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.

MSC_two_6792asdad				96.52 1	97.78 60	90.86 1		96.85 80					99.61 7	96.03 27	99.06 9	99.07 5

No_MVS				96.52 1	97.78 60	90.86 1		96.85 80					99.61 7	96.03 27	99.06 9	99.07 5

OPU-MVS				96.21 3	98.00 48	90.85 3	97.13 19				97.08 70	92.59 2	98.94 92	92.25 92	98.99 14	98.84 19

HPM-MVS++		95.14 13	94.91 26	95.83 4	98.25 35	89.65 4	95.92 87	96.96 68	91.75 13	94.02 72	96.83 82	88.12 28	99.55 20	93.41 66	98.94 16	98.28 61

MM		95.10 14	94.91 26	95.68 5	96.09 116	88.34 10	96.68 38	94.37 298	95.08 1	94.68 58	97.72 39	82.94 100	99.64 4	97.85 5	98.76 33	99.06 7

SMA-MVS		95.20 10	95.07 20	95.59 6	98.14 41	88.48 9	96.26 54	97.28 40	85.90 206	97.67 4	98.10 14	88.41 24	99.56 16	94.66 48	99.19 1	98.71 25
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
3Dnovator+	87.14 4	92.42 103	91.37 125	95.55 7	95.63 143	88.73 7	97.07 23	96.77 92	90.84 26	84.02 331	96.62 95	75.95 213	99.34 42	87.77 181	97.68 97	98.59 29

TestfortrainingZip a		95.70 4	95.76 5	95.51 8	98.88 1	87.98 11	97.32 10	97.86 1	88.11 130	97.21 14	97.54 44	92.42 4	99.67 1	93.66 60	98.85 20	98.89 15

CNVR-MVS		95.40 9	95.37 11	95.50 9	98.11 42	88.51 8	95.29 131	96.96 68	92.09 10	95.32 50	97.08 70	89.49 17	99.33 45	95.10 43	98.85 20	98.66 26

TestfortrainingZip				95.40 10	97.32 74	88.97 6	97.32 10	96.82 85	89.07 88	95.69 45	96.49 100	89.27 19	99.29 50		95.80 141	97.95 96

MGCNet		94.18 50	93.80 64	95.34 11	94.91 183	87.62 16	95.97 82	93.01 349	92.58 6	94.22 63	97.20 64	80.56 139	99.59 11	97.04 20	98.68 41	98.81 22

ACMMP_NAP		94.74 25	94.56 33	95.28 12	98.02 47	87.70 13	95.68 107	97.34 30	88.28 121	95.30 51	97.67 41	85.90 55	99.54 24	93.91 56	98.95 15	98.60 28

DPE-MVS		95.57 6	95.67 6	95.25 13	98.36 31	87.28 20	95.56 119	97.51 11	89.13 87	97.14 18	97.91 32	91.64 9	99.62 5	94.61 49	99.17 2	98.86 16
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
SF-MVS		94.97 17	94.90 28	95.20 14	97.84 56	87.76 12	96.65 39	97.48 16	87.76 151	95.71 44	97.70 40	88.28 27	99.35 41	93.89 57	98.78 30	98.48 35

MCST-MVS		94.45 34	94.20 51	95.19 15	98.46 22	87.50 18	95.00 154	97.12 55	87.13 172	92.51 113	96.30 105	89.24 20	99.34 42	93.46 63	98.62 50	98.73 23

NCCC		94.81 22	94.69 32	95.17 16	97.83 57	87.46 19	95.66 110	96.93 72	92.34 7	93.94 73	96.58 97	87.74 31	99.44 33	92.83 75	98.40 58	98.62 27

DPM-MVS		92.58 99	91.74 109	95.08 17	96.19 107	89.31 5	92.66 311	96.56 113	83.44 280	91.68 139	95.04 185	86.60 47	98.99 82	85.60 215	97.92 85	96.93 185

ZNCC-MVS		94.47 33	94.28 45	95.03 18	98.52 18	86.96 22	96.85 33	97.32 34	88.24 122	93.15 88	97.04 73	86.17 52	99.62 5	92.40 86	98.81 27	98.52 31

test_0728_SECOND				95.01 19	98.79 5	86.43 41	97.09 21	97.49 12					99.61 7	95.62 34	99.08 7	98.99 9

MTAPA		94.42 39	94.22 48	95.00 20	98.42 24	86.95 23	94.36 208	96.97 65	91.07 22	93.14 89	97.56 43	84.30 81	99.56 16	93.43 64	98.75 34	98.47 38

MSP-MVS		95.42 8	95.56 8	94.98 21	98.49 20	86.52 38	96.91 30	97.47 17	91.73 14	96.10 36	96.69 87	89.90 14	99.30 48	94.70 47	98.04 80	99.13 2
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
region2R		94.43 36	94.27 47	94.92 22	98.65 11	86.67 32	96.92 29	97.23 43	88.60 112	93.58 80	97.27 58	85.22 64	99.54 24	92.21 94	98.74 35	98.56 30

APDe-MVS		95.46 7	95.64 7	94.91 23	98.26 34	86.29 48	97.46 7	97.40 26	89.03 93	96.20 35	98.10 14	89.39 18	99.34 42	95.88 29	99.03 11	99.10 4
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
ACMMPR		94.43 36	94.28 45	94.91 23	98.63 12	86.69 30	96.94 25	97.32 34	88.63 109	93.53 83	97.26 60	85.04 68	99.54 24	92.35 89	98.78 30	98.50 32

GST-MVS		94.21 45	93.97 60	94.90 25	98.41 25	86.82 26	96.54 41	97.19 44	88.24 122	93.26 85	96.83 82	85.48 60	99.59 11	91.43 120	98.40 58	98.30 55

HFP-MVS		94.52 31	94.40 38	94.86 26	98.61 13	86.81 27	96.94 25	97.34 30	88.63 109	93.65 78	97.21 62	86.10 53	99.49 30	92.35 89	98.77 32	98.30 55

sasdasda		93.27 82	92.75 92	94.85 27	95.70 139	87.66 14	96.33 44	96.41 123	90.00 51	94.09 68	94.60 210	82.33 109	98.62 133	92.40 86	92.86 230	98.27 63

MP-MVS-pluss		94.21 45	94.00 59	94.85 27	98.17 39	86.65 33	94.82 167	97.17 49	86.26 198	92.83 98	97.87 34	85.57 59	99.56 16	94.37 52	98.92 17	98.34 48
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
canonicalmvs		93.27 82	92.75 92	94.85 27	95.70 139	87.66 14	96.33 44	96.41 123	90.00 51	94.09 68	94.60 210	82.33 109	98.62 133	92.40 86	92.86 230	98.27 63

XVS		94.45 34	94.32 41	94.85 27	98.54 16	86.60 36	96.93 27	97.19 44	90.66 34	92.85 96	97.16 68	85.02 69	99.49 30	91.99 105	98.56 54	98.47 38

X-MVStestdata		88.31 233	86.13 282	94.85 27	98.54 16	86.60 36	96.93 27	97.19 44	90.66 34	92.85 96	23.41 497	85.02 69	99.49 30	91.99 105	98.56 54	98.47 38

SteuartSystems-ACMMP		95.20 10	95.32 13	94.85 27	96.99 82	86.33 44	97.33 8	97.30 37	91.38 19	95.39 49	97.46 50	88.98 23	99.40 34	94.12 53	98.89 18	98.82 21
Skip Steuart: Steuart Systems R&D Blog.
MED-MVS test				94.84 33	98.88 1	85.89 65	97.32 10	97.86 1	88.11 130	97.21 14	97.54 44		99.67 1	95.27 40	98.85 20	98.95 11

MED-MVS		95.74 3	96.04 3	94.84 33	98.88 1	85.89 65	97.32 10	97.86 1	89.01 95	97.21 14	97.54 44	92.42 4	99.67 1	95.27 40	98.85 20	98.95 11

DVP-MVS++		95.98 1	96.36 1	94.82 35	97.78 60	86.00 54	98.29 1	97.49 12	90.75 29	97.62 8	98.06 22	92.59 2	99.61 7	95.64 32	99.02 12	98.86 16

ME-MVS		95.17 12	95.29 14	94.81 36	98.39 28	85.89 65	95.91 88	97.55 9	89.01 95	95.86 42	97.54 44	89.24 20	99.59 11	95.27 40	98.85 20	98.95 11

alignmvs		93.08 90	92.50 98	94.81 36	95.62 144	87.61 17	95.99 79	96.07 168	89.77 64	94.12 67	94.87 194	80.56 139	98.66 125	92.42 85	93.10 226	98.15 75

SED-MVS		95.91 2	96.28 2	94.80 38	98.77 8	85.99 56	97.13 19	97.44 21	90.31 41	97.71 2	98.07 20	92.31 6	99.58 14	95.66 30	99.13 3	98.84 19

DeepC-MVS_fast	89.43 2	94.04 53	93.79 65	94.80 38	97.48 70	86.78 28	95.65 112	96.89 77	89.40 75	92.81 99	96.97 75	85.37 62	99.24 52	90.87 129	98.69 39	98.38 47
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MP-MVS		94.25 42	94.07 56	94.77 40	98.47 21	86.31 46	96.71 36	96.98 64	89.04 91	91.98 124	97.19 65	85.43 61	99.56 16	92.06 103	98.79 28	98.44 42
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
APD-MVS		94.24 43	94.07 56	94.75 41	98.06 45	86.90 25	95.88 90	96.94 71	85.68 213	95.05 56	97.18 66	87.31 39	99.07 65	91.90 111	98.61 52	98.28 61
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CP-MVS		94.34 40	94.21 50	94.74 42	98.39 28	86.64 34	97.60 5	97.24 41	88.53 114	92.73 104	97.23 61	85.20 65	99.32 46	92.15 97	98.83 26	98.25 68

PGM-MVS		93.96 58	93.72 70	94.68 43	98.43 23	86.22 49	95.30 129	97.78 4	87.45 162	93.26 85	97.33 56	84.62 78	99.51 28	90.75 131	98.57 53	98.32 54

DVP-MVS		95.67 5	96.02 4	94.64 44	98.78 6	85.93 59	97.09 21	96.73 98	90.27 45	97.04 22	98.05 25	91.47 10	99.55 20	95.62 34	99.08 7	98.45 41
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
mPP-MVS		93.99 56	93.78 66	94.63 45	98.50 19	85.90 64	96.87 31	96.91 75	88.70 107	91.83 134	97.17 67	83.96 85	99.55 20	91.44 119	98.64 49	98.43 43

PHI-MVS		93.89 60	93.65 74	94.62 46	96.84 85	86.43 41	96.69 37	97.49 12	85.15 237	93.56 82	96.28 106	85.60 58	99.31 47	92.45 83	98.79 28	98.12 80

TSAR-MVS + MP.		94.85 19	94.94 24	94.58 47	98.25 35	86.33 44	96.11 67	96.62 108	88.14 127	96.10 36	96.96 76	89.09 22	98.94 92	94.48 50	98.68 41	98.48 35
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
CANet		93.54 69	93.20 83	94.55 48	95.65 141	85.73 72	94.94 157	96.69 104	91.89 12	90.69 165	95.88 137	81.99 121	99.54 24	93.14 70	97.95 84	98.39 45

train_agg		93.44 75	93.08 85	94.52 49	97.53 67	86.49 39	94.07 228	96.78 90	81.86 324	92.77 101	96.20 109	87.63 33	99.12 63	92.14 98	98.69 39	97.94 97

CDPH-MVS		92.83 94	92.30 101	94.44 50	97.79 58	86.11 53	94.06 230	96.66 105	80.09 355	92.77 101	96.63 94	86.62 45	99.04 69	87.40 188	98.66 45	98.17 73

3Dnovator	86.66 5	91.73 121	90.82 140	94.44 50	94.59 209	86.37 43	97.18 17	97.02 62	89.20 84	84.31 326	96.66 90	73.74 254	99.17 57	86.74 198	97.96 83	97.79 120

SR-MVS		94.23 44	94.17 54	94.43 52	98.21 38	85.78 70	96.40 43	96.90 76	88.20 125	94.33 62	97.40 53	84.75 77	99.03 70	93.35 67	97.99 82	98.48 35

HPM-MVS		94.02 54	93.88 61	94.43 52	98.39 28	85.78 70	97.25 15	97.07 60	86.90 182	92.62 110	96.80 86	84.85 75	99.17 57	92.43 84	98.65 48	98.33 50
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
TSAR-MVS + GP.		93.66 67	93.41 78	94.41 54	96.59 92	86.78 28	94.40 200	93.93 316	89.77 64	94.21 64	95.59 157	87.35 38	98.61 135	92.72 78	96.15 136	97.83 116

reproduce-ours		94.82 20	94.97 22	94.38 55	97.91 53	85.46 75	95.86 91	97.15 51	89.82 57	95.23 53	98.10 14	87.09 41	99.37 37	95.30 38	98.25 67	98.30 55

our_new_method		94.82 20	94.97 22	94.38 55	97.91 53	85.46 75	95.86 91	97.15 51	89.82 57	95.23 53	98.10 14	87.09 41	99.37 37	95.30 38	98.25 67	98.30 55

NormalMVS		93.46 72	93.16 84	94.37 57	98.40 26	86.20 50	96.30 47	96.27 136	91.65 17	92.68 106	96.13 119	77.97 183	98.84 106	90.75 131	98.26 63	98.07 82

test12				94.34 58	97.13 80	86.15 52		96.29 132		91.04 161		85.08 67	99.01 75		98.13 75	97.86 111

SymmetryMVS		92.81 96	92.31 100	94.32 59	96.15 108	86.20 50	96.30 47	94.43 294	91.65 17	92.68 106	96.13 119	77.97 183	98.84 106	90.75 131	94.72 168	97.92 105

ACMMP		93.24 84	92.88 90	94.30 60	98.09 44	85.33 79	96.86 32	97.45 20	88.33 118	90.15 180	97.03 74	81.44 129	99.51 28	90.85 130	95.74 144	98.04 89
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
reproduce_model		94.76 24	94.92 25	94.29 61	97.92 49	85.18 81	95.95 85	97.19 44	89.67 67	95.27 52	98.16 6	86.53 48	99.36 40	95.42 37	98.15 73	98.33 50

DeepC-MVS	88.79 3	93.31 81	92.99 88	94.26 62	96.07 118	85.83 68	94.89 160	96.99 63	89.02 94	89.56 189	97.37 55	82.51 106	99.38 35	92.20 95	98.30 61	97.57 135
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MGCFI-Net		93.03 91	92.63 95	94.23 63	95.62 144	85.92 61	96.08 69	96.33 130	89.86 55	93.89 75	94.66 207	82.11 116	98.50 141	92.33 91	92.82 233	98.27 63

fmvsm_l_conf0.5_n_3		94.80 23	95.01 21	94.15 64	95.64 142	85.08 82	96.09 68	97.36 28	90.98 24	97.09 20	98.12 10	84.98 73	98.94 92	97.07 17	97.80 92	98.43 43

EPNet		91.79 112	91.02 134	94.10 65	90.10 410	85.25 80	96.03 76	92.05 376	92.83 5	87.39 237	95.78 148	79.39 165	99.01 75	88.13 175	97.48 100	98.05 88
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
lecture		95.10 14	95.46 10	94.01 66	98.40 26	84.36 107	97.70 3	97.78 4	91.19 20	96.22 34	98.08 19	86.64 44	99.37 37	94.91 45	98.26 63	98.29 60

test_fmvsmconf_n		94.60 28	94.81 30	93.98 67	94.62 205	84.96 85	96.15 62	97.35 29	89.37 76	96.03 39	98.11 11	86.36 49	99.01 75	97.45 10	97.83 90	97.96 95

