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	95.60 203	99.09 334	98.84 65	93.32 211	96.74 226	99.72 95	86.04 266	100.00 1	98.01 155	99.43 130	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 42	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 97	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 37	100.00 1	99.74 44	100.00 1	100.00 1

mPP-MVS		98.39 56	98.20 54	98.97 93	99.97 3	96.92 140	99.95 75	98.38 185	95.04 124	98.61 142	99.80 59	93.39 118	100.00 1	98.64 116	100.00 1	99.98 57

CPTT-MVS		97.64 110	97.32 114	98.58 122	99.97 3	95.77 192	99.96 56	98.35 191	89.90 357	98.36 157	99.79 63	91.18 182	99.99 40	98.37 133	99.99 21	99.99 26

DP-MVS Recon		98.41 53	98.02 68	99.56 30	99.97 3	98.70 54	99.92 103	98.44 148	92.06 283	98.40 156	99.84 49	95.68 49	100.00 1	98.19 144	99.71 92	99.97 67

PAPR		98.52 43	98.16 58	99.58 29	99.97 3	98.77 48	99.95 75	98.43 156	95.35 118	98.03 172	99.75 81	94.03 103	99.98 52	98.11 149	99.83 81	99.99 26

aaatest				99.60 24	99.96 9	98.79 43	99.97 42	98.88 55	96.36 90	99.07 112	99.93 12		100.00 1	99.98 9	99.96 48	99.99 26

MED-MVS		99.24 8	99.12 5	99.60 24	99.96 9	98.79 43	99.97 42	98.88 55	96.91 62	99.07 112	99.92 16	97.36 18	100.00 1	99.98 9	99.98 32	100.00 1

TestfortrainingZip a		99.01 16	98.78 21	99.69 17	99.96 9	99.09 26	99.97 42	98.74 76	96.91 62	99.86 16	99.92 16	96.29 38	99.99 40	98.32 136	99.09 151	100.00 1

HFP-MVS		98.56 39	98.37 43	99.14 73	99.96 9	97.43 116	99.95 75	98.61 99	94.77 134	99.31 95	99.85 38	94.22 96	100.00 1	98.70 111	99.98 32	99.98 57

region2R		98.54 41	98.37 43	99.05 83	99.96 9	97.18 126	99.96 56	98.55 119	94.87 131	99.45 81	99.85 38	94.07 102	100.00 1	98.67 113	100.00 1	99.98 57

ACMMPR		98.50 44	98.32 47	99.05 83	99.96 9	97.18 126	99.95 75	98.60 101	94.77 134	99.31 95	99.84 49	93.73 112	100.00 1	98.70 111	99.98 32	99.98 57

NCCC		99.37 2	99.25 2	99.71 16	99.96 9	99.15 24	99.97 42	98.62 98	98.02 22	99.90 7	99.95 4	97.33 19	100.00 1	99.54 59	100.00 1	100.00 1

CP-MVS		98.45 48	98.32 47	98.87 98	99.96 9	96.62 155	99.97 42	98.39 181	94.43 152	98.90 122	99.87 32	94.30 93	100.00 1	99.04 87	99.99 21	99.99 26

test-260524					99.95 17	99.33 9		98.42 168		99.04 115		96.44 36	100.00 1	99.98 9	99.98 32

test_one_0601					99.94 18	99.30 14		98.41 174	96.63 75	99.75 42	99.93 12	97.49 11

test_0728_SECOND				99.82 8	99.94 18	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 18	97.50 112	99.94 93	98.42 168	96.22 93	99.41 87	99.78 67	94.34 90	99.96 77	98.92 96	99.95 54	99.99 26

X-MVStestdata		93.83 291	92.06 326	99.15 71	99.94 18	97.50 112	99.94 93	98.42 168	96.22 93	99.41 87	41.37 553	94.34 90	99.96 77	98.92 96	99.95 54	99.99 26

test_prior				99.43 41	99.94 18	98.49 67		98.65 88					99.80 144			99.99 26

MSLP-MVS++		99.13 9	99.01 12	99.49 37	99.94 18	98.46 68	99.98 24	98.86 59	97.10 53	99.80 28	99.94 5	95.92 45	100.00 1	99.51 60	100.00 1	100.00 1

APDe-MVS		99.06 13	98.91 15	99.51 34	99.94 18	98.76 51	99.91 111	98.39 181	97.20 51	99.46 80	99.85 38	95.53 53	99.79 146	99.86 28	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 18	97.17 129	99.95 75	98.39 181	94.70 138	98.26 163	99.81 58	91.84 173	100.00 1	98.85 102	99.97 44	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 18	98.73 52	99.87 133	98.33 196	93.97 180	99.76 41	99.87 32	94.99 69	99.75 155	98.55 120	100.00 1	99.98 57

PAPM_NR		98.12 75	97.93 78	98.70 109	99.94 18	96.13 181	99.82 167	98.43 156	94.56 142	97.52 192	99.70 101	94.40 85	99.98 52	97.00 199	99.98 32	99.99 26

MG-MVS		98.91 22	98.65 27	99.68 18	99.94 18	99.07 27	99.64 242	99.44 19	97.33 44	99.00 118	99.72 95	94.03 103	99.98 52	98.73 110	100.00 1	100.00 1

aaEdge-Enhanced		99.07 11	98.89 17	99.59 27	99.93 29	98.79 43	99.95 75	98.80 71	95.89 103	99.28 99	99.93 12	96.28 39	99.98 52	99.98 9	99.96 48	99.99 26

SED-MVS		99.28 5	99.11 8	99.77 9	99.93 29	99.30 14	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 29	99.31 12		98.41 174	97.71 31	99.84 23				100.00 1	100.00 1	100.00 1

test_241102_ONE					99.93 29	99.30 14		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 29	99.29 17	99.95 75	98.32 198	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 29	99.29 17	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 29	97.24 123	99.95 75	98.42 168	97.50 38	99.52 76	99.88 29	97.43 17	99.71 161	99.50 62	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 29	98.77 48		98.43 156		99.63 59			99.85 131

FOURS1					99.92 37	97.66 106	99.95 75	98.36 189	95.58 112	99.52 76

ZD-MVS					99.92 37	98.57 62		98.52 128	92.34 271	99.31 95	99.83 51	95.06 64	99.80 144	99.70 50	99.97 44

GST-MVS		98.27 63	97.97 72	99.17 66	99.92 37	97.57 108	99.93 100	98.39 181	94.04 178	98.80 127	99.74 88	92.98 135	100.00 1	98.16 146	99.76 89	99.93 88

TEST9					99.92 37	98.92 32	99.96 56	98.43 156	93.90 186	99.71 49	99.86 34	95.88 46	99.85 131

train_agg		98.88 23	98.65 27	99.59 27	99.92 37	98.92 32	99.96 56	98.43 156	94.35 157	99.71 49	99.86 34	95.94 43	99.85 131	99.69 51	99.98 32	99.99 26

test_8					99.92 37	98.88 35	99.96 56	98.43 156	94.35 157	99.69 51	99.85 38	95.94 43	99.85 131

PGM-MVS		98.34 58	98.13 60	98.99 90	99.92 37	97.00 136	99.75 201	99.50 17	93.90 186	99.37 92	99.76 73	93.24 128	100.00 1	97.75 176	99.96 48	99.98 57

ACMMP		97.74 103	97.44 107	98.66 113	99.92 37	96.13 181	99.18 326	99.45 18	94.84 132	96.41 245	99.71 98	91.40 176	99.99 40	97.99 157	98.03 192	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 45	99.31 12	99.95 75	98.43 156	96.48 80	99.80 28	99.93 12	97.44 15	100.00 1	99.92 17	99.98 32	100.00 1

MSC_two_6792asdad				99.93 2	99.91 45	99.80 2		98.41 174					100.00 1	99.96 13	100.00 1	100.00 1

No_MVS				99.93 2	99.91 45	99.80 2		98.41 174					100.00 1	99.96 13	100.00 1	100.00 1

HPM-MVS++		99.07 11	98.88 18	99.63 19	99.90 48	99.02 28	99.95 75	98.56 113	97.56 37	99.44 82	99.85 38	95.38 57	100.00 1	99.31 72	99.99 21	99.87 100

APD-MVS		98.62 36	98.35 46	99.41 44	99.90 48	98.51 65	99.87 133	98.36 189	94.08 173	99.74 45	99.73 92	94.08 101	99.74 157	99.42 68	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 48	98.85 38	99.24 321	98.47 140	98.14 16	99.08 110	99.91 19	93.09 132	100.00 1	99.04 87	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 51	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 51	99.24 21	99.87 133	98.44 148	97.48 39	99.64 58	99.94 5	96.68 31	99.99 40	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 51	99.25 20				99.49 79

CSCG		97.10 136	97.04 126	97.27 243	99.89 51	91.92 341	99.90 117	99.07 37	88.67 381	95.26 278	99.82 54	93.17 131	99.98 52	98.15 147	99.47 125	99.90 96

ZNCC-MVS		98.31 60	98.03 67	99.17 66	99.88 55	97.59 107	99.94 93	98.44 148	94.31 161	98.50 149	99.82 54	93.06 133	99.99 40	98.30 138	99.99 21	99.93 88

SR-MVS		98.46 47	98.30 50	98.93 96	99.88 55	97.04 135	99.84 152	98.35 191	94.92 128	99.32 94	99.80 59	93.35 120	99.78 148	99.30 73	99.95 54	99.96 75

9.14			98.38 41		99.87 57		99.91 111	98.33 196	93.22 214	99.78 39	99.89 27	94.57 81	99.85 131	99.84 30	99.97 44

SMA-MVS		98.76 29	98.48 35	99.62 22	99.87 57	98.87 36	99.86 144	98.38 185	93.19 216	99.77 40	99.94 5	95.54 51	100.00 1	99.74 44	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 59	95.39 213	99.61 249	97.78 272	96.52 78	98.61 142	99.31 157	92.73 143	99.67 169	96.77 215	99.48 122	99.06 256

lecture		98.67 33	98.46 36	99.28 53	99.86 59	97.88 93	99.97 42	99.25 30	96.07 97	99.79 37	99.70 101	92.53 152	99.98 52	99.51 60	99.48 122	99.97 67

PHI-MVS		98.41 53	98.21 53	99.03 85	99.86 59	97.10 133	99.98 24	98.80 71	90.78 334	99.62 62	99.78 67	95.30 58	100.00 1	99.80 33	99.93 65	99.99 26

MTAPA		98.29 62	97.96 75	99.30 52	99.85 62	97.93 91	99.39 294	98.28 205	95.76 106	97.18 207	99.88 29	92.74 142	100.00 1	98.67 113	99.88 77	99.99 26

LS3D		95.84 213	95.11 231	98.02 167	99.85 62	95.10 233	98.74 385	98.50 137	87.22 406	93.66 302	99.86 34	87.45 241	99.95 86	90.94 338	99.81 87	99.02 264

HPM-MVS		97.96 80	97.72 90	98.68 110	99.84 64	96.39 167	99.90 117	98.17 223	92.61 252	98.62 141	99.57 131	91.87 172	99.67 169	98.87 101	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 65	96.59 159	99.40 290	98.51 131	95.29 120	98.51 148	99.76 73	93.60 116	99.71 161	98.53 123	99.52 115	99.95 83

save fliter					99.82 66	98.79 43	99.96 56	98.40 178	97.66 33

PLC	95.54 3	97.93 83	97.89 82	98.05 165	99.82 66	94.77 246	99.92 103	98.46 142	93.93 183	97.20 205	99.27 165	95.44 56	99.97 65	97.41 183	99.51 118	99.41 199
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 68	96.60 157	99.82 167	98.30 203	93.95 182	99.37 92	99.77 71	92.84 139	99.76 154	98.95 92	99.92 68	99.97 67

EI-MVSNet-UG-set		98.14 74	97.99 70	98.60 118	99.80 69	96.27 170	99.36 300	98.50 137	95.21 122	98.30 160	99.75 81	93.29 125	99.73 160	98.37 133	99.30 140	99.81 109

SR-MVS-dyc-post		98.31 60	98.17 57	98.71 108	99.79 70	96.37 168	99.76 194	98.31 200	94.43 152	99.40 89	99.75 81	93.28 126	99.78 148	98.90 99	99.92 68	99.97 67

RE-MVS-def			98.13 60		99.79 70	96.37 168	99.76 194	98.31 200	94.43 152	99.40 89	99.75 81	92.95 136		98.90 99	99.92 68	99.97 67

HPM-MVS_fast		97.80 97	97.50 103	98.68 110	99.79 70	96.42 163	99.88 130	98.16 228	91.75 295	98.94 120	99.54 134	91.82 174	99.65 173	97.62 180	99.99 21	99.99 26

SF-MVS		98.67 33	98.40 39	99.50 35	99.77 73	98.67 55	99.90 117	98.21 218	93.53 198	99.81 26	99.89 27	94.70 77	99.86 130	99.84 30	99.93 65	99.96 75

MGCNet		99.06 13	98.84 19	99.72 14	99.76 74	99.21 23	99.99 8	99.34 25	98.70 2	99.44 82	99.75 81	93.24 128	99.99 40	99.94 15	99.41 132	99.95 83

旧先验1					99.76 74	97.52 110		98.64 91			99.85 38	95.63 50			99.94 59	99.99 26

OMC-MVS		97.28 126	97.23 118	97.41 232	99.76 74	93.36 306	99.65 238	97.95 251	96.03 98	97.41 198	99.70 101	89.61 208	99.51 179	96.73 218	98.25 182	99.38 202

新几何1				99.42 43	99.75 77	98.27 72		98.63 97	92.69 247	99.55 71	99.82 54	94.40 85	100.00 1	91.21 330	99.94 59	99.99 26

MP-MVS-pluss		98.07 78	97.64 96	99.38 49	99.74 78	98.41 70	99.74 205	98.18 222	93.35 209	96.45 238	99.85 38	92.64 147	99.97 65	98.91 98	99.89 74	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 78	98.67 55	99.77 187	98.38 185	96.73 71	99.88 13	99.74 88	94.89 71	99.59 175	99.80 33	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 78	98.56 63		98.40 178		99.65 55		94.76 74	99.75 155		99.98 32	99.99 26

原ACMM1				98.96 94	99.73 81	96.99 137		98.51 131	94.06 176	99.62 62	99.85 38	94.97 70	99.96 77	95.11 250	99.95 54	99.92 93

TSAR-MVS + GP.		98.60 37	98.51 34	98.86 99	99.73 81	96.63 154	99.97 42	97.92 256	98.07 19	98.76 133	99.55 132	95.00 68	99.94 95	99.91 20	97.68 199	99.99 26

CANet		98.27 63	97.82 87	99.63 19	99.72 83	99.10 25	99.98 24	98.51 131	97.00 59	98.52 146	99.71 98	87.80 232	99.95 86	99.75 42	99.38 134	99.83 105

reproduce_model		98.75 30	98.66 26	99.03 85	99.71 84	97.10 133	99.73 212	98.23 213	97.02 58	99.18 105	99.90 23	94.54 82	99.99 40	99.77 38	99.90 73	99.99 26

F-COLMAP		96.93 148	96.95 129	96.87 261	99.71 84	91.74 351	99.85 147	97.95 251	93.11 224	95.72 267	99.16 186	92.35 158	99.94 95	95.32 246	99.35 138	98.92 272

reproduce-ours		98.78 27	98.67 24	99.09 80	99.70 86	97.30 120	99.74 205	98.25 209	97.10 53	99.10 108	99.90 23	94.59 78	99.99 40	99.77 38	99.91 71	99.99 26

our_new_method		98.78 27	98.67 24	99.09 80	99.70 86	97.30 120	99.74 205	98.25 209	97.10 53	99.10 108	99.90 23	94.59 78	99.99 40	99.77 38	99.91 71	99.99 26

SD-MVS		98.92 21	98.70 23	99.56 30	99.70 86	98.73 52	99.94 93	98.34 195	96.38 86	99.81 26	99.76 73	94.59 78	99.98 52	99.84 30	99.96 48	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	95.59 306	99.67 89	86.91 438	99.95 75	98.89 52	97.60 34	99.90 7	99.76 73	96.54 34	99.98 52	99.94 15	99.82 85	99.88 98

ACMMP_NAP		98.49 45	98.14 59	99.54 32	99.66 90	98.62 61	99.85 147	98.37 188	94.68 139	99.53 74	99.83 51	92.87 138	100.00 1	98.66 115	99.84 80	99.99 26

DeepPCF-MVS	95.94 2	97.71 107	98.98 13	93.92 380	99.63 91	81.76 475	99.96 56	98.56 113	99.47 1	99.19 104	99.99 1	94.16 100	100.00 1	99.92 17	99.93 65	100.00 1

