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 6	96.03 27	99.06 9	99.07 7

No_MVS				96.52 1	97.78 60	90.86 1		96.85 80					99.61 6	96.03 27	99.06 9	99.07 7

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	94.37 302	95.08 1	94.68 58	97.72 41	82.94 100	99.64 3	97.85 5	98.76 32	99.06 9

SMA-MVS		95.20 10	95.07 20	95.59 6	98.14 41	88.48 9	96.26 54	97.28 40	85.90 208	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	92.42 104	91.37 126	95.55 7	95.63 143	88.73 7	97.07 23	96.77 92	90.84 26	84.02 335	96.62 95	75.95 217	99.34 42	87.77 183	97.68 96	98.59 29

CNVR-MVS		95.40 8	95.37 11	95.50 8	98.11 42	88.51 8	95.29 131	96.96 68	92.09 10	95.32 50	97.08 70	89.49 16	99.33 45	95.10 43	98.85 20	98.66 26

TestfortrainingZip				95.40 9	97.32 74	88.97 6	97.32 10	96.82 85	89.07 91	95.69 45	96.49 100	89.27 18	99.29 50		95.80 142	97.95 97

MGCNet		94.18 50	93.80 64	95.34 10	94.91 183	87.62 15	95.97 82	93.01 353	92.58 6	94.22 63	97.20 64	80.56 140	99.59 10	97.04 20	98.68 40	98.81 22

ACMMP_NAP		94.74 25	94.56 33	95.28 11	98.02 47	87.70 12	95.68 107	97.34 30	88.28 123	95.30 51	97.67 43	85.90 55	99.54 24	93.91 56	98.95 15	98.60 28

DPE-MVS		95.57 5	95.67 5	95.25 12	98.36 31	87.28 19	95.56 119	97.51 10	89.13 90	97.14 17	97.91 34	91.64 8	99.62 4	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 13	97.84 56	87.76 11	96.65 39	97.48 15	87.76 153	95.71 44	97.70 42	88.28 27	99.35 41	93.89 57	98.78 29	98.48 35

MCST-MVS		94.45 34	94.20 51	95.19 14	98.46 22	87.50 17	95.00 155	97.12 55	87.13 174	92.51 113	96.30 105	89.24 19	99.34 42	93.46 63	98.62 49	98.73 23

NCCC		94.81 22	94.69 32	95.17 15	97.83 57	87.46 18	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 57	98.62 27

DPM-MVS		92.58 99	91.74 110	95.08 16	96.19 107	89.31 5	92.66 313	96.56 113	83.44 283	91.68 139	95.04 189	86.60 47	98.99 82	85.60 217	97.92 84	96.93 189

ZNCC-MVS		94.47 33	94.28 45	95.03 17	98.52 18	86.96 21	96.85 33	97.32 34	88.24 124	93.15 88	97.04 73	86.17 52	99.62 4	92.40 86	98.81 26	98.52 31

test_0728_SECOND				95.01 18	98.79 5	86.43 40	97.09 21	97.49 11					99.61 6	95.62 34	99.08 7	98.99 11

MTAPA		94.42 39	94.22 48	95.00 19	98.42 24	86.95 22	94.36 210	96.97 65	91.07 22	93.14 89	97.56 45	84.30 81	99.56 16	93.43 64	98.75 33	98.47 38

TestfortrainingZip a		95.33 9	95.44 10	94.99 20	98.88 1	86.26 48	97.32 10	97.43 25	90.76 29	96.80 26	98.09 18	89.00 22	99.58 13	93.66 60	96.99 111	99.14 2

MSP-MVS		95.42 7	95.56 7	94.98 21	98.49 20	86.52 37	96.91 30	97.47 16	91.73 14	96.10 36	96.69 87	89.90 13	99.30 48	94.70 47	98.04 79	99.13 4
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 31	96.92 29	97.23 43	88.60 114	93.58 80	97.27 58	85.22 64	99.54 24	92.21 94	98.74 34	98.56 30

APDe-MVS		95.46 6	95.64 6	94.91 23	98.26 34	86.29 47	97.46 7	97.40 26	89.03 96	96.20 35	98.10 14	89.39 17	99.34 42	95.88 29	99.03 11	99.10 6
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 29	96.94 25	97.32 34	88.63 111	93.53 83	97.26 60	85.04 68	99.54 24	92.35 89	98.78 29	98.50 32

MED-MVS		95.95 2	96.29 2	94.90 25	98.88 1	85.89 65	97.32 10	97.86 1	90.76 29	97.21 14	98.09 18	92.42 4	99.67 1	95.27 40	98.85 20	99.14 2

GST-MVS		94.21 45	93.97 60	94.90 25	98.41 25	86.82 25	96.54 41	97.19 44	88.24 124	93.26 85	96.83 82	85.48 60	99.59 10	91.43 120	98.40 57	98.30 55

HFP-MVS		94.52 31	94.40 38	94.86 27	98.61 13	86.81 26	96.94 25	97.34 30	88.63 111	93.65 78	97.21 62	86.10 53	99.49 30	92.35 89	98.77 31	98.30 55

sasdasda		93.27 82	92.75 92	94.85 28	95.70 139	87.66 13	96.33 44	96.41 123	90.00 53	94.09 68	94.60 214	82.33 109	98.62 133	92.40 86	92.86 234	98.27 64

MP-MVS-pluss		94.21 45	94.00 59	94.85 28	98.17 39	86.65 32	94.82 168	97.17 49	86.26 200	92.83 98	97.87 36	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 28	95.70 139	87.66 13	96.33 44	96.41 123	90.00 53	94.09 68	94.60 214	82.33 109	98.62 133	92.40 86	92.86 234	98.27 64

XVS		94.45 34	94.32 41	94.85 28	98.54 16	86.60 35	96.93 27	97.19 44	90.66 36	92.85 96	97.16 68	85.02 69	99.49 30	91.99 105	98.56 53	98.47 38

X-MVStestdata		88.31 237	86.13 286	94.85 28	98.54 16	86.60 35	96.93 27	97.19 44	90.66 36	92.85 96	23.41 519	85.02 69	99.49 30	91.99 105	98.56 53	98.47 38

SteuartSystems-ACMMP		95.20 10	95.32 13	94.85 28	96.99 82	86.33 43	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 34	98.88 1	85.89 65	97.32 10	97.86 1	88.11 132	97.21 14	97.54 46		99.67 1	95.27 40	98.85 20	98.95 13

DVP-MVS++		95.98 1	96.36 1	94.82 35	97.78 60	86.00 54	98.29 1	97.49 11	90.75 31	97.62 8	98.06 24	92.59 2	99.61 6	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 8	89.01 98	95.86 42	97.54 46	89.24 19	99.59 10	95.27 40	98.85 20	98.95 13

alignmvs		93.08 90	92.50 98	94.81 36	95.62 144	87.61 16	95.99 79	96.07 169	89.77 66	94.12 67	94.87 198	80.56 140	98.66 125	92.42 85	93.10 229	98.15 76

SED-MVS		95.91 3	96.28 3	94.80 38	98.77 8	85.99 56	97.13 19	97.44 20	90.31 43	97.71 2	98.07 22	92.31 5	99.58 13	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 27	95.65 112	96.89 77	89.40 78	92.81 99	96.97 75	85.37 62	99.24 52	90.87 129	98.69 38	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 45	96.71 36	96.98 64	89.04 94	91.98 124	97.19 65	85.43 61	99.56 16	92.06 103	98.79 27	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 24	95.88 90	96.94 71	85.68 215	95.05 56	97.18 66	87.31 39	99.07 65	91.90 111	98.61 51	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 33	97.60 5	97.24 41	88.53 116	92.73 104	97.23 61	85.20 65	99.32 46	92.15 97	98.83 25	98.25 69

PGM-MVS		93.96 58	93.72 70	94.68 43	98.43 23	86.22 49	95.30 129	97.78 3	87.45 164	93.26 85	97.33 56	84.62 78	99.51 28	90.75 131	98.57 52	98.32 54

DVP-MVS		95.67 4	96.02 4	94.64 44	98.78 6	85.93 59	97.09 21	96.73 98	90.27 47	97.04 21	98.05 27	91.47 9	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	88.70 109	91.83 134	97.17 67	83.96 85	99.55 20	91.44 119	98.64 48	98.43 43

PHI-MVS		93.89 60	93.65 74	94.62 46	96.84 85	86.43 40	96.69 37	97.49 11	85.15 239	93.56 82	96.28 106	85.60 58	99.31 47	92.45 83	98.79 27	98.12 81

TSAR-MVS + MP.		94.85 19	94.94 24	94.58 47	98.25 35	86.33 43	96.11 67	96.62 108	88.14 129	96.10 36	96.96 76	89.09 21	98.94 92	94.48 50	98.68 40	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	94.94 158	96.69 104	91.89 12	90.69 166	95.88 138	81.99 122	99.54 24	93.14 70	97.95 83	98.39 45

train_agg		93.44 75	93.08 85	94.52 49	97.53 67	86.49 38	94.07 230	96.78 90	81.86 328	92.77 101	96.20 109	87.63 33	99.12 63	92.14 98	98.69 38	97.94 98

CDPH-MVS		92.83 94	92.30 102	94.44 50	97.79 58	86.11 53	94.06 232	96.66 105	80.09 359	92.77 101	96.63 94	86.62 45	99.04 69	87.40 190	98.66 44	98.17 74

3Dnovator	86.66 5	91.73 122	90.82 141	94.44 50	94.59 209	86.37 42	97.18 17	97.02 62	89.20 87	84.31 330	96.66 90	73.74 258	99.17 57	86.74 200	97.96 82	97.79 122

SR-MVS		94.23 44	94.17 54	94.43 52	98.21 38	85.78 70	96.40 43	96.90 76	88.20 127	94.33 62	97.40 53	84.75 77	99.03 70	93.35 67	97.99 81	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	86.90 184	92.62 110	96.80 86	84.85 75	99.17 57	92.43 84	98.65 47	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 27	94.40 202	93.93 320	89.77 66	94.21 64	95.59 159	87.35 38	98.61 135	92.72 78	96.15 136	97.83 118

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 59	95.23 53	98.10 14	87.09 41	99.37 37	95.30 38	98.25 66	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 59	95.23 53	98.10 14	87.09 41	99.37 37	95.30 38	98.25 66	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	96.13 120	77.97 187	98.84 106	90.75 131	98.26 62	98.07 83

test12				94.34 58	97.13 80	86.15 52		96.29 132		91.04 162		85.08 67	99.01 75		98.13 74	97.86 113

SymmetryMVS		92.81 96	92.31 101	94.32 59	96.15 108	86.20 50	96.30 47	94.43 298	91.65 17	92.68 106	96.13 120	77.97 187	98.84 106	90.75 131	94.72 169	97.92 107

ACMMP		93.24 84	92.88 90	94.30 60	98.09 44	85.33 79	96.86 32	97.45 19	88.33 120	90.15 184	97.03 74	81.44 130	99.51 28	90.85 130	95.74 145	98.04 90
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 69	95.27 52	98.16 6	86.53 48	99.36 40	95.42 37	98.15 72	98.33 50

DeepC-MVS	88.79 3	93.31 81	92.99 88	94.26 62	96.07 118	85.83 68	94.89 161	96.99 63	89.02 97	89.56 193	97.37 55	82.51 106	99.38 35	92.20 95	98.30 60	97.57 137
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 57	93.89 75	94.66 211	82.11 117	98.50 141	92.33 91	92.82 237	98.27 64

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 19	98.12 10	84.98 73	98.94 92	97.07 17	97.80 91	98.43 43

EPNet		91.79 113	91.02 135	94.10 65	90.10 414	85.25 80	96.03 76	92.05 380	92.83 5	87.39 241	95.78 149	79.39 166	99.01 75	88.13 177	97.48 99	98.05 89
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
lecture		95.10 14	95.46 9	94.01 66	98.40 26	84.36 107	97.70 3	97.78 3	91.19 20	96.22 34	98.08 21	86.64 44	99.37 37	94.91 45	98.26 62	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 79	96.03 39	98.11 11	86.36 49	99.01 75	97.45 10	97.83 89	97.96 96

DELS-MVS		93.43 79	93.25 81	93.97 68	95.42 152	85.04 83	93.06 296	97.13 54	90.74 33	91.84 132	95.09 188	86.32 50	99.21 55	91.22 121	98.45 55	97.65 131
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		91.95 110	91.28 129	93.96 69	98.33 33	85.92 61	94.66 181	96.66 105	82.69 305	90.03 186	95.82 145	82.30 111	99.03 70	84.57 236	96.48 129	96.91 191

HPM-MVS_fast		93.40 80	93.22 82	93.94 70	98.36 31	84.83 87	97.15 18	96.80 89	85.77 212	92.47 114	97.13 69	82.38 107	99.07 65	90.51 136	98.40 57	97.92 107

test_fmvsmconf0.1_n		94.20 47	94.31 43	93.88 71	92.46 328	84.80 88	96.18 59	96.82 85	89.29 84	95.68 46	98.11 11	85.10 66	98.99 82	97.38 11	97.75 95	97.86 113

SD-MVS		94.96 18	95.33 12	93.88 71	97.25 79	86.69 29	96.19 57	97.11 58	90.42 39	96.95 23	97.27 58	89.53 15	96.91 319	94.38 51	98.85 20	98.03 91
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	93.24 287	97.24 41	88.76 106	91.60 140	95.85 142	86.07 54	98.66 125	91.91 109	98.16 70	98.03 91

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	87.46 162	93.75 76	97.43 51	84.24 82	99.01 75	92.73 76	97.80 91	97.88 111

test_prior				93.82 74	97.29 77	84.49 98		96.88 78					98.87 100			98.11 82

APD-MVS_3200maxsize		93.78 63	93.77 67	93.80 76	97.92 49	84.19 111	96.30 47	96.87 79	86.96 180	93.92 74	97.47 49	83.88 86	98.96 89	92.71 79	97.87 87	98.26 68

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	86.56 192	96.84 25	97.81 39	87.56 36	98.77 115	97.14 15	96.82 119	97.16 170

CSCG		93.23 85	93.05 86	93.76 78	98.04 46	84.07 113	96.22 56	97.37 27	84.15 264	90.05 185	95.66 155	87.77 30	99.15 61	89.91 147	98.27 61	98.07 83

GDP-MVS		92.04 108	91.46 123	93.75 79	94.55 215	84.69 91	95.60 118	96.56 113	87.83 150	93.07 92	95.89 137	73.44 262	98.65 127	90.22 140	96.03 138	97.91 109

BP-MVS1		92.48 101	92.07 105	93.72 80	94.50 219	84.39 106	95.90 89	94.30 305	90.39 40	92.67 108	95.94 133	74.46 241	98.65 127	93.14 70	97.35 103	98.13 78

test_fmvsmconf0.01_n		93.19 86	93.02 87	93.71 81	89.25 427	84.42 105	96.06 73	96.29 132	89.06 92	94.68 58	98.13 7	79.22 168	98.98 86	97.22 13	97.24 105	97.74 125

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	84.47 238	95.47 152	97.45 144

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	86.31 198	96.75 28	97.86 37	87.40 37	98.74 119	97.07 17	97.02 110	97.07 175

QAPM		89.51 194	88.15 221	93.59 84	94.92 181	84.58 93	96.82 34	96.70 103	78.43 386	83.41 353	96.19 113	73.18 267	99.30 48	77.11 363	96.54 126	96.89 192

test_fmvsm_n_1920		94.71 26	95.11 19	93.50 85	95.79 133	84.62 92	96.15 62	97.64 5	89.85 58	97.19 16	97.89 35	86.28 51	98.71 122	97.11 16	98.08 78	97.17 163

fmvsm_s_conf0.5_n_9		94.99 16	95.50 8	93.44 86	96.51 100	82.25 185	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 84	98.90 15