DELS-MVS		93.43 79	93.25 81	93.97 68	95.42 152	85.04 83	93.06 294	97.13 54	90.74 31	91.84 132	95.09 184	86.32 50	99.21 55	91.22 121	98.45 56	97.65 129
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
DP-MVS Recon		91.95 109	91.28 128	93.96 69	98.33 33	85.92 61	94.66 179	96.66 105	82.69 302	90.03 182	95.82 144	82.30 111	99.03 70	84.57 233	96.48 129	96.91 187

HPM-MVS_fast		93.40 80	93.22 82	93.94 70	98.36 31	84.83 87	97.15 18	96.80 89	85.77 210	92.47 114	97.13 69	82.38 107	99.07 65	90.51 136	98.40 58	97.92 105

test_fmvsmconf0.1_n		94.20 47	94.31 43	93.88 71	92.46 324	84.80 88	96.18 59	96.82 85	89.29 81	95.68 46	98.11 11	85.10 66	98.99 82	97.38 11	97.75 96	97.86 111

SD-MVS		94.96 18	95.33 12	93.88 71	97.25 79	86.69 30	96.19 57	97.11 58	90.42 37	96.95 24	97.27 58	89.53 16	96.91 315	94.38 51	98.85 20	98.03 90
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
MVS_111021_HR		93.45 74	93.31 79	93.84 73	96.99 82	84.84 86	93.24 285	97.24 41	88.76 104	91.60 140	95.85 141	86.07 54	98.66 125	91.91 109	98.16 71	98.03 90

SR-MVS-dyc-post		93.82 62	93.82 63	93.82 74	97.92 49	84.57 94	96.28 51	96.76 93	87.46 160	93.75 76	97.43 51	84.24 82	99.01 75	92.73 76	97.80 92	97.88 109

test_prior				93.82 74	97.29 77	84.49 98		96.88 78					98.87 100			98.11 81

APD-MVS_3200maxsize		93.78 63	93.77 67	93.80 76	97.92 49	84.19 111	96.30 47	96.87 79	86.96 178	93.92 74	97.47 49	83.88 86	98.96 89	92.71 79	97.87 88	98.26 67

fmvsm_l_conf0.5_n		94.29 41	94.46 36	93.79 77	95.28 158	85.43 77	95.68 107	96.43 121	86.56 190	96.84 26	97.81 37	87.56 36	98.77 115	97.14 15	96.82 119	97.16 166

CSCG		93.23 85	93.05 86	93.76 78	98.04 46	84.07 113	96.22 56	97.37 27	84.15 261	90.05 181	95.66 154	87.77 30	99.15 61	89.91 145	98.27 62	98.07 82

GDP-MVS		92.04 107	91.46 122	93.75 79	94.55 215	84.69 91	95.60 118	96.56 113	87.83 148	93.07 92	95.89 136	73.44 258	98.65 127	90.22 139	96.03 138	97.91 107

BP-MVS1		92.48 101	92.07 104	93.72 80	94.50 218	84.39 106	95.90 89	94.30 301	90.39 38	92.67 108	95.94 132	74.46 237	98.65 127	93.14 70	97.35 104	98.13 77

test_fmvsmconf0.01_n		93.19 86	93.02 87	93.71 81	89.25 423	84.42 105	96.06 73	96.29 132	89.06 89	94.68 58	98.13 7	79.22 167	98.98 86	97.22 13	97.24 106	97.74 123

UA-Net		92.83 94	92.54 97	93.68 82	96.10 115	84.71 90	95.66 110	96.39 125	91.92 11	93.22 87	96.49 100	83.16 95	98.87 100	84.47 235	95.47 151	97.45 141

fmvsm_l_conf0.5_n_a		94.20 47	94.40 38	93.60 83	95.29 157	84.98 84	95.61 115	96.28 135	86.31 196	96.75 28	97.86 35	87.40 37	98.74 119	97.07 17	97.02 111	97.07 171

QAPM		89.51 190	88.15 217	93.59 84	94.92 181	84.58 93	96.82 34	96.70 103	78.43 382	83.41 349	96.19 112	73.18 263	99.30 48	77.11 358	96.54 126	96.89 188

test_fmvsm_n_1920		94.71 26	95.11 19	93.50 85	95.79 133	84.62 92	96.15 62	97.64 6	89.85 56	97.19 17	97.89 33	86.28 51	98.71 122	97.11 16	98.08 79	97.17 159

fmvsm_s_conf0.5_n_9		94.99 16	95.50 9	93.44 86	96.51 100	82.25 184	95.76 102	96.92 73	93.37 3	97.63 7	98.43 1	84.82 76	99.16 60	98.15 1	97.92 85	98.90 14

KinetiMVS		91.82 111	91.30 126	93.39 87	94.72 197	83.36 138	95.45 122	96.37 127	90.33 40	92.17 119	96.03 126	72.32 275	98.75 116	87.94 178	96.34 131	98.07 82

casdiffmvs_mvg		92.96 93	92.83 91	93.35 88	94.59 209	83.40 136	95.00 154	96.34 129	90.30 43	92.05 122	96.05 123	83.43 89	98.15 177	92.07 100	95.67 145	98.49 34
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
fmvsm_s_conf0.5_n_5		93.96 58	94.18 53	93.30 89	94.79 190	83.81 122	95.77 100	96.74 97	88.02 136	96.23 33	97.84 36	83.36 93	98.83 109	97.49 8	97.34 105	97.25 152

EI-MVSNet-Vis-set		93.01 92	92.92 89	93.29 90	95.01 172	83.51 133	94.48 188	95.77 195	90.87 25	92.52 112	96.67 89	84.50 79	99.00 80	91.99 105	94.44 181	97.36 144

Vis-MVSNet		91.75 119	91.23 129	93.29 90	95.32 156	83.78 123	96.14 64	95.98 174	89.89 53	90.45 169	96.58 97	75.09 226	98.31 168	84.75 227	96.90 115	97.78 121
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
balanced_conf03		93.98 57	94.22 48	93.26 92	96.13 110	83.29 140	96.27 53	96.52 116	89.82 57	95.56 48	95.51 160	84.50 79	98.79 113	94.83 46	98.86 19	97.72 125

SPE-MVS-test		94.02 54	94.29 44	93.24 93	96.69 88	83.24 141	97.49 6	96.92 73	92.14 9	92.90 94	95.77 149	85.02 69	98.33 165	93.03 72	98.62 50	98.13 77

VNet		92.24 105	91.91 107	93.24 93	96.59 92	83.43 134	94.84 166	96.44 120	89.19 85	94.08 71	95.90 135	77.85 189	98.17 175	88.90 165	93.38 215	98.13 77

fmvsm_s_conf0.5_n_10		94.43 36	94.84 29	93.20 95	95.73 136	83.19 144	95.99 79	97.31 36	91.08 21	97.67 4	98.11 11	81.87 123	99.22 53	97.86 4	97.91 87	97.20 157

VDD-MVS		90.74 148	89.92 162	93.20 95	96.27 105	83.02 156	95.73 104	93.86 320	88.42 117	92.53 111	96.84 81	62.09 389	98.64 130	90.95 127	92.62 240	97.93 104

Elysia		90.12 167	89.10 185	93.18 97	93.16 294	84.05 115	95.22 138	96.27 136	85.16 235	90.59 166	94.68 203	64.64 368	98.37 158	86.38 204	95.77 142	97.12 168

StellarMVS		90.12 167	89.10 185	93.18 97	93.16 294	84.05 115	95.22 138	96.27 136	85.16 235	90.59 166	94.68 203	64.64 368	98.37 158	86.38 204	95.77 142	97.12 168

CS-MVS		94.12 51	94.44 37	93.17 99	96.55 95	83.08 153	97.63 4	96.95 70	91.71 15	93.50 84	96.21 108	85.61 57	98.24 170	93.64 61	98.17 70	98.19 71

nrg030		91.08 143	90.39 146	93.17 99	93.07 301	86.91 24	96.41 42	96.26 140	88.30 120	88.37 214	94.85 197	82.19 115	97.64 239	91.09 122	82.95 366	94.96 273

MVSMamba_PlusPlus		93.44 75	93.54 76	93.14 101	96.58 94	83.05 154	96.06 73	96.50 118	84.42 258	94.09 68	95.56 159	85.01 72	98.69 124	94.96 44	98.66 45	97.67 128

EI-MVSNet-UG-set		92.74 97	92.62 96	93.12 102	94.86 186	83.20 143	94.40 200	95.74 198	90.71 33	92.05 122	96.60 96	84.00 84	98.99 82	91.55 117	93.63 205	97.17 159

test_fmvsmvis_n_1920		93.44 75	93.55 75	93.10 103	93.67 280	84.26 109	95.83 95	96.14 159	89.00 97	92.43 115	97.50 48	83.37 92	98.72 120	96.61 24	97.44 101	96.32 213

新几何1				93.10 103	97.30 76	84.35 108		95.56 216	71.09 459	91.26 149	96.24 107	82.87 102	98.86 102	79.19 336	98.10 76	96.07 229

OMC-MVS		91.23 135	90.62 145	93.08 105	96.27 105	84.07 113	93.52 267	95.93 180	86.95 179	89.51 190	96.13 119	78.50 177	98.35 162	85.84 213	92.90 229	96.83 195

OpenMVS	83.78 11	88.74 220	87.29 239	93.08 105	92.70 319	85.39 78	96.57 40	96.43 121	78.74 377	80.85 381	96.07 122	69.64 312	99.01 75	78.01 349	96.65 124	94.83 281

MAR-MVS		90.30 163	89.37 178	93.07 107	96.61 91	84.48 99	95.68 107	95.67 207	82.36 307	87.85 224	92.85 277	76.63 202	98.80 111	80.01 316	96.68 123	95.91 235
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
lupinMVS		90.92 144	90.21 150	93.03 108	93.86 265	83.88 120	92.81 305	93.86 320	79.84 358	91.76 136	94.29 224	77.92 186	98.04 198	90.48 137	97.11 107	97.17 159

Effi-MVS+		91.59 129	91.11 131	93.01 109	94.35 233	83.39 137	94.60 181	95.10 251	87.10 173	90.57 168	93.10 272	81.43 130	98.07 192	89.29 157	94.48 179	97.59 134

fmvsm_s_conf0.5_n_a		93.57 68	93.76 68	93.00 110	95.02 171	83.67 126	96.19 57	96.10 165	87.27 166	95.98 40	98.05 25	83.07 99	98.45 151	96.68 23	95.51 148	96.88 189

MVS_111021_LR		92.47 102	92.29 102	92.98 111	95.99 125	84.43 103	93.08 291	96.09 166	88.20 125	91.12 154	95.72 152	81.33 131	97.76 228	91.74 113	97.37 103	96.75 197

fmvsm_s_conf0.1_n_a		93.19 86	93.26 80	92.97 112	92.49 322	83.62 129	96.02 77	95.72 202	86.78 184	96.04 38	98.19 4	82.30 111	98.43 155	96.38 25	95.42 154	96.86 190

ETV-MVS		92.74 97	92.66 94	92.97 112	95.20 164	84.04 117	95.07 150	96.51 117	90.73 32	92.96 93	91.19 338	84.06 83	98.34 163	91.72 114	96.54 126	96.54 208

LFMVS		90.08 170	89.13 184	92.95 114	96.71 87	82.32 183	96.08 69	89.91 434	86.79 183	92.15 121	96.81 84	62.60 387	98.34 163	87.18 192	93.90 195	98.19 71

UGNet		89.95 177	88.95 193	92.95 114	94.51 217	83.31 139	95.70 106	95.23 244	89.37 76	87.58 231	93.94 240	64.00 376	98.78 114	83.92 242	96.31 132	96.74 198
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
jason		90.80 146	90.10 154	92.90 116	93.04 304	83.53 132	93.08 291	94.15 309	80.22 352	91.41 146	94.91 191	76.87 196	97.93 218	90.28 138	96.90 115	97.24 153
jason: jason.
DP-MVS		87.25 274	85.36 313	92.90 116	97.65 64	83.24 141	94.81 168	92.00 378	74.99 426	81.92 370	95.00 187	72.66 268	99.05 67	66.92 439	92.33 245	96.40 210

fmvsm_s_conf0.5_n_8		94.56 30	95.12 18	92.87 118	95.96 128	81.32 213	95.76 102	97.57 8	93.48 2	97.53 10	98.32 3	81.78 126	99.13 62	97.91 2	97.81 91	98.16 74

fmvsm_s_conf0.5_n		93.76 64	94.06 58	92.86 119	95.62 144	83.17 145	96.14 64	96.12 163	88.13 128	95.82 43	98.04 28	83.43 89	98.48 143	96.97 21	96.23 133	96.92 186

fmvsm_s_conf0.1_n		93.46 72	93.66 73	92.85 120	93.75 272	83.13 147	96.02 77	95.74 198	87.68 154	95.89 41	98.17 5	82.78 103	98.46 147	96.71 22	96.17 135	96.98 180

CANet_DTU		90.26 165	89.41 177	92.81 121	93.46 287	83.01 157	93.48 268	94.47 293	89.43 74	87.76 229	94.23 229	70.54 300	99.03 70	84.97 222	96.39 130	96.38 211

MVSFormer		91.68 127	91.30 126	92.80 122	93.86 265	83.88 120	95.96 83	95.90 184	84.66 254	91.76 136	94.91 191	77.92 186	97.30 280	89.64 153	97.11 107	97.24 153

PVSNet_Blended_VisFu		91.38 132	90.91 137	92.80 122	96.39 102	83.17 145	94.87 162	96.66 105	83.29 285	89.27 196	94.46 219	80.29 142	99.17 57	87.57 185	95.37 155	96.05 232

fmvsm_l_conf0.5_n_9		94.65 27	95.28 15	92.77 124	95.95 129	81.83 195	95.53 120	97.12 55	91.68 16	97.89 1	98.06 22	85.71 56	98.65 127	97.32 12	98.26 63	97.83 116

LuminaMVS		90.55 159	89.81 164	92.77 124	92.78 317	84.21 110	94.09 226	94.17 308	85.82 207	91.54 141	94.14 231	69.93 306	97.92 219	91.62 116	94.21 189	96.18 221

balanced_ft_v1		92.23 106	92.05 105	92.77 124	95.40 153	81.78 199	95.80 96	95.69 206	87.94 140	91.92 129	95.04 185	75.91 214	98.71 122	93.83 58	96.94 112	97.82 118

fmvsm_s_conf0.5_n_6		94.11 52	94.56 33	92.76 127	94.98 176	81.96 193	95.79 98	97.29 39	89.31 79	97.52 11	97.61 42	83.25 94	98.88 99	97.05 19	98.22 69	97.43 143

