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 99	96.80 104	98.51 106	99.99 1	95.60 161	99.09 246	98.84 58	93.32 164	96.74 163	99.72 81	86.04 217	100.00 1	98.01 115	99.43 110	99.94 74

CNVR-MVS		99.40 1	99.26 1	99.84 6	99.98 2	99.51 6	99.98 14	98.69 68	98.20 7	99.93 1	99.98 2	96.82 23	100.00 1	99.75 28	100.00 1	99.99 23

MCST-MVS		99.32 3	99.14 4	99.86 5	99.97 3	99.59 5	99.97 27	98.64 76	98.47 2	99.13 85	99.92 13	96.38 30	100.00 1	99.74 30	100.00 1	100.00 1

mPP-MVS		98.39 46	98.20 45	98.97 74	99.97 3	96.92 112	99.95 52	98.38 154	95.04 97	98.61 112	99.80 51	93.39 97	100.00 1	98.64 89	100.00 1	99.98 48

CPTT-MVS		97.64 83	97.32 86	98.58 98	99.97 3	95.77 152	99.96 34	98.35 160	89.90 267	98.36 122	99.79 55	91.18 153	99.99 36	98.37 99	99.99 21	99.99 23

DP-MVS Recon		98.41 44	98.02 56	99.56 25	99.97 3	98.70 46	99.92 78	98.44 120	92.06 213	98.40 121	99.84 41	95.68 40	100.00 1	98.19 105	99.71 83	99.97 58

PAPR		98.52 34	98.16 48	99.58 24	99.97 3	98.77 40	99.95 52	98.43 128	95.35 91	98.03 132	99.75 69	94.03 84	99.98 43	98.11 110	99.83 72	99.99 23

HFP-MVS		98.56 31	98.37 35	99.14 59	99.96 8	97.43 94	99.95 52	98.61 82	94.77 105	99.31 76	99.85 30	94.22 77	100.00 1	98.70 84	99.98 32	99.98 48

region2R		98.54 32	98.37 35	99.05 66	99.96 8	97.18 101	99.96 34	98.55 96	94.87 103	99.45 64	99.85 30	94.07 83	100.00 1	98.67 86	100.00 1	99.98 48

ACMMPR		98.50 35	98.32 39	99.05 66	99.96 8	97.18 101	99.95 52	98.60 84	94.77 105	99.31 76	99.84 41	93.73 92	100.00 1	98.70 84	99.98 32	99.98 48

NCCC		99.37 2	99.25 2	99.71 14	99.96 8	99.15 21	99.97 27	98.62 81	98.02 13	99.90 3	99.95 3	97.33 17	100.00 1	99.54 39	100.00 1	100.00 1

CP-MVS		98.45 39	98.32 39	98.87 79	99.96 8	96.62 121	99.97 27	98.39 150	94.43 117	98.90 94	99.87 24	94.30 75	100.00 1	99.04 63	99.99 21	99.99 23

test_one_0601					99.94 13	99.30 12		98.41 143	96.63 56	99.75 29	99.93 11	97.49 10

test_0728_SECOND				99.82 7	99.94 13	99.47 7	99.95 52	98.43 128					100.00 1	99.99 5	100.00 1	100.00 1

XVS		98.70 25	98.55 25	99.15 57	99.94 13	97.50 90	99.94 68	98.42 139	96.22 71	99.41 68	99.78 59	94.34 73	99.96 61	98.92 70	99.95 49	99.99 23

X-MVStestdata		93.83 204	92.06 237	99.15 57	99.94 13	97.50 90	99.94 68	98.42 139	96.22 71	99.41 68	41.37 402	94.34 73	99.96 61	98.92 70	99.95 49	99.99 23

test_prior				99.43 35	99.94 13	98.49 58		98.65 74					99.80 121			99.99 23

MSLP-MVS++		99.13 8	99.01 11	99.49 32	99.94 13	98.46 59	99.98 14	98.86 53	97.10 40	99.80 17	99.94 4	95.92 36	100.00 1	99.51 40	100.00 1	100.00 1

APDe-MVS		99.06 11	98.91 14	99.51 29	99.94 13	98.76 43	99.91 82	98.39 150	97.20 38	99.46 63	99.85 30	95.53 44	99.79 123	99.86 21	100.00 1	99.99 23
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
MP-MVS		98.23 56	97.97 58	99.03 68	99.94 13	97.17 104	99.95 52	98.39 150	94.70 109	98.26 128	99.81 50	91.84 144	100.00 1	98.85 76	99.97 42	99.93 76
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CDPH-MVS		98.65 27	98.36 37	99.49 32	99.94 13	98.73 44	99.87 100	98.33 165	93.97 143	99.76 28	99.87 24	94.99 57	99.75 132	98.55 93	100.00 1	99.98 48

PAPM_NR		98.12 59	97.93 63	98.70 87	99.94 13	96.13 143	99.82 131	98.43 128	94.56 113	97.52 144	99.70 85	94.40 68	99.98 43	97.00 149	99.98 32	99.99 23

MG-MVS		98.91 18	98.65 20	99.68 15	99.94 13	99.07 24	99.64 179	99.44 20	97.33 31	99.00 90	99.72 81	94.03 84	99.98 43	98.73 83	100.00 1	100.00 1

SED-MVS		99.28 5	99.11 7	99.77 8	99.93 24	99.30 12	99.96 34	98.43 128	97.27 34	99.80 17	99.94 4	96.71 24	100.00 1	100.00 1	100.00 1	100.00 1

IU-MVS					99.93 24	99.31 10		98.41 143	97.71 19	99.84 12				100.00 1	100.00 1	100.00 1

test_241102_ONE					99.93 24	99.30 12		98.43 128	97.26 36	99.80 17	99.88 21	96.71 24	100.00 1

DVP-MVS		99.30 4	99.16 3	99.73 12	99.93 24	99.29 15	99.95 52	98.32 167	97.28 32	99.83 13	99.91 14	97.22 19	100.00 1	99.99 5	100.00 1	99.89 84
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 24	99.29 15	99.96 34	98.42 139	97.28 32	99.86 7	99.94 4	97.22 19

MSP-MVS		99.09 9	99.12 5	98.98 73	99.93 24	97.24 98	99.95 52	98.42 139	97.50 26	99.52 59	99.88 21	97.43 16	99.71 138	99.50 41	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 24	98.77 40		98.43 128		99.63 43			99.85 108

FOURS1					99.92 31	97.66 83	99.95 52	98.36 158	95.58 85	99.52 59

ZD-MVS					99.92 31	98.57 54		98.52 102	92.34 205	99.31 76	99.83 43	95.06 52	99.80 121	99.70 34	99.97 42

GST-MVS		98.27 51	97.97 58	99.17 53	99.92 31	97.57 85	99.93 75	98.39 150	94.04 141	98.80 99	99.74 76	92.98 112	100.00 1	98.16 107	99.76 80	99.93 76

TEST9					99.92 31	98.92 28	99.96 34	98.43 128	93.90 148	99.71 34	99.86 26	95.88 37	99.85 108

train_agg		98.88 19	98.65 20	99.59 23	99.92 31	98.92 28	99.96 34	98.43 128	94.35 122	99.71 34	99.86 26	95.94 34	99.85 108	99.69 35	99.98 32	99.99 23

test_8					99.92 31	98.88 31	99.96 34	98.43 128	94.35 122	99.69 36	99.85 30	95.94 34	99.85 108

PGM-MVS		98.34 47	98.13 50	98.99 72	99.92 31	97.00 108	99.75 151	99.50 18	93.90 148	99.37 73	99.76 63	93.24 106	100.00 1	97.75 132	99.96 46	99.98 48

ACMMP		97.74 78	97.44 80	98.66 90	99.92 31	96.13 143	99.18 240	99.45 19	94.84 104	96.41 173	99.71 83	91.40 147	99.99 36	97.99 117	98.03 157	99.87 87
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 9	99.77 8	99.91 39	99.31 10	99.95 52	98.43 128	96.48 59	99.80 17	99.93 11	97.44 14	100.00 1	99.92 12	99.98 32	100.00 1

MSC_two_6792asdad				99.93 2	99.91 39	99.80 2		98.41 143					100.00 1	99.96 9	100.00 1	100.00 1

No_MVS				99.93 2	99.91 39	99.80 2		98.41 143					100.00 1	99.96 9	100.00 1	100.00 1

HPM-MVS++		99.07 10	98.88 16	99.63 17	99.90 42	99.02 25	99.95 52	98.56 90	97.56 25	99.44 65	99.85 30	95.38 46	100.00 1	99.31 51	99.99 21	99.87 87

APD-MVS		98.62 28	98.35 38	99.41 38	99.90 42	98.51 57	99.87 100	98.36 158	94.08 135	99.74 31	99.73 78	94.08 82	99.74 134	99.42 47	99.99 21	99.99 23
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
DeepC-MVS_fast	96.59 1	98.81 22	98.54 26	99.62 20	99.90 42	98.85 34	99.24 235	98.47 113	98.14 10	99.08 86	99.91 14	93.09 109	100.00 1	99.04 63	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 45	99.80 2	99.96 34				99.80 51	97.44 14	100.00 1	100.00 1	99.98 32	100.00 1

DPE-MVS		99.26 6	99.10 8	99.74 11	99.89 45	99.24 19	99.87 100	98.44 120	97.48 27	99.64 42	99.94 4	96.68 26	99.99 36	99.99 5	100.00 1	99.99 23
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
test_part2					99.89 45	99.25 18				99.49 62

CSCG		97.10 102	97.04 96	97.27 175	99.89 45	91.92 260	99.90 87	99.07 34	88.67 290	95.26 195	99.82 46	93.17 108	99.98 43	98.15 108	99.47 104	99.90 83

ZNCC-MVS		98.31 48	98.03 55	99.17 53	99.88 49	97.59 84	99.94 68	98.44 120	94.31 125	98.50 116	99.82 46	93.06 110	99.99 36	98.30 103	99.99 21	99.93 76

SR-MVS		98.46 38	98.30 42	98.93 77	99.88 49	97.04 106	99.84 121	98.35 160	94.92 101	99.32 75	99.80 51	93.35 99	99.78 125	99.30 52	99.95 49	99.96 64

9.14			98.38 33		99.87 51		99.91 82	98.33 165	93.22 167	99.78 26	99.89 19	94.57 65	99.85 108	99.84 22	99.97 42

SMA-MVS		98.76 23	98.48 28	99.62 20	99.87 51	98.87 32	99.86 113	98.38 154	93.19 168	99.77 27	99.94 4	95.54 42	100.00 1	99.74 30	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
PHI-MVS		98.41 44	98.21 44	99.03 68	99.86 53	97.10 105	99.98 14	98.80 62	90.78 253	99.62 46	99.78 59	95.30 47	100.00 1	99.80 25	99.93 60	99.99 23

MTAPA		98.29 50	97.96 61	99.30 42	99.85 54	97.93 73	99.39 216	98.28 174	95.76 80	97.18 152	99.88 21	92.74 120	100.00 1	98.67 86	99.88 68	99.99 23

LS3D		95.84 152	95.11 162	98.02 132	99.85 54	95.10 181	98.74 288	98.50 110	87.22 311	93.66 213	99.86 26	87.45 201	99.95 69	90.94 253	99.81 78	99.02 196

HPM-MVS		97.96 62	97.72 70	98.68 88	99.84 56	96.39 130	99.90 87	98.17 186	92.61 190	98.62 111	99.57 107	91.87 143	99.67 145	98.87 75	99.99 21	99.99 23
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 51	98.11 52	98.75 85	99.83 57	96.59 123	99.40 212	98.51 105	95.29 93	98.51 115	99.76 63	93.60 96	99.71 138	98.53 94	99.52 99	99.95 71

save fliter					99.82 58	98.79 38	99.96 34	98.40 147	97.66 21

PLC	95.54 3	97.93 64	97.89 66	98.05 131	99.82 58	94.77 190	99.92 78	98.46 115	93.93 146	97.20 151	99.27 132	95.44 45	99.97 53	97.41 137	99.51 102	99.41 162
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
APD-MVS_3200maxsize		98.25 54	98.08 54	98.78 82	99.81 60	96.60 122	99.82 131	98.30 172	93.95 145	99.37 73	99.77 61	92.84 116	99.76 131	98.95 67	99.92 63	99.97 58

