19.87 521	46.76 507	60.24 510

Anonymous20240521		85.15 421	83.81 423	89.16 443	88.32 469	82.69 456	98.80 372	95.74 446	79.72 456	81.53 446	90.99 464	65.38 451	94.16 462	72.69 469	81.11 406	90.63 464

OpenMVS_ROB	79.82 20	83.77 433	81.68 436	90.03 437	88.30 470	82.82 455	98.46 394	95.22 462	73.92 478	76.00 471	91.29 463	55.00 475	96.94 380	68.40 477	88.51 342	90.34 465

test20.03		84.72 427	83.99 419	86.91 455	88.19 471	80.62 472	98.88 360	95.94 443	88.36 379	78.87 458	94.62 423	68.75 435	89.11 490	66.52 482	75.82 443	91.00 459

RoMa-SfM		74.91 453	72.77 455	81.35 468	88.00 472	67.35 488	93.55 478	86.23 504	68.27 486	66.79 487	92.92 446	30.40 493	87.68 492	66.14 484	62.62 483	89.02 482

gbinet_0.2-2-1-0.02		87.63 408	85.51 414	93.99 369	87.22 473	91.56 358	99.81 167	97.36 320	79.54 459	88.60 376	93.29 444	73.76 414	96.34 420	89.27 358	60.78 491	94.06 392

blend_shiyan4		90.13 377	88.79 382	94.17 354	87.12 474	91.83 339	99.75 194	97.08 379	79.27 464	88.69 372	92.53 450	92.25 160	96.50 407	89.35 355	73.04 452	94.18 370

KD-MVS_self_test		83.59 434	82.06 434	88.20 451	86.93 475	80.70 471	97.21 435	96.38 433	82.87 442	82.49 440	88.97 474	67.63 442	92.32 479	73.75 468	62.30 485	91.58 455

DKM		72.18 455	69.80 458	79.34 471	86.79 476	65.15 490	92.70 482	84.00 505	67.67 487	61.97 490	89.63 470	23.69 508	85.17 498	67.39 479	54.35 501	87.70 486

MIMVSNet1		82.58 438	80.51 443	88.78 446	86.68 477	84.20 446	96.65 449	95.41 457	78.75 465	78.59 461	92.44 451	51.88 481	89.76 488	65.26 486	78.95 422	92.38 448

wanda-best-256-512		87.82 404	85.71 410	94.15 357	86.66 478	91.88 335	99.76 188	97.08 379	79.46 460	88.37 385	92.36 455	78.01 371	96.43 413	88.39 371	61.26 487	94.14 381

FE-blended-shiyan7		87.82 404	85.71 410	94.15 357	86.66 478	91.88 335	99.76 188	97.08 379	79.46 460	88.37 385	92.36 455	78.01 371	96.43 413	88.39 371	61.26 487	94.14 381

usedtu_blend_shiyan5		86.75 412	84.29 418	94.16 355	86.66 478	91.83 339	97.42 429	95.23 461	69.94 484	88.37 385	92.36 455	78.01 371	96.50 407	89.35 355	61.26 487	94.14 381

SP-NN		55.28 472	53.59 474	60.34 490	86.63 481	39.01 521	86.70 499	56.31 519	31.08 515	43.77 510	68.45 508	23.39 509	60.24 514	29.19 514	56.76 498	81.77 496

LoFTR		74.41 454	70.88 457	84.99 461	86.56 482	67.85 487	93.74 474	89.63 497	69.46 485	54.95 498	87.39 484	30.76 492	96.92 381	61.37 492	64.06 479	90.19 468

blended_shiyan8		87.82 404	85.71 410	94.16 355	86.54 483	91.79 341	99.72 209	97.08 379	79.32 462	88.44 379	92.35 458	77.88 375	96.56 404	88.53 367	61.51 486	94.15 377

blended_shiyan6		87.74 407	85.62 413	94.09 362	86.53 484	91.73 347	99.72 209	97.08 379	79.32 462	88.22 389	92.31 460	77.82 376	96.43 413	88.31 373	61.26 487	94.13 386

CL-MVSNet_self_test		84.50 428	83.15 428	88.53 449	86.00 485	81.79 464	98.82 368	97.35 321	85.12 423	83.62 437	90.91 466	76.66 388	91.40 482	69.53 475	60.36 492	92.40 447

MatchFormer		70.84 456	66.72 462	83.19 465	85.99 486	64.61 491	93.58 477	88.62 500	59.32 495	50.64 501	82.31 495	28.00 498	96.79 393	52.52 500	59.50 494	88.18 484

