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 62

MM		95.10 14	94.91 26	95.68 5	96.09 116	88.34 10	96.68 38	94.37 306	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	85.90 212	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	92.42 105	91.37 127	95.55 7	95.63 143	88.73 7	97.07 23	96.77 92	90.84 26	84.02 339	96.62 95	75.95 221	99.34 42	87.77 187	97.68 96	98.59 29

CNVR-MVS		95.40 8	95.37 11	95.50 8	98.11 42	88.51 8	95.29 132	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 92	95.69 45	96.49 100	89.27 18	99.29 50		95.80 142	97.95 98

MGCNet		94.18 50	93.80 64	95.34 10	94.91 183	87.62 15	95.97 82	93.01 357	92.58 6	94.22 63	97.20 64	80.56 142	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	88.28 125	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 91	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	87.76 155	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	95.00 156	97.12 55	87.13 176	92.51 113	96.30 106	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		92.58 100	91.74 111	95.08 16	96.19 107	89.31 5	92.66 316	96.56 113	83.44 287	91.68 140	95.04 193	86.60 47	98.99 82	85.60 221	97.92 84	96.93 193

ZNCC-MVS		94.47 33	94.28 45	95.03 17	98.52 18	86.96 21	96.85 33	97.32 34	88.24 126	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	94.36 211	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	88.60 115	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 97	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	88.63 112	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	88.24 126	93.26 85	96.83 82	85.48 60	99.59 10	91.43 121	98.40 57	98.30 56

HFP-MVS		94.52 31	94.40 38	94.86 27	98.61 13	86.81 26	96.94 25	97.34 30	88.63 112	93.65 78	97.21 62	86.10 53	99.49 30	92.35 89	98.77 31	98.30 56

sasdasda		93.27 82	92.75 92	94.85 28	95.70 139	87.66 13	96.33 44	96.41 123	90.00 54	94.09 68	94.60 218	82.33 110	98.62 133	92.40 86	92.86 238	98.27 65

MP-MVS-pluss		94.21 45	94.00 59	94.85 28	98.17 39	86.65 32	94.82 169	97.17 49	86.26 204	92.83 98	97.87 36	85.57 59	99.56 16	94.37 52	98.92 17	98.34 49
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 54	94.09 68	94.60 218	82.33 110	98.62 133	92.40 86	92.86 238	98.27 65

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		88.31 241	86.13 290	94.85 28	98.54 16	86.60 35	96.93 27	97.19 44	90.66 36	92.85 96	23.41 528	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	88.11 134	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 99	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 99	94.81 36	95.62 144	87.61 16	95.99 79	96.07 170	89.77 67	94.12 67	94.87 202	80.56 142	98.66 125	92.42 85	93.10 233	98.15 77

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 44	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 79	92.81 99	96.97 75	85.37 62	99.24 52	90.87 132	98.69 38	98.38 48
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 95	91.98 124	97.19 65	85.43 61	99.56 16	92.06 103	98.79 27	98.44 43
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	85.68 219	95.05 56	97.18 66	87.31 39	99.07 65	91.90 111	98.61 51	98.28 62
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	88.53 117	92.73 104	97.23 61	85.20 65	99.32 46	92.15 97	98.83 25	98.25 70

PGM-MVS		93.96 58	93.72 70	94.68 43	98.43 23	86.22 49	95.30 130	97.78 3	87.45 166	93.26 85	97.33 56	84.62 78	99.51 28	90.75 135	98.57 52	98.32 55

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 48	97.04 21	98.05 27	91.47 9	99.55 20	95.62 34	99.08 7	98.45 42
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	88.70 110	91.83 134	97.17 67	83.96 85	99.55 20	91.44 120	98.64 48	98.43 44

PHI-MVS		93.89 60	93.65 74	94.62 46	96.84 85	86.43 40	96.69 37	97.49 11	85.15 243	93.56 82	96.28 107	85.60 58	99.31 47	92.45 83	98.79 27	98.12 82

TSAR-MVS + MP.		94.85 19	94.94 24	94.58 47	98.25 35	86.33 43	96.11 67	96.62 108	88.14 131	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	94.94 159	96.69 104	91.89 12	90.69 168	95.88 139	81.99 123	99.54 24	93.14 70	97.95 83	98.39 46

train_agg		93.44 75	93.08 85	94.52 49	97.53 67	86.49 38	94.07 231	96.78 90	81.86 332	92.77 101	96.20 110	87.63 33	99.12 63	92.14 98	98.69 38	97.94 99

CDPH-MVS		92.83 94	92.30 103	94.44 50	97.79 58	86.11 53	94.06 233	96.66 105	80.09 363	92.77 101	96.63 94	86.62 45	99.04 69	87.40 194	98.66 44	98.17 75

3Dnovator	86.66 5	91.73 123	90.82 144	94.44 50	94.59 210	86.37 42	97.18 17	97.02 62	89.20 88	84.31 334	96.66 90	73.74 262	99.17 57	86.74 204	97.96 82	97.79 123

SR-MVS		94.23 44	94.17 54	94.43 52	98.21 38	85.78 70	96.40 43	96.90 76	88.20 129	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	86.90 187	92.62 110	96.80 86	84.85 75	99.17 57	92.43 84	98.65 47	98.33 51
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	94.40 203	93.93 324	89.77 67	94.21 64	95.59 161	87.35 38	98.61 135	92.72 78	96.15 136	97.83 119

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 60	95.23 53	98.10 14	87.09 41	99.37 37	95.30 38	98.25 66	98.30 56

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 60	95.23 53	98.10 14	87.09 41	99.37 37	95.30 38	98.25 66	98.30 56

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	96.13 121	77.97 191	98.84 106	90.75 135	98.26 62	98.07 84

test12				94.34 58	97.13 80	86.15 52		96.29 132		91.04 163		85.08 67	99.01 75		98.13 74	97.86 114

SymmetryMVS		92.81 97	92.31 102	94.32 59	96.15 108	86.20 50	96.30 47	94.43 302	91.65 17	92.68 106	96.13 121	77.97 191	98.84 106	90.75 135	94.72 170	97.92 108

ACMMP		93.24 84	92.88 90	94.30 60	98.09 44	85.33 79	96.86 32	97.45 19	88.33 121	90.15 188	97.03 74	81.44 131	99.51 28	90.85 133	95.74 145	98.04 91
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 70	95.27 52	98.16 6	86.53 48	99.36 40	95.42 37	98.15 72	98.33 51

DeepC-MVS	88.79 3	93.31 81	92.99 88	94.26 62	96.07 118	85.83 68	94.89 162	96.99 63	89.02 98	89.56 197	97.37 55	82.51 107	99.38 35	92.20 95	98.30 60	97.57 139
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 96	94.23 63	95.62 144	85.92 61	96.08 69	96.33 130	89.86 58	93.89 75	94.66 215	82.11 118	98.50 141	92.33 91	92.82 241	98.27 65

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 44

EPNet		91.79 114	91.02 138	94.10 65	90.10 418	85.25 80	96.03 76	92.05 384	92.83 5	87.39 245	95.78 151	79.39 169	99.01 75	88.13 181	97.48 99	98.05 90
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 61

test_fmvsmconf_n		94.60 28	94.81 30	93.98 67	94.62 206	84.96 85	96.15 62	97.35 29	89.37 80	96.03 39	98.11 11	86.36 49	99.01 75	97.45 10	97.83 89	97.96 97

DELS-MVS		93.43 79	93.25 81	93.97 68	95.42 152	85.04 83	93.06 297	97.13 54	90.74 33	91.84 132	95.09 192	86.32 50	99.21 55	91.22 123	98.45 55	97.65 132
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		91.95 111	91.28 131	93.96 69	98.33 33	85.92 61	94.66 182	96.66 105	82.69 309	90.03 190	95.82 146	82.30 112	99.03 70	84.57 240	96.48 129	96.91 195

HPM-MVS_fast		93.40 80	93.22 82	93.94 70	98.36 31	84.83 87	97.15 18	96.80 89	85.77 216	92.47 114	97.13 69	82.38 108	99.07 65	90.51 140	98.40 57	97.92 108

test_fmvsmconf0.1_n		94.20 47	94.31 43	93.88 71	92.46 332	84.80 88	96.18 59	96.82 85	89.29 85	95.68 46	98.11 11	85.10 66	98.99 82	97.38 11	97.75 95	97.86 114

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 40	96.95 23	97.27 58	89.53 15	96.91 323	94.38 51	98.85 20	98.03 92
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	93.24 288	97.24 41	88.76 107	91.60 141	95.85 143	86.07 54	98.66 125	91.91 109	98.16 70	98.03 92

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	87.46 164	93.75 76	97.43 51	84.24 82	99.01 75	92.73 76	97.80 91	97.88 112

test_prior				93.82 74	97.29 77	84.49 98		96.88 78					98.87 100			98.11 83

APD-MVS_3200maxsize		93.78 63	93.77 67	93.80 76	97.92 49	84.19 111	96.30 47	96.87 79	86.96 183	93.92 74	97.47 49	83.88 86	98.96 89	92.71 79	97.87 87	98.26 69

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	86.56 195	96.84 25	97.81 39	87.56 36	98.77 115	97.14 15	96.82 119	97.16 174

CSCG		93.23 85	93.05 86	93.76 78	98.04 46	84.07 113	96.22 56	97.37 27	84.15 268	90.05 189	95.66 157	87.77 30	99.15 61	89.91 151	98.27 61	98.07 84

GDP-MVS		92.04 109	91.46 124	93.75 79	94.55 216	84.69 91	95.60 118	96.56 113	87.83 152	93.07 92	95.89 138	73.44 266	98.65 127	90.22 144	96.03 138	97.91 110

BP-MVS1		92.48 102	92.07 106	93.72 80	94.50 220	84.39 106	95.90 89	94.30 309	90.39 41	92.67 108	95.94 134	74.46 245	98.65 127	93.14 70	97.35 103	98.13 79

test_fmvsmconf0.01_n		93.19 86	93.02 87	93.71 81	89.25 431	84.42 105	96.06 73	96.29 132	89.06 93	94.68 58	98.13 7	79.22 171	98.98 86	97.22 13	97.24 105	97.74 126

UA-Net		92.83 94	92.54 98	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	84.47 242	95.47 153	97.45 148

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	86.31 202	96.75 28	97.86 37	87.40 37	98.74 119	97.07 17	97.02 110	97.07 179

QAPM		89.51 198	88.15 225	93.59 84	94.92 181	84.58 93	96.82 34	96.70 103	78.43 391	83.41 357	96.19 114	73.18 271	99.30 48	77.11 367	96.54 126	96.89 196

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 59	97.19 16	97.89 35	86.28 51	98.71 122	97.11 16	98.08 78	97.17 167

fmvsm_s_conf0.5_n_9		94.99 16	95.50 8	93.44 86	96.51 100	82.25 186	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		91.82 113	91.30 129	93.39 87	94.72 198	83.36 138	95.45 122	96.37 127	90.33 43	92.17 119	96.03 128	72.32 283	98.75 116	87.94 184	96.34 131	98.07 84

casdiffmvs_mvg		92.96 93	92.83 91	93.35 88	94.59 210	83.40 136	95.00 156	96.34 129	90.30 46	92.05 122	96.05 125	83.43 89	98.15 178	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	88.02 139	96.23 33	97.84 38	83.36 93	98.83 109	97.49 8	97.34 104	97.25 159

EI-MVSNet-Vis-set		93.01 92	92.92 89	93.29 90	95.01 172	83.51 133	94.48 191	95.77 198	90.87 25	92.52 112	96.67 89	84.50 79	99.00 80	91.99 105	94.44 183	97.36 151

Vis-MVSNet		91.75 121	91.23 132	93.29 90	95.32 156	83.78 123	96.14 64	95.98 177	89.89 56	90.45 173	96.58 97	75.09 234	98.31 168	84.75 234	96.90 115	97.78 124
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 60	95.56 48	95.51 166	84.50 79	98.79 113	94.83 46	98.86 19	97.72 128

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	95.77 152	85.02 69	98.33 165	93.03 72	98.62 49	98.13 79

VNet		92.24 107	91.91 109	93.24 93	96.59 92	83.43 134	94.84 168	96.44 120	89.19 89	94.08 71	95.90 137	77.85 197	98.17 176	88.90 171	93.38 222	98.13 79

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	81.87 125	99.22 53	97.86 4	97.91 86	97.20 165

VDD-MVS		90.74 154	89.92 169	93.20 95	96.27 105	83.02 156	95.73 104	93.86 328	88.42 120	92.53 111	96.84 81	62.09 399	98.64 130	90.95 130	92.62 248	97.93 107

Elysia		90.12 174	89.10 193	93.18 97	93.16 297	84.05 115	95.22 139	96.27 136	85.16 241	90.59 170	94.68 211	64.64 377	98.37 158	86.38 210	95.77 143	97.12 176

StellarMVS		90.12 174	89.10 193	93.18 97	93.16 297	84.05 115	95.22 139	96.27 136	85.16 241	90.59 170	94.68 211	64.64 377	98.37 158	86.38 210	95.77 143	97.12 176

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	96.21 109	85.61 57	98.24 170	93.64 61	98.17 69	98.19 73

nrg030		91.08 148	90.39 153	93.17 99	93.07 304	86.91 23	96.41 42	96.26 140	88.30 123	88.37 222	94.85 205	82.19 117	97.64 242	91.09 124	82.95 375	94.96 281

MVSMamba_PlusPlus		93.44 75	93.54 76	93.14 101	96.58 94	83.05 154	96.06 73	96.50 118	84.42 265	94.09 68	95.56 163	85.01 72	98.69 124	94.96 44	98.66 44	97.67 131

EI-MVSNet-UG-set		92.74 98	92.62 97	93.12 102	94.86 186	83.20 143	94.40 203	95.74 201	90.71 35	92.05 122	96.60 96	84.00 84	98.99 82	91.55 117	93.63 211	97.17 167

test_fmvsmvis_n_1920		93.44 75	93.55 75	93.10 103	93.67 283	84.26 109	95.83 95	96.14 161	89.00 100	92.43 115	97.50 48	83.37 92	98.72 120	96.61 24	97.44 100	96.32 221

新几何1				93.10 103	97.30 76	84.35 108		95.56 219	71.09 469	91.26 151	96.24 108	82.87 102	98.86 102	79.19 344	98.10 75	96.07 237

OMC-MVS		91.23 138	90.62 150	93.08 105	96.27 105	84.07 113	93.52 270	95.93 183	86.95 184	89.51 198	96.13 121	78.50 185	98.35 162	85.84 219	92.90 237	96.83 203

OpenMVS	83.78 11	88.74 228	87.29 247	93.08 105	92.70 326	85.39 78	96.57 40	96.43 121	78.74 385	80.85 390	96.07 124	69.64 320	99.01 75	78.01 358	96.65 124	94.83 289

MAR-MVS		90.30 170	89.37 186	93.07 107	96.61 91	84.48 99	95.68 107	95.67 210	82.36 314	87.85 232	92.85 286	76.63 210	98.80 111	80.01 324	96.68 123	95.91 243
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
cashybrid2		92.82 96	92.75 92	93.03 108	94.79 190	82.44 178	95.39 124	96.24 143	90.58 38	91.79 136	96.43 104	82.73 104	98.19 175	91.31 122	95.54 148	98.46 41

lupinMVS		90.92 150	90.21 157	93.03 108	93.86 268	83.88 120	92.81 310	93.86 328	79.84 366	91.76 137	94.29 232	77.92 194	98.04 200	90.48 141	97.11 106	97.17 167