KinetiMVS		91.82 112	91.30 127	93.39 87	94.72 197	83.36 138	95.45 122	96.37 127	90.33 42	92.17 119	96.03 127	72.32 279	98.75 116	87.94 180	96.34 131	98.07 83

casdiffmvs_mvg		92.96 93	92.83 91	93.35 88	94.59 209	83.40 136	95.00 155	96.34 129	90.30 45	92.05 122	96.05 124	83.43 89	98.15 177	92.07 100	95.67 146	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	88.02 137	96.23 33	97.84 38	83.36 93	98.83 109	97.49 8	97.34 104	97.25 155

EI-MVSNet-Vis-set		93.01 92	92.92 89	93.29 90	95.01 172	83.51 133	94.48 190	95.77 197	90.87 25	92.52 112	96.67 89	84.50 79	99.00 80	91.99 105	94.44 182	97.36 147

Vis-MVSNet		91.75 120	91.23 130	93.29 90	95.32 156	83.78 123	96.14 64	95.98 176	89.89 55	90.45 170	96.58 97	75.09 230	98.31 168	84.75 230	96.90 115	97.78 123
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
BridgeMVS		93.98 57	94.22 48	93.26 92	96.13 110	83.29 140	96.27 53	96.52 116	89.82 59	95.56 48	95.51 163	84.50 79	98.79 113	94.83 46	98.86 19	97.72 127

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	95.77 150	85.02 69	98.33 165	93.03 72	98.62 49	98.13 78

VNet		92.24 106	91.91 108	93.24 93	96.59 92	83.43 134	94.84 167	96.44 120	89.19 88	94.08 71	95.90 136	77.85 193	98.17 175	88.90 167	93.38 218	98.13 78

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	81.87 124	99.22 53	97.86 4	97.91 86	97.20 161

VDD-MVS		90.74 150	89.92 165	93.20 95	96.27 105	83.02 156	95.73 104	93.86 324	88.42 119	92.53 111	96.84 81	62.09 394	98.64 130	90.95 127	92.62 244	97.93 106

Elysia		90.12 170	89.10 189	93.18 97	93.16 296	84.05 115	95.22 138	96.27 136	85.16 237	90.59 167	94.68 207	64.64 373	98.37 158	86.38 206	95.77 143	97.12 172

StellarMVS		90.12 170	89.10 189	93.18 97	93.16 296	84.05 115	95.22 138	96.27 136	85.16 237	90.59 167	94.68 207	64.64 373	98.37 158	86.38 206	95.77 143	97.12 172

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 69	98.19 72

nrg030		91.08 145	90.39 149	93.17 99	93.07 303	86.91 23	96.41 42	96.26 140	88.30 122	88.37 218	94.85 201	82.19 116	97.64 241	91.09 122	82.95 371	94.96 277

MVSMamba_PlusPlus		93.44 75	93.54 76	93.14 101	96.58 94	83.05 154	96.06 73	96.50 118	84.42 261	94.09 68	95.56 161	85.01 72	98.69 124	94.96 44	98.66 44	97.67 130

EI-MVSNet-UG-set		92.74 97	92.62 96	93.12 102	94.86 186	83.20 143	94.40 202	95.74 200	90.71 35	92.05 122	96.60 96	84.00 84	98.99 82	91.55 117	93.63 207	97.17 163

test_fmvsmvis_n_1920		93.44 75	93.55 75	93.10 103	93.67 282	84.26 109	95.83 95	96.14 160	89.00 99	92.43 115	97.50 48	83.37 92	98.72 120	96.61 24	97.44 100	96.32 217

新几何1				93.10 103	97.30 76	84.35 108		95.56 218	71.09 464	91.26 150	96.24 107	82.87 102	98.86 102	79.19 340	98.10 75	96.07 233

OMC-MVS		91.23 136	90.62 146	93.08 105	96.27 105	84.07 113	93.52 269	95.93 182	86.95 181	89.51 194	96.13 120	78.50 181	98.35 162	85.84 215	92.90 233	96.83 199

OpenMVS	83.78 11	88.74 224	87.29 243	93.08 105	92.70 322	85.39 78	96.57 40	96.43 121	78.74 381	80.85 386	96.07 123	69.64 316	99.01 75	78.01 354	96.65 124	94.83 285

MAR-MVS		90.30 166	89.37 182	93.07 107	96.61 91	84.48 99	95.68 107	95.67 209	82.36 310	87.85 228	92.85 282	76.63 206	98.80 111	80.01 320	96.68 123	95.91 239
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		90.92 146	90.21 153	93.03 108	93.86 267	83.88 120	92.81 307	93.86 324	79.84 362	91.76 136	94.29 228	77.92 190	98.04 199	90.48 137	97.11 106	97.17 163

Effi-MVS+		91.59 130	91.11 132	93.01 109	94.35 234	83.39 137	94.60 183	95.10 254	87.10 175	90.57 169	93.10 277	81.43 131	98.07 193	89.29 159	94.48 180	97.59 136

fmvsm_s_conf0.5_n_a		93.57 68	93.76 68	93.00 110	95.02 171	83.67 126	96.19 57	96.10 166	87.27 168	95.98 40	98.05 27	83.07 99	98.45 151	96.68 23	95.51 149	96.88 193

MVS_111021_LR		92.47 102	92.29 103	92.98 111	95.99 125	84.43 103	93.08 293	96.09 167	88.20 127	91.12 155	95.72 153	81.33 132	97.76 230	91.74 113	97.37 102	96.75 201

fmvsm_s_conf0.1_n_a		93.19 86	93.26 80	92.97 112	92.49 326	83.62 129	96.02 77	95.72 204	86.78 186	96.04 38	98.19 4	82.30 111	98.43 155	96.38 25	95.42 155	96.86 194

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 34	92.96 93	91.19 343	84.06 83	98.34 163	91.72 114	96.54 126	96.54 212

LFMVS		90.08 173	89.13 188	92.95 114	96.71 87	82.32 184	96.08 69	89.91 439	86.79 185	92.15 121	96.81 84	62.60 392	98.34 163	87.18 194	93.90 196	98.19 72

UGNet		89.95 180	88.95 197	92.95 114	94.51 217	83.31 139	95.70 106	95.23 246	89.37 79	87.58 235	93.94 244	64.00 381	98.78 114	83.92 246	96.31 132	96.74 202
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		90.80 148	90.10 157	92.90 116	93.04 306	83.53 132	93.08 293	94.15 313	80.22 356	91.41 146	94.91 195	76.87 200	97.93 219	90.28 138	96.90 115	97.24 156
jason: jason.
DP-MVS		87.25 278	85.36 317	92.90 116	97.65 64	83.24 141	94.81 169	92.00 382	74.99 431	81.92 375	95.00 191	72.66 272	99.05 67	66.92 444	92.33 249	96.40 214

fmvsm_s_conf0.5_n_8		94.56 30	95.12 18	92.87 118	95.96 128	81.32 215	95.76 102	97.57 7	93.48 2	97.53 10	98.32 3	81.78 127	99.13 62	97.91 2	97.81 90	98.16 75

fmvsm_s_conf0.5_n		93.76 64	94.06 58	92.86 119	95.62 144	83.17 145	96.14 64	96.12 164	88.13 130	95.82 43	98.04 30	83.43 89	98.48 143	96.97 21	96.23 133	96.92 190

fmvsm_s_conf0.1_n		93.46 72	93.66 73	92.85 120	93.75 274	83.13 147	96.02 77	95.74 200	87.68 156	95.89 41	98.17 5	82.78 103	98.46 147	96.71 22	96.17 135	96.98 184

casdiffseed414692147		91.11 143	90.55 147	92.81 121	94.27 242	82.58 177	94.81 169	96.03 174	87.93 143	90.17 182	95.62 157	78.51 180	97.90 223	84.18 242	93.45 216	97.94 98

CANet_DTU		90.26 168	89.41 181	92.81 121	93.46 289	83.01 157	93.48 270	94.47 297	89.43 77	87.76 233	94.23 233	70.54 304	99.03 70	84.97 225	96.39 130	96.38 215

MVSFormer		91.68 128	91.30 127	92.80 123	93.86 267	83.88 120	95.96 83	95.90 186	84.66 257	91.76 136	94.91 195	77.92 190	97.30 284	89.64 155	97.11 106	97.24 156

PVSNet_Blended_VisFu		91.38 133	90.91 138	92.80 123	96.39 102	83.17 145	94.87 163	96.66 105	83.29 288	89.27 200	94.46 223	80.29 143	99.17 57	87.57 187	95.37 156	96.05 236

fmvsm_l_conf0.5_n_9		94.65 27	95.28 15	92.77 125	95.95 129	81.83 197	95.53 120	97.12 55	91.68 16	97.89 1	98.06 24	85.71 56	98.65 127	97.32 12	98.26 62	97.83 118

LuminaMVS		90.55 162	89.81 167	92.77 125	92.78 320	84.21 110	94.09 228	94.17 312	85.82 209	91.54 141	94.14 235	69.93 310	97.92 220	91.62 116	94.21 190	96.18 225

balanced_ft_v1		92.23 107	92.05 106	92.77 125	95.40 153	81.78 201	95.80 96	95.69 208	87.94 141	91.92 129	95.04 189	75.91 218	98.71 122	93.83 58	96.94 112	97.82 120

fmvsm_s_conf0.5_n_6		94.11 52	94.56 33	92.76 128	94.98 176	81.96 194	95.79 98	97.29 39	89.31 82	97.52 11	97.61 44	83.25 94	98.88 99	97.05 19	98.22 68	97.43 146

VDDNet		89.56 193	88.49 212	92.76 128	95.07 170	82.09 188	96.30 47	93.19 348	81.05 350	91.88 130	96.86 80	61.16 410	98.33 165	88.43 174	92.49 248	97.84 117

viewdifsd2359ckpt09		91.18 139	90.65 145	92.75 130	94.61 208	82.36 183	94.32 211	95.74 200	84.72 254	89.66 192	95.15 186	79.69 161	98.04 199	87.70 184	94.27 189	97.85 116

h-mvs33		90.80 148	90.15 156	92.75 130	96.01 121	82.66 170	95.43 123	95.53 222	89.80 62	93.08 90	95.64 156	75.77 219	99.00 80	92.07 100	78.05 428	96.60 207

hybridcas		92.43 103	92.33 100	92.74 132	94.51 217	81.84 196	95.05 153	96.16 158	89.60 71	91.40 147	96.20 109	82.23 113	98.09 188	89.95 146	95.87 140	98.28 61

casdiffmvs		92.51 100	92.43 99	92.74 132	94.41 229	81.98 192	94.54 187	96.23 144	89.57 73	91.96 126	96.17 114	82.58 105	98.01 206	90.95 127	95.45 154	98.23 70
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		90.69 153	90.02 163	92.71 134	95.72 137	82.41 181	94.11 224	95.12 252	85.63 216	91.49 143	94.70 205	74.75 234	98.42 156	86.13 210	92.53 246	97.31 148

DCV-MVSNet		90.69 153	90.02 163	92.71 134	95.72 137	82.41 181	94.11 224	95.12 252	85.63 216	91.49 143	94.70 205	74.75 234	98.42 156	86.13 210	92.53 246	97.31 148

PCF-MVS	84.11 10	87.74 252	86.08 290	92.70 136	94.02 256	84.43 103	89.27 415	95.87 191	73.62 446	84.43 322	94.33 225	78.48 183	98.86 102	70.27 418	94.45 181	94.81 286
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	92.69 137	96.05 120	82.00 190	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 70	97.17 163

SSM_0404		90.73 151	90.08 158	92.69 137	95.00 175	83.13 147	94.32 211	95.00 262	85.41 227	89.84 187	95.35 172	76.13 209	97.98 211	85.46 220	94.18 191	96.95 186

baseline		92.39 105	92.29 103	92.69 137	94.46 224	81.77 202	94.14 221	96.27 136	89.22 86	91.88 130	96.00 128	82.35 108	97.99 208	91.05 123	95.27 160	98.30 55

MSLP-MVS++		93.72 66	94.08 55	92.65 140	97.31 75	83.43 134	95.79 98	97.33 32	90.03 52	93.58 80	96.96 76	84.87 74	97.76 230	92.19 96	98.66 44	96.76 200

EC-MVSNet		93.44 75	93.71 71	92.63 141	95.21 163	82.43 178	97.27 14	96.71 101	90.57 38	92.88 95	95.80 146	83.16 95	98.16 176	93.68 59	98.14 73	97.31 148

ab-mvs		89.41 201	88.35 214	92.60 142	95.15 168	82.65 174	92.20 335	95.60 216	83.97 268	88.55 214	93.70 258	74.16 249	98.21 174	82.46 270	89.37 294	96.94 188

LS3D		87.89 247	86.32 279	92.59 143	96.07 118	82.92 160	95.23 136	94.92 272	75.66 423	82.89 361	95.98 130	72.48 276	99.21 55	68.43 432	95.23 161	95.64 253

Anonymous20240529		88.09 243	86.59 268	92.58 144	96.53 97	81.92 195	95.99 79	95.84 193	74.11 441	89.06 204	95.21 181	61.44 402	98.81 110	83.67 253	87.47 325	97.01 182

fmvsm_s_conf0.5_n_3		94.49 32	95.13 17	92.56 145	95.49 150	81.10 225	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 98	97.78 123

CPTT-MVS		91.99 109	91.80 109	92.55 146	98.24 37	81.98 192	96.76 35	96.49 119	81.89 327	90.24 175	96.44 103	78.59 177	98.61 135	89.68 153	97.85 88	97.06 176

viewdifsd2359ckpt13		91.20 138	90.75 143	92.54 147	94.30 240	82.13 187	94.03 234	95.89 188	85.60 218	90.20 177	95.36 171	79.69 161	97.90 223	87.85 182	93.86 197	97.61 133

114514_t		89.51 194	88.50 210	92.54 147	98.11 42	81.99 191	95.16 146	96.36 128	70.19 468	85.81 275	95.25 177	76.70 204	98.63 132	82.07 280	96.86 118	97.00 183

PAPM_NR		91.22 137	90.78 142	92.52 149	97.60 65	81.46 211	94.37 208	96.24 143	86.39 197	87.41 238	94.80 203	82.06 120	98.48 143	82.80 265	95.37 156	97.61 133

mamba_0408		89.06 214	87.92 228	92.50 150	94.76 191	82.66 170	79.84 488	94.64 290	85.18 232	88.96 206	95.00 191	76.00 214	97.98 211	83.74 250	93.15 226	96.85 195

DeepPCF-MVS	89.96 1	94.20 47	94.77 31	92.49 151	96.52 98	80.00 276	94.00 239	97.08 59	90.05 51	95.65 47	97.29 57	89.66 14	98.97 87	93.95 55	98.71 35	98.50 32

SSM_0407		90.47 164	89.80 168	92.46 152	94.76 191	82.66 170	93.98 241	95.00 262	85.41 227	88.96 206	95.35 172	76.13 209	97.88 225	85.46 220	93.15 226	96.85 195

IS-MVSNet		91.43 132	91.09 134	92.46 152	95.87 132	81.38 214	96.95 24	93.69 337	89.72 68	89.50 196	95.98 130	78.57 178	97.77 229	83.02 259	96.50 128	98.22 71