EI-MVSNet-UG-set		98.14 58	97.99 57	98.60 95	99.80 61	96.27 133	99.36 221	98.50 110	95.21 95	98.30 125	99.75 69	93.29 103	99.73 137	98.37 99	99.30 116	99.81 94

SR-MVS-dyc-post		98.31 48	98.17 47	98.71 86	99.79 62	96.37 131	99.76 148	98.31 169	94.43 117	99.40 70	99.75 69	93.28 104	99.78 125	98.90 73	99.92 63	99.97 58

RE-MVS-def			98.13 50		99.79 62	96.37 131	99.76 148	98.31 169	94.43 117	99.40 70	99.75 69	92.95 113		98.90 73	99.92 63	99.97 58

HPM-MVS_fast		97.80 73	97.50 78	98.68 88	99.79 62	96.42 126	99.88 97	98.16 190	91.75 223	98.94 92	99.54 110	91.82 145	99.65 147	97.62 135	99.99 21	99.99 23

SF-MVS		98.67 26	98.40 31	99.50 30	99.77 65	98.67 47	99.90 87	98.21 181	93.53 158	99.81 15	99.89 19	94.70 63	99.86 107	99.84 22	99.93 60	99.96 64

旧先验1					99.76 66	97.52 87		98.64 76			99.85 30	95.63 41			99.94 54	99.99 23

OMC-MVS		97.28 96	97.23 88	97.41 166	99.76 66	93.36 229	99.65 175	97.95 209	96.03 75	97.41 148	99.70 85	89.61 177	99.51 152	96.73 156	98.25 149	99.38 164

新几何1				99.42 37	99.75 68	98.27 61		98.63 80	92.69 185	99.55 54	99.82 46	94.40 68	100.00 1	91.21 245	99.94 54	99.99 23

MP-MVS-pluss		98.07 61	97.64 73	99.38 41	99.74 69	98.41 60	99.74 154	98.18 185	93.35 162	96.45 170	99.85 30	92.64 122	99.97 53	98.91 72	99.89 66	99.77 101
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TSAR-MVS + MP.		98.93 16	98.77 18	99.41 38	99.74 69	98.67 47	99.77 143	98.38 154	96.73 53	99.88 6	99.74 76	94.89 59	99.59 149	99.80 25	99.98 32	99.97 58
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 35	99.74 69	98.56 55		98.40 147		99.65 40		94.76 60	99.75 132		99.98 32	99.99 23

原ACMM1				98.96 75	99.73 72	96.99 109		98.51 105	94.06 138	99.62 46	99.85 30	94.97 58	99.96 61	95.11 175	99.95 49	99.92 81

TSAR-MVS + GP.		98.60 29	98.51 27	98.86 80	99.73 72	96.63 120	99.97 27	97.92 214	98.07 11	98.76 103	99.55 108	95.00 56	99.94 77	99.91 15	97.68 162	99.99 23

CANet		98.27 51	97.82 68	99.63 17	99.72 74	99.10 23	99.98 14	98.51 105	97.00 43	98.52 114	99.71 83	87.80 196	99.95 69	99.75 28	99.38 112	99.83 91

F-COLMAP		96.93 110	96.95 99	96.87 184	99.71 75	91.74 265	99.85 116	97.95 209	93.11 171	95.72 188	99.16 143	92.35 132	99.94 77	95.32 173	99.35 114	98.92 198

SD-MVS		98.92 17	98.70 19	99.56 25	99.70 76	98.73 44	99.94 68	98.34 164	96.38 65	99.81 15	99.76 63	94.59 64	99.98 43	99.84 22	99.96 46	99.97 58
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 55	99.12 5	95.59 218	99.67 77	86.91 338	99.95 52	98.89 49	97.60 22	99.90 3	99.76 63	96.54 28	99.98 43	99.94 11	99.82 76	99.88 85

ACMMP_NAP		98.49 36	98.14 49	99.54 27	99.66 78	98.62 53	99.85 116	98.37 157	94.68 110	99.53 57	99.83 43	92.87 115	100.00 1	98.66 88	99.84 71	99.99 23

DeepPCF-MVS	95.94 2	97.71 81	98.98 12	93.92 282	99.63 79	81.76 365	99.96 34	98.56 90	99.47 1	99.19 83	99.99 1	94.16 81	100.00 1	99.92 12	99.93 60	100.00 1

EPNet		98.49 36	98.40 31	98.77 84	99.62 80	96.80 117	99.90 87	99.51 17	97.60 22	99.20 81	99.36 126	93.71 93	99.91 89	97.99 117	98.71 137	99.61 127
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet_BlendedMVS		96.05 145	95.82 142	96.72 189	99.59 81	96.99 109	99.95 52	99.10 31	94.06 138	98.27 126	95.80 286	89.00 188	99.95 69	99.12 58	87.53 279	93.24 337

PVSNet_Blended		97.94 63	97.64 73	98.83 81	99.59 81	96.99 109	100.00 1	99.10 31	95.38 90	98.27 126	99.08 146	89.00 188	99.95 69	99.12 58	99.25 118	99.57 137

PatchMatch-RL		96.04 146	95.40 151	97.95 133	99.59 81	95.22 177	99.52 197	99.07 34	93.96 144	96.49 169	98.35 210	82.28 246	99.82 120	90.15 269	99.22 121	98.81 205

dcpmvs_2		97.42 91	98.09 53	95.42 223	99.58 84	87.24 334	99.23 236	96.95 308	94.28 127	98.93 93	99.73 78	94.39 71	99.16 170	99.89 16	99.82 76	99.86 89

test222					99.55 85	97.41 96	99.34 222	98.55 96	91.86 218	99.27 80	99.83 43	93.84 90			99.95 49	99.99 23

CNLPA		97.76 77	97.38 82	98.92 78	99.53 86	96.84 114	99.87 100	98.14 194	93.78 151	96.55 168	99.69 87	92.28 134	99.98 43	97.13 144	99.44 108	99.93 76

API-MVS		97.86 67	97.66 72	98.47 108	99.52 87	95.41 168	99.47 206	98.87 52	91.68 224	98.84 96	99.85 30	92.34 133	99.99 36	98.44 96	99.96 46	100.00 1

PVSNet	91.05 13	97.13 101	96.69 108	98.45 110	99.52 87	95.81 150	99.95 52	99.65 12	94.73 107	99.04 88	99.21 139	84.48 232	99.95 69	94.92 181	98.74 136	99.58 136

114514_t		97.41 92	96.83 102	99.14 59	99.51 89	97.83 75	99.89 95	98.27 176	88.48 294	99.06 87	99.66 96	90.30 169	99.64 148	96.32 160	99.97 42	99.96 64

cl22		93.77 208	93.25 212	95.33 227	99.49 90	94.43 194	99.61 183	98.09 196	90.38 258	89.16 282	95.61 293	90.56 165	97.34 270	91.93 237	84.45 300	94.21 282

testdata				98.42 113	99.47 91	95.33 171		98.56 90	93.78 151	99.79 25	99.85 30	93.64 95	99.94 77	94.97 179	99.94 54	100.00 1

MAR-MVS		97.43 87	97.19 90	98.15 126	99.47 91	94.79 189	99.05 257	98.76 63	92.65 188	98.66 109	99.82 46	88.52 193	99.98 43	98.12 109	99.63 88	99.67 113
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		94.54 186	93.42 205	97.91 137	99.46 93	94.04 207	98.93 269	97.48 254	81.15 361	90.04 255	99.55 108	87.02 207	99.95 69	88.97 279	98.11 153	99.73 105

MVS_111021_LR		98.42 43	98.38 33	98.53 105	99.39 94	95.79 151	99.87 100	99.86 2	96.70 54	98.78 100	99.79 55	92.03 140	99.90 91	99.17 57	99.86 70	99.88 85

CHOSEN 280x420		99.01 13	99.03 10	98.95 76	99.38 95	98.87 32	98.46 305	99.42 22	97.03 42	99.02 89	99.09 145	99.35 1	98.21 235	99.73 32	99.78 79	99.77 101

MVS_111021_HR		98.72 24	98.62 22	99.01 71	99.36 96	97.18 101	99.93 75	99.90 1	96.81 51	98.67 108	99.77 61	93.92 86	99.89 96	99.27 53	99.94 54	99.96 64

DPM-MVS		98.83 21	98.46 29	99.97 1	99.33 97	99.92 1	99.96 34	98.44 120	97.96 14	99.55 54	99.94 4	97.18 21	100.00 1	93.81 210	99.94 54	99.98 48

TAPA-MVS	92.12 8	94.42 191	93.60 198	96.90 183	99.33 97	91.78 264	99.78 140	98.00 203	89.89 268	94.52 201	99.47 114	91.97 141	99.18 168	69.90 375	99.52 99	99.73 105
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CS-MVS-test		97.88 66	97.94 62	97.70 150	99.28 99	95.20 178	99.98 14	97.15 286	95.53 87	99.62 46	99.79 55	92.08 139	98.38 218	98.75 82	99.28 117	99.52 147

test_fmvsm_n_1920		98.44 40	98.61 23	97.92 135	99.27 100	95.18 179	100.00 1	98.90 47	98.05 12	99.80 17	99.73 78	92.64 122	99.99 36	99.58 38	99.51 102	98.59 215

fmvsm_l_conf0.5_n_a		99.00 14	98.91 14	99.28 43	99.21 101	97.91 74	99.98 14	98.85 56	98.25 4	99.92 2	99.75 69	94.72 61	99.97 53	99.87 19	99.64 87	99.95 71

test_yl		97.83 69	97.37 83	99.21 47	99.18 102	97.98 70	99.64 179	99.27 27	91.43 233	97.88 138	98.99 155	95.84 38	99.84 116	98.82 77	95.32 214	99.79 97

DCV-MVSNet		97.83 69	97.37 83	99.21 47	99.18 102	97.98 70	99.64 179	99.27 27	91.43 233	97.88 138	98.99 155	95.84 38	99.84 116	98.82 77	95.32 214	99.79 97

MVS_0304		98.87 20	98.61 23	99.67 16	99.18 102	99.13 22	99.87 100	99.65 12	98.17 8	98.75 105	99.75 69	92.76 119	99.94 77	99.88 18	99.44 108	99.94 74

fmvsm_l_conf0.5_n		98.94 15	98.84 17	99.25 44	99.17 105	97.81 77	99.98 14	98.86 53	98.25 4	99.90 3	99.76 63	94.21 79	99.97 53	99.87 19	99.52 99	99.98 48

DeepC-MVS	94.51 4	96.92 111	96.40 117	98.45 110	99.16 106	95.90 148	99.66 173	98.06 199	96.37 68	94.37 204	99.49 113	83.29 242	99.90 91	97.63 134	99.61 93	99.55 139
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 32	98.22 43	99.50 30	99.15 107	98.65 51	100.00 1	98.58 86	97.70 20	98.21 130	99.24 137	92.58 125	99.94 77	98.63 91	99.94 54	99.92 81
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
CS-MVS		97.79 75	97.91 64	97.43 165	99.10 108	94.42 195	99.99 4	97.10 291	95.07 96	99.68 37	99.75 69	92.95 113	98.34 222	98.38 98	99.14 123	99.54 143

Anonymous202405211		93.10 226	91.99 239	96.40 199	99.10 108	89.65 309	98.88 274	97.93 211	83.71 348	94.00 210	98.75 184	68.79 343	99.88 102	95.08 177	91.71 238	99.68 111

fmvsm_s_conf0.5_n		97.80 73	97.85 67	97.67 151	99.06 110	94.41 196	99.98 14	98.97 40	97.34 29	99.63 43	99.69 87	87.27 203	99.97 53	99.62 37	99.06 127	98.62 214

HyFIR lowres test		96.66 124	96.43 116	97.36 171	99.05 111	93.91 212	99.70 167	99.80 3	90.54 256	96.26 176	98.08 216	92.15 137	98.23 234	96.84 155	95.46 209	99.93 76

LFMVS		94.75 180	93.56 201	98.30 119	99.03 112	95.70 157	98.74 288	97.98 206	87.81 304	98.47 117	99.39 123	67.43 351	99.53 150	98.01 115	95.20 216	99.67 113