Effi-MVS+		91.59 131	91.11 134	93.01 110	94.35 235	83.39 137	94.60 184	95.10 258	87.10 177	90.57 172	93.10 281	81.43 132	98.07 194	89.29 163	94.48 181	97.59 138

fmvsm_s_conf0.5_n_a		93.57 68	93.76 68	93.00 111	95.02 171	83.67 126	96.19 57	96.10 167	87.27 170	95.98 40	98.05 27	83.07 99	98.45 151	96.68 23	95.51 150	96.88 197

MVS_111021_LR		92.47 103	92.29 104	92.98 112	95.99 125	84.43 103	93.08 294	96.09 168	88.20 129	91.12 156	95.72 155	81.33 133	97.76 231	91.74 113	97.37 102	96.75 205

fmvsm_s_conf0.1_n_a		93.19 86	93.26 80	92.97 113	92.49 330	83.62 129	96.02 77	95.72 205	86.78 189	96.04 38	98.19 4	82.30 112	98.43 155	96.38 25	95.42 156	96.86 198

ETV-MVS		92.74 98	92.66 95	92.97 113	95.20 164	84.04 117	95.07 151	96.51 117	90.73 34	92.96 93	91.19 347	84.06 83	98.34 163	91.72 114	96.54 126	96.54 216

LFMVS		90.08 177	89.13 192	92.95 115	96.71 87	82.32 185	96.08 69	89.91 444	86.79 188	92.15 121	96.81 84	62.60 397	98.34 163	87.18 198	93.90 199	98.19 73

UGNet		89.95 184	88.95 201	92.95 115	94.51 218	83.31 139	95.70 106	95.23 249	89.37 80	87.58 239	93.94 248	64.00 385	98.78 114	83.92 250	96.31 132	96.74 206
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		90.80 152	90.10 161	92.90 117	93.04 308	83.53 132	93.08 294	94.15 317	80.22 360	91.41 147	94.91 199	76.87 204	97.93 220	90.28 142	96.90 115	97.24 160
jason: jason.
DP-MVS		87.25 282	85.36 321	92.90 117	97.65 64	83.24 141	94.81 170	92.00 386	74.99 436	81.92 379	95.00 195	72.66 276	99.05 67	66.92 450	92.33 253	96.40 218

fmvsm_s_conf0.5_n_8		94.56 30	95.12 18	92.87 119	95.96 128	81.32 216	95.76 102	97.57 7	93.48 2	97.53 10	98.32 3	81.78 128	99.13 62	97.91 2	97.81 90	98.16 76

fmvsm_s_conf0.5_n		93.76 64	94.06 58	92.86 120	95.62 144	83.17 145	96.14 64	96.12 165	88.13 132	95.82 43	98.04 30	83.43 89	98.48 143	96.97 21	96.23 133	96.92 194

fmvsm_s_conf0.1_n		93.46 72	93.66 73	92.85 121	93.75 275	83.13 147	96.02 77	95.74 201	87.68 158	95.89 41	98.17 5	82.78 103	98.46 147	96.71 22	96.17 135	96.98 188

casdiffseed414692147		91.11 146	90.55 151	92.81 122	94.27 243	82.58 177	94.81 170	96.03 175	87.93 145	90.17 186	95.62 159	78.51 184	97.90 224	84.18 246	93.45 220	97.94 99

CANet_DTU		90.26 172	89.41 185	92.81 122	93.46 290	83.01 157	93.48 271	94.47 301	89.43 78	87.76 237	94.23 237	70.54 308	99.03 70	84.97 229	96.39 130	96.38 219

MVSFormer		91.68 129	91.30 129	92.80 124	93.86 268	83.88 120	95.96 83	95.90 187	84.66 261	91.76 137	94.91 199	77.92 194	97.30 288	89.64 159	97.11 106	97.24 160

PVSNet_Blended_VisFu		91.38 134	90.91 141	92.80 124	96.39 102	83.17 145	94.87 164	96.66 105	83.29 292	89.27 204	94.46 227	80.29 145	99.17 57	87.57 191	95.37 157	96.05 240

fmvsm_l_conf0.5_n_9		94.65 27	95.28 15	92.77 126	95.95 129	81.83 198	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	97.83 119

LuminaMVS		90.55 166	89.81 171	92.77 126	92.78 323	84.21 110	94.09 229	94.17 316	85.82 213	91.54 142	94.14 239	69.93 314	97.92 221	91.62 116	94.21 191	96.18 229

balanced_ft_v1		92.23 108	92.05 107	92.77 126	95.40 153	81.78 202	95.80 96	95.69 209	87.94 143	91.92 129	95.04 193	75.91 222	98.71 122	93.83 58	96.94 112	97.82 121

fmvsm_s_conf0.5_n_6		94.11 52	94.56 33	92.76 129	94.98 176	81.96 195	95.79 98	97.29 39	89.31 83	97.52 11	97.61 44	83.25 94	98.88 99	97.05 19	98.22 68	97.43 150

VDDNet		89.56 197	88.49 216	92.76 129	95.07 170	82.09 189	96.30 47	93.19 352	81.05 354	91.88 130	96.86 80	61.16 415	98.33 165	88.43 178	92.49 252	97.84 118

viewdifsd2359ckpt09		91.18 142	90.65 149	92.75 131	94.61 209	82.36 184	94.32 212	95.74 201	84.72 258	89.66 196	95.15 190	79.69 164	98.04 200	87.70 188	94.27 190	97.85 117

h-mvs33		90.80 152	90.15 160	92.75 131	96.01 121	82.66 170	95.43 123	95.53 223	89.80 63	93.08 90	95.64 158	75.77 223	99.00 80	92.07 100	78.05 432	96.60 211

hybridcas		92.43 104	92.33 101	92.74 133	94.51 218	81.84 197	95.05 154	96.16 159	89.60 72	91.40 148	96.20 110	82.23 114	98.09 189	89.95 150	95.87 140	98.28 62

casdiffmvs		92.51 101	92.43 100	92.74 133	94.41 230	81.98 193	94.54 188	96.23 145	89.57 74	91.96 126	96.17 115	82.58 106	98.01 207	90.95 130	95.45 155	98.23 71
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		90.69 157	90.02 167	92.71 135	95.72 137	82.41 182	94.11 225	95.12 256	85.63 220	91.49 144	94.70 209	74.75 238	98.42 156	86.13 214	92.53 250	97.31 152

DCV-MVSNet		90.69 157	90.02 167	92.71 135	95.72 137	82.41 182	94.11 225	95.12 256	85.63 220	91.49 144	94.70 209	74.75 238	98.42 156	86.13 214	92.53 250	97.31 152

PCF-MVS	84.11 10	87.74 256	86.08 294	92.70 137	94.02 257	84.43 103	89.27 419	95.87 192	73.62 451	84.43 326	94.33 229	78.48 187	98.86 102	70.27 424	94.45 182	94.81 290
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	92.69 138	96.05 120	82.00 191	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	97.17 167

SSM_0404		90.73 155	90.08 162	92.69 138	95.00 175	83.13 147	94.32 212	95.00 266	85.41 231	89.84 191	95.35 176	76.13 213	97.98 212	85.46 224	94.18 192	96.95 190

baseline		92.39 106	92.29 104	92.69 138	94.46 225	81.77 203	94.14 222	96.27 136	89.22 87	91.88 130	96.00 129	82.35 109	97.99 209	91.05 125	95.27 161	98.30 56

MSLP-MVS++		93.72 66	94.08 55	92.65 141	97.31 75	83.43 134	95.79 98	97.33 32	90.03 53	93.58 80	96.96 76	84.87 74	97.76 231	92.19 96	98.66 44	96.76 204

EC-MVSNet		93.44 75	93.71 71	92.63 142	95.21 163	82.43 179	97.27 14	96.71 101	90.57 39	92.88 95	95.80 148	83.16 95	98.16 177	93.68 59	98.14 73	97.31 152

ab-mvs		89.41 205	88.35 218	92.60 143	95.15 168	82.65 174	92.20 339	95.60 217	83.97 272	88.55 218	93.70 262	74.16 253	98.21 174	82.46 274	89.37 298	96.94 192

LS3D		87.89 251	86.32 283	92.59 144	96.07 118	82.92 160	95.23 137	94.92 276	75.66 428	82.89 365	95.98 131	72.48 280	99.21 55	68.43 438	95.23 162	95.64 257

Anonymous20240529		88.09 247	86.59 272	92.58 145	96.53 97	81.92 196	95.99 79	95.84 194	74.11 446	89.06 208	95.21 185	61.44 407	98.81 110	83.67 257	87.47 329	97.01 186

fmvsm_s_conf0.5_n_3		94.49 32	95.13 17	92.56 146	95.49 150	81.10 226	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	97.78 124

CPTT-MVS		91.99 110	91.80 110	92.55 147	98.24 37	81.98 193	96.76 35	96.49 119	81.89 331	90.24 179	96.44 103	78.59 181	98.61 135	89.68 157	97.85 88	97.06 180

viewdifsd2359ckpt13		91.20 141	90.75 146	92.54 148	94.30 241	82.13 188	94.03 235	95.89 189	85.60 222	90.20 181	95.36 175	79.69 164	97.90 224	87.85 186	93.86 200	97.61 135

114514_t		89.51 198	88.50 214	92.54 148	98.11 42	81.99 192	95.16 147	96.36 128	70.19 473	85.81 279	95.25 181	76.70 208	98.63 132	82.07 284	96.86 118	97.00 187

PAPM_NR		91.22 140	90.78 145	92.52 150	97.60 65	81.46 212	94.37 209	96.24 143	86.39 201	87.41 242	94.80 207	82.06 121	98.48 143	82.80 269	95.37 157	97.61 135

mamba_0408		89.06 218	87.92 232	92.50 151	94.76 192	82.66 170	79.84 494	94.64 294	85.18 236	88.96 210	95.00 195	76.00 218	97.98 212	83.74 254	93.15 230	96.85 199

DeepPCF-MVS	89.96 1	94.20 47	94.77 31	92.49 152	96.52 98	80.00 277	94.00 240	97.08 59	90.05 52	95.65 47	97.29 57	89.66 14	98.97 87	93.95 55	98.71 35	98.50 32

SSM_0407		90.47 168	89.80 172	92.46 153	94.76 192	82.66 170	93.98 242	95.00 266	85.41 231	88.96 210	95.35 176	76.13 213	97.88 226	85.46 224	93.15 230	96.85 199

IS-MVSNet		91.43 133	91.09 137	92.46 153	95.87 132	81.38 215	96.95 24	93.69 341	89.72 69	89.50 200	95.98 131	78.57 182	97.77 230	83.02 263	96.50 128	98.22 72

API-MVS		90.66 161	90.07 163	92.45 155	96.36 103	84.57 94	96.06 73	95.22 251	82.39 312	89.13 205	94.27 235	80.32 144	98.46 147	80.16 322	96.71 122	94.33 313

xiu_mvs_v1_base_debu		90.64 162	90.05 164	92.40 156	93.97 263	84.46 100	93.32 279	95.46 227	85.17 238	92.25 116	94.03 240	70.59 304	98.57 138	90.97 126	94.67 172	94.18 317

xiu_mvs_v1_base		90.64 162	90.05 164	92.40 156	93.97 263	84.46 100	93.32 279	95.46 227	85.17 238	92.25 116	94.03 240	70.59 304	98.57 138	90.97 126	94.67 172	94.18 317

xiu_mvs_v1_base_debi		90.64 162	90.05 164	92.40 156	93.97 263	84.46 100	93.32 279	95.46 227	85.17 238	92.25 116	94.03 240	70.59 304	98.57 138	90.97 126	94.67 172	94.18 317

fmvsm_s_conf0.5_n_2		93.47 71	93.83 62	92.39 159	95.36 154	81.19 222	95.20 144	96.56 113	90.37 42	97.13 18	98.03 31	77.47 200	98.96 89	97.79 6	96.58 125	97.03 183

viewmacassd2359aftdt		91.67 130	91.43 126	92.37 160	93.95 266	81.00 230	93.90 250	95.97 180	87.75 156	91.45 146	96.04 127	79.92 152	97.97 214	89.26 164	94.67 172	98.14 78

viewmanbaseed2359cas		91.78 117	91.58 116	92.37 160	94.32 238	81.07 227	93.76 256	95.96 181	87.26 171	91.50 143	95.88 139	80.92 139	97.97 214	89.70 156	94.92 166	98.07 84

fmvsm_s_conf0.1_n_2		93.16 88	93.42 77	92.37 160	94.62 206	81.13 224	95.23 137	95.89 189	90.30 46	96.74 29	98.02 32	76.14 212	98.95 91	97.64 7	96.21 134	97.03 183

AdaColmap		89.89 187	89.07 195	92.37 160	97.41 71	83.03 155	94.42 198	95.92 184	82.81 306	86.34 268	94.65 216	73.89 258	99.02 73	80.69 311	95.51 150	95.05 276

CNLPA		89.07 217	87.98 229	92.34 164	96.87 84	84.78 89	94.08 230	93.24 349	81.41 345	84.46 324	95.13 191	75.57 230	96.62 340	77.21 365	93.84 202	95.61 260

fmvsm_s_conf0.5_n_4		93.86 61	94.37 40	92.33 165	95.13 169	80.95 233	95.64 113	96.97 65	89.60 72	96.85 24	97.77 40	83.08 98	98.92 95	97.49 8	96.78 120	97.13 175

ET-MVSNet_ETH3D		87.51 270	85.91 302	92.32 166	93.70 282	83.93 118	92.33 331	90.94 419	84.16 267	72.09 471	92.52 299	69.90 315	95.85 392	89.20 165	88.36 316	97.17 167

E4		91.74 122	91.55 119	92.31 167	94.27 243	80.80 243	93.81 253	96.17 157	87.97 141	91.11 157	96.05 125	80.75 140	98.08 192	89.78 152	94.02 195	98.06 89

E2		91.79 114	91.61 114	92.31 167	94.49 221	80.86 239	93.74 258	96.19 150	87.63 161	91.16 152	95.94 134	81.31 134	98.06 195	89.76 153	94.29 188	97.99 94

Anonymous202405211		87.68 257	86.13 290	92.31 167	96.66 89	80.74 245	94.87 164	91.49 403	80.47 359	89.46 201	95.44 169	54.72 456	98.23 171	82.19 280	89.89 288	97.97 96

E3		91.78 117	91.61 114	92.30 170	94.48 222	80.86 239	93.73 259	96.19 150	87.63 161	91.16 152	95.95 133	81.30 135	98.06 195	89.76 153	94.29 188	97.99 94

CHOSEN 1792x2688		88.84 224	87.69 237	92.30 170	96.14 109	81.42 214	90.01 406	95.86 193	74.52 441	87.41 242	93.94 248	75.46 231	98.36 160	80.36 317	95.53 149	97.12 176

viewcassd2359sk11		91.79 114	91.62 113	92.29 172	94.62 206	80.88 237	93.70 263	96.18 156	87.38 168	91.13 155	95.85 143	81.62 130	98.06 195	89.71 155	94.40 184	97.94 99

HY-MVS	83.01 12	89.03 220	87.94 231	92.29 172	94.86 186	82.77 162	92.08 344	94.49 300	81.52 344	86.93 249	92.79 292	78.32 189	98.23 171	79.93 325	90.55 275	95.88 246