EPNet		98.49 45	98.40 39	98.77 105	99.62 92	96.80 148	99.90 117	99.51 16	97.60 34	99.20 102	99.36 152	93.71 113	99.91 112	97.99 157	98.71 167	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 93	99.33 9	99.99 8	99.76 6	98.39 4	99.39 91	99.80 59	90.49 197	99.96 77	99.89 22	99.43 130	99.98 57

PVSNet_BlendedMVS		96.05 203	95.82 196	96.72 267	99.59 93	96.99 137	99.95 75	99.10 34	94.06 176	98.27 161	95.80 376	89.00 220	99.95 86	99.12 81	87.53 365	93.24 438

PVSNet_Blended		97.94 82	97.64 96	98.83 100	99.59 93	96.99 137	100.00 1	99.10 34	95.38 117	98.27 161	99.08 191	89.00 220	99.95 86	99.12 81	99.25 142	99.57 162

PatchMatch-RL		96.04 204	95.40 214	97.95 170	99.59 93	95.22 227	99.52 271	99.07 37	93.96 181	96.49 236	98.35 285	82.28 328	99.82 143	90.15 354	99.22 145	98.81 280

dcpmvs_2		97.42 121	98.09 63	95.42 313	99.58 97	87.24 434	99.23 322	96.95 408	94.28 164	98.93 121	99.73 92	94.39 88	99.16 208	99.89 22	99.82 85	99.86 102

test222					99.55 98	97.41 118	99.34 302	98.55 119	91.86 289	99.27 100	99.83 51	93.84 110			99.95 54	99.99 26

CNLPA		97.76 101	97.38 110	98.92 97	99.53 99	96.84 142	99.87 133	98.14 232	93.78 190	96.55 234	99.69 105	92.28 160	99.98 52	97.13 194	99.44 129	99.93 88

API-MVS		97.86 88	97.66 94	98.47 135	99.52 100	95.41 211	99.47 281	98.87 58	91.68 298	98.84 124	99.85 38	92.34 159	99.99 40	98.44 128	99.96 48	100.00 1

PVSNet	91.05 13	97.13 135	96.69 144	98.45 138	99.52 100	95.81 190	99.95 75	99.65 12	94.73 136	99.04 115	99.21 178	84.48 303	99.95 86	94.92 256	98.74 166	99.58 160

114514_t		97.41 122	96.83 135	99.14 73	99.51 102	97.83 95	99.89 127	98.27 207	88.48 386	99.06 114	99.66 116	90.30 200	99.64 174	96.32 230	99.97 44	99.96 75

cl22		93.77 296	93.25 296	95.33 317	99.49 103	94.43 258	99.61 249	98.09 235	90.38 345	89.16 374	95.61 385	90.56 195	97.34 358	91.93 321	84.45 388	94.21 376

testdata				98.42 142	99.47 104	95.33 217		98.56 113	93.78 190	99.79 37	99.85 38	93.64 115	99.94 95	94.97 254	99.94 59	100.00 1

MAR-MVS		97.43 117	97.19 120	98.15 158	99.47 104	94.79 245	99.05 345	98.76 73	92.65 250	98.66 138	99.82 54	88.52 226	99.98 52	98.12 148	99.63 99	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		94.54 264	93.42 286	97.91 176	99.46 106	94.04 277	98.93 364	97.48 309	81.15 463	90.04 345	99.55 132	87.02 249	99.95 86	88.97 369	98.11 188	99.73 120

MVS_111021_LR		98.42 52	98.38 41	98.53 130	99.39 107	95.79 191	99.87 133	99.86 2	96.70 72	98.78 128	99.79 63	92.03 169	99.90 114	99.17 80	99.86 79	99.88 98

CHOSEN 280x420		99.01 16	99.03 11	98.95 95	99.38 108	98.87 36	98.46 404	99.42 21	97.03 57	99.02 117	99.09 190	99.35 2	98.21 320	99.73 46	99.78 88	99.77 116

MVS_111021_HR		98.72 31	98.62 29	99.01 89	99.36 109	97.18 126	99.93 100	99.90 1	96.81 69	98.67 137	99.77 71	93.92 105	99.89 119	99.27 75	99.94 59	99.96 75

fmvsm_s_conf0.5_n_11		98.03 79	97.89 82	98.46 137	99.35 110	97.76 99	99.99 8	98.04 242	98.20 9	99.90 7	99.78 67	86.21 264	99.95 86	99.89 22	99.68 94	97.65 319

DPM-MVS		98.83 24	98.46 36	99.97 1	99.33 111	99.92 1	99.96 56	98.44 148	97.96 23	99.55 71	99.94 5	97.18 23	100.00 1	93.81 287	99.94 59	99.98 57

TAPA-MVS	92.12 8	94.42 272	93.60 278	96.90 260	99.33 111	91.78 350	99.78 181	98.00 245	89.89 358	94.52 287	99.47 138	91.97 170	99.18 205	69.90 487	99.52 115	99.73 120
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
reproduce_monomvs		95.38 236	95.07 233	96.32 283	99.32 113	96.60 157	99.76 194	98.85 62	96.65 74	87.83 403	96.05 373	99.52 1	98.11 325	96.58 222	81.07 418	94.25 369

fmvsm_s_conf0.5_n_9		98.15 73	98.02 68	98.55 124	99.28 114	95.84 189	99.99 8	98.57 107	98.17 13	99.93 3	99.74 88	87.04 248	99.97 65	99.86 28	99.59 109	99.83 105

SPE-MVS-test		97.88 86	97.94 77	97.70 196	99.28 114	95.20 228	99.98 24	97.15 367	95.53 114	99.62 62	99.79 63	92.08 168	98.38 302	98.75 109	99.28 141	99.52 174

test_fmvsm_n_1920		98.44 49	98.61 30	97.92 174	99.27 116	95.18 229	100.00 1	98.90 50	98.05 20	99.80 28	99.73 92	92.64 147	99.99 40	99.58 58	99.51 118	98.59 290

fmvsm_s_conf0.5_n_10		98.24 69	97.90 80	99.26 55	99.24 117	97.88 93	99.99 8	98.76 73	98.20 9	99.92 5	99.74 88	85.97 268	99.94 95	99.72 47	99.53 114	99.96 75

fmvsm_l_conf0.5_n_a		99.00 18	98.91 15	99.28 53	99.21 118	97.91 92	99.98 24	98.85 62	98.25 5	99.92 5	99.75 81	94.72 75	99.97 65	99.87 26	99.64 98	99.95 83

fmvsm_s_conf0.5_n_8		98.38 57	98.05 66	99.35 50	99.20 119	98.12 78	99.98 24	98.81 67	98.22 7	99.80 28	99.71 98	87.37 243	99.97 65	99.91 20	99.48 122	99.97 67

test_yl		97.83 92	97.37 111	99.21 60	99.18 120	97.98 87	99.64 242	99.27 27	91.43 307	97.88 182	98.99 208	95.84 47	99.84 139	98.82 103	95.32 292	99.79 112

DCV-MVSNet		97.83 92	97.37 111	99.21 60	99.18 120	97.98 87	99.64 242	99.27 27	91.43 307	97.88 182	98.99 208	95.84 47	99.84 139	98.82 103	95.32 292	99.79 112

fmvsm_l_conf0.5_n		98.94 19	98.84 19	99.25 56	99.17 122	97.81 97	99.98 24	98.86 59	98.25 5	99.90 7	99.76 73	94.21 98	99.97 65	99.87 26	99.52 115	99.98 57

DeepC-MVS	94.51 4	96.92 149	96.40 160	98.45 138	99.16 123	95.90 187	99.66 237	98.06 239	96.37 89	94.37 293	99.49 137	83.29 321	99.90 114	97.63 179	99.61 105	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	99.15 124	98.65 59	100.00 1	98.58 105	97.70 32	98.21 167	99.24 174	92.58 150	99.94 95	98.63 118	99.94 59	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	99.12 125	98.29 71	99.98 24	98.64 91	98.14 16	99.86 16	99.76 73	87.99 231	99.97 65	99.72 47	99.54 112	99.91 95

fmvsm_l_conf0.5_n_9		98.55 40	98.23 51	99.49 37	99.10 126	98.50 66	99.99 8	98.70 80	98.14 16	99.94 2	99.68 112	89.02 219	99.98 52	99.89 22	99.61 105	99.99 26

CS-MVS		97.79 99	97.91 79	97.43 228	99.10 126	94.42 259	99.99 8	97.10 381	95.07 123	99.68 52	99.75 81	92.95 136	98.34 306	98.38 131	99.14 147	99.54 168

Anonymous202405211		93.10 314	91.99 327	96.40 279	99.10 126	89.65 403	98.88 370	97.93 253	83.71 446	94.00 299	98.75 246	68.79 443	99.88 125	95.08 251	91.71 325	99.68 131

fmvsm_s_conf0.5_n		97.80 97	97.85 85	97.67 197	99.06 129	94.41 260	99.98 24	98.97 43	97.34 42	99.63 59	99.69 105	87.27 244	99.97 65	99.62 56	99.06 153	98.62 289

HyFIR lowres test		96.66 167	96.43 157	97.36 237	99.05 130	93.91 283	99.70 228	99.80 3	90.54 340	96.26 248	98.08 298	92.15 166	98.23 319	96.84 209	95.46 287	99.93 88

LFMVS		94.75 258	93.56 281	98.30 148	99.03 131	95.70 197	98.74 385	97.98 248	87.81 399	98.47 150	99.39 149	67.43 452	99.53 176	98.01 155	95.20 295	99.67 133

fmvsm_s_conf0.5_n_4		97.75 102	97.86 84	97.42 229	99.01 132	94.69 249	99.97 42	98.76 73	97.91 25	99.87 14	99.76 73	86.70 255	99.93 105	99.67 53	99.12 150	97.64 320

fmvsm_s_conf0.5_n_2		97.59 112	97.28 115	98.53 130	99.01 132	98.15 73	99.98 24	98.59 103	98.17 13	99.75 42	99.63 122	81.83 334	99.94 95	99.78 36	98.79 164	97.51 328

AllTest		92.48 331	91.64 334	95.00 326	99.01 132	88.43 421	98.94 361	96.82 424	86.50 416	88.71 379	98.47 280	74.73 417	99.88 125	85.39 416	96.18 260	96.71 334

TestCases				95.00 326	99.01 132	88.43 421		96.82 424	86.50 416	88.71 379	98.47 280	74.73 417	99.88 125	85.39 416	96.18 260	96.71 334

COLMAP_ROB	90.47 14	92.18 338	91.49 340	94.25 361	99.00 136	88.04 427	98.42 410	96.70 431	82.30 458	88.43 391	99.01 201	76.97 392	99.85 131	86.11 412	96.50 251	94.86 345
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	98.99 137	98.07 81	99.98 24	98.81 67	98.18 12	99.89 11	99.70 101	84.15 307	99.97 65	99.76 41	99.50 120	98.39 297

test_fmvs1		95.35 237	95.68 203	94.36 357	98.99 137	84.98 450	99.96 56	96.65 433	97.60 34	99.73 47	98.96 214	71.58 433	99.93 105	98.31 137	99.37 135	98.17 303

HY-MVS	92.50 7	97.79 99	97.17 122	99.63 19	98.98 139	99.32 11	97.49 440	99.52 14	95.69 109	98.32 159	97.41 320	93.32 122	99.77 151	98.08 152	95.75 276	99.81 109

VNet		97.21 131	96.57 149	99.13 77	98.97 140	97.82 96	99.03 348	99.21 32	94.31 161	99.18 105	98.88 227	86.26 263	99.89 119	98.93 94	94.32 305	99.69 130

thres200		96.96 145	96.21 168	99.22 59	98.97 140	98.84 39	99.85 147	99.71 7	93.17 218	96.26 248	98.88 227	89.87 205	99.51 179	94.26 275	94.91 297	99.31 220

tfpn200view9		96.79 154	95.99 179	99.19 62	98.94 142	98.82 40	99.78 181	99.71 7	92.86 234	96.02 258	98.87 234	89.33 212	99.50 181	93.84 284	94.57 301	99.27 230

thres400		96.78 156	95.99 179	99.16 69	98.94 142	98.82 40	99.78 181	99.71 7	92.86 234	96.02 258	98.87 234	89.33 212	99.50 181	93.84 284	94.57 301	99.16 243

sasdasda		97.09 138	96.32 162	99.39 46	98.93 144	98.95 30	99.72 216	97.35 323	94.45 148	97.88 182	99.42 142	86.71 253	99.52 177	98.48 125	93.97 311	99.72 122

Anonymous20231211		89.86 389	88.44 397	94.13 369	98.93 144	90.68 381	98.54 401	98.26 208	76.28 482	86.73 417	95.54 389	70.60 439	97.56 351	90.82 341	80.27 427	94.15 385

canonicalmvs		97.09 138	96.32 162	99.39 46	98.93 144	98.95 30	99.72 216	97.35 323	94.45 148	97.88 182	99.42 142	86.71 253	99.52 177	98.48 125	93.97 311	99.72 122

SDMVSNet		94.80 253	93.96 268	97.33 240	98.92 147	95.42 210	99.59 254	98.99 40	92.41 267	92.55 317	97.85 310	75.81 407	98.93 223	97.90 164	91.62 326	97.64 320

sd_testset		93.55 303	92.83 307	95.74 304	98.92 147	90.89 377	98.24 418	98.85 62	92.41 267	92.55 317	97.85 310	71.07 438	98.68 264	93.93 281	91.62 326	97.64 320

EPNet_dtu		95.71 224	95.39 215	96.66 269	98.92 147	93.41 302	99.57 260	98.90 50	96.19 95	97.52 192	98.56 270	92.65 146	97.36 356	77.89 468	98.33 177	99.20 240
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	98.92 147	99.28 19	99.89 127	99.52 14	95.58 112	98.24 165	99.39 149	93.33 121	99.74 157	97.98 159	95.58 285	99.78 115

CHOSEN 1792x2688		96.81 153	96.53 150	97.64 201	98.91 151	93.07 309	99.65 238	99.80 3	95.64 110	95.39 274	98.86 236	84.35 305	99.90 114	96.98 201	99.16 146	99.95 83

thres100view900		96.74 162	95.92 191	99.18 63	98.90 152	98.77 48	99.74 205	99.71 7	92.59 254	95.84 261	98.86 236	89.25 214	99.50 181	93.84 284	94.57 301	99.27 230

thres600view7		96.69 165	95.87 195	99.14 73	98.90 152	98.78 47	99.74 205	99.71 7	92.59 254	95.84 261	98.86 236	89.25 214	99.50 181	93.44 297	94.50 304	99.16 243

MSDG		94.37 274	93.36 293	97.40 233	98.88 154	93.95 282	99.37 298	97.38 318	85.75 427	90.80 336	99.17 183	84.11 309	99.88 125	86.35 408	98.43 175	98.36 299

MGCFI-Net		97.00 143	96.22 167	99.34 51	98.86 155	98.80 42	99.67 236	97.30 335	94.31 161	97.77 188	99.41 146	86.36 261	99.50 181	98.38 131	93.90 313	99.72 122

h-mvs33		94.92 250	94.36 253	96.59 272	98.85 156	91.29 369	98.93 364	98.94 44	95.90 101	98.77 130	98.42 283	90.89 190	99.77 151	97.80 169	70.76 473	98.72 286

Anonymous20240529		92.10 339	90.65 351	96.47 274	98.82 157	90.61 383	98.72 387	98.67 87	75.54 486	93.90 301	98.58 268	66.23 457	99.90 114	94.70 265	90.67 329	98.90 275

PVSNet_Blended_VisFu		97.27 127	96.81 137	98.66 113	98.81 158	96.67 153	99.92 103	98.64 91	94.51 144	96.38 246	98.49 276	89.05 218	99.88 125	97.10 196	98.34 176	99.43 195

PS-MVSNAJ		98.44 49	98.20 54	99.16 69	98.80 159	98.92 32	99.54 269	98.17 223	97.34 42	99.85 20	99.85 38	91.20 179	99.89 119	99.41 69	99.67 95	98.69 287

CANet_DTU		96.76 157	96.15 171	98.60 118	98.78 160	97.53 109	99.84 152	97.63 286	97.25 50	99.20 102	99.64 119	81.36 340	99.98 52	92.77 309	98.89 158	98.28 301

mvsany_test1		97.82 95	97.90 80	97.55 212	98.77 161	93.04 312	99.80 175	97.93 253	96.95 61	99.61 69	99.68 112	90.92 187	99.83 141	99.18 79	98.29 181	99.80 111

alignmvs		97.81 96	97.33 113	99.25 56	98.77 161	98.66 57	99.99 8	98.44 148	94.40 156	98.41 154	99.47 138	93.65 114	99.42 191	98.57 119	94.26 307	99.67 133