AllTest		92.48 241	91.64 244	95.00 237	99.01 113	88.43 323	98.94 268	96.82 323	86.50 320	88.71 288	98.47 206	74.73 320	99.88 102	85.39 317	96.18 191	96.71 241

TestCases				95.00 237	99.01 113	88.43 323		96.82 323	86.50 320	88.71 288	98.47 206	74.73 320	99.88 102	85.39 317	96.18 191	96.71 241

COLMAP_ROB	90.47 14	92.18 248	91.49 250	94.25 270	99.00 115	88.04 329	98.42 310	96.70 330	82.30 357	88.43 295	99.01 152	76.97 296	99.85 108	86.11 313	96.50 187	94.86 252
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
test_fmvs1		95.35 166	95.68 147	94.36 267	98.99 116	84.98 347	99.96 34	96.65 332	97.60 22	99.73 32	98.96 161	71.58 333	99.93 85	98.31 102	99.37 113	98.17 221

HY-MVS	92.50 7	97.79 75	97.17 92	99.63 17	98.98 117	99.32 9	97.49 334	99.52 15	95.69 82	98.32 124	97.41 237	93.32 101	99.77 128	98.08 113	95.75 205	99.81 94

VNet		97.21 100	96.57 112	99.13 63	98.97 118	97.82 76	99.03 260	99.21 29	94.31 125	99.18 84	98.88 172	86.26 216	99.89 96	98.93 69	94.32 222	99.69 110

thres200		96.96 108	96.21 121	99.22 46	98.97 118	98.84 35	99.85 116	99.71 7	93.17 169	96.26 176	98.88 172	89.87 174	99.51 152	94.26 199	94.91 217	99.31 174

tfpn200view9		96.79 115	95.99 126	99.19 49	98.94 120	98.82 36	99.78 140	99.71 7	92.86 174	96.02 181	98.87 175	89.33 181	99.50 154	93.84 207	94.57 218	99.27 179

thres400		96.78 116	95.99 126	99.16 55	98.94 120	98.82 36	99.78 140	99.71 7	92.86 174	96.02 181	98.87 175	89.33 181	99.50 154	93.84 207	94.57 218	99.16 186

Anonymous20231211		89.86 297	88.44 304	94.13 273	98.93 122	90.68 287	98.54 302	98.26 177	76.28 373	86.73 316	95.54 297	70.60 339	97.56 263	90.82 256	80.27 335	94.15 290

canonicalmvs		97.09 104	96.32 118	99.39 40	98.93 122	98.95 27	99.72 162	97.35 265	94.45 115	97.88 138	99.42 118	86.71 210	99.52 151	98.48 95	93.97 228	99.72 107

SDMVSNet		94.80 176	93.96 189	97.33 173	98.92 124	95.42 167	99.59 185	98.99 37	92.41 202	92.55 228	97.85 226	75.81 310	98.93 178	97.90 123	91.62 239	97.64 232

sd_testset		93.55 215	92.83 219	95.74 216	98.92 124	90.89 284	98.24 316	98.85 56	92.41 202	92.55 228	97.85 226	71.07 338	98.68 194	93.93 204	91.62 239	97.64 232

EPNet_dtu		95.71 156	95.39 152	96.66 191	98.92 124	93.41 226	99.57 189	98.90 47	96.19 73	97.52 144	98.56 198	92.65 121	97.36 268	77.89 358	98.33 144	99.20 184
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
WTY-MVS		98.10 60	97.60 75	99.60 22	98.92 124	99.28 17	99.89 95	99.52 15	95.58 85	98.24 129	99.39 123	93.33 100	99.74 134	97.98 119	95.58 208	99.78 100

CHOSEN 1792x2688		96.81 114	96.53 113	97.64 153	98.91 128	93.07 231	99.65 175	99.80 3	95.64 83	95.39 192	98.86 177	84.35 235	99.90 91	96.98 150	99.16 122	99.95 71

thres100view900		96.74 119	95.92 138	99.18 50	98.90 129	98.77 40	99.74 154	99.71 7	92.59 192	95.84 184	98.86 177	89.25 183	99.50 154	93.84 207	94.57 218	99.27 179

thres600view7		96.69 122	95.87 141	99.14 59	98.90 129	98.78 39	99.74 154	99.71 7	92.59 192	95.84 184	98.86 177	89.25 183	99.50 154	93.44 219	94.50 221	99.16 186

MSDG		94.37 193	93.36 209	97.40 167	98.88 131	93.95 211	99.37 219	97.38 263	85.75 331	90.80 247	99.17 142	84.11 237	99.88 102	86.35 310	98.43 142	98.36 219

h-mvs33		94.92 174	94.36 178	96.59 193	98.85 132	91.29 276	98.93 269	98.94 41	95.90 76	98.77 101	98.42 209	90.89 161	99.77 128	97.80 125	70.76 369	98.72 211

Anonymous20240529		92.10 249	90.65 260	96.47 194	98.82 133	90.61 289	98.72 290	98.67 73	75.54 377	93.90 212	98.58 196	66.23 355	99.90 91	94.70 190	90.67 241	98.90 201

PVSNet_Blended_VisFu		97.27 97	96.81 103	98.66 90	98.81 134	96.67 119	99.92 78	98.64 76	94.51 114	96.38 174	98.49 202	89.05 187	99.88 102	97.10 146	98.34 143	99.43 160

PS-MVSNAJ		98.44 40	98.20 45	99.16 55	98.80 135	98.92 28	99.54 195	98.17 186	97.34 29	99.85 9	99.85 30	91.20 150	99.89 96	99.41 48	99.67 85	98.69 212

CANet_DTU		96.76 117	96.15 122	98.60 95	98.78 136	97.53 86	99.84 121	97.63 233	97.25 37	99.20 81	99.64 99	81.36 255	99.98 43	92.77 230	98.89 130	98.28 220

mvsany_test1		97.82 71	97.90 65	97.55 158	98.77 137	93.04 234	99.80 137	97.93 211	96.95 45	99.61 52	99.68 93	90.92 158	99.83 118	99.18 56	98.29 148	99.80 96

alignmvs		97.81 72	97.33 85	99.25 44	98.77 137	98.66 49	99.99 4	98.44 120	94.40 121	98.41 119	99.47 114	93.65 94	99.42 162	98.57 92	94.26 224	99.67 113

SteuartSystems-ACMMP		99.02 12	98.97 13	99.18 50	98.72 139	97.71 79	99.98 14	98.44 120	96.85 46	99.80 17	99.91 14	97.57 8	99.85 108	99.44 46	99.99 21	99.99 23
Skip Steuart: Steuart Systems R&D Blog.
xiu_mvs_v2_base		98.23 56	97.97 58	99.02 70	98.69 140	98.66 49	99.52 197	98.08 198	97.05 41	99.86 7	99.86 26	90.65 163	99.71 138	99.39 50	98.63 138	98.69 212

miper_enhance_ethall		94.36 195	93.98 188	95.49 219	98.68 141	95.24 175	99.73 159	97.29 273	93.28 166	89.86 260	95.97 284	94.37 72	97.05 292	92.20 234	84.45 300	94.19 283

test2506		97.53 85	97.19 90	98.58 98	98.66 142	96.90 113	98.81 283	99.77 5	94.93 99	97.95 134	98.96 161	92.51 127	99.20 166	94.93 180	98.15 150	99.64 119

ECVR-MVS		95.66 159	95.05 164	97.51 161	98.66 142	93.71 216	98.85 280	98.45 116	94.93 99	96.86 159	98.96 161	75.22 316	99.20 166	95.34 172	98.15 150	99.64 119

fmvsm_s_conf0.5_n_a		97.73 80	97.72 70	97.77 145	98.63 144	94.26 201	99.96 34	98.92 46	97.18 39	99.75 29	99.69 87	87.00 208	99.97 53	99.46 44	98.89 130	99.08 194

testing222		97.08 106	96.75 106	98.06 130	98.56 145	96.82 115	99.85 116	98.61 82	92.53 196	98.84 96	98.84 181	93.36 98	98.30 226	95.84 168	94.30 223	99.05 195

test1111		95.57 161	94.98 167	97.37 169	98.56 145	93.37 228	98.86 278	98.45 116	94.95 98	96.63 165	98.95 166	75.21 317	99.11 171	95.02 178	98.14 152	99.64 119

MVSTER		95.53 162	95.22 158	96.45 196	98.56 145	97.72 78	99.91 82	97.67 231	92.38 204	91.39 239	97.14 244	97.24 18	97.30 275	94.80 186	87.85 273	94.34 274

VDD-MVS		93.77 208	92.94 216	96.27 204	98.55 148	90.22 298	98.77 287	97.79 225	90.85 249	96.82 161	99.42 118	61.18 372	99.77 128	98.95 67	94.13 225	98.82 204

tpmvs		94.28 197	93.57 200	96.40 199	98.55 148	91.50 274	95.70 368	98.55 96	87.47 306	92.15 232	94.26 343	91.42 146	98.95 177	88.15 289	95.85 201	98.76 207

UGNet		95.33 167	94.57 175	97.62 156	98.55 148	94.85 185	98.67 296	99.32 26	95.75 81	96.80 162	96.27 275	72.18 330	99.96 61	94.58 193	99.05 128	98.04 225
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	95.18 168	94.10 185	98.43 112	98.55 148	95.99 146	97.91 329	97.31 270	90.35 260	89.48 271	99.22 138	85.19 225	99.89 96	90.40 266	98.47 141	99.41 162
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
test_vis1_n_1920		95.44 164	95.31 155	95.82 214	98.50 152	88.74 317	99.98 14	97.30 271	97.84 16	99.85 9	99.19 140	66.82 353	99.97 53	98.82 77	99.46 106	98.76 207

BH-w/o		95.71 156	95.38 153	96.68 190	98.49 153	92.28 251	99.84 121	97.50 252	92.12 210	92.06 235	98.79 182	84.69 230	98.67 195	95.29 174	99.66 86	99.09 192

baseline1		95.78 153	94.86 169	98.54 103	98.47 154	98.07 65	99.06 253	97.99 204	92.68 186	94.13 209	98.62 193	93.28 104	98.69 193	93.79 212	85.76 288	98.84 203

iter_conf05		96.07 144	95.95 134	96.44 198	98.43 155	97.52 87	99.91 82	96.85 319	94.16 131	92.49 230	97.98 222	98.20 4	97.34 270	97.26 141	88.29 266	94.45 264

EPMVS		96.53 128	96.01 125	98.09 128	98.43 155	96.12 145	96.36 355	99.43 21	93.53 158	97.64 142	95.04 321	94.41 67	98.38 218	91.13 247	98.11 153	99.75 103

iter_conf_final		96.01 147	95.93 136	96.28 203	98.38 157	97.03 107	99.87 100	97.03 299	94.05 140	92.61 226	97.98 222	98.01 5	97.34 270	97.02 148	88.39 265	94.47 258

sss		97.57 84	97.03 97	99.18 50	98.37 158	98.04 67	99.73 159	99.38 23	93.46 160	98.76 103	99.06 148	91.21 149	99.89 96	96.33 159	97.01 179	99.62 124

BH-untuned		95.18 168	94.83 170	96.22 205	98.36 159	91.22 277	99.80 137	97.32 269	90.91 247	91.08 243	98.67 186	83.51 239	98.54 201	94.23 200	99.61 93	98.92 198

ET-MVSNet_ETH3D		94.37 193	93.28 211	97.64 153	98.30 160	97.99 69	99.99 4	97.61 238	94.35 122	71.57 380	99.45 117	96.23 31	95.34 350	96.91 154	85.14 295	99.59 130

AUN-MVS		93.28 220	92.60 225	95.34 226	98.29 161	90.09 301	99.31 226	98.56 90	91.80 222	96.35 175	98.00 219	89.38 180	98.28 229	92.46 231	69.22 374	97.64 232

FMVSNet3		92.69 237	91.58 246	95.99 209	98.29 161	97.42 95	99.26 234	97.62 235	89.80 269	89.68 264	95.32 311	81.62 253	96.27 330	87.01 306	85.65 289	94.29 276

PMMVS		96.76 117	96.76 105	96.76 187	98.28 163	92.10 255	99.91 82	97.98 206	94.12 133	99.53 57	99.39 123	86.93 209	98.73 188	96.95 152	97.73 160	99.45 157

hse-mvs2		94.38 192	94.08 186	95.31 228	98.27 164	90.02 303	99.29 231	98.56 90	95.90 76	98.77 101	98.00 219	90.89 161	98.26 233	97.80 125	69.20 375	97.64 232