VDDNet		89.56 189	88.49 208	92.76 127	95.07 170	82.09 187	96.30 47	93.19 344	81.05 346	91.88 130	96.86 80	61.16 405	98.33 165	88.43 172	92.49 244	97.84 115

viewdifsd2359ckpt09		91.18 138	90.65 144	92.75 129	94.61 208	82.36 182	94.32 209	95.74 198	84.72 251	89.66 188	95.15 182	79.69 160	98.04 198	87.70 182	94.27 188	97.85 114

h-mvs33		90.80 146	90.15 153	92.75 129	96.01 121	82.66 170	95.43 123	95.53 220	89.80 60	93.08 90	95.64 155	75.77 215	99.00 80	92.07 100	78.05 423	96.60 203

casdiffmvs		92.51 100	92.43 99	92.74 131	94.41 228	81.98 191	94.54 185	96.23 144	89.57 70	91.96 126	96.17 113	82.58 105	98.01 205	90.95 127	95.45 153	98.23 69
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
test_yl		90.69 151	90.02 160	92.71 132	95.72 137	82.41 180	94.11 222	95.12 249	85.63 214	91.49 143	94.70 201	74.75 230	98.42 156	86.13 208	92.53 242	97.31 145

DCV-MVSNet		90.69 151	90.02 160	92.71 132	95.72 137	82.41 180	94.11 222	95.12 249	85.63 214	91.49 143	94.70 201	74.75 230	98.42 156	86.13 208	92.53 242	97.31 145

PCF-MVS	84.11 10	87.74 248	86.08 286	92.70 134	94.02 254	84.43 103	89.27 411	95.87 189	73.62 441	84.43 318	94.33 221	78.48 179	98.86 102	70.27 413	94.45 180	94.81 282
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
fmvsm_s_conf0.5_n_11		94.60 28	95.23 16	92.69 135	96.05 120	82.00 189	96.31 46	96.71 101	92.27 8	96.68 30	98.39 2	85.32 63	98.92 95	97.20 14	98.16 71	97.17 159

SSM_0404		90.73 149	90.08 155	92.69 135	95.00 175	83.13 147	94.32 209	95.00 259	85.41 225	89.84 183	95.35 168	76.13 205	97.98 210	85.46 218	94.18 190	96.95 182

baseline		92.39 104	92.29 102	92.69 135	94.46 223	81.77 200	94.14 219	96.27 136	89.22 83	91.88 130	96.00 127	82.35 108	97.99 207	91.05 123	95.27 159	98.30 55

MSLP-MVS++		93.72 66	94.08 55	92.65 138	97.31 75	83.43 134	95.79 98	97.33 32	90.03 50	93.58 80	96.96 76	84.87 74	97.76 228	92.19 96	98.66 45	96.76 196

EC-MVSNet		93.44 75	93.71 71	92.63 139	95.21 163	82.43 177	97.27 14	96.71 101	90.57 36	92.88 95	95.80 145	83.16 95	98.16 176	93.68 59	98.14 74	97.31 145

ab-mvs		89.41 197	88.35 210	92.60 140	95.15 168	82.65 174	92.20 331	95.60 214	83.97 265	88.55 210	93.70 254	74.16 245	98.21 174	82.46 266	89.37 289	96.94 184

LS3D		87.89 243	86.32 275	92.59 141	96.07 118	82.92 160	95.23 136	94.92 268	75.66 418	82.89 356	95.98 129	72.48 272	99.21 55	68.43 427	95.23 160	95.64 249

Anonymous20240529		88.09 239	86.59 264	92.58 142	96.53 97	81.92 194	95.99 79	95.84 191	74.11 436	89.06 200	95.21 177	61.44 397	98.81 110	83.67 249	87.47 320	97.01 178

fmvsm_s_conf0.5_n_3		94.49 32	95.13 17	92.56 143	95.49 150	81.10 223	95.93 86	97.16 50	92.96 4	97.39 12	98.13 7	83.63 88	98.80 111	97.89 3	97.61 99	97.78 121

CPTT-MVS		91.99 108	91.80 108	92.55 144	98.24 37	81.98 191	96.76 35	96.49 119	81.89 323	90.24 173	96.44 103	78.59 175	98.61 135	89.68 151	97.85 89	97.06 172

viewdifsd2359ckpt13		91.20 137	90.75 142	92.54 145	94.30 239	82.13 186	94.03 232	95.89 186	85.60 216	90.20 175	95.36 167	79.69 160	97.90 222	87.85 180	93.86 196	97.61 131

114514_t		89.51 190	88.50 206	92.54 145	98.11 42	81.99 190	95.16 146	96.36 128	70.19 463	85.81 271	95.25 173	76.70 200	98.63 132	82.07 276	96.86 118	97.00 179

PAPM_NR		91.22 136	90.78 141	92.52 147	97.60 65	81.46 209	94.37 206	96.24 143	86.39 195	87.41 234	94.80 199	82.06 119	98.48 143	82.80 261	95.37 155	97.61 131

mamba_0408		89.06 210	87.92 224	92.50 148	94.76 191	82.66 170	79.84 483	94.64 286	85.18 230	88.96 202	95.00 187	76.00 210	97.98 210	83.74 246	93.15 223	96.85 191

DeepPCF-MVS	89.96 1	94.20 47	94.77 31	92.49 149	96.52 98	80.00 274	94.00 237	97.08 59	90.05 49	95.65 47	97.29 57	89.66 15	98.97 87	93.95 55	98.71 36	98.50 32

SSM_0407		90.47 161	89.80 165	92.46 150	94.76 191	82.66 170	93.98 239	95.00 259	85.41 225	88.96 202	95.35 168	76.13 205	97.88 223	85.46 218	93.15 223	96.85 191

IS-MVSNet		91.43 131	91.09 133	92.46 150	95.87 132	81.38 212	96.95 24	93.69 333	89.72 66	89.50 192	95.98 129	78.57 176	97.77 227	83.02 255	96.50 128	98.22 70

API-MVS		90.66 154	90.07 156	92.45 152	96.36 103	84.57 94	96.06 73	95.22 246	82.39 305	89.13 197	94.27 227	80.32 141	98.46 147	80.16 314	96.71 122	94.33 305

xiu_mvs_v1_base_debu		90.64 155	90.05 157	92.40 153	93.97 260	84.46 100	93.32 276	95.46 224	85.17 232	92.25 116	94.03 232	70.59 296	98.57 138	90.97 124	94.67 170	94.18 309

xiu_mvs_v1_base		90.64 155	90.05 157	92.40 153	93.97 260	84.46 100	93.32 276	95.46 224	85.17 232	92.25 116	94.03 232	70.59 296	98.57 138	90.97 124	94.67 170	94.18 309

xiu_mvs_v1_base_debi		90.64 155	90.05 157	92.40 153	93.97 260	84.46 100	93.32 276	95.46 224	85.17 232	92.25 116	94.03 232	70.59 296	98.57 138	90.97 124	94.67 170	94.18 309

fmvsm_s_conf0.5_n_2		93.47 71	93.83 62	92.39 156	95.36 154	81.19 219	95.20 143	96.56 113	90.37 39	97.13 19	98.03 29	77.47 192	98.96 89	97.79 6	96.58 125	97.03 175

viewmacassd2359aftdt		91.67 128	91.43 124	92.37 157	93.95 263	81.00 227	93.90 247	95.97 177	87.75 152	91.45 145	96.04 125	79.92 148	97.97 212	89.26 158	94.67 170	98.14 76

viewmanbaseed2359cas		91.78 115	91.58 114	92.37 157	94.32 236	81.07 224	93.76 253	95.96 178	87.26 167	91.50 142	95.88 137	80.92 137	97.97 212	89.70 150	94.92 164	98.07 82

fmvsm_s_conf0.1_n_2		93.16 88	93.42 77	92.37 157	94.62 205	81.13 221	95.23 136	95.89 186	90.30 43	96.74 29	98.02 30	76.14 204	98.95 91	97.64 7	96.21 134	97.03 175

AdaColmap		89.89 180	89.07 187	92.37 157	97.41 71	83.03 155	94.42 195	95.92 181	82.81 299	86.34 260	94.65 208	73.89 250	99.02 73	80.69 303	95.51 148	95.05 268

CNLPA		89.07 209	87.98 221	92.34 161	96.87 84	84.78 89	94.08 227	93.24 341	81.41 337	84.46 316	95.13 183	75.57 222	96.62 332	77.21 356	93.84 198	95.61 252

fmvsm_s_conf0.5_n_4		93.86 61	94.37 40	92.33 162	95.13 169	80.95 230	95.64 113	96.97 65	89.60 69	96.85 25	97.77 38	83.08 98	98.92 95	97.49 8	96.78 120	97.13 167

ET-MVSNet_ETH3D		87.51 262	85.91 294	92.32 163	93.70 279	83.93 118	92.33 325	90.94 410	84.16 260	72.09 461	92.52 290	69.90 307	95.85 384	89.20 159	88.36 307	97.17 159

E4		91.74 120	91.55 117	92.31 164	94.27 241	80.80 240	93.81 250	96.17 156	87.97 138	91.11 155	96.05 123	80.75 138	98.08 190	89.78 146	94.02 192	98.06 87

E2		91.79 112	91.61 112	92.31 164	94.49 219	80.86 236	93.74 255	96.19 149	87.63 157	91.16 150	95.94 132	81.31 132	98.06 193	89.76 147	94.29 186	97.99 92

Anonymous202405211		87.68 249	86.13 282	92.31 164	96.66 89	80.74 242	94.87 162	91.49 395	80.47 351	89.46 193	95.44 163	54.72 446	98.23 171	82.19 272	89.89 279	97.97 94

E3		91.78 115	91.61 112	92.30 167	94.48 220	80.86 236	93.73 256	96.19 149	87.63 157	91.16 150	95.95 131	81.30 133	98.06 193	89.76 147	94.29 186	97.99 92

CHOSEN 1792x2688		88.84 216	87.69 229	92.30 167	96.14 109	81.42 211	90.01 398	95.86 190	74.52 431	87.41 234	93.94 240	75.46 223	98.36 160	80.36 309	95.53 147	97.12 168

viewcassd2359sk11		91.79 112	91.62 111	92.29 169	94.62 205	80.88 234	93.70 260	96.18 155	87.38 164	91.13 153	95.85 141	81.62 128	98.06 193	89.71 149	94.40 182	97.94 97

HY-MVS	83.01 12	89.03 212	87.94 223	92.29 169	94.86 186	82.77 162	92.08 336	94.49 292	81.52 336	86.93 241	92.79 283	78.32 181	98.23 171	79.93 317	90.55 266	95.88 238

CDS-MVSNet		89.45 193	88.51 205	92.29 169	93.62 282	83.61 131	93.01 295	94.68 284	81.95 318	87.82 227	93.24 266	78.69 173	96.99 309	80.34 310	93.23 220	96.28 216
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PAPR		90.02 173	89.27 183	92.29 169	95.78 134	80.95 230	92.68 310	96.22 145	81.91 320	86.66 251	93.75 252	82.23 113	98.44 153	79.40 335	94.79 167	97.48 139

E3new		91.76 118	91.58 114	92.28 173	94.69 202	80.90 233	93.68 263	96.17 156	87.15 170	91.09 160	95.70 153	81.75 127	98.05 197	89.67 152	94.35 183	97.90 108

mvsmamba		90.33 162	89.69 168	92.25 174	95.17 165	81.64 202	95.27 134	93.36 339	84.88 244	89.51 190	94.27 227	69.29 321	97.42 265	89.34 156	96.12 137	97.68 127

E5new		91.71 122	91.55 117	92.20 175	94.33 234	80.62 246	94.41 196	96.19 149	88.06 132	91.11 155	96.16 114	79.92 148	98.03 201	90.00 140	93.80 200	97.94 97

E6new		91.71 122	91.55 117	92.20 175	94.32 236	80.62 246	94.41 196	96.19 149	88.06 132	91.11 155	96.16 114	79.92 148	98.03 201	90.00 140	93.80 200	97.94 97

E6		91.71 122	91.55 117	92.20 175	94.32 236	80.62 246	94.41 196	96.19 149	88.06 132	91.11 155	96.16 114	79.92 148	98.03 201	90.00 140	93.80 200	97.94 97

E5		91.71 122	91.55 117	92.20 175	94.33 234	80.62 246	94.41 196	96.19 149	88.06 132	91.11 155	96.16 114	79.92 148	98.03 201	90.00 140	93.80 200	97.94 97

PLC	84.53 7	89.06 210	88.03 219	92.15 179	97.27 78	82.69 169	94.29 211	95.44 229	79.71 360	84.01 332	94.18 230	76.68 201	98.75 116	77.28 355	93.41 214	95.02 269
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
EPP-MVSNet		91.70 126	91.56 116	92.13 180	95.88 130	80.50 253	97.33 8	95.25 243	86.15 201	89.76 187	95.60 156	83.42 91	98.32 167	87.37 190	93.25 219	97.56 136

patch_mono-2		93.74 65	94.32 41	92.01 181	97.54 66	78.37 323	93.40 272	97.19 44	88.02 136	94.99 57	97.21 62	88.35 25	98.44 153	94.07 54	98.09 77	99.23 1

原ACMM1				92.01 181	97.34 73	81.05 225		96.81 88	78.89 371	90.45 169	95.92 134	82.65 104	98.84 106	80.68 304	98.26 63	96.14 223

UniMVSNet (Re)		89.80 183	89.07 187	92.01 181	93.60 283	84.52 97	94.78 170	97.47 17	89.26 82	86.44 257	92.32 296	82.10 117	97.39 276	84.81 226	80.84 400	94.12 313

MG-MVS		91.77 117	91.70 110	92.00 184	97.08 81	80.03 272	93.60 265	95.18 247	87.85 147	90.89 163	96.47 102	82.06 119	98.36 160	85.07 221	97.04 110	97.62 130

EIA-MVS		91.95 109	91.94 106	91.98 185	95.16 166	80.01 273	95.36 124	96.73 98	88.44 115	89.34 194	92.16 301	83.82 87	98.45 151	89.35 155	97.06 109	97.48 139

PVSNet_Blended		90.73 149	90.32 148	91.98 185	96.12 111	81.25 215	92.55 315	96.83 83	82.04 316	89.10 198	92.56 289	81.04 135	98.85 104	86.72 200	95.91 139	95.84 240

guyue		91.12 141	90.84 139	91.96 187	94.59 209	80.57 251	94.87 162	93.71 332	88.96 98	91.14 152	95.22 174	73.22 262	97.76 228	92.01 104	93.81 199	97.54 138

PS-MVSNAJ		91.18 138	90.92 136	91.96 187	95.26 161	82.60 176	92.09 335	95.70 204	86.27 197	91.84 132	92.46 291	79.70 157	98.99 82	89.08 160	95.86 140	94.29 306

TAMVS		89.21 203	88.29 214	91.96 187	93.71 277	82.62 175	93.30 280	94.19 306	82.22 310	87.78 228	93.94 240	78.83 170	96.95 312	77.70 351	92.98 228	96.32 213

SDMVSNet		90.19 166	89.61 171	91.93 190	96.00 122	83.09 152	92.89 302	95.98 174	88.73 105	86.85 247	95.20 178	72.09 279	97.08 300	88.90 165	89.85 281	95.63 250

FA-MVS(test-final)		89.66 185	88.91 195	91.93 190	94.57 213	80.27 257	91.36 356	94.74 281	84.87 245	89.82 184	92.61 288	74.72 233	98.47 146	83.97 241	93.53 209	97.04 174

MVS_Test		91.31 134	91.11 131	91.93 190	94.37 229	80.14 262	93.46 270	95.80 193	86.46 193	91.35 148	93.77 250	82.21 114	98.09 188	87.57 185	94.95 163	97.55 137