UnsupCasMVSNet_bld		79.97 448	77.03 453	88.78 446	85.62 487	81.98 462	93.66 475	97.35 321	75.51 474	70.79 482	83.05 492	48.70 485	94.91 455	78.31 454	60.29 493	89.46 479

mvs5depth		84.87 424	82.90 430	90.77 426	85.59 488	84.84 443	91.10 491	93.29 485	83.14 439	85.07 428	94.33 431	62.17 462	97.32 352	78.83 452	72.59 457	90.14 469

SP-LightGlue		55.29 470	53.65 473	60.20 492	85.58 489	39.12 520	86.36 502	57.52 516	32.34 514	44.34 509	67.75 511	24.36 506	59.32 517	29.62 512	54.98 499	82.17 494

SP-SuperGlue		55.29 470	53.71 472	60.00 494	85.11 490	38.86 522	86.96 498	57.95 515	32.77 512	44.54 508	68.00 509	23.90 507	59.51 516	29.61 513	54.59 500	81.63 497

SP-MNN		53.97 475	52.04 479	59.73 496	84.72 491	38.63 523	86.51 500	55.94 520	29.25 516	40.20 515	67.48 512	22.18 511	59.59 515	27.79 515	54.33 502	80.98 498

Patchmatch-RL test		86.90 410	85.98 409	89.67 439	84.45 492	75.59 479	89.71 494	92.43 487	86.89 402	77.83 465	90.94 465	94.22 95	93.63 469	87.75 383	69.61 463	99.79 112

pmmvs-eth3d		84.03 431	81.97 435	90.20 434	84.15 493	87.09 427	98.10 416	94.73 470	83.05 440	74.10 478	87.77 481	65.56 450	94.01 463	81.08 437	69.24 465	89.49 478

test_fmvs3		79.99 447	80.17 445	79.45 470	84.02 494	62.83 492	99.05 336	93.49 484	88.29 381	80.06 455	86.65 487	28.09 497	88.00 491	88.63 363	73.27 451	87.54 488

PM-MVS		80.47 444	78.88 448	85.26 459	83.79 495	72.22 483	95.89 465	91.08 492	85.71 417	76.56 470	88.30 477	36.64 491	93.90 465	82.39 429	69.57 464	89.66 477

new-patchmatchnet		81.19 440	79.34 447	86.76 456	82.86 496	80.36 474	97.92 420	95.27 460	82.09 447	72.02 480	86.87 486	62.81 461	90.74 486	71.10 472	63.08 481	89.19 481

FE-MVSNET2		83.57 435	81.36 438	90.20 434	82.83 497	87.59 421	98.28 405	96.04 441	85.33 422	74.13 477	87.45 482	59.16 470	93.26 473	79.12 450	69.91 461	89.77 474

FE-MVSNET		81.05 442	78.81 449	87.79 453	81.98 498	83.70 448	98.23 409	91.78 491	81.27 450	74.29 476	87.44 483	60.92 468	90.67 487	64.92 487	68.43 468	89.01 483

mvsany_test3		82.12 439	81.14 440	85.06 460	81.87 499	70.41 484	97.09 439	92.14 488	91.27 305	77.84 464	88.73 475	39.31 489	95.49 443	90.75 335	71.24 459	89.29 480

WB-MVS		76.28 450	77.28 452	73.29 478	81.18 500	54.68 503	97.87 422	94.19 475	81.30 449	69.43 484	90.70 467	77.02 382	82.06 501	35.71 508	68.11 471	83.13 492

test_f		78.40 449	77.59 451	80.81 469	80.82 501	62.48 495	96.96 443	93.08 486	83.44 437	74.57 475	84.57 491	27.95 499	92.63 477	84.15 415	72.79 453	87.32 489

SSC-MVS		75.42 452	76.40 454	72.49 482	80.68 502	53.62 504	97.42 429	94.06 477	80.42 454	68.75 485	90.14 469	76.54 390	81.66 502	33.25 509	66.34 475	82.19 493

pmmvs3		80.27 445	77.77 450	87.76 454	80.32 503	82.43 459	98.23 409	91.97 489	72.74 480	78.75 459	87.97 480	57.30 474	90.99 485	70.31 473	62.37 484	89.87 472

testf1		68.38 460	66.92 460	72.78 480	78.80 504	50.36 507	90.95 492	87.35 502	55.47 497	58.95 492	88.14 478	20.64 514	87.60 493	57.28 496	64.69 477	80.39 500