CDS-MVSNet		89.45 201	88.51 213	92.29 172	93.62 285	83.61 131	93.01 298	94.68 292	81.95 326	87.82 235	93.24 275	78.69 179	96.99 317	80.34 318	93.23 227	96.28 224
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PAPR		90.02 180	89.27 191	92.29 172	95.78 134	80.95 233	92.68 315	96.22 146	81.91 328	86.66 259	93.75 260	82.23 114	98.44 153	79.40 343	94.79 169	97.48 146

E3new		91.76 120	91.58 116	92.28 176	94.69 203	80.90 236	93.68 266	96.17 157	87.15 174	91.09 162	95.70 156	81.75 129	98.05 199	89.67 158	94.35 185	97.90 111

mvsmamba		90.33 169	89.69 175	92.25 177	95.17 165	81.64 205	95.27 135	93.36 347	84.88 251	89.51 198	94.27 235	69.29 330	97.42 270	89.34 162	96.12 137	97.68 130

E5new		91.71 124	91.55 119	92.20 178	94.33 236	80.62 249	94.41 199	96.19 150	88.06 135	91.11 157	96.16 116	79.92 152	98.03 203	90.00 145	93.80 204	97.94 99

E6new		91.71 124	91.55 119	92.20 178	94.32 238	80.62 249	94.41 199	96.19 150	88.06 135	91.11 157	96.16 116	79.92 152	98.03 203	90.00 145	93.80 204	97.94 99

E6		91.71 124	91.55 119	92.20 178	94.32 238	80.62 249	94.41 199	96.19 150	88.06 135	91.11 157	96.16 116	79.92 152	98.03 203	90.00 145	93.80 204	97.94 99

E5		91.71 124	91.55 119	92.20 178	94.33 236	80.62 249	94.41 199	96.19 150	88.06 135	91.11 157	96.16 116	79.92 152	98.03 203	90.00 145	93.80 204	97.94 99

PLC	84.53 7	89.06 218	88.03 227	92.15 182	97.27 78	82.69 169	94.29 214	95.44 232	79.71 368	84.01 340	94.18 238	76.68 209	98.75 116	77.28 364	93.41 221	95.02 277
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
EPP-MVSNet		91.70 128	91.56 118	92.13 183	95.88 130	80.50 256	97.33 8	95.25 248	86.15 207	89.76 195	95.60 160	83.42 91	98.32 167	87.37 196	93.25 226	97.56 140

patch_mono-2		93.74 65	94.32 41	92.01 184	97.54 66	78.37 330	93.40 275	97.19 44	88.02 139	94.99 57	97.21 62	88.35 25	98.44 153	94.07 54	98.09 76	99.23 1

原ACMM1				92.01 184	97.34 73	81.05 228		96.81 88	78.89 379	90.45 173	95.92 136	82.65 105	98.84 106	80.68 312	98.26 62	96.14 231

UniMVSNet (Re)		89.80 190	89.07 195	92.01 184	93.60 286	84.52 97	94.78 173	97.47 16	89.26 86	86.44 265	92.32 305	82.10 119	97.39 281	84.81 233	80.84 409	94.12 321

MG-MVS		91.77 119	91.70 112	92.00 187	97.08 81	80.03 275	93.60 268	95.18 254	87.85 151	90.89 166	96.47 102	82.06 121	98.36 160	85.07 228	97.04 109	97.62 133

EIA-MVS		91.95 111	91.94 108	91.98 188	95.16 166	80.01 276	95.36 125	96.73 98	88.44 118	89.34 202	92.16 310	83.82 87	98.45 151	89.35 161	97.06 108	97.48 146

PVSNet_Blended		90.73 155	90.32 155	91.98 188	96.12 111	81.25 218	92.55 320	96.83 83	82.04 324	89.10 206	92.56 298	81.04 137	98.85 104	86.72 206	95.91 139	95.84 248

guyue		91.12 145	90.84 143	91.96 190	94.59 210	80.57 254	94.87 164	93.71 340	88.96 101	91.14 154	95.22 182	73.22 270	97.76 231	92.01 104	93.81 203	97.54 144

PS-MVSNAJ		91.18 142	90.92 140	91.96 190	95.26 161	82.60 176	92.09 343	95.70 207	86.27 203	91.84 132	92.46 300	79.70 161	98.99 82	89.08 166	95.86 141	94.29 314

TAMVS		89.21 211	88.29 222	91.96 190	93.71 280	82.62 175	93.30 283	94.19 314	82.22 318	87.78 236	93.94 248	78.83 176	96.95 320	77.70 360	92.98 235	96.32 221

SDMVSNet		90.19 173	89.61 178	91.93 193	96.00 122	83.09 152	92.89 305	95.98 177	88.73 108	86.85 255	95.20 186	72.09 287	97.08 308	88.90 171	89.85 290	95.63 258

FA-MVS(test-final)		89.66 193	88.91 203	91.93 193	94.57 214	80.27 260	91.36 364	94.74 289	84.87 252	89.82 192	92.61 297	74.72 241	98.47 146	83.97 249	93.53 215	97.04 182

MVS_Test		91.31 137	91.11 134	91.93 193	94.37 231	80.14 265	93.46 273	95.80 196	86.46 198	91.35 150	93.77 258	82.21 116	98.09 189	87.57 191	94.95 165	97.55 142

NR-MVSNet		88.58 234	87.47 243	91.93 193	93.04 308	84.16 112	94.77 174	96.25 142	89.05 94	80.04 404	93.29 273	79.02 174	97.05 313	81.71 295	80.05 419	94.59 297

HyFIR lowres test		88.09 247	86.81 260	91.93 193	96.00 122	80.63 247	90.01 406	95.79 197	73.42 453	87.68 238	92.10 316	73.86 259	97.96 216	80.75 310	91.70 258	97.19 166

GeoE		90.05 178	89.43 183	91.90 198	95.16 166	80.37 259	95.80 96	94.65 293	83.90 273	87.55 241	94.75 208	78.18 190	97.62 244	81.28 300	93.63 211	97.71 129

thisisatest0530		88.67 229	87.61 239	91.86 199	94.87 185	80.07 270	94.63 183	89.90 445	84.00 271	88.46 220	93.78 257	66.88 354	98.46 147	83.30 259	92.65 243	97.06 180

xiu_mvs_v2_base		91.13 144	90.89 142	91.86 199	94.97 177	82.42 180	92.24 336	95.64 215	86.11 211	91.74 139	93.14 279	79.67 166	98.89 98	89.06 167	95.46 154	94.28 315

DU-MVS		89.34 210	88.50 214	91.85 201	93.04 308	83.72 124	94.47 194	96.59 110	89.50 75	86.46 262	93.29 273	77.25 202	97.23 297	84.92 230	81.02 405	94.59 297

AstraMVS		90.69 157	90.30 156	91.84 202	93.81 271	79.85 284	94.76 175	92.39 372	88.96 101	91.01 165	95.87 142	70.69 302	97.94 219	92.49 82	92.70 242	97.73 127

OPM-MVS		90.12 174	89.56 179	91.82 203	93.14 299	83.90 119	94.16 221	95.74 201	88.96 101	87.86 231	95.43 171	72.48 280	97.91 222	88.10 183	90.18 282	93.65 355
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
HQP_MVS		90.60 165	90.19 158	91.82 203	94.70 201	82.73 166	95.85 93	96.22 146	90.81 27	86.91 251	94.86 203	74.23 249	98.12 179	88.15 179	89.99 284	94.63 294

UniMVSNet_NR-MVSNet		89.92 186	89.29 189	91.81 205	93.39 292	83.72 124	94.43 197	97.12 55	89.80 63	86.46 262	93.32 270	83.16 95	97.23 297	84.92 230	81.02 405	94.49 307

diffmvs		91.37 136	91.23 132	91.77 206	93.09 302	80.27 260	92.36 326	95.52 224	87.03 180	91.40 148	94.93 198	80.08 148	97.44 267	92.13 99	94.56 178	97.61 135
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		91.51 132	91.44 125	91.73 207	93.09 302	80.27 260	92.51 321	95.58 218	87.22 172	91.80 135	95.57 162	79.96 151	97.48 259	92.23 93	94.97 164	97.45 148

1112_ss		88.42 236	87.33 246	91.72 208	94.92 181	80.98 231	92.97 302	94.54 297	78.16 397	83.82 343	93.88 253	78.78 178	97.91 222	79.45 339	89.41 297	96.26 225

Fast-Effi-MVS+		89.41 205	88.64 209	91.71 209	94.74 195	80.81 242	93.54 269	95.10 258	83.11 296	86.82 257	90.67 370	79.74 160	97.75 235	80.51 315	93.55 213	96.57 214

WTY-MVS		89.60 195	88.92 202	91.67 210	95.47 151	81.15 223	92.38 325	94.78 287	83.11 296	89.06 208	94.32 230	78.67 180	96.61 343	81.57 296	90.89 271	97.24 160

TAPA-MVS	84.62 6	88.16 245	87.01 255	91.62 211	96.64 90	80.65 246	94.39 205	96.21 149	76.38 420	86.19 272	95.44 169	79.75 159	98.08 192	62.75 468	95.29 159	96.13 232
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
onestephybrid01		91.23 138	91.10 136	91.61 212	93.07 304	79.86 282	92.83 308	95.34 242	87.07 178	91.04 163	95.53 164	80.01 150	97.43 268	90.96 129	94.08 194	97.56 140

VPA-MVSNet		89.62 194	88.96 200	91.60 213	93.86 268	82.89 161	95.46 121	97.33 32	87.91 146	88.43 221	93.31 271	74.17 252	97.40 278	87.32 197	82.86 380	94.52 302

nocashy02		91.38 134	91.32 128	91.58 214	93.02 311	79.63 293	92.83 308	95.38 236	88.29 124	90.66 169	95.81 147	80.63 141	97.50 257	91.52 118	93.71 209	97.62 133

FE-MVS		87.40 275	86.02 296	91.57 215	94.56 215	79.69 292	90.27 393	93.72 339	80.57 357	88.80 214	91.62 336	65.32 370	98.59 137	74.97 390	94.33 187	96.44 217

hybridnocas07		90.93 149	90.72 147	91.54 216	92.75 324	79.72 290	92.35 328	95.21 252	86.41 200	90.44 176	95.40 172	79.17 173	97.39 281	90.83 134	93.94 198	97.50 145

XVG-OURS		89.40 207	88.70 208	91.52 217	94.06 255	81.46 212	91.27 369	96.07 170	86.14 208	88.89 213	95.77 152	68.73 339	97.26 294	87.39 195	89.96 286	95.83 249

hse-mvs2		89.88 188	89.34 187	91.51 218	94.83 188	81.12 225	93.94 244	93.91 327	89.80 63	93.08 90	93.60 263	75.77 223	97.66 239	92.07 100	77.07 439	95.74 253

TranMVSNet+NR-MVSNet		88.84 224	87.95 230	91.49 219	92.68 327	83.01 157	94.92 161	96.31 131	89.88 57	85.53 288	93.85 255	76.63 210	96.96 319	81.91 288	79.87 422	94.50 305

AUN-MVS		87.78 255	86.54 275	91.48 220	94.82 189	81.05 228	93.91 248	93.93 324	83.00 301	86.93 249	93.53 265	69.50 324	97.67 237	86.14 212	77.12 438	95.73 255

XVG-OURS-SEG-HR		89.95 184	89.45 181	91.47 221	94.00 261	81.21 221	91.87 348	96.06 172	85.78 215	88.55 218	95.73 154	74.67 242	97.27 292	88.71 175	89.64 295	95.91 243

MVS		87.44 273	86.10 293	91.44 222	92.61 329	83.62 129	92.63 317	95.66 212	67.26 480	81.47 382	92.15 311	77.95 193	98.22 173	79.71 328	95.48 152	92.47 406

hybrid		90.69 157	90.45 152	91.43 223	92.67 328	79.42 301	92.28 335	95.21 252	85.15 243	90.39 177	95.37 174	78.93 175	97.32 287	90.27 143	93.74 208	97.55 142

viewdifsd2359ckpt07		91.11 146	91.02 138	91.41 224	94.21 248	78.37 330	92.91 304	95.71 206	87.50 163	90.32 178	95.88 139	80.27 146	97.99 209	88.78 174	93.55 213	97.86 114

F-COLMAP		87.95 250	86.80 261	91.40 225	96.35 104	80.88 237	94.73 177	95.45 230	79.65 369	82.04 377	94.61 217	71.13 294	98.50 141	76.24 377	91.05 269	94.80 291

dcpmvs_2		93.49 70	94.19 52	91.38 226	97.69 63	76.78 374	94.25 216	96.29 132	88.33 121	94.46 60	96.88 79	88.07 29	98.64 130	93.62 62	98.09 76	98.73 23

thisisatest0515		87.33 278	85.99 297	91.37 227	93.49 288	79.55 294	90.63 385	89.56 453	80.17 361	87.56 240	90.86 360	67.07 351	98.28 169	81.50 297	93.02 234	96.29 223

HQP-MVS		89.80 190	89.28 190	91.34 228	94.17 250	81.56 206	94.39 205	96.04 173	88.81 104	85.43 297	93.97 247	73.83 260	97.96 216	87.11 201	89.77 293	94.50 305

fmvsm_s_conf0.5_n_7		93.15 89	93.76 68	91.31 229	94.42 229	79.48 296	94.52 189	97.14 53	89.33 82	94.17 66	98.09 18	81.83 126	97.49 258	96.33 26	98.02 80	96.95 190

RRT-MVS		90.85 151	90.70 148	91.30 230	94.25 245	76.83 373	94.85 167	96.13 164	89.04 95	90.23 180	94.88 201	70.15 313	98.72 120	91.86 112	94.88 167	98.34 49

FMVSNet3		87.40 275	86.11 292	91.30 230	93.79 274	83.64 128	94.20 220	94.81 285	83.89 274	84.37 327	91.87 327	68.45 342	96.56 352	78.23 355	85.36 348	93.70 354

FMVSNet2		87.19 288	85.82 305	91.30 230	94.01 258	83.67 126	94.79 172	94.94 271	83.57 282	83.88 342	92.05 320	66.59 359	96.51 356	77.56 362	85.01 351	93.73 352

RPMNet		83.95 368	81.53 379	91.21 233	90.58 406	79.34 306	85.24 471	96.76 93	71.44 467	85.55 286	82.97 472	70.87 299	98.91 97	61.01 472	89.36 299	95.40 264

IB-MVS	80.51 15	85.24 344	83.26 363	91.19 234	92.13 341	79.86 282	91.75 352	91.29 409	83.28 293	80.66 394	88.49 419	61.28 409	98.46 147	80.99 306	79.46 426	95.25 270
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		89.47 200	88.90 204	91.18 235	94.22 247	82.07 190	92.13 341	96.09 168	87.90 147	85.37 303	92.45 301	74.38 247	97.56 249	87.15 199	90.43 277	93.93 332
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		89.45 201	88.90 204	91.12 236	94.47 223	81.49 210	95.30 130	96.14 161	86.73 191	85.45 294	95.16 188	69.89 316	98.10 181	87.70 188	89.23 302	93.77 348

LGP-MVS_train				91.12 236	94.47 223	81.49 210		96.14 161	86.73 191	85.45 294	95.16 188	69.89 316	98.10 181	87.70 188	89.23 302	93.77 348