SymmetryMVS		97.64 110	97.46 104	98.17 154	98.74 163	95.39 213	99.61 249	99.26 29	96.52 78	98.61 142	99.31 157	92.73 143	99.67 169	96.77 215	95.63 283	99.45 191

SteuartSystems-ACMMP		99.02 15	98.97 14	99.18 63	98.72 164	97.71 101	99.98 24	98.44 148	96.85 64	99.80 28	99.91 19	97.57 9	99.85 131	99.44 67	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	98.69 165	98.66 57	99.52 271	98.08 238	97.05 56	99.86 16	99.86 34	90.65 192	99.71 161	99.39 71	98.63 168	98.69 287

miper_enhance_ethall		94.36 276	93.98 267	95.49 307	98.68 166	95.24 225	99.73 212	97.29 343	93.28 213	89.86 350	95.97 374	94.37 89	97.05 379	92.20 313	84.45 388	94.19 377

fmvsm_s_conf0.5_n_5		98.08 77	97.71 92	99.17 66	98.67 167	97.69 105	99.99 8	98.57 107	97.40 40	99.89 11	99.69 105	85.99 267	99.96 77	99.80 33	99.40 133	99.85 103

ETVMVS		97.03 142	96.64 145	98.20 153	98.67 167	97.12 130	99.89 127	98.57 107	91.10 320	98.17 168	98.59 265	93.86 109	98.19 321	95.64 243	95.24 294	99.28 227

test2506		97.53 114	97.19 120	98.58 122	98.66 169	96.90 141	98.81 379	99.77 5	94.93 126	97.95 176	98.96 214	92.51 153	99.20 203	94.93 255	98.15 185	99.64 139

ECVR-MVS		95.66 228	95.05 234	97.51 217	98.66 169	93.71 287	98.85 376	98.45 143	94.93 126	96.86 219	98.96 214	75.22 413	99.20 203	95.34 245	98.15 185	99.64 139

BridgeMVS		98.27 63	97.99 70	99.11 78	98.64 171	98.43 69	99.47 281	97.79 268	94.56 142	99.74 45	98.35 285	94.33 92	99.25 197	99.12 81	99.96 48	99.64 139

fmvsm_s_conf0.5_n_a		97.73 105	97.72 90	97.77 189	98.63 172	94.26 268	99.96 56	98.92 49	97.18 52	99.75 42	99.69 105	87.00 250	99.97 65	99.46 65	98.89 158	99.08 254

MVSMamba_PlusPlus		97.83 92	97.45 106	98.99 90	98.60 173	98.15 73	99.58 256	97.74 277	90.34 348	99.26 101	98.32 288	94.29 94	99.23 198	99.03 90	99.89 74	99.58 160

balanced_ft_v1		96.88 150	96.52 151	97.96 169	98.60 173	94.94 238	99.41 289	97.56 298	93.53 198	99.42 86	97.89 309	83.33 320	99.31 194	99.29 74	99.62 100	99.64 139

PRO-TEST		95.68 227	96.10 173	94.41 355	98.58 175	84.60 454	99.77 187	96.84 420	94.33 160	97.96 175	98.12 296	80.76 351	99.12 209	99.21 78	99.36 136	99.53 172

testing222		97.08 141	96.75 140	98.06 164	98.56 176	96.82 143	99.85 147	98.61 99	92.53 262	98.84 124	98.84 240	93.36 119	98.30 311	95.84 239	94.30 306	99.05 258

test1111		95.57 231	94.98 237	97.37 235	98.56 176	93.37 305	98.86 374	98.45 143	94.95 125	96.63 228	98.95 219	75.21 414	99.11 210	95.02 252	98.14 187	99.64 139

MVSTER		95.53 232	95.22 226	96.45 277	98.56 176	97.72 100	99.91 111	97.67 282	92.38 270	91.39 327	97.14 327	97.24 20	97.30 363	94.80 261	87.85 358	94.34 364

testing3-2		97.72 106	97.43 109	98.60 118	98.55 179	97.11 132	100.00 1	99.23 31	93.78 190	97.90 178	98.73 248	95.50 54	99.69 165	98.53 123	94.63 299	98.99 266

VDD-MVS		93.77 296	92.94 305	96.27 284	98.55 179	90.22 392	98.77 384	97.79 268	90.85 326	96.82 223	99.42 142	61.18 477	99.77 151	98.95 92	94.13 308	98.82 279

tpmvs		94.28 278	93.57 280	96.40 279	98.55 179	91.50 367	95.70 479	98.55 119	87.47 401	92.15 320	94.26 443	91.42 175	98.95 222	88.15 386	95.85 271	98.76 282

UGNet		95.33 238	94.57 249	97.62 205	98.55 179	94.85 240	98.67 393	99.32 26	95.75 107	96.80 225	96.27 363	72.18 430	99.96 77	94.58 268	99.05 154	98.04 308
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	95.18 241	94.10 261	98.43 140	98.55 179	95.99 185	97.91 433	97.31 334	90.35 347	89.48 363	99.22 175	85.19 285	99.89 119	90.40 351	98.47 174	99.41 199
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
UWE-MVS-28		95.95 207	96.49 152	94.34 358	98.51 184	89.99 397	99.39 294	98.57 107	93.14 221	97.33 201	98.31 290	93.44 117	94.68 470	93.69 294	95.98 265	98.34 300

UWE-MVS		96.79 154	96.72 142	97.00 254	98.51 184	93.70 288	99.71 221	98.60 101	92.96 229	97.09 209	98.34 287	96.67 33	98.85 230	92.11 319	96.50 251	98.44 295

myMVS_eth3d28		97.86 88	97.59 100	98.68 110	98.50 186	97.26 122	99.92 103	98.55 119	93.79 189	98.26 163	98.75 246	95.20 59	99.48 187	98.93 94	96.40 254	99.29 225

test_vis1_n_1920		95.44 234	95.31 222	95.82 301	98.50 186	88.74 415	99.98 24	97.30 335	97.84 28	99.85 20	99.19 181	66.82 455	99.97 65	98.82 103	99.46 127	98.76 282

BH-w/o		95.71 224	95.38 220	96.68 268	98.49 188	92.28 332	99.84 152	97.50 307	92.12 280	92.06 323	98.79 244	84.69 298	98.67 266	95.29 247	99.66 96	99.09 252

baseline1		95.78 220	94.86 240	98.54 128	98.47 189	98.07 81	99.06 341	97.99 246	92.68 248	94.13 298	98.62 262	93.28 126	98.69 263	93.79 289	85.76 375	98.84 278

fmvsm_s_conf0.5_n_7		97.70 108	97.74 89	97.59 210	98.44 190	95.16 231	99.97 42	98.65 88	97.95 24	99.62 62	99.78 67	86.09 265	99.94 95	99.69 51	99.50 120	97.66 318

EPMVS		96.53 176	96.01 178	98.09 162	98.43 191	96.12 183	96.36 466	99.43 20	93.53 198	97.64 190	95.04 417	94.41 84	98.38 302	91.13 332	98.11 188	99.75 118

kuosan		93.17 311	92.60 313	94.86 333	98.40 192	89.54 405	98.44 406	98.53 126	84.46 441	88.49 386	97.92 306	90.57 194	97.05 379	83.10 433	93.49 316	97.99 309

WBMVS		94.52 267	94.03 265	95.98 291	98.38 193	96.68 152	99.92 103	97.63 286	90.75 335	89.64 358	95.25 410	96.77 27	96.90 392	94.35 273	83.57 395	94.35 362

UBG		97.84 91	97.69 93	98.29 149	98.38 193	96.59 159	99.90 117	98.53 126	93.91 185	98.52 146	98.42 283	96.77 27	99.17 206	98.54 121	96.20 259	99.11 250

sss		97.57 113	97.03 127	99.18 63	98.37 195	98.04 84	99.73 212	99.38 22	93.46 203	98.76 133	99.06 195	91.21 178	99.89 119	96.33 229	97.01 237	99.62 147

testing11		97.48 116	97.27 116	98.10 161	98.36 196	96.02 184	99.92 103	98.45 143	93.45 205	98.15 169	98.70 252	95.48 55	99.22 199	97.85 166	95.05 296	99.07 255

BH-untuned		95.18 241	94.83 241	96.22 285	98.36 196	91.22 370	99.80 175	97.32 333	90.91 324	91.08 330	98.67 254	83.51 313	98.54 283	94.23 276	99.61 105	98.92 272

testing91		97.16 133	96.90 131	97.97 168	98.35 198	95.67 200	99.91 111	98.42 168	92.91 232	97.33 201	98.72 249	94.81 73	99.21 200	96.98 201	94.63 299	99.03 263

testing99		97.17 132	96.91 130	97.95 170	98.35 198	95.70 197	99.91 111	98.43 156	92.94 230	97.36 199	98.72 249	94.83 72	99.21 200	97.00 199	94.64 298	98.95 268

ET-MVSNet_ETH3D		94.37 274	93.28 295	97.64 201	98.30 200	97.99 86	99.99 8	97.61 292	94.35 157	71.57 494	99.45 141	96.23 40	95.34 459	96.91 207	85.14 382	99.59 154

AUN-MVS		93.28 308	92.60 313	95.34 316	98.29 201	90.09 395	99.31 308	98.56 113	91.80 293	96.35 247	98.00 301	89.38 211	98.28 314	92.46 310	69.22 480	97.64 320

FMVSNet3		92.69 326	91.58 336	95.99 290	98.29 201	97.42 117	99.26 320	97.62 289	89.80 359	89.68 354	95.32 404	81.62 338	96.27 433	87.01 404	85.65 376	94.29 366

PMMVS		96.76 157	96.76 139	96.76 265	98.28 203	92.10 336	99.91 111	97.98 248	94.12 171	99.53 74	99.39 149	86.93 251	98.73 254	96.95 204	97.73 196	99.45 191

hse-mvs2		94.38 273	94.08 264	95.31 318	98.27 204	90.02 396	99.29 315	98.56 113	95.90 101	98.77 130	98.00 301	90.89 190	98.26 318	97.80 169	69.20 481	97.64 320

PVSNet_0	88.03 19	91.80 346	90.27 360	96.38 281	98.27 204	90.46 387	99.94 93	99.61 13	93.99 179	86.26 427	97.39 322	71.13 437	99.89 119	98.77 107	67.05 487	98.79 281

UA-Net		96.54 175	95.96 185	98.27 150	98.23 206	95.71 196	98.00 430	98.45 143	93.72 194	98.41 154	99.27 165	88.71 225	99.66 172	91.19 331	97.69 197	99.44 194

test_cas_vis1_n_1920		96.59 171	96.23 165	97.65 200	98.22 207	94.23 270	99.99 8	97.25 349	97.77 29	99.58 70	99.08 191	77.10 387	99.97 65	97.64 178	99.45 128	98.74 284

FE-MVS		95.70 226	95.01 236	97.79 185	98.21 208	94.57 251	95.03 480	98.69 82	88.90 375	97.50 194	96.19 365	92.60 149	99.49 186	89.99 356	97.94 194	99.31 220

GG-mvs-BLEND				98.54 128	98.21 208	98.01 85	93.87 485	98.52 128		97.92 177	97.92 306	99.02 3	97.94 338	98.17 145	99.58 110	99.67 133

mvs_anonymous		95.65 229	95.03 235	97.53 214	98.19 210	95.74 194	99.33 303	97.49 308	90.87 325	90.47 339	97.10 329	88.23 228	97.16 370	95.92 237	97.66 200	99.68 131

MVS_Test		96.46 179	95.74 199	98.61 117	98.18 211	97.23 124	99.31 308	97.15 367	91.07 321	98.84 124	97.05 333	88.17 229	98.97 219	94.39 270	97.50 202	99.61 151

BH-RMVSNet		95.18 241	94.31 256	97.80 183	98.17 212	95.23 226	99.76 194	97.53 303	92.52 263	94.27 296	99.25 172	76.84 394	98.80 243	90.89 340	99.54 112	99.35 210

dongtai		91.55 352	91.13 345	92.82 410	98.16 213	86.35 439	99.47 281	98.51 131	83.24 449	85.07 438	97.56 315	90.33 199	94.94 465	76.09 476	91.73 324	97.18 331

RPSCF		91.80 346	92.79 309	88.83 453	98.15 214	69.87 499	98.11 426	96.60 435	83.93 444	94.33 294	99.27 165	79.60 365	99.46 190	91.99 320	93.16 321	97.18 331

ETV-MVS		97.92 84	97.80 88	98.25 151	98.14 215	96.48 161	99.98 24	97.63 286	95.61 111	99.29 98	99.46 140	92.55 151	98.82 234	99.02 91	98.54 172	99.46 186

IS-MVSNet		96.29 192	95.90 192	97.45 224	98.13 216	94.80 244	99.08 336	97.61 292	92.02 285	95.54 272	98.96 214	90.64 193	98.08 327	93.73 292	97.41 206	99.47 184

test_fmvsmconf_n		98.43 51	98.32 47	98.78 103	98.12 217	96.41 164	99.99 8	98.83 66	98.22 7	99.67 53	99.64 119	91.11 183	99.94 95	99.67 53	99.62 100	99.98 57

fmvsm_s_conf0.1_n_2		97.25 128	96.85 134	98.43 140	98.08 218	98.08 80	99.92 103	97.76 276	98.05 20	99.65 55	99.58 128	80.88 348	99.93 105	99.59 57	98.17 183	97.29 329

ab-mvs		94.69 259	93.42 286	98.51 133	98.07 219	96.26 171	96.49 464	98.68 84	90.31 349	94.54 286	97.00 336	76.30 402	99.71 161	95.98 236	93.38 319	99.56 163

XVG-OURS-SEG-HR		94.79 254	94.70 248	95.08 323	98.05 220	89.19 407	99.08 336	97.54 301	93.66 195	94.87 281	99.58 128	78.78 373	99.79 146	97.31 186	93.40 318	96.25 338

EIA-MVS		97.53 114	97.46 104	97.76 191	98.04 221	94.84 241	99.98 24	97.61 292	94.41 155	97.90 178	99.59 125	92.40 157	98.87 227	98.04 154	99.13 148	99.59 154

XVG-OURS		94.82 251	94.74 247	95.06 324	98.00 222	89.19 407	99.08 336	97.55 299	94.10 172	94.71 283	99.62 123	80.51 356	99.74 157	96.04 235	93.06 323	96.25 338

mvsmamba		96.94 146	96.73 141	97.55 212	97.99 223	94.37 264	99.62 245	97.70 279	93.13 222	98.42 153	97.92 306	88.02 230	98.75 252	98.78 106	99.01 155	99.52 174

dp		95.05 245	94.43 251	96.91 258	97.99 223	92.73 320	96.29 469	97.98 248	89.70 360	95.93 260	94.67 432	93.83 111	98.45 289	86.91 407	96.53 250	99.54 168

tpmrst		96.27 194	95.98 181	97.13 249	97.96 225	93.15 308	96.34 467	98.17 223	92.07 281	98.71 136	95.12 414	93.91 106	98.73 254	94.91 258	96.62 248	99.50 180

TR-MVS		94.54 264	93.56 281	97.49 222	97.96 225	94.34 266	98.71 388	97.51 306	90.30 350	94.51 288	98.69 253	75.56 408	98.77 248	92.82 308	95.99 264	99.35 210

Vis-MVSNet (Re-imp)		96.32 189	95.98 181	97.35 239	97.93 227	94.82 243	99.47 281	98.15 231	91.83 290	95.09 279	99.11 189	91.37 177	97.47 354	93.47 296	97.43 203	99.74 119

MDTV_nov1_ep13			95.69 201		97.90 228	94.15 274	95.98 475	98.44 148	93.12 223	97.98 174	95.74 378	95.10 62	98.58 276	90.02 355	96.92 239

Fast-Effi-MVS+		95.02 247	94.19 259	97.52 216	97.88 229	94.55 252	99.97 42	97.08 385	88.85 377	94.47 289	97.96 305	84.59 300	98.41 294	89.84 358	97.10 227	99.59 154

ADS-MVSNet2		93.80 295	93.88 271	93.55 393	97.87 230	85.94 444	94.24 481	96.84 420	90.07 353	96.43 243	94.48 437	90.29 201	95.37 458	87.44 393	97.23 214	99.36 206