PVSNet_0	88.03 19	91.80 256	90.27 269	96.38 201	98.27 164	90.46 293	99.94 68	99.61 14	93.99 142	86.26 326	97.39 239	71.13 337	99.89 96	98.77 80	67.05 380	98.79 206

UA-Net		96.54 127	95.96 132	98.27 120	98.23 166	95.71 156	98.00 327	98.45 116	93.72 154	98.41 119	99.27 132	88.71 192	99.66 146	91.19 246	97.69 161	99.44 159

test_cas_vis1_n_1920		96.59 126	96.23 120	97.65 152	98.22 167	94.23 202	99.99 4	97.25 277	97.77 17	99.58 53	99.08 146	77.10 293	99.97 53	97.64 133	99.45 107	98.74 209

FE-MVS		95.70 158	95.01 166	97.79 142	98.21 168	94.57 191	95.03 369	98.69 68	88.90 285	97.50 146	96.19 277	92.60 124	99.49 158	89.99 271	97.94 159	99.31 174

GG-mvs-BLEND				98.54 103	98.21 168	98.01 68	93.87 374	98.52 102		97.92 135	97.92 225	99.02 2	97.94 251	98.17 106	99.58 96	99.67 113

mvs_anonymous		95.65 160	95.03 165	97.53 159	98.19 170	95.74 154	99.33 223	97.49 253	90.87 248	90.47 250	97.10 246	88.23 194	97.16 283	95.92 166	97.66 163	99.68 111

MVS_Test		96.46 130	95.74 143	98.61 94	98.18 171	97.23 99	99.31 226	97.15 286	91.07 244	98.84 96	97.05 250	88.17 195	98.97 175	94.39 195	97.50 165	99.61 127

BH-RMVSNet		95.18 168	94.31 181	97.80 140	98.17 172	95.23 176	99.76 148	97.53 248	92.52 198	94.27 207	99.25 136	76.84 298	98.80 182	90.89 255	99.54 98	99.35 169

RPSCF		91.80 256	92.79 221	88.83 345	98.15 173	69.87 383	98.11 323	96.60 334	83.93 346	94.33 205	99.27 132	79.60 275	99.46 161	91.99 236	93.16 235	97.18 239

ETV-MVS		97.92 65	97.80 69	98.25 121	98.14 174	96.48 124	99.98 14	97.63 233	95.61 84	99.29 79	99.46 116	92.55 126	98.82 181	99.02 66	98.54 139	99.46 155

IS-MVSNet		96.29 140	95.90 139	97.45 163	98.13 175	94.80 188	99.08 248	97.61 238	92.02 215	95.54 191	98.96 161	90.64 164	98.08 240	93.73 215	97.41 169	99.47 154

test_fmvsmconf_n		98.43 42	98.32 39	98.78 82	98.12 176	96.41 127	99.99 4	98.83 59	98.22 6	99.67 38	99.64 99	91.11 154	99.94 77	99.67 36	99.62 89	99.98 48

ab-mvs		94.69 181	93.42 205	98.51 106	98.07 177	96.26 134	96.49 353	98.68 70	90.31 261	94.54 200	97.00 252	76.30 305	99.71 138	95.98 165	93.38 233	99.56 138

XVG-OURS-SEG-HR		94.79 177	94.70 174	95.08 234	98.05 178	89.19 312	99.08 248	97.54 246	93.66 155	94.87 198	99.58 106	78.78 283	99.79 123	97.31 139	93.40 232	96.25 245

EIA-MVS		97.53 85	97.46 79	97.76 147	98.04 179	94.84 186	99.98 14	97.61 238	94.41 120	97.90 136	99.59 104	92.40 131	98.87 179	98.04 114	99.13 124	99.59 130

XVG-OURS		94.82 175	94.74 173	95.06 235	98.00 180	89.19 312	99.08 248	97.55 244	94.10 134	94.71 199	99.62 102	80.51 267	99.74 134	96.04 164	93.06 237	96.25 245

dp		95.05 171	94.43 177	96.91 182	97.99 181	92.73 241	96.29 358	97.98 206	89.70 270	95.93 183	94.67 334	93.83 91	98.45 207	86.91 309	96.53 186	99.54 143

tpmrst		96.27 142	95.98 128	97.13 177	97.96 182	93.15 230	96.34 356	98.17 186	92.07 211	98.71 107	95.12 319	93.91 87	98.73 188	94.91 183	96.62 184	99.50 151

TR-MVS		94.54 186	93.56 201	97.49 162	97.96 182	94.34 199	98.71 291	97.51 251	90.30 262	94.51 202	98.69 185	75.56 311	98.77 185	92.82 229	95.99 195	99.35 169

Vis-MVSNet (Re-imp)		96.32 137	95.98 128	97.35 172	97.93 184	94.82 187	99.47 206	98.15 193	91.83 219	95.09 196	99.11 144	91.37 148	97.47 266	93.47 218	97.43 166	99.74 104

MDTV_nov1_ep13			95.69 145		97.90 185	94.15 204	95.98 364	98.44 120	93.12 170	97.98 133	95.74 288	95.10 50	98.58 198	90.02 270	96.92 181

Fast-Effi-MVS+		95.02 172	94.19 183	97.52 160	97.88 186	94.55 192	99.97 27	97.08 294	88.85 287	94.47 203	97.96 224	84.59 231	98.41 210	89.84 273	97.10 174	99.59 130

ADS-MVSNet2		93.80 207	93.88 192	93.55 296	97.87 187	85.94 341	94.24 370	96.84 320	90.07 264	96.43 171	94.48 339	90.29 170	95.37 349	87.44 296	97.23 171	99.36 167

ADS-MVSNet		94.79 177	94.02 187	97.11 179	97.87 187	93.79 213	94.24 370	98.16 190	90.07 264	96.43 171	94.48 339	90.29 170	98.19 236	87.44 296	97.23 171	99.36 167

Effi-MVS+		96.30 139	95.69 145	98.16 123	97.85 189	96.26 134	97.41 336	97.21 279	90.37 259	98.65 110	98.58 196	86.61 212	98.70 192	97.11 145	97.37 170	99.52 147

PatchmatchNet		95.94 149	95.45 150	97.39 168	97.83 190	94.41 196	96.05 362	98.40 147	92.86 174	97.09 153	95.28 316	94.21 79	98.07 242	89.26 277	98.11 153	99.70 108
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
cascas		94.64 184	93.61 196	97.74 149	97.82 191	96.26 134	99.96 34	97.78 226	85.76 329	94.00 210	97.54 233	76.95 297	99.21 165	97.23 142	95.43 211	97.76 231

1112_ss		96.01 147	95.20 159	98.42 113	97.80 192	96.41 127	99.65 175	96.66 331	92.71 183	92.88 223	99.40 121	92.16 136	99.30 163	91.92 238	93.66 229	99.55 139

Test_1112_low_res		95.72 154	94.83 170	98.42 113	97.79 193	96.41 127	99.65 175	96.65 332	92.70 184	92.86 224	96.13 280	92.15 137	99.30 163	91.88 239	93.64 230	99.55 139

Effi-MVS+-dtu		94.53 188	95.30 156	92.22 318	97.77 194	82.54 358	99.59 185	97.06 296	94.92 101	95.29 194	95.37 309	85.81 218	97.89 252	94.80 186	97.07 175	96.23 247

tpm cat1		93.51 216	92.52 230	96.47 194	97.77 194	91.47 275	96.13 360	98.06 199	80.98 362	92.91 222	93.78 347	89.66 175	98.87 179	87.03 305	96.39 189	99.09 192

FA-MVS(test-final)		95.86 150	95.09 163	98.15 126	97.74 196	95.62 160	96.31 357	98.17 186	91.42 235	96.26 176	96.13 280	90.56 165	99.47 160	92.18 235	97.07 175	99.35 169

xiu_mvs_v1_base_debu		97.43 87	97.06 93	98.55 100	97.74 196	98.14 62	99.31 226	97.86 220	96.43 62	99.62 46	99.69 87	85.56 220	99.68 142	99.05 60	98.31 145	97.83 227

xiu_mvs_v1_base		97.43 87	97.06 93	98.55 100	97.74 196	98.14 62	99.31 226	97.86 220	96.43 62	99.62 46	99.69 87	85.56 220	99.68 142	99.05 60	98.31 145	97.83 227

xiu_mvs_v1_base_debi		97.43 87	97.06 93	98.55 100	97.74 196	98.14 62	99.31 226	97.86 220	96.43 62	99.62 46	99.69 87	85.56 220	99.68 142	99.05 60	98.31 145	97.83 227

EPP-MVSNet		96.69 122	96.60 110	96.96 181	97.74 196	93.05 233	99.37 219	98.56 90	88.75 288	95.83 186	99.01 152	96.01 32	98.56 199	96.92 153	97.20 173	99.25 181

gg-mvs-nofinetune		93.51 216	91.86 243	98.47 108	97.72 201	97.96 72	92.62 378	98.51 105	74.70 380	97.33 149	69.59 393	98.91 3	97.79 255	97.77 130	99.56 97	99.67 113

IB-MVS	92.85 6	94.99 173	93.94 190	98.16 123	97.72 201	95.69 158	99.99 4	98.81 60	94.28 127	92.70 225	96.90 254	95.08 51	99.17 169	96.07 163	73.88 364	99.60 129
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 92	97.02 98	98.59 97	97.71 203	97.52 87	99.97 27	98.54 99	91.83 219	97.45 147	99.04 149	97.50 9	99.10 172	94.75 188	96.37 190	99.16 186

Syy-MVS		90.00 295	90.63 261	88.11 352	97.68 204	74.66 380	99.71 164	98.35 160	90.79 251	92.10 233	98.67 186	79.10 281	93.09 372	63.35 386	95.95 198	96.59 243

myMVS_eth3d		94.46 190	94.76 172	93.55 296	97.68 204	90.97 279	99.71 164	98.35 160	90.79 251	92.10 233	98.67 186	92.46 130	93.09 372	87.13 302	95.95 198	96.59 243

test_fmvs1_n		94.25 198	94.36 178	93.92 282	97.68 204	83.70 353	99.90 87	96.57 335	97.40 28	99.67 38	98.88 172	61.82 369	99.92 88	98.23 104	99.13 124	98.14 224

diffmvs		97.00 107	96.64 109	98.09 128	97.64 207	96.17 142	99.81 133	97.19 280	94.67 111	98.95 91	99.28 129	86.43 213	98.76 186	98.37 99	97.42 168	99.33 172
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
Vis-MVSNet		95.72 154	95.15 161	97.45 163	97.62 208	94.28 200	99.28 232	98.24 178	94.27 129	96.84 160	98.94 168	79.39 276	98.76 186	93.25 220	98.49 140	99.30 176
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
thisisatest0530		97.10 102	96.72 107	98.22 122	97.60 209	96.70 118	99.92 78	98.54 99	91.11 243	97.07 154	98.97 159	97.47 12	99.03 173	93.73 215	96.09 193	98.92 198

miper_ehance_all_eth		93.16 223	92.60 225	94.82 245	97.57 210	93.56 220	99.50 201	97.07 295	88.75 288	88.85 287	95.52 299	90.97 157	96.74 311	90.77 257	84.45 300	94.17 284

testing3		93.92 202	94.23 182	92.99 310	97.54 211	90.23 297	99.99 4	99.16 30	90.57 255	91.33 242	98.63 192	92.99 111	92.52 376	82.46 335	95.39 212	96.22 248

LCM-MVSNet-Re		92.31 245	92.60 225	91.43 325	97.53 212	79.27 375	99.02 261	91.83 389	92.07 211	80.31 355	94.38 342	83.50 240	95.48 347	97.22 143	97.58 164	99.54 143

GBi-Net		90.88 272	89.82 278	94.08 274	97.53 212	91.97 256	98.43 307	96.95 308	87.05 312	89.68 264	94.72 330	71.34 334	96.11 335	87.01 306	85.65 289	94.17 284

test1		90.88 272	89.82 278	94.08 274	97.53 212	91.97 256	98.43 307	96.95 308	87.05 312	89.68 264	94.72 330	71.34 334	96.11 335	87.01 306	85.65 289	94.17 284