API-MVS		90.66 157	90.07 159	92.45 154	96.36 103	84.57 94	96.06 73	95.22 248	82.39 308	89.13 201	94.27 231	80.32 142	98.46 147	80.16 318	96.71 122	94.33 309

xiu_mvs_v1_base_debu		90.64 158	90.05 160	92.40 155	93.97 262	84.46 100	93.32 278	95.46 226	85.17 234	92.25 116	94.03 236	70.59 300	98.57 138	90.97 124	94.67 171	94.18 313

xiu_mvs_v1_base		90.64 158	90.05 160	92.40 155	93.97 262	84.46 100	93.32 278	95.46 226	85.17 234	92.25 116	94.03 236	70.59 300	98.57 138	90.97 124	94.67 171	94.18 313

xiu_mvs_v1_base_debi		90.64 158	90.05 160	92.40 155	93.97 262	84.46 100	93.32 278	95.46 226	85.17 234	92.25 116	94.03 236	70.59 300	98.57 138	90.97 124	94.67 171	94.18 313

fmvsm_s_conf0.5_n_2		93.47 71	93.83 62	92.39 158	95.36 154	81.19 221	95.20 143	96.56 113	90.37 41	97.13 18	98.03 31	77.47 196	98.96 89	97.79 6	96.58 125	97.03 179

viewmacassd2359aftdt		91.67 129	91.43 125	92.37 159	93.95 265	81.00 229	93.90 249	95.97 179	87.75 154	91.45 145	96.04 126	79.92 149	97.97 213	89.26 160	94.67 171	98.14 77

viewmanbaseed2359cas		91.78 116	91.58 115	92.37 159	94.32 237	81.07 226	93.76 255	95.96 180	87.26 169	91.50 142	95.88 138	80.92 138	97.97 213	89.70 152	94.92 165	98.07 83

fmvsm_s_conf0.1_n_2		93.16 88	93.42 77	92.37 159	94.62 205	81.13 223	95.23 136	95.89 188	90.30 45	96.74 29	98.02 32	76.14 208	98.95 91	97.64 7	96.21 134	97.03 179

AdaColmap		89.89 183	89.07 191	92.37 159	97.41 71	83.03 155	94.42 197	95.92 183	82.81 302	86.34 264	94.65 212	73.89 254	99.02 73	80.69 307	95.51 149	95.05 272

CNLPA		89.07 213	87.98 225	92.34 163	96.87 84	84.78 89	94.08 229	93.24 345	81.41 341	84.46 320	95.13 187	75.57 226	96.62 336	77.21 361	93.84 199	95.61 256

fmvsm_s_conf0.5_n_4		93.86 61	94.37 40	92.33 164	95.13 169	80.95 232	95.64 113	96.97 65	89.60 71	96.85 24	97.77 40	83.08 98	98.92 95	97.49 8	96.78 120	97.13 171

ET-MVSNet_ETH3D		87.51 266	85.91 298	92.32 165	93.70 281	83.93 118	92.33 327	90.94 415	84.16 263	72.09 466	92.52 295	69.90 311	95.85 388	89.20 161	88.36 312	97.17 163

E4		91.74 121	91.55 118	92.31 166	94.27 242	80.80 242	93.81 252	96.17 156	87.97 139	91.11 156	96.05 124	80.75 139	98.08 191	89.78 148	94.02 193	98.06 88

E2		91.79 113	91.61 113	92.31 166	94.49 220	80.86 238	93.74 257	96.19 149	87.63 159	91.16 151	95.94 133	81.31 133	98.06 194	89.76 149	94.29 187	97.99 93

Anonymous202405211		87.68 253	86.13 286	92.31 166	96.66 89	80.74 244	94.87 163	91.49 399	80.47 355	89.46 197	95.44 166	54.72 451	98.23 171	82.19 276	89.89 284	97.97 95

E3		91.78 116	91.61 113	92.30 169	94.48 221	80.86 238	93.73 258	96.19 149	87.63 159	91.16 151	95.95 132	81.30 134	98.06 194	89.76 149	94.29 187	97.99 93

CHOSEN 1792x2688		88.84 220	87.69 233	92.30 169	96.14 109	81.42 213	90.01 402	95.86 192	74.52 436	87.41 238	93.94 244	75.46 227	98.36 160	80.36 313	95.53 148	97.12 172

viewcassd2359sk11		91.79 113	91.62 112	92.29 171	94.62 205	80.88 236	93.70 262	96.18 155	87.38 166	91.13 154	95.85 142	81.62 129	98.06 194	89.71 151	94.40 183	97.94 98

HY-MVS	83.01 12	89.03 216	87.94 227	92.29 171	94.86 186	82.77 162	92.08 340	94.49 296	81.52 340	86.93 245	92.79 288	78.32 185	98.23 171	79.93 321	90.55 271	95.88 242

CDS-MVSNet		89.45 197	88.51 209	92.29 171	93.62 284	83.61 131	93.01 297	94.68 288	81.95 322	87.82 231	93.24 271	78.69 175	96.99 313	80.34 314	93.23 223	96.28 220
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PAPR		90.02 176	89.27 187	92.29 171	95.78 134	80.95 232	92.68 312	96.22 145	81.91 324	86.66 255	93.75 256	82.23 113	98.44 153	79.40 339	94.79 168	97.48 142

E3new		91.76 119	91.58 115	92.28 175	94.69 202	80.90 235	93.68 265	96.17 156	87.15 172	91.09 161	95.70 154	81.75 128	98.05 198	89.67 154	94.35 184	97.90 110

mvsmamba		90.33 165	89.69 171	92.25 176	95.17 165	81.64 204	95.27 134	93.36 343	84.88 247	89.51 194	94.27 231	69.29 326	97.42 267	89.34 158	96.12 137	97.68 129

E5new		91.71 123	91.55 118	92.20 177	94.33 235	80.62 248	94.41 198	96.19 149	88.06 133	91.11 156	96.16 115	79.92 149	98.03 202	90.00 141	93.80 201	97.94 98

E6new		91.71 123	91.55 118	92.20 177	94.32 237	80.62 248	94.41 198	96.19 149	88.06 133	91.11 156	96.16 115	79.92 149	98.03 202	90.00 141	93.80 201	97.94 98

E6		91.71 123	91.55 118	92.20 177	94.32 237	80.62 248	94.41 198	96.19 149	88.06 133	91.11 156	96.16 115	79.92 149	98.03 202	90.00 141	93.80 201	97.94 98

E5		91.71 123	91.55 118	92.20 177	94.33 235	80.62 248	94.41 198	96.19 149	88.06 133	91.11 156	96.16 115	79.92 149	98.03 202	90.00 141	93.80 201	97.94 98

PLC	84.53 7	89.06 214	88.03 223	92.15 181	97.27 78	82.69 169	94.29 213	95.44 231	79.71 364	84.01 336	94.18 234	76.68 205	98.75 116	77.28 360	93.41 217	95.02 273
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
EPP-MVSNet		91.70 127	91.56 117	92.13 182	95.88 130	80.50 255	97.33 8	95.25 245	86.15 203	89.76 191	95.60 158	83.42 91	98.32 167	87.37 192	93.25 222	97.56 138

patch_mono-2		93.74 65	94.32 41	92.01 183	97.54 66	78.37 326	93.40 274	97.19 44	88.02 137	94.99 57	97.21 62	88.35 25	98.44 153	94.07 54	98.09 76	99.23 1

原ACMM1				92.01 183	97.34 73	81.05 227		96.81 88	78.89 375	90.45 170	95.92 135	82.65 104	98.84 106	80.68 308	98.26 62	96.14 227

UniMVSNet (Re)		89.80 186	89.07 191	92.01 183	93.60 285	84.52 97	94.78 172	97.47 16	89.26 85	86.44 261	92.32 301	82.10 118	97.39 278	84.81 229	80.84 405	94.12 317

MG-MVS		91.77 118	91.70 111	92.00 186	97.08 81	80.03 274	93.60 267	95.18 250	87.85 149	90.89 164	96.47 102	82.06 120	98.36 160	85.07 224	97.04 109	97.62 132

EIA-MVS		91.95 110	91.94 107	91.98 187	95.16 166	80.01 275	95.36 124	96.73 98	88.44 117	89.34 198	92.16 306	83.82 87	98.45 151	89.35 157	97.06 108	97.48 142

PVSNet_Blended		90.73 151	90.32 151	91.98 187	96.12 111	81.25 217	92.55 317	96.83 83	82.04 320	89.10 202	92.56 294	81.04 136	98.85 104	86.72 202	95.91 139	95.84 244

guyue		91.12 142	90.84 140	91.96 189	94.59 209	80.57 253	94.87 163	93.71 336	88.96 100	91.14 153	95.22 178	73.22 266	97.76 230	92.01 104	93.81 200	97.54 141

PS-MVSNAJ		91.18 139	90.92 137	91.96 189	95.26 161	82.60 176	92.09 339	95.70 206	86.27 199	91.84 132	92.46 296	79.70 158	98.99 82	89.08 162	95.86 141	94.29 310

TAMVS		89.21 207	88.29 218	91.96 189	93.71 279	82.62 175	93.30 282	94.19 310	82.22 314	87.78 232	93.94 244	78.83 172	96.95 316	77.70 356	92.98 231	96.32 217

SDMVSNet		90.19 169	89.61 174	91.93 192	96.00 122	83.09 152	92.89 304	95.98 176	88.73 107	86.85 251	95.20 182	72.09 283	97.08 304	88.90 167	89.85 286	95.63 254

FA-MVS(test-final)		89.66 189	88.91 199	91.93 192	94.57 213	80.27 259	91.36 360	94.74 285	84.87 248	89.82 188	92.61 293	74.72 237	98.47 146	83.97 245	93.53 211	97.04 178

MVS_Test		91.31 135	91.11 132	91.93 192	94.37 230	80.14 264	93.46 272	95.80 195	86.46 195	91.35 149	93.77 254	82.21 115	98.09 188	87.57 187	94.95 164	97.55 139

NR-MVSNet		88.58 230	87.47 239	91.93 192	93.04 306	84.16 112	94.77 173	96.25 142	89.05 93	80.04 400	93.29 269	79.02 170	97.05 309	81.71 291	80.05 415	94.59 293

HyFIR lowres test		88.09 243	86.81 256	91.93 192	96.00 122	80.63 246	90.01 402	95.79 196	73.42 448	87.68 234	92.10 312	73.86 255	97.96 215	80.75 306	91.70 254	97.19 162

GeoE		90.05 174	89.43 179	91.90 197	95.16 166	80.37 258	95.80 96	94.65 289	83.90 269	87.55 237	94.75 204	78.18 186	97.62 243	81.28 296	93.63 207	97.71 128

thisisatest0530		88.67 225	87.61 235	91.86 198	94.87 185	80.07 269	94.63 182	89.90 440	84.00 267	88.46 216	93.78 253	66.88 350	98.46 147	83.30 255	92.65 239	97.06 176

xiu_mvs_v2_base		91.13 141	90.89 139	91.86 198	94.97 177	82.42 179	92.24 332	95.64 214	86.11 207	91.74 138	93.14 275	79.67 163	98.89 98	89.06 163	95.46 153	94.28 311

DU-MVS		89.34 206	88.50 210	91.85 200	93.04 306	83.72 124	94.47 193	96.59 110	89.50 74	86.46 258	93.29 269	77.25 198	97.23 293	84.92 226	81.02 401	94.59 293

AstraMVS		90.69 153	90.30 152	91.84 201	93.81 270	79.85 282	94.76 174	92.39 368	88.96 100	91.01 163	95.87 141	70.69 298	97.94 218	92.49 82	92.70 238	97.73 126

OPM-MVS		90.12 170	89.56 175	91.82 202	93.14 298	83.90 119	94.16 220	95.74 200	88.96 100	87.86 227	95.43 168	72.48 276	97.91 221	88.10 179	90.18 278	93.65 351
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
HQP_MVS		90.60 161	90.19 154	91.82 202	94.70 200	82.73 166	95.85 93	96.22 145	90.81 27	86.91 247	94.86 199	74.23 245	98.12 178	88.15 175	89.99 280	94.63 290

UniMVSNet_NR-MVSNet		89.92 182	89.29 185	91.81 204	93.39 291	83.72 124	94.43 196	97.12 55	89.80 62	86.46 258	93.32 266	83.16 95	97.23 293	84.92 226	81.02 401	94.49 303

diffmvs		91.37 134	91.23 130	91.77 205	93.09 301	80.27 259	92.36 323	95.52 223	87.03 177	91.40 147	94.93 194	80.08 146	97.44 265	92.13 99	94.56 177	97.61 133
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		91.51 131	91.44 124	91.73 206	93.09 301	80.27 259	92.51 318	95.58 217	87.22 170	91.80 135	95.57 160	79.96 148	97.48 257	92.23 93	94.97 163	97.45 144

1112_ss		88.42 232	87.33 242	91.72 207	94.92 181	80.98 230	92.97 301	94.54 293	78.16 392	83.82 339	93.88 249	78.78 174	97.91 221	79.45 335	89.41 293	96.26 221

Fast-Effi-MVS+		89.41 201	88.64 205	91.71 208	94.74 194	80.81 241	93.54 268	95.10 254	83.11 292	86.82 253	90.67 366	79.74 157	97.75 234	80.51 311	93.55 209	96.57 210

WTY-MVS		89.60 191	88.92 198	91.67 209	95.47 151	81.15 222	92.38 322	94.78 283	83.11 292	89.06 204	94.32 226	78.67 176	96.61 339	81.57 292	90.89 267	97.24 156

TAPA-MVS	84.62 6	88.16 241	87.01 251	91.62 210	96.64 90	80.65 245	94.39 204	96.21 148	76.38 415	86.19 268	95.44 166	79.75 156	98.08 191	62.75 462	95.29 158	96.13 228
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
VPA-MVSNet		89.62 190	88.96 196	91.60 211	93.86 267	82.89 161	95.46 121	97.33 32	87.91 144	88.43 217	93.31 267	74.17 248	97.40 275	87.32 193	82.86 376	94.52 298

FE-MVS		87.40 271	86.02 292	91.57 212	94.56 214	79.69 289	90.27 389	93.72 335	80.57 353	88.80 210	91.62 332	65.32 366	98.59 137	74.97 386	94.33 186	96.44 213

XVG-OURS		89.40 203	88.70 204	91.52 213	94.06 254	81.46 211	91.27 365	96.07 169	86.14 204	88.89 209	95.77 150	68.73 335	97.26 290	87.39 191	89.96 282	95.83 245

hse-mvs2		89.88 184	89.34 183	91.51 214	94.83 188	81.12 224	93.94 243	93.91 323	89.80 62	93.08 90	93.60 259	75.77 219	97.66 238	92.07 100	77.07 435	95.74 249

TranMVSNet+NR-MVSNet		88.84 220	87.95 226	91.49 215	92.68 323	83.01 157	94.92 160	96.31 131	89.88 56	85.53 284	93.85 251	76.63 206	96.96 315	81.91 284	79.87 418	94.50 301

AUN-MVS		87.78 251	86.54 271	91.48 216	94.82 189	81.05 227	93.91 247	93.93 320	83.00 297	86.93 245	93.53 261	69.50 320	97.67 236	86.14 208	77.12 434	95.73 251

XVG-OURS-SEG-HR		89.95 180	89.45 177	91.47 217	94.00 260	81.21 220	91.87 344	96.06 171	85.78 211	88.55 214	95.73 152	74.67 238	97.27 288	88.71 171	89.64 291	95.91 239

MVS		87.44 269	86.10 289	91.44 218	92.61 325	83.62 129	92.63 314	95.66 211	67.26 474	81.47 378	92.15 307	77.95 189	98.22 173	79.71 324	95.48 151	92.47 402

hybrid		90.69 153	90.45 148	91.43 219	92.67 324	79.42 297	92.28 331	95.21 249	85.15 239	90.39 173	95.37 170	78.93 171	97.32 283	90.27 139	93.74 205	97.55 139

viewdifsd2359ckpt07		91.11 143	91.02 135	91.41 220	94.21 247	78.37 326	92.91 303	95.71 205	87.50 161	90.32 174	95.88 138	80.27 144	97.99 208	88.78 170	93.55 209	97.86 113

F-COLMAP		87.95 246	86.80 257	91.40 221	96.35 104	80.88 236	94.73 176	95.45 229	79.65 365	82.04 373	94.61 213	71.13 290	98.50 141	76.24 373	91.05 265	94.80 287

dcpmvs_2		93.49 70	94.19 52	91.38 222	97.69 63	76.78 370	94.25 215	96.29 132	88.33 120	94.46 60	96.88 79	88.07 29	98.64 130	93.62 62	98.09 76	98.73 23

thisisatest0515		87.33 274	85.99 293	91.37 223	93.49 287	79.55 290	90.63 381	89.56 448	80.17 357	87.56 236	90.86 356	67.07 347	98.28 169	81.50 293	93.02 230	96.29 219