ADS-MVSNet		94.79 254	94.02 266	97.11 251	97.87 230	93.79 284	94.24 481	98.16 228	90.07 353	96.43 243	94.48 437	90.29 201	98.19 321	87.44 393	97.23 214	99.36 206

Effi-MVS+		96.30 191	95.69 201	98.16 155	97.85 232	96.26 171	97.41 443	97.21 357	90.37 346	98.65 140	98.58 268	86.61 257	98.70 261	97.11 195	97.37 208	99.52 174

PatchmatchNet		95.94 208	95.45 210	97.39 234	97.83 233	94.41 260	96.05 473	98.40 178	92.86 234	97.09 209	95.28 409	94.21 98	98.07 329	89.26 367	98.11 188	99.70 125
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
cascas		94.64 262	93.61 276	97.74 193	97.82 234	96.26 171	99.96 56	97.78 272	85.76 425	94.00 299	97.54 316	76.95 393	99.21 200	97.23 191	95.43 289	97.76 317

1112_ss		96.01 205	95.20 227	98.42 142	97.80 235	96.41 164	99.65 238	96.66 432	92.71 245	92.88 313	99.40 147	92.16 165	99.30 195	91.92 322	93.66 314	99.55 164

E3new		96.75 159	96.43 157	97.71 194	97.79 236	94.83 242	99.80 175	97.33 327	93.52 201	97.49 195	99.31 157	87.73 233	98.83 231	97.52 181	97.40 207	99.48 183

Test_1112_low_res		95.72 222	94.83 241	98.42 142	97.79 236	96.41 164	99.65 238	96.65 433	92.70 246	92.86 314	96.13 369	92.15 166	99.30 195	91.88 323	93.64 315	99.55 164

Effi-MVS+-dtu		94.53 266	95.30 223	92.22 418	97.77 238	82.54 468	99.59 254	97.06 394	94.92 128	95.29 276	95.37 402	85.81 269	97.89 339	94.80 261	97.07 228	96.23 340

tpm cat1		93.51 304	92.52 319	96.47 274	97.77 238	91.47 368	96.13 471	98.06 239	80.98 464	92.91 312	93.78 448	89.66 206	98.87 227	87.03 403	96.39 255	99.09 252

FA-MVS(test-final)		95.86 211	95.09 232	98.15 158	97.74 240	95.62 202	96.31 468	98.17 223	91.42 309	96.26 248	96.13 369	90.56 195	99.47 189	92.18 314	97.07 228	99.35 210

xiu_mvs_v1_base_debu		97.43 117	97.06 123	98.55 124	97.74 240	98.14 75	99.31 308	97.86 262	96.43 83	99.62 62	99.69 105	85.56 277	99.68 166	99.05 84	98.31 178	97.83 313

xiu_mvs_v1_base		97.43 117	97.06 123	98.55 124	97.74 240	98.14 75	99.31 308	97.86 262	96.43 83	99.62 62	99.69 105	85.56 277	99.68 166	99.05 84	98.31 178	97.83 313

xiu_mvs_v1_base_debi		97.43 117	97.06 123	98.55 124	97.74 240	98.14 75	99.31 308	97.86 262	96.43 83	99.62 62	99.69 105	85.56 277	99.68 166	99.05 84	98.31 178	97.83 313

EPP-MVSNet		96.69 165	96.60 147	96.96 256	97.74 240	93.05 311	99.37 298	98.56 113	88.75 379	95.83 263	99.01 201	96.01 41	98.56 279	96.92 205	97.20 216	99.25 234

gg-mvs-nofinetune		93.51 304	91.86 331	98.47 135	97.72 245	97.96 90	92.62 496	98.51 131	74.70 489	97.33 201	69.59 525	98.91 4	97.79 342	97.77 174	99.56 111	99.67 133

IB-MVS	92.85 6	94.99 248	93.94 269	98.16 155	97.72 245	95.69 199	99.99 8	98.81 67	94.28 164	92.70 315	96.90 340	95.08 63	99.17 206	96.07 234	73.88 461	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	97.71 247	97.52 110	99.97 42	98.54 123	91.83 290	97.45 196	99.04 197	97.50 10	99.10 211	94.75 263	96.37 256	99.16 243

VortexMVS		94.11 282	93.50 283	95.94 293	97.70 248	96.61 156	99.35 301	97.18 360	93.52 201	89.57 361	95.74 378	87.55 238	96.97 387	95.76 242	85.13 383	94.23 371

viewdifsd2359ckpt09		96.21 198	95.77 197	97.53 214	97.69 249	94.50 255	99.78 181	97.23 354	92.88 233	96.58 231	99.26 169	84.85 291	98.66 269	96.61 220	97.02 235	99.43 195

Syy-MVS		90.00 387	90.63 352	88.11 462	97.68 250	74.66 495	99.71 221	98.35 191	90.79 332	92.10 321	98.67 254	79.10 371	93.09 486	63.35 505	95.95 268	96.59 336

myMVS_eth3d		94.46 271	94.76 246	93.55 393	97.68 250	90.97 372	99.71 221	98.35 191	90.79 332	92.10 321	98.67 254	92.46 156	93.09 486	87.13 400	95.95 268	96.59 336

test_fmvs1_n		94.25 279	94.36 253	93.92 380	97.68 250	83.70 458	99.90 117	96.57 436	97.40 40	99.67 53	98.88 227	61.82 474	99.92 111	98.23 143	99.13 148	98.14 306

fmvsm_s_conf0.5_n_6		98.27 63	97.96 75	99.23 58	97.66 253	98.11 79	99.98 24	98.64 91	97.85 27	99.87 14	99.72 95	88.86 222	99.93 105	99.64 55	99.36 136	99.63 146

RRT-MVS		96.24 196	95.68 203	97.94 173	97.65 254	94.92 239	99.27 318	97.10 381	92.79 240	97.43 197	97.99 303	81.85 333	99.37 193	98.46 127	98.57 169	99.53 172

nomal-1		96.23 197	96.10 173	96.64 271	97.64 255	92.37 331	99.76 194	98.09 235	91.73 296	94.59 285	97.47 317	93.31 124	98.45 289	96.77 215	95.52 286	99.10 251

diffmvs		97.00 143	96.64 145	98.09 162	97.64 255	96.17 180	99.81 169	97.19 358	94.67 140	98.95 119	99.28 161	86.43 258	98.76 250	98.37 133	97.42 205	99.33 213
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		96.59 171	96.23 165	97.66 199	97.63 257	94.70 247	99.77 187	97.33 327	93.41 206	97.34 200	99.17 183	86.72 252	98.83 231	97.40 184	97.32 211	99.46 186

viewdifsd2359ckpt13		96.19 199	95.77 197	97.45 224	97.62 258	94.40 262	99.70 228	97.23 354	92.76 242	96.63 228	99.05 196	84.96 290	98.64 272	96.65 219	97.35 209	99.31 220

Vis-MVSNet		95.72 222	95.15 230	97.45 224	97.62 258	94.28 267	99.28 316	98.24 211	94.27 166	96.84 221	98.94 221	79.39 366	98.76 250	93.25 299	98.49 173	99.30 223
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	97.60 260	96.70 149	99.92 103	98.54 123	91.11 319	97.07 211	98.97 212	97.47 13	99.03 214	93.73 292	96.09 262	98.92 272

GDP-MVS		97.88 86	97.59 100	98.75 106	97.59 261	97.81 97	99.95 75	97.37 321	94.44 151	99.08 110	99.58 128	97.13 25	99.08 212	94.99 253	98.17 183	99.37 204

miper_ehance_all_eth		93.16 312	92.60 313	94.82 334	97.57 262	93.56 297	99.50 275	97.07 393	88.75 379	88.85 378	95.52 391	90.97 186	96.74 403	90.77 342	84.45 388	94.17 379

guyue		97.15 134	96.82 136	98.15 158	97.56 263	96.25 175	99.71 221	97.84 265	95.75 107	98.13 170	98.65 257	87.58 237	98.82 234	98.29 139	97.91 195	99.36 206

viewmanbaseed2359cas		96.45 180	96.07 175	97.59 210	97.55 264	94.59 250	99.70 228	97.33 327	93.62 197	97.00 215	99.32 154	85.57 276	98.71 258	97.26 190	97.33 210	99.47 184

testing3		93.92 288	94.23 258	92.99 407	97.54 265	90.23 391	99.99 8	99.16 33	90.57 339	91.33 329	98.63 261	92.99 134	92.52 490	82.46 438	95.39 290	96.22 341

SSM_0404		95.75 221	95.16 229	97.50 219	97.53 266	95.39 213	99.11 332	97.25 349	90.81 328	95.27 277	98.83 241	84.74 295	98.67 266	95.24 248	97.69 197	98.45 294

LCM-MVSNet-Re		92.31 335	92.60 313	91.43 427	97.53 266	79.27 486	99.02 350	91.83 504	92.07 281	80.31 464	94.38 441	83.50 314	95.48 455	97.22 192	97.58 201	99.54 168

GBi-Net		90.88 363	89.82 369	94.08 371	97.53 266	91.97 337	98.43 407	96.95 408	87.05 407	89.68 354	94.72 428	71.34 434	96.11 439	87.01 404	85.65 376	94.17 379

test1		90.88 363	89.82 369	94.08 371	97.53 266	91.97 337	98.43 407	96.95 408	87.05 407	89.68 354	94.72 428	71.34 434	96.11 439	87.01 404	85.65 376	94.17 379

FMVSNet2		91.02 360	89.56 374	95.41 314	97.53 266	95.74 194	98.98 353	97.41 316	87.05 407	88.43 391	95.00 422	71.34 434	96.24 435	85.12 419	85.21 381	94.25 369

tttt0517		96.85 151	96.49 152	97.92 174	97.48 271	95.89 188	99.85 147	98.54 123	90.72 336	96.63 228	98.93 224	97.47 13	99.02 215	93.03 306	95.76 275	98.85 277

onestephybrid01		96.75 159	96.44 156	97.71 194	97.47 272	95.03 234	99.83 160	97.27 345	94.15 169	98.66 138	99.25 172	85.72 271	98.81 238	98.42 129	97.17 222	99.28 227

Casviewmamba		96.25 195	95.89 193	97.32 242	97.45 273	93.68 290	99.80 175	97.22 356	93.38 207	96.86 219	99.28 161	84.64 299	98.87 227	97.18 193	97.19 217	99.41 199

BP-MVS1		98.33 59	98.18 56	98.81 101	97.44 274	97.98 87	99.96 56	98.17 223	94.88 130	98.77 130	99.59 125	97.59 8	99.08 212	98.24 142	98.93 157	99.36 206

casdiffmvs_mvg		96.43 181	95.94 189	97.89 178	97.44 274	95.47 206	99.86 144	97.29 343	93.35 209	96.03 256	99.19 181	85.39 281	98.72 257	97.89 165	97.04 232	99.49 182
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		96.36 186	95.95 187	97.60 207	97.41 276	94.52 253	99.71 221	97.33 327	93.20 215	97.02 212	99.07 193	85.37 282	98.82 234	97.27 187	97.14 224	99.46 186

EC-MVSNet		97.38 124	97.24 117	97.80 183	97.41 276	95.64 201	99.99 8	97.06 394	94.59 141	99.63 59	99.32 154	89.20 217	98.14 323	98.76 108	99.23 144	99.62 147

viewdifsd2359ckpt07		95.83 214	95.42 212	97.07 252	97.40 278	93.04 312	99.60 252	97.24 352	92.39 269	96.09 255	99.14 188	83.07 324	98.93 223	97.02 198	96.87 240	99.23 237

c3_l		92.53 330	91.87 330	94.52 346	97.40 278	92.99 314	99.40 290	96.93 413	87.86 397	88.69 381	95.44 396	89.95 204	96.44 421	90.45 348	80.69 423	94.14 389

hybrid		96.53 176	96.15 171	97.67 197	97.39 280	95.12 232	99.80 175	97.15 367	93.38 207	98.23 166	99.16 186	85.20 284	98.70 261	97.92 161	97.15 223	99.20 240

viewmambaseed2359dif		95.92 210	95.55 208	97.04 253	97.38 281	93.41 302	99.78 181	96.97 406	91.14 318	96.58 231	99.27 165	84.85 291	98.75 252	96.87 208	97.12 226	98.97 267

fmvsm_s_conf0.1_n		97.30 125	97.21 119	97.60 207	97.38 281	94.40 262	99.90 117	98.64 91	96.47 82	99.51 78	99.65 118	84.99 289	99.93 105	99.22 77	99.09 151	98.46 293

hybridcas		96.09 202	95.62 205	97.50 219	97.37 283	94.44 256	99.84 152	97.16 364	93.16 219	96.03 256	99.21 178	84.19 306	98.65 271	96.53 224	97.07 228	99.42 198

E3		96.36 186	95.95 187	97.60 207	97.37 283	94.52 253	99.71 221	97.33 327	93.18 217	97.02 212	99.07 193	85.45 280	98.82 234	97.27 187	97.14 224	99.46 186

CDS-MVSNet		96.34 188	96.07 175	97.13 249	97.37 283	94.96 236	99.53 270	97.91 257	91.55 301	95.37 275	98.32 288	95.05 65	97.13 373	93.80 288	95.75 276	99.30 223
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
hybridnocas07		96.57 173	96.16 170	97.81 182	97.36 286	95.32 218	99.81 169	97.12 373	94.17 168	98.02 173	98.90 225	85.05 287	98.80 243	97.85 166	97.18 218	99.32 215

TESTMET0.1,1		96.74 162	96.26 164	98.16 155	97.36 286	96.48 161	99.96 56	98.29 204	91.93 286	95.77 264	98.07 299	95.54 51	98.29 312	90.55 346	98.89 158	99.70 125

miper_lstm_enhance		91.81 343	91.39 342	93.06 406	97.34 288	89.18 409	99.38 296	96.79 426	86.70 415	87.47 409	95.22 411	90.00 203	95.86 448	88.26 382	81.37 412	94.15 385

baseline		96.43 181	95.98 181	97.76 191	97.34 288	95.17 230	99.51 273	97.17 362	93.92 184	96.90 218	99.28 161	85.37 282	98.64 272	97.50 182	96.86 242	99.46 186

cl____		92.31 335	91.58 336	94.52 346	97.33 290	92.77 316	99.57 260	96.78 427	86.97 411	87.56 407	95.51 392	89.43 210	96.62 410	88.60 372	82.44 404	94.16 384

SD_0403		92.63 329	93.38 290	90.40 441	97.32 291	77.91 488	97.75 438	98.03 244	91.89 287	90.83 335	98.29 292	82.00 330	93.79 479	88.51 377	95.75 276	99.52 174

DIV-MVS_self_test		92.32 334	91.60 335	94.47 350	97.31 292	92.74 318	99.58 256	96.75 428	86.99 410	87.64 405	95.54 389	89.55 209	96.50 416	88.58 373	82.44 404	94.17 379

casdiffmvs		96.42 183	95.97 184	97.77 189	97.30 293	94.98 235	99.84 152	97.09 384	93.75 193	96.58 231	99.26 169	85.07 286	98.78 247	97.77 174	97.04 232	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		94.36 276	93.48 284	96.99 255	97.29 294	93.54 298	99.96 56	96.72 430	88.35 390	93.43 303	98.94 221	82.05 329	98.05 330	88.12 388	96.48 253	99.37 204

eth_miper_zixun_eth		92.41 333	91.93 328	93.84 384	97.28 295	90.68 381	98.83 377	96.97 406	88.57 384	89.19 373	95.73 381	89.24 216	96.69 408	89.97 357	81.55 410	94.15 385

MVSFormer		96.94 146	96.60 147	97.95 170	97.28 295	97.70 103	99.55 267	97.27 345	91.17 315	99.43 84	99.54 134	90.92 187	96.89 393	94.67 266	99.62 100	99.25 234

lupinMVS		97.85 90	97.60 98	98.62 116	97.28 295	97.70 103	99.99 8	97.55 299	95.50 116	99.43 84	99.67 114	90.92 187	98.71 258	98.40 130	99.62 100	99.45 191

viewmamba		96.61 169	96.34 161	97.42 229	97.26 298	94.37 264	99.83 160	97.16 364	94.51 144	97.89 180	99.26 169	86.38 259	98.66 269	97.70 177	97.06 231	99.23 237

dtuplus		95.79 219	95.42 212	96.93 257	97.24 299	93.16 307	99.78 181	96.93 413	91.69 297	96.18 253	99.29 160	83.80 311	98.73 254	96.83 210	97.02 235	98.89 276

diffmvs_AUTHOR		96.75 159	96.41 159	97.79 185	97.20 300	95.46 207	99.69 231	97.15 367	94.46 147	98.78 128	99.21 178	85.64 274	98.77 248	98.27 140	97.31 212	99.13 247

mamba_0408		94.98 249	94.09 262	97.64 201	97.14 301	95.31 219	93.48 491	97.08 385	90.48 342	94.40 290	98.62 262	84.49 301	98.67 266	93.99 279	97.18 218	98.93 269