NR-MVSNet		88.58 226	87.47 235	91.93 190	93.04 304	84.16 112	94.77 171	96.25 142	89.05 90	80.04 395	93.29 264	79.02 169	97.05 305	81.71 287	80.05 410	94.59 289

HyFIR lowres test		88.09 239	86.81 252	91.93 190	96.00 122	80.63 244	90.01 398	95.79 194	73.42 443	87.68 230	92.10 307	73.86 251	97.96 214	80.75 302	91.70 249	97.19 158

GeoE		90.05 171	89.43 176	91.90 195	95.16 166	80.37 256	95.80 96	94.65 285	83.90 266	87.55 233	94.75 200	78.18 182	97.62 241	81.28 292	93.63 205	97.71 126

thisisatest0530		88.67 221	87.61 231	91.86 196	94.87 185	80.07 267	94.63 180	89.90 435	84.00 264	88.46 212	93.78 249	66.88 345	98.46 147	83.30 251	92.65 235	97.06 172

xiu_mvs_v2_base		91.13 140	90.89 138	91.86 196	94.97 177	82.42 178	92.24 328	95.64 212	86.11 205	91.74 138	93.14 270	79.67 162	98.89 98	89.06 161	95.46 152	94.28 307

DU-MVS		89.34 202	88.50 206	91.85 198	93.04 304	83.72 124	94.47 191	96.59 110	89.50 71	86.46 254	93.29 264	77.25 194	97.23 289	84.92 223	81.02 396	94.59 289

AstraMVS		90.69 151	90.30 149	91.84 199	93.81 268	79.85 280	94.76 172	92.39 364	88.96 98	91.01 162	95.87 140	70.69 294	97.94 217	92.49 82	92.70 234	97.73 124

OPM-MVS		90.12 167	89.56 172	91.82 200	93.14 296	83.90 119	94.16 218	95.74 198	88.96 98	87.86 223	95.43 165	72.48 272	97.91 220	88.10 177	90.18 273	93.65 346
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
HQP_MVS		90.60 158	90.19 151	91.82 200	94.70 200	82.73 166	95.85 93	96.22 145	90.81 27	86.91 243	94.86 195	74.23 241	98.12 178	88.15 173	89.99 275	94.63 286

UniMVSNet_NR-MVSNet		89.92 179	89.29 181	91.81 202	93.39 289	83.72 124	94.43 194	97.12 55	89.80 60	86.46 254	93.32 261	83.16 95	97.23 289	84.92 223	81.02 396	94.49 299

diffmvs		91.37 133	91.23 129	91.77 203	93.09 299	80.27 257	92.36 321	95.52 221	87.03 175	91.40 147	94.93 190	80.08 145	97.44 263	92.13 99	94.56 176	97.61 131
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
diffmvs_AUTHOR		91.51 130	91.44 123	91.73 204	93.09 299	80.27 257	92.51 316	95.58 215	87.22 168	91.80 135	95.57 158	79.96 147	97.48 255	92.23 93	94.97 162	97.45 141

1112_ss		88.42 228	87.33 238	91.72 205	94.92 181	80.98 228	92.97 299	94.54 289	78.16 388	83.82 335	93.88 245	78.78 172	97.91 220	79.45 331	89.41 288	96.26 217

Fast-Effi-MVS+		89.41 197	88.64 201	91.71 206	94.74 194	80.81 239	93.54 266	95.10 251	83.11 289	86.82 249	90.67 361	79.74 156	97.75 232	80.51 307	93.55 207	96.57 206

WTY-MVS		89.60 187	88.92 194	91.67 207	95.47 151	81.15 220	92.38 320	94.78 279	83.11 289	89.06 200	94.32 222	78.67 174	96.61 335	81.57 288	90.89 262	97.24 153

TAPA-MVS	84.62 6	88.16 237	87.01 247	91.62 208	96.64 90	80.65 243	94.39 202	96.21 148	76.38 410	86.19 264	95.44 163	79.75 155	98.08 190	62.75 457	95.29 157	96.13 224
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
VPA-MVSNet		89.62 186	88.96 192	91.60 209	93.86 265	82.89 161	95.46 121	97.33 32	87.91 142	88.43 213	93.31 262	74.17 244	97.40 273	87.32 191	82.86 371	94.52 294

FE-MVS		87.40 267	86.02 288	91.57 210	94.56 214	79.69 287	90.27 385	93.72 331	80.57 349	88.80 206	91.62 327	65.32 361	98.59 137	74.97 381	94.33 185	96.44 209

XVG-OURS		89.40 199	88.70 200	91.52 211	94.06 252	81.46 209	91.27 361	96.07 168	86.14 202	88.89 205	95.77 149	68.73 330	97.26 286	87.39 189	89.96 277	95.83 241

hse-mvs2		89.88 181	89.34 179	91.51 212	94.83 188	81.12 222	93.94 241	93.91 319	89.80 60	93.08 90	93.60 255	75.77 215	97.66 236	92.07 100	77.07 430	95.74 245

TranMVSNet+NR-MVSNet		88.84 216	87.95 222	91.49 213	92.68 320	83.01 157	94.92 159	96.31 131	89.88 54	85.53 280	93.85 247	76.63 202	96.96 311	81.91 280	79.87 413	94.50 297

AUN-MVS		87.78 247	86.54 267	91.48 214	94.82 189	81.05 225	93.91 245	93.93 316	83.00 294	86.93 241	93.53 256	69.50 315	97.67 234	86.14 206	77.12 429	95.73 247

XVG-OURS-SEG-HR		89.95 177	89.45 174	91.47 215	94.00 258	81.21 218	91.87 340	96.06 170	85.78 209	88.55 210	95.73 151	74.67 234	97.27 284	88.71 169	89.64 286	95.91 235

MVS		87.44 265	86.10 285	91.44 216	92.61 321	83.62 129	92.63 312	95.66 209	67.26 469	81.47 373	92.15 302	77.95 185	98.22 173	79.71 320	95.48 150	92.47 397

viewdifsd2359ckpt07		91.11 142	91.02 134	91.41 217	94.21 245	78.37 323	92.91 301	95.71 203	87.50 159	90.32 172	95.88 137	80.27 143	97.99 207	88.78 168	93.55 207	97.86 111

F-COLMAP		87.95 242	86.80 253	91.40 218	96.35 104	80.88 234	94.73 174	95.45 227	79.65 361	82.04 368	94.61 209	71.13 286	98.50 141	76.24 368	91.05 260	94.80 283

dcpmvs_2		93.49 70	94.19 52	91.38 219	97.69 63	76.78 366	94.25 213	96.29 132	88.33 118	94.46 60	96.88 79	88.07 29	98.64 130	93.62 62	98.09 77	98.73 23

thisisatest0515		87.33 270	85.99 289	91.37 220	93.49 285	79.55 288	90.63 377	89.56 443	80.17 353	87.56 232	90.86 351	67.07 342	98.28 169	81.50 289	93.02 227	96.29 215

HQP-MVS		89.80 183	89.28 182	91.34 221	94.17 247	81.56 203	94.39 202	96.04 171	88.81 101	85.43 289	93.97 239	73.83 252	97.96 214	87.11 195	89.77 284	94.50 297

fmvsm_s_conf0.5_n_7		93.15 89	93.76 68	91.31 222	94.42 227	79.48 290	94.52 186	97.14 53	89.33 78	94.17 66	98.09 18	81.83 124	97.49 254	96.33 26	98.02 81	96.95 182

RRT-MVS		90.85 145	90.70 143	91.30 223	94.25 242	76.83 365	94.85 165	96.13 162	89.04 91	90.23 174	94.88 193	70.15 305	98.72 120	91.86 112	94.88 165	98.34 48

FMVSNet3		87.40 267	86.11 284	91.30 223	93.79 271	83.64 128	94.20 217	94.81 277	83.89 267	84.37 319	91.87 318	68.45 333	96.56 344	78.23 346	85.36 339	93.70 345

FMVSNet2		87.19 280	85.82 297	91.30 223	94.01 255	83.67 126	94.79 169	94.94 263	83.57 275	83.88 334	92.05 311	66.59 350	96.51 348	77.56 353	85.01 342	93.73 343

RPMNet		83.95 359	81.53 370	91.21 226	90.58 398	79.34 299	85.24 461	96.76 93	71.44 457	85.55 278	82.97 461	70.87 291	98.91 97	61.01 461	89.36 290	95.40 256

IB-MVS	80.51 15	85.24 336	83.26 354	91.19 227	92.13 333	79.86 279	91.75 344	91.29 400	83.28 286	80.66 385	88.49 410	61.28 399	98.46 147	80.99 298	79.46 417	95.25 262
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
CLD-MVS		89.47 192	88.90 196	91.18 228	94.22 244	82.07 188	92.13 333	96.09 166	87.90 143	85.37 295	92.45 292	74.38 239	97.56 246	87.15 193	90.43 268	93.93 323
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
LPG-MVS_test		89.45 193	88.90 196	91.12 229	94.47 221	81.49 207	95.30 129	96.14 159	86.73 186	85.45 286	95.16 180	69.89 308	98.10 180	87.70 182	89.23 293	93.77 339

LGP-MVS_train				91.12 229	94.47 221	81.49 207		96.14 159	86.73 186	85.45 286	95.16 180	69.89 308	98.10 180	87.70 182	89.23 293	93.77 339

ACMM	84.12 9	89.14 205	88.48 209	91.12 229	94.65 204	81.22 217	95.31 127	96.12 163	85.31 229	85.92 269	94.34 220	70.19 304	98.06 193	85.65 214	88.86 298	94.08 317
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tttt0517		88.61 223	87.78 228	91.11 232	94.96 178	77.81 341	95.35 125	89.69 438	85.09 239	88.05 221	94.59 212	66.93 343	98.48 143	83.27 252	92.13 247	97.03 175

GBi-Net		87.26 272	85.98 290	91.08 233	94.01 255	83.10 149	95.14 147	94.94 263	83.57 275	84.37 319	91.64 323	66.59 350	96.34 362	78.23 346	85.36 339	93.79 334

test1		87.26 272	85.98 290	91.08 233	94.01 255	83.10 149	95.14 147	94.94 263	83.57 275	84.37 319	91.64 323	66.59 350	96.34 362	78.23 346	85.36 339	93.79 334

FMVSNet1		85.85 321	84.11 341	91.08 233	92.81 315	83.10 149	95.14 147	94.94 263	81.64 331	82.68 358	91.64 323	59.01 421	96.34 362	75.37 375	83.78 355	93.79 334

Test_1112_low_res		87.65 251	86.51 268	91.08 233	94.94 180	79.28 303	91.77 343	94.30 301	76.04 416	83.51 345	92.37 294	77.86 188	97.73 233	78.69 341	89.13 295	96.22 218

PS-MVSNAJss		89.97 175	89.62 170	91.02 237	91.90 342	80.85 238	95.26 135	95.98 174	86.26 198	86.21 263	94.29 224	79.70 157	97.65 237	88.87 167	88.10 309	94.57 291

BH-RMVSNet		88.37 231	87.48 234	91.02 237	95.28 158	79.45 292	92.89 302	93.07 347	85.45 224	86.91 243	94.84 198	70.35 301	97.76 228	73.97 390	94.59 175	95.85 239

UniMVSNet_ETH3D		87.53 261	86.37 272	91.00 239	92.44 325	78.96 308	94.74 173	95.61 213	84.07 263	85.36 296	94.52 214	59.78 413	97.34 278	82.93 256	87.88 314	96.71 199

FIs		90.51 160	90.35 147	90.99 240	93.99 259	80.98 228	95.73 104	97.54 10	89.15 86	86.72 250	94.68 203	81.83 124	97.24 288	85.18 220	88.31 308	94.76 284

ACMP	84.23 8	89.01 214	88.35 210	90.99 240	94.73 195	81.27 214	95.07 150	95.89 186	86.48 191	83.67 340	94.30 223	69.33 317	97.99 207	87.10 197	88.55 300	93.72 344
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
Anonymous20231211		86.59 304	85.13 319	90.98 242	96.52 98	81.50 205	96.14 64	96.16 158	73.78 439	83.65 341	92.15 302	63.26 382	97.37 277	82.82 260	81.74 385	94.06 318

IMVS_0403		89.97 175	89.64 169	90.96 243	93.72 273	77.75 346	93.00 296	95.34 238	85.53 220	88.77 207	94.49 215	78.49 178	97.84 224	84.75 227	92.65 235	97.28 148

sss		88.93 215	88.26 216	90.94 244	94.05 253	80.78 241	91.71 345	95.38 233	81.55 335	88.63 209	93.91 244	75.04 227	95.47 403	82.47 265	91.61 250	96.57 206

IMVS_0407		89.85 182	89.51 173	90.88 245	93.72 273	77.75 346	93.07 293	95.34 238	85.53 220	88.34 215	94.49 215	77.69 190	97.60 242	84.75 227	92.65 235	97.28 148

viewmambaseed2359dif		90.04 172	89.78 166	90.83 246	92.85 314	77.92 335	92.23 329	95.01 255	81.90 321	90.20 175	95.45 162	79.64 164	97.34 278	87.52 187	93.17 221	97.23 156

sd_testset		88.59 225	87.85 227	90.83 246	96.00 122	80.42 255	92.35 323	94.71 282	88.73 105	86.85 247	95.20 178	67.31 337	96.43 356	79.64 323	89.85 281	95.63 250

PVSNet_BlendedMVS		89.98 174	89.70 167	90.82 248	96.12 111	81.25 215	93.92 243	96.83 83	83.49 279	89.10 198	92.26 299	81.04 135	98.85 104	86.72 200	87.86 315	92.35 404

cascas		86.43 312	84.98 322	90.80 249	92.10 335	80.92 232	90.24 389	95.91 183	73.10 446	83.57 344	88.39 411	65.15 363	97.46 259	84.90 225	91.43 252	94.03 320

ECVR-MVS		89.09 208	88.53 204	90.77 250	95.62 144	75.89 379	96.16 60	84.22 472	87.89 145	90.20 175	96.65 91	63.19 384	98.10 180	85.90 211	96.94 112	98.33 50

GA-MVS		86.61 302	85.27 316	90.66 251	91.33 365	78.71 312	90.40 384	93.81 326	85.34 228	85.12 299	89.57 392	61.25 400	97.11 298	80.99 298	89.59 287	96.15 222

thres600view7		87.65 251	86.67 259	90.59 252	96.08 117	78.72 310	94.88 161	91.58 391	87.06 174	88.08 219	92.30 297	68.91 327	98.10 180	70.05 420	91.10 255	94.96 273

thres400		87.62 256	86.64 260	90.57 253	95.99 125	78.64 313	94.58 182	91.98 380	86.94 180	88.09 217	91.77 319	69.18 323	98.10 180	70.13 417	91.10 255	94.96 273

baseline1		88.10 238	87.28 240	90.57 253	94.96 178	80.07 267	94.27 212	91.29 400	86.74 185	87.41 234	94.00 237	76.77 199	96.20 367	80.77 301	79.31 419	95.44 254

viewdifsd2359ckpt11		89.43 195	89.05 189	90.56 255	92.89 312	77.00 361	92.81 305	94.52 290	87.03 175	89.77 185	95.79 146	74.67 234	97.51 250	88.97 163	84.98 343	97.17 159

viewmsd2359difaftdt		89.43 195	89.05 189	90.56 255	92.89 312	77.00 361	92.81 305	94.52 290	87.03 175	89.77 185	95.79 146	74.67 234	97.51 250	88.97 163	84.98 343	97.17 159

usedtu_dtu_shiyan1		86.84 291	85.61 305	90.53 257	90.50 402	81.80 197	90.97 369	94.96 261	83.05 291	83.50 346	90.32 368	72.15 276	96.65 326	79.49 328	85.55 337	93.15 369