APD_test2		68.38 460	66.92 460	72.78 480	78.80 504	50.36 507	90.95 492	87.35 502	55.47 497	58.95 492	88.14 478	20.64 514	87.60 493	57.28 496	64.69 477	80.39 500

ambc				83.23 464	77.17 506	62.61 493	87.38 496	94.55 474		76.72 469	86.65 487	30.16 494	96.36 419	84.85 414	69.86 462	90.73 462

test_vis3_rt		68.82 458	66.69 463	75.21 477	76.24 507	60.41 497	96.44 453	68.71 512	75.13 475	50.54 502	69.52 506	16.42 520	96.32 422	80.27 442	66.92 474	68.89 506

PDCNetPlus		59.83 467	57.26 470	67.55 486	76.18 508	56.71 501	87.01 497	45.27 525	59.54 494	48.80 504	83.01 493	26.63 501	76.54 506	62.12 491	26.78 517	69.40 505

usedtu_dtu_shiyan2		75.87 451	72.37 456	86.39 457	76.18 508	75.49 480	96.53 451	93.82 481	64.74 489	72.53 479	88.48 476	37.67 490	91.12 484	64.13 488	57.22 496	92.56 442

TDRefinement		84.76 425	82.56 432	91.38 420	74.58 510	84.80 444	97.36 433	94.56 473	84.73 428	80.21 453	96.12 362	63.56 457	98.39 290	87.92 381	63.97 480	90.95 461

SIFT-NN		35.94 486	36.54 489	34.16 502	73.93 511	29.52 525	62.74 515	37.28 526	19.65 520	27.91 522	49.19 521	11.66 523	46.35 521	9.19 522	37.30 510	26.61 519

ELoFTR		64.32 466	60.56 469	75.60 476	73.46 512	53.20 505	86.50 501	80.09 508	60.74 493	45.95 506	82.48 494	16.05 521	89.20 489	56.48 499	43.34 508	84.38 490

E-PMN		52.30 478	52.18 478	52.67 498	71.51 513	45.40 513	93.62 476	76.60 510	36.01 507	43.50 511	64.13 515	27.11 500	67.31 512	31.06 510	26.06 518	45.30 518

EMVS		51.44 480	51.22 481	52.11 499	70.71 514	44.97 515	94.04 471	75.66 511	35.34 509	42.40 513	61.56 519	28.93 496	65.87 513	27.64 516	24.73 519	45.49 516

PMMVS2		67.15 463	64.15 466	76.14 475	70.56 515	62.07 496	93.89 472	87.52 501	58.09 496	60.02 491	78.32 497	22.38 510	84.54 499	59.56 494	47.03 506	81.80 495

SIFT-MNN		34.10 487	34.41 490	33.17 504	68.99 516	28.51 526	60.22 517	36.81 527	19.08 523	24.04 524	47.28 524	10.06 527	45.04 522	8.72 523	34.47 512	25.97 522

SIFT-NCM-Cal		31.73 489	31.67 492	31.91 507	67.18 517	27.55 532	58.36 519	33.09 531	18.38 526	14.93 531	45.16 530	8.60 530	43.82 524	7.62 532	31.68 515	24.36 525

SIFT-NN-NCMNet		33.88 488	34.14 491	33.10 505	66.88 518	28.42 527	60.42 516	36.72 528	19.15 521	24.06 523	47.14 525	10.24 525	44.77 523	8.72 523	33.94 514	26.10 521

FPMVS		68.72 459	68.72 459	68.71 484	65.95 519	44.27 517	95.97 464	94.74 469	51.13 500	53.26 499	90.50 468	25.11 504	83.00 500	60.80 493	80.97 411	78.87 502

SP-DiffGlue		56.84 468	55.72 471	60.19 493	65.70 520	40.86 518	81.89 504	60.28 514	34.62 511	50.39 503	76.88 499	26.61 502	58.81 518	48.21 502	56.94 497	80.90 499

wuyk23d		20.37 501	20.84 504	18.99 518	65.34 521	27.73 530	50.43 527	7.67 542	9.50 535	8.01 536	6.34 536	6.13 538	26.24 535	23.40 518	10.69 533	2.99 533

SIFT-ConvMatch		30.09 492	29.76 496	31.09 509	65.16 522	27.56 531	54.13 523	31.17 532	18.55 525	17.88 527	45.89 527	8.40 531	42.26 528	8.11 528	18.51 524	23.46 527

SIFT-CM-Cal		28.34 495	27.90 499	29.63 511	63.75 523	25.98 536	50.66 526	26.18 536	18.12 529	16.88 529	44.64 531	8.08 533	39.70 529	7.65 531	15.19 529	23.22 528

LCM-MVSNet		67.77 462	64.73 465	76.87 474	62.95 524	56.25 502	89.37 495	93.74 482	44.53 502	61.99 489	80.74 496	20.42 516	86.53 497	69.37 476	59.50 494	87.84 485