FMVSNet2		91.02 269	89.56 283	95.41 224	97.53 212	95.74 154	98.98 263	97.41 261	87.05 312	88.43 295	95.00 324	71.34 334	96.24 332	85.12 319	85.21 294	94.25 279

tttt0517		96.85 112	96.49 114	97.92 135	97.48 216	95.89 149	99.85 116	98.54 99	90.72 254	96.63 165	98.93 170	97.47 12	99.02 174	93.03 227	95.76 204	98.85 202

casdiffmvs_mvg		96.43 131	95.94 135	97.89 139	97.44 217	95.47 164	99.86 113	97.29 273	93.35 162	96.03 180	99.19 140	85.39 223	98.72 190	97.89 124	97.04 177	99.49 153
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
EC-MVSNet		97.38 94	97.24 87	97.80 140	97.41 218	95.64 159	99.99 4	97.06 296	94.59 112	99.63 43	99.32 128	89.20 186	98.14 237	98.76 81	99.23 120	99.62 124

c3_l		92.53 240	91.87 242	94.52 257	97.40 219	92.99 235	99.40 212	96.93 313	87.86 302	88.69 290	95.44 303	89.95 173	96.44 323	90.45 263	80.69 331	94.14 293

fmvsm_s_conf0.1_n		97.30 95	97.21 89	97.60 157	97.38 220	94.40 198	99.90 87	98.64 76	96.47 61	99.51 61	99.65 98	84.99 228	99.93 85	99.22 55	99.09 126	98.46 216

CDS-MVSNet		96.34 136	96.07 123	97.13 177	97.37 221	94.96 183	99.53 196	97.91 215	91.55 227	95.37 193	98.32 211	95.05 53	97.13 286	93.80 211	95.75 205	99.30 176
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
TESTMET0.1,1		96.74 119	96.26 119	98.16 123	97.36 222	96.48 124	99.96 34	98.29 173	91.93 216	95.77 187	98.07 217	95.54 42	98.29 227	90.55 261	98.89 130	99.70 108

miper_lstm_enhance		91.81 253	91.39 252	93.06 309	97.34 223	89.18 314	99.38 217	96.79 325	86.70 319	87.47 308	95.22 317	90.00 172	95.86 344	88.26 287	81.37 321	94.15 290

baseline		96.43 131	95.98 128	97.76 147	97.34 223	95.17 180	99.51 199	97.17 283	93.92 147	96.90 158	99.28 129	85.37 224	98.64 196	97.50 136	96.86 183	99.46 155

cl____		92.31 245	91.58 246	94.52 257	97.33 225	92.77 237	99.57 189	96.78 326	86.97 316	87.56 306	95.51 300	89.43 179	96.62 316	88.60 282	82.44 313	94.16 289

DIV-MVS_self_test		92.32 244	91.60 245	94.47 261	97.31 226	92.74 239	99.58 187	96.75 327	86.99 315	87.64 304	95.54 297	89.55 178	96.50 320	88.58 283	82.44 313	94.17 284

casdiffmvs		96.42 133	95.97 131	97.77 145	97.30 227	94.98 182	99.84 121	97.09 293	93.75 153	96.58 167	99.26 135	85.07 226	98.78 184	97.77 130	97.04 177	99.54 143
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		94.36 195	93.48 203	96.99 180	97.29 228	93.54 221	99.96 34	96.72 329	88.35 297	93.43 214	98.94 168	82.05 247	98.05 243	88.12 291	96.48 188	99.37 166

eth_miper_zixun_eth		92.41 243	91.93 240	93.84 286	97.28 229	90.68 287	98.83 281	96.97 307	88.57 293	89.19 281	95.73 290	89.24 185	96.69 314	89.97 272	81.55 319	94.15 290

MVSFormer		96.94 109	96.60 110	97.95 133	97.28 229	97.70 81	99.55 193	97.27 275	91.17 240	99.43 66	99.54 110	90.92 158	96.89 304	94.67 191	99.62 89	99.25 181

lupinMVS		97.85 68	97.60 75	98.62 93	97.28 229	97.70 81	99.99 4	97.55 244	95.50 89	99.43 66	99.67 94	90.92 158	98.71 191	98.40 97	99.62 89	99.45 157

SCA		94.69 181	93.81 194	97.33 173	97.10 232	94.44 193	98.86 278	98.32 167	93.30 165	96.17 179	95.59 295	76.48 303	97.95 249	91.06 249	97.43 166	99.59 130

TAMVS		95.85 151	95.58 148	96.65 192	97.07 233	93.50 222	99.17 241	97.82 224	91.39 237	95.02 197	98.01 218	92.20 135	97.30 275	93.75 214	95.83 202	99.14 189

Fast-Effi-MVS+-dtu		93.72 211	93.86 193	93.29 301	97.06 234	86.16 339	99.80 137	96.83 321	92.66 187	92.58 227	97.83 228	81.39 254	97.67 260	89.75 274	96.87 182	96.05 250

CostFormer		96.10 143	95.88 140	96.78 186	97.03 235	92.55 247	97.08 344	97.83 223	90.04 266	98.72 106	94.89 328	95.01 55	98.29 227	96.54 158	95.77 203	99.50 151

test_fmvsmvis_n_1920		97.67 82	97.59 77	97.91 137	97.02 236	95.34 170	99.95 52	98.45 116	97.87 15	97.02 155	99.59 104	89.64 176	99.98 43	99.41 48	99.34 115	98.42 217

test-LLR		96.47 129	96.04 124	97.78 143	97.02 236	95.44 165	99.96 34	98.21 181	94.07 136	95.55 189	96.38 271	93.90 88	98.27 231	90.42 264	98.83 134	99.64 119

test-mter		96.39 134	95.93 136	97.78 143	97.02 236	95.44 165	99.96 34	98.21 181	91.81 221	95.55 189	96.38 271	95.17 48	98.27 231	90.42 264	98.83 134	99.64 119

gm-plane-assit					96.97 239	93.76 215			91.47 231		98.96 161		98.79 183	94.92 181

WB-MVSnew		92.90 230	92.77 222	93.26 303	96.95 240	93.63 218	99.71 164	98.16 190	91.49 228	94.28 206	98.14 214	81.33 256	96.48 321	79.47 350	95.46 209	89.68 373

QAPM		95.40 165	94.17 184	99.10 64	96.92 241	97.71 79	99.40 212	98.68 70	89.31 273	88.94 285	98.89 171	82.48 245	99.96 61	93.12 226	99.83 72	99.62 124

KD-MVS_2432*1600		88.00 314	86.10 318	93.70 292	96.91 242	94.04 207	97.17 341	97.12 289	84.93 339	81.96 346	92.41 358	92.48 128	94.51 360	79.23 351	52.68 392	92.56 347

miper_refine_blended		88.00 314	86.10 318	93.70 292	96.91 242	94.04 207	97.17 341	97.12 289	84.93 339	81.96 346	92.41 358	92.48 128	94.51 360	79.23 351	52.68 392	92.56 347

tpm2		95.47 163	95.18 160	96.35 202	96.91 242	91.70 269	96.96 347	97.93 211	88.04 301	98.44 118	95.40 305	93.32 101	97.97 246	94.00 202	95.61 207	99.38 164

FMVSNet5		88.32 311	87.47 313	90.88 328	96.90 245	88.39 325	97.28 338	95.68 355	82.60 356	84.67 335	92.40 360	79.83 273	91.16 381	76.39 365	81.51 320	93.09 339

3Dnovator+	91.53 11	96.31 138	95.24 157	99.52 28	96.88 246	98.64 52	99.72 162	98.24 178	95.27 94	88.42 297	98.98 157	82.76 244	99.94 77	97.10 146	99.83 72	99.96 64

Patchmatch-test		92.65 239	91.50 249	96.10 208	96.85 247	90.49 292	91.50 383	97.19 280	82.76 355	90.23 252	95.59 295	95.02 54	98.00 245	77.41 360	96.98 180	99.82 92

MVS		96.60 125	95.56 149	99.72 13	96.85 247	99.22 20	98.31 313	98.94 41	91.57 226	90.90 246	99.61 103	86.66 211	99.96 61	97.36 138	99.88 68	99.99 23

3Dnovator	91.47 12	96.28 141	95.34 154	99.08 65	96.82 249	97.47 93	99.45 209	98.81 60	95.52 88	89.39 272	99.00 154	81.97 248	99.95 69	97.27 140	99.83 72	99.84 90

EI-MVSNet		93.73 210	93.40 208	94.74 246	96.80 250	92.69 242	99.06 253	97.67 231	88.96 282	91.39 239	99.02 150	88.75 191	97.30 275	91.07 248	87.85 273	94.22 280

CVMVSNet		94.68 183	94.94 168	93.89 285	96.80 250	86.92 337	99.06 253	98.98 38	94.45 115	94.23 208	99.02 150	85.60 219	95.31 351	90.91 254	95.39 212	99.43 160

IterMVS-LS		92.69 237	92.11 235	94.43 265	96.80 250	92.74 239	99.45 209	96.89 316	88.98 280	89.65 267	95.38 308	88.77 190	96.34 327	90.98 252	82.04 316	94.22 280
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS		90.91 271	90.17 273	93.12 306	96.78 253	90.42 295	98.89 272	97.05 298	89.03 277	86.49 321	95.42 304	76.59 301	95.02 353	87.22 301	84.09 303	93.93 311
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
1314		96.84 113	95.96 132	99.48 34	96.74 254	98.52 56	98.31 313	98.86 53	95.82 78	89.91 258	98.98 157	87.49 200	99.96 61	97.80 125	99.73 82	99.96 64

IterMVS-SCA-FT		90.85 274	90.16 274	92.93 311	96.72 255	89.96 304	98.89 272	96.99 303	88.95 283	86.63 318	95.67 291	76.48 303	95.00 354	87.04 304	84.04 306	93.84 318

MVS-HIRNet		86.22 321	83.19 334	95.31 228	96.71 256	90.29 296	92.12 380	97.33 268	62.85 387	86.82 315	70.37 392	69.37 342	97.49 265	75.12 367	97.99 158	98.15 222

VDDNet		93.12 225	91.91 241	96.76 187	96.67 257	92.65 245	98.69 294	98.21 181	82.81 354	97.75 141	99.28 129	61.57 370	99.48 159	98.09 112	94.09 226	98.15 222

dmvs_re		93.20 222	93.15 213	93.34 299	96.54 258	83.81 352	98.71 291	98.51 105	91.39 237	92.37 231	98.56 198	78.66 285	97.83 254	93.89 205	89.74 242	98.38 218

MIMVSNet		90.30 287	88.67 301	95.17 233	96.45 259	91.64 271	92.39 379	97.15 286	85.99 326	90.50 249	93.19 354	66.95 352	94.86 357	82.01 339	93.43 231	99.01 197

CR-MVSNet		93.45 219	92.62 224	95.94 210	96.29 260	92.66 243	92.01 381	96.23 344	92.62 189	96.94 156	93.31 352	91.04 155	96.03 340	79.23 351	95.96 196	99.13 190

RPMNet		89.76 299	87.28 314	97.19 176	96.29 260	92.66 243	92.01 381	98.31 169	70.19 386	96.94 156	85.87 385	87.25 204	99.78 125	62.69 387	95.96 196	99.13 190

tt0805		91.28 264	90.18 272	94.60 252	96.26 262	87.55 331	98.39 311	98.72 65	89.00 279	89.22 278	98.47 206	62.98 366	98.96 176	90.57 260	88.00 272	97.28 238

Patchmtry		89.70 300	88.49 303	93.33 300	96.24 263	89.94 307	91.37 384	96.23 344	78.22 370	87.69 303	93.31 352	91.04 155	96.03 340	80.18 349	82.10 315	94.02 301

test_vis1_rt		86.87 319	86.05 321	89.34 341	96.12 264	78.07 376	99.87 100	83.54 400	92.03 214	78.21 365	89.51 371	45.80 386	99.91 89	96.25 161	93.11 236	90.03 370

JIA-IIPM		91.76 259	90.70 259	94.94 239	96.11 265	87.51 332	93.16 377	98.13 195	75.79 376	97.58 143	77.68 390	92.84 116	97.97 246	88.47 286	96.54 185	99.33 172