FE-MVSNET3		86.84 291	85.61 305	90.53 257	90.50 402	81.80 197	90.97 369	94.96 261	83.05 291	83.50 346	90.32 368	72.15 276	96.65 326	79.49 328	85.55 337	93.15 369

FC-MVSNet-test		90.27 164	90.18 152	90.53 257	93.71 277	79.85 280	95.77 100	97.59 7	89.31 79	86.27 261	94.67 206	81.93 122	97.01 308	84.26 237	88.09 311	94.71 285

PAPM		86.68 301	85.39 311	90.53 257	93.05 303	79.33 302	89.79 401	94.77 280	78.82 374	81.95 369	93.24 266	76.81 197	97.30 280	66.94 437	93.16 222	94.95 277

WR-MVS		88.38 230	87.67 230	90.52 261	93.30 291	80.18 260	93.26 283	95.96 178	88.57 113	85.47 285	92.81 281	76.12 207	96.91 315	81.24 293	82.29 376	94.47 302

SSM_04072		88.57 227	87.92 224	90.51 262	94.76 191	82.66 170	79.84 483	94.64 286	85.18 230	88.96 202	95.00 187	76.00 210	92.03 453	83.74 246	93.15 223	96.85 191

MVSTER		88.84 216	88.29 214	90.51 262	92.95 309	80.44 254	93.73 256	95.01 255	84.66 254	87.15 238	93.12 271	72.79 267	97.21 291	87.86 179	87.36 323	93.87 328

testdata				90.49 264	96.40 101	77.89 338		95.37 235	72.51 451	93.63 79	96.69 87	82.08 118	97.65 237	83.08 253	97.39 102	95.94 234

test1111		89.10 206	88.64 201	90.48 265	95.53 149	74.97 389	96.08 69	84.89 470	88.13 128	90.16 179	96.65 91	63.29 381	98.10 180	86.14 206	96.90 115	98.39 45

tt0805		86.92 288	85.74 303	90.48 265	92.22 329	79.98 275	95.63 114	94.88 271	83.83 269	84.74 308	92.80 282	57.61 428	97.67 234	85.48 217	84.42 348	93.79 334

jajsoiax		88.24 235	87.50 233	90.48 265	90.89 386	80.14 262	95.31 127	95.65 211	84.97 242	84.24 327	94.02 235	65.31 362	97.42 265	88.56 170	88.52 302	93.89 324

PatchMatch-RL		86.77 298	85.54 307	90.47 268	95.88 130	82.71 168	90.54 380	92.31 368	79.82 359	84.32 324	91.57 331	68.77 329	96.39 358	73.16 396	93.48 213	92.32 405

0.4-1-1-0.1		81.55 391	78.59 413	90.42 269	87.55 444	79.90 277	88.56 423	89.19 448	77.01 402	79.72 402	77.71 475	54.84 443	97.11 298	80.50 308	72.20 443	94.26 308

tfpn200view9		87.58 259	86.64 260	90.41 270	95.99 125	78.64 313	94.58 182	91.98 380	86.94 180	88.09 217	91.77 319	69.18 323	98.10 180	70.13 417	91.10 255	94.48 300

VPNet		88.20 236	87.47 235	90.39 271	93.56 284	79.46 291	94.04 231	95.54 219	88.67 108	86.96 240	94.58 213	69.33 317	97.15 293	84.05 240	80.53 405	94.56 292

ACMH	80.38 17	85.36 331	83.68 348	90.39 271	94.45 224	80.63 244	94.73 174	94.85 273	82.09 312	77.24 430	92.65 286	60.01 411	97.58 244	72.25 401	84.87 345	92.96 376
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
thres100view900		87.63 254	86.71 256	90.38 273	96.12 111	78.55 316	95.03 153	91.58 391	87.15 170	88.06 220	92.29 298	68.91 327	98.10 180	70.13 417	91.10 255	94.48 300

mvs_tets		88.06 241	87.28 240	90.38 273	90.94 382	79.88 278	95.22 138	95.66 209	85.10 238	84.21 328	93.94 240	63.53 379	97.40 273	88.50 171	88.40 306	93.87 328

1314		87.51 262	86.57 265	90.34 275	92.42 326	79.74 285	92.63 312	95.35 237	78.35 383	80.14 392	91.62 327	74.05 246	97.15 293	81.05 294	93.53 209	94.12 313

LTVRE_ROB	82.13 13	86.26 315	84.90 325	90.34 275	94.44 225	81.50 205	92.31 327	94.89 269	83.03 293	79.63 404	92.67 285	69.69 311	97.79 226	71.20 406	86.26 332	91.72 415
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
0.3-1-1-0.015		80.75 404	77.58 418	90.25 277	86.55 448	79.72 286	87.46 444	89.48 446	76.43 409	77.93 425	75.94 476	52.31 455	97.05 305	80.25 313	71.85 447	93.99 322

test_djsdf		89.03 212	88.64 201	90.21 278	90.74 393	79.28 303	95.96 83	95.90 184	84.66 254	85.33 297	92.94 276	74.02 247	97.30 280	89.64 153	88.53 301	94.05 319

v2v482		87.84 244	87.06 244	90.17 279	90.99 378	79.23 306	94.00 237	95.13 248	84.87 245	85.53 280	92.07 310	74.45 238	97.45 260	84.71 232	81.75 384	93.85 331

pmmvs4		85.43 329	83.86 346	90.16 280	90.02 413	82.97 159	90.27 385	92.67 359	75.93 417	80.73 383	91.74 321	71.05 287	95.73 392	78.85 340	83.46 362	91.78 414

V42		87.68 249	86.86 249	90.15 281	90.58 398	80.14 262	94.24 215	95.28 242	83.66 273	85.67 275	91.33 333	74.73 232	97.41 271	84.43 236	81.83 382	92.89 379

MSDG		84.86 344	83.09 357	90.14 282	93.80 269	80.05 269	89.18 414	93.09 346	78.89 371	78.19 421	91.91 316	65.86 360	97.27 284	68.47 426	88.45 304	93.11 371

sc_t1		81.53 392	78.67 412	90.12 283	90.78 390	78.64 313	93.91 245	90.20 424	68.42 466	80.82 382	89.88 385	46.48 470	96.76 320	76.03 371	71.47 448	94.96 273

anonymousdsp		87.84 244	87.09 243	90.12 283	89.13 424	80.54 252	94.67 178	95.55 217	82.05 314	83.82 335	92.12 304	71.47 284	97.15 293	87.15 193	87.80 318	92.67 386

thres200		87.21 278	86.24 279	90.12 283	95.36 154	78.53 317	93.26 283	92.10 374	86.42 194	88.00 222	91.11 344	69.24 322	98.00 206	69.58 421	91.04 261	93.83 333

CR-MVSNet		85.35 332	83.76 347	90.12 283	90.58 398	79.34 299	85.24 461	91.96 382	78.27 385	85.55 278	87.87 421	71.03 288	95.61 395	73.96 391	89.36 290	95.40 256

0.4-1-1-0.2		80.84 403	77.77 416	90.06 287	86.18 452	79.35 297	86.75 449	89.54 444	76.23 414	78.59 420	75.46 479	55.03 442	96.99 309	80.11 315	72.05 445	93.85 331

v1144		87.61 257	86.79 254	90.06 287	91.01 377	79.34 299	93.95 240	95.42 232	83.36 284	85.66 276	91.31 336	74.98 228	97.42 265	83.37 250	82.06 378	93.42 355

XXY-MVS		87.65 251	86.85 250	90.03 289	92.14 332	80.60 250	93.76 253	95.23 244	82.94 296	84.60 310	94.02 235	74.27 240	95.49 402	81.04 295	83.68 358	94.01 321

Vis-MVSNet (Re-imp)		89.59 188	89.44 175	90.03 289	95.74 135	75.85 380	95.61 115	90.80 414	87.66 156	87.83 226	95.40 166	76.79 198	96.46 353	78.37 342	96.73 121	97.80 119

test2506		87.21 278	86.28 277	90.02 291	95.62 144	73.64 405	96.25 55	71.38 495	87.89 145	90.45 169	96.65 91	55.29 440	98.09 188	86.03 210	96.94 112	98.33 50

BH-untuned		88.60 224	88.13 218	90.01 292	95.24 162	78.50 319	93.29 281	94.15 309	84.75 250	84.46 316	93.40 258	75.76 217	97.40 273	77.59 352	94.52 178	94.12 313

v1192		87.25 274	86.33 274	90.00 293	90.76 392	79.04 307	93.80 251	95.48 222	82.57 303	85.48 284	91.18 340	73.38 261	97.42 265	82.30 269	82.06 378	93.53 349

v7n		86.81 293	85.76 301	89.95 294	90.72 394	79.25 305	95.07 150	95.92 181	84.45 257	82.29 362	90.86 351	72.60 271	97.53 248	79.42 334	80.52 406	93.08 373

testing91		87.11 283	86.18 280	89.92 295	94.43 226	75.38 388	91.53 351	92.27 370	86.48 191	86.50 252	90.24 371	61.19 403	97.53 248	82.10 274	90.88 263	96.84 194

IMVS_0404		87.60 258	86.84 251	89.89 296	93.72 273	77.75 346	88.56 423	95.34 238	85.53 220	79.98 396	94.49 215	66.54 353	94.64 416	84.75 227	92.65 235	97.28 148

v8		87.50 264	86.71 256	89.89 296	91.37 362	79.40 295	94.50 187	95.38 233	84.81 248	83.60 343	91.33 333	76.05 208	97.42 265	82.84 259	80.51 407	92.84 381

v10		87.25 274	86.38 271	89.85 298	91.19 368	79.50 289	94.48 188	95.45 227	83.79 271	83.62 342	91.19 338	75.13 225	97.42 265	81.94 279	80.60 402	92.63 388

baseline2		86.50 308	85.39 311	89.84 299	91.12 373	76.70 368	91.88 339	88.58 450	82.35 308	79.95 397	90.95 349	73.42 259	97.63 240	80.27 312	89.95 278	95.19 263

pm-mvs1		86.61 302	85.54 307	89.82 300	91.44 357	80.18 260	95.28 133	94.85 273	83.84 268	81.66 371	92.62 287	72.45 274	96.48 350	79.67 322	78.06 422	92.82 382

TR-MVS		86.78 295	85.76 301	89.82 300	94.37 229	78.41 321	92.47 317	92.83 353	81.11 345	86.36 258	92.40 293	68.73 330	97.48 255	73.75 394	89.85 281	93.57 348

ACMH+	81.04 14	85.05 339	83.46 351	89.82 300	94.66 203	79.37 296	94.44 193	94.12 312	82.19 311	78.04 423	92.82 280	58.23 424	97.54 247	73.77 393	82.90 370	92.54 394

EI-MVSNet		89.10 206	88.86 198	89.80 303	91.84 344	78.30 326	93.70 260	95.01 255	85.73 211	87.15 238	95.28 171	79.87 154	97.21 291	83.81 244	87.36 323	93.88 327

gbinet_0.2-2-1-0.02		82.59 373	80.19 385	89.77 304	85.23 463	80.05 269	91.59 350	93.52 335	77.60 391	79.78 401	82.87 463	63.26 382	96.45 354	78.93 338	68.97 458	92.81 383

usedtu_blend_shiyan5		82.39 378	79.93 392	89.75 305	85.12 464	80.08 265	92.36 321	93.26 340	74.29 434	79.00 412	82.72 464	64.29 373	96.60 339	79.60 324	68.75 462	92.55 391

v144192		87.19 280	86.35 273	89.74 306	90.64 396	78.24 328	93.92 243	95.43 230	81.93 319	85.51 282	91.05 347	74.21 243	97.45 260	82.86 258	81.56 386	93.53 349

COLMAP_ROB	80.39 16	83.96 358	82.04 367	89.74 306	95.28 158	79.75 284	94.25 213	92.28 369	75.17 424	78.02 424	93.77 250	58.60 423	97.84 224	65.06 448	85.92 333	91.63 417
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
SCA		86.32 314	85.18 318	89.73 308	92.15 331	76.60 369	91.12 365	91.69 387	83.53 278	85.50 283	88.81 404	66.79 346	96.48 350	76.65 361	90.35 270	96.12 225

blend_shiyan4		81.94 381	79.35 400	89.70 309	85.52 459	80.08 265	91.29 359	93.82 323	77.12 400	79.31 408	82.94 462	54.81 444	96.60 339	79.60 324	69.78 453	92.41 400

IterMVS-LS		88.36 232	87.91 226	89.70 309	93.80 269	78.29 327	93.73 256	95.08 253	85.73 211	84.75 307	91.90 317	79.88 153	96.92 314	83.83 243	82.51 372	93.89 324
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
blended_shiyan8		82.79 368	80.49 378	89.69 311	85.50 460	79.83 282	91.38 354	93.82 323	77.14 397	79.39 407	83.73 454	64.95 367	96.63 329	79.75 319	68.77 461	92.62 390

testing11		86.44 311	85.35 314	89.69 311	94.29 240	75.40 387	91.30 358	90.53 419	84.76 249	85.06 301	90.13 377	58.95 422	97.45 260	82.08 275	91.09 259	96.21 220

testing99		86.72 299	85.73 304	89.69 311	94.23 243	74.91 391	91.35 357	90.97 408	86.14 202	86.36 258	90.22 372	59.41 416	97.48 255	82.24 271	90.66 265	96.69 201

v1921920		86.97 287	86.06 287	89.69 311	90.53 401	78.11 331	93.80 251	95.43 230	81.90 321	85.33 297	91.05 347	72.66 268	97.41 271	82.05 277	81.80 383	93.53 349

icg_test_0407_2		89.15 204	88.97 191	89.68 315	93.72 273	77.75 346	88.26 429	95.34 238	85.53 220	88.34 215	94.49 215	77.69 190	93.99 428	84.75 227	92.65 235	97.28 148

blended_shiyan6		82.78 369	80.48 379	89.67 316	85.53 458	79.76 283	91.37 355	93.82 323	77.14 397	79.30 409	83.73 454	64.96 366	96.63 329	79.68 321	68.75 462	92.63 388

VortexMVS		88.42 228	88.01 220	89.63 317	93.89 264	78.82 309	93.82 249	95.47 223	86.67 188	84.53 314	91.99 313	72.62 270	96.65 326	89.02 162	84.09 352	93.41 356

Fast-Effi-MVS+-dtu		87.44 265	86.72 255	89.63 317	92.04 336	77.68 351	94.03 232	93.94 315	85.81 208	82.42 361	91.32 335	70.33 302	97.06 303	80.33 311	90.23 272	94.14 312

v1240		86.78 295	85.85 296	89.56 319	90.45 405	77.79 343	93.61 264	95.37 235	81.65 330	85.43 289	91.15 342	71.50 283	97.43 264	81.47 290	82.05 380	93.47 353

Effi-MVS+-dtu		88.65 222	88.35 210	89.54 320	93.33 290	76.39 373	94.47 191	94.36 299	87.70 153	85.43 289	89.56 393	73.45 257	97.26 286	85.57 216	91.28 254	94.97 270

wanda-best-256-512		82.44 375	80.07 387	89.53 321	85.12 464	79.44 293	90.49 381	93.75 329	76.97 403	79.00 412	82.72 464	64.29 373	96.61 335	79.56 326	68.75 462	92.55 391

