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	94.37 304	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	85.90 210	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	84.02 337	96.62 95	75.95 219	99.34 42	87.77 185	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	93.01 355	92.58 6	94.22 63	97.20 64	80.56 141	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	88.28 124	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	87.76 154	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	87.13 175	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	92.66 314	96.56 113	83.44 285	91.68 140	95.04 191	86.60 47	98.99 82	85.60 219	97.92 84	96.93 191

ZNCC-MVS		94.47 33	94.28 45	95.03 17	98.52 18	86.96 21	96.85 33	97.32 34	88.24 125	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	88.24 125	93.26 85	96.83 82	85.48 60	99.59 10	91.43 120	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	94.60 216	82.33 110	98.62 133	92.40 86	92.86 236	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	86.26 202	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	94.60 216	82.33 110	98.62 133	92.40 86	92.86 236	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		88.31 239	86.13 288	94.85 28	98.54 16	86.60 35	96.93 27	97.19 44	90.66 36	92.85 96	23.41 526	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	88.11 133	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	94.87 200	80.56 141	98.66 125	92.42 85	93.10 231	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	90.87 130	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	85.68 217	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	87.45 165	93.26 85	97.33 56	84.62 78	99.51 28	90.75 133	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	91.44 119	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	85.15 241	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	88.14 130	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	90.69 167	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	81.86 330	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	80.09 361	92.77 101	96.63 94	86.62 45	99.04 69	87.40 192	98.66 44	98.17 75

3Dnovator	86.66 5	91.73 123	90.82 142	94.44 50	94.59 210	86.37 42	97.18 17	97.02 62	89.20 88	84.31 332	96.66 90	73.74 260	99.17 57	86.74 202	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	88.20 128	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	86.90 185	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	93.93 322	89.77 67	94.21 64	95.59 160	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	77.97 189	98.84 106	90.75 133	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	94.43 300	91.65 17	92.68 106	96.13 121	77.97 189	98.84 106	90.75 133	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	90.15 186	97.03 74	81.44 131	99.51 28	90.85 131	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	89.56 195	97.37 55	82.51 107	99.38 35	92.20 95	98.30 60	97.57 138
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	94.66 213	82.11 118	98.50 141	92.33 91	92.82 239	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	91.02 136	94.10 65	90.10 416	85.25 80	96.03 76	92.05 382	92.83 5	87.39 243	95.78 150	79.39 167	99.01 75	88.13 179	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	95.09 190	86.32 50	99.21 55	91.22 122	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	91.28 130	93.96 69	98.33 33	85.92 61	94.66 182	96.66 105	82.69 307	90.03 188	95.82 146	82.30 112	99.03 70	84.57 238	96.48 129	96.91 193

HPM-MVS_fast		93.40 80	93.22 82	93.94 70	98.36 31	84.83 87	97.15 18	96.80 89	85.77 214	92.47 114	97.13 69	82.38 108	99.07 65	90.51 138	98.40 57	97.92 108

test_fmvsmconf0.1_n		94.20 47	94.31 43	93.88 71	92.46 330	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	96.91 321	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	87.46 163	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	86.96 181	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	86.56 193	96.84 25	97.81 39	87.56 36	98.77 115	97.14 15	96.82 119	97.16 172

CSCG		93.23 85	93.05 86	93.76 78	98.04 46	84.07 113	96.22 56	97.37 27	84.15 266	90.05 187	95.66 156	87.77 30	99.15 61	89.91 149	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	87.83 151	93.07 92	95.89 138	73.44 264	98.65 127	90.22 142	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	94.30 307	90.39 41	92.67 108	95.94 134	74.46 243	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	89.25 429	84.42 105	96.06 73	96.29 132	89.06 93	94.68 58	98.13 7	79.22 169	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	84.47 240	95.47 153	97.45 146

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	86.31 200	96.75 28	97.86 37	87.40 37	98.74 119	97.07 17	97.02 110	97.07 177

QAPM		89.51 196	88.15 223	93.59 84	94.92 181	84.58 93	96.82 34	96.70 103	78.43 389	83.41 355	96.19 114	73.18 269	99.30 48	77.11 365	96.54 126	96.89 194

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	97.17 165

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	91.30 128	93.39 87	94.72 198	83.36 138	95.45 122	96.37 127	90.33 43	92.17 119	96.03 128	72.32 281	98.75 116	87.94 182	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	88.02 138	96.23 33	97.84 38	83.36 93	98.83 109	97.49 8	97.34 104	97.25 157

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	97.36 149

Vis-MVSNet		91.75 121	91.23 131	93.29 90	95.32 156	83.78 123	96.14 64	95.98 177	89.89 56	90.45 171	96.58 97	75.09 232	98.31 168	84.75 232	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	95.51 164	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	95.77 151	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	77.85 195	98.17 176	88.90 169	93.38 220	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	97.20 163

VDD-MVS		90.74 152	89.92 167	93.20 95	96.27 105	83.02 156	95.73 104	93.86 326	88.42 120	92.53 111	96.84 81	62.09 397	98.64 130	90.95 128	92.62 246	97.93 107