SIFT-NN-CMatch		31.71 490	31.56 493	32.16 506	62.58 525	27.53 533	56.45 520	33.28 530	19.00 524	23.65 525	47.34 522	10.05 528	42.72 526	8.71 525	22.96 522	26.24 520

SIFT-UM-Cal		27.47 496	27.02 500	28.83 514	62.12 526	24.58 538	53.60 524	23.46 537	18.14 528	12.85 533	45.56 528	7.49 534	39.45 530	7.68 530	12.30 530	22.45 529

SIFT-UMatch		29.40 494	28.87 498	30.98 510	62.08 527	26.57 535	56.09 521	29.45 534	18.31 527	15.86 530	46.00 526	8.23 532	42.54 527	7.99 529	15.81 527	23.85 526

GLUNet-SfM		51.10 481	46.61 484	64.56 487	61.54 528	39.88 519	79.38 510	65.13 513	36.09 506	33.36 519	69.94 504	14.50 522	78.76 504	42.46 506	17.10 526	75.02 504

SIFT-NN-UMatch		31.23 491	31.05 495	31.79 508	60.08 529	27.23 534	58.49 518	33.65 529	19.14 522	17.30 528	47.31 523	10.12 526	42.88 525	8.67 526	24.67 520	25.27 523

XFeat-NN		42.54 482	42.87 486	41.54 501	59.73 530	27.86 529	69.53 512	45.34 524	24.36 517	37.16 516	64.79 513	20.84 513	51.40 520	30.01 511	34.12 513	45.36 517

MVE	53.74 22	51.54 479	47.86 483	62.60 488	59.56 531	50.93 506	79.41 509	77.69 509	35.69 508	36.27 517	61.76 518	5.79 539	69.63 510	37.97 507	36.61 511	67.24 507
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
SIFT-NN-PointCN		29.63 493	29.72 497	29.36 512	57.55 532	23.55 539	56.07 522	30.57 533	17.99 530	20.99 526	45.21 529	9.94 529	39.33 531	8.40 527	20.81 523	25.20 524

SIFT-PointCN		25.49 497	25.71 501	24.84 515	56.17 533	18.65 540	51.37 525	26.53 535	16.31 531	12.78 534	39.87 534	6.41 537	34.09 533	6.51 534	15.42 528	21.77 530

SIFT-PCN-Cal		24.67 498	24.81 502	24.24 516	56.13 534	18.04 541	49.05 528	23.39 538	16.07 532	12.99 532	40.17 533	6.97 536	34.68 532	6.71 533	11.81 531	19.99 531

XFeat-MNN		41.51 483	41.24 487	42.32 500	55.40 535	28.19 528	69.39 513	46.53 523	23.57 518	34.47 518	63.21 517	20.04 517	52.41 519	27.43 517	31.08 516	46.37 515

SIFT-NCMNet		21.21 500	21.22 503	21.17 517	52.99 536	16.41 542	42.12 529	14.05 540	15.89 533	10.70 535	35.85 535	5.14 540	29.82 534	5.80 535	8.44 534	17.28 532

ANet_high		56.10 469	52.24 477	67.66 485	49.27 537	56.82 500	83.94 503	82.02 507	70.47 482	33.28 520	64.54 514	17.23 519	69.16 511	45.59 504	23.85 521	77.02 503

tmp_tt		65.23 465	62.94 468	72.13 483	44.90 538	50.03 509	81.05 508	89.42 499	38.45 504	48.51 505	99.90 23	54.09 477	78.70 505	91.84 316	18.26 525	87.64 487

PMVS	49.05 23	53.75 476	51.34 480	60.97 489	40.80 539	34.68 524	74.82 511	89.62 498	37.55 505	28.67 521	72.12 500	7.09 535	81.63 503	43.17 505	68.21 470	66.59 508
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test123		37.68 485	39.14 488	33.31 503	19.94 540	24.83 537	98.36 402	9.75 541	15.53 534	51.31 500	87.14 485	19.62 518	17.74 536	47.10 503	3.47 535	57.36 513

testmvs		40.60 484	44.45 485	29.05 513	19.49 541	14.11 543	99.68 227	18.47 539	20.74 519	64.59 488	98.48 273	10.95 524	17.09 537	56.66 498	11.01 532	55.94 514

mmdepth		0.00 504	0.00 507	0.00 519	0.00 542	0.00 544	0.00 530	0.00 543	0.00 537	0.00 538	0.00 538	0.00 541	0.00 538	0.00 536	0.00 536	0.00 534