FE-blended-shiyan7		82.44 375	80.07 387	89.53 321	85.12 464	79.44 293	90.49 381	93.75 329	76.97 403	79.00 412	82.72 464	64.29 373	96.61 335	79.56 326	68.75 462	92.55 391

AllTest		83.42 365	81.39 371	89.52 323	95.01 172	77.79 343	93.12 287	90.89 412	77.41 393	76.12 439	93.34 259	54.08 449	97.51 250	68.31 428	84.27 350	93.26 359

TestCases				89.52 323	95.01 172	77.79 343		90.89 412	77.41 393	76.12 439	93.34 259	54.08 449	97.51 250	68.31 428	84.27 350	93.26 359

mvs_anonymous		89.37 201	89.32 180	89.51 325	93.47 286	74.22 398	91.65 348	94.83 275	82.91 297	85.45 286	93.79 248	81.23 134	96.36 361	86.47 202	94.09 191	97.94 97

XVG-ACMP-BASELINE		86.00 317	84.84 327	89.45 326	91.20 367	78.00 333	91.70 346	95.55 217	85.05 240	82.97 355	92.25 300	54.49 447	97.48 255	82.93 256	87.45 322	92.89 379

testing222		84.84 345	83.32 352	89.43 327	94.15 250	75.94 378	91.09 366	89.41 447	84.90 243	85.78 272	89.44 394	52.70 454	96.28 365	70.80 412	91.57 251	96.07 229

MVP-Stereo		85.97 318	84.86 326	89.32 328	90.92 384	82.19 185	92.11 334	94.19 306	78.76 376	78.77 419	91.63 326	68.38 334	96.56 344	75.01 380	93.95 194	89.20 457
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PatchmatchNet		85.85 321	84.70 329	89.29 329	91.76 348	75.54 384	88.49 425	91.30 399	81.63 332	85.05 302	88.70 408	71.71 280	96.24 366	74.61 386	89.05 296	96.08 228
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v148		87.04 285	86.32 275	89.21 330	90.94 382	77.26 357	93.71 259	94.43 294	84.84 247	84.36 322	90.80 355	76.04 209	97.05 305	82.12 273	79.60 416	93.31 358

tfpnnormal		84.72 347	83.23 355	89.20 331	92.79 316	80.05 269	94.48 188	95.81 192	82.38 306	81.08 379	91.21 337	69.01 326	96.95 312	61.69 459	80.59 403	90.58 443

cl22		86.78 295	85.98 290	89.18 332	92.34 327	77.62 352	90.84 373	94.13 311	81.33 339	83.97 333	90.15 376	73.96 248	96.60 339	84.19 238	82.94 367	93.33 357

BH-w/o		87.57 260	87.05 245	89.12 333	94.90 184	77.90 337	92.41 318	93.51 336	82.89 298	83.70 339	91.34 332	75.75 218	97.07 302	75.49 373	93.49 211	92.39 402

WR-MVS_H		87.80 246	87.37 237	89.10 334	93.23 292	78.12 330	95.61 115	97.30 37	87.90 143	83.72 338	92.01 312	79.65 163	96.01 376	76.36 365	80.54 404	93.16 367

miper_enhance_ethall		86.90 289	86.18 280	89.06 335	91.66 353	77.58 353	90.22 391	94.82 276	79.16 367	84.48 315	89.10 398	79.19 168	96.66 325	84.06 239	82.94 367	92.94 377

c3_l		87.14 282	86.50 269	89.04 336	92.20 330	77.26 357	91.22 364	94.70 283	82.01 317	84.34 323	90.43 366	78.81 171	96.61 335	83.70 248	81.09 393	93.25 361

miper_ehance_all_eth		87.22 277	86.62 263	89.02 337	92.13 333	77.40 355	90.91 372	94.81 277	81.28 340	84.32 324	90.08 379	79.26 166	96.62 332	83.81 244	82.94 367	93.04 374

gg-mvs-nofinetune		81.77 385	79.37 399	88.99 338	90.85 388	77.73 350	86.29 453	79.63 483	74.88 429	83.19 354	69.05 486	60.34 408	96.11 371	75.46 374	94.64 174	93.11 371

ETVMVS		84.43 352	82.92 361	88.97 339	94.37 229	74.67 392	91.23 363	88.35 452	83.37 283	86.06 267	89.04 399	55.38 438	95.67 394	67.12 435	91.34 253	96.58 205

pmmvs6		83.42 365	81.60 369	88.87 340	88.01 439	77.87 339	94.96 156	94.24 305	74.67 430	78.80 418	91.09 345	60.17 410	96.49 349	77.06 360	75.40 436	92.23 407

test_cas_vis1_n_1920		88.83 219	88.85 199	88.78 341	91.15 372	76.72 367	93.85 248	94.93 267	83.23 288	92.81 99	96.00 127	61.17 404	94.45 417	91.67 115	94.84 166	95.17 264

MIMVSNet		82.59 373	80.53 376	88.76 342	91.51 355	78.32 325	86.57 452	90.13 427	79.32 363	80.70 384	88.69 409	52.98 453	93.07 444	66.03 443	88.86 298	94.90 278

cl____		86.52 307	85.78 298	88.75 343	92.03 337	76.46 371	90.74 374	94.30 301	81.83 326	83.34 351	90.78 356	75.74 220	96.57 342	81.74 285	81.54 387	93.22 363

DIV-MVS_self_test		86.53 306	85.78 298	88.75 343	92.02 338	76.45 372	90.74 374	94.30 301	81.83 326	83.34 351	90.82 354	75.75 218	96.57 342	81.73 286	81.52 388	93.24 362

CP-MVSNet		87.63 254	87.26 242	88.74 345	93.12 297	76.59 370	95.29 131	96.58 111	88.43 116	83.49 348	92.98 275	75.28 224	95.83 385	78.97 337	81.15 392	93.79 334

eth_miper_zixun_eth		86.50 308	85.77 300	88.68 346	91.94 339	75.81 381	90.47 383	94.89 269	82.05 314	84.05 330	90.46 365	75.96 212	96.77 319	82.76 262	79.36 418	93.46 354

CHOSEN 280x420		85.15 337	83.99 344	88.65 347	92.47 323	78.40 322	79.68 485	92.76 356	74.90 428	81.41 375	89.59 391	69.85 310	95.51 399	79.92 318	95.29 157	92.03 410

PS-CasMVS		87.32 271	86.88 248	88.63 348	92.99 307	76.33 375	95.33 126	96.61 109	88.22 124	83.30 353	93.07 273	73.03 265	95.79 389	78.36 343	81.00 398	93.75 341

TransMVSNet (Re)		84.43 352	83.06 359	88.54 349	91.72 349	78.44 320	95.18 144	92.82 355	82.73 301	79.67 403	92.12 304	73.49 256	95.96 378	71.10 410	68.73 466	91.21 430

tt0320-xc		79.63 417	76.66 426	88.52 350	91.03 376	78.72 310	93.00 296	89.53 445	66.37 471	76.11 441	87.11 432	46.36 472	95.32 407	72.78 398	67.67 467	91.51 422

EG-PatchMatch MVS		82.37 379	80.34 381	88.46 351	90.27 407	79.35 297	92.80 308	94.33 300	77.14 397	73.26 458	90.18 375	47.47 467	96.72 321	70.25 414	87.32 325	89.30 454

PEN-MVS		86.80 294	86.27 278	88.40 352	92.32 328	75.71 383	95.18 144	96.38 126	87.97 138	82.82 357	93.15 269	73.39 260	95.92 380	76.15 369	79.03 421	93.59 347

Baseline_NR-MVSNet		87.07 284	86.63 262	88.40 352	91.44 357	77.87 339	94.23 216	92.57 361	84.12 262	85.74 274	92.08 308	77.25 194	96.04 372	82.29 270	79.94 411	91.30 428

UBG		85.51 327	84.57 334	88.35 354	94.21 245	71.78 430	90.07 396	89.66 440	82.28 309	85.91 270	89.01 400	61.30 398	97.06 303	76.58 364	92.06 248	96.22 218

D2MVS		85.90 319	85.09 320	88.35 354	90.79 389	77.42 354	91.83 342	95.70 204	80.77 348	80.08 394	90.02 381	66.74 348	96.37 359	81.88 281	87.97 313	91.26 429

pmmvs5		84.21 354	82.84 364	88.34 356	88.95 426	76.94 363	92.41 318	91.91 384	75.63 419	80.28 389	91.18 340	64.59 370	95.57 396	77.09 359	83.47 361	92.53 395

tt0320		80.13 410	77.41 419	88.29 357	90.50 402	78.02 332	93.10 290	90.71 417	66.06 474	76.75 434	86.97 433	49.56 462	95.40 404	71.65 402	71.41 449	91.46 425

LCM-MVSNet-Re		88.30 234	88.32 213	88.27 358	94.71 199	72.41 425	93.15 286	90.98 407	87.77 150	79.25 410	91.96 314	78.35 180	95.75 390	83.04 254	95.62 146	96.65 202

CostFormer		85.77 324	84.94 324	88.26 359	91.16 371	72.58 423	89.47 409	91.04 406	76.26 413	86.45 256	89.97 383	70.74 293	96.86 318	82.35 268	87.07 328	95.34 260

ITE_SJBPF				88.24 360	91.88 343	77.05 360		92.92 350	85.54 218	80.13 393	93.30 263	57.29 429	96.20 367	72.46 400	84.71 346	91.49 423

PVSNet	78.82 18	85.55 326	84.65 330	88.23 361	94.72 197	71.93 426	87.12 447	92.75 357	78.80 375	84.95 304	90.53 363	64.43 371	96.71 323	74.74 383	93.86 196	96.06 231

IterMVS-SCA-FT		85.45 328	84.53 335	88.18 362	91.71 350	76.87 364	90.19 393	92.65 360	85.40 227	81.44 374	90.54 362	66.79 346	95.00 413	81.04 295	81.05 394	92.66 387

EPNet_dtu		86.49 310	85.94 293	88.14 363	90.24 408	72.82 415	94.11 222	92.20 372	86.66 189	79.42 406	92.36 295	73.52 255	95.81 387	71.26 405	93.66 204	95.80 243
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Patchmtry		82.71 371	80.93 375	88.06 364	90.05 412	76.37 374	84.74 466	91.96 382	72.28 454	81.32 377	87.87 421	71.03 288	95.50 401	68.97 423	80.15 409	92.32 405

test_vis1_n_1920		89.39 200	89.84 163	88.04 365	92.97 308	72.64 420	94.71 176	96.03 173	86.18 200	91.94 128	96.56 99	61.63 393	95.74 391	93.42 65	95.11 161	95.74 245

DTE-MVSNet		86.11 316	85.48 309	87.98 366	91.65 354	74.92 390	94.93 158	95.75 197	87.36 165	82.26 363	93.04 274	72.85 266	95.82 386	74.04 389	77.46 427	93.20 365

PMMVS		85.71 325	84.96 323	87.95 367	88.90 427	77.09 359	88.68 421	90.06 429	72.32 453	86.47 253	90.76 357	72.15 276	94.40 420	81.78 284	93.49 211	92.36 403

GG-mvs-BLEND				87.94 368	89.73 419	77.91 336	87.80 435	78.23 488		80.58 386	83.86 452	59.88 412	95.33 406	71.20 406	92.22 246	90.60 442

MonoMVSNet		86.89 290	86.55 266	87.92 369	89.46 422	73.75 402	94.12 220	93.10 345	87.82 149	85.10 300	90.76 357	69.59 313	94.94 414	86.47 202	82.50 373	95.07 267

reproduce_monomvs		86.37 313	85.87 295	87.87 370	93.66 281	73.71 403	93.44 271	95.02 254	88.61 111	82.64 360	91.94 315	57.88 426	96.68 324	89.96 144	79.71 415	93.22 363

pmmvs-eth3d		80.97 401	78.72 411	87.74 371	84.99 467	79.97 276	90.11 395	91.65 389	75.36 421	73.51 456	86.03 441	59.45 415	93.96 431	75.17 377	72.21 442	89.29 456

MS-PatchMatch		85.05 339	84.16 339	87.73 372	91.42 360	78.51 318	91.25 362	93.53 334	77.50 392	80.15 391	91.58 329	61.99 390	95.51 399	75.69 372	94.35 183	89.16 458

mmtdpeth		85.04 341	84.15 340	87.72 373	93.11 298	75.74 382	94.37 206	92.83 353	84.98 241	89.31 195	86.41 438	61.61 395	97.14 296	92.63 81	62.11 478	90.29 444

test_0402		81.30 397	79.17 405	87.67 374	93.19 293	78.17 329	92.98 298	91.71 385	75.25 423	76.02 442	90.31 370	59.23 417	96.37 359	50.22 480	83.63 359	88.47 466

IterMVS		84.88 343	83.98 345	87.60 375	91.44 357	76.03 377	90.18 394	92.41 363	83.24 287	81.06 380	90.42 367	66.60 349	94.28 424	79.46 330	80.98 399	92.48 396
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Patchmatch-test		81.37 395	79.30 401	87.58 376	90.92 384	74.16 400	80.99 478	87.68 457	70.52 461	76.63 436	88.81 404	71.21 285	92.76 448	60.01 466	86.93 329	95.83 241

EPMVS		83.90 361	82.70 365	87.51 377	90.23 409	72.67 418	88.62 422	81.96 478	81.37 338	85.01 303	88.34 412	66.31 354	94.45 417	75.30 376	87.12 326	95.43 255

ADS-MVSNet2		81.66 388	79.71 396	87.50 378	91.35 363	74.19 399	83.33 471	88.48 451	72.90 448	82.24 364	85.77 444	64.98 364	93.20 442	64.57 450	83.74 356	95.12 265

OurMVSNet-221017-0		85.35 332	84.64 332	87.49 379	90.77 391	72.59 422	94.01 235	94.40 297	84.72 251	79.62 405	93.17 268	61.91 391	96.72 321	81.99 278	81.16 390	93.16 367

tpm2		84.08 356	82.94 360	87.48 380	91.39 361	71.27 435	89.23 413	90.37 421	71.95 455	84.64 309	89.33 395	67.30 338	96.55 346	75.17 377	87.09 327	94.63 286

RPSCF		85.07 338	84.27 336	87.48 380	92.91 311	70.62 444	91.69 347	92.46 362	76.20 415	82.67 359	95.22 174	63.94 377	97.29 283	77.51 354	85.80 334	94.53 293

myMVS_eth3d28		85.80 323	85.26 317	87.42 382	94.73 195	69.92 450	90.60 378	90.95 409	87.21 169	86.06 267	90.04 380	59.47 414	96.02 374	74.89 382	93.35 218	96.33 212

FE-MVSNET2		81.82 384	79.99 390	87.34 383	84.74 468	77.36 356	92.72 309	94.55 288	82.09 312	73.79 455	86.46 435	57.80 427	94.45 417	74.65 384	73.10 438	90.20 445

WBMVS		84.97 342	84.18 338	87.34 383	94.14 251	71.62 434	90.20 392	92.35 365	81.61 333	84.06 329	90.76 357	61.82 392	96.52 347	78.93 338	83.81 354	93.89 324

miper_lstm_enhance		85.27 335	84.59 333	87.31 385	91.28 366	74.63 393	87.69 440	94.09 313	81.20 344	81.36 376	89.85 387	74.97 229	94.30 423	81.03 297	79.84 414	93.01 375