ACMM	84.12 9	89.14 213	88.48 217	91.12 236	94.65 205	81.22 220	95.31 128	96.12 165	85.31 235	85.92 277	94.34 228	70.19 312	98.06 195	85.65 220	88.86 307	94.08 325
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tttt0517		88.61 231	87.78 236	91.11 239	94.96 178	77.81 349	95.35 126	89.69 448	85.09 246	88.05 229	94.59 220	66.93 352	98.48 143	83.27 260	92.13 255	97.03 183

GBi-Net		87.26 280	85.98 298	91.08 240	94.01 258	83.10 149	95.14 148	94.94 271	83.57 282	84.37 327	91.64 332	66.59 359	96.34 370	78.23 355	85.36 348	93.79 343

test1		87.26 280	85.98 298	91.08 240	94.01 258	83.10 149	95.14 148	94.94 271	83.57 282	84.37 327	91.64 332	66.59 359	96.34 370	78.23 355	85.36 348	93.79 343

FMVSNet1		85.85 329	84.11 350	91.08 240	92.81 321	83.10 149	95.14 148	94.94 271	81.64 339	82.68 367	91.64 332	59.01 431	96.34 370	75.37 384	83.78 364	93.79 343

Test_1112_low_res		87.65 259	86.51 276	91.08 240	94.94 180	79.28 310	91.77 351	94.30 309	76.04 426	83.51 353	92.37 303	77.86 196	97.73 236	78.69 350	89.13 304	96.22 226

PS-MVSNAJss		89.97 182	89.62 177	91.02 244	91.90 350	80.85 241	95.26 136	95.98 177	86.26 204	86.21 271	94.29 232	79.70 161	97.65 240	88.87 173	88.10 318	94.57 299

BH-RMVSNet		88.37 239	87.48 242	91.02 244	95.28 158	79.45 298	92.89 305	93.07 355	85.45 230	86.91 251	94.84 206	70.35 309	97.76 231	73.97 399	94.59 177	95.85 247

UniMVSNet_ETH3D		87.53 269	86.37 280	91.00 246	92.44 333	78.96 315	94.74 176	95.61 216	84.07 270	85.36 304	94.52 222	59.78 423	97.34 285	82.93 264	87.88 323	96.71 207

FIs		90.51 167	90.35 154	90.99 247	93.99 262	80.98 231	95.73 104	97.54 9	89.15 90	86.72 258	94.68 211	81.83 126	97.24 296	85.18 226	88.31 317	94.76 292

ACMP	84.23 8	89.01 222	88.35 218	90.99 247	94.73 196	81.27 217	95.07 151	95.89 189	86.48 196	83.67 348	94.30 231	69.33 326	97.99 209	87.10 203	88.55 309	93.72 353
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
Anonymous20231211		86.59 312	85.13 327	90.98 249	96.52 98	81.50 208	96.14 64	96.16 159	73.78 449	83.65 349	92.15 311	63.26 391	97.37 284	82.82 268	81.74 394	94.06 326

IMVS_0403		89.97 182	89.64 176	90.96 250	93.72 276	77.75 354	93.00 299	95.34 242	85.53 226	88.77 215	94.49 223	78.49 186	97.84 227	84.75 234	92.65 243	97.28 155

sss		88.93 223	88.26 224	90.94 251	94.05 256	80.78 244	91.71 353	95.38 236	81.55 343	88.63 217	93.91 252	75.04 235	95.47 411	82.47 273	91.61 259	96.57 214

IMVS_0407		89.85 189	89.51 180	90.88 252	93.72 276	77.75 354	93.07 296	95.34 242	85.53 226	88.34 223	94.49 223	77.69 198	97.60 245	84.75 234	92.65 243	97.28 155

dtuplus		89.78 192	89.43 183	90.85 253	92.83 320	77.91 343	92.32 333	94.97 268	82.33 316	90.20 181	95.53 164	78.56 183	97.38 283	85.15 227	92.95 236	97.24 160

viewmambaseed2359dif		90.04 179	89.78 173	90.83 254	92.85 319	77.92 342	92.23 337	95.01 262	81.90 329	90.20 181	95.45 168	79.64 168	97.34 285	87.52 193	93.17 228	97.23 164

sd_testset		88.59 233	87.85 235	90.83 254	96.00 122	80.42 258	92.35 328	94.71 290	88.73 108	86.85 255	95.20 186	67.31 346	96.43 364	79.64 331	89.85 290	95.63 258

PVSNet_BlendedMVS		89.98 181	89.70 174	90.82 256	96.12 111	81.25 218	93.92 246	96.83 83	83.49 286	89.10 206	92.26 308	81.04 137	98.85 104	86.72 206	87.86 324	92.35 413

cascas		86.43 320	84.98 330	90.80 257	92.10 343	80.92 235	90.24 397	95.91 186	73.10 456	83.57 352	88.39 420	65.15 372	97.46 263	84.90 232	91.43 261	94.03 328

ECVR-MVS		89.09 216	88.53 212	90.77 258	95.62 144	75.89 387	96.16 60	84.22 482	87.89 149	90.20 181	96.65 91	63.19 393	98.10 181	85.90 217	96.94 112	98.33 51

GA-MVS		86.61 310	85.27 324	90.66 259	91.33 373	78.71 319	90.40 392	93.81 334	85.34 234	85.12 307	89.57 401	61.25 410	97.11 306	80.99 306	89.59 296	96.15 230

thres600view7		87.65 259	86.67 267	90.59 260	96.08 117	78.72 317	94.88 163	91.58 399	87.06 179	88.08 227	92.30 306	68.91 336	98.10 181	70.05 431	91.10 264	94.96 281

thres400		87.62 264	86.64 268	90.57 261	95.99 125	78.64 320	94.58 185	91.98 388	86.94 185	88.09 225	91.77 328	69.18 332	98.10 181	70.13 428	91.10 264	94.96 281

baseline1		88.10 246	87.28 248	90.57 261	94.96 178	80.07 270	94.27 215	91.29 409	86.74 190	87.41 242	94.00 245	76.77 207	96.20 375	80.77 309	79.31 428	95.44 262

viewdifsd2359ckpt11		89.43 203	89.05 197	90.56 263	92.89 317	77.00 369	92.81 310	94.52 298	87.03 180	89.77 193	95.79 149	74.67 242	97.51 253	88.97 169	84.98 352	97.17 167

viewmsd2359difaftdt		89.43 203	89.05 197	90.56 263	92.89 317	77.00 369	92.81 310	94.52 298	87.03 180	89.77 193	95.79 149	74.67 242	97.51 253	88.97 169	84.98 352	97.17 167

usedtu_dtu_shiyan1		86.84 299	85.61 313	90.53 265	90.50 410	81.80 200	90.97 377	94.96 269	83.05 298	83.50 354	90.32 377	72.15 284	96.65 334	79.49 336	85.55 346	93.15 378

FE-MVSNET3		86.84 299	85.61 313	90.53 265	90.50 410	81.80 200	90.97 377	94.96 269	83.05 298	83.50 354	90.32 377	72.15 284	96.65 334	79.49 336	85.55 346	93.15 378

FC-MVSNet-test		90.27 171	90.18 159	90.53 265	93.71 280	79.85 284	95.77 100	97.59 6	89.31 83	86.27 269	94.67 214	81.93 124	97.01 316	84.26 244	88.09 320	94.71 293

PAPM		86.68 309	85.39 319	90.53 265	93.05 307	79.33 309	89.79 409	94.77 288	78.82 382	81.95 378	93.24 275	76.81 205	97.30 288	66.94 448	93.16 229	94.95 285

WR-MVS		88.38 238	87.67 238	90.52 269	93.30 294	80.18 263	93.26 286	95.96 181	88.57 116	85.47 293	92.81 290	76.12 215	96.91 323	81.24 301	82.29 385	94.47 310

SSM_04072		88.57 235	87.92 232	90.51 270	94.76 192	82.66 170	79.84 494	94.64 294	85.18 236	88.96 210	95.00 195	76.00 218	92.03 462	83.74 254	93.15 230	96.85 199

MVSTER		88.84 224	88.29 222	90.51 270	92.95 314	80.44 257	93.73 259	95.01 262	84.66 261	87.15 246	93.12 280	72.79 275	97.21 299	87.86 185	87.36 332	93.87 337

testdata				90.49 272	96.40 101	77.89 346		95.37 239	72.51 461	93.63 79	96.69 87	82.08 120	97.65 240	83.08 261	97.39 101	95.94 242

test1111		89.10 214	88.64 209	90.48 273	95.53 149	74.97 397	96.08 69	84.89 480	88.13 132	90.16 187	96.65 91	63.29 390	98.10 181	86.14 212	96.90 115	98.39 46

tt0805		86.92 296	85.74 311	90.48 273	92.22 337	79.98 278	95.63 114	94.88 279	83.83 276	84.74 316	92.80 291	57.61 438	97.67 237	85.48 223	84.42 357	93.79 343

jajsoiax		88.24 243	87.50 241	90.48 273	90.89 394	80.14 265	95.31 128	95.65 214	84.97 249	84.24 335	94.02 243	65.31 371	97.42 270	88.56 176	88.52 311	93.89 333

PatchMatch-RL		86.77 306	85.54 315	90.47 276	95.88 130	82.71 168	90.54 388	92.31 376	79.82 367	84.32 332	91.57 340	68.77 338	96.39 366	73.16 405	93.48 219	92.32 414

0.4-1-1-0.1		81.55 400	78.59 423	90.42 277	87.55 453	79.90 280	88.56 432	89.19 458	77.01 412	79.72 411	77.71 488	54.84 453	97.11 306	80.50 316	72.20 453	94.26 316

tfpn200view9		87.58 267	86.64 268	90.41 278	95.99 125	78.64 320	94.58 185	91.98 388	86.94 185	88.09 225	91.77 328	69.18 332	98.10 181	70.13 428	91.10 264	94.48 308

VPNet		88.20 244	87.47 243	90.39 279	93.56 287	79.46 297	94.04 234	95.54 222	88.67 111	86.96 248	94.58 221	69.33 326	97.15 301	84.05 248	80.53 414	94.56 300

ACMH	80.38 17	85.36 339	83.68 357	90.39 279	94.45 226	80.63 247	94.73 177	94.85 281	82.09 320	77.24 439	92.65 295	60.01 421	97.58 247	72.25 410	84.87 354	92.96 385
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
thres100view900		87.63 262	86.71 264	90.38 281	96.12 111	78.55 323	95.03 155	91.58 399	87.15 174	88.06 228	92.29 307	68.91 336	98.10 181	70.13 428	91.10 264	94.48 308

mvs_tets		88.06 249	87.28 248	90.38 281	90.94 390	79.88 281	95.22 139	95.66 212	85.10 245	84.21 336	93.94 248	63.53 388	97.40 278	88.50 177	88.40 315	93.87 337

1314		87.51 270	86.57 273	90.34 283	92.42 334	79.74 289	92.63 317	95.35 241	78.35 392	80.14 401	91.62 336	74.05 254	97.15 301	81.05 302	93.53 215	94.12 321

LTVRE_ROB	82.13 13	86.26 323	84.90 333	90.34 283	94.44 227	81.50 208	92.31 334	94.89 277	83.03 300	79.63 413	92.67 294	69.69 319	97.79 229	71.20 416	86.26 341	91.72 424
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		80.75 413	77.58 428	90.25 285	86.55 458	79.72 290	87.46 453	89.48 456	76.43 419	77.93 434	75.94 491	52.31 465	97.05 313	80.25 321	71.85 457	93.99 330

test_djsdf		89.03 220	88.64 209	90.21 286	90.74 401	79.28 310	95.96 83	95.90 187	84.66 261	85.33 305	92.94 285	74.02 255	97.30 288	89.64 159	88.53 310	94.05 327

v2v482		87.84 252	87.06 252	90.17 287	90.99 386	79.23 313	94.00 240	95.13 255	84.87 252	85.53 288	92.07 319	74.45 246	97.45 264	84.71 239	81.75 393	93.85 340

pmmvs4		85.43 337	83.86 355	90.16 288	90.02 421	82.97 159	90.27 393	92.67 367	75.93 427	80.73 392	91.74 330	71.05 295	95.73 400	78.85 349	83.46 371	91.78 423

V42		87.68 257	86.86 257	90.15 289	90.58 406	80.14 265	94.24 218	95.28 247	83.66 280	85.67 283	91.33 342	74.73 240	97.41 276	84.43 243	81.83 391	92.89 388

MSDG		84.86 352	83.09 366	90.14 290	93.80 272	80.05 272	89.18 422	93.09 354	78.89 379	78.19 430	91.91 325	65.86 369	97.27 292	68.47 437	88.45 313	93.11 380

sc_t1		81.53 401	78.67 422	90.12 291	90.78 398	78.64 320	93.91 248	90.20 434	68.42 476	80.82 391	89.88 394	46.48 480	96.76 328	76.03 380	71.47 458	94.96 281

anonymousdsp		87.84 252	87.09 251	90.12 291	89.13 432	80.54 255	94.67 181	95.55 220	82.05 322	83.82 343	92.12 313	71.47 292	97.15 301	87.15 199	87.80 327	92.67 395

thres200		87.21 286	86.24 287	90.12 291	95.36 154	78.53 324	93.26 286	92.10 382	86.42 199	88.00 230	91.11 353	69.24 331	98.00 208	69.58 432	91.04 270	93.83 342

CR-MVSNet		85.35 340	83.76 356	90.12 291	90.58 406	79.34 306	85.24 471	91.96 390	78.27 394	85.55 286	87.87 430	71.03 296	95.61 403	73.96 400	89.36 299	95.40 264

0.4-1-1-0.2		80.84 412	77.77 426	90.06 295	86.18 462	79.35 304	86.75 459	89.54 454	76.23 424	78.59 429	75.46 494	55.03 452	96.99 317	80.11 323	72.05 455	93.85 340

v1144		87.61 265	86.79 262	90.06 295	91.01 385	79.34 306	93.95 243	95.42 235	83.36 291	85.66 284	91.31 345	74.98 236	97.42 270	83.37 258	82.06 387	93.42 364

XXY-MVS		87.65 259	86.85 258	90.03 297	92.14 340	80.60 253	93.76 256	95.23 249	82.94 303	84.60 318	94.02 243	74.27 248	95.49 410	81.04 303	83.68 367	94.01 329

Vis-MVSNet (Re-imp)		89.59 196	89.44 182	90.03 297	95.74 135	75.85 388	95.61 115	90.80 423	87.66 160	87.83 234	95.40 172	76.79 206	96.46 361	78.37 351	96.73 121	97.80 122

test2506		87.21 286	86.28 285	90.02 299	95.62 144	73.64 413	96.25 55	71.38 506	87.89 149	90.45 173	96.65 91	55.29 450	98.09 189	86.03 216	96.94 112	98.33 51

BH-untuned		88.60 232	88.13 226	90.01 300	95.24 162	78.50 326	93.29 284	94.15 317	84.75 257	84.46 324	93.40 267	75.76 225	97.40 278	77.59 361	94.52 180	94.12 321

v1192		87.25 282	86.33 282	90.00 301	90.76 400	79.04 314	93.80 254	95.48 225	82.57 310	85.48 292	91.18 349	73.38 269	97.42 270	82.30 277	82.06 387	93.53 358

v7n		86.81 301	85.76 309	89.95 302	90.72 402	79.25 312	95.07 151	95.92 184	84.45 264	82.29 371	90.86 360	72.60 279	97.53 251	79.42 342	80.52 415	93.08 382

testing91		87.11 291	86.18 288	89.92 303	94.43 228	75.38 396	91.53 359	92.27 378	86.48 196	86.50 260	90.24 380	61.19 413	97.53 251	82.10 282	90.88 272	96.84 202