HQP-MVS		89.80 186	89.28 186	91.34 224	94.17 249	81.56 205	94.39 204	96.04 172	88.81 103	85.43 293	93.97 243	73.83 256	97.96 215	87.11 197	89.77 289	94.50 301

fmvsm_s_conf0.5_n_7		93.15 89	93.76 68	91.31 225	94.42 228	79.48 292	94.52 188	97.14 53	89.33 81	94.17 66	98.09 18	81.83 125	97.49 256	96.33 26	98.02 80	96.95 186

RRT-MVS		90.85 147	90.70 144	91.30 226	94.25 244	76.83 369	94.85 166	96.13 163	89.04 94	90.23 176	94.88 197	70.15 309	98.72 120	91.86 112	94.88 166	98.34 48

FMVSNet3		87.40 271	86.11 288	91.30 226	93.79 273	83.64 128	94.20 219	94.81 281	83.89 270	84.37 323	91.87 323	68.45 338	96.56 348	78.23 351	85.36 344	93.70 350

FMVSNet2		87.19 284	85.82 301	91.30 226	94.01 257	83.67 126	94.79 171	94.94 267	83.57 278	83.88 338	92.05 316	66.59 355	96.51 352	77.56 358	85.01 347	93.73 348

RPMNet		83.95 364	81.53 375	91.21 229	90.58 402	79.34 302	85.24 466	96.76 93	71.44 462	85.55 282	82.97 466	70.87 295	98.91 97	61.01 466	89.36 295	95.40 260

IB-MVS	80.51 15	85.24 340	83.26 359	91.19 230	92.13 337	79.86 281	91.75 348	91.29 405	83.28 289	80.66 390	88.49 415	61.28 404	98.46 147	80.99 302	79.46 422	95.25 266
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		89.47 196	88.90 200	91.18 231	94.22 246	82.07 189	92.13 337	96.09 167	87.90 145	85.37 299	92.45 297	74.38 243	97.56 248	87.15 195	90.43 273	93.93 328
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		89.45 197	88.90 200	91.12 232	94.47 222	81.49 209	95.30 129	96.14 160	86.73 188	85.45 290	95.16 184	69.89 312	98.10 180	87.70 184	89.23 298	93.77 344

LGP-MVS_train				91.12 232	94.47 222	81.49 209		96.14 160	86.73 188	85.45 290	95.16 184	69.89 312	98.10 180	87.70 184	89.23 298	93.77 344

ACMM	84.12 9	89.14 209	88.48 213	91.12 232	94.65 204	81.22 219	95.31 127	96.12 164	85.31 231	85.92 273	94.34 224	70.19 308	98.06 194	85.65 216	88.86 303	94.08 321
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tttt0517		88.61 227	87.78 232	91.11 235	94.96 178	77.81 345	95.35 125	89.69 443	85.09 242	88.05 225	94.59 216	66.93 348	98.48 143	83.27 256	92.13 251	97.03 179

GBi-Net		87.26 276	85.98 294	91.08 236	94.01 257	83.10 149	95.14 147	94.94 267	83.57 278	84.37 323	91.64 328	66.59 355	96.34 366	78.23 351	85.36 344	93.79 339

test1		87.26 276	85.98 294	91.08 236	94.01 257	83.10 149	95.14 147	94.94 267	83.57 278	84.37 323	91.64 328	66.59 355	96.34 366	78.23 351	85.36 344	93.79 339

FMVSNet1		85.85 325	84.11 346	91.08 236	92.81 318	83.10 149	95.14 147	94.94 267	81.64 335	82.68 363	91.64 328	59.01 426	96.34 366	75.37 380	83.78 360	93.79 339

Test_1112_low_res		87.65 255	86.51 272	91.08 236	94.94 180	79.28 306	91.77 347	94.30 305	76.04 421	83.51 349	92.37 299	77.86 192	97.73 235	78.69 346	89.13 300	96.22 222

PS-MVSNAJss		89.97 178	89.62 173	91.02 240	91.90 346	80.85 240	95.26 135	95.98 176	86.26 200	86.21 267	94.29 228	79.70 158	97.65 239	88.87 169	88.10 314	94.57 295

BH-RMVSNet		88.37 235	87.48 238	91.02 240	95.28 158	79.45 294	92.89 304	93.07 351	85.45 226	86.91 247	94.84 202	70.35 305	97.76 230	73.97 395	94.59 176	95.85 243

UniMVSNet_ETH3D		87.53 265	86.37 276	91.00 242	92.44 329	78.96 311	94.74 175	95.61 215	84.07 266	85.36 300	94.52 218	59.78 418	97.34 281	82.93 260	87.88 319	96.71 203

FIs		90.51 163	90.35 150	90.99 243	93.99 261	80.98 230	95.73 104	97.54 9	89.15 89	86.72 254	94.68 207	81.83 125	97.24 292	85.18 222	88.31 313	94.76 288

ACMP	84.23 8	89.01 218	88.35 214	90.99 243	94.73 195	81.27 216	95.07 150	95.89 188	86.48 193	83.67 344	94.30 227	69.33 322	97.99 208	87.10 199	88.55 305	93.72 349
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
Anonymous20231211		86.59 308	85.13 323	90.98 245	96.52 98	81.50 207	96.14 64	96.16 158	73.78 444	83.65 345	92.15 307	63.26 387	97.37 280	82.82 264	81.74 390	94.06 322

IMVS_0403		89.97 178	89.64 172	90.96 246	93.72 275	77.75 350	93.00 298	95.34 240	85.53 222	88.77 211	94.49 219	78.49 182	97.84 226	84.75 230	92.65 239	97.28 151

sss		88.93 219	88.26 220	90.94 247	94.05 255	80.78 243	91.71 349	95.38 235	81.55 339	88.63 213	93.91 248	75.04 231	95.47 407	82.47 269	91.61 255	96.57 210

IMVS_0407		89.85 185	89.51 176	90.88 248	93.72 275	77.75 350	93.07 295	95.34 240	85.53 222	88.34 219	94.49 219	77.69 194	97.60 244	84.75 230	92.65 239	97.28 151

dtuplus		89.78 188	89.43 179	90.85 249	92.83 317	77.91 339	92.32 329	94.97 264	82.33 312	90.20 177	95.53 162	78.56 179	97.38 279	85.15 223	92.95 232	97.24 156

viewmambaseed2359dif		90.04 175	89.78 169	90.83 250	92.85 316	77.92 338	92.23 333	95.01 258	81.90 325	90.20 177	95.45 165	79.64 165	97.34 281	87.52 189	93.17 224	97.23 160

sd_testset		88.59 229	87.85 231	90.83 250	96.00 122	80.42 257	92.35 325	94.71 286	88.73 107	86.85 251	95.20 182	67.31 342	96.43 360	79.64 327	89.85 286	95.63 254

PVSNet_BlendedMVS		89.98 177	89.70 170	90.82 252	96.12 111	81.25 217	93.92 245	96.83 83	83.49 282	89.10 202	92.26 304	81.04 136	98.85 104	86.72 202	87.86 320	92.35 409

cascas		86.43 316	84.98 326	90.80 253	92.10 339	80.92 234	90.24 393	95.91 185	73.10 451	83.57 348	88.39 416	65.15 368	97.46 261	84.90 228	91.43 257	94.03 324

ECVR-MVS		89.09 212	88.53 208	90.77 254	95.62 144	75.89 383	96.16 60	84.22 477	87.89 147	90.20 177	96.65 91	63.19 389	98.10 180	85.90 213	96.94 112	98.33 50

GA-MVS		86.61 306	85.27 320	90.66 255	91.33 369	78.71 315	90.40 388	93.81 330	85.34 230	85.12 303	89.57 397	61.25 405	97.11 302	80.99 302	89.59 292	96.15 226

thres600view7		87.65 255	86.67 263	90.59 256	96.08 117	78.72 313	94.88 162	91.58 395	87.06 176	88.08 223	92.30 302	68.91 332	98.10 180	70.05 425	91.10 260	94.96 277

thres400		87.62 260	86.64 264	90.57 257	95.99 125	78.64 316	94.58 184	91.98 384	86.94 182	88.09 221	91.77 324	69.18 328	98.10 180	70.13 422	91.10 260	94.96 277

baseline1		88.10 242	87.28 244	90.57 257	94.96 178	80.07 269	94.27 214	91.29 405	86.74 187	87.41 238	94.00 241	76.77 203	96.20 371	80.77 305	79.31 424	95.44 258

viewdifsd2359ckpt11		89.43 199	89.05 193	90.56 259	92.89 314	77.00 365	92.81 307	94.52 294	87.03 177	89.77 189	95.79 147	74.67 238	97.51 252	88.97 165	84.98 348	97.17 163

viewmsd2359difaftdt		89.43 199	89.05 193	90.56 259	92.89 314	77.00 365	92.81 307	94.52 294	87.03 177	89.77 189	95.79 147	74.67 238	97.51 252	88.97 165	84.98 348	97.17 163

usedtu_dtu_shiyan1		86.84 295	85.61 309	90.53 261	90.50 406	81.80 199	90.97 373	94.96 265	83.05 294	83.50 350	90.32 373	72.15 280	96.65 330	79.49 332	85.55 342	93.15 374

FE-MVSNET3		86.84 295	85.61 309	90.53 261	90.50 406	81.80 199	90.97 373	94.96 265	83.05 294	83.50 350	90.32 373	72.15 280	96.65 330	79.49 332	85.55 342	93.15 374

FC-MVSNet-test		90.27 167	90.18 155	90.53 261	93.71 279	79.85 282	95.77 100	97.59 6	89.31 82	86.27 265	94.67 210	81.93 123	97.01 312	84.26 240	88.09 316	94.71 289

PAPM		86.68 305	85.39 315	90.53 261	93.05 305	79.33 305	89.79 405	94.77 284	78.82 378	81.95 374	93.24 271	76.81 201	97.30 284	66.94 442	93.16 225	94.95 281

WR-MVS		88.38 234	87.67 234	90.52 265	93.30 293	80.18 262	93.26 285	95.96 180	88.57 115	85.47 289	92.81 286	76.12 211	96.91 319	81.24 297	82.29 381	94.47 306

SSM_04072		88.57 231	87.92 228	90.51 266	94.76 191	82.66 170	79.84 488	94.64 290	85.18 232	88.96 206	95.00 191	76.00 214	92.03 458	83.74 250	93.15 226	96.85 195

MVSTER		88.84 220	88.29 218	90.51 266	92.95 311	80.44 256	93.73 258	95.01 258	84.66 257	87.15 242	93.12 276	72.79 271	97.21 295	87.86 181	87.36 328	93.87 333

testdata				90.49 268	96.40 101	77.89 342		95.37 237	72.51 456	93.63 79	96.69 87	82.08 119	97.65 239	83.08 257	97.39 101	95.94 238

test1111		89.10 210	88.64 205	90.48 269	95.53 149	74.97 393	96.08 69	84.89 475	88.13 130	90.16 183	96.65 91	63.29 386	98.10 180	86.14 208	96.90 115	98.39 45

tt0805		86.92 292	85.74 307	90.48 269	92.22 333	79.98 277	95.63 114	94.88 275	83.83 272	84.74 312	92.80 287	57.61 433	97.67 236	85.48 219	84.42 353	93.79 339

jajsoiax		88.24 239	87.50 237	90.48 269	90.89 390	80.14 264	95.31 127	95.65 213	84.97 245	84.24 331	94.02 239	65.31 367	97.42 267	88.56 172	88.52 307	93.89 329

PatchMatch-RL		86.77 302	85.54 311	90.47 272	95.88 130	82.71 168	90.54 384	92.31 372	79.82 363	84.32 328	91.57 336	68.77 334	96.39 362	73.16 401	93.48 215	92.32 410

0.4-1-1-0.1		81.55 396	78.59 418	90.42 273	87.55 448	79.90 279	88.56 427	89.19 453	77.01 407	79.72 407	77.71 481	54.84 448	97.11 302	80.50 312	72.20 448	94.26 312

tfpn200view9		87.58 263	86.64 264	90.41 274	95.99 125	78.64 316	94.58 184	91.98 384	86.94 182	88.09 221	91.77 324	69.18 328	98.10 180	70.13 422	91.10 260	94.48 304

VPNet		88.20 240	87.47 239	90.39 275	93.56 286	79.46 293	94.04 233	95.54 221	88.67 110	86.96 244	94.58 217	69.33 322	97.15 297	84.05 244	80.53 410	94.56 296

ACMH	80.38 17	85.36 335	83.68 353	90.39 275	94.45 225	80.63 246	94.73 176	94.85 277	82.09 316	77.24 435	92.65 291	60.01 416	97.58 246	72.25 406	84.87 350	92.96 381
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
thres100view900		87.63 258	86.71 260	90.38 277	96.12 111	78.55 319	95.03 154	91.58 395	87.15 172	88.06 224	92.29 303	68.91 332	98.10 180	70.13 422	91.10 260	94.48 304

mvs_tets		88.06 245	87.28 244	90.38 277	90.94 386	79.88 280	95.22 138	95.66 211	85.10 241	84.21 332	93.94 244	63.53 384	97.40 275	88.50 173	88.40 311	93.87 333

1314		87.51 266	86.57 269	90.34 279	92.42 330	79.74 287	92.63 314	95.35 239	78.35 387	80.14 397	91.62 332	74.05 250	97.15 297	81.05 298	93.53 211	94.12 317

LTVRE_ROB	82.13 13	86.26 319	84.90 329	90.34 279	94.44 226	81.50 207	92.31 330	94.89 273	83.03 296	79.63 409	92.67 290	69.69 315	97.79 228	71.20 411	86.26 337	91.72 420
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		80.75 409	77.58 423	90.25 281	86.55 453	79.72 288	87.46 448	89.48 451	76.43 414	77.93 430	75.94 483	52.31 460	97.05 309	80.25 317	71.85 452	93.99 326

test_djsdf		89.03 216	88.64 205	90.21 282	90.74 397	79.28 306	95.96 83	95.90 186	84.66 257	85.33 301	92.94 281	74.02 251	97.30 284	89.64 155	88.53 306	94.05 323

v2v482		87.84 248	87.06 248	90.17 283	90.99 382	79.23 309	94.00 239	95.13 251	84.87 248	85.53 284	92.07 315	74.45 242	97.45 262	84.71 235	81.75 389	93.85 336

pmmvs4		85.43 333	83.86 351	90.16 284	90.02 417	82.97 159	90.27 389	92.67 363	75.93 422	80.73 388	91.74 326	71.05 291	95.73 396	78.85 345	83.46 367	91.78 419

V42		87.68 253	86.86 253	90.15 285	90.58 402	80.14 264	94.24 217	95.28 244	83.66 276	85.67 279	91.33 338	74.73 236	97.41 273	84.43 239	81.83 387	92.89 384

MSDG		84.86 348	83.09 362	90.14 286	93.80 271	80.05 271	89.18 418	93.09 350	78.89 375	78.19 426	91.91 321	65.86 365	97.27 288	68.47 431	88.45 309	93.11 376

sc_t1		81.53 397	78.67 417	90.12 287	90.78 394	78.64 316	93.91 247	90.20 429	68.42 471	80.82 387	89.88 390	46.48 475	96.76 324	76.03 376	71.47 453	94.96 277

anonymousdsp		87.84 248	87.09 247	90.12 287	89.13 428	80.54 254	94.67 180	95.55 219	82.05 318	83.82 339	92.12 309	71.47 288	97.15 297	87.15 195	87.80 323	92.67 391

thres200		87.21 282	86.24 283	90.12 287	95.36 154	78.53 320	93.26 285	92.10 378	86.42 196	88.00 226	91.11 349	69.24 327	98.00 207	69.58 426	91.04 266	93.83 338