SSM_04072		94.77 256	94.09 262	96.82 262	97.14 301	95.31 219	93.48 491	97.08 385	90.48 342	94.40 290	98.62 262	84.49 301	96.21 436	93.99 279	97.18 218	98.93 269

SSM_0407		95.62 230	94.95 238	97.61 206	97.14 301	95.31 219	99.00 351	97.25 349	90.81 328	94.40 290	98.83 241	84.74 295	98.58 276	95.24 248	97.18 218	98.93 269

SCA		94.69 259	93.81 273	97.33 240	97.10 304	94.44 256	98.86 374	98.32 198	93.30 212	96.17 254	95.59 387	76.48 400	97.95 336	91.06 334	97.43 203	99.59 154

viewmacassd2359aftdt		95.93 209	95.45 210	97.36 237	97.09 305	94.12 276	99.57 260	97.26 348	93.05 227	96.50 235	99.17 183	82.76 325	98.68 264	96.61 220	97.04 232	99.28 227

KinetiMVS		96.10 200	95.29 224	98.53 130	97.08 306	97.12 130	99.56 264	98.12 234	94.78 133	98.44 151	98.94 221	80.30 360	99.39 192	91.56 327	98.79 164	99.06 256

TAMVS		95.85 212	95.58 206	96.65 270	97.07 307	93.50 299	99.17 327	97.82 267	91.39 311	95.02 280	98.01 300	92.20 164	97.30 363	93.75 291	95.83 272	99.14 246

Fast-Effi-MVS+-dtu		93.72 299	93.86 272	93.29 398	97.06 308	86.16 441	99.80 175	96.83 422	92.66 249	92.58 316	97.83 312	81.39 339	97.67 347	89.75 359	96.87 240	96.05 343

E4		96.01 205	95.53 209	97.44 227	97.05 309	94.23 270	99.57 260	97.30 335	92.72 243	96.47 237	99.03 198	83.98 310	98.83 231	96.92 205	96.77 243	99.27 230

E5new		95.83 214	95.39 215	97.15 245	97.03 310	93.59 292	99.32 306	97.30 335	92.58 256	96.45 238	99.00 205	83.37 317	98.81 238	96.81 211	96.65 246	99.04 259

E5		95.83 214	95.39 215	97.15 245	97.03 310	93.59 292	99.32 306	97.30 335	92.58 256	96.45 238	99.00 205	83.37 317	98.81 238	96.81 211	96.65 246	99.04 259

CostFormer		96.10 200	95.88 194	96.78 264	97.03 310	92.55 326	97.08 452	97.83 266	90.04 355	98.72 135	94.89 426	95.01 67	98.29 312	96.54 223	95.77 274	99.50 180

test_fmvsmvis_n_1920		97.67 109	97.59 100	97.91 176	97.02 313	95.34 216	99.95 75	98.45 143	97.87 26	97.02 212	99.59 125	89.64 207	99.98 52	99.41 69	99.34 139	98.42 296

test-LLR		96.47 178	96.04 177	97.78 187	97.02 313	95.44 208	99.96 56	98.21 218	94.07 174	95.55 270	96.38 358	93.90 107	98.27 316	90.42 349	98.83 162	99.64 139

test-mter		96.39 184	95.93 190	97.78 187	97.02 313	95.44 208	99.96 56	98.21 218	91.81 292	95.55 270	96.38 358	95.17 60	98.27 316	90.42 349	98.83 162	99.64 139

casdiffseed414692147		95.07 244	94.26 257	97.50 219	97.01 316	94.70 247	99.58 256	97.02 398	91.27 313	94.66 284	98.82 243	80.79 350	98.55 282	93.39 298	95.79 273	99.27 230

E6new		95.83 214	95.39 215	97.14 247	97.00 317	93.58 294	99.31 308	97.30 335	92.57 258	96.45 238	99.01 201	83.44 315	98.81 238	96.80 213	96.66 244	99.04 259

E6		95.83 214	95.39 215	97.14 247	97.00 317	93.58 294	99.31 308	97.30 335	92.57 258	96.45 238	99.01 201	83.44 315	98.81 238	96.80 213	96.66 244	99.04 259

icg_test_0407_2		95.04 246	94.78 245	95.84 300	96.97 319	91.64 359	98.63 396	97.12 373	92.33 272	95.60 268	98.88 227	85.65 272	96.56 413	92.12 315	95.70 279	99.32 215

IMVS_0407		95.21 240	94.80 244	96.46 276	96.97 319	91.64 359	98.81 379	97.12 373	92.33 272	95.60 268	98.88 227	85.65 272	98.42 292	92.12 315	95.70 279	99.32 215

IMVS_0404		93.83 291	93.17 297	95.80 302	96.97 319	91.64 359	97.78 437	97.12 373	92.33 272	90.87 334	98.88 227	76.78 395	96.43 422	92.12 315	95.70 279	99.32 215

IMVS_0403		95.25 239	94.81 243	96.58 273	96.97 319	91.64 359	98.97 358	97.12 373	92.33 272	95.43 273	98.88 227	85.78 270	98.79 245	92.12 315	95.70 279	99.32 215

gm-plane-assit					96.97 319	93.76 286			91.47 305		98.96 214		98.79 245	94.92 256

WB-MVSnew		92.90 318	92.77 310	93.26 400	96.95 324	93.63 291	99.71 221	98.16 228	91.49 302	94.28 295	98.14 295	81.33 341	96.48 419	79.47 457	95.46 287	89.68 487

QAPM		95.40 235	94.17 260	99.10 79	96.92 325	97.71 101	99.40 290	98.68 84	89.31 363	88.94 377	98.89 226	82.48 327	99.96 77	93.12 305	99.83 81	99.62 147

KD-MVS_2432*1600		88.00 409	86.10 413	93.70 389	96.91 326	94.04 277	97.17 449	97.12 373	84.93 436	81.96 453	92.41 464	92.48 154	94.51 472	79.23 459	52.68 518	92.56 451

miper_refine_blended		88.00 409	86.10 413	93.70 389	96.91 326	94.04 277	97.17 449	97.12 373	84.93 436	81.96 453	92.41 464	92.48 154	94.51 472	79.23 459	52.68 518	92.56 451

tpm2		95.47 233	95.18 228	96.35 282	96.91 326	91.70 356	96.96 455	97.93 253	88.04 395	98.44 151	95.40 398	93.32 122	97.97 333	94.00 278	95.61 284	99.38 202

FMVSNet5		88.32 405	87.47 407	90.88 430	96.90 329	88.39 423	97.28 446	95.68 459	82.60 457	84.67 440	92.40 466	79.83 363	91.16 496	76.39 475	81.51 411	93.09 441

3Dnovator+	91.53 11	96.31 190	95.24 225	99.52 33	96.88 330	98.64 60	99.72 216	98.24 211	95.27 121	88.42 393	98.98 210	82.76 325	99.94 95	97.10 196	99.83 81	99.96 75

Patchmatch-test		92.65 328	91.50 339	96.10 288	96.85 331	90.49 386	91.50 502	97.19 358	82.76 456	90.23 340	95.59 387	95.02 66	98.00 332	77.41 470	96.98 238	99.82 107

MVS		96.60 170	95.56 207	99.72 14	96.85 331	99.22 22	98.31 414	98.94 44	91.57 300	90.90 333	99.61 124	86.66 256	99.96 77	97.36 185	99.88 77	99.99 26

3Dnovator	91.47 12	96.28 193	95.34 221	99.08 82	96.82 333	97.47 115	99.45 286	98.81 67	95.52 115	89.39 364	99.00 205	81.97 331	99.95 86	97.27 187	99.83 81	99.84 104

EI-MVSNet		93.73 298	93.40 289	94.74 335	96.80 334	92.69 321	99.06 341	97.67 282	88.96 372	91.39 327	99.02 199	88.75 224	97.30 363	91.07 333	87.85 358	94.22 374

CVMVSNet		94.68 261	94.94 239	93.89 383	96.80 334	86.92 437	99.06 341	98.98 41	94.45 148	94.23 297	99.02 199	85.60 275	95.31 460	90.91 339	95.39 290	99.43 195

IterMVS-LS		92.69 326	92.11 324	94.43 354	96.80 334	92.74 318	99.45 286	96.89 417	88.98 370	89.65 357	95.38 401	88.77 223	96.34 429	90.98 337	82.04 407	94.22 374
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
AstraMVS		96.57 173	96.46 155	96.91 258	96.79 337	92.50 327	99.90 117	97.38 318	96.02 99	97.79 187	99.32 154	86.36 261	98.99 216	98.26 141	96.33 257	99.23 237

IterMVS		90.91 362	90.17 364	93.12 403	96.78 338	90.42 389	98.89 368	97.05 397	89.03 367	86.49 422	95.42 397	76.59 398	95.02 462	87.22 399	84.09 391	93.93 412
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
1314		96.84 152	95.96 185	99.48 40	96.74 339	98.52 64	98.31 414	98.86 59	95.82 104	89.91 348	98.98 210	87.49 240	99.96 77	97.80 169	99.73 91	99.96 75

IterMVS-SCA-FT		90.85 365	90.16 365	92.93 408	96.72 340	89.96 398	98.89 368	96.99 402	88.95 373	86.63 419	95.67 382	76.48 400	95.00 463	87.04 402	84.04 394	93.84 419

MVS-HIRNet		86.22 422	83.19 438	95.31 318	96.71 341	90.29 390	92.12 498	97.33 327	62.85 506	86.82 416	70.37 523	69.37 442	97.49 353	75.12 478	97.99 193	98.15 304

viewdifsd2359ckpt11		94.09 284	93.63 275	95.46 311	96.68 342	88.92 412	99.62 245	97.12 373	93.07 225	95.73 265	99.22 175	77.05 388	98.88 226	96.52 225	87.69 363	98.58 291

viewmsd2359difaftdt		94.09 284	93.64 274	95.46 311	96.68 342	88.92 412	99.62 245	97.13 372	93.07 225	95.73 265	99.22 175	77.05 388	98.89 225	96.52 225	87.70 362	98.58 291

VDDNet		93.12 313	91.91 329	96.76 265	96.67 344	92.65 324	98.69 391	98.21 218	82.81 455	97.75 189	99.28 161	61.57 475	99.48 187	98.09 151	94.09 309	98.15 304

dmvs_re		93.20 310	93.15 299	93.34 396	96.54 345	83.81 457	98.71 388	98.51 131	91.39 311	92.37 319	98.56 270	78.66 375	97.83 341	93.89 282	89.74 330	98.38 298

Elysia		94.50 268	93.38 290	97.85 180	96.49 346	96.70 149	98.98 353	97.78 272	90.81 328	96.19 251	98.55 272	73.63 425	98.98 217	89.41 360	98.56 170	97.88 311

StellarMVS		94.50 268	93.38 290	97.85 180	96.49 346	96.70 149	98.98 353	97.78 272	90.81 328	96.19 251	98.55 272	73.63 425	98.98 217	89.41 360	98.56 170	97.88 311

MIMVSNet		90.30 378	88.67 393	95.17 322	96.45 348	91.64 359	92.39 497	97.15 367	85.99 422	90.50 338	93.19 457	66.95 453	94.86 468	82.01 442	93.43 317	99.01 265

CR-MVSNet		93.45 307	92.62 312	95.94 293	96.29 349	92.66 322	92.01 499	96.23 445	92.62 251	96.94 216	93.31 454	91.04 184	96.03 444	79.23 459	95.96 266	99.13 247

RPMNet		89.76 391	87.28 408	97.19 244	96.29 349	92.66 322	92.01 499	98.31 200	70.19 497	96.94 216	85.87 508	87.25 245	99.78 148	62.69 508	95.96 266	99.13 247

tt0805		91.28 355	90.18 363	94.60 341	96.26 351	87.55 430	98.39 412	98.72 78	89.00 369	89.22 370	98.47 280	62.98 470	98.96 221	90.57 345	88.00 357	97.28 330

Patchmtry		89.70 392	88.49 396	93.33 397	96.24 352	89.94 401	91.37 503	96.23 445	78.22 479	87.69 404	93.31 454	91.04 184	96.03 444	80.18 456	82.10 406	94.02 402

test_vis1_rt		86.87 419	86.05 416	89.34 449	96.12 353	78.07 487	99.87 133	83.54 521	92.03 284	78.21 476	89.51 487	45.80 498	99.91 112	96.25 231	93.11 322	90.03 483

JIA-IIPM		91.76 349	90.70 350	94.94 328	96.11 354	87.51 431	93.16 494	98.13 233	75.79 485	97.58 191	77.68 518	92.84 139	97.97 333	88.47 378	96.54 249	99.33 213

OpenMVS	90.15 15	94.77 256	93.59 279	98.33 146	96.07 355	97.48 114	99.56 264	98.57 107	90.46 344	86.51 421	98.95 219	78.57 376	99.94 95	93.86 283	99.74 90	97.57 325

PAPM		98.60 37	98.42 38	99.14 73	96.05 356	98.96 29	99.90 117	99.35 24	96.68 73	98.35 158	99.66 116	96.45 35	98.51 284	99.45 66	99.89 74	99.96 75

CLD-MVS		94.06 287	93.90 270	94.55 345	96.02 357	90.69 380	99.98 24	97.72 278	96.62 77	91.05 332	98.85 239	77.21 386	98.47 285	98.11 149	89.51 336	94.48 350
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
PatchT		90.38 375	88.75 392	95.25 320	95.99 358	90.16 393	91.22 504	97.54 301	76.80 481	97.26 204	86.01 507	91.88 171	96.07 443	66.16 499	95.91 270	99.51 178

ACMH+	89.98 16	90.35 376	89.54 375	92.78 412	95.99 358	86.12 442	98.81 379	97.18 360	89.38 362	83.14 449	97.76 313	68.42 447	98.43 291	89.11 368	86.05 374	93.78 422

DeepMVS_CX				82.92 478	95.98 360	58.66 515		96.01 451	92.72 243	78.34 475	95.51 392	58.29 482	98.08 327	82.57 436	85.29 379	92.03 462

ACMP	92.05 9	92.74 324	92.42 321	93.73 385	95.91 361	88.72 416	99.81 169	97.53 303	94.13 170	87.00 415	98.23 293	74.07 421	98.47 285	96.22 232	88.86 343	93.99 407
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test_vis1_n		93.61 302	93.03 302	95.35 315	95.86 362	86.94 436	99.87 133	96.36 443	96.85 64	99.54 73	98.79 244	52.41 490	99.83 141	98.64 116	98.97 156	99.29 225

HQP-NCC					95.78 363		99.87 133		96.82 66	93.37 304

ACMP_Plane					95.78 363		99.87 133		96.82 66	93.37 304

HQP-MVS		94.61 263	94.50 250	94.92 329	95.78 363	91.85 344	99.87 133	97.89 258	96.82 66	93.37 304	98.65 257	80.65 354	98.39 298	97.92 161	89.60 331	94.53 346

NP-MVS					95.77 366	91.79 348					98.65 257

test_fmvsmconf0.1_n		97.74 103	97.44 107	98.64 115	95.76 367	96.20 177	99.94 93	98.05 241	98.17 13	98.89 123	99.42 142	87.65 235	99.90 114	99.50 62	99.60 108	99.82 107

plane_prior6					95.76 367	91.72 355						80.47 358

ACMM	91.95 10	92.88 319	92.52 319	93.98 379	95.75 369	89.08 411	99.77 187	97.52 305	93.00 228	89.95 347	97.99 303	76.17 404	98.46 288	93.63 295	88.87 342	94.39 358
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GA-MVS		93.83 291	92.84 306	96.80 263	95.73 370	93.57 296	99.88 130	97.24 352	92.57 258	92.92 311	96.66 350	78.73 374	97.67 347	87.75 391	94.06 310	99.17 242

plane_prior1					95.73 370

jason		97.24 129	96.86 133	98.38 145	95.73 370	97.32 119	99.97 42	97.40 317	95.34 119	98.60 145	99.54 134	87.70 234	98.56 279	97.94 160	99.47 125	99.25 234
jason: jason.
mmtdpeth		88.52 403	87.75 405	90.85 432	95.71 373	83.47 463	98.94 361	94.85 477	88.78 378	97.19 206	89.58 485	63.29 468	98.97 219	98.54 121	62.86 496	90.10 482