OpenMVS	90.15 15	94.77 179	93.59 199	98.33 117	96.07 266	97.48 92	99.56 191	98.57 88	90.46 257	86.51 320	98.95 166	78.57 286	99.94 77	93.86 206	99.74 81	97.57 236

PAPM		98.60 29	98.42 30	99.14 59	96.05 267	98.96 26	99.90 87	99.35 25	96.68 55	98.35 123	99.66 96	96.45 29	98.51 202	99.45 45	99.89 66	99.96 64

CLD-MVS		94.06 201	93.90 191	94.55 256	96.02 268	90.69 286	99.98 14	97.72 227	96.62 58	91.05 245	98.85 180	77.21 292	98.47 203	98.11 110	89.51 248	94.48 257
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
PatchT		90.38 284	88.75 300	95.25 230	95.99 269	90.16 299	91.22 385	97.54 246	76.80 372	97.26 150	86.01 384	91.88 142	96.07 339	66.16 383	95.91 200	99.51 149

ACMH+	89.98 16	90.35 285	89.54 284	92.78 314	95.99 269	86.12 340	98.81 283	97.18 282	89.38 272	83.14 342	97.76 230	68.42 347	98.43 208	89.11 278	86.05 287	93.78 321

DeepMVS_CX				82.92 362	95.98 271	58.66 393		96.01 349	92.72 182	78.34 364	95.51 300	58.29 375	98.08 240	82.57 334	85.29 292	92.03 355

ACMP	92.05 9	92.74 234	92.42 232	93.73 288	95.91 272	88.72 318	99.81 133	97.53 248	94.13 132	87.00 314	98.23 212	74.07 324	98.47 203	96.22 162	88.86 255	93.99 306
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test_vis1_n		93.61 214	93.03 215	95.35 225	95.86 273	86.94 336	99.87 100	96.36 342	96.85 46	99.54 56	98.79 182	52.41 382	99.83 118	98.64 89	98.97 129	99.29 178

HQP-NCC					95.78 274		99.87 100		96.82 48	93.37 215

ACMP_Plane					95.78 274		99.87 100		96.82 48	93.37 215

HQP-MVS		94.61 185	94.50 176	94.92 240	95.78 274	91.85 261	99.87 100	97.89 216	96.82 48	93.37 215	98.65 189	80.65 265	98.39 214	97.92 121	89.60 243	94.53 253

NP-MVS					95.77 277	91.79 263					98.65 189

test_fmvsmconf0.1_n		97.74 78	97.44 80	98.64 92	95.76 278	96.20 139	99.94 68	98.05 201	98.17 8	98.89 95	99.42 118	87.65 198	99.90 91	99.50 41	99.60 95	99.82 92

plane_prior6					95.76 278	91.72 268						80.47 269

ACMM	91.95 10	92.88 231	92.52 230	93.98 281	95.75 280	89.08 315	99.77 143	97.52 250	93.00 172	89.95 257	97.99 221	76.17 307	98.46 206	93.63 217	88.87 254	94.39 268
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GA-MVS		93.83 204	92.84 218	96.80 185	95.73 281	93.57 219	99.88 97	97.24 278	92.57 194	92.92 221	96.66 263	78.73 284	97.67 260	87.75 294	94.06 227	99.17 185

plane_prior1					95.73 281

jason		97.24 98	96.86 101	98.38 116	95.73 281	97.32 97	99.97 27	97.40 262	95.34 92	98.60 113	99.54 110	87.70 197	98.56 199	97.94 120	99.47 104	99.25 181
jason: jason.
HQP_MVS		94.49 189	94.36 178	94.87 241	95.71 284	91.74 265	99.84 121	97.87 218	96.38 65	93.01 219	98.59 194	80.47 269	98.37 220	97.79 128	89.55 246	94.52 255

plane_prior7					95.71 284	91.59 273

ITE_SJBPF				92.38 316	95.69 286	85.14 345		95.71 354	92.81 178	89.33 275	98.11 215	70.23 340	98.42 209	85.91 315	88.16 269	93.59 329

fmvsm_s_conf0.1_n_a		97.09 104	96.90 100	97.63 155	95.65 287	94.21 203	99.83 128	98.50 110	96.27 70	99.65 40	99.64 99	84.72 229	99.93 85	99.04 63	98.84 133	98.74 209

ACMH	89.72 17	90.64 278	89.63 281	93.66 294	95.64 288	88.64 321	98.55 300	97.45 255	89.03 277	81.62 349	97.61 232	69.75 341	98.41 210	89.37 275	87.62 278	93.92 312
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
baseline2		96.71 121	96.49 114	97.37 169	95.63 289	95.96 147	99.74 154	98.88 51	92.94 173	91.61 237	98.97 159	97.72 7	98.62 197	94.83 185	98.08 156	97.53 237

FMVSNet1		88.50 310	86.64 316	94.08 274	95.62 290	91.97 256	98.43 307	96.95 308	83.00 352	86.08 328	94.72 330	59.09 374	96.11 335	81.82 341	84.07 304	94.17 284

LPG-MVS_test		92.96 228	92.71 223	93.71 290	95.43 291	88.67 319	99.75 151	97.62 235	92.81 178	90.05 253	98.49 202	75.24 314	98.40 212	95.84 168	89.12 250	94.07 298

LGP-MVS_train				93.71 290	95.43 291	88.67 319		97.62 235	92.81 178	90.05 253	98.49 202	75.24 314	98.40 212	95.84 168	89.12 250	94.07 298

tpm		93.70 212	93.41 207	94.58 254	95.36 293	87.41 333	97.01 345	96.90 315	90.85 249	96.72 164	94.14 344	90.40 168	96.84 307	90.75 258	88.54 262	99.51 149

D2MVS		92.76 233	92.59 228	93.27 302	95.13 294	89.54 311	99.69 168	99.38 23	92.26 207	87.59 305	94.61 336	85.05 227	97.79 255	91.59 242	88.01 271	92.47 350

VPA-MVSNet		92.70 236	91.55 248	96.16 206	95.09 295	96.20 139	98.88 274	99.00 36	91.02 246	91.82 236	95.29 315	76.05 309	97.96 248	95.62 171	81.19 322	94.30 275

LTVRE_ROB	88.28 18	90.29 288	89.05 295	94.02 277	95.08 296	90.15 300	97.19 340	97.43 257	84.91 341	83.99 338	97.06 249	74.00 325	98.28 229	84.08 324	87.71 276	93.62 328
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		87.87 316	86.51 317	91.94 321	95.05 297	85.57 343	97.65 333	94.08 377	84.40 344	81.82 348	96.85 258	62.14 368	98.33 223	80.25 348	86.37 286	91.91 357

test0.0.03 1		93.86 203	93.61 196	94.64 250	95.02 298	92.18 254	99.93 75	98.58 86	94.07 136	87.96 301	98.50 201	93.90 88	94.96 355	81.33 342	93.17 234	96.78 240

UniMVSNet (Re)		93.07 227	92.13 234	95.88 211	94.84 299	96.24 138	99.88 97	98.98 38	92.49 200	89.25 276	95.40 305	87.09 206	97.14 285	93.13 225	78.16 345	94.26 277

USDC		90.00 295	88.96 296	93.10 308	94.81 300	88.16 327	98.71 291	95.54 359	93.66 155	83.75 340	97.20 243	65.58 357	98.31 225	83.96 327	87.49 280	92.85 344

VPNet		91.81 253	90.46 263	95.85 213	94.74 301	95.54 163	98.98 263	98.59 85	92.14 209	90.77 248	97.44 236	68.73 345	97.54 264	94.89 184	77.89 347	94.46 259

FIs		94.10 199	93.43 204	96.11 207	94.70 302	96.82 115	99.58 187	98.93 45	92.54 195	89.34 274	97.31 240	87.62 199	97.10 289	94.22 201	86.58 284	94.40 266

UniMVSNet_ETH3D		90.06 294	88.58 302	94.49 260	94.67 303	88.09 328	97.81 332	97.57 243	83.91 347	88.44 293	97.41 237	57.44 376	97.62 262	91.41 243	88.59 261	97.77 230

UniMVSNet_NR-MVSNet		92.95 229	92.11 235	95.49 219	94.61 304	95.28 173	99.83 128	99.08 33	91.49 228	89.21 279	96.86 257	87.14 205	96.73 312	93.20 221	77.52 350	94.46 259

test_fmvs2		89.47 303	89.70 280	88.77 348	94.54 305	75.74 377	99.83 128	94.70 373	94.71 108	91.08 243	96.82 262	54.46 379	97.78 257	92.87 228	88.27 267	92.80 345

WR-MVS		92.31 245	91.25 253	95.48 222	94.45 306	95.29 172	99.60 184	98.68 70	90.10 263	88.07 300	96.89 255	80.68 264	96.80 310	93.14 224	79.67 338	94.36 270

nrg030		93.51 216	92.53 229	96.45 196	94.36 307	97.20 100	99.81 133	97.16 285	91.60 225	89.86 260	97.46 235	86.37 214	97.68 259	95.88 167	80.31 334	94.46 259

tfpnnormal		89.29 306	87.61 312	94.34 268	94.35 308	94.13 205	98.95 267	98.94 41	83.94 345	84.47 336	95.51 300	74.84 319	97.39 267	77.05 363	80.41 332	91.48 360

FC-MVSNet-test		93.81 206	93.15 213	95.80 215	94.30 309	96.20 139	99.42 211	98.89 49	92.33 206	89.03 284	97.27 242	87.39 202	96.83 308	93.20 221	86.48 285	94.36 270

MS-PatchMatch		90.65 277	90.30 268	91.71 324	94.22 310	85.50 344	98.24 316	97.70 228	88.67 290	86.42 323	96.37 273	67.82 349	98.03 244	83.62 329	99.62 89	91.60 358

WR-MVS_H		91.30 262	90.35 266	94.15 271	94.17 311	92.62 246	99.17 241	98.94 41	88.87 286	86.48 322	94.46 341	84.36 233	96.61 317	88.19 288	78.51 343	93.21 338

DU-MVS		92.46 242	91.45 251	95.49 219	94.05 312	95.28 173	99.81 133	98.74 64	92.25 208	89.21 279	96.64 265	81.66 251	96.73 312	93.20 221	77.52 350	94.46 259

NR-MVSNet		91.56 261	90.22 270	95.60 217	94.05 312	95.76 153	98.25 315	98.70 67	91.16 242	80.78 354	96.64 265	83.23 243	96.57 318	91.41 243	77.73 349	94.46 259

CP-MVSNet		91.23 266	90.22 270	94.26 269	93.96 314	92.39 250	99.09 246	98.57 88	88.95 283	86.42 323	96.57 268	79.19 279	96.37 325	90.29 267	78.95 340	94.02 301

XXY-MVS		91.82 252	90.46 263	95.88 211	93.91 315	95.40 169	98.87 277	97.69 229	88.63 292	87.87 302	97.08 247	74.38 323	97.89 252	91.66 241	84.07 304	94.35 273

PS-CasMVS		90.63 279	89.51 286	93.99 280	93.83 316	91.70 269	98.98 263	98.52 102	88.48 294	86.15 327	96.53 270	75.46 312	96.31 329	88.83 280	78.86 342	93.95 309

test_0402		85.58 323	83.94 328	90.50 332	93.81 317	85.04 346	98.55 300	95.20 367	76.01 374	79.72 359	95.13 318	64.15 363	96.26 331	66.04 384	86.88 283	90.21 369

XVG-ACMP-BASELINE		91.22 267	90.75 258	92.63 315	93.73 318	85.61 342	98.52 304	97.44 256	92.77 181	89.90 259	96.85 258	66.64 354	98.39 214	92.29 233	88.61 259	93.89 314

TranMVSNet+NR-MVSNet		91.68 260	90.61 262	94.87 241	93.69 319	93.98 210	99.69 168	98.65 74	91.03 245	88.44 293	96.83 261	80.05 272	96.18 333	90.26 268	76.89 358	94.45 264

mvsmamba		94.10 199	93.72 195	95.25 230	93.57 320	94.13 205	99.67 172	96.45 340	93.63 157	91.34 241	97.77 229	86.29 215	97.22 281	96.65 157	88.10 270	94.40 266