Elysia		90.12 172	89.10 191	93.18 97	93.16 297	84.05 115	95.22 139	96.27 136	85.16 239	90.59 168	94.68 209	64.64 375	98.37 158	86.38 208	95.77 143	97.12 174

StellarMVS		90.12 172	89.10 191	93.18 97	93.16 297	84.05 115	95.22 139	96.27 136	85.16 239	90.59 168	94.68 209	64.64 375	98.37 158	86.38 208	95.77 143	97.12 174

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		91.08 146	90.39 151	93.17 99	93.07 304	86.91 23	96.41 42	96.26 140	88.30 123	88.37 220	94.85 203	82.19 117	97.64 242	91.09 123	82.95 373	94.96 279

MVSMamba_PlusPlus		93.44 75	93.54 76	93.14 101	96.58 94	83.05 154	96.06 73	96.50 118	84.42 263	94.09 68	95.56 162	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	93.63 209	97.17 165

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	96.32 219

新几何1				93.10 103	97.30 76	84.35 108		95.56 219	71.09 467	91.26 151	96.24 108	82.87 102	98.86 102	79.19 342	98.10 75	96.07 235

OMC-MVS		91.23 137	90.62 148	93.08 105	96.27 105	84.07 113	93.52 270	95.93 183	86.95 182	89.51 196	96.13 121	78.50 183	98.35 162	85.84 217	92.90 235	96.83 201

OpenMVS	83.78 11	88.74 226	87.29 245	93.08 105	92.70 324	85.39 78	96.57 40	96.43 121	78.74 383	80.85 388	96.07 124	69.64 318	99.01 75	78.01 356	96.65 124	94.83 287

MAR-MVS		90.30 168	89.37 184	93.07 107	96.61 91	84.48 99	95.68 107	95.67 210	82.36 312	87.85 230	92.85 284	76.63 208	98.80 111	80.01 322	96.68 123	95.91 241
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	91.31 121	95.54 148	98.46 41

lupinMVS		90.92 148	90.21 155	93.03 108	93.86 268	83.88 120	92.81 308	93.86 326	79.84 364	91.76 137	94.29 230	77.92 192	98.04 200	90.48 139	97.11 106	97.17 165

Effi-MVS+		91.59 131	91.11 133	93.01 110	94.35 235	83.39 137	94.60 184	95.10 256	87.10 176	90.57 170	93.10 279	81.43 132	98.07 194	89.29 161	94.48 181	97.59 137

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	87.27 169	95.98 40	98.05 27	83.07 99	98.45 151	96.68 23	95.51 150	96.88 195

MVS_111021_LR		92.47 103	92.29 104	92.98 112	95.99 125	84.43 103	93.08 294	96.09 168	88.20 128	91.12 156	95.72 154	81.33 133	97.76 231	91.74 113	97.37 102	96.75 203

fmvsm_s_conf0.1_n_a		93.19 86	93.26 80	92.97 113	92.49 328	83.62 129	96.02 77	95.72 205	86.78 187	96.04 38	98.19 4	82.30 112	98.43 155	96.38 25	95.42 156	96.86 196

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	91.19 345	84.06 83	98.34 163	91.72 114	96.54 126	96.54 214

LFMVS		90.08 175	89.13 190	92.95 115	96.71 87	82.32 185	96.08 69	89.91 442	86.79 186	92.15 121	96.81 84	62.60 395	98.34 163	87.18 196	93.90 198	98.19 73

UGNet		89.95 182	88.95 199	92.95 115	94.51 218	83.31 139	95.70 106	95.23 247	89.37 80	87.58 237	93.94 246	64.00 383	98.78 114	83.92 248	96.31 132	96.74 204
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		90.80 150	90.10 159	92.90 117	93.04 307	83.53 132	93.08 294	94.15 315	80.22 358	91.41 147	94.91 197	76.87 202	97.93 220	90.28 140	96.90 115	97.24 158
jason: jason.
DP-MVS		87.25 280	85.36 319	92.90 117	97.65 64	83.24 141	94.81 170	92.00 384	74.99 434	81.92 377	95.00 193	72.66 274	99.05 67	66.92 448	92.33 251	96.40 216

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	88.13 131	95.82 43	98.04 30	83.43 89	98.48 143	96.97 21	96.23 133	96.92 192

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	87.68 157	95.89 41	98.17 5	82.78 103	98.46 147	96.71 22	96.17 135	96.98 186

casdiffseed414692147		91.11 144	90.55 149	92.81 122	94.27 243	82.58 177	94.81 170	96.03 175	87.93 144	90.17 184	95.62 158	78.51 182	97.90 224	84.18 244	93.45 218	97.94 99

CANet_DTU		90.26 170	89.41 183	92.81 122	93.46 290	83.01 157	93.48 271	94.47 299	89.43 78	87.76 235	94.23 235	70.54 306	99.03 70	84.97 227	96.39 130	96.38 217