monomultidepth		0.00 504	0.00 507	0.00 519	0.00 542	0.00 544	0.00 530	0.00 543	0.00 537	0.00 538	0.00 538	0.00 541	0.00 538	0.00 536	0.00 536	0.00 534

test_blank		0.00 504	0.00 507	0.00 519	0.00 542	0.00 544	0.00 530	0.00 543	0.00 537	0.00 538	0.02 537	0.00 541	0.00 538	0.00 536	0.00 536	0.00 534

eth-test2					0.00 542

eth-test					0.00 542

uanet_test		0.00 504	0.00 507	0.00 519	0.00 542	0.00 544	0.00 530	0.00 543	0.00 537	0.00 538	0.00 538	0.00 541	0.00 538	0.00 536	0.00 536	0.00 534

DCPMVS		0.00 504	0.00 507	0.00 519	0.00 542	0.00 544	0.00 530	0.00 543	0.00 537	0.00 538	0.00 538	0.00 541	0.00 538	0.00 536	0.00 536	0.00 534

cdsmvs_eth3d_5k		23.43 499	31.24 494	0.00 519	0.00 542	0.00 544	0.00 530	98.09 234	0.00 537	0.00 538	99.67 114	83.37 310	0.00 538	0.00 536	0.00 536	0.00 534

pcd_1.5k_mvsjas		7.60 503	10.13 506	0.00 519	0.00 542	0.00 544	0.00 530	0.00 543	0.00 537	0.00 538	0.00 538	91.20 177	0.00 538	0.00 536	0.00 536	0.00 534

sosnet-low-res		0.00 504	0.00 507	0.00 519	0.00 542	0.00 544	0.00 530	0.00 543	0.00 537	0.00 538	0.00 538	0.00 541	0.00 538	0.00 536	0.00 536	0.00 534

sosnet		0.00 504	0.00 507	0.00 519	0.00 542	0.00 544	0.00 530	0.00 543	0.00 537	0.00 538	0.00 538	0.00 541	0.00 538	0.00 536	0.00 536	0.00 534

uncertanet		0.00 504	0.00 507	0.00 519	0.00 542	0.00 544	0.00 530	0.00 543	0.00 537	0.00 538	0.00 538	0.00 541	0.00 538	0.00 536	0.00 536	0.00 534

Regformer		0.00 504	0.00 507	0.00 519	0.00 542	0.00 544	0.00 530	0.00 543	0.00 537	0.00 538	0.00 538	0.00 541	0.00 538	0.00 536	0.00 536	0.00 534

ab-mvs-re		8.28 502	11.04 505	0.00 519	0.00 542	0.00 544	0.00 530	0.00 543	0.00 537	0.00 538	99.40 147	0.00 541	0.00 538	0.00 536	0.00 536	0.00 534

uanet		0.00 504	0.00 507	0.00 519	0.00 542	0.00 544	0.00 530	0.00 543	0.00 537	0.00 538	0.00 538	0.00 541	0.00 538	0.00 536	0.00 536	0.00 534

WAC-MVS						90.97 364								86.10 404

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							95.78 466				59.23 520	93.20 128	97.74 336	91.06 326

test_post											63.35 516	94.43 82	98.13 315

patchmatchnet-post											91.70 462	95.12 60	97.95 327

MTMP							99.87 133	96.49 431

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							99.46 278		94.21 165	99.85 20			99.95 85	96.96 197

新几何2							99.40 283

无先验							99.49 270	98.71 79	93.46 199				100.00 1	94.36 263		99.99 26

原ACMM2							99.90 117

testdata2													99.99 39	90.54 339

segment_acmp												96.68 31

testdata1							99.28 309		96.35 91

plane_prior5								97.87 258					98.37 296	97.79 168	89.55 325	94.52 340

plane_prior4											98.59 259

plane_prior3						91.64 351			96.63 75	93.01 300

plane_prior2							99.84 152		96.38 86

plane_prior						91.74 344	99.86 144		96.76 70						89.59 324

n2								0.00 543

nn								0.00 543

door-mid								89.69 496

test11								98.44 148

door								90.31 493

HQP5-MVS						91.85 337

BP-MVS														97.92 158

HQP4-MVS										93.37 295			98.39 290			94.53 338

HQP3-MVS								97.89 256							89.60 322

HQP2-MVS												80.65 345

MDTV_nov1_ep13_2view						96.26 170	96.11 460		91.89 282	98.06 169		94.40 84		94.30 266		99.67 133

ACMMP++_ref															87.04 358

ACMMP++															88.23 345

Test By Simon												92.82 139

High-resolution multi-view benchmark