IMVS_0404		87.60 266	86.84 259	89.89 304	93.72 276	77.75 354	88.56 432	95.34 242	85.53 226	79.98 405	94.49 223	66.54 362	94.64 424	84.75 234	92.65 243	97.28 155

v8		87.50 272	86.71 264	89.89 304	91.37 370	79.40 302	94.50 190	95.38 236	84.81 255	83.60 351	91.33 342	76.05 216	97.42 270	82.84 267	80.51 416	92.84 390

v10		87.25 282	86.38 279	89.85 306	91.19 376	79.50 295	94.48 191	95.45 230	83.79 278	83.62 350	91.19 347	75.13 233	97.42 270	81.94 287	80.60 411	92.63 397

baseline2		86.50 316	85.39 319	89.84 307	91.12 381	76.70 376	91.88 347	88.58 460	82.35 315	79.95 406	90.95 358	73.42 267	97.63 243	80.27 320	89.95 287	95.19 271

pm-mvs1		86.61 310	85.54 315	89.82 308	91.44 365	80.18 263	95.28 134	94.85 281	83.84 275	81.66 380	92.62 296	72.45 282	96.48 358	79.67 330	78.06 431	92.82 391

TR-MVS		86.78 303	85.76 309	89.82 308	94.37 231	78.41 328	92.47 322	92.83 361	81.11 353	86.36 266	92.40 302	68.73 339	97.48 259	73.75 403	89.85 290	93.57 357

ACMH+	81.04 14	85.05 347	83.46 360	89.82 308	94.66 204	79.37 303	94.44 196	94.12 320	82.19 319	78.04 432	92.82 289	58.23 434	97.54 250	73.77 402	82.90 379	92.54 403

EI-MVSNet		89.10 214	88.86 206	89.80 311	91.84 352	78.30 333	93.70 263	95.01 262	85.73 217	87.15 246	95.28 179	79.87 158	97.21 299	83.81 252	87.36 332	93.88 336

gbinet_0.2-2-1-0.02		82.59 382	80.19 394	89.77 312	85.23 473	80.05 272	91.59 358	93.52 343	77.60 401	79.78 410	82.87 474	63.26 391	96.45 362	78.93 347	68.97 468	92.81 392

usedtu_blend_shiyan5		82.39 387	79.93 401	89.75 313	85.12 474	80.08 268	92.36 326	93.26 348	74.29 444	79.00 421	82.72 475	64.29 382	96.60 347	79.60 332	68.75 472	92.55 400

v144192		87.19 288	86.35 281	89.74 314	90.64 404	78.24 335	93.92 246	95.43 233	81.93 327	85.51 290	91.05 356	74.21 251	97.45 264	82.86 266	81.56 395	93.53 358

COLMAP_ROB	80.39 16	83.96 367	82.04 376	89.74 314	95.28 158	79.75 288	94.25 216	92.28 377	75.17 434	78.02 433	93.77 258	58.60 433	97.84 227	65.06 459	85.92 342	91.63 426
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
SCA		86.32 322	85.18 326	89.73 316	92.15 339	76.60 377	91.12 373	91.69 395	83.53 285	85.50 291	88.81 413	66.79 355	96.48 358	76.65 370	90.35 279	96.12 233

blend_shiyan4		81.94 390	79.35 409	89.70 317	85.52 469	80.08 268	91.29 367	93.82 331	77.12 410	79.31 417	82.94 473	54.81 454	96.60 347	79.60 332	69.78 463	92.41 409

IterMVS-LS		88.36 240	87.91 234	89.70 317	93.80 272	78.29 334	93.73 259	95.08 260	85.73 217	84.75 315	91.90 326	79.88 157	96.92 322	83.83 251	82.51 381	93.89 333
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
blended_shiyan8		82.79 377	80.49 387	89.69 319	85.50 470	79.83 286	91.38 362	93.82 331	77.14 407	79.39 416	83.73 465	64.95 376	96.63 337	79.75 327	68.77 471	92.62 399

testing11		86.44 319	85.35 322	89.69 319	94.29 242	75.40 395	91.30 366	90.53 429	84.76 256	85.06 309	90.13 386	58.95 432	97.45 264	82.08 283	91.09 268	96.21 228

testing99		86.72 307	85.73 312	89.69 319	94.23 246	74.91 399	91.35 365	90.97 417	86.14 208	86.36 266	90.22 381	59.41 426	97.48 259	82.24 279	90.66 274	96.69 209

v1921920		86.97 295	86.06 295	89.69 319	90.53 409	78.11 338	93.80 254	95.43 233	81.90 329	85.33 305	91.05 356	72.66 276	97.41 276	82.05 285	81.80 392	93.53 358

icg_test_0407_2		89.15 212	88.97 199	89.68 323	93.72 276	77.75 354	88.26 438	95.34 242	85.53 226	88.34 223	94.49 223	77.69 198	93.99 437	84.75 234	92.65 243	97.28 155

blended_shiyan6		82.78 378	80.48 388	89.67 324	85.53 468	79.76 287	91.37 363	93.82 331	77.14 407	79.30 418	83.73 465	64.96 375	96.63 337	79.68 329	68.75 472	92.63 397

VortexMVS		88.42 236	88.01 228	89.63 325	93.89 267	78.82 316	93.82 252	95.47 226	86.67 193	84.53 322	91.99 322	72.62 278	96.65 334	89.02 168	84.09 361	93.41 365

Fast-Effi-MVS+-dtu		87.44 273	86.72 263	89.63 325	92.04 344	77.68 359	94.03 235	93.94 323	85.81 214	82.42 370	91.32 344	70.33 310	97.06 311	80.33 319	90.23 281	94.14 320

v1240		86.78 303	85.85 304	89.56 327	90.45 413	77.79 351	93.61 267	95.37 239	81.65 338	85.43 297	91.15 351	71.50 291	97.43 268	81.47 298	82.05 389	93.47 362

Effi-MVS+-dtu		88.65 230	88.35 218	89.54 328	93.33 293	76.39 381	94.47 194	94.36 307	87.70 157	85.43 297	89.56 402	73.45 265	97.26 294	85.57 222	91.28 263	94.97 278

wanda-best-256-512		82.44 384	80.07 396	89.53 329	85.12 474	79.44 299	90.49 389	93.75 337	76.97 413	79.00 421	82.72 475	64.29 382	96.61 343	79.56 334	68.75 472	92.55 400

FE-blended-shiyan7		82.44 384	80.07 396	89.53 329	85.12 474	79.44 299	90.49 389	93.75 337	76.97 413	79.00 421	82.72 475	64.29 382	96.61 343	79.56 334	68.75 472	92.55 400

AllTest		83.42 374	81.39 380	89.52 331	95.01 172	77.79 351	93.12 290	90.89 421	77.41 403	76.12 448	93.34 268	54.08 459	97.51 253	68.31 439	84.27 359	93.26 368

TestCases				89.52 331	95.01 172	77.79 351		90.89 421	77.41 403	76.12 448	93.34 268	54.08 459	97.51 253	68.31 439	84.27 359	93.26 368

mvs_anonymous		89.37 209	89.32 188	89.51 333	93.47 289	74.22 406	91.65 356	94.83 283	82.91 304	85.45 294	93.79 256	81.23 136	96.36 369	86.47 208	94.09 193	97.94 99

XVG-ACMP-BASELINE		86.00 325	84.84 335	89.45 334	91.20 375	78.00 340	91.70 354	95.55 220	85.05 247	82.97 364	92.25 309	54.49 457	97.48 259	82.93 264	87.45 331	92.89 388

testing222		84.84 353	83.32 361	89.43 335	94.15 253	75.94 386	91.09 374	89.41 457	84.90 250	85.78 280	89.44 403	52.70 464	96.28 373	70.80 422	91.57 260	96.07 237

MVP-Stereo		85.97 326	84.86 334	89.32 336	90.92 392	82.19 187	92.11 342	94.19 314	78.76 384	78.77 428	91.63 335	68.38 343	96.56 352	75.01 389	93.95 197	89.20 466
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PatchmatchNet		85.85 329	84.70 337	89.29 337	91.76 356	75.54 392	88.49 434	91.30 408	81.63 340	85.05 310	88.70 417	71.71 288	96.24 374	74.61 395	89.05 305	96.08 236
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v148		87.04 293	86.32 283	89.21 338	90.94 390	77.26 365	93.71 262	94.43 302	84.84 254	84.36 330	90.80 364	76.04 217	97.05 313	82.12 281	79.60 425	93.31 367

tfpnnormal		84.72 355	83.23 364	89.20 339	92.79 322	80.05 272	94.48 191	95.81 195	82.38 313	81.08 388	91.21 346	69.01 335	96.95 320	61.69 470	80.59 412	90.58 452

cl22		86.78 303	85.98 298	89.18 340	92.34 335	77.62 360	90.84 381	94.13 319	81.33 347	83.97 341	90.15 385	73.96 256	96.60 347	84.19 245	82.94 376	93.33 366

BH-w/o		87.57 268	87.05 253	89.12 341	94.90 184	77.90 345	92.41 323	93.51 344	82.89 305	83.70 347	91.34 341	75.75 226	97.07 310	75.49 382	93.49 217	92.39 411

WR-MVS_H		87.80 254	87.37 245	89.10 342	93.23 295	78.12 337	95.61 115	97.30 37	87.90 147	83.72 346	92.01 321	79.65 167	96.01 384	76.36 374	80.54 413	93.16 376

miper_enhance_ethall		86.90 297	86.18 288	89.06 343	91.66 361	77.58 361	90.22 399	94.82 284	79.16 375	84.48 323	89.10 407	79.19 172	96.66 333	84.06 247	82.94 376	92.94 386

c3_l		87.14 290	86.50 277	89.04 344	92.20 338	77.26 365	91.22 372	94.70 291	82.01 325	84.34 331	90.43 375	78.81 177	96.61 343	83.70 256	81.09 402	93.25 370

miper_ehance_all_eth		87.22 285	86.62 271	89.02 345	92.13 341	77.40 363	90.91 380	94.81 285	81.28 348	84.32 332	90.08 388	79.26 170	96.62 340	83.81 252	82.94 376	93.04 383

gg-mvs-nofinetune		81.77 394	79.37 408	88.99 346	90.85 396	77.73 358	86.29 463	79.63 493	74.88 439	83.19 363	69.05 504	60.34 418	96.11 379	75.46 383	94.64 176	93.11 380

ETVMVS		84.43 360	82.92 370	88.97 347	94.37 231	74.67 400	91.23 371	88.35 462	83.37 290	86.06 275	89.04 408	55.38 448	95.67 402	67.12 446	91.34 262	96.58 213

pmmvs6		83.42 374	81.60 378	88.87 348	88.01 448	77.87 347	94.96 158	94.24 313	74.67 440	78.80 427	91.09 354	60.17 420	96.49 357	77.06 369	75.40 445	92.23 416

test_cas_vis1_n_1920		88.83 227	88.85 207	88.78 349	91.15 380	76.72 375	93.85 251	94.93 275	83.23 295	92.81 99	96.00 129	61.17 414	94.45 425	91.67 115	94.84 168	95.17 272

MIMVSNet		82.59 382	80.53 385	88.76 350	91.51 363	78.32 332	86.57 462	90.13 437	79.32 371	80.70 393	88.69 418	52.98 463	93.07 453	66.03 454	88.86 307	94.90 286

cl____		86.52 315	85.78 306	88.75 351	92.03 345	76.46 379	90.74 382	94.30 309	81.83 334	83.34 359	90.78 365	75.74 228	96.57 350	81.74 293	81.54 396	93.22 372

DIV-MVS_self_test		86.53 314	85.78 306	88.75 351	92.02 346	76.45 380	90.74 382	94.30 309	81.83 334	83.34 359	90.82 363	75.75 226	96.57 350	81.73 294	81.52 397	93.24 371

CP-MVSNet		87.63 262	87.26 250	88.74 353	93.12 300	76.59 378	95.29 132	96.58 111	88.43 119	83.49 356	92.98 284	75.28 232	95.83 393	78.97 346	81.15 401	93.79 343

eth_miper_zixun_eth		86.50 316	85.77 308	88.68 354	91.94 347	75.81 389	90.47 391	94.89 277	82.05 322	84.05 338	90.46 374	75.96 220	96.77 327	82.76 270	79.36 427	93.46 363

CHOSEN 280x420		85.15 345	83.99 353	88.65 355	92.47 331	78.40 329	79.68 496	92.76 364	74.90 438	81.41 384	89.59 400	69.85 318	95.51 407	79.92 326	95.29 159	92.03 419

PS-CasMVS		87.32 279	86.88 256	88.63 356	92.99 312	76.33 383	95.33 127	96.61 109	88.22 128	83.30 361	93.07 282	73.03 273	95.79 397	78.36 352	81.00 407	93.75 350

TransMVSNet (Re)		84.43 360	83.06 368	88.54 357	91.72 357	78.44 327	95.18 145	92.82 363	82.73 308	79.67 412	92.12 313	73.49 264	95.96 386	71.10 420	68.73 476	91.21 439

tt0320-xc		79.63 427	76.66 436	88.52 358	91.03 384	78.72 317	93.00 299	89.53 455	66.37 482	76.11 450	87.11 441	46.36 482	95.32 415	72.78 407	67.67 477	91.51 431

EG-PatchMatch MVS		82.37 388	80.34 390	88.46 359	90.27 415	79.35 304	92.80 313	94.33 308	77.14 407	73.26 467	90.18 384	47.47 477	96.72 329	70.25 425	87.32 334	89.30 463

PEN-MVS		86.80 302	86.27 286	88.40 360	92.32 336	75.71 391	95.18 145	96.38 126	87.97 141	82.82 366	93.15 278	73.39 268	95.92 388	76.15 378	79.03 430	93.59 356

Baseline_NR-MVSNet		87.07 292	86.63 270	88.40 360	91.44 365	77.87 347	94.23 219	92.57 369	84.12 269	85.74 282	92.08 317	77.25 202	96.04 380	82.29 278	79.94 420	91.30 437

UBG		85.51 335	84.57 342	88.35 362	94.21 248	71.78 438	90.07 404	89.66 450	82.28 317	85.91 278	89.01 409	61.30 408	97.06 311	76.58 373	92.06 256	96.22 226

D2MVS		85.90 327	85.09 328	88.35 362	90.79 397	77.42 362	91.83 350	95.70 207	80.77 356	80.08 403	90.02 390	66.74 357	96.37 367	81.88 289	87.97 322	91.26 438

pmmvs5		84.21 363	82.84 373	88.34 364	88.95 434	76.94 371	92.41 323	91.91 392	75.63 429	80.28 398	91.18 349	64.59 379	95.57 404	77.09 368	83.47 370	92.53 404

tt0320		80.13 419	77.41 429	88.29 365	90.50 410	78.02 339	93.10 293	90.71 426	66.06 485	76.75 443	86.97 442	49.56 472	95.40 412	71.65 411	71.41 459	91.46 434

LCM-MVSNet-Re		88.30 242	88.32 221	88.27 366	94.71 200	72.41 433	93.15 289	90.98 416	87.77 154	79.25 419	91.96 323	78.35 188	95.75 398	83.04 262	95.62 147	96.65 210