TransMVSNet (Re)		87.25 317	85.28 324	93.16 305	93.56 321	91.03 278	98.54 302	94.05 379	83.69 349	81.09 352	96.16 278	75.32 313	96.40 324	76.69 364	68.41 376	92.06 354

v10		90.25 289	88.82 298	94.57 255	93.53 322	93.43 225	99.08 248	96.87 318	85.00 338	87.34 312	94.51 337	80.93 261	97.02 299	82.85 333	79.23 339	93.26 336

testgi		89.01 308	88.04 309	91.90 322	93.49 323	84.89 348	99.73 159	95.66 356	93.89 150	85.14 333	98.17 213	59.68 373	94.66 359	77.73 359	88.88 253	96.16 249

v8		90.54 281	89.17 291	94.66 249	93.43 324	93.40 227	99.20 238	96.94 312	85.76 329	87.56 306	94.51 337	81.96 249	97.19 282	84.94 321	78.25 344	93.38 334

V42		91.28 264	90.12 275	94.74 246	93.42 325	93.46 223	99.68 170	97.02 300	87.36 308	89.85 262	95.05 320	81.31 257	97.34 270	87.34 299	80.07 336	93.40 332

pm-mvs1		89.36 305	87.81 311	94.01 278	93.40 326	91.93 259	98.62 299	96.48 339	86.25 324	83.86 339	96.14 279	73.68 326	97.04 294	86.16 312	75.73 362	93.04 341

RRT_MVS		93.14 224	92.92 217	93.78 287	93.31 327	90.04 302	99.66 173	97.69 229	92.53 196	88.91 286	97.76 230	84.36 233	96.93 302	95.10 176	86.99 282	94.37 269

v1144		91.09 268	89.83 277	94.87 241	93.25 328	93.69 217	99.62 182	96.98 305	86.83 318	89.64 268	94.99 325	80.94 260	97.05 292	85.08 320	81.16 323	93.87 316

v1192		90.62 280	89.25 290	94.72 248	93.13 329	93.07 231	99.50 201	97.02 300	86.33 323	89.56 270	95.01 322	79.22 278	97.09 291	82.34 337	81.16 323	94.01 303

v2v482		91.30 262	90.07 276	95.01 236	93.13 329	93.79 213	99.77 143	97.02 300	88.05 300	89.25 276	95.37 309	80.73 263	97.15 284	87.28 300	80.04 337	94.09 297

OPM-MVS		93.21 221	92.80 220	94.44 263	93.12 331	90.85 285	99.77 143	97.61 238	96.19 73	91.56 238	98.65 189	75.16 318	98.47 203	93.78 213	89.39 249	93.99 306
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
v144192		90.79 275	89.52 285	94.59 253	93.11 332	92.77 237	99.56 191	96.99 303	86.38 322	89.82 263	94.95 327	80.50 268	97.10 289	83.98 326	80.41 332	93.90 313

bld_raw_dy_0_64		92.74 234	92.03 238	94.87 241	93.09 333	93.46 223	99.12 243	95.41 361	92.84 177	90.44 251	97.54 233	78.08 290	97.04 294	93.94 203	87.77 275	94.11 295

PEN-MVS		90.19 291	89.06 294	93.57 295	93.06 334	90.90 283	99.06 253	98.47 113	88.11 299	85.91 329	96.30 274	76.67 299	95.94 343	87.07 303	76.91 357	93.89 314

v1240		90.20 290	88.79 299	94.44 263	93.05 335	92.27 252	99.38 217	96.92 314	85.89 327	89.36 273	94.87 329	77.89 291	97.03 297	80.66 345	81.08 326	94.01 303

v148		90.70 276	89.63 281	93.92 282	92.97 336	90.97 279	99.75 151	96.89 316	87.51 305	88.27 298	95.01 322	81.67 250	97.04 294	87.40 298	77.17 355	93.75 322

v1921920		90.46 282	89.12 292	94.50 259	92.96 337	92.46 248	99.49 203	96.98 305	86.10 325	89.61 269	95.30 312	78.55 287	97.03 297	82.17 338	80.89 330	94.01 303

Baseline_NR-MVSNet		90.33 286	89.51 286	92.81 313	92.84 338	89.95 305	99.77 143	93.94 380	84.69 343	89.04 283	95.66 292	81.66 251	96.52 319	90.99 251	76.98 356	91.97 356

test_method		80.79 343	79.70 347	84.08 359	92.83 339	67.06 385	99.51 199	95.42 360	54.34 391	81.07 353	93.53 349	44.48 387	92.22 378	78.90 355	77.23 354	92.94 342

pmmvs4		92.10 249	91.07 256	95.18 232	92.82 340	94.96 183	99.48 205	96.83 321	87.45 307	88.66 291	96.56 269	83.78 238	96.83 308	89.29 276	84.77 298	93.75 322

LF4IMVS		89.25 307	88.85 297	90.45 334	92.81 341	81.19 368	98.12 322	94.79 370	91.44 232	86.29 325	97.11 245	65.30 360	98.11 239	88.53 285	85.25 293	92.07 353

DTE-MVSNet		89.40 304	88.24 307	92.88 312	92.66 342	89.95 305	99.10 245	98.22 180	87.29 309	85.12 334	96.22 276	76.27 306	95.30 352	83.56 330	75.74 361	93.41 331

EU-MVSNet		90.14 293	90.34 267	89.54 340	92.55 343	81.06 369	98.69 294	98.04 202	91.41 236	86.59 319	96.84 260	80.83 262	93.31 371	86.20 311	81.91 317	94.26 277

APD_test1		81.15 342	80.92 343	81.86 363	92.45 344	59.76 392	96.04 363	93.61 383	73.29 383	77.06 368	96.64 265	44.28 388	96.16 334	72.35 371	82.52 311	89.67 374

our_test_3		90.39 283	89.48 288	93.12 306	92.40 345	89.57 310	99.33 223	96.35 343	87.84 303	85.30 332	94.99 325	84.14 236	96.09 338	80.38 346	84.56 299	93.71 327

ppachtmachnet_test		89.58 302	88.35 305	93.25 304	92.40 345	90.44 294	99.33 223	96.73 328	85.49 334	85.90 330	95.77 287	81.09 259	96.00 342	76.00 366	82.49 312	93.30 335

v7n		89.65 301	88.29 306	93.72 289	92.22 347	90.56 291	99.07 252	97.10 291	85.42 336	86.73 316	94.72 330	80.06 271	97.13 286	81.14 343	78.12 346	93.49 330

dmvs_testset		83.79 336	86.07 320	76.94 367	92.14 348	48.60 402	96.75 350	90.27 392	89.48 271	78.65 362	98.55 200	79.25 277	86.65 390	66.85 381	82.69 310	95.57 251

PS-MVSNAJss		93.64 213	93.31 210	94.61 251	92.11 349	92.19 253	99.12 243	97.38 263	92.51 199	88.45 292	96.99 253	91.20 150	97.29 278	94.36 196	87.71 276	94.36 270

pmmvs5		90.17 292	89.09 293	93.40 298	92.10 350	89.77 308	99.74 154	95.58 358	85.88 328	87.24 313	95.74 288	73.41 327	96.48 321	88.54 284	83.56 307	93.95 309

N_pmnet		80.06 346	80.78 344	77.89 366	91.94 351	45.28 404	98.80 285	56.82 406	78.10 371	80.08 357	93.33 350	77.03 294	95.76 345	68.14 379	82.81 309	92.64 346

test_djsdf		92.83 232	92.29 233	94.47 261	91.90 352	92.46 248	99.55 193	97.27 275	91.17 240	89.96 256	96.07 283	81.10 258	96.89 304	94.67 191	88.91 252	94.05 300

SixPastTwentyTwo		88.73 309	88.01 310	90.88 328	91.85 353	82.24 360	98.22 319	95.18 368	88.97 281	82.26 345	96.89 255	71.75 332	96.67 315	84.00 325	82.98 308	93.72 326

K. test v3		88.05 313	87.24 315	90.47 333	91.82 354	82.23 361	98.96 266	97.42 259	89.05 276	76.93 370	95.60 294	68.49 346	95.42 348	85.87 316	81.01 328	93.75 322

OurMVSNet-221017-0		89.81 298	89.48 288	90.83 330	91.64 355	81.21 367	98.17 321	95.38 363	91.48 230	85.65 331	97.31 240	72.66 328	97.29 278	88.15 289	84.83 297	93.97 308

mvs_tets		91.81 253	91.08 255	94.00 279	91.63 356	90.58 290	98.67 296	97.43 257	92.43 201	87.37 311	97.05 250	71.76 331	97.32 274	94.75 188	88.68 258	94.11 295

Gipuma		66.95 359	65.00 359	72.79 372	91.52 357	67.96 384	66.16 395	95.15 369	47.89 393	58.54 390	67.99 395	29.74 392	87.54 389	50.20 394	77.83 348	62.87 395
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_fmvsmconf0.01_n		96.39 134	95.74 143	98.32 118	91.47 358	95.56 162	99.84 121	97.30 271	97.74 18	97.89 137	99.35 127	79.62 274	99.85 108	99.25 54	99.24 119	99.55 139

jajsoiax		91.92 251	91.18 254	94.15 271	91.35 359	90.95 282	99.00 262	97.42 259	92.61 190	87.38 310	97.08 247	72.46 329	97.36 268	94.53 194	88.77 256	94.13 294

MDA-MVSNet-bldmvs		84.09 334	81.52 341	91.81 323	91.32 360	88.00 330	98.67 296	95.92 351	80.22 365	55.60 393	93.32 351	68.29 348	93.60 369	73.76 368	76.61 359	93.82 320

MVP-Stereo		90.93 270	90.45 265	92.37 317	91.25 361	88.76 316	98.05 326	96.17 346	87.27 310	84.04 337	95.30 312	78.46 288	97.27 280	83.78 328	99.70 84	91.09 361
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MDA-MVSNet_test_wron		85.51 325	83.32 333	92.10 319	90.96 362	88.58 322	99.20 238	96.52 337	79.70 367	57.12 392	92.69 356	79.11 280	93.86 366	77.10 362	77.46 352	93.86 317

YYNet1		85.50 326	83.33 332	92.00 320	90.89 363	88.38 326	99.22 237	96.55 336	79.60 368	57.26 391	92.72 355	79.09 282	93.78 367	77.25 361	77.37 353	93.84 318

anonymousdsp		91.79 258	90.92 257	94.41 266	90.76 364	92.93 236	98.93 269	97.17 283	89.08 275	87.46 309	95.30 312	78.43 289	96.92 303	92.38 232	88.73 257	93.39 333

lessismore_v0				90.53 331	90.58 365	80.90 370		95.80 352		77.01 369	95.84 285	66.15 356	96.95 300	83.03 332	75.05 363	93.74 325

EG-PatchMatch MVS		85.35 327	83.81 330	89.99 338	90.39 366	81.89 363	98.21 320	96.09 348	81.78 359	74.73 376	93.72 348	51.56 384	97.12 288	79.16 354	88.61 259	90.96 363

EGC-MVSNET		69.38 352	63.76 362	86.26 356	90.32 367	81.66 366	96.24 359	93.85 381	0.99 403	3.22 404	92.33 361	52.44 381	92.92 374	59.53 390	84.90 296	84.21 384

CMPMVS	61.59 21	84.75 330	85.14 325	83.57 360	90.32 367	62.54 388	96.98 346	97.59 242	74.33 381	69.95 382	96.66 263	64.17 362	98.32 224	87.88 293	88.41 264	89.84 372
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
new_pmnet		84.49 333	82.92 336	89.21 342	90.03 369	82.60 357	96.89 349	95.62 357	80.59 363	75.77 375	89.17 372	65.04 361	94.79 358	72.12 372	81.02 327	90.23 368

pmmvs6		85.69 322	83.84 329	91.26 327	90.00 370	84.41 350	97.82 331	96.15 347	75.86 375	81.29 351	95.39 307	61.21 371	96.87 306	83.52 331	73.29 365	92.50 349

DSMNet-mixed		88.28 312	88.24 307	88.42 350	89.64 371	75.38 379	98.06 325	89.86 393	85.59 333	88.20 299	92.14 362	76.15 308	91.95 379	78.46 356	96.05 194	97.92 226