FMVSNet5		81.52 393	79.60 397	87.27 386	91.17 369	77.95 334	91.49 352	92.26 371	76.87 405	76.16 438	87.91 420	51.67 456	92.34 451	67.74 432	81.16 390	91.52 421

USDC		82.76 370	81.26 373	87.26 387	91.17 369	74.55 394	89.27 411	93.39 338	78.26 386	75.30 446	92.08 308	54.43 448	96.63 329	71.64 403	85.79 335	90.61 440

test-LLR		85.87 320	85.41 310	87.25 388	90.95 380	71.67 432	89.55 405	89.88 436	83.41 281	84.54 312	87.95 418	67.25 339	95.11 410	81.82 282	93.37 216	94.97 270

test-mter		84.54 351	83.64 349	87.25 388	90.95 380	71.67 432	89.55 405	89.88 436	79.17 366	84.54 312	87.95 418	55.56 435	95.11 410	81.82 282	93.37 216	94.97 270

JIA-IIPM		81.04 398	78.98 409	87.25 388	88.64 428	73.48 407	81.75 477	89.61 442	73.19 445	82.05 367	73.71 482	66.07 359	95.87 383	71.18 408	84.60 347	92.41 400

TDRefinement		79.81 414	77.34 420	87.22 391	79.24 484	75.48 385	93.12 287	92.03 377	76.45 408	75.01 447	91.58 329	49.19 463	96.44 355	70.22 416	69.18 457	89.75 450

tpmvs		83.35 367	82.07 366	87.20 392	91.07 375	71.00 441	88.31 428	91.70 386	78.91 369	80.49 388	87.18 430	69.30 320	97.08 300	68.12 431	83.56 360	93.51 352

ppachtmachnet_test		81.84 383	80.07 387	87.15 393	88.46 432	74.43 397	89.04 417	92.16 373	75.33 422	77.75 427	88.99 401	66.20 356	95.37 405	65.12 447	77.60 425	91.65 416

dmvs_re		84.20 355	83.22 356	87.14 394	91.83 346	77.81 341	90.04 397	90.19 425	84.70 253	81.49 372	89.17 397	64.37 372	91.13 464	71.58 404	85.65 336	92.46 398

tpm cat1		81.96 380	80.27 382	87.01 395	91.09 374	71.02 440	87.38 445	91.53 394	66.25 472	80.17 390	86.35 440	68.22 335	96.15 370	69.16 422	82.29 376	93.86 330

test_fmvs1_n		87.03 286	87.04 246	86.97 396	89.74 418	71.86 427	94.55 184	94.43 294	78.47 380	91.95 127	95.50 161	51.16 458	93.81 432	93.02 73	94.56 176	95.26 261

OpenMVS_ROB	74.94 19	79.51 418	77.03 425	86.93 397	87.00 446	76.23 376	92.33 325	90.74 416	68.93 465	74.52 451	88.23 415	49.58 461	96.62 332	57.64 472	84.29 349	87.94 469

SixPastTwentyTwo		83.91 360	82.90 362	86.92 398	90.99 378	70.67 443	93.48 268	91.99 379	85.54 218	77.62 429	92.11 306	60.59 407	96.87 317	76.05 370	77.75 424	93.20 365

ADS-MVSNet		81.56 390	79.78 393	86.90 399	91.35 363	71.82 428	83.33 471	89.16 449	72.90 448	82.24 364	85.77 444	64.98 364	93.76 433	64.57 450	83.74 356	95.12 265

PatchT		82.68 372	81.27 372	86.89 400	90.09 411	70.94 442	84.06 468	90.15 426	74.91 427	85.63 277	83.57 456	69.37 316	94.87 415	65.19 445	88.50 303	94.84 280

tpm		84.73 346	84.02 343	86.87 401	90.33 406	68.90 453	89.06 416	89.94 433	80.85 347	85.75 273	89.86 386	68.54 332	95.97 377	77.76 350	84.05 353	95.75 244

Patchmatch-RL test		81.67 387	79.96 391	86.81 402	85.42 461	71.23 436	82.17 476	87.50 458	78.47 380	77.19 431	82.50 468	70.81 292	93.48 437	82.66 263	72.89 441	95.71 248

test_vis1_n		86.56 305	86.49 270	86.78 403	88.51 429	72.69 417	94.68 177	93.78 328	79.55 362	90.70 164	95.31 170	48.75 464	93.28 440	93.15 69	93.99 193	94.38 304

testing3-2		86.72 299	86.71 256	86.74 404	96.11 114	65.92 465	93.39 273	89.65 441	89.46 72	87.84 225	92.79 283	59.17 419	97.60 242	81.31 291	90.72 264	96.70 200

test_fmvs1		87.34 269	87.56 232	86.68 405	90.59 397	71.80 429	94.01 235	94.04 314	78.30 384	91.97 125	95.22 174	56.28 433	93.71 434	92.89 74	94.71 169	94.52 294

MDA-MVSNet-bldmvs		78.85 423	76.31 428	86.46 406	89.76 417	73.88 401	88.79 419	90.42 420	79.16 367	59.18 482	88.33 413	60.20 409	94.04 426	62.00 458	68.96 459	91.48 424

mvs5depth		80.98 400	79.15 406	86.45 407	84.57 469	73.29 410	87.79 436	91.67 388	80.52 350	82.20 366	89.72 389	55.14 441	95.93 379	73.93 392	66.83 469	90.12 447

tpmrst		85.35 332	84.99 321	86.43 408	90.88 387	67.88 458	88.71 420	91.43 397	80.13 354	86.08 266	88.80 406	73.05 264	96.02 374	82.48 264	83.40 364	95.40 256

TESTMET0.1,1		83.74 363	82.85 363	86.42 409	89.96 414	71.21 437	89.55 405	87.88 454	77.41 393	83.37 350	87.31 426	56.71 431	93.65 436	80.62 305	92.85 232	94.40 303

our_test_3		81.93 382	80.46 380	86.33 410	88.46 432	73.48 407	88.46 426	91.11 402	76.46 407	76.69 435	88.25 414	66.89 344	94.36 421	68.75 424	79.08 420	91.14 432

lessismore_v0				86.04 411	88.46 432	68.78 454		80.59 481		73.01 459	90.11 378	55.39 437	96.43 356	75.06 379	65.06 473	92.90 378

TinyColmap		79.76 415	77.69 417	85.97 412	91.71 350	73.12 411	89.55 405	90.36 422	75.03 425	72.03 462	90.19 374	46.22 473	96.19 369	63.11 454	81.03 395	88.59 465

KD-MVS_2432*1600		78.50 424	76.02 432	85.93 413	86.22 450	74.47 395	84.80 464	92.33 366	79.29 364	76.98 432	85.92 442	53.81 451	93.97 429	67.39 433	57.42 483	89.36 452

miper_refine_blended		78.50 424	76.02 432	85.93 413	86.22 450	74.47 395	84.80 464	92.33 366	79.29 364	76.98 432	85.92 442	53.81 451	93.97 429	67.39 433	57.42 483	89.36 452

K. test v3		81.59 389	80.15 386	85.91 415	89.89 416	69.42 452	92.57 314	87.71 456	85.56 217	73.44 457	89.71 390	55.58 434	95.52 398	77.17 357	69.76 454	92.78 384

SSC-MVS3.2		84.60 350	84.19 337	85.85 416	92.74 318	68.07 455	88.15 431	93.81 326	87.42 163	83.76 337	91.07 346	62.91 385	95.73 392	74.56 387	83.24 365	93.75 341

mvsany_test1		85.42 330	85.30 315	85.77 417	87.95 441	75.41 386	87.61 443	80.97 480	76.82 406	88.68 208	95.83 143	77.44 193	90.82 466	85.90 211	86.51 330	91.08 436

MIMVSNet1		79.38 419	77.28 421	85.69 418	86.35 449	73.67 404	91.61 349	92.75 357	78.11 389	72.64 460	88.12 416	48.16 465	91.97 457	60.32 463	77.49 426	91.43 426

UWE-MVS		83.69 364	83.09 357	85.48 419	93.06 302	65.27 470	90.92 371	86.14 462	79.90 357	86.26 262	90.72 360	57.17 430	95.81 387	71.03 411	92.62 240	95.35 259

UnsupCasMVSNet_eth		80.07 411	78.27 415	85.46 420	85.24 462	72.63 421	88.45 427	94.87 272	82.99 295	71.64 465	88.07 417	56.34 432	91.75 459	73.48 395	63.36 476	92.01 411

CL-MVSNet_self_test		81.74 386	80.53 376	85.36 421	85.96 453	72.45 424	90.25 387	93.07 347	81.24 342	79.85 400	87.29 427	70.93 290	92.52 449	66.95 436	69.23 456	91.11 434

MDA-MVSNet_test_wron		79.21 421	77.19 423	85.29 422	88.22 436	72.77 416	85.87 455	90.06 429	74.34 432	62.62 479	87.56 424	66.14 357	91.99 456	66.90 440	73.01 439	91.10 435

YYNet1		79.22 420	77.20 422	85.28 423	88.20 437	72.66 419	85.87 455	90.05 431	74.33 433	62.70 477	87.61 423	66.09 358	92.03 453	66.94 437	72.97 440	91.15 431

WB-MVSnew		83.77 362	83.28 353	85.26 424	91.48 356	71.03 439	91.89 338	87.98 453	78.91 369	84.78 306	90.22 372	69.11 325	94.02 427	64.70 449	90.44 267	90.71 438

dp		81.47 394	80.23 383	85.17 425	89.92 415	65.49 468	86.74 450	90.10 428	76.30 412	81.10 378	87.12 431	62.81 386	95.92 380	68.13 430	79.88 412	94.09 316

UnsupCasMVSNet_bld		76.23 434	73.27 438	85.09 426	83.79 471	72.92 413	85.65 458	93.47 337	71.52 456	68.84 471	79.08 474	49.77 460	93.21 441	66.81 441	60.52 480	89.13 460

usedtu_dtu_shiyan2		74.72 436	71.30 441	84.98 427	77.78 486	70.58 445	91.85 341	90.76 415	67.24 470	68.06 473	82.17 469	37.13 482	92.78 447	60.69 462	66.03 470	91.59 420

SD_0403		84.71 348	84.65 330	84.92 428	92.95 309	65.95 464	92.07 337	93.23 342	83.82 270	79.03 411	93.73 253	73.90 249	92.91 446	63.02 456	90.05 274	95.89 237

Anonymous20231206		81.03 399	79.77 395	84.82 429	87.85 442	70.26 447	91.42 353	92.08 375	73.67 440	77.75 427	89.25 396	62.43 388	93.08 443	61.50 460	82.00 381	91.12 433

FE-MVSNET		78.19 426	76.03 431	84.69 430	83.70 472	73.31 409	90.58 379	90.00 432	77.11 401	71.91 463	85.47 446	55.53 436	91.94 458	59.69 467	70.24 451	88.83 462

test0.0.03 1		82.41 377	81.69 368	84.59 431	88.23 435	72.89 414	90.24 389	87.83 455	83.41 281	79.86 399	89.78 388	67.25 339	88.99 476	65.18 446	83.42 363	91.90 413

CMPMVS	59.16 21	80.52 405	79.20 404	84.48 432	83.98 470	67.63 461	89.95 400	93.84 322	64.79 476	66.81 474	91.14 343	57.93 425	95.17 408	76.25 367	88.10 309	90.65 439
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
CVMVSNet		84.69 349	84.79 328	84.37 433	91.84 344	64.92 471	93.70 260	91.47 396	66.19 473	86.16 265	95.28 171	67.18 341	93.33 439	80.89 300	90.42 269	94.88 279

PVSNet_0	73.20 20	77.22 430	74.83 436	84.37 433	90.70 395	71.10 438	83.09 473	89.67 439	72.81 450	73.93 454	83.13 458	60.79 406	93.70 435	68.54 425	50.84 488	88.30 467

LF4IMVS		80.37 408	79.07 408	84.27 435	86.64 447	69.87 451	89.39 410	91.05 405	76.38 410	74.97 448	90.00 382	47.85 466	94.25 425	74.55 388	80.82 401	88.69 464

Anonymous20240521		80.44 407	79.21 403	84.11 436	85.75 456	67.89 457	92.86 304	93.23 342	75.61 420	75.59 445	87.47 425	50.03 459	94.33 422	71.14 409	81.21 389	90.12 447

PM-MVS		78.11 427	76.12 430	84.09 437	83.54 473	70.08 448	88.97 418	85.27 469	79.93 356	74.73 450	86.43 437	34.70 485	93.48 437	79.43 333	72.06 444	88.72 463

test_fmvs2		83.98 357	84.03 342	83.83 438	87.16 445	67.53 462	93.93 242	92.89 351	77.62 390	86.89 246	93.53 256	47.18 468	92.02 455	90.54 134	86.51 330	91.93 412

testgi		80.94 402	80.20 384	83.18 439	87.96 440	66.29 463	91.28 360	90.70 418	83.70 272	78.12 422	92.84 278	51.37 457	90.82 466	63.34 453	82.46 374	92.43 399

KD-MVS_self_test		80.20 409	79.24 402	83.07 440	85.64 457	65.29 469	91.01 368	93.93 316	78.71 378	76.32 437	86.40 439	59.20 418	92.93 445	72.59 399	69.35 455	91.00 437

testing3		80.46 406	79.59 398	83.06 441	93.44 288	64.64 472	93.33 275	85.47 467	84.34 259	79.93 398	90.84 353	44.35 476	92.39 450	57.06 474	87.56 319	92.16 409

ambc				83.06 441	79.99 482	63.51 476	77.47 486	92.86 352		74.34 453	84.45 451	28.74 486	95.06 412	73.06 397	68.89 460	90.61 440

test20.03		79.95 413	79.08 407	82.55 443	85.79 455	67.74 460	91.09 366	91.08 403	81.23 343	74.48 452	89.96 384	61.63 393	90.15 468	60.08 464	76.38 432	89.76 449

MVStest1		72.91 439	69.70 444	82.54 444	78.14 485	73.05 412	88.21 430	86.21 461	60.69 480	64.70 475	90.53 363	46.44 471	85.70 483	58.78 470	53.62 485	88.87 461

test_vis1_rt		77.96 428	76.46 427	82.48 445	85.89 454	71.74 431	90.25 387	78.89 484	71.03 460	71.30 466	81.35 471	42.49 478	91.05 465	84.55 234	82.37 375	84.65 472

EU-MVSNet		81.32 396	80.95 374	82.42 446	88.50 431	63.67 475	93.32 276	91.33 398	64.02 477	80.57 387	92.83 279	61.21 402	92.27 452	76.34 366	80.38 408	91.32 427

myMVS_eth3d		79.67 416	78.79 410	82.32 447	91.92 340	64.08 473	89.75 403	87.40 459	81.72 328	78.82 416	87.20 428	45.33 474	91.29 462	59.09 469	87.84 316	91.60 418

ttmdpeth		76.55 432	74.64 437	82.29 448	82.25 478	67.81 459	89.76 402	85.69 465	70.35 462	75.76 443	91.69 322	46.88 469	89.77 470	66.16 442	63.23 477	89.30 454

pmmvs3		71.81 442	68.71 445	81.11 449	75.86 488	70.42 446	86.74 450	83.66 473	58.95 483	68.64 472	80.89 472	36.93 483	89.52 472	63.10 455	63.59 475	83.39 473