CostFormer		85.77 332	84.94 332	88.26 367	91.16 379	72.58 431	89.47 417	91.04 415	76.26 423	86.45 264	89.97 392	70.74 301	96.86 326	82.35 276	87.07 337	95.34 268

ITE_SJBPF				88.24 368	91.88 351	77.05 368		92.92 358	85.54 224	80.13 402	93.30 272	57.29 439	96.20 375	72.46 409	84.71 355	91.49 432

PVSNet	78.82 18	85.55 334	84.65 338	88.23 369	94.72 198	71.93 434	87.12 456	92.75 365	78.80 383	84.95 312	90.53 372	64.43 380	96.71 331	74.74 392	93.86 200	96.06 239

IterMVS-SCA-FT		85.45 336	84.53 343	88.18 370	91.71 358	76.87 372	90.19 401	92.65 368	85.40 233	81.44 383	90.54 371	66.79 355	95.00 421	81.04 303	81.05 403	92.66 396

EPNet_dtu		86.49 318	85.94 301	88.14 371	90.24 416	72.82 423	94.11 225	92.20 380	86.66 194	79.42 415	92.36 304	73.52 263	95.81 395	71.26 415	93.66 210	95.80 251
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Patchmtry		82.71 380	80.93 384	88.06 372	90.05 420	76.37 382	84.74 476	91.96 390	72.28 464	81.32 386	87.87 430	71.03 296	95.50 409	68.97 434	80.15 418	92.32 414

test_vis1_n_1920		89.39 208	89.84 170	88.04 373	92.97 313	72.64 428	94.71 179	96.03 175	86.18 206	91.94 128	96.56 99	61.63 403	95.74 399	93.42 65	95.11 163	95.74 253

DTE-MVSNet		86.11 324	85.48 317	87.98 374	91.65 362	74.92 398	94.93 160	95.75 200	87.36 169	82.26 372	93.04 283	72.85 274	95.82 394	74.04 398	77.46 436	93.20 374

PMMVS		85.71 333	84.96 331	87.95 375	88.90 435	77.09 367	88.68 430	90.06 439	72.32 463	86.47 261	90.76 366	72.15 284	94.40 428	81.78 292	93.49 217	92.36 412

GG-mvs-BLEND				87.94 376	89.73 427	77.91 343	87.80 444	78.23 498		80.58 395	83.86 463	59.88 422	95.33 414	71.20 416	92.22 254	90.60 451

MonoMVSNet		86.89 298	86.55 274	87.92 377	89.46 430	73.75 410	94.12 223	93.10 353	87.82 153	85.10 308	90.76 366	69.59 321	94.94 422	86.47 208	82.50 382	95.07 275

reproduce_monomvs		86.37 321	85.87 303	87.87 378	93.66 284	73.71 411	93.44 274	95.02 261	88.61 114	82.64 369	91.94 324	57.88 436	96.68 332	89.96 149	79.71 424	93.22 372

pmmvs-eth3d		80.97 410	78.72 421	87.74 379	84.99 477	79.97 279	90.11 403	91.65 397	75.36 431	73.51 465	86.03 451	59.45 425	93.96 440	75.17 386	72.21 452	89.29 465

MS-PatchMatch		85.05 347	84.16 348	87.73 380	91.42 368	78.51 325	91.25 370	93.53 342	77.50 402	80.15 400	91.58 338	61.99 400	95.51 407	75.69 381	94.35 185	89.16 467

mmtdpeth		85.04 349	84.15 349	87.72 381	93.11 301	75.74 390	94.37 209	92.83 361	84.98 248	89.31 203	86.41 448	61.61 405	97.14 304	92.63 81	62.11 488	90.29 453

test_0402		81.30 406	79.17 414	87.67 382	93.19 296	78.17 336	92.98 301	91.71 393	75.25 433	76.02 451	90.31 379	59.23 427	96.37 367	50.22 493	83.63 368	88.47 476

IterMVS		84.88 351	83.98 354	87.60 383	91.44 365	76.03 385	90.18 402	92.41 371	83.24 294	81.06 389	90.42 376	66.60 358	94.28 432	79.46 338	80.98 408	92.48 405
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Patchmatch-test		81.37 404	79.30 410	87.58 384	90.92 392	74.16 408	80.99 489	87.68 467	70.52 471	76.63 445	88.81 413	71.21 293	92.76 457	60.01 477	86.93 338	95.83 249

EPMVS		83.90 370	82.70 374	87.51 385	90.23 417	72.67 426	88.62 431	81.96 488	81.37 346	85.01 311	88.34 421	66.31 363	94.45 425	75.30 385	87.12 335	95.43 263

ADS-MVSNet2		81.66 397	79.71 405	87.50 386	91.35 371	74.19 407	83.33 482	88.48 461	72.90 458	82.24 373	85.77 455	64.98 373	93.20 451	64.57 461	83.74 365	95.12 273

OurMVSNet-221017-0		85.35 340	84.64 340	87.49 387	90.77 399	72.59 430	94.01 238	94.40 305	84.72 258	79.62 414	93.17 277	61.91 401	96.72 329	81.99 286	81.16 399	93.16 376

tpm2		84.08 365	82.94 369	87.48 388	91.39 369	71.27 443	89.23 421	90.37 431	71.95 465	84.64 317	89.33 404	67.30 347	96.55 354	75.17 386	87.09 336	94.63 294

RPSCF		85.07 346	84.27 345	87.48 388	92.91 316	70.62 452	91.69 355	92.46 370	76.20 425	82.67 368	95.22 182	63.94 386	97.29 291	77.51 363	85.80 343	94.53 301

myMVS_eth3d28		85.80 331	85.26 325	87.42 390	94.73 196	69.92 458	90.60 386	90.95 418	87.21 173	86.06 275	90.04 389	59.47 424	96.02 382	74.89 391	93.35 225	96.33 220

FE-MVSNET2		81.82 393	79.99 399	87.34 391	84.74 478	77.36 364	92.72 314	94.55 296	82.09 320	73.79 464	86.46 445	57.80 437	94.45 425	74.65 393	73.10 447	90.20 454

WBMVS		84.97 350	84.18 347	87.34 391	94.14 254	71.62 442	90.20 400	92.35 373	81.61 341	84.06 337	90.76 366	61.82 402	96.52 355	78.93 347	83.81 363	93.89 333

miper_lstm_enhance		85.27 343	84.59 341	87.31 393	91.28 374	74.63 401	87.69 449	94.09 321	81.20 352	81.36 385	89.85 396	74.97 237	94.30 431	81.03 305	79.84 423	93.01 384

FMVSNet5		81.52 402	79.60 406	87.27 394	91.17 377	77.95 341	91.49 360	92.26 379	76.87 415	76.16 447	87.91 429	51.67 466	92.34 460	67.74 443	81.16 399	91.52 430

USDC		82.76 379	81.26 382	87.26 395	91.17 377	74.55 402	89.27 419	93.39 346	78.26 395	75.30 455	92.08 317	54.43 458	96.63 337	71.64 412	85.79 344	90.61 449

test-LLR		85.87 328	85.41 318	87.25 396	90.95 388	71.67 440	89.55 413	89.88 446	83.41 288	84.54 320	87.95 427	67.25 348	95.11 418	81.82 290	93.37 223	94.97 278

test-mter		84.54 359	83.64 358	87.25 396	90.95 388	71.67 440	89.55 413	89.88 446	79.17 374	84.54 320	87.95 427	55.56 445	95.11 418	81.82 290	93.37 223	94.97 278

JIA-IIPM		81.04 407	78.98 419	87.25 396	88.64 436	73.48 415	81.75 488	89.61 452	73.19 455	82.05 376	73.71 498	66.07 368	95.87 391	71.18 418	84.60 356	92.41 409

TDRefinement		79.81 423	77.34 430	87.22 399	79.24 495	75.48 393	93.12 290	92.03 385	76.45 418	75.01 456	91.58 338	49.19 473	96.44 363	70.22 427	69.18 467	89.75 459

tpmvs		83.35 376	82.07 375	87.20 400	91.07 383	71.00 449	88.31 437	91.70 394	78.91 377	80.49 397	87.18 439	69.30 329	97.08 308	68.12 442	83.56 369	93.51 361

ppachtmachnet_test		81.84 392	80.07 396	87.15 401	88.46 440	74.43 405	89.04 425	92.16 381	75.33 432	77.75 436	88.99 410	66.20 365	95.37 413	65.12 458	77.60 434	91.65 425

dmvs_re		84.20 364	83.22 365	87.14 402	91.83 354	77.81 349	90.04 405	90.19 435	84.70 260	81.49 381	89.17 406	64.37 381	91.13 473	71.58 413	85.65 345	92.46 407

tpm cat1		81.96 389	80.27 391	87.01 403	91.09 382	71.02 448	87.38 454	91.53 402	66.25 483	80.17 399	86.35 450	68.22 344	96.15 378	69.16 433	82.29 385	93.86 339

test_fmvs1_n		87.03 294	87.04 254	86.97 404	89.74 426	71.86 435	94.55 187	94.43 302	78.47 389	91.95 127	95.50 167	51.16 468	93.81 441	93.02 73	94.56 178	95.26 269

OpenMVS_ROB	74.94 19	79.51 428	77.03 435	86.93 405	87.00 455	76.23 384	92.33 331	90.74 425	68.93 475	74.52 460	88.23 424	49.58 471	96.62 340	57.64 483	84.29 358	87.94 479

SixPastTwentyTwo		83.91 369	82.90 371	86.92 406	90.99 386	70.67 451	93.48 271	91.99 387	85.54 224	77.62 438	92.11 315	60.59 417	96.87 325	76.05 379	77.75 433	93.20 374

ADS-MVSNet		81.56 399	79.78 402	86.90 407	91.35 371	71.82 436	83.33 482	89.16 459	72.90 458	82.24 373	85.77 455	64.98 373	93.76 442	64.57 461	83.74 365	95.12 273

PatchT		82.68 381	81.27 381	86.89 408	90.09 419	70.94 450	84.06 479	90.15 436	74.91 437	85.63 285	83.57 467	69.37 325	94.87 423	65.19 456	88.50 312	94.84 288

tpm		84.73 354	84.02 352	86.87 409	90.33 414	68.90 461	89.06 424	89.94 443	80.85 355	85.75 281	89.86 395	68.54 341	95.97 385	77.76 359	84.05 362	95.75 252

Patchmatch-RL test		81.67 396	79.96 400	86.81 410	85.42 471	71.23 444	82.17 487	87.50 468	78.47 389	77.19 440	82.50 479	70.81 300	93.48 446	82.66 271	72.89 450	95.71 256

test_vis1_n		86.56 313	86.49 278	86.78 411	88.51 437	72.69 425	94.68 180	93.78 336	79.55 370	90.70 167	95.31 178	48.75 474	93.28 449	93.15 69	93.99 196	94.38 312

testing3-2		86.72 307	86.71 264	86.74 412	96.11 114	65.92 474	93.39 276	89.65 451	89.46 76	87.84 233	92.79 292	59.17 429	97.60 245	81.31 299	90.72 273	96.70 208

test_fmvs1		87.34 277	87.56 240	86.68 413	90.59 405	71.80 437	94.01 238	94.04 322	78.30 393	91.97 125	95.22 182	56.28 443	93.71 443	92.89 74	94.71 171	94.52 302

MDA-MVSNet-bldmvs		78.85 433	76.31 438	86.46 414	89.76 425	73.88 409	88.79 428	90.42 430	79.16 375	59.18 493	88.33 422	60.20 419	94.04 435	62.00 469	68.96 469	91.48 433

mvs5depth		80.98 409	79.15 415	86.45 415	84.57 479	73.29 418	87.79 445	91.67 396	80.52 358	82.20 375	89.72 398	55.14 451	95.93 387	73.93 401	66.83 479	90.12 456

tpmrst		85.35 340	84.99 329	86.43 416	90.88 395	67.88 467	88.71 429	91.43 406	80.13 362	86.08 274	88.80 415	73.05 272	96.02 382	82.48 272	83.40 373	95.40 264

TESTMET0.1,1		83.74 372	82.85 372	86.42 417	89.96 422	71.21 445	89.55 413	87.88 464	77.41 403	83.37 358	87.31 435	56.71 441	93.65 445	80.62 313	92.85 240	94.40 311

our_test_3		81.93 391	80.46 389	86.33 418	88.46 440	73.48 415	88.46 435	91.11 411	76.46 417	76.69 444	88.25 423	66.89 353	94.36 429	68.75 435	79.08 429	91.14 441

lessismore_v0				86.04 419	88.46 440	68.78 462		80.59 491		73.01 469	90.11 387	55.39 447	96.43 364	75.06 388	65.06 483	92.90 387

TinyColmap		79.76 424	77.69 427	85.97 420	91.71 358	73.12 419	89.55 413	90.36 432	75.03 435	72.03 472	90.19 383	46.22 483	96.19 377	63.11 465	81.03 404	88.59 475

KD-MVS_2432*1600		78.50 434	76.02 442	85.93 421	86.22 460	74.47 403	84.80 474	92.33 374	79.29 372	76.98 441	85.92 452	53.81 461	93.97 438	67.39 444	57.42 493	89.36 461

miper_refine_blended		78.50 434	76.02 442	85.93 421	86.22 460	74.47 403	84.80 474	92.33 374	79.29 372	76.98 441	85.92 452	53.81 461	93.97 438	67.39 444	57.42 493	89.36 461

K. test v3		81.59 398	80.15 395	85.91 423	89.89 424	69.42 460	92.57 319	87.71 466	85.56 223	73.44 466	89.71 399	55.58 444	95.52 406	77.17 366	69.76 464	92.78 393

SSC-MVS3.2		84.60 358	84.19 346	85.85 424	92.74 325	68.07 464	88.15 440	93.81 334	87.42 167	83.76 345	91.07 355	62.91 395	95.73 400	74.56 396	83.24 374	93.75 350

mvsany_test1		85.42 338	85.30 323	85.77 425	87.95 450	75.41 394	87.61 452	80.97 490	76.82 416	88.68 216	95.83 145	77.44 201	90.82 476	85.90 217	86.51 339	91.08 445

MIMVSNet1		79.38 429	77.28 431	85.69 426	86.35 459	73.67 412	91.61 357	92.75 365	78.11 398	72.64 470	88.12 425	48.16 475	91.97 466	60.32 474	77.49 435	91.43 435

UWE-MVS		83.69 373	83.09 366	85.48 427	93.06 306	65.27 479	90.92 379	86.14 472	79.90 365	86.26 270	90.72 369	57.17 440	95.81 395	71.03 421	92.62 248	95.35 267

UnsupCasMVSNet_eth		80.07 420	78.27 425	85.46 428	85.24 472	72.63 429	88.45 436	94.87 280	82.99 302	71.64 475	88.07 426	56.34 442	91.75 468	73.48 404	63.36 486	92.01 420

CL-MVSNet_self_test		81.74 395	80.53 385	85.36 429	85.96 463	72.45 432	90.25 395	93.07 355	81.24 350	79.85 409	87.29 436	70.93 298	92.52 458	66.95 447	69.23 466	91.11 443

MDA-MVSNet_test_wron		79.21 431	77.19 433	85.29 430	88.22 445	72.77 424	85.87 465	90.06 439	74.34 442	62.62 490	87.56 433	66.14 366	91.99 465	66.90 451	73.01 448	91.10 444

YYNet1		79.22 430	77.20 432	85.28 431	88.20 446	72.66 427	85.87 465	90.05 441	74.33 443	62.70 488	87.61 432	66.09 367	92.03 462	66.94 448	72.97 449	91.15 440