CR-MVSNet		85.35 336	83.76 352	90.12 287	90.58 402	79.34 302	85.24 466	91.96 386	78.27 389	85.55 282	87.87 426	71.03 292	95.61 399	73.96 396	89.36 295	95.40 260

0.4-1-1-0.2		80.84 408	77.77 421	90.06 291	86.18 457	79.35 300	86.75 454	89.54 449	76.23 419	78.59 425	75.46 486	55.03 447	96.99 313	80.11 319	72.05 450	93.85 336

v1144		87.61 261	86.79 258	90.06 291	91.01 381	79.34 302	93.95 242	95.42 234	83.36 287	85.66 280	91.31 341	74.98 232	97.42 267	83.37 254	82.06 383	93.42 360

XXY-MVS		87.65 255	86.85 254	90.03 293	92.14 336	80.60 252	93.76 255	95.23 246	82.94 299	84.60 314	94.02 239	74.27 244	95.49 406	81.04 299	83.68 363	94.01 325

Vis-MVSNet (Re-imp)		89.59 192	89.44 178	90.03 293	95.74 135	75.85 384	95.61 115	90.80 419	87.66 158	87.83 230	95.40 169	76.79 202	96.46 357	78.37 347	96.73 121	97.80 121

test2506		87.21 282	86.28 281	90.02 295	95.62 144	73.64 409	96.25 55	71.38 500	87.89 147	90.45 170	96.65 91	55.29 445	98.09 188	86.03 212	96.94 112	98.33 50

BH-untuned		88.60 228	88.13 222	90.01 296	95.24 162	78.50 322	93.29 283	94.15 313	84.75 253	84.46 320	93.40 263	75.76 221	97.40 275	77.59 357	94.52 179	94.12 317

v1192		87.25 278	86.33 278	90.00 297	90.76 396	79.04 310	93.80 253	95.48 224	82.57 306	85.48 288	91.18 345	73.38 265	97.42 267	82.30 273	82.06 383	93.53 354

v7n		86.81 297	85.76 305	89.95 298	90.72 398	79.25 308	95.07 150	95.92 183	84.45 260	82.29 367	90.86 356	72.60 275	97.53 250	79.42 338	80.52 411	93.08 378

testing91		87.11 287	86.18 284	89.92 299	94.43 227	75.38 392	91.53 355	92.27 374	86.48 193	86.50 256	90.24 376	61.19 408	97.53 250	82.10 278	90.88 268	96.84 198

IMVS_0404		87.60 262	86.84 255	89.89 300	93.72 275	77.75 350	88.56 427	95.34 240	85.53 222	79.98 401	94.49 219	66.54 358	94.64 420	84.75 230	92.65 239	97.28 151

v8		87.50 268	86.71 260	89.89 300	91.37 366	79.40 298	94.50 189	95.38 235	84.81 251	83.60 347	91.33 338	76.05 212	97.42 267	82.84 263	80.51 412	92.84 386

v10		87.25 278	86.38 275	89.85 302	91.19 372	79.50 291	94.48 190	95.45 229	83.79 274	83.62 346	91.19 343	75.13 229	97.42 267	81.94 283	80.60 407	92.63 393

baseline2		86.50 312	85.39 315	89.84 303	91.12 377	76.70 372	91.88 343	88.58 455	82.35 311	79.95 402	90.95 354	73.42 263	97.63 242	80.27 316	89.95 283	95.19 267

pm-mvs1		86.61 306	85.54 311	89.82 304	91.44 361	80.18 262	95.28 133	94.85 277	83.84 271	81.66 376	92.62 292	72.45 278	96.48 354	79.67 326	78.06 427	92.82 387

TR-MVS		86.78 299	85.76 305	89.82 304	94.37 230	78.41 324	92.47 319	92.83 357	81.11 349	86.36 262	92.40 298	68.73 335	97.48 257	73.75 399	89.85 286	93.57 353

ACMH+	81.04 14	85.05 343	83.46 356	89.82 304	94.66 203	79.37 299	94.44 195	94.12 316	82.19 315	78.04 428	92.82 285	58.23 429	97.54 249	73.77 398	82.90 375	92.54 399

EI-MVSNet		89.10 210	88.86 202	89.80 307	91.84 348	78.30 329	93.70 262	95.01 258	85.73 213	87.15 242	95.28 175	79.87 155	97.21 295	83.81 248	87.36 328	93.88 332

gbinet_0.2-2-1-0.02		82.59 378	80.19 390	89.77 308	85.23 468	80.05 271	91.59 354	93.52 339	77.60 396	79.78 406	82.87 468	63.26 387	96.45 358	78.93 343	68.97 463	92.81 388

usedtu_blend_shiyan5		82.39 383	79.93 397	89.75 309	85.12 469	80.08 267	92.36 323	93.26 344	74.29 439	79.00 417	82.72 469	64.29 378	96.60 343	79.60 328	68.75 467	92.55 396

v144192		87.19 284	86.35 277	89.74 310	90.64 400	78.24 331	93.92 245	95.43 232	81.93 323	85.51 286	91.05 352	74.21 247	97.45 262	82.86 262	81.56 391	93.53 354

COLMAP_ROB	80.39 16	83.96 363	82.04 372	89.74 310	95.28 158	79.75 286	94.25 215	92.28 373	75.17 429	78.02 429	93.77 254	58.60 428	97.84 226	65.06 453	85.92 338	91.63 422
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
SCA		86.32 318	85.18 322	89.73 312	92.15 335	76.60 373	91.12 369	91.69 391	83.53 281	85.50 287	88.81 409	66.79 351	96.48 354	76.65 366	90.35 275	96.12 229

blend_shiyan4		81.94 386	79.35 405	89.70 313	85.52 464	80.08 267	91.29 363	93.82 327	77.12 405	79.31 413	82.94 467	54.81 449	96.60 343	79.60 328	69.78 458	92.41 405

IterMVS-LS		88.36 236	87.91 230	89.70 313	93.80 271	78.29 330	93.73 258	95.08 256	85.73 213	84.75 311	91.90 322	79.88 154	96.92 318	83.83 247	82.51 377	93.89 329
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
blended_shiyan8		82.79 373	80.49 383	89.69 315	85.50 465	79.83 284	91.38 358	93.82 327	77.14 402	79.39 412	83.73 459	64.95 372	96.63 333	79.75 323	68.77 466	92.62 395

testing11		86.44 315	85.35 318	89.69 315	94.29 241	75.40 391	91.30 362	90.53 424	84.76 252	85.06 305	90.13 382	58.95 427	97.45 262	82.08 279	91.09 264	96.21 224

testing99		86.72 303	85.73 308	89.69 315	94.23 245	74.91 395	91.35 361	90.97 413	86.14 204	86.36 262	90.22 377	59.41 421	97.48 257	82.24 275	90.66 270	96.69 205

v1921920		86.97 291	86.06 291	89.69 315	90.53 405	78.11 334	93.80 253	95.43 232	81.90 325	85.33 301	91.05 352	72.66 272	97.41 273	82.05 281	81.80 388	93.53 354

icg_test_0407_2		89.15 208	88.97 195	89.68 319	93.72 275	77.75 350	88.26 433	95.34 240	85.53 222	88.34 219	94.49 219	77.69 194	93.99 433	84.75 230	92.65 239	97.28 151

blended_shiyan6		82.78 374	80.48 384	89.67 320	85.53 463	79.76 285	91.37 359	93.82 327	77.14 402	79.30 414	83.73 459	64.96 371	96.63 333	79.68 325	68.75 467	92.63 393

VortexMVS		88.42 232	88.01 224	89.63 321	93.89 266	78.82 312	93.82 251	95.47 225	86.67 190	84.53 318	91.99 318	72.62 274	96.65 330	89.02 164	84.09 357	93.41 361

Fast-Effi-MVS+-dtu		87.44 269	86.72 259	89.63 321	92.04 340	77.68 355	94.03 234	93.94 319	85.81 210	82.42 366	91.32 340	70.33 306	97.06 307	80.33 315	90.23 277	94.14 316

v1240		86.78 299	85.85 300	89.56 323	90.45 409	77.79 347	93.61 266	95.37 237	81.65 334	85.43 293	91.15 347	71.50 287	97.43 266	81.47 294	82.05 385	93.47 358

Effi-MVS+-dtu		88.65 226	88.35 214	89.54 324	93.33 292	76.39 377	94.47 193	94.36 303	87.70 155	85.43 293	89.56 398	73.45 261	97.26 290	85.57 218	91.28 259	94.97 274

wanda-best-256-512		82.44 380	80.07 392	89.53 325	85.12 469	79.44 295	90.49 385	93.75 333	76.97 408	79.00 417	82.72 469	64.29 378	96.61 339	79.56 330	68.75 467	92.55 396

FE-blended-shiyan7		82.44 380	80.07 392	89.53 325	85.12 469	79.44 295	90.49 385	93.75 333	76.97 408	79.00 417	82.72 469	64.29 378	96.61 339	79.56 330	68.75 467	92.55 396

AllTest		83.42 370	81.39 376	89.52 327	95.01 172	77.79 347	93.12 289	90.89 417	77.41 398	76.12 444	93.34 264	54.08 454	97.51 252	68.31 433	84.27 355	93.26 364

TestCases				89.52 327	95.01 172	77.79 347		90.89 417	77.41 398	76.12 444	93.34 264	54.08 454	97.51 252	68.31 433	84.27 355	93.26 364

mvs_anonymous		89.37 205	89.32 184	89.51 329	93.47 288	74.22 402	91.65 352	94.83 279	82.91 300	85.45 290	93.79 252	81.23 135	96.36 365	86.47 204	94.09 192	97.94 98

XVG-ACMP-BASELINE		86.00 321	84.84 331	89.45 330	91.20 371	78.00 336	91.70 350	95.55 219	85.05 243	82.97 360	92.25 305	54.49 452	97.48 257	82.93 260	87.45 327	92.89 384

testing222		84.84 349	83.32 357	89.43 331	94.15 252	75.94 382	91.09 370	89.41 452	84.90 246	85.78 276	89.44 399	52.70 459	96.28 369	70.80 417	91.57 256	96.07 233

MVP-Stereo		85.97 322	84.86 330	89.32 332	90.92 388	82.19 186	92.11 338	94.19 310	78.76 380	78.77 424	91.63 331	68.38 339	96.56 348	75.01 385	93.95 195	89.20 462
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PatchmatchNet		85.85 325	84.70 333	89.29 333	91.76 352	75.54 388	88.49 429	91.30 404	81.63 336	85.05 306	88.70 413	71.71 284	96.24 370	74.61 391	89.05 301	96.08 232
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v148		87.04 289	86.32 279	89.21 334	90.94 386	77.26 361	93.71 261	94.43 298	84.84 250	84.36 326	90.80 360	76.04 213	97.05 309	82.12 277	79.60 421	93.31 363

tfpnnormal		84.72 351	83.23 360	89.20 335	92.79 319	80.05 271	94.48 190	95.81 194	82.38 309	81.08 384	91.21 342	69.01 331	96.95 316	61.69 464	80.59 408	90.58 448

cl22		86.78 299	85.98 294	89.18 336	92.34 331	77.62 356	90.84 377	94.13 315	81.33 343	83.97 337	90.15 381	73.96 252	96.60 343	84.19 241	82.94 372	93.33 362

BH-w/o		87.57 264	87.05 249	89.12 337	94.90 184	77.90 341	92.41 320	93.51 340	82.89 301	83.70 343	91.34 337	75.75 222	97.07 306	75.49 378	93.49 213	92.39 407

WR-MVS_H		87.80 250	87.37 241	89.10 338	93.23 294	78.12 333	95.61 115	97.30 37	87.90 145	83.72 342	92.01 317	79.65 164	96.01 380	76.36 370	80.54 409	93.16 372

miper_enhance_ethall		86.90 293	86.18 284	89.06 339	91.66 357	77.58 357	90.22 395	94.82 280	79.16 371	84.48 319	89.10 403	79.19 169	96.66 329	84.06 243	82.94 372	92.94 382

c3_l		87.14 286	86.50 273	89.04 340	92.20 334	77.26 361	91.22 368	94.70 287	82.01 321	84.34 327	90.43 371	78.81 173	96.61 339	83.70 252	81.09 398	93.25 366

miper_ehance_all_eth		87.22 281	86.62 267	89.02 341	92.13 337	77.40 359	90.91 376	94.81 281	81.28 344	84.32 328	90.08 384	79.26 167	96.62 336	83.81 248	82.94 372	93.04 379

gg-mvs-nofinetune		81.77 390	79.37 404	88.99 342	90.85 392	77.73 354	86.29 458	79.63 488	74.88 434	83.19 359	69.05 496	60.34 413	96.11 375	75.46 379	94.64 175	93.11 376

ETVMVS		84.43 356	82.92 366	88.97 343	94.37 230	74.67 396	91.23 367	88.35 457	83.37 286	86.06 271	89.04 404	55.38 443	95.67 398	67.12 440	91.34 258	96.58 209

pmmvs6		83.42 370	81.60 374	88.87 344	88.01 443	77.87 343	94.96 157	94.24 309	74.67 435	78.80 423	91.09 350	60.17 415	96.49 353	77.06 365	75.40 441	92.23 412

test_cas_vis1_n_1920		88.83 223	88.85 203	88.78 345	91.15 376	76.72 371	93.85 250	94.93 271	83.23 291	92.81 99	96.00 128	61.17 409	94.45 421	91.67 115	94.84 167	95.17 268

MIMVSNet		82.59 378	80.53 381	88.76 346	91.51 359	78.32 328	86.57 457	90.13 432	79.32 367	80.70 389	88.69 414	52.98 458	93.07 449	66.03 448	88.86 303	94.90 282

cl____		86.52 311	85.78 302	88.75 347	92.03 341	76.46 375	90.74 378	94.30 305	81.83 330	83.34 355	90.78 361	75.74 224	96.57 346	81.74 289	81.54 392	93.22 368

DIV-MVS_self_test		86.53 310	85.78 302	88.75 347	92.02 342	76.45 376	90.74 378	94.30 305	81.83 330	83.34 355	90.82 359	75.75 222	96.57 346	81.73 290	81.52 393	93.24 367

CP-MVSNet		87.63 258	87.26 246	88.74 349	93.12 299	76.59 374	95.29 131	96.58 111	88.43 118	83.49 352	92.98 280	75.28 228	95.83 389	78.97 342	81.15 397	93.79 339

eth_miper_zixun_eth		86.50 312	85.77 304	88.68 350	91.94 343	75.81 385	90.47 387	94.89 273	82.05 318	84.05 334	90.46 370	75.96 216	96.77 323	82.76 266	79.36 423	93.46 359

CHOSEN 280x420		85.15 341	83.99 349	88.65 351	92.47 327	78.40 325	79.68 490	92.76 360	74.90 433	81.41 380	89.59 396	69.85 314	95.51 403	79.92 322	95.29 158	92.03 415

PS-CasMVS		87.32 275	86.88 252	88.63 352	92.99 309	76.33 379	95.33 126	96.61 109	88.22 126	83.30 357	93.07 278	73.03 269	95.79 393	78.36 348	81.00 403	93.75 346

TransMVSNet (Re)		84.43 356	83.06 364	88.54 353	91.72 353	78.44 323	95.18 144	92.82 359	82.73 304	79.67 408	92.12 309	73.49 260	95.96 382	71.10 415	68.73 471	91.21 435

tt0320-xc		79.63 422	76.66 431	88.52 354	91.03 380	78.72 313	93.00 298	89.53 450	66.37 476	76.11 446	87.11 437	46.36 477	95.32 411	72.78 403	67.67 472	91.51 427

EG-PatchMatch MVS		82.37 384	80.34 386	88.46 355	90.27 411	79.35 300	92.80 310	94.33 304	77.14 402	73.26 463	90.18 380	47.47 472	96.72 325	70.25 419	87.32 330	89.30 459