HQP_MVS		94.49 270	94.36 253	94.87 330	95.71 373	91.74 351	99.84 152	97.87 260	96.38 86	93.01 309	98.59 265	80.47 358	98.37 304	97.79 172	89.55 334	94.52 348

plane_prior7					95.71 373	91.59 365

ITE_SJBPF				92.38 415	95.69 376	85.14 448		95.71 458	92.81 237	89.33 367	98.11 297	70.23 440	98.42 292	85.91 414	88.16 355	93.59 430

fmvsm_s_conf0.1_n_a		97.09 138	96.90 131	97.63 204	95.65 377	94.21 272	99.83 160	98.50 137	96.27 92	99.65 55	99.64 119	84.72 297	99.93 105	99.04 87	98.84 161	98.74 284

ACMH	89.72 17	90.64 369	89.63 372	93.66 391	95.64 378	88.64 419	98.55 399	97.45 310	89.03 367	81.62 456	97.61 314	69.75 441	98.41 294	89.37 362	87.62 364	93.92 413
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
baseline2		96.71 164	96.49 152	97.37 235	95.63 379	95.96 186	99.74 205	98.88 55	92.94 230	91.61 325	98.97 212	97.72 7	98.62 274	94.83 260	98.08 191	97.53 327

FMVSNet1		88.50 404	86.64 411	94.08 371	95.62 380	91.97 337	98.43 407	96.95 408	83.00 453	86.08 429	94.72 428	59.09 481	96.11 439	81.82 444	84.07 392	94.17 379

LuminaMVS		96.63 168	96.21 168	97.87 179	95.58 381	96.82 143	99.12 330	97.67 282	94.47 146	97.88 182	98.31 290	87.50 239	98.71 258	98.07 153	97.29 213	98.10 307

0.3-1-1-0.015		94.22 280	93.13 301	97.49 222	95.50 382	94.17 273	100.00 1	98.22 214	88.44 388	97.14 208	97.04 335	92.73 143	98.59 275	96.45 227	72.65 467	99.70 125

0.4-1-1-0.2		94.14 281	93.02 303	97.51 217	95.45 383	94.25 269	100.00 1	98.22 214	88.53 385	96.83 222	96.95 338	92.25 162	98.57 278	96.34 228	72.65 467	99.70 125

LPG-MVS_test		92.96 316	92.71 311	93.71 387	95.43 384	88.67 417	99.75 201	97.62 289	92.81 237	90.05 343	98.49 276	75.24 411	98.40 296	95.84 239	89.12 338	94.07 398

LGP-MVS_train				93.71 387	95.43 384	88.67 417		97.62 289	92.81 237	90.05 343	98.49 276	75.24 411	98.40 296	95.84 239	89.12 338	94.07 398

tpm		93.70 300	93.41 288	94.58 343	95.36 386	87.41 432	97.01 453	96.90 416	90.85 326	96.72 227	94.14 445	90.40 198	96.84 397	90.75 343	88.54 350	99.51 178

0.4-1-1-0.1		94.07 286	92.95 304	97.42 229	95.24 387	94.00 280	100.00 1	98.22 214	88.27 392	96.81 224	96.93 339	92.27 161	98.56 279	96.21 233	72.63 469	99.70 125

D2MVS		92.76 323	92.59 317	93.27 399	95.13 388	89.54 405	99.69 231	99.38 22	92.26 277	87.59 406	94.61 434	85.05 287	97.79 342	91.59 326	88.01 356	92.47 455

VPA-MVSNet		92.70 325	91.55 338	96.16 286	95.09 389	96.20 177	98.88 370	99.00 39	91.02 323	91.82 324	95.29 408	76.05 406	97.96 335	95.62 244	81.19 413	94.30 365

LTVRE_ROB	88.28 18	90.29 379	89.05 386	94.02 374	95.08 390	90.15 394	97.19 448	97.43 312	84.91 438	83.99 445	97.06 332	74.00 422	98.28 314	84.08 425	87.71 360	93.62 429
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		87.87 411	86.51 412	91.94 421	95.05 391	85.57 446	97.65 439	94.08 489	84.40 442	81.82 455	96.85 344	62.14 473	98.33 307	80.25 455	86.37 371	91.91 464

test0.0.03 1		93.86 290	93.61 276	94.64 339	95.02 392	92.18 335	99.93 100	98.58 105	94.07 174	87.96 401	98.50 275	93.90 107	94.96 464	81.33 445	93.17 320	96.78 333

UniMVSNet (Re)		93.07 315	92.13 323	95.88 297	94.84 393	96.24 176	99.88 130	98.98 41	92.49 265	89.25 368	95.40 398	87.09 247	97.14 372	93.13 304	78.16 438	94.26 367

USDC		90.00 387	88.96 387	93.10 405	94.81 394	88.16 425	98.71 388	95.54 463	93.66 195	83.75 447	97.20 326	65.58 459	98.31 309	83.96 428	87.49 366	92.85 447

VPNet		91.81 343	90.46 354	95.85 299	94.74 395	95.54 205	98.98 353	98.59 103	92.14 279	90.77 337	97.44 319	68.73 445	97.54 352	94.89 259	77.89 440	94.46 351

FIs		94.10 283	93.43 285	96.11 287	94.70 396	96.82 143	99.58 256	98.93 48	92.54 261	89.34 366	97.31 323	87.62 236	97.10 376	94.22 277	86.58 369	94.40 357

UniMVSNet_ETH3D		90.06 386	88.58 395	94.49 349	94.67 397	88.09 426	97.81 436	97.57 297	83.91 445	88.44 388	97.41 320	57.44 483	97.62 349	91.41 328	88.59 349	97.77 316

UniMVSNet_NR-MVSNet		92.95 317	92.11 324	95.49 307	94.61 398	95.28 223	99.83 160	99.08 36	91.49 302	89.21 371	96.86 343	87.14 246	96.73 404	93.20 300	77.52 443	94.46 351

test_fmvs2		89.47 396	89.70 371	88.77 456	94.54 399	75.74 491	99.83 160	94.70 483	94.71 137	91.08 330	96.82 348	54.46 486	97.78 344	92.87 307	88.27 353	92.80 448

MonoMVSNet		94.82 251	94.43 251	95.98 291	94.54 399	90.73 379	99.03 348	97.06 394	93.16 219	93.15 308	95.47 395	88.29 227	97.57 350	97.85 166	91.33 328	99.62 147

WR-MVS		92.31 335	91.25 343	95.48 310	94.45 401	95.29 222	99.60 252	98.68 84	90.10 352	88.07 400	96.89 341	80.68 353	96.80 401	93.14 303	79.67 430	94.36 359

dtuonly		93.89 289	93.16 298	96.08 289	94.37 402	91.67 358	99.15 329	95.04 475	91.79 294	94.74 282	98.72 249	81.01 345	98.31 309	87.29 397	96.33 257	98.27 302

nrg030		93.51 304	92.53 318	96.45 277	94.36 403	97.20 125	99.81 169	97.16 364	91.60 299	89.86 350	97.46 318	86.37 260	97.68 346	95.88 238	80.31 426	94.46 351

tfpnnormal		89.29 399	87.61 406	94.34 358	94.35 404	94.13 275	98.95 360	98.94 44	83.94 443	84.47 441	95.51 392	74.84 416	97.39 355	77.05 473	80.41 424	91.48 467

FC-MVSNet-test		93.81 294	93.15 299	95.80 302	94.30 405	96.20 177	99.42 288	98.89 52	92.33 272	89.03 376	97.27 325	87.39 242	96.83 399	93.20 300	86.48 370	94.36 359

SSC-MVS3.2		89.59 394	88.66 394	92.38 415	94.29 406	86.12 442	99.49 277	97.66 285	90.28 351	88.63 384	95.18 412	64.46 464	96.88 395	85.30 418	82.66 401	94.14 389

MS-PatchMatch		90.65 368	90.30 359	91.71 426	94.22 407	85.50 447	98.24 418	97.70 279	88.67 381	86.42 424	96.37 360	67.82 450	98.03 331	83.62 430	99.62 100	91.60 465

WR-MVS_H		91.30 353	90.35 357	94.15 365	94.17 408	92.62 325	99.17 327	98.94 44	88.87 376	86.48 423	94.46 439	84.36 304	96.61 411	88.19 384	78.51 435	93.21 439

DU-MVS		92.46 332	91.45 341	95.49 307	94.05 409	95.28 223	99.81 169	98.74 76	92.25 278	89.21 371	96.64 352	81.66 336	96.73 404	93.20 300	77.52 443	94.46 351

NR-MVSNet		91.56 351	90.22 361	95.60 305	94.05 409	95.76 193	98.25 417	98.70 80	91.16 317	80.78 463	96.64 352	83.23 322	96.57 412	91.41 328	77.73 442	94.46 351

CP-MVSNet		91.23 357	90.22 361	94.26 360	93.96 411	92.39 330	99.09 334	98.57 107	88.95 373	86.42 424	96.57 355	79.19 369	96.37 427	90.29 352	78.95 432	94.02 402

XXY-MVS		91.82 342	90.46 354	95.88 297	93.91 412	95.40 212	98.87 373	97.69 281	88.63 383	87.87 402	97.08 330	74.38 420	97.89 339	91.66 325	84.07 392	94.35 362

PS-CasMVS		90.63 370	89.51 377	93.99 377	93.83 413	91.70 356	98.98 353	98.52 128	88.48 386	86.15 428	96.53 357	75.46 409	96.31 432	88.83 370	78.86 434	93.95 410

test_0402		85.58 426	83.94 432	90.50 438	93.81 414	85.04 449	98.55 399	95.20 472	76.01 483	79.72 469	95.13 413	64.15 466	96.26 434	66.04 501	86.88 368	90.21 479

XVG-ACMP-BASELINE		91.22 358	90.75 349	92.63 414	93.73 415	85.61 445	98.52 403	97.44 311	92.77 241	89.90 349	96.85 344	66.64 456	98.39 298	92.29 312	88.61 347	93.89 415

TranMVSNet+NR-MVSNet		91.68 350	90.61 353	94.87 330	93.69 416	93.98 281	99.69 231	98.65 88	91.03 322	88.44 388	96.83 347	80.05 362	96.18 437	90.26 353	76.89 451	94.45 356

TransMVSNet (Re)		87.25 417	85.28 425	93.16 402	93.56 417	91.03 371	98.54 401	94.05 491	83.69 447	81.09 460	96.16 366	75.32 410	96.40 426	76.69 474	68.41 483	92.06 461

v10		90.25 380	88.82 389	94.57 344	93.53 418	93.43 301	99.08 336	96.87 419	85.00 435	87.34 413	94.51 435	80.93 347	97.02 386	82.85 435	79.23 431	93.26 437

testgi		89.01 401	88.04 402	91.90 422	93.49 419	84.89 451	99.73 212	95.66 460	93.89 188	85.14 435	98.17 294	59.68 479	94.66 471	77.73 469	88.88 341	96.16 342

v8		90.54 372	89.17 382	94.66 338	93.43 420	93.40 304	99.20 324	96.94 412	85.76 425	87.56 407	94.51 435	81.96 332	97.19 369	84.94 421	78.25 437	93.38 435

V42		91.28 355	90.12 366	94.74 335	93.42 421	93.46 300	99.68 234	97.02 398	87.36 403	89.85 352	95.05 416	81.31 342	97.34 358	87.34 396	80.07 428	93.40 433

pm-mvs1		89.36 398	87.81 404	94.01 375	93.40 422	91.93 340	98.62 397	96.48 441	86.25 420	83.86 446	96.14 368	73.68 424	97.04 382	86.16 411	75.73 456	93.04 443

v1144		91.09 359	89.83 368	94.87 330	93.25 423	93.69 289	99.62 245	96.98 404	86.83 413	89.64 358	94.99 423	80.94 346	97.05 379	85.08 420	81.16 414	93.87 417

v1192		90.62 371	89.25 381	94.72 337	93.13 424	93.07 309	99.50 275	97.02 398	86.33 419	89.56 362	95.01 420	79.22 368	97.09 378	82.34 440	81.16 414	94.01 404

v2v482		91.30 353	90.07 367	95.01 325	93.13 424	93.79 284	99.77 187	97.02 398	88.05 394	89.25 368	95.37 402	80.73 352	97.15 371	87.28 398	80.04 429	94.09 397

OPM-MVS		93.21 309	92.80 308	94.44 352	93.12 426	90.85 378	99.77 187	97.61 292	96.19 95	91.56 326	98.65 257	75.16 415	98.47 285	93.78 290	89.39 337	93.99 407
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
v144192		90.79 366	89.52 376	94.59 342	93.11 427	92.77 316	99.56 264	96.99 402	86.38 418	89.82 353	94.95 425	80.50 357	97.10 376	83.98 427	80.41 424	93.90 414

PEN-MVS		90.19 382	89.06 385	93.57 392	93.06 428	90.90 376	99.06 341	98.47 140	88.11 393	85.91 430	96.30 362	76.67 396	95.94 447	87.07 401	76.91 450	93.89 415

v1240		90.20 381	88.79 390	94.44 352	93.05 429	92.27 333	99.38 296	96.92 415	85.89 423	89.36 365	94.87 427	77.89 383	97.03 384	80.66 450	81.08 417	94.01 404

usedtu_dtu_shiyan1		92.78 321	91.73 332	95.92 295	93.03 430	96.82 143	99.83 160	97.79 268	90.58 337	90.09 341	95.04 417	84.75 293	96.72 406	88.19 384	86.23 372	94.23 371

FE-MVSNET3		92.78 321	91.73 332	95.92 295	93.03 430	96.82 143	99.83 160	97.79 268	90.58 337	90.09 341	95.04 417	84.75 293	96.72 406	88.20 383	86.23 372	94.23 371

ArgMatch-SfM		85.25 431	84.17 429	88.48 458	92.99 432	77.23 490	97.92 431	94.24 487	90.50 341	85.08 437	95.65 384	49.84 494	95.83 449	81.06 448	70.22 474	92.39 457

v148		90.70 367	89.63 372	93.92 380	92.97 433	90.97 372	99.75 201	96.89 417	87.51 400	88.27 397	95.01 420	81.67 335	97.04 382	87.40 395	77.17 448	93.75 423

v1921920		90.46 373	89.12 383	94.50 348	92.96 434	92.46 328	99.49 277	96.98 404	86.10 421	89.61 360	95.30 405	78.55 377	97.03 384	82.17 441	80.89 422	94.01 404

MVStest1		85.03 433	82.76 442	91.83 423	92.95 435	89.16 410	98.57 398	94.82 478	71.68 494	68.54 499	95.11 415	83.17 323	95.66 453	74.69 479	65.32 490	90.65 474

tt0320-xc		82.94 448	80.35 455	90.72 436	92.90 436	83.54 461	96.85 458	94.73 481	63.12 505	79.85 468	93.77 449	49.43 496	95.46 456	80.98 449	71.54 471	93.16 440

ArgMatch-Sym		85.85 424	85.07 427	88.21 460	92.84 437	77.63 489	98.42 410	94.70 483	89.91 356	84.33 442	96.72 349	51.42 493	94.89 467	82.48 437	74.80 459	92.10 459

Baseline_NR-MVSNet		90.33 377	89.51 377	92.81 411	92.84 437	89.95 399	99.77 187	93.94 492	84.69 440	89.04 375	95.66 383	81.66 336	96.52 415	90.99 336	76.98 449	91.97 463

test_method		80.79 454	79.70 457	84.08 473	92.83 439	67.06 503	99.51 273	95.42 465	54.34 516	81.07 461	93.53 451	44.48 499	92.22 493	78.90 464	77.23 447	92.94 445

pmmvs4		92.10 339	91.07 347	95.18 321	92.82 440	94.96 236	99.48 280	96.83 422	87.45 402	88.66 383	96.56 356	83.78 312	96.83 399	89.29 365	84.77 386	93.75 423

LF4IMVS		89.25 400	88.85 388	90.45 440	92.81 441	81.19 478	98.12 425	94.79 479	91.44 306	86.29 426	97.11 328	65.30 462	98.11 325	88.53 375	85.25 380	92.07 460

tt0320		83.56 447	81.15 450	90.77 434	92.77 442	83.58 460	96.83 459	95.52 464	63.26 504	81.36 458	92.54 461	53.26 488	95.77 451	80.45 451	74.38 460	92.96 444

DTE-MVSNet		89.40 397	88.24 400	92.88 409	92.66 443	89.95 399	99.10 333	98.22 214	87.29 404	85.12 436	96.22 364	76.27 403	95.30 461	83.56 431	75.74 455	93.41 432