WB-MVSnew		83.77 371	83.28 362	85.26 432	91.48 364	71.03 447	91.89 346	87.98 463	78.91 377	84.78 314	90.22 381	69.11 334	94.02 436	64.70 460	90.44 276	90.71 447

dp		81.47 403	80.23 392	85.17 433	89.92 423	65.49 477	86.74 460	90.10 438	76.30 422	81.10 387	87.12 440	62.81 396	95.92 388	68.13 441	79.88 421	94.09 324

UnsupCasMVSNet_bld		76.23 444	73.27 448	85.09 434	83.79 481	72.92 421	85.65 468	93.47 345	71.52 466	68.84 481	79.08 486	49.77 470	93.21 450	66.81 452	60.52 490	89.13 469

usedtu_dtu_shiyan2		74.72 446	71.30 451	84.98 435	77.78 497	70.58 453	91.85 349	90.76 424	67.24 481	68.06 483	82.17 480	37.13 492	92.78 456	60.69 473	66.03 480	91.59 429

SD_0403		84.71 356	84.65 338	84.92 436	92.95 314	65.95 473	92.07 345	93.23 350	83.82 277	79.03 420	93.73 261	73.90 257	92.91 455	63.02 467	90.05 283	95.89 245

Anonymous20231206		81.03 408	79.77 404	84.82 437	87.85 451	70.26 455	91.42 361	92.08 383	73.67 450	77.75 436	89.25 405	62.43 398	93.08 452	61.50 471	82.00 390	91.12 442

FE-MVSNET		78.19 436	76.03 441	84.69 438	83.70 482	73.31 417	90.58 387	90.00 442	77.11 411	71.91 473	85.47 457	55.53 446	91.94 467	59.69 478	70.24 461	88.83 471

test0.0.03 1		82.41 386	81.69 377	84.59 439	88.23 444	72.89 422	90.24 397	87.83 465	83.41 288	79.86 408	89.78 397	67.25 348	88.99 486	65.18 457	83.42 372	91.90 422

CMPMVS	59.16 21	80.52 414	79.20 413	84.48 440	83.98 480	67.63 470	89.95 408	93.84 330	64.79 487	66.81 484	91.14 352	57.93 435	95.17 416	76.25 376	88.10 318	90.65 448
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
CVMVSNet		84.69 357	84.79 336	84.37 441	91.84 352	64.92 480	93.70 263	91.47 405	66.19 484	86.16 273	95.28 179	67.18 350	93.33 448	80.89 308	90.42 278	94.88 287

PVSNet_0	73.20 20	77.22 440	74.83 446	84.37 441	90.70 403	71.10 446	83.09 484	89.67 449	72.81 460	73.93 463	83.13 469	60.79 416	93.70 444	68.54 436	50.84 499	88.30 477

LF4IMVS		80.37 417	79.07 417	84.27 443	86.64 456	69.87 459	89.39 418	91.05 414	76.38 420	74.97 457	90.00 391	47.85 476	94.25 433	74.55 397	80.82 410	88.69 473

Anonymous20240521		80.44 416	79.21 412	84.11 444	85.75 466	67.89 466	92.86 307	93.23 350	75.61 430	75.59 454	87.47 434	50.03 469	94.33 430	71.14 419	81.21 398	90.12 456

PM-MVS		78.11 437	76.12 440	84.09 445	83.54 483	70.08 456	88.97 426	85.27 479	79.93 364	74.73 459	86.43 447	34.70 495	93.48 446	79.43 341	72.06 454	88.72 472

dtuonly		84.33 362	84.48 344	83.87 446	86.63 457	63.54 485	86.79 458	91.48 404	78.02 399	83.20 362	93.56 264	69.53 323	94.11 434	79.08 345	92.02 257	93.97 331

test_fmvs2		83.98 366	84.03 351	83.83 447	87.16 454	67.53 471	93.93 245	92.89 359	77.62 400	86.89 254	93.53 265	47.18 478	92.02 464	90.54 138	86.51 339	91.93 421

testgi		80.94 411	80.20 393	83.18 448	87.96 449	66.29 472	91.28 368	90.70 427	83.70 279	78.12 431	92.84 287	51.37 467	90.82 476	63.34 464	82.46 383	92.43 408

KD-MVS_self_test		80.20 418	79.24 411	83.07 449	85.64 467	65.29 478	91.01 376	93.93 324	78.71 386	76.32 446	86.40 449	59.20 428	92.93 454	72.59 408	69.35 465	91.00 446

testing3		80.46 415	79.59 407	83.06 450	93.44 291	64.64 481	93.33 278	85.47 477	84.34 266	79.93 407	90.84 362	44.35 486	92.39 459	57.06 485	87.56 328	92.16 418

ambc				83.06 450	79.99 493	63.51 486	77.47 497	92.86 360		74.34 462	84.45 462	28.74 496	95.06 420	73.06 406	68.89 470	90.61 449

test20.03		79.95 422	79.08 416	82.55 452	85.79 465	67.74 469	91.09 374	91.08 412	81.23 351	74.48 461	89.96 393	61.63 403	90.15 478	60.08 475	76.38 441	89.76 458

MVStest1		72.91 449	69.70 454	82.54 453	78.14 496	73.05 420	88.21 439	86.21 471	60.69 491	64.70 486	90.53 372	46.44 481	85.70 494	58.78 481	53.62 496	88.87 470

test_vis1_rt		77.96 438	76.46 437	82.48 454	85.89 464	71.74 439	90.25 395	78.89 494	71.03 470	71.30 476	81.35 482	42.49 488	91.05 474	84.55 241	82.37 384	84.65 483

EU-MVSNet		81.32 405	80.95 383	82.42 455	88.50 439	63.67 484	93.32 279	91.33 407	64.02 488	80.57 396	92.83 288	61.21 412	92.27 461	76.34 375	80.38 417	91.32 436

myMVS_eth3d		79.67 425	78.79 420	82.32 456	91.92 348	64.08 482	89.75 411	87.40 469	81.72 336	78.82 425	87.20 437	45.33 484	91.29 471	59.09 480	87.84 325	91.60 427

ttmdpeth		76.55 442	74.64 447	82.29 457	82.25 488	67.81 468	89.76 410	85.69 475	70.35 472	75.76 452	91.69 331	46.88 479	89.77 480	66.16 453	63.23 487	89.30 463

dtuonlycased		79.67 425	79.05 418	81.54 458	88.34 443	68.44 463	88.96 427	90.65 428	78.48 388	73.21 468	85.88 454	63.18 394	91.00 475	70.40 423	72.32 451	85.19 482

pmmvs3		71.81 452	68.71 455	81.11 459	75.86 499	70.42 454	86.74 460	83.66 483	58.95 494	68.64 482	80.89 484	36.93 493	89.52 482	63.10 466	63.59 485	83.39 484

Syy-MVS		80.07 420	79.78 402	80.94 460	91.92 348	59.93 493	89.75 411	87.40 469	81.72 336	78.82 425	87.20 437	66.29 364	91.29 471	47.06 498	87.84 325	91.60 427

UWE-MVS-28		78.98 432	78.38 424	80.80 461	88.18 447	60.66 492	90.65 384	78.51 495	78.84 381	77.93 434	90.93 359	59.08 430	89.02 485	50.96 491	90.33 280	92.72 394

new-patchmatchnet		76.41 443	75.17 445	80.13 462	82.65 487	59.61 494	87.66 450	91.08 412	78.23 396	69.85 479	83.22 468	54.76 455	91.63 470	64.14 463	64.89 484	89.16 467

mvsany_test3		74.95 445	73.26 449	80.02 463	74.61 500	63.16 487	85.53 469	78.42 496	74.16 445	74.89 458	86.46 445	36.02 494	89.09 484	82.39 275	66.91 478	87.82 480

test_fmvs3		77.67 439	77.16 434	79.22 464	79.52 494	61.14 489	92.34 330	91.64 398	73.98 447	78.86 424	86.59 444	27.38 499	87.03 488	88.12 182	75.97 443	89.50 460

DSMNet-mixed		76.94 441	76.29 439	78.89 465	83.10 485	56.11 502	87.78 446	79.77 492	60.65 492	75.64 453	88.71 416	61.56 406	88.34 487	60.07 476	89.29 301	92.21 417

EGC-MVSNET		61.97 461	56.37 466	78.77 466	89.63 428	73.50 414	89.12 423	82.79 485	0.21 547	1.24 548	84.80 460	39.48 489	90.04 479	44.13 500	75.94 444	72.79 499

ArgMatch-SfM		70.39 453	67.69 457	78.49 467	81.44 489	60.73 490	84.71 477	75.65 505	68.09 478	66.71 485	86.79 443	20.42 505	86.05 493	71.50 414	53.87 495	88.67 474

new_pmnet		72.15 450	70.13 453	78.20 468	82.95 486	65.68 475	83.91 480	82.40 487	62.94 490	64.47 487	79.82 485	42.85 487	86.26 492	57.41 484	74.44 446	82.65 488

MVS-HIRNet		73.70 448	72.20 450	78.18 469	91.81 355	56.42 501	82.94 485	82.58 486	55.24 495	68.88 480	66.48 506	55.32 449	95.13 417	58.12 482	88.42 314	83.01 486

LCM-MVSNet		66.00 458	62.16 463	77.51 470	64.51 515	58.29 496	83.87 481	90.90 420	48.17 500	54.69 496	73.31 499	16.83 509	86.75 489	65.47 455	61.67 489	87.48 481

APD_test1		69.04 454	66.26 460	77.36 471	80.51 492	62.79 488	85.46 470	83.51 484	54.11 497	59.14 494	84.79 461	23.40 502	89.61 481	55.22 486	70.24 461	79.68 493

test_f		71.95 451	70.87 452	75.21 472	74.21 503	59.37 495	85.07 473	85.82 474	65.25 486	70.42 478	83.13 469	23.62 500	82.93 500	78.32 353	71.94 456	83.33 485

ANet_high		58.88 465	54.22 470	72.86 473	56.50 520	56.67 498	80.75 490	86.00 473	73.09 457	37.39 511	64.63 510	22.17 503	79.49 504	43.51 501	23.96 514	82.43 489

test_vis3_rt		65.12 459	62.60 461	72.69 474	71.44 505	60.71 491	87.17 455	65.55 508	63.80 489	53.22 497	65.65 509	14.54 510	89.44 483	76.65 370	65.38 482	67.91 506

LoFTR		57.22 468	52.62 472	71.00 475	72.03 504	48.57 508	72.00 505	70.08 507	44.40 505	40.92 509	76.42 490	8.12 514	82.76 501	42.28 504	47.33 502	81.66 490

FPMVS		64.63 460	62.55 462	70.88 476	70.80 506	56.71 497	84.42 478	84.42 481	51.78 498	49.57 498	81.61 481	23.49 501	81.48 502	40.61 506	76.25 442	74.46 498

dmvs_testset		74.57 447	75.81 444	70.86 477	87.72 452	40.47 517	87.05 457	77.90 500	82.75 307	71.15 477	85.47 457	67.98 345	84.12 498	45.26 499	76.98 440	88.00 478

DenseAffine		56.77 469	52.17 473	70.54 478	74.27 501	53.25 504	77.23 498	50.43 516	49.87 499	47.26 503	77.37 489	7.99 515	79.10 505	50.35 492	34.79 508	79.28 494

N_pmnet		68.89 455	68.44 456	70.23 479	89.07 433	28.79 526	88.06 441	19.50 526	69.47 474	71.86 474	84.93 459	61.24 411	91.75 468	54.70 487	77.15 437	90.15 455

testf1		59.54 463	56.11 467	69.85 480	69.28 507	56.61 499	80.37 491	76.55 503	42.58 507	45.68 504	75.61 492	11.26 511	84.18 496	43.20 502	60.44 491	68.75 504

APD_test2		59.54 463	56.11 467	69.85 480	69.28 507	56.61 499	80.37 491	76.55 503	42.58 507	45.68 504	75.61 492	11.26 511	84.18 496	43.20 502	60.44 491	68.75 504

WB-MVS		67.92 456	67.49 458	69.21 482	81.09 490	41.17 516	88.03 442	78.00 499	73.50 452	62.63 489	83.11 471	63.94 386	86.52 490	25.66 514	51.45 498	79.94 492

PMMVS2		59.60 462	56.40 465	69.21 482	68.83 509	46.58 509	73.02 504	77.48 501	55.07 496	49.21 499	72.95 500	17.43 508	80.04 503	49.32 495	44.33 503	80.99 491

SSC-MVS		67.06 457	66.56 459	68.56 484	80.54 491	40.06 518	87.77 447	77.37 502	72.38 462	61.75 491	82.66 478	63.37 389	86.45 491	24.48 515	48.69 501	79.16 495

Gipuma		57.99 467	54.91 469	67.24 485	88.51 437	65.59 476	52.21 512	90.33 433	43.58 506	42.84 507	51.18 515	20.29 506	85.07 495	34.77 508	70.45 460	51.05 514
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
RoMa-SfM		53.80 470	49.39 474	67.06 486	67.87 511	48.86 506	75.04 499	38.06 522	47.23 502	47.40 502	78.96 487	7.40 516	76.66 507	48.89 496	33.62 509	75.64 497

DKM		50.92 474	46.13 478	65.30 487	66.27 513	45.98 511	73.05 503	31.91 524	45.08 503	42.04 508	75.01 496	4.95 524	73.81 509	47.90 497	28.96 511	76.09 496

MatchFormer		51.11 473	46.66 477	64.46 488	67.11 512	43.39 514	70.54 506	63.67 510	33.19 511	37.22 512	70.30 502	6.67 519	78.17 506	30.29 511	40.94 505	71.81 502

PMVS	47.18 22	52.22 472	48.46 476	63.48 489	45.72 524	46.20 510	73.41 502	78.31 497	41.03 509	30.06 516	65.68 508	6.05 520	83.43 499	30.04 512	65.86 481	60.80 508
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
dongtai		58.82 466	58.24 464	60.56 490	83.13 484	45.09 513	82.32 486	48.22 518	67.61 479	61.70 492	69.15 503	38.75 490	76.05 508	32.01 510	41.31 504	60.55 509

PDCNetPlus		48.34 476	45.15 479	57.91 491	61.43 517	41.85 515	65.98 507	38.30 521	47.59 501	37.96 510	71.85 501	10.18 513	66.85 514	52.94 489	20.14 525	65.03 507

MVE	39.65 23	43.39 478	38.59 484	57.77 492	56.52 519	48.77 507	55.38 510	58.64 513	29.33 515	28.96 517	52.65 514	4.68 527	64.62 515	28.11 513	33.07 510	59.93 510
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		50.52 475	48.47 475	56.66 493	52.26 523	18.98 531	41.51 518	81.40 489	10.10 521	44.59 506	75.01 496	28.51 497	68.16 510	53.54 488	49.31 500	82.83 487

DeepMVS_CX				56.31 494	74.23 502	51.81 505		56.67 514	44.85 504	48.54 500	75.16 495	27.87 498	58.74 517	40.92 505	52.22 497	58.39 512

ELoFTR		40.15 481	35.08 485	55.36 495	41.27 531	28.17 528	47.70 514	43.76 519	29.15 516	30.35 515	65.97 507	2.17 534	66.90 513	34.51 509	20.83 524	71.00 503

kuosan		53.51 471	53.30 471	54.13 496	76.06 498	45.36 512	80.11 493	48.36 517	59.63 493	54.84 495	63.43 511	37.41 491	62.07 516	20.73 517	39.10 506	54.96 513