PEN-MVS		86.80 298	86.27 282	88.40 356	92.32 332	75.71 387	95.18 144	96.38 126	87.97 139	82.82 362	93.15 274	73.39 264	95.92 384	76.15 374	79.03 426	93.59 352

Baseline_NR-MVSNet		87.07 288	86.63 266	88.40 356	91.44 361	77.87 343	94.23 218	92.57 365	84.12 265	85.74 278	92.08 313	77.25 198	96.04 376	82.29 274	79.94 416	91.30 433

UBG		85.51 331	84.57 338	88.35 358	94.21 247	71.78 434	90.07 400	89.66 445	82.28 313	85.91 274	89.01 405	61.30 403	97.06 307	76.58 369	92.06 252	96.22 222

D2MVS		85.90 323	85.09 324	88.35 358	90.79 393	77.42 358	91.83 346	95.70 206	80.77 352	80.08 399	90.02 386	66.74 353	96.37 363	81.88 285	87.97 318	91.26 434

pmmvs5		84.21 359	82.84 369	88.34 360	88.95 430	76.94 367	92.41 320	91.91 388	75.63 424	80.28 394	91.18 345	64.59 375	95.57 400	77.09 364	83.47 366	92.53 400

tt0320		80.13 415	77.41 424	88.29 361	90.50 406	78.02 335	93.10 292	90.71 422	66.06 479	76.75 439	86.97 438	49.56 467	95.40 408	71.65 407	71.41 454	91.46 430

LCM-MVSNet-Re		88.30 238	88.32 217	88.27 362	94.71 199	72.41 429	93.15 288	90.98 412	87.77 152	79.25 415	91.96 319	78.35 184	95.75 394	83.04 258	95.62 147	96.65 206

CostFormer		85.77 328	84.94 328	88.26 363	91.16 375	72.58 427	89.47 413	91.04 411	76.26 418	86.45 260	89.97 388	70.74 297	96.86 322	82.35 272	87.07 333	95.34 264

ITE_SJBPF				88.24 364	91.88 347	77.05 364		92.92 354	85.54 220	80.13 398	93.30 268	57.29 434	96.20 371	72.46 405	84.71 351	91.49 428

PVSNet	78.82 18	85.55 330	84.65 334	88.23 365	94.72 197	71.93 430	87.12 451	92.75 361	78.80 379	84.95 308	90.53 368	64.43 376	96.71 327	74.74 388	93.86 197	96.06 235

IterMVS-SCA-FT		85.45 332	84.53 339	88.18 366	91.71 354	76.87 368	90.19 397	92.65 364	85.40 229	81.44 379	90.54 367	66.79 351	95.00 417	81.04 299	81.05 399	92.66 392

EPNet_dtu		86.49 314	85.94 297	88.14 367	90.24 412	72.82 419	94.11 224	92.20 376	86.66 191	79.42 411	92.36 300	73.52 259	95.81 391	71.26 410	93.66 206	95.80 247
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Patchmtry		82.71 376	80.93 380	88.06 368	90.05 416	76.37 378	84.74 471	91.96 386	72.28 459	81.32 382	87.87 426	71.03 292	95.50 405	68.97 428	80.15 414	92.32 410

test_vis1_n_1920		89.39 204	89.84 166	88.04 369	92.97 310	72.64 424	94.71 178	96.03 174	86.18 202	91.94 128	96.56 99	61.63 398	95.74 395	93.42 65	95.11 162	95.74 249

DTE-MVSNet		86.11 320	85.48 313	87.98 370	91.65 358	74.92 394	94.93 159	95.75 199	87.36 167	82.26 368	93.04 279	72.85 270	95.82 390	74.04 394	77.46 432	93.20 370

PMMVS		85.71 329	84.96 327	87.95 371	88.90 431	77.09 363	88.68 425	90.06 434	72.32 458	86.47 257	90.76 362	72.15 280	94.40 424	81.78 288	93.49 213	92.36 408

GG-mvs-BLEND				87.94 372	89.73 423	77.91 339	87.80 439	78.23 493		80.58 391	83.86 457	59.88 417	95.33 410	71.20 411	92.22 250	90.60 447

MonoMVSNet		86.89 294	86.55 270	87.92 373	89.46 426	73.75 406	94.12 222	93.10 349	87.82 151	85.10 304	90.76 362	69.59 317	94.94 418	86.47 204	82.50 378	95.07 271

reproduce_monomvs		86.37 317	85.87 299	87.87 374	93.66 283	73.71 407	93.44 273	95.02 257	88.61 113	82.64 365	91.94 320	57.88 431	96.68 328	89.96 145	79.71 420	93.22 368

pmmvs-eth3d		80.97 406	78.72 416	87.74 375	84.99 472	79.97 278	90.11 399	91.65 393	75.36 426	73.51 461	86.03 446	59.45 420	93.96 436	75.17 382	72.21 447	89.29 461

MS-PatchMatch		85.05 343	84.16 344	87.73 376	91.42 364	78.51 321	91.25 366	93.53 338	77.50 397	80.15 396	91.58 334	61.99 395	95.51 403	75.69 377	94.35 184	89.16 463

mmtdpeth		85.04 345	84.15 345	87.72 377	93.11 300	75.74 386	94.37 208	92.83 357	84.98 244	89.31 199	86.41 443	61.61 400	97.14 300	92.63 81	62.11 483	90.29 449

test_0402		81.30 402	79.17 410	87.67 378	93.19 295	78.17 332	92.98 300	91.71 389	75.25 428	76.02 447	90.31 375	59.23 422	96.37 363	50.22 486	83.63 364	88.47 471

IterMVS		84.88 347	83.98 350	87.60 379	91.44 361	76.03 381	90.18 398	92.41 367	83.24 290	81.06 385	90.42 372	66.60 354	94.28 428	79.46 334	80.98 404	92.48 401
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Patchmatch-test		81.37 400	79.30 406	87.58 380	90.92 388	74.16 404	80.99 483	87.68 462	70.52 466	76.63 441	88.81 409	71.21 289	92.76 453	60.01 471	86.93 334	95.83 245

EPMVS		83.90 366	82.70 370	87.51 381	90.23 413	72.67 422	88.62 426	81.96 483	81.37 342	85.01 307	88.34 417	66.31 359	94.45 421	75.30 381	87.12 331	95.43 259

ADS-MVSNet2		81.66 393	79.71 401	87.50 382	91.35 367	74.19 403	83.33 476	88.48 456	72.90 453	82.24 369	85.77 449	64.98 369	93.20 447	64.57 455	83.74 361	95.12 269

OurMVSNet-221017-0		85.35 336	84.64 336	87.49 383	90.77 395	72.59 426	94.01 237	94.40 301	84.72 254	79.62 410	93.17 273	61.91 396	96.72 325	81.99 282	81.16 395	93.16 372

tpm2		84.08 361	82.94 365	87.48 384	91.39 365	71.27 439	89.23 417	90.37 426	71.95 460	84.64 313	89.33 400	67.30 343	96.55 350	75.17 382	87.09 332	94.63 290

RPSCF		85.07 342	84.27 341	87.48 384	92.91 313	70.62 448	91.69 351	92.46 366	76.20 420	82.67 364	95.22 178	63.94 382	97.29 287	77.51 359	85.80 339	94.53 297

myMVS_eth3d28		85.80 327	85.26 321	87.42 386	94.73 195	69.92 454	90.60 382	90.95 414	87.21 171	86.06 271	90.04 385	59.47 419	96.02 378	74.89 387	93.35 221	96.33 216

FE-MVSNET2		81.82 389	79.99 395	87.34 387	84.74 473	77.36 360	92.72 311	94.55 292	82.09 316	73.79 460	86.46 440	57.80 432	94.45 421	74.65 389	73.10 443	90.20 450

WBMVS		84.97 346	84.18 343	87.34 387	94.14 253	71.62 438	90.20 396	92.35 369	81.61 337	84.06 333	90.76 362	61.82 397	96.52 351	78.93 343	83.81 359	93.89 329

miper_lstm_enhance		85.27 339	84.59 337	87.31 389	91.28 370	74.63 397	87.69 444	94.09 317	81.20 348	81.36 381	89.85 392	74.97 233	94.30 427	81.03 301	79.84 419	93.01 380

FMVSNet5		81.52 398	79.60 402	87.27 390	91.17 373	77.95 337	91.49 356	92.26 375	76.87 410	76.16 443	87.91 425	51.67 461	92.34 456	67.74 437	81.16 395	91.52 426

USDC		82.76 375	81.26 378	87.26 391	91.17 373	74.55 398	89.27 415	93.39 342	78.26 390	75.30 451	92.08 313	54.43 453	96.63 333	71.64 408	85.79 340	90.61 445

test-LLR		85.87 324	85.41 314	87.25 392	90.95 384	71.67 436	89.55 409	89.88 441	83.41 284	84.54 316	87.95 423	67.25 344	95.11 414	81.82 286	93.37 219	94.97 274

test-mter		84.54 355	83.64 354	87.25 392	90.95 384	71.67 436	89.55 409	89.88 441	79.17 370	84.54 316	87.95 423	55.56 440	95.11 414	81.82 286	93.37 219	94.97 274

JIA-IIPM		81.04 403	78.98 414	87.25 392	88.64 432	73.48 411	81.75 482	89.61 447	73.19 450	82.05 372	73.71 490	66.07 364	95.87 387	71.18 413	84.60 352	92.41 405

TDRefinement		79.81 419	77.34 425	87.22 395	79.24 489	75.48 389	93.12 289	92.03 381	76.45 413	75.01 452	91.58 334	49.19 468	96.44 359	70.22 421	69.18 462	89.75 455

tpmvs		83.35 372	82.07 371	87.20 396	91.07 379	71.00 445	88.31 432	91.70 390	78.91 373	80.49 393	87.18 435	69.30 325	97.08 304	68.12 436	83.56 365	93.51 357

ppachtmachnet_test		81.84 388	80.07 392	87.15 397	88.46 436	74.43 401	89.04 421	92.16 377	75.33 427	77.75 432	88.99 406	66.20 361	95.37 409	65.12 452	77.60 430	91.65 421

dmvs_re		84.20 360	83.22 361	87.14 398	91.83 350	77.81 345	90.04 401	90.19 430	84.70 256	81.49 377	89.17 402	64.37 377	91.13 469	71.58 409	85.65 341	92.46 403

tpm cat1		81.96 385	80.27 387	87.01 399	91.09 378	71.02 444	87.38 449	91.53 398	66.25 477	80.17 395	86.35 445	68.22 340	96.15 374	69.16 427	82.29 381	93.86 335

test_fmvs1_n		87.03 290	87.04 250	86.97 400	89.74 422	71.86 431	94.55 186	94.43 298	78.47 384	91.95 127	95.50 164	51.16 463	93.81 437	93.02 73	94.56 177	95.26 265

OpenMVS_ROB	74.94 19	79.51 423	77.03 430	86.93 401	87.00 450	76.23 380	92.33 327	90.74 421	68.93 470	74.52 456	88.23 420	49.58 466	96.62 336	57.64 477	84.29 354	87.94 474

SixPastTwentyTwo		83.91 365	82.90 367	86.92 402	90.99 382	70.67 447	93.48 270	91.99 383	85.54 220	77.62 434	92.11 311	60.59 412	96.87 321	76.05 375	77.75 429	93.20 370

ADS-MVSNet		81.56 395	79.78 398	86.90 403	91.35 367	71.82 432	83.33 476	89.16 454	72.90 453	82.24 369	85.77 449	64.98 369	93.76 438	64.57 455	83.74 361	95.12 269

PatchT		82.68 377	81.27 377	86.89 404	90.09 415	70.94 446	84.06 473	90.15 431	74.91 432	85.63 281	83.57 461	69.37 321	94.87 419	65.19 450	88.50 308	94.84 284

tpm		84.73 350	84.02 348	86.87 405	90.33 410	68.90 457	89.06 420	89.94 438	80.85 351	85.75 277	89.86 391	68.54 337	95.97 381	77.76 355	84.05 358	95.75 248

Patchmatch-RL test		81.67 392	79.96 396	86.81 406	85.42 466	71.23 440	82.17 481	87.50 463	78.47 384	77.19 436	82.50 473	70.81 296	93.48 442	82.66 267	72.89 446	95.71 252

test_vis1_n		86.56 309	86.49 274	86.78 407	88.51 433	72.69 421	94.68 179	93.78 332	79.55 366	90.70 165	95.31 174	48.75 469	93.28 445	93.15 69	93.99 194	94.38 308

testing3-2		86.72 303	86.71 260	86.74 408	96.11 114	65.92 469	93.39 275	89.65 446	89.46 75	87.84 229	92.79 288	59.17 424	97.60 244	81.31 295	90.72 269	96.70 204

test_fmvs1		87.34 273	87.56 236	86.68 409	90.59 401	71.80 433	94.01 237	94.04 318	78.30 388	91.97 125	95.22 178	56.28 438	93.71 439	92.89 74	94.71 170	94.52 298

MDA-MVSNet-bldmvs		78.85 428	76.31 433	86.46 410	89.76 421	73.88 405	88.79 423	90.42 425	79.16 371	59.18 487	88.33 418	60.20 414	94.04 431	62.00 463	68.96 464	91.48 429

mvs5depth		80.98 405	79.15 411	86.45 411	84.57 474	73.29 414	87.79 440	91.67 392	80.52 354	82.20 371	89.72 394	55.14 446	95.93 383	73.93 397	66.83 474	90.12 452

tpmrst		85.35 336	84.99 325	86.43 412	90.88 391	67.88 462	88.71 424	91.43 402	80.13 358	86.08 270	88.80 411	73.05 268	96.02 378	82.48 268	83.40 369	95.40 260

TESTMET0.1,1		83.74 368	82.85 368	86.42 413	89.96 418	71.21 441	89.55 409	87.88 459	77.41 398	83.37 354	87.31 431	56.71 436	93.65 441	80.62 309	92.85 236	94.40 307

our_test_3		81.93 387	80.46 385	86.33 414	88.46 436	73.48 411	88.46 430	91.11 407	76.46 412	76.69 440	88.25 419	66.89 349	94.36 425	68.75 429	79.08 425	91.14 437

lessismore_v0				86.04 415	88.46 436	68.78 458		80.59 486		73.01 464	90.11 383	55.39 442	96.43 360	75.06 384	65.06 478	92.90 383

TinyColmap		79.76 420	77.69 422	85.97 416	91.71 354	73.12 415	89.55 409	90.36 427	75.03 430	72.03 467	90.19 379	46.22 478	96.19 373	63.11 459	81.03 400	88.59 470

KD-MVS_2432*1600		78.50 429	76.02 437	85.93 417	86.22 455	74.47 399	84.80 469	92.33 370	79.29 368	76.98 437	85.92 447	53.81 456	93.97 434	67.39 438	57.42 488	89.36 457

miper_refine_blended		78.50 429	76.02 437	85.93 417	86.22 455	74.47 399	84.80 469	92.33 370	79.29 368	76.98 437	85.92 447	53.81 456	93.97 434	67.39 438	57.42 488	89.36 457

K. test v3		81.59 394	80.15 391	85.91 419	89.89 420	69.42 456	92.57 316	87.71 461	85.56 219	73.44 462	89.71 395	55.58 439	95.52 402	77.17 362	69.76 459	92.78 389

SSC-MVS3.2		84.60 354	84.19 342	85.85 420	92.74 321	68.07 459	88.15 435	93.81 330	87.42 165	83.76 341	91.07 351	62.91 390	95.73 396	74.56 392	83.24 370	93.75 346

mvsany_test1		85.42 334	85.30 319	85.77 421	87.95 445	75.41 390	87.61 447	80.97 485	76.82 411	88.68 212	95.83 144	77.44 197	90.82 471	85.90 213	86.51 335	91.08 441

MIMVSNet1		79.38 424	77.28 426	85.69 422	86.35 454	73.67 408	91.61 353	92.75 361	78.11 393	72.64 465	88.12 421	48.16 470	91.97 462	60.32 468	77.49 431	91.43 431