EU-MVSNet		90.14 384	90.34 358	89.54 448	92.55 444	81.06 479	98.69 391	98.04 242	91.41 310	86.59 420	96.84 346	80.83 349	93.31 484	86.20 410	81.91 408	94.26 367

APD_test1		81.15 452	80.92 452	81.86 479	92.45 445	59.76 514	96.04 474	93.61 496	73.29 492	77.06 479	96.64 352	44.28 500	96.16 438	72.35 483	82.52 402	89.67 488

sc_t1		85.01 434	82.46 444	92.67 413	92.44 446	83.09 464	97.39 444	95.72 457	65.06 502	85.64 433	96.16 366	49.50 495	97.34 358	84.86 422	75.39 457	97.57 325

our_test_3		90.39 374	89.48 379	93.12 403	92.40 447	89.57 404	99.33 303	96.35 444	87.84 398	85.30 434	94.99 423	84.14 308	96.09 442	80.38 453	84.56 387	93.71 428

ppachtmachnet_test		89.58 395	88.35 398	93.25 401	92.40 447	90.44 388	99.33 303	96.73 429	85.49 430	85.90 431	95.77 377	81.09 344	96.00 446	76.00 477	82.49 403	93.30 436

v7n		89.65 393	88.29 399	93.72 386	92.22 449	90.56 385	99.07 340	97.10 381	85.42 432	86.73 417	94.72 428	80.06 361	97.13 373	81.14 446	78.12 439	93.49 431

dmvs_testset		83.79 443	86.07 415	76.94 486	92.14 450	48.60 528	96.75 460	90.27 508	89.48 361	78.65 473	98.55 272	79.25 367	86.65 511	66.85 497	82.69 400	95.57 344

PS-MVSNAJss		93.64 301	93.31 294	94.61 340	92.11 451	92.19 334	99.12 330	97.38 318	92.51 264	88.45 387	96.99 337	91.20 179	97.29 366	94.36 271	87.71 360	94.36 359

pmmvs5		90.17 383	89.09 384	93.40 395	92.10 452	89.77 402	99.74 205	95.58 462	85.88 424	87.24 414	95.74 378	73.41 427	96.48 419	88.54 374	83.56 396	93.95 410

N_pmnet		80.06 457	80.78 453	77.89 484	91.94 453	45.28 533	98.80 382	56.82 536	78.10 480	80.08 466	93.33 452	77.03 390	95.76 452	68.14 493	82.81 399	92.64 450

test_djsdf		92.83 320	92.29 322	94.47 350	91.90 454	92.46 328	99.55 267	97.27 345	91.17 315	89.96 346	96.07 372	81.10 343	96.89 393	94.67 266	88.91 340	94.05 401

SixPastTwentyTwo		88.73 402	88.01 403	90.88 430	91.85 455	82.24 470	98.22 422	95.18 473	88.97 371	82.26 452	96.89 341	71.75 432	96.67 409	84.00 426	82.98 397	93.72 427

dtuonlycased		86.10 423	85.82 418	86.95 465	91.84 456	79.57 485	99.27 318	94.89 476	86.79 414	79.46 470	94.46 439	66.85 454	90.93 499	80.41 452	78.44 436	90.34 476

K. test v3		88.05 408	87.24 409	90.47 439	91.82 457	82.23 471	98.96 359	97.42 314	89.05 366	76.93 481	95.60 386	68.49 446	95.42 457	85.87 415	81.01 420	93.75 423

OurMVSNet-221017-0		89.81 390	89.48 379	90.83 433	91.64 458	81.21 477	98.17 424	95.38 467	91.48 304	85.65 432	97.31 323	72.66 428	97.29 366	88.15 386	84.83 385	93.97 409

mvs_tets		91.81 343	91.08 346	94.00 376	91.63 459	90.58 384	98.67 393	97.43 312	92.43 266	87.37 412	97.05 333	71.76 431	97.32 361	94.75 263	88.68 346	94.11 396

Gipuma		66.95 479	65.00 479	72.79 494	91.52 460	67.96 500	66.16 534	95.15 474	47.89 519	58.54 512	67.99 531	29.74 509	87.54 510	50.20 523	77.83 441	62.87 530
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_fmvsmconf0.01_n		96.39 184	95.74 199	98.32 147	91.47 461	95.56 204	99.84 152	97.30 335	97.74 30	97.89 180	99.35 153	79.62 364	99.85 131	99.25 76	99.24 143	99.55 164

jajsoiax		91.92 341	91.18 344	94.15 365	91.35 462	90.95 375	99.00 351	97.42 314	92.61 252	87.38 411	97.08 330	72.46 429	97.36 356	94.53 269	88.77 344	94.13 394

MDA-MVSNet-bldmvs		84.09 441	81.52 448	91.81 424	91.32 463	88.00 428	98.67 393	95.92 453	80.22 467	55.60 516	93.32 453	68.29 448	93.60 482	73.76 480	76.61 452	93.82 421

MVP-Stereo		90.93 361	90.45 356	92.37 417	91.25 464	88.76 414	98.05 429	96.17 447	87.27 405	84.04 443	95.30 405	78.46 378	97.27 368	83.78 429	99.70 93	91.09 468
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MDA-MVSNet_test_wron		85.51 428	83.32 437	92.10 419	90.96 465	88.58 420	99.20 324	96.52 438	79.70 469	57.12 514	92.69 460	79.11 370	93.86 478	77.10 472	77.46 445	93.86 418

YYNet1		85.50 429	83.33 436	92.00 420	90.89 466	88.38 424	99.22 323	96.55 437	79.60 470	57.26 513	92.72 459	79.09 372	93.78 480	77.25 471	77.37 446	93.84 419

ALIKED-NN		54.48 490	52.67 494	59.89 512	90.79 467	45.45 531	81.25 525	55.75 540	34.99 528	44.87 527	71.98 521	25.50 518	74.36 528	21.88 541	47.04 525	59.85 532

anonymousdsp		91.79 348	90.92 348	94.41 355	90.76 468	92.93 315	98.93 364	97.17 362	89.08 365	87.46 410	95.30 405	78.43 379	96.92 390	92.38 311	88.73 345	93.39 434

lessismore_v0				90.53 437	90.58 469	80.90 480		95.80 454		77.01 480	95.84 375	66.15 458	96.95 388	83.03 434	75.05 458	93.74 426

EG-PatchMatch MVS		85.35 430	83.81 434	89.99 446	90.39 470	81.89 473	98.21 423	96.09 449	81.78 460	74.73 487	93.72 450	51.56 492	97.12 375	79.16 462	88.61 347	90.96 471

EGC-MVSNET		69.38 470	63.76 482	86.26 469	90.32 471	81.66 476	96.24 470	93.85 493	0.99 558	3.22 559	92.33 471	52.44 489	92.92 488	59.53 516	84.90 384	84.21 508

CMPMVS	61.59 21	84.75 437	85.14 426	83.57 474	90.32 471	62.54 508	96.98 454	97.59 296	74.33 490	69.95 496	96.66 350	64.17 465	98.32 308	87.88 390	88.41 352	89.84 485
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
ALIKED-MNN		52.51 495	50.15 502	59.60 514	90.05 473	44.33 535	81.60 523	54.93 543	32.36 531	40.96 535	68.77 527	20.90 529	75.30 526	20.00 542	41.78 530	59.18 533

new_pmnet		84.49 440	82.92 440	89.21 450	90.03 474	82.60 467	96.89 457	95.62 461	80.59 465	75.77 486	89.17 488	65.04 463	94.79 469	72.12 484	81.02 419	90.23 478

pmmvs6		85.69 425	83.84 433	91.26 429	90.00 475	84.41 455	97.82 435	96.15 448	75.86 484	81.29 459	95.39 400	61.21 476	96.87 396	83.52 432	73.29 463	92.50 454

ttmdpeth		88.23 407	87.06 410	91.75 425	89.91 476	87.35 433	98.92 367	95.73 456	87.92 396	84.02 444	96.31 361	68.23 449	96.84 397	86.33 409	76.12 453	91.06 469

DSMNet-mixed		88.28 406	88.24 400	88.42 459	89.64 477	75.38 494	98.06 428	89.86 509	85.59 429	88.20 399	92.14 473	76.15 405	91.95 494	78.46 466	96.05 263	97.92 310

DenseAffine		75.91 463	73.39 467	83.47 475	89.52 478	71.86 497	93.39 493	89.29 514	71.44 495	66.83 500	90.32 482	30.65 506	89.67 503	68.20 492	60.88 505	88.88 496

UnsupCasMVSNet_eth		85.52 427	83.99 430	90.10 444	89.36 479	83.51 462	96.65 461	97.99 246	89.14 364	75.89 485	93.83 447	63.25 469	93.92 476	81.92 443	67.90 486	92.88 446

Anonymous20231206		86.32 421	85.42 424	89.02 452	89.11 480	80.53 483	99.05 345	95.28 468	85.43 431	82.82 450	93.92 446	74.40 419	93.44 483	66.99 495	81.83 409	93.08 442

ALIKED-LG		54.29 491	52.28 495	60.32 508	88.90 481	45.51 530	81.66 522	56.33 537	38.60 521	42.62 533	70.81 522	25.00 520	75.20 527	19.87 543	46.76 527	60.24 531

Anonymous20240521		85.15 432	83.81 434	89.16 451	88.32 482	82.69 466	98.80 382	95.74 455	79.72 468	81.53 457	90.99 476	65.38 461	94.16 474	72.69 482	81.11 416	90.63 475

OpenMVS_ROB	79.82 20	83.77 444	81.68 447	90.03 445	88.30 483	82.82 465	98.46 404	95.22 471	73.92 491	76.00 484	91.29 475	55.00 485	96.94 389	68.40 490	88.51 351	90.34 476

test20.03		84.72 438	83.99 430	86.91 466	88.19 484	80.62 482	98.88 370	95.94 452	88.36 389	78.87 471	94.62 433	68.75 444	89.11 505	66.52 498	75.82 454	91.00 470

RoMa-SfM		74.91 466	72.77 468	81.35 480	88.00 485	67.35 502	93.55 490	86.23 519	68.27 500	66.79 501	92.92 458	30.40 507	87.68 507	66.14 500	62.62 497	89.02 494

gbinet_0.2-2-1-0.02		87.63 416	85.51 423	93.99 377	87.22 486	91.56 366	99.81 169	97.36 322	79.54 471	88.60 385	93.29 456	73.76 423	96.34 429	89.27 366	60.78 506	94.06 400

blend_shiyan4		90.13 385	88.79 390	94.17 362	87.12 487	91.83 346	99.75 201	97.08 385	79.27 476	88.69 381	92.53 462	92.25 162	96.50 416	89.35 363	73.04 465	94.18 378

KD-MVS_self_test		83.59 445	82.06 445	88.20 461	86.93 488	80.70 481	97.21 447	96.38 442	82.87 454	82.49 451	88.97 489	67.63 451	92.32 491	73.75 481	62.30 499	91.58 466

DKM		72.18 468	69.80 471	79.34 483	86.79 489	65.15 504	92.70 495	84.00 520	67.67 501	61.97 506	89.63 484	23.69 524	85.17 513	67.39 494	54.35 516	87.70 500

MIMVSNet1		82.58 449	80.51 454	88.78 454	86.68 490	84.20 456	96.65 461	95.41 466	78.75 477	78.59 474	92.44 463	51.88 491	89.76 502	65.26 502	78.95 432	92.38 458

wanda-best-256-512		87.82 412	85.71 419	94.15 365	86.66 491	91.88 342	99.76 194	97.08 385	79.46 472	88.37 394	92.36 467	78.01 380	96.43 422	88.39 379	61.26 501	94.14 389

FE-blended-shiyan7		87.82 412	85.71 419	94.15 365	86.66 491	91.88 342	99.76 194	97.08 385	79.46 472	88.37 394	92.36 467	78.01 380	96.43 422	88.39 379	61.26 501	94.14 389

usedtu_blend_shiyan5		86.75 420	84.29 428	94.16 363	86.66 491	91.83 346	97.42 441	95.23 470	69.94 498	88.37 394	92.36 467	78.01 380	96.50 416	89.35 363	61.26 501	94.14 389

SP-NN		55.28 489	53.59 491	60.34 507	86.63 494	39.01 540	86.70 515	56.31 538	31.08 533	43.77 530	68.45 529	23.39 525	60.24 533	29.19 536	56.76 513	81.77 514

LoFTR		74.41 467	70.88 470	84.99 472	86.56 495	67.85 501	93.74 486	89.63 511	69.46 499	54.95 517	87.39 500	30.76 505	96.92 390	61.37 511	64.06 493	90.19 480

blended_shiyan8		87.82 412	85.71 419	94.16 363	86.54 496	91.79 348	99.72 216	97.08 385	79.32 474	88.44 388	92.35 470	77.88 384	96.56 413	88.53 375	61.51 500	94.15 385

blended_shiyan6		87.74 415	85.62 422	94.09 370	86.53 497	91.73 354	99.72 216	97.08 385	79.32 474	88.22 398	92.31 472	77.82 385	96.43 422	88.31 381	61.26 501	94.13 394

CL-MVSNet_self_test		84.50 439	83.15 439	88.53 457	86.00 498	81.79 474	98.82 378	97.35 323	85.12 434	83.62 448	90.91 478	76.66 397	91.40 495	69.53 488	60.36 507	92.40 456

MatchFormer		70.84 469	66.72 476	83.19 477	85.99 499	64.61 505	93.58 489	88.62 515	59.32 511	50.64 520	82.31 515	28.00 512	96.79 402	52.52 522	59.50 509	88.18 497

UnsupCasMVSNet_bld		79.97 459	77.03 465	88.78 454	85.62 500	81.98 472	93.66 487	97.35 323	75.51 487	70.79 495	83.05 511	48.70 497	94.91 466	78.31 467	60.29 508	89.46 491

mvs5depth		84.87 435	82.90 441	90.77 434	85.59 501	84.84 452	91.10 505	93.29 498	83.14 451	85.07 438	94.33 442	62.17 472	97.32 361	78.83 465	72.59 470	90.14 481

SP-LightGlue		55.29 487	53.65 490	60.20 509	85.58 502	39.12 539	86.36 518	57.52 535	32.34 532	44.34 529	67.75 532	24.36 522	59.32 536	29.62 534	54.98 514	82.17 512

SP-SuperGlue		55.29 487	53.71 489	60.00 511	85.11 503	38.86 541	86.96 514	57.95 534	32.77 530	44.54 528	68.00 530	23.90 523	59.51 535	29.61 535	54.59 515	81.63 515

SP-MNN		53.97 492	52.04 498	59.73 513	84.72 504	38.63 542	86.51 516	55.94 539	29.25 534	40.20 536	67.48 533	22.18 527	59.59 534	27.79 537	54.33 517	80.98 517

Patchmatch-RL test		86.90 418	85.98 417	89.67 447	84.45 505	75.59 492	89.71 510	92.43 500	86.89 412	77.83 478	90.94 477	94.22 96	93.63 481	87.75 391	69.61 477	99.79 112

DKM-HiRes		68.91 472	66.34 478	76.62 488	84.17 506	60.69 511	90.78 509	78.55 524	62.17 508	58.82 511	87.54 497	20.94 528	82.56 517	63.05 506	51.00 522	86.61 504

MASt3R-SfM		78.94 460	79.57 458	77.07 485	84.15 507	50.74 524	91.56 501	92.34 501	83.22 450	80.84 462	94.16 444	36.67 503	92.30 492	79.45 458	73.71 462	88.16 498

pmmvs-eth3d		84.03 442	81.97 446	90.20 442	84.15 507	87.09 435	98.10 427	94.73 481	83.05 452	74.10 491	87.77 496	65.56 460	94.01 475	81.08 447	69.24 479	89.49 490

test_fmvs3		79.99 458	80.17 456	79.45 482	84.02 509	62.83 506	99.05 345	93.49 497	88.29 391	80.06 467	86.65 504	28.09 511	88.00 506	88.63 371	73.27 464	87.54 502

PM-MVS		80.47 455	78.88 460	85.26 470	83.79 510	72.22 496	95.89 477	91.08 506	85.71 428	76.56 483	88.30 492	36.64 504	93.90 477	82.39 439	69.57 478	89.66 489

RoMa-HiRes		69.18 471	67.02 473	75.65 490	83.52 511	60.31 513	90.80 508	76.82 526	62.46 507	62.85 504	90.44 481	24.75 521	83.07 515	60.58 513	50.97 523	83.58 509

new-patchmatchnet		81.19 451	79.34 459	86.76 467	82.86 512	80.36 484	97.92 431	95.27 469	82.09 459	72.02 493	86.87 503	62.81 471	90.74 500	71.10 485	63.08 495	89.19 493