PMatch-SfM		38.18 482	33.34 486	52.72 497	43.67 526	28.18 527	52.96 511	16.29 530	29.70 514	31.24 514	68.56 505	1.08 546	57.70 518	38.73 507	17.80 527	72.30 501

MASt3R-SfM		45.78 477	43.96 480	51.24 498	45.04 525	29.83 525	57.88 509	38.83 520	31.88 513	47.48 501	81.30 483	7.16 517	51.15 520	49.56 494	36.51 507	72.74 500

GLUNet-SfM		31.36 484	26.25 489	46.70 499	35.51 533	24.89 529	33.71 523	36.36 523	19.08 517	23.78 520	52.69 513	3.82 532	56.26 519	19.75 519	11.56 536	58.95 511

E-PMN		43.23 479	42.29 481	46.03 500	65.58 514	37.41 519	73.51 501	64.62 509	33.99 510	28.47 518	47.87 516	19.90 507	67.91 511	22.23 516	24.45 512	32.77 520

EMVS		42.07 480	41.12 482	44.92 501	63.45 516	35.56 521	73.65 500	63.48 511	33.05 512	26.88 519	45.45 517	21.27 504	67.14 512	19.80 518	23.02 516	32.06 521

ALIKED-LG		28.00 485	26.54 488	32.41 502	58.12 518	31.80 522	47.26 515	21.21 525	14.15 518	19.16 522	41.93 519	6.72 518	35.73 521	5.96 528	24.32 513	29.69 522

ALIKED-MNN		26.28 486	24.57 491	31.39 503	56.22 521	31.73 523	45.54 516	19.13 528	11.12 519	17.11 524	39.35 521	5.01 523	34.53 522	5.54 530	22.12 518	27.92 523

ALIKED-NN		26.07 487	24.75 490	30.02 504	55.08 522	30.61 524	44.20 517	19.22 527	10.98 520	17.98 523	40.71 520	5.39 522	32.83 523	5.59 529	23.63 515	26.63 524

tmp_tt		35.64 483	39.24 483	24.84 505	14.87 550	23.90 530	62.71 508	51.51 515	6.58 529	36.66 513	62.08 512	44.37 485	30.34 525	52.40 490	22.00 519	20.27 526

wuyk23d		21.27 489	20.48 492	23.63 506	68.59 510	36.41 520	49.57 513	6.85 542	9.37 522	7.89 531	4.46 547	4.03 531	31.37 524	17.47 520	16.07 529	3.12 542

SP-LightGlue		20.24 490	20.15 494	20.49 507	43.51 527	12.27 539	38.68 520	14.56 533	7.54 526	12.90 528	30.07 525	4.75 525	14.38 529	7.60 524	21.75 520	34.82 515

SP-SuperGlue		20.22 491	20.18 493	20.36 508	43.26 528	12.27 539	38.71 519	14.77 532	7.64 525	13.04 527	30.21 524	4.73 526	14.21 531	7.59 525	21.65 521	34.59 516

SP-DiffGlue		20.02 492	19.96 495	20.21 509	19.64 547	13.14 538	30.51 524	15.49 531	8.39 523	19.98 521	43.75 518	5.48 521	13.72 532	13.75 521	22.65 517	33.78 518

SP-MNN		19.61 493	19.42 496	20.19 510	42.15 529	11.42 545	38.15 521	14.24 534	6.55 530	11.64 530	29.88 527	4.16 529	14.56 528	7.09 527	20.92 523	34.58 517

SP-NN		19.44 494	19.37 497	19.67 511	41.70 530	11.48 544	37.75 522	13.72 536	6.86 527	11.86 529	29.97 526	4.23 528	14.25 530	7.13 526	21.07 522	33.30 519

XFeat-MNN		17.43 495	16.95 498	18.86 512	16.90 548	11.28 546	27.31 525	17.08 529	8.08 524	15.61 526	35.73 522	4.06 530	22.95 526	10.20 522	17.59 528	22.35 525

XFeat-NN		15.96 496	15.86 499	16.25 513	15.78 549	9.87 549	25.17 526	13.83 535	6.76 528	15.68 525	34.83 523	3.61 533	19.28 527	9.22 523	17.90 526	19.58 527

SIFT-NN		12.98 497	13.18 500	12.37 514	36.49 532	16.03 532	22.41 527	7.69 538	4.89 531	7.41 532	20.48 529	1.69 535	11.46 534	1.88 533	15.70 530	9.61 529

SIFT-MNN		12.44 498	12.55 501	12.11 515	34.55 534	15.21 533	20.91 528	7.74 537	4.86 532	6.54 534	20.09 530	1.51 536	11.47 533	1.88 533	14.87 532	9.64 528

SIFT-NN-NCMNet		12.12 499	12.25 502	11.75 516	32.82 536	14.83 534	20.73 529	7.58 539	4.72 534	6.60 533	19.53 531	1.49 537	11.15 536	1.74 535	15.02 531	9.28 530

SIFT-NCM-Cal		11.58 500	11.64 503	11.40 517	33.45 535	14.10 535	19.75 531	6.89 540	4.68 537	4.55 541	18.60 536	1.34 541	11.28 535	1.53 541	13.95 533	8.82 534

SIFT-NN-CMatch		11.26 501	11.31 505	11.13 518	30.21 540	13.40 537	18.43 532	6.79 543	4.71 535	6.47 535	19.53 531	1.43 539	10.72 538	1.71 536	12.49 535	9.26 531

SIFT-ConvMatch		10.91 503	10.94 508	10.84 519	32.07 537	13.57 536	17.23 535	6.35 544	4.71 535	5.18 538	18.94 534	1.30 542	10.76 537	1.65 539	11.02 538	8.19 535

SIFT-NN-UMatch		11.06 502	11.19 507	10.66 520	28.66 542	12.16 541	19.79 530	6.86 541	4.73 533	5.21 537	19.47 533	1.46 538	10.70 539	1.71 536	12.79 534	9.13 532

SIFT-UMatch		10.58 504	10.73 509	10.15 521	31.05 538	11.65 543	18.01 533	5.92 546	4.65 538	4.72 539	18.93 535	1.25 544	10.62 540	1.66 538	10.39 539	8.16 536

SIFT-CM-Cal		10.08 506	10.13 512	9.92 522	30.71 539	11.88 542	15.35 537	5.44 547	4.59 539	4.72 539	18.04 539	1.26 543	10.19 541	1.46 543	9.60 540	7.69 537

SIFT-NN-PointCN		10.26 505	10.46 510	9.65 523	27.18 543	9.89 548	17.89 534	6.17 545	4.40 541	5.65 536	18.29 537	1.43 539	10.09 542	1.61 540	11.55 537	8.99 533

SIFT-UM-Cal		9.80 507	10.00 513	9.22 524	30.05 541	10.15 547	16.31 536	4.85 549	4.54 540	4.19 542	18.23 538	1.19 545	9.95 543	1.52 542	9.11 542	7.57 538

SIFT-PCN-Cal		8.65 511	8.88 515	7.98 525	26.74 544	7.47 551	13.90 539	4.61 550	4.09 543	3.82 543	15.86 540	1.01 547	8.94 544	1.34 544	8.52 543	7.53 539

SIFT-PointCN		8.76 509	9.03 514	7.96 526	26.50 545	7.60 550	14.94 538	5.08 548	4.10 542	3.74 544	15.46 541	0.94 548	8.92 545	1.33 545	9.14 541	7.37 540

SIFT-NCMNet		7.46 513	7.71 517	6.72 527	25.03 546	6.86 552	11.42 540	2.98 551	4.05 544	3.38 545	13.68 542	0.84 549	7.65 546	1.13 546	6.87 544	5.66 541

test123		8.76 509	11.22 506	1.39 528	0.85 552	0.97 553	85.76 467	0.35 553	0.54 546	2.45 547	8.14 546	0.60 550	0.48 547	2.16 532	0.17 546	2.71 543

testmvs		8.92 508	11.52 504	1.12 529	1.06 551	0.46 554	86.02 464	0.65 552	0.62 545	2.74 546	9.52 545	0.31 551	0.45 548	2.38 531	0.39 545	2.46 544

mmdepth		0.00 515	0.00 518	0.00 530	0.00 553	0.00 555	0.00 541	0.00 554	0.00 548	0.00 549	0.00 548	0.00 552	0.00 549	0.00 547	0.00 547	0.00 545

monomultidepth		0.00 515	0.00 518	0.00 530	0.00 553	0.00 555	0.00 541	0.00 554	0.00 548	0.00 549	0.00 548	0.00 552	0.00 549	0.00 547	0.00 547	0.00 545

test_blank		0.00 515	0.00 518	0.00 530	0.00 553	0.00 555	0.00 541	0.00 554	0.00 548	0.00 549	0.00 548	0.00 552	0.00 549	0.00 547	0.00 547	0.00 545

uanet_test		0.00 515	0.00 518	0.00 530	0.00 553	0.00 555	0.00 541	0.00 554	0.00 548	0.00 549	0.00 548	0.00 552	0.00 549	0.00 547	0.00 547	0.00 545

DCPMVS		0.00 515	0.00 518	0.00 530	0.00 553	0.00 555	0.00 541	0.00 554	0.00 548	0.00 549	0.00 548	0.00 552	0.00 549	0.00 547	0.00 547	0.00 545

cdsmvs_eth3d_5k		22.14 488	29.52 487	0.00 530	0.00 553	0.00 555	0.00 541	95.76 199	0.00 548	0.00 549	94.29 232	75.66 229	0.00 549	0.00 547	0.00 547	0.00 545

pcd_1.5k_mvsjas		6.64 514	8.86 516	0.00 530	0.00 553	0.00 555	0.00 541	0.00 554	0.00 548	0.00 549	0.00 548	79.70 161	0.00 549	0.00 547	0.00 547	0.00 545

sosnet-low-res		0.00 515	0.00 518	0.00 530	0.00 553	0.00 555	0.00 541	0.00 554	0.00 548	0.00 549	0.00 548	0.00 552	0.00 549	0.00 547	0.00 547	0.00 545

sosnet		0.00 515	0.00 518	0.00 530	0.00 553	0.00 555	0.00 541	0.00 554	0.00 548	0.00 549	0.00 548	0.00 552	0.00 549	0.00 547	0.00 547	0.00 545

uncertanet		0.00 515	0.00 518	0.00 530	0.00 553	0.00 555	0.00 541	0.00 554	0.00 548	0.00 549	0.00 548	0.00 552	0.00 549	0.00 547	0.00 547	0.00 545

Regformer		0.00 515	0.00 518	0.00 530	0.00 553	0.00 555	0.00 541	0.00 554	0.00 548	0.00 549	0.00 548	0.00 552	0.00 549	0.00 547	0.00 547	0.00 545

ab-mvs-re		7.82 512	10.43 511	0.00 530	0.00 553	0.00 555	0.00 541	0.00 554	0.00 548	0.00 549	93.88 253	0.00 552	0.00 549	0.00 547	0.00 547	0.00 545

uanet		0.00 515	0.00 518	0.00 530	0.00 553	0.00 555	0.00 541	0.00 554	0.00 548	0.00 549	0.00 548	0.00 552	0.00 549	0.00 547	0.00 547	0.00 545

WAC-MVS						64.08 482								59.14 479

FOURS1					98.86 4	85.54 74	98.29 1	97.49 11	89.79 66	96.29 32

PC_three_1452									82.47 311	97.09 19	97.07 72	92.72 1	98.04 200	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					0.00 553

eth-test					0.00 553

ZD-MVS					98.15 40	86.62 34		97.07 60	83.63 281	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	87.46 164	93.75 76	97.43 51	82.94 100		92.73 76	97.80 91	97.88 112

IU-MVS					98.77 8	86.00 54		96.84 82	81.26 349	97.26 13				95.50 36	99.13 3	99.03 10

test_241102_TWO								97.44 20	90.31 44	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 50	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	86.13 210	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	95.21 142	96.82 85	89.44 77

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 48	97.64 6	98.13 7	91.47 9

GSMVS																96.12 233

test_part2					98.55 15	87.22 20				96.40 31

sam_mvs1												71.70 289				96.12 233

sam_mvs												70.60 303

MTGPA								96.97 65

test_post1							88.00 443				9.81 544	69.31 328	95.53 405	76.65 370

test_post											10.29 543	70.57 307	95.91 390

patchmatchnet-post											83.76 464	71.53 290	96.48 358

MTMP							96.16 60	60.64 512

gm-plane-assit					89.60 429	68.00 465			77.28 406		88.99 410		97.57 248	79.44 340

test9_res														91.91 109	98.71 35	98.07 84

TEST9					97.53 67	86.49 38	94.07 231	96.78 90	81.61 341	92.77 101	96.20 110	87.71 32	99.12 63

test_8					97.49 69	86.30 46	94.02 237	96.76 93	81.86 332	92.70 105	96.20 110	87.63 33	99.02 73

agg_prior2														90.54 138	98.68 40	98.27 65

agg_prior					97.38 72	85.92 61		96.72 100		92.16 120			98.97 87

test_prior4						85.96 58	94.11 225

test_prior2							94.12 223		87.67 159	92.63 109	96.39 105	86.62 45		91.50 119	98.67 43

旧先验2							93.36 277		71.25 468	94.37 61			97.13 305	86.74 204

新几何2							93.11 292

旧先验1					96.79 86	81.81 199		95.67 210			96.81 84	86.69 43			97.66 97	96.97 189

无先验							93.28 285	96.26 140	73.95 448				99.05 67	80.56 314		96.59 212

原ACMM2							92.94 303

test222					96.55 95	81.70 204	92.22 338	95.01 262	68.36 477	90.20 181	96.14 120	80.26 147			97.80 91	96.05 240

testdata2													98.75 116	78.30 354

segment_acmp												87.16 40

testdata1							92.15 340		87.94 143

plane_prior7					94.70 201	82.74 165

plane_prior6					94.52 217	82.75 163						74.23 249

plane_prior5								96.22 146					98.12 179	88.15 179	89.99 284	94.63 294

plane_prior4											94.86 203

plane_prior3						82.75 163			90.26 50	86.91 251

plane_prior2							95.85 93		90.81 27

plane_prior1					94.59 210

plane_prior						82.73 166	95.21 142		89.66 71						89.88 289

n2								0.00 554

nn								0.00 554

door-mid								85.49 476

test11								96.57 112

door								85.33 478

HQP5-MVS						81.56 206

HQP-NCC					94.17 250		94.39 205		88.81 104	85.43 297

ACMP_Plane					94.17 250		94.39 205		88.81 104	85.43 297

BP-MVS														87.11 201

HQP4-MVS										85.43 297			97.96 216			94.51 304

HQP3-MVS								96.04 173							89.77 293

HQP2-MVS												73.83 260

NP-MVS					94.37 231	82.42 180					93.98 246

MDTV_nov1_ep13_2view						55.91 503	87.62 451		73.32 454	84.59 319		70.33 310		74.65 393		95.50 261

MDTV_nov1_ep13			83.56 359		91.69 360	69.93 457	87.75 448	91.54 401	78.60 387	84.86 313	88.90 412	69.54 322	96.03 381	70.25 425	88.93 306

ACMMP++_ref															87.47 329

ACMMP++															88.01 321

Test By Simon												80.02 149

High-resolution multi-view benchmark