UWE-MVS		83.69 369	83.09 362	85.48 423	93.06 304	65.27 474	90.92 375	86.14 467	79.90 361	86.26 266	90.72 365	57.17 435	95.81 391	71.03 416	92.62 244	95.35 263

UnsupCasMVSNet_eth		80.07 416	78.27 420	85.46 424	85.24 467	72.63 425	88.45 431	94.87 276	82.99 298	71.64 470	88.07 422	56.34 437	91.75 464	73.48 400	63.36 481	92.01 416

CL-MVSNet_self_test		81.74 391	80.53 381	85.36 425	85.96 458	72.45 428	90.25 391	93.07 351	81.24 346	79.85 405	87.29 432	70.93 294	92.52 454	66.95 441	69.23 461	91.11 439

MDA-MVSNet_test_wron		79.21 426	77.19 428	85.29 426	88.22 440	72.77 420	85.87 460	90.06 434	74.34 437	62.62 484	87.56 429	66.14 362	91.99 461	66.90 445	73.01 444	91.10 440

YYNet1		79.22 425	77.20 427	85.28 427	88.20 441	72.66 423	85.87 460	90.05 436	74.33 438	62.70 482	87.61 428	66.09 363	92.03 458	66.94 442	72.97 445	91.15 436

WB-MVSnew		83.77 367	83.28 358	85.26 428	91.48 360	71.03 443	91.89 342	87.98 458	78.91 373	84.78 310	90.22 377	69.11 330	94.02 432	64.70 454	90.44 272	90.71 443

dp		81.47 399	80.23 388	85.17 429	89.92 419	65.49 472	86.74 455	90.10 433	76.30 417	81.10 383	87.12 436	62.81 391	95.92 384	68.13 435	79.88 417	94.09 320

UnsupCasMVSNet_bld		76.23 439	73.27 443	85.09 430	83.79 476	72.92 417	85.65 463	93.47 341	71.52 461	68.84 476	79.08 479	49.77 465	93.21 446	66.81 446	60.52 485	89.13 465

usedtu_dtu_shiyan2		74.72 441	71.30 446	84.98 431	77.78 491	70.58 449	91.85 345	90.76 420	67.24 475	68.06 478	82.17 474	37.13 487	92.78 452	60.69 467	66.03 475	91.59 425

SD_0403		84.71 352	84.65 334	84.92 432	92.95 311	65.95 468	92.07 341	93.23 346	83.82 273	79.03 416	93.73 257	73.90 253	92.91 451	63.02 461	90.05 279	95.89 241

Anonymous20231206		81.03 404	79.77 400	84.82 433	87.85 446	70.26 451	91.42 357	92.08 379	73.67 445	77.75 432	89.25 401	62.43 393	93.08 448	61.50 465	82.00 386	91.12 438

FE-MVSNET		78.19 431	76.03 436	84.69 434	83.70 477	73.31 413	90.58 383	90.00 437	77.11 406	71.91 468	85.47 451	55.53 441	91.94 463	59.69 472	70.24 456	88.83 467

test0.0.03 1		82.41 382	81.69 373	84.59 435	88.23 439	72.89 418	90.24 393	87.83 460	83.41 284	79.86 404	89.78 393	67.25 344	88.99 481	65.18 451	83.42 368	91.90 418

CMPMVS	59.16 21	80.52 410	79.20 409	84.48 436	83.98 475	67.63 465	89.95 404	93.84 326	64.79 481	66.81 479	91.14 348	57.93 430	95.17 412	76.25 372	88.10 314	90.65 444
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
CVMVSNet		84.69 353	84.79 332	84.37 437	91.84 348	64.92 475	93.70 262	91.47 401	66.19 478	86.16 269	95.28 175	67.18 346	93.33 444	80.89 304	90.42 274	94.88 283

PVSNet_0	73.20 20	77.22 435	74.83 441	84.37 437	90.70 399	71.10 442	83.09 478	89.67 444	72.81 455	73.93 459	83.13 463	60.79 411	93.70 440	68.54 430	50.84 493	88.30 472

LF4IMVS		80.37 413	79.07 413	84.27 439	86.64 451	69.87 455	89.39 414	91.05 410	76.38 415	74.97 453	90.00 387	47.85 471	94.25 429	74.55 393	80.82 406	88.69 469

Anonymous20240521		80.44 412	79.21 408	84.11 440	85.75 461	67.89 461	92.86 306	93.23 346	75.61 425	75.59 450	87.47 430	50.03 464	94.33 426	71.14 414	81.21 394	90.12 452

PM-MVS		78.11 432	76.12 435	84.09 441	83.54 478	70.08 452	88.97 422	85.27 474	79.93 360	74.73 455	86.43 442	34.70 490	93.48 442	79.43 337	72.06 449	88.72 468

dtuonly		84.33 358	84.48 340	83.87 442	86.63 452	63.54 480	86.79 453	91.48 400	78.02 394	83.20 358	93.56 260	69.53 319	94.11 430	79.08 341	92.02 253	93.97 327

test_fmvs2		83.98 362	84.03 347	83.83 443	87.16 449	67.53 466	93.93 244	92.89 355	77.62 395	86.89 250	93.53 261	47.18 473	92.02 460	90.54 134	86.51 335	91.93 417

testgi		80.94 407	80.20 389	83.18 444	87.96 444	66.29 467	91.28 364	90.70 423	83.70 275	78.12 427	92.84 283	51.37 462	90.82 471	63.34 458	82.46 379	92.43 404

KD-MVS_self_test		80.20 414	79.24 407	83.07 445	85.64 462	65.29 473	91.01 372	93.93 320	78.71 382	76.32 442	86.40 444	59.20 423	92.93 450	72.59 404	69.35 460	91.00 442

testing3		80.46 411	79.59 403	83.06 446	93.44 290	64.64 476	93.33 277	85.47 472	84.34 262	79.93 403	90.84 358	44.35 481	92.39 455	57.06 479	87.56 324	92.16 414

ambc				83.06 446	79.99 487	63.51 481	77.47 491	92.86 356		74.34 458	84.45 456	28.74 491	95.06 416	73.06 402	68.89 465	90.61 445

test20.03		79.95 418	79.08 412	82.55 448	85.79 460	67.74 464	91.09 370	91.08 408	81.23 347	74.48 457	89.96 389	61.63 398	90.15 473	60.08 469	76.38 437	89.76 454

MVStest1		72.91 444	69.70 449	82.54 449	78.14 490	73.05 416	88.21 434	86.21 466	60.69 485	64.70 480	90.53 368	46.44 476	85.70 488	58.78 475	53.62 490	88.87 466

test_vis1_rt		77.96 433	76.46 432	82.48 450	85.89 459	71.74 435	90.25 391	78.89 489	71.03 465	71.30 471	81.35 476	42.49 483	91.05 470	84.55 237	82.37 380	84.65 477

EU-MVSNet		81.32 401	80.95 379	82.42 451	88.50 435	63.67 479	93.32 278	91.33 403	64.02 482	80.57 392	92.83 284	61.21 407	92.27 457	76.34 371	80.38 413	91.32 432

myMVS_eth3d		79.67 421	78.79 415	82.32 452	91.92 344	64.08 477	89.75 407	87.40 464	81.72 332	78.82 421	87.20 433	45.33 479	91.29 467	59.09 474	87.84 321	91.60 423

ttmdpeth		76.55 437	74.64 442	82.29 453	82.25 483	67.81 463	89.76 406	85.69 470	70.35 467	75.76 448	91.69 327	46.88 474	89.77 475	66.16 447	63.23 482	89.30 459

pmmvs3		71.81 447	68.71 450	81.11 454	75.86 493	70.42 450	86.74 455	83.66 478	58.95 488	68.64 477	80.89 477	36.93 488	89.52 477	63.10 460	63.59 480	83.39 478

Syy-MVS		80.07 416	79.78 398	80.94 455	91.92 344	59.93 487	89.75 407	87.40 464	81.72 332	78.82 421	87.20 433	66.29 360	91.29 467	47.06 490	87.84 321	91.60 423

UWE-MVS-28		78.98 427	78.38 419	80.80 456	88.18 442	60.66 486	90.65 380	78.51 490	78.84 377	77.93 430	90.93 355	59.08 425	89.02 480	50.96 485	90.33 276	92.72 390

new-patchmatchnet		76.41 438	75.17 440	80.13 457	82.65 482	59.61 488	87.66 445	91.08 408	78.23 391	69.85 474	83.22 462	54.76 450	91.63 466	64.14 457	64.89 479	89.16 463

mvsany_test3		74.95 440	73.26 444	80.02 458	74.61 494	63.16 482	85.53 464	78.42 491	74.16 440	74.89 454	86.46 440	36.02 489	89.09 479	82.39 271	66.91 473	87.82 475

test_fmvs3		77.67 434	77.16 429	79.22 459	79.52 488	61.14 484	92.34 326	91.64 394	73.98 442	78.86 420	86.59 439	27.38 494	87.03 483	88.12 178	75.97 439	89.50 456

DSMNet-mixed		76.94 436	76.29 434	78.89 460	83.10 480	56.11 496	87.78 441	79.77 487	60.65 486	75.64 449	88.71 412	61.56 401	88.34 482	60.07 470	89.29 297	92.21 413

EGC-MVSNET		61.97 455	56.37 460	78.77 461	89.63 424	73.50 410	89.12 419	82.79 480	0.21 538	1.24 539	84.80 454	39.48 484	90.04 474	44.13 492	75.94 440	72.79 492

new_pmnet		72.15 445	70.13 448	78.20 462	82.95 481	65.68 470	83.91 474	82.40 482	62.94 484	64.47 481	79.82 478	42.85 482	86.26 487	57.41 478	74.44 442	82.65 482

MVS-HIRNet		73.70 443	72.20 445	78.18 463	91.81 351	56.42 495	82.94 479	82.58 481	55.24 489	68.88 475	66.48 497	55.32 444	95.13 413	58.12 476	88.42 310	83.01 480

LCM-MVSNet		66.00 452	62.16 457	77.51 464	64.51 508	58.29 490	83.87 475	90.90 416	48.17 493	54.69 490	73.31 491	16.83 503	86.75 484	65.47 449	61.67 484	87.48 476

APD_test1		69.04 448	66.26 454	77.36 465	80.51 486	62.79 483	85.46 465	83.51 479	54.11 491	59.14 488	84.79 455	23.40 497	89.61 476	55.22 480	70.24 456	79.68 487

test_f		71.95 446	70.87 447	75.21 466	74.21 496	59.37 489	85.07 468	85.82 469	65.25 480	70.42 473	83.13 463	23.62 495	82.93 494	78.32 349	71.94 451	83.33 479

ANet_high		58.88 459	54.22 464	72.86 467	56.50 513	56.67 492	80.75 484	86.00 468	73.09 452	37.39 503	64.63 501	22.17 498	79.49 498	43.51 493	23.96 506	82.43 483

test_vis3_rt		65.12 453	62.60 455	72.69 468	71.44 498	60.71 485	87.17 450	65.55 502	63.80 483	53.22 491	65.65 500	14.54 504	89.44 478	76.65 366	65.38 477	67.91 497

LoFTR		57.22 462	52.62 466	71.00 469	72.03 497	48.57 501	72.00 498	70.08 501	44.40 498	40.92 501	76.42 482	8.12 508	82.76 495	42.28 496	47.33 496	81.66 484

FPMVS		64.63 454	62.55 456	70.88 470	70.80 499	56.71 491	84.42 472	84.42 476	51.78 492	49.57 492	81.61 475	23.49 496	81.48 496	40.61 498	76.25 438	74.46 491

dmvs_testset		74.57 442	75.81 439	70.86 471	87.72 447	40.47 510	87.05 452	77.90 495	82.75 303	71.15 472	85.47 451	67.98 341	84.12 492	45.26 491	76.98 436	88.00 473

N_pmnet		68.89 449	68.44 451	70.23 472	89.07 429	28.79 518	88.06 436	19.50 518	69.47 469	71.86 469	84.93 453	61.24 406	91.75 464	54.70 481	77.15 433	90.15 451

testf1		59.54 457	56.11 461	69.85 473	69.28 500	56.61 493	80.37 485	76.55 498	42.58 500	45.68 496	75.61 484	11.26 505	84.18 490	43.20 494	60.44 486	68.75 495

APD_test2		59.54 457	56.11 461	69.85 473	69.28 500	56.61 493	80.37 485	76.55 498	42.58 500	45.68 496	75.61 484	11.26 505	84.18 490	43.20 494	60.44 486	68.75 495

WB-MVS		67.92 450	67.49 452	69.21 475	81.09 484	41.17 509	88.03 437	78.00 494	73.50 447	62.63 483	83.11 465	63.94 382	86.52 485	25.66 505	51.45 492	79.94 486

PMMVS2		59.60 456	56.40 459	69.21 475	68.83 502	46.58 502	73.02 497	77.48 496	55.07 490	49.21 493	72.95 492	17.43 502	80.04 497	49.32 487	44.33 497	80.99 485

SSC-MVS		67.06 451	66.56 453	68.56 477	80.54 485	40.06 511	87.77 442	77.37 497	72.38 457	61.75 485	82.66 472	63.37 385	86.45 486	24.48 506	48.69 495	79.16 488

Gipuma		57.99 461	54.91 463	67.24 478	88.51 433	65.59 471	52.21 503	90.33 428	43.58 499	42.84 499	51.18 506	20.29 500	85.07 489	34.77 499	70.45 455	51.05 505
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
RoMa-SfM		53.80 463	49.39 467	67.06 479	67.87 504	48.86 499	75.04 492	38.06 514	47.23 495	47.40 495	78.96 480	7.40 509	76.66 500	48.89 488	33.62 501	75.64 490

DKM		50.92 467	46.13 471	65.30 480	66.27 506	45.98 504	73.05 496	31.91 516	45.08 496	42.04 500	75.01 488	4.95 516	73.81 502	47.90 489	28.96 503	76.09 489

MatchFormer		51.11 466	46.66 470	64.46 481	67.11 505	43.39 507	70.54 499	63.67 504	33.19 504	37.22 504	70.30 494	6.67 511	78.17 499	30.29 502	40.94 499	71.81 493

PMVS	47.18 22	52.22 465	48.46 469	63.48 482	45.72 517	46.20 503	73.41 495	78.31 492	41.03 502	30.06 507	65.68 499	6.05 512	83.43 493	30.04 503	65.86 476	60.80 499
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
dongtai		58.82 460	58.24 458	60.56 483	83.13 479	45.09 506	82.32 480	48.22 511	67.61 473	61.70 486	69.15 495	38.75 485	76.05 501	32.01 501	41.31 498	60.55 500

PDCNetPlus		48.34 469	45.15 472	57.91 484	61.43 510	41.85 508	65.98 500	38.30 513	47.59 494	37.96 502	71.85 493	10.18 507	66.85 507	52.94 483	20.14 517	65.03 498

MVE	39.65 23	43.39 470	38.59 476	57.77 485	56.52 512	48.77 500	55.38 502	58.64 507	29.33 506	28.96 508	52.65 505	4.68 519	64.62 508	28.11 504	33.07 502	59.93 501
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		50.52 468	48.47 468	56.66 486	52.26 516	18.98 522	41.51 509	81.40 484	10.10 512	44.59 498	75.01 488	28.51 492	68.16 503	53.54 482	49.31 494	82.83 481

DeepMVS_CX				56.31 487	74.23 495	51.81 498		56.67 508	44.85 497	48.54 494	75.16 487	27.87 493	58.74 510	40.92 497	52.22 491	58.39 503

ELoFTR		40.15 473	35.08 477	55.36 488	41.27 522	28.17 519	47.70 505	43.76 512	29.15 507	30.35 506	65.97 498	2.17 526	66.90 506	34.51 500	20.83 516	71.00 494

kuosan		53.51 464	53.30 465	54.13 489	76.06 492	45.36 505	80.11 487	48.36 510	59.63 487	54.84 489	63.43 502	37.41 486	62.07 509	20.73 508	39.10 500	54.96 504