Syy-MVS		80.07 411	79.78 393	80.94 450	91.92 340	59.93 482	89.75 403	87.40 459	81.72 328	78.82 416	87.20 428	66.29 355	91.29 462	47.06 482	87.84 316	91.60 418

UWE-MVS-28		78.98 422	78.38 414	80.80 451	88.18 438	60.66 481	90.65 376	78.51 485	78.84 373	77.93 425	90.93 350	59.08 420	89.02 475	50.96 479	90.33 271	92.72 385

new-patchmatchnet		76.41 433	75.17 435	80.13 452	82.65 477	59.61 483	87.66 441	91.08 403	78.23 387	69.85 469	83.22 457	54.76 445	91.63 461	64.14 452	64.89 474	89.16 458

mvsany_test3		74.95 435	73.26 439	80.02 453	74.61 489	63.16 477	85.53 459	78.42 486	74.16 435	74.89 449	86.46 435	36.02 484	89.09 474	82.39 267	66.91 468	87.82 470

test_fmvs3		77.67 429	77.16 424	79.22 454	79.52 483	61.14 479	92.34 324	91.64 390	73.98 437	78.86 415	86.59 434	27.38 489	87.03 478	88.12 176	75.97 434	89.50 451

DSMNet-mixed		76.94 431	76.29 429	78.89 455	83.10 475	56.11 491	87.78 437	79.77 482	60.65 481	75.64 444	88.71 407	61.56 396	88.34 477	60.07 465	89.29 292	92.21 408

EGC-MVSNET		61.97 450	56.37 455	78.77 456	89.63 420	73.50 406	89.12 415	82.79 475	0.21 502	1.24 503	84.80 449	39.48 479	90.04 469	44.13 484	75.94 435	72.79 484

new_pmnet		72.15 440	70.13 443	78.20 457	82.95 476	65.68 466	83.91 469	82.40 477	62.94 479	64.47 476	79.82 473	42.85 477	86.26 482	57.41 473	74.44 437	82.65 477

MVS-HIRNet		73.70 438	72.20 440	78.18 458	91.81 347	56.42 490	82.94 474	82.58 476	55.24 484	68.88 470	66.48 487	55.32 439	95.13 409	58.12 471	88.42 305	83.01 475

LCM-MVSNet		66.00 447	62.16 452	77.51 459	64.51 499	58.29 485	83.87 470	90.90 411	48.17 488	54.69 485	73.31 483	16.83 498	86.75 479	65.47 444	61.67 479	87.48 471

APD_test1		69.04 443	66.26 449	77.36 460	80.51 481	62.79 478	85.46 460	83.51 474	54.11 486	59.14 483	84.79 450	23.40 492	89.61 471	55.22 475	70.24 451	79.68 481

test_f		71.95 441	70.87 442	75.21 461	74.21 491	59.37 484	85.07 463	85.82 464	65.25 475	70.42 468	83.13 458	23.62 490	82.93 489	78.32 344	71.94 446	83.33 474

ANet_high		58.88 454	54.22 459	72.86 462	56.50 502	56.67 487	80.75 479	86.00 463	73.09 447	37.39 494	64.63 490	22.17 493	79.49 492	43.51 485	23.96 496	82.43 478

test_vis3_rt		65.12 448	62.60 450	72.69 463	71.44 492	60.71 480	87.17 446	65.55 496	63.80 478	53.22 486	65.65 489	14.54 499	89.44 473	76.65 361	65.38 472	67.91 487

FPMVS		64.63 449	62.55 451	70.88 464	70.80 493	56.71 486	84.42 467	84.42 471	51.78 487	49.57 487	81.61 470	23.49 491	81.48 490	40.61 489	76.25 433	74.46 483

dmvs_testset		74.57 437	75.81 434	70.86 465	87.72 443	40.47 500	87.05 448	77.90 490	82.75 300	71.15 467	85.47 446	67.98 336	84.12 487	45.26 483	76.98 431	88.00 468

N_pmnet		68.89 444	68.44 446	70.23 466	89.07 425	28.79 505	88.06 432	19.50 505	69.47 464	71.86 464	84.93 448	61.24 401	91.75 459	54.70 476	77.15 428	90.15 446

testf1		59.54 452	56.11 456	69.85 467	69.28 494	56.61 488	80.37 480	76.55 493	42.58 491	45.68 490	75.61 477	11.26 500	84.18 485	43.20 486	60.44 481	68.75 485

APD_test2		59.54 452	56.11 456	69.85 467	69.28 494	56.61 488	80.37 480	76.55 493	42.58 491	45.68 490	75.61 477	11.26 500	84.18 485	43.20 486	60.44 481	68.75 485

WB-MVS		67.92 445	67.49 447	69.21 469	81.09 479	41.17 499	88.03 433	78.00 489	73.50 442	62.63 478	83.11 460	63.94 377	86.52 480	25.66 494	51.45 487	79.94 480

PMMVS2		59.60 451	56.40 454	69.21 469	68.83 496	46.58 495	73.02 490	77.48 491	55.07 485	49.21 488	72.95 484	17.43 497	80.04 491	49.32 481	44.33 491	80.99 479

SSC-MVS		67.06 446	66.56 448	68.56 471	80.54 480	40.06 501	87.77 438	77.37 492	72.38 452	61.75 480	82.66 467	63.37 380	86.45 481	24.48 495	48.69 490	79.16 482

Gipuma		57.99 456	54.91 458	67.24 472	88.51 429	65.59 467	52.21 493	90.33 423	43.58 490	42.84 493	51.18 494	20.29 495	85.07 484	34.77 490	70.45 450	51.05 493
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	47.18 22	52.22 458	48.46 462	63.48 473	45.72 504	46.20 496	73.41 489	78.31 487	41.03 493	30.06 496	65.68 488	6.05 502	83.43 488	30.04 492	65.86 471	60.80 488
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
dongtai		58.82 455	58.24 453	60.56 474	83.13 474	45.09 498	82.32 475	48.22 504	67.61 468	61.70 481	69.15 485	38.75 480	76.05 493	32.01 491	41.31 492	60.55 489

MVE	39.65 23	43.39 460	38.59 466	57.77 475	56.52 501	48.77 494	55.38 492	58.64 500	29.33 496	28.96 497	52.65 493	4.68 503	64.62 497	28.11 493	33.07 494	59.93 490
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		50.52 459	48.47 461	56.66 476	52.26 503	18.98 507	41.51 495	81.40 479	10.10 497	44.59 492	75.01 481	28.51 487	68.16 494	53.54 477	49.31 489	82.83 476

DeepMVS_CX				56.31 477	74.23 490	51.81 493		56.67 501	44.85 489	48.54 489	75.16 480	27.87 488	58.74 499	40.92 488	52.22 486	58.39 491

kuosan		53.51 457	53.30 460	54.13 478	76.06 487	45.36 497	80.11 482	48.36 503	59.63 482	54.84 484	63.43 491	37.41 481	62.07 498	20.73 497	39.10 493	54.96 492

E-PMN		43.23 461	42.29 463	46.03 479	65.58 498	37.41 502	73.51 488	64.62 497	33.99 494	28.47 498	47.87 495	19.90 496	67.91 495	22.23 496	24.45 495	32.77 494

EMVS		42.07 462	41.12 464	44.92 480	63.45 500	35.56 504	73.65 487	63.48 498	33.05 495	26.88 499	45.45 496	21.27 494	67.14 496	19.80 498	23.02 497	32.06 495

tmp_tt		35.64 463	39.24 465	24.84 481	14.87 505	23.90 506	62.71 491	51.51 502	6.58 499	36.66 495	62.08 492	44.37 475	30.34 501	52.40 478	22.00 498	20.27 496

wuyk23d		21.27 465	20.48 468	23.63 482	68.59 497	36.41 503	49.57 494	6.85 506	9.37 498	7.89 500	4.46 502	4.03 504	31.37 500	17.47 499	16.07 499	3.12 497

test123		8.76 467	11.22 470	1.39 483	0.85 507	0.97 508	85.76 457	0.35 508	0.54 501	2.45 502	8.14 501	0.60 505	0.48 502	2.16 501	0.17 501	2.71 498

testmvs		8.92 466	11.52 469	1.12 484	1.06 506	0.46 509	86.02 454	0.65 507	0.62 500	2.74 501	9.52 500	0.31 506	0.45 503	2.38 500	0.39 500	2.46 499

mmdepth		0.00 470	0.00 473	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.00 503	0.00 507	0.00 504	0.00 502	0.00 502	0.00 500

monomultidepth		0.00 470	0.00 473	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.00 503	0.00 507	0.00 504	0.00 502	0.00 502	0.00 500

test_blank		0.00 470	0.00 473	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.00 503	0.00 507	0.00 504	0.00 502	0.00 502	0.00 500

uanet_test		0.00 470	0.00 473	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.00 503	0.00 507	0.00 504	0.00 502	0.00 502	0.00 500

DCPMVS		0.00 470	0.00 473	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.00 503	0.00 507	0.00 504	0.00 502	0.00 502	0.00 500

cdsmvs_eth3d_5k		22.14 464	29.52 467	0.00 485	0.00 508	0.00 510	0.00 496	95.76 196	0.00 503	0.00 504	94.29 224	75.66 221	0.00 504	0.00 502	0.00 502	0.00 500

pcd_1.5k_mvsjas		6.64 469	8.86 472	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.00 503	79.70 157	0.00 504	0.00 502	0.00 502	0.00 500

sosnet-low-res		0.00 470	0.00 473	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.00 503	0.00 507	0.00 504	0.00 502	0.00 502	0.00 500

sosnet		0.00 470	0.00 473	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.00 503	0.00 507	0.00 504	0.00 502	0.00 502	0.00 500

uncertanet		0.00 470	0.00 473	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.00 503	0.00 507	0.00 504	0.00 502	0.00 502	0.00 500

Regformer		0.00 470	0.00 473	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.00 503	0.00 507	0.00 504	0.00 502	0.00 502	0.00 500

ab-mvs-re		7.82 468	10.43 471	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	93.88 245	0.00 507	0.00 504	0.00 502	0.00 502	0.00 500

uanet		0.00 470	0.00 473	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.00 503	0.00 507	0.00 504	0.00 502	0.00 502	0.00 500

WAC-MVS						64.08 473								59.14 468

FOURS1					98.86 4	85.54 74	98.29 1	97.49 12	89.79 63	96.29 32

PC_three_1452									82.47 304	97.09 20	97.07 72	92.72 1	98.04 198	92.70 80	99.02 12	98.86 16

test_one_0601					98.58 14	85.83 68		97.44 21	91.05 23	96.78 27	98.06 22	91.45 13

eth-test2					0.00 508

eth-test					0.00 508

ZD-MVS					98.15 40	86.62 35		97.07 60	83.63 274	94.19 65	96.91 78	87.57 35	99.26 51	91.99 105	98.44 57

RE-MVS-def			93.68 72		97.92 49	84.57 94	96.28 51	96.76 93	87.46 160	93.75 76	97.43 51	82.94 100		92.73 76	97.80 92	97.88 109

IU-MVS					98.77 8	86.00 54		96.84 82	81.26 341	97.26 13				95.50 36	99.13 3	99.03 8

test_241102_TWO								97.44 21	90.31 41	97.62 8	98.07 20	91.46 12	99.58 14	95.66 30	99.12 6	98.98 10

test_241102_ONE					98.77 8	85.99 56		97.44 21	90.26 47	97.71 2	97.96 31	92.31 6	99.38 35

9.14			94.47 35		97.79 58		96.08 69	97.44 21	86.13 204	95.10 55	97.40 53	88.34 26	99.22 53	93.25 68	98.70 38

save fliter					97.85 55	85.63 73	95.21 141	96.82 85	89.44 73

test_0728_THIRD									90.75 29	97.04 22	98.05 25	92.09 8	99.55 20	95.64 32	99.13 3	99.13 2

test0726					98.78 6	85.93 59	97.19 16	97.47 17	90.27 45	97.64 6	98.13 7	91.47 10

GSMVS																96.12 225

test_part2					98.55 15	87.22 21				96.40 31

sam_mvs1												71.70 281				96.12 225

sam_mvs												70.60 295

MTGPA								96.97 65

test_post1							88.00 434				9.81 499	69.31 319	95.53 397	76.65 361

test_post											10.29 498	70.57 299	95.91 382

patchmatchnet-post											83.76 453	71.53 282	96.48 350

MTMP							96.16 60	60.64 499

gm-plane-assit					89.60 421	68.00 456			77.28 396		88.99 401		97.57 245	79.44 332

test9_res														91.91 109	98.71 36	98.07 82

TEST9					97.53 67	86.49 39	94.07 228	96.78 90	81.61 333	92.77 101	96.20 109	87.71 32	99.12 63

test_8					97.49 69	86.30 47	94.02 234	96.76 93	81.86 324	92.70 105	96.20 109	87.63 33	99.02 73

agg_prior2														90.54 134	98.68 41	98.27 63

agg_prior					97.38 72	85.92 61		96.72 100		92.16 120			98.97 87

test_prior4						85.96 58	94.11 222

test_prior2							94.12 220		87.67 155	92.63 109	96.39 104	86.62 45		91.50 118	98.67 44

旧先验2							93.36 274		71.25 458	94.37 61			97.13 297	86.74 198

新几何2							93.11 289

旧先验1					96.79 86	81.81 196		95.67 207			96.81 84	86.69 43			97.66 98	96.97 181

无先验							93.28 282	96.26 140	73.95 438				99.05 67	80.56 306		96.59 204

原ACMM2							92.94 300

test222					96.55 95	81.70 201	92.22 330	95.01 255	68.36 467	90.20 175	96.14 118	80.26 144			97.80 92	96.05 232

testdata2													98.75 116	78.30 345

segment_acmp												87.16 40

testdata1							92.15 332		87.94 140

plane_prior7					94.70 200	82.74 165

plane_prior6					94.52 216	82.75 163						74.23 241

plane_prior5								96.22 145					98.12 178	88.15 173	89.99 275	94.63 286

plane_prior4											94.86 195

plane_prior3						82.75 163			90.26 47	86.91 243

plane_prior2							95.85 93		90.81 27

plane_prior1					94.59 209

plane_prior						82.73 166	95.21 141		89.66 68						89.88 280

n2								0.00 509

nn								0.00 509

door-mid								85.49 466

test11								96.57 112

door								85.33 468

HQP5-MVS						81.56 203

HQP-NCC					94.17 247		94.39 202		88.81 101	85.43 289

ACMP_Plane					94.17 247		94.39 202		88.81 101	85.43 289

BP-MVS														87.11 195

HQP4-MVS										85.43 289			97.96 214			94.51 296

HQP3-MVS								96.04 171							89.77 284

HQP2-MVS												73.83 252

NP-MVS					94.37 229	82.42 178					93.98 238

MDTV_nov1_ep13_2view						55.91 492	87.62 442		73.32 444	84.59 311		70.33 302		74.65 384		95.50 253

MDTV_nov1_ep13			83.56 350		91.69 352	69.93 449	87.75 439	91.54 393	78.60 379	84.86 305	88.90 403	69.54 314	96.03 373	70.25 414	88.93 297

ACMMP++_ref															87.47 320

ACMMP++															88.01 312

Test By Simon												80.02 146

High-resolution multi-view benchmark