UnsupCasMVSNet_eth		85.52 324	83.99 326	90.10 336	89.36 372	83.51 354	96.65 351	97.99 204	89.14 274	75.89 374	93.83 346	63.25 365	93.92 364	81.92 340	67.90 379	92.88 343

Anonymous20231206		86.32 320	85.42 323	89.02 344	89.11 373	80.53 373	99.05 257	95.28 364	85.43 335	82.82 343	93.92 345	74.40 322	93.44 370	66.99 380	81.83 318	93.08 340

Anonymous20240521		85.15 328	83.81 330	89.16 343	88.32 374	82.69 356	98.80 285	95.74 353	79.72 366	81.53 350	90.99 365	65.38 359	94.16 362	72.69 370	81.11 325	90.63 366

OpenMVS_ROB	79.82 20	83.77 337	81.68 340	90.03 337	88.30 375	82.82 355	98.46 305	95.22 366	73.92 382	76.00 373	91.29 364	55.00 378	96.94 301	68.40 378	88.51 263	90.34 367

test20.03		84.72 331	83.99 326	86.91 354	88.19 376	80.62 372	98.88 274	95.94 350	88.36 296	78.87 360	94.62 335	68.75 344	89.11 385	66.52 382	75.82 360	91.00 362

KD-MVS_self_test		83.59 338	82.06 338	88.20 351	86.93 377	80.70 371	97.21 339	96.38 341	82.87 353	82.49 344	88.97 373	67.63 350	92.32 377	73.75 369	62.30 388	91.58 359

MIMVSNet1		82.58 339	80.51 345	88.78 346	86.68 378	84.20 351	96.65 351	95.41 361	78.75 369	78.59 363	92.44 357	51.88 383	89.76 384	65.26 385	78.95 340	92.38 352

CL-MVSNet_self_test		84.50 332	83.15 335	88.53 349	86.00 379	81.79 364	98.82 282	97.35 265	85.12 337	83.62 341	90.91 367	76.66 300	91.40 380	69.53 376	60.36 389	92.40 351

UnsupCasMVSNet_bld		79.97 348	77.03 353	88.78 346	85.62 380	81.98 362	93.66 375	97.35 265	75.51 378	70.79 381	83.05 387	48.70 385	94.91 356	78.31 357	60.29 390	89.46 377

Patchmatch-RL test		86.90 318	85.98 322	89.67 339	84.45 381	75.59 378	89.71 388	92.43 386	86.89 317	77.83 367	90.94 366	94.22 77	93.63 368	87.75 294	69.61 371	99.79 97

pmmvs-eth3d		84.03 335	81.97 339	90.20 335	84.15 382	87.09 335	98.10 324	94.73 372	83.05 351	74.10 378	87.77 379	65.56 358	94.01 363	81.08 344	69.24 373	89.49 376

test_fmvs3		79.99 347	80.17 346	79.45 365	84.02 383	62.83 386	99.05 257	93.49 384	88.29 298	80.06 358	86.65 382	28.09 394	88.00 386	88.63 281	73.27 366	87.54 382

PM-MVS		80.47 344	78.88 349	85.26 357	83.79 384	72.22 381	95.89 366	91.08 390	85.71 332	76.56 372	88.30 375	36.64 390	93.90 365	82.39 336	69.57 372	89.66 375

new-patchmatchnet		81.19 341	79.34 348	86.76 355	82.86 385	80.36 374	97.92 328	95.27 365	82.09 358	72.02 379	86.87 381	62.81 367	90.74 383	71.10 373	63.08 386	89.19 379

mvsany_test3		82.12 340	81.14 342	85.06 358	81.87 386	70.41 382	97.09 343	92.14 387	91.27 239	77.84 366	88.73 374	39.31 389	95.49 346	90.75 258	71.24 368	89.29 378

WB-MVS		76.28 350	77.28 352	73.29 371	81.18 387	54.68 396	97.87 330	94.19 376	81.30 360	69.43 383	90.70 368	77.02 295	82.06 394	35.71 399	68.11 378	83.13 385

test_f		78.40 349	77.59 351	80.81 364	80.82 388	62.48 389	96.96 347	93.08 385	83.44 350	74.57 377	84.57 386	27.95 395	92.63 375	84.15 323	72.79 367	87.32 383

SSC-MVS		75.42 351	76.40 354	72.49 375	80.68 389	53.62 397	97.42 335	94.06 378	80.42 364	68.75 384	90.14 370	76.54 302	81.66 395	33.25 400	66.34 382	82.19 386

pmmvs3		80.27 345	77.77 350	87.76 353	80.32 390	82.43 359	98.23 318	91.97 388	72.74 384	78.75 361	87.97 378	57.30 377	90.99 382	70.31 374	62.37 387	89.87 371

testf1		68.38 355	66.92 356	72.78 373	78.80 391	50.36 399	90.95 386	87.35 398	55.47 389	58.95 388	88.14 376	20.64 399	87.60 387	57.28 391	64.69 383	80.39 388

APD_test2		68.38 355	66.92 356	72.78 373	78.80 391	50.36 399	90.95 386	87.35 398	55.47 389	58.95 388	88.14 376	20.64 399	87.60 387	57.28 391	64.69 383	80.39 388

ambc				83.23 361	77.17 393	62.61 387	87.38 390	94.55 375		76.72 371	86.65 382	30.16 391	96.36 326	84.85 322	69.86 370	90.73 365

test_vis3_rt		68.82 353	66.69 358	75.21 370	76.24 394	60.41 391	96.44 354	68.71 405	75.13 379	50.54 396	69.52 394	16.42 404	96.32 328	80.27 347	66.92 381	68.89 392

TDRefinement		84.76 329	82.56 337	91.38 326	74.58 395	84.80 349	97.36 337	94.56 374	84.73 342	80.21 356	96.12 282	63.56 364	98.39 214	87.92 292	63.97 385	90.95 364

E-PMN		52.30 363	52.18 365	52.67 381	71.51 396	45.40 403	93.62 376	76.60 403	36.01 397	43.50 398	64.13 397	27.11 396	67.31 400	31.06 401	26.06 396	45.30 399

EMVS		51.44 365	51.22 367	52.11 382	70.71 397	44.97 405	94.04 372	75.66 404	35.34 399	42.40 399	61.56 400	28.93 393	65.87 401	27.64 402	24.73 397	45.49 398

PMMVS2		67.15 358	64.15 361	76.14 369	70.56 398	62.07 390	93.89 373	87.52 397	58.09 388	60.02 387	78.32 389	22.38 398	84.54 392	59.56 389	47.03 394	81.80 387

FPMVS		68.72 354	68.72 355	68.71 377	65.95 399	44.27 406	95.97 365	94.74 371	51.13 392	53.26 394	90.50 369	25.11 397	83.00 393	60.80 388	80.97 329	78.87 390

wuyk23d		20.37 369	20.84 372	18.99 385	65.34 400	27.73 408	50.43 396	7.67 409	9.50 402	8.01 403	6.34 403	6.13 407	26.24 402	23.40 403	10.69 401	2.99 400

LCM-MVSNet		67.77 357	64.73 360	76.87 368	62.95 401	56.25 395	89.37 389	93.74 382	44.53 394	61.99 386	80.74 388	20.42 401	86.53 391	69.37 377	59.50 391	87.84 380

MVE	53.74 22	51.54 364	47.86 368	62.60 379	59.56 402	50.93 398	79.41 393	77.69 402	35.69 398	36.27 400	61.76 399	5.79 408	69.63 398	37.97 398	36.61 395	67.24 393
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ANet_high		56.10 361	52.24 364	67.66 378	49.27 403	56.82 394	83.94 391	82.02 401	70.47 385	33.28 401	64.54 396	17.23 403	69.16 399	45.59 396	23.85 398	77.02 391

tmp_tt		65.23 360	62.94 363	72.13 376	44.90 404	50.03 401	81.05 392	89.42 396	38.45 395	48.51 397	99.90 18	54.09 380	78.70 397	91.84 240	18.26 399	87.64 381

PMVS	49.05 23	53.75 362	51.34 366	60.97 380	40.80 405	34.68 407	74.82 394	89.62 395	37.55 396	28.67 402	72.12 391	7.09 406	81.63 396	43.17 397	68.21 377	66.59 394
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test123		37.68 367	39.14 370	33.31 383	19.94 406	24.83 409	98.36 312	9.75 408	15.53 401	51.31 395	87.14 380	19.62 402	17.74 403	47.10 395	3.47 402	57.36 396

testmvs		40.60 366	44.45 369	29.05 384	19.49 407	14.11 410	99.68 170	18.47 407	20.74 400	64.59 385	98.48 205	10.95 405	17.09 404	56.66 393	11.01 400	55.94 397

test_blank		0.00 372	0.00 375	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.02 404	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

eth-test2					0.00 408

eth-test					0.00 408

uanet_test		0.00 372	0.00 375	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.00 405	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

DCPMVS		0.00 372	0.00 375	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.00 405	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

cdsmvs_eth3d_5k		23.43 368	31.24 371	0.00 386	0.00 408	0.00 411	0.00 397	98.09 196	0.00 404	0.00 405	99.67 94	83.37 241	0.00 405	0.00 404	0.00 403	0.00 401

pcd_1.5k_mvsjas		7.60 371	10.13 374	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.00 405	91.20 150	0.00 405	0.00 404	0.00 403	0.00 401

sosnet-low-res		0.00 372	0.00 375	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.00 405	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

sosnet		0.00 372	0.00 375	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.00 405	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

uncertanet		0.00 372	0.00 375	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.00 405	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

Regformer		0.00 372	0.00 375	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.00 405	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

ab-mvs-re		8.28 370	11.04 373	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	99.40 121	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

uanet		0.00 372	0.00 375	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.00 405	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

MM				99.76 10		99.33 8	99.99 4	99.76 6	98.39 3	99.39 72	99.80 51	90.49 167	99.96 61	99.89 16	99.43 110	99.98 48

WAC-MVS						90.97 279								86.10 314

PC_three_1452									96.96 44	99.80 17	99.79 55	97.49 10	100.00 1	99.99 5	99.98 32	100.00 1

test_241102_TWO								98.43 128	97.27 34	99.80 17	99.94 4	97.18 21	100.00 1	100.00 1	100.00 1	100.00 1

test_0728_THIRD									96.48 59	99.83 13	99.91 14	97.87 6	100.00 1	99.92 12	100.00 1	100.00 1

GSMVS																99.59 130

sam_mvs1												94.72 61				99.59 130

sam_mvs												94.25 76

MTGPA								98.28 174

test_post1							95.78 367				59.23 401	93.20 107	97.74 258	91.06 249

test_post											63.35 398	94.43 66	98.13 238

patchmatchnet-post											91.70 363	95.12 49	97.95 249

MTMP							99.87 100	96.49 338

test9_res														99.71 33	99.99 21	100.00 1

agg_prior2														99.48 43	100.00 1	100.00 1

test_prior4						98.05 66	99.94 68

test_prior2							99.95 52		95.78 79	99.73 32	99.76 63	96.00 33		99.78 27	100.00 1

旧先验2							99.46 208		94.21 130	99.85 9			99.95 69	96.96 151

新几何2							99.40 212

无先验							99.49 203	98.71 66	93.46 160				100.00 1	94.36 196		99.99 23

原ACMM2							99.90 87

testdata2													99.99 36	90.54 262

segment_acmp												96.68 26

testdata1							99.28 232		96.35 69

plane_prior5								97.87 218					98.37 220	97.79 128	89.55 246	94.52 255

plane_prior4											98.59 194

plane_prior3						91.64 271			96.63 56	93.01 219

plane_prior2							99.84 121		96.38 65

plane_prior						91.74 265	99.86 113		96.76 52						89.59 245

n2								0.00 410

nn								0.00 410

door-mid								89.69 394

test11								98.44 120

door								90.31 391

HQP5-MVS						91.85 261

BP-MVS														97.92 121

HQP4-MVS										93.37 215			98.39 214			94.53 253

HQP3-MVS								97.89 216							89.60 243

HQP2-MVS												80.65 265

MDTV_nov1_ep13_2view						96.26 134	96.11 361		91.89 217	98.06 131		94.40 68		94.30 198		99.67 113

ACMMP++_ref															87.04 281

ACMMP++															88.23 268

Test By Simon												92.82 118

High-resolution multi-view benchmark