GLUNet-SfM		31.36 475	26.25 480	46.70 490	35.51 524	24.89 520	33.71 514	36.36 515	19.08 508	23.78 511	52.69 504	3.82 524	56.26 511	19.75 510	11.56 527	58.95 502

E-PMN		43.23 471	42.29 473	46.03 491	65.58 507	37.41 512	73.51 494	64.62 503	33.99 503	28.47 509	47.87 507	19.90 501	67.91 504	22.23 507	24.45 504	32.77 511

EMVS		42.07 472	41.12 474	44.92 492	63.45 509	35.56 514	73.65 493	63.48 505	33.05 505	26.88 510	45.45 508	21.27 499	67.14 505	19.80 509	23.02 508	32.06 512

ALIKED-LG		28.00 476	26.54 479	32.41 493	58.12 511	31.80 515	47.26 506	21.21 517	14.15 509	19.16 513	41.93 510	6.72 510	35.73 512	5.96 519	24.32 505	29.69 513

ALIKED-MNN		26.28 477	24.57 482	31.39 494	56.22 514	31.73 516	45.54 507	19.13 520	11.12 510	17.11 515	39.35 512	5.01 515	34.53 513	5.54 521	22.12 510	27.92 514

ALIKED-NN		26.07 478	24.75 481	30.02 495	55.08 515	30.61 517	44.20 508	19.22 519	10.98 511	17.98 514	40.71 511	5.39 514	32.83 514	5.59 520	23.63 507	26.63 515

tmp_tt		35.64 474	39.24 475	24.84 496	14.87 541	23.90 521	62.71 501	51.51 509	6.58 520	36.66 505	62.08 503	44.37 480	30.34 516	52.40 484	22.00 511	20.27 517

wuyk23d		21.27 480	20.48 483	23.63 497	68.59 503	36.41 513	49.57 504	6.85 533	9.37 513	7.89 522	4.46 538	4.03 523	31.37 515	17.47 511	16.07 520	3.12 533

SP-LightGlue		20.24 481	20.15 485	20.49 498	43.51 518	12.27 530	38.68 511	14.56 524	7.54 517	12.90 519	30.07 516	4.75 517	14.38 520	7.60 515	21.75 512	34.82 506

SP-SuperGlue		20.22 482	20.18 484	20.36 499	43.26 519	12.27 530	38.71 510	14.77 523	7.64 516	13.04 518	30.21 515	4.73 518	14.21 522	7.59 516	21.65 513	34.59 507

SP-DiffGlue		20.02 483	19.96 486	20.21 500	19.64 538	13.14 529	30.51 515	15.49 522	8.39 514	19.98 512	43.75 509	5.48 513	13.72 523	13.75 512	22.65 509	33.78 509

SP-MNN		19.61 484	19.42 487	20.19 501	42.15 520	11.42 536	38.15 512	14.24 525	6.55 521	11.64 521	29.88 518	4.16 521	14.56 519	7.09 518	20.92 515	34.58 508

SP-NN		19.44 485	19.37 488	19.67 502	41.70 521	11.48 535	37.75 513	13.72 527	6.86 518	11.86 520	29.97 517	4.23 520	14.25 521	7.13 517	21.07 514	33.30 510

XFeat-MNN		17.43 486	16.95 489	18.86 503	16.90 539	11.28 537	27.31 516	17.08 521	8.08 515	15.61 517	35.73 513	4.06 522	22.95 517	10.20 513	17.59 519	22.35 516

XFeat-NN		15.96 487	15.86 490	16.25 504	15.78 540	9.87 540	25.17 517	13.83 526	6.76 519	15.68 516	34.83 514	3.61 525	19.28 518	9.22 514	17.90 518	19.58 518

SIFT-NN		12.98 488	13.18 491	12.37 505	36.49 523	16.03 523	22.41 518	7.69 529	4.89 522	7.41 523	20.48 520	1.69 527	11.46 525	1.88 524	15.70 521	9.61 520

SIFT-MNN		12.44 489	12.55 492	12.11 506	34.55 525	15.21 524	20.91 519	7.74 528	4.86 523	6.54 525	20.09 521	1.51 528	11.47 524	1.88 524	14.87 523	9.64 519

SIFT-NN-NCMNet		12.12 490	12.25 493	11.75 507	32.82 527	14.83 525	20.73 520	7.58 530	4.72 525	6.60 524	19.53 522	1.49 529	11.15 527	1.74 526	15.02 522	9.28 521

SIFT-NCM-Cal		11.58 491	11.64 494	11.40 508	33.45 526	14.10 526	19.75 522	6.89 531	4.68 528	4.55 532	18.60 527	1.34 533	11.28 526	1.53 532	13.95 524	8.82 525

SIFT-NN-CMatch		11.26 492	11.31 496	11.13 509	30.21 531	13.40 528	18.43 523	6.79 534	4.71 526	6.47 526	19.53 522	1.43 531	10.72 529	1.71 527	12.49 526	9.26 522

SIFT-ConvMatch		10.91 494	10.94 499	10.84 510	32.07 528	13.57 527	17.23 526	6.35 535	4.71 526	5.18 529	18.94 525	1.30 534	10.76 528	1.65 530	11.02 529	8.19 526

SIFT-NN-UMatch		11.06 493	11.19 498	10.66 511	28.66 533	12.16 532	19.79 521	6.86 532	4.73 524	5.21 528	19.47 524	1.46 530	10.70 530	1.71 527	12.79 525	9.13 523

SIFT-UMatch		10.58 495	10.73 500	10.15 512	31.05 529	11.65 534	18.01 524	5.92 537	4.65 529	4.72 530	18.93 526	1.25 536	10.62 531	1.66 529	10.39 530	8.16 527

SIFT-CM-Cal		10.08 497	10.13 503	9.92 513	30.71 530	11.88 533	15.35 528	5.44 538	4.59 530	4.72 530	18.04 530	1.26 535	10.19 532	1.46 534	9.60 531	7.69 528

SIFT-NN-PointCN		10.26 496	10.46 501	9.65 514	27.18 534	9.89 539	17.89 525	6.17 536	4.40 532	5.65 527	18.29 528	1.43 531	10.09 533	1.61 531	11.55 528	8.99 524

SIFT-UM-Cal		9.80 498	10.00 504	9.22 515	30.05 532	10.15 538	16.31 527	4.85 540	4.54 531	4.19 533	18.23 529	1.19 537	9.95 534	1.52 533	9.11 533	7.57 529

SIFT-PCN-Cal		8.65 502	8.88 506	7.98 516	26.74 535	7.47 542	13.90 530	4.61 541	4.09 534	3.82 534	15.86 531	1.01 538	8.94 535	1.34 535	8.52 534	7.53 530

SIFT-PointCN		8.76 500	9.03 505	7.96 517	26.50 536	7.60 541	14.94 529	5.08 539	4.10 533	3.74 535	15.46 532	0.94 539	8.92 536	1.33 536	9.14 532	7.37 531

SIFT-NCMNet		7.46 504	7.71 508	6.72 518	25.03 537	6.86 543	11.42 531	2.98 542	4.05 535	3.38 536	13.68 533	0.84 540	7.65 537	1.13 537	6.87 535	5.66 532

test123		8.76 500	11.22 497	1.39 519	0.85 543	0.97 544	85.76 462	0.35 544	0.54 537	2.45 538	8.14 537	0.60 541	0.48 538	2.16 523	0.17 537	2.71 534

testmvs		8.92 499	11.52 495	1.12 520	1.06 542	0.46 545	86.02 459	0.65 543	0.62 536	2.74 537	9.52 536	0.31 542	0.45 539	2.38 522	0.39 536	2.46 535

mmdepth		0.00 506	0.00 509	0.00 521	0.00 544	0.00 546	0.00 532	0.00 545	0.00 539	0.00 540	0.00 539	0.00 543	0.00 540	0.00 538	0.00 538	0.00 536

monomultidepth		0.00 506	0.00 509	0.00 521	0.00 544	0.00 546	0.00 532	0.00 545	0.00 539	0.00 540	0.00 539	0.00 543	0.00 540	0.00 538	0.00 538	0.00 536

test_blank		0.00 506	0.00 509	0.00 521	0.00 544	0.00 546	0.00 532	0.00 545	0.00 539	0.00 540	0.00 539	0.00 543	0.00 540	0.00 538	0.00 538	0.00 536

uanet_test		0.00 506	0.00 509	0.00 521	0.00 544	0.00 546	0.00 532	0.00 545	0.00 539	0.00 540	0.00 539	0.00 543	0.00 540	0.00 538	0.00 538	0.00 536

DCPMVS		0.00 506	0.00 509	0.00 521	0.00 544	0.00 546	0.00 532	0.00 545	0.00 539	0.00 540	0.00 539	0.00 543	0.00 540	0.00 538	0.00 538	0.00 536

cdsmvs_eth3d_5k		22.14 479	29.52 478	0.00 521	0.00 544	0.00 546	0.00 532	95.76 198	0.00 539	0.00 540	94.29 228	75.66 225	0.00 540	0.00 538	0.00 538	0.00 536

pcd_1.5k_mvsjas		6.64 505	8.86 507	0.00 521	0.00 544	0.00 546	0.00 532	0.00 545	0.00 539	0.00 540	0.00 539	79.70 158	0.00 540	0.00 538	0.00 538	0.00 536

sosnet-low-res		0.00 506	0.00 509	0.00 521	0.00 544	0.00 546	0.00 532	0.00 545	0.00 539	0.00 540	0.00 539	0.00 543	0.00 540	0.00 538	0.00 538	0.00 536

sosnet		0.00 506	0.00 509	0.00 521	0.00 544	0.00 546	0.00 532	0.00 545	0.00 539	0.00 540	0.00 539	0.00 543	0.00 540	0.00 538	0.00 538	0.00 536

uncertanet		0.00 506	0.00 509	0.00 521	0.00 544	0.00 546	0.00 532	0.00 545	0.00 539	0.00 540	0.00 539	0.00 543	0.00 540	0.00 538	0.00 538	0.00 536

Regformer		0.00 506	0.00 509	0.00 521	0.00 544	0.00 546	0.00 532	0.00 545	0.00 539	0.00 540	0.00 539	0.00 543	0.00 540	0.00 538	0.00 538	0.00 536

ab-mvs-re		7.82 503	10.43 502	0.00 521	0.00 544	0.00 546	0.00 532	0.00 545	0.00 539	0.00 540	93.88 249	0.00 543	0.00 540	0.00 538	0.00 538	0.00 536

uanet		0.00 506	0.00 509	0.00 521	0.00 544	0.00 546	0.00 532	0.00 545	0.00 539	0.00 540	0.00 539	0.00 543	0.00 540	0.00 538	0.00 538	0.00 536

WAC-MVS						64.08 477								59.14 473

FOURS1					98.86 4	85.54 74	98.29 1	97.49 11	89.79 65	96.29 32

PC_three_1452									82.47 307	97.09 19	97.07 72	92.72 1	98.04 199	92.70 80	99.02 12	98.86 16

test_one_0601					98.58 14	85.83 68		97.44 20	91.05 23	96.78 27	98.06 24	91.45 12

eth-test2					0.00 544

eth-test					0.00 544

ZD-MVS					98.15 40	86.62 34		97.07 60	83.63 277	94.19 65	96.91 78	87.57 35	99.26 51	91.99 105	98.44 56

RE-MVS-def			93.68 72		97.92 49	84.57 94	96.28 51	96.76 93	87.46 162	93.75 76	97.43 51	82.94 100		92.73 76	97.80 91	97.88 111

IU-MVS					98.77 8	86.00 54		96.84 82	81.26 345	97.26 13				95.50 36	99.13 3	99.03 10

test_241102_TWO								97.44 20	90.31 43	97.62 8	98.07 22	91.46 11	99.58 13	95.66 30	99.12 6	98.98 12

test_241102_ONE					98.77 8	85.99 56		97.44 20	90.26 49	97.71 2	97.96 33	92.31 5	99.38 35

9.14			94.47 35		97.79 58		96.08 69	97.44 20	86.13 206	95.10 55	97.40 53	88.34 26	99.22 53	93.25 68	98.70 37

save fliter					97.85 55	85.63 73	95.21 141	96.82 85	89.44 76

test_0728_THIRD									90.75 31	97.04 21	98.05 27	92.09 7	99.55 20	95.64 32	99.13 3	99.13 4

test0726					98.78 6	85.93 59	97.19 16	97.47 16	90.27 47	97.64 6	98.13 7	91.47 9

GSMVS																96.12 229

test_part2					98.55 15	87.22 20				96.40 31

sam_mvs1												71.70 285				96.12 229

sam_mvs												70.60 299

MTGPA								96.97 65

test_post1							88.00 438				9.81 535	69.31 324	95.53 401	76.65 366

test_post											10.29 534	70.57 303	95.91 386

patchmatchnet-post											83.76 458	71.53 286	96.48 354

MTMP							96.16 60	60.64 506

gm-plane-assit					89.60 425	68.00 460			77.28 401		88.99 406		97.57 247	79.44 336

test9_res														91.91 109	98.71 35	98.07 83

TEST9					97.53 67	86.49 38	94.07 230	96.78 90	81.61 337	92.77 101	96.20 109	87.71 32	99.12 63

test_8					97.49 69	86.30 46	94.02 236	96.76 93	81.86 328	92.70 105	96.20 109	87.63 33	99.02 73

agg_prior2														90.54 134	98.68 40	98.27 64

agg_prior					97.38 72	85.92 61		96.72 100		92.16 120			98.97 87

test_prior4						85.96 58	94.11 224

test_prior2							94.12 222		87.67 157	92.63 109	96.39 104	86.62 45		91.50 118	98.67 43

旧先验2							93.36 276		71.25 463	94.37 61			97.13 301	86.74 200

新几何2							93.11 291

旧先验1					96.79 86	81.81 198		95.67 209			96.81 84	86.69 43			97.66 97	96.97 185

无先验							93.28 284	96.26 140	73.95 443				99.05 67	80.56 310		96.59 208

原ACMM2							92.94 302

test222					96.55 95	81.70 203	92.22 334	95.01 258	68.36 472	90.20 177	96.14 119	80.26 145			97.80 91	96.05 236

testdata2													98.75 116	78.30 350

segment_acmp												87.16 40

testdata1							92.15 336		87.94 141

plane_prior7					94.70 200	82.74 165

plane_prior6					94.52 216	82.75 163						74.23 245

plane_prior5								96.22 145					98.12 178	88.15 175	89.99 280	94.63 290

plane_prior4											94.86 199

plane_prior3						82.75 163			90.26 49	86.91 247

plane_prior2							95.85 93		90.81 27

plane_prior1					94.59 209

plane_prior						82.73 166	95.21 141		89.66 70						89.88 285

n2								0.00 545

nn								0.00 545

door-mid								85.49 471

test11								96.57 112

door								85.33 473

HQP5-MVS						81.56 205

HQP-NCC					94.17 249		94.39 204		88.81 103	85.43 293

ACMP_Plane					94.17 249		94.39 204		88.81 103	85.43 293

BP-MVS														87.11 197

HQP4-MVS										85.43 293			97.96 215			94.51 300

HQP3-MVS								96.04 172							89.77 289

HQP2-MVS												73.83 256

NP-MVS					94.37 230	82.42 179					93.98 242

MDTV_nov1_ep13_2view						55.91 497	87.62 446		73.32 449	84.59 315		70.33 306		74.65 389		95.50 257

MDTV_nov1_ep13			83.56 355		91.69 356	69.93 453	87.75 443	91.54 397	78.60 383	84.86 309	88.90 408	69.54 318	96.03 377	70.25 419	88.93 302

ACMMP++_ref															87.47 325

ACMMP++															88.01 317

Test By Simon												80.02 147

High-resolution multi-view benchmark