FE-MVSNET2		83.57 446	81.36 449	90.20 442	82.83 513	87.59 429	98.28 416	96.04 450	85.33 433	74.13 490	87.45 498	59.16 480	93.26 485	79.12 463	69.91 475	89.77 486

FE-MVSNET		81.05 453	78.81 461	87.79 463	81.98 514	83.70 458	98.23 420	91.78 505	81.27 462	74.29 489	87.44 499	60.92 478	90.67 501	64.92 503	68.43 482	89.01 495

mvsany_test3		82.12 450	81.14 451	85.06 471	81.87 515	70.41 498	97.09 451	92.14 502	91.27 313	77.84 477	88.73 490	39.31 501	95.49 454	90.75 343	71.24 472	89.29 492

WB-MVS		76.28 462	77.28 464	73.29 493	81.18 516	54.68 519	97.87 434	94.19 488	81.30 461	69.43 497	90.70 479	77.02 391	82.06 518	35.71 530	68.11 485	83.13 510

test_f		78.40 461	77.59 463	80.81 481	80.82 517	62.48 509	96.96 455	93.08 499	83.44 448	74.57 488	84.57 510	27.95 513	92.63 489	84.15 424	72.79 466	87.32 503

SSC-MVS		75.42 465	76.40 466	72.49 498	80.68 518	53.62 520	97.42 441	94.06 490	80.42 466	68.75 498	90.14 483	76.54 399	81.66 519	33.25 531	66.34 489	82.19 511

pmmvs3		80.27 456	77.77 462	87.76 464	80.32 519	82.43 469	98.23 420	91.97 503	72.74 493	78.75 472	87.97 495	57.30 484	90.99 498	70.31 486	62.37 498	89.87 484

testf1		68.38 475	66.92 474	72.78 495	78.80 520	50.36 525	90.95 506	87.35 517	55.47 514	58.95 509	88.14 493	20.64 531	87.60 508	57.28 517	64.69 491	80.39 519

APD_test2		68.38 475	66.92 474	72.78 495	78.80 520	50.36 525	90.95 506	87.35 517	55.47 514	58.95 509	88.14 493	20.64 531	87.60 508	57.28 517	64.69 491	80.39 519

ambc				83.23 476	77.17 522	62.61 507	87.38 512	94.55 486		76.72 482	86.65 504	30.16 508	96.36 428	84.85 423	69.86 476	90.73 473

test_vis3_rt		68.82 473	66.69 477	75.21 492	76.24 523	60.41 512	96.44 465	68.71 530	75.13 488	50.54 521	69.52 526	16.42 538	96.32 431	80.27 454	66.92 488	68.89 527

PDCNetPlus		59.83 483	57.26 486	67.55 503	76.18 524	56.71 517	87.01 513	45.27 546	59.54 510	48.80 523	83.01 512	26.63 515	76.54 525	62.12 510	26.78 539	69.40 526

usedtu_dtu_shiyan2		75.87 464	72.37 469	86.39 468	76.18 524	75.49 493	96.53 463	93.82 494	64.74 503	72.53 492	88.48 491	37.67 502	91.12 497	64.13 504	57.22 511	92.56 451

TDRefinement		84.76 436	82.56 443	91.38 428	74.58 526	84.80 453	97.36 445	94.56 485	84.73 439	80.21 465	96.12 371	63.56 467	98.39 298	87.92 389	63.97 494	90.95 472

PMatch-SfM		62.12 482	58.57 485	72.76 497	74.34 527	52.97 522	84.95 519	65.57 531	56.89 513	46.61 525	85.70 509	9.51 549	80.54 521	60.53 514	43.03 529	84.77 505

SIFT-NN		35.94 506	36.54 509	34.16 522	73.93 528	29.52 544	62.74 535	37.28 547	19.65 540	27.91 543	49.19 542	11.66 542	46.35 541	9.19 545	37.30 531	26.61 541

ELoFTR		64.32 481	60.56 484	75.60 491	73.46 529	53.20 521	86.50 517	80.09 523	60.74 509	45.95 526	82.48 514	16.05 539	89.20 504	56.48 521	43.34 528	84.38 507

E-PMN		52.30 496	52.18 497	52.67 515	71.51 530	45.40 532	93.62 488	76.60 527	36.01 525	43.50 531	64.13 536	27.11 514	67.31 531	31.06 532	26.06 540	45.30 540

EMVS		51.44 499	51.22 500	52.11 516	70.71 531	44.97 534	94.04 483	75.66 528	35.34 527	42.40 534	61.56 540	28.93 510	65.87 532	27.64 538	24.73 541	45.49 537

PMMVS2		67.15 478	64.15 481	76.14 489	70.56 532	62.07 510	93.89 484	87.52 516	58.09 512	60.02 508	78.32 517	22.38 526	84.54 514	59.56 515	47.03 526	81.80 513

PMatch-Up-SfM		57.92 484	53.93 488	69.90 500	69.97 533	46.69 529	81.36 524	55.29 542	51.90 517	43.17 532	82.54 513	7.86 554	78.44 524	57.13 519	36.17 533	84.58 506

SIFT-MNN		34.10 507	34.41 510	33.17 524	68.99 534	28.51 545	60.22 537	36.81 548	19.08 543	24.04 546	47.28 545	10.06 546	45.04 542	8.72 546	34.47 534	25.97 544

SIFT-NCM-Cal		31.73 509	31.67 512	31.91 527	67.18 535	27.55 551	58.36 540	33.09 552	18.38 547	14.93 553	45.16 551	8.60 550	43.82 545	7.62 555	31.68 537	24.36 547

SIFT-NN-NCMNet		33.88 508	34.14 511	33.10 525	66.88 536	28.42 546	60.42 536	36.72 549	19.15 541	24.06 545	47.14 546	10.24 544	44.77 543	8.72 546	33.94 536	26.10 543

FPMVS		68.72 474	68.72 472	68.71 501	65.95 537	44.27 536	95.97 476	94.74 480	51.13 518	53.26 518	90.50 480	25.11 519	83.00 516	60.80 512	80.97 421	78.87 521

SP-DiffGlue		56.84 485	55.72 487	60.19 510	65.70 538	40.86 537	81.89 521	60.28 533	34.62 529	50.39 522	76.88 519	26.61 516	58.81 537	48.21 524	56.94 512	80.90 518

wuyk23d		20.37 521	20.84 524	18.99 538	65.34 539	27.73 549	50.43 548	7.67 564	9.50 556	8.01 558	6.34 557	6.13 559	26.24 557	23.40 540	10.69 556	2.99 555

SIFT-ConvMatch		30.09 512	29.76 516	31.09 529	65.16 540	27.56 550	54.13 544	31.17 553	18.55 546	17.88 549	45.89 548	8.40 551	42.26 549	8.11 551	18.51 547	23.46 549

MVS_clip		48.84 501	50.24 501	44.65 519	64.05 541	23.54 559	58.84 538	20.46 560	18.73 545	60.84 507	89.57 486	25.96 517	29.22 556	62.25 509	51.44 521	81.19 516

SIFT-CM-Cal		28.34 515	27.90 519	29.63 531	63.75 542	25.98 555	50.66 547	26.18 557	18.12 550	16.88 551	44.64 552	8.08 553	39.70 550	7.65 554	15.19 552	23.22 550

LCM-MVSNet		67.77 477	64.73 480	76.87 487	62.95 543	56.25 518	89.37 511	93.74 495	44.53 520	61.99 505	80.74 516	20.42 533	86.53 512	69.37 489	59.50 509	87.84 499

SIFT-NN-CMatch		31.71 510	31.56 513	32.16 526	62.58 544	27.53 552	56.45 541	33.28 551	19.00 544	23.65 547	47.34 543	10.05 547	42.72 547	8.71 548	22.96 544	26.24 542

SIFT-UM-Cal		27.47 516	27.02 520	28.83 534	62.12 545	24.58 557	53.60 545	23.46 558	18.14 549	12.85 555	45.56 549	7.49 555	39.45 551	7.68 553	12.30 553	22.45 551

SIFT-UMatch		29.40 514	28.87 518	30.98 530	62.08 546	26.57 554	56.09 542	29.45 555	18.31 548	15.86 552	46.00 547	8.23 552	42.54 548	7.99 552	15.81 550	23.85 548

GLUNet-SfM		51.10 500	46.61 504	64.56 504	61.54 547	39.88 538	79.38 528	65.13 532	36.09 524	33.36 540	69.94 524	14.50 541	78.76 522	42.46 528	17.10 549	75.02 524

SIFT-NN-UMatch		31.23 511	31.05 515	31.79 528	60.08 548	27.23 553	58.49 539	33.65 550	19.14 542	17.30 550	47.31 544	10.12 545	42.88 546	8.67 549	24.67 542	25.27 545

XFeat-NN		42.54 502	42.87 506	41.54 521	59.73 549	27.86 548	69.53 532	45.34 545	24.36 535	37.16 537	64.79 534	20.84 530	51.40 540	30.01 533	34.12 535	45.36 539

MVE	53.74 22	51.54 498	47.86 503	62.60 505	59.56 550	50.93 523	79.41 527	77.69 525	35.69 526	36.27 538	61.76 539	5.79 560	69.63 529	37.97 529	36.61 532	67.24 528
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
SIFT-NN-PointCN		29.63 513	29.72 517	29.36 532	57.55 551	23.55 558	56.07 543	30.57 554	17.99 551	20.99 548	45.21 550	9.94 548	39.33 552	8.40 550	20.81 545	25.20 546

SIFT-PointCN		25.49 517	25.71 521	24.84 535	56.17 552	18.65 562	51.37 546	26.53 556	16.31 552	12.78 556	39.87 555	6.41 558	34.09 554	6.51 557	15.42 551	21.77 552

SIFT-PCN-Cal		24.67 518	24.81 522	24.24 536	56.13 553	18.04 563	49.05 549	23.39 559	16.07 553	12.99 554	40.17 554	6.97 557	34.68 553	6.71 556	11.81 554	19.99 553

XFeat-MNN		41.51 503	41.24 507	42.32 520	55.40 554	28.19 547	69.39 533	46.53 544	23.57 536	34.47 539	63.21 538	20.04 534	52.41 539	27.43 539	31.08 538	46.37 536

SIFT-NCMNet		21.21 520	21.22 523	21.17 537	52.99 555	16.41 564	42.12 550	14.05 562	15.89 554	10.70 557	35.85 556	5.14 561	29.82 555	5.80 558	8.44 557	17.28 554

ANet_high		56.10 486	52.24 496	67.66 502	49.27 556	56.82 516	83.94 520	82.02 522	70.47 496	33.28 541	64.54 535	17.23 537	69.16 530	45.59 526	23.85 543	77.02 523

VLMVS		51.63 497	52.90 493	47.80 518	47.64 557	20.83 560	69.98 530	55.61 541	20.15 539	63.34 503	87.24 501	19.48 536	43.90 544	62.94 507	49.76 524	78.65 522

tmp_tt		65.23 480	62.94 483	72.13 499	44.90 558	50.03 527	81.05 526	89.42 513	38.45 522	48.51 524	99.90 23	54.09 487	78.70 523	91.84 324	18.26 548	87.64 501

VLMVS_CLIP		52.57 494	53.54 492	49.65 517	41.84 559	19.27 561	69.54 531	70.45 529	22.22 537	56.57 515	86.16 506	15.89 540	54.77 538	66.88 496	52.29 520	74.91 525

PMVS	49.05 23	53.75 493	51.34 499	60.97 506	40.80 560	34.68 543	74.82 529	89.62 512	37.55 523	28.67 542	72.12 520	7.09 556	81.63 520	43.17 527	68.21 484	66.59 529
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVS_baseline		18.28 522	19.10 525	15.85 539	22.71 561	1.80 566	10.32 551	3.08 565	1.00 557	27.16 544	68.73 528	2.83 562	0.36 560	17.05 544	18.98 546	45.38 538

test123		37.68 505	39.14 508	33.31 523	19.94 562	24.83 556	98.36 413	9.75 563	15.53 555	51.31 519	87.14 502	19.62 535	17.74 558	47.10 525	3.47 558	57.36 534

testmvs		40.60 504	44.45 505	29.05 533	19.49 563	14.11 565	99.68 234	18.47 561	20.74 538	64.59 502	98.48 279	10.95 543	17.09 559	56.66 520	11.01 555	55.94 535

PatchmatchNet2					0.00 564	86.19 440	98.94 361	96.51 439	78.40 478
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
mmdepth		0.00 525	0.00 528	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.00 559	0.00 563	0.00 561	0.00 559	0.00 559	0.00 556

monomultidepth		0.00 525	0.00 528	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.00 559	0.00 563	0.00 561	0.00 559	0.00 559	0.00 556

test_blank		0.00 525	0.00 528	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.02 558	0.00 563	0.00 561	0.00 559	0.00 559	0.00 556

eth-test2					0.00 564

eth-test					0.00 564

uanet_test		0.00 525	0.00 528	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.00 559	0.00 563	0.00 561	0.00 559	0.00 559	0.00 556

DCPMVS		0.00 525	0.00 528	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.00 559	0.00 563	0.00 561	0.00 559	0.00 559	0.00 556

cdsmvs_eth3d_5k		23.43 519	31.24 514	0.00 540	0.00 564	0.00 567	0.00 552	98.09 235	0.00 559	0.00 560	99.67 114	83.37 317	0.00 561	0.00 559	0.00 559	0.00 556

pcd_1.5k_mvsjas		7.60 524	10.13 527	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.00 559	91.20 179	0.00 561	0.00 559	0.00 559	0.00 556

sosnet-low-res		0.00 525	0.00 528	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.00 559	0.00 563	0.00 561	0.00 559	0.00 559	0.00 556

sosnet		0.00 525	0.00 528	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.00 559	0.00 563	0.00 561	0.00 559	0.00 559	0.00 556

uncertanet		0.00 525	0.00 528	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.00 559	0.00 563	0.00 561	0.00 559	0.00 559	0.00 556

Regformer		0.00 525	0.00 528	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.00 559	0.00 563	0.00 561	0.00 559	0.00 559	0.00 556

ab-mvs-re		8.28 523	11.04 526	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	99.40 147	0.00 563	0.00 561	0.00 559	0.00 559	0.00 556

uanet		0.00 525	0.00 528	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.00 559	0.00 563	0.00 561	0.00 559	0.00 559	0.00 556

PatchmatchNet1														68.29 491	82.87 398	92.70 449
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet3													95.80 450
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
WAC-MVS						90.97 372								86.10 413

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 17	100.00 1	100.00 1

GSMVS																99.59 154

sam_mvs1												94.72 75				99.59 154

sam_mvs												94.25 95

MTGPA								98.28 205

test_post1							95.78 478				59.23 541	93.20 130	97.74 345	91.06 334

test_post											63.35 537	94.43 83	98.13 324

patchmatchnet-post											91.70 474	95.12 61	97.95 336

MTMP							99.87 133	96.49 440

test9_res														99.71 49	99.99 21	100.00 1

agg_prior2														99.48 64	100.00 1	100.00 1

test_prior4						98.05 83	99.94 93

test_prior2							99.95 75		95.78 105	99.73 47	99.76 73	96.00 42		99.78 36	100.00 1

旧先验2							99.46 285		94.21 167	99.85 20			99.95 86	96.96 203

新几何2							99.40 290

无先验							99.49 277	98.71 79	93.46 203				100.00 1	94.36 271		99.99 26

原ACMM2							99.90 117

testdata2													99.99 40	90.54 347

segment_acmp												96.68 31

testdata1							99.28 316		96.35 91

plane_prior5								97.87 260					98.37 304	97.79 172	89.55 334	94.52 348

plane_prior4											98.59 265

plane_prior3						91.64 359			96.63 75	93.01 309

plane_prior2							99.84 152		96.38 86

plane_prior						91.74 351	99.86 144		96.76 70						89.59 333

n2								0.00 566

nn								0.00 566

door-mid								89.69 510

test11								98.44 148

door								90.31 507

HQP5-MVS						91.85 344

BP-MVS														97.92 161

HQP4-MVS										93.37 304			98.39 298			94.53 346

HQP3-MVS								97.89 258							89.60 331

HQP2-MVS												80.65 354

MDTV_nov1_ep13_2view						96.26 171	96.11 472		91.89 287	98.06 171		94.40 85		94.30 274		99.67 133

ACMMP++_ref															87.04 367

ACMMP++															88.23 354

Test By Simon												92.82 141

High-resolution multi-view benchmark