MVSFormer		91.68 129	91.30 128	92.80 124	93.86 268	83.88 120	95.96 83	95.90 187	84.66 259	91.76 137	94.91 197	77.92 192	97.30 286	89.64 157	97.11 106	97.24 158

PVSNet_Blended_VisFu		91.38 134	90.91 139	92.80 124	96.39 102	83.17 145	94.87 164	96.66 105	83.29 290	89.27 202	94.46 225	80.29 144	99.17 57	87.57 189	95.37 157	96.05 238

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		90.55 164	89.81 169	92.77 126	92.78 321	84.21 110	94.09 229	94.17 314	85.82 211	91.54 142	94.14 237	69.93 312	97.92 221	91.62 116	94.21 191	96.18 227

balanced_ft_v1		92.23 108	92.05 107	92.77 126	95.40 153	81.78 202	95.80 96	95.69 209	87.94 142	91.92 129	95.04 191	75.91 220	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	97.43 148

VDDNet		89.56 195	88.49 214	92.76 129	95.07 170	82.09 189	96.30 47	93.19 350	81.05 352	91.88 130	96.86 80	61.16 413	98.33 165	88.43 176	92.49 250	97.84 118

viewdifsd2359ckpt09		91.18 140	90.65 147	92.75 131	94.61 209	82.36 184	94.32 212	95.74 201	84.72 256	89.66 194	95.15 188	79.69 162	98.04 200	87.70 186	94.27 190	97.85 117

h-mvs33		90.80 150	90.15 158	92.75 131	96.01 121	82.66 170	95.43 123	95.53 223	89.80 63	93.08 90	95.64 157	75.77 221	99.00 80	92.07 100	78.05 430	96.60 209

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	89.95 148	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	90.95 128	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		90.69 155	90.02 165	92.71 135	95.72 137	82.41 182	94.11 225	95.12 254	85.63 218	91.49 144	94.70 207	74.75 236	98.42 156	86.13 212	92.53 248	97.31 150

DCV-MVSNet		90.69 155	90.02 165	92.71 135	95.72 137	82.41 182	94.11 225	95.12 254	85.63 218	91.49 144	94.70 207	74.75 236	98.42 156	86.13 212	92.53 248	97.31 150

PCF-MVS	84.11 10	87.74 254	86.08 292	92.70 137	94.02 257	84.43 103	89.27 417	95.87 192	73.62 449	84.43 324	94.33 227	78.48 185	98.86 102	70.27 422	94.45 182	94.81 288
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	97.17 165

SSM_0404		90.73 153	90.08 160	92.69 138	95.00 175	83.13 147	94.32 212	95.00 264	85.41 229	89.84 189	95.35 174	76.13 211	97.98 212	85.46 222	94.18 192	96.95 188

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	91.05 124	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	96.76 202

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	95.80 147	83.16 95	98.16 177	93.68 59	98.14 73	97.31 150

ab-mvs		89.41 203	88.35 216	92.60 143	95.15 168	82.65 174	92.20 337	95.60 217	83.97 270	88.55 216	93.70 260	74.16 251	98.21 174	82.46 272	89.37 296	96.94 190

LS3D		87.89 249	86.32 281	92.59 144	96.07 118	82.92 160	95.23 137	94.92 274	75.66 426	82.89 363	95.98 131	72.48 278	99.21 55	68.43 436	95.23 162	95.64 255

Anonymous20240529		88.09 245	86.59 270	92.58 145	96.53 97	81.92 196	95.99 79	95.84 194	74.11 444	89.06 206	95.21 183	61.44 405	98.81 110	83.67 255	87.47 327	97.01 184

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	81.89 329	90.24 177	96.44 103	78.59 179	98.61 135	89.68 155	97.85 88	97.06 178

viewdifsd2359ckpt13		91.20 139	90.75 144	92.54 148	94.30 241	82.13 188	94.03 235	95.89 189	85.60 220	90.20 179	95.36 173	79.69 162	97.90 224	87.85 184	93.86 199	97.61 134

114514_t		89.51 196	88.50 212	92.54 148	98.11 42	81.99 192	95.16 147	96.36 128	70.19 471	85.81 277	95.25 179	76.70 206	98.63 132	82.07 282	96.86 118	97.00 185

PAPM_NR		91.22 138	90.78 143	92.52 150	97.60 65	81.46 212	94.37 209	96.24 143	86.39 199	87.41 240	94.80 205	82.06 121	98.48 143	82.80 267	95.37 157	97.61 134

mamba_0408		89.06 216	87.92 230	92.50 151	94.76 192	82.66 170	79.84 492	94.64 292	85.18 234	88.96 208	95.00 193	76.00 216	97.98 212	83.74 252	93.15 228	96.85 197

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		90.47 166	89.80 170	92.46 153	94.76 192	82.66 170	93.98 242	95.00 264	85.41 229	88.96 208	95.35 174	76.13 211	97.88 226	85.46 222	93.15 228	96.85 197

IS-MVSNet		91.43 133	91.09 135	92.46 153	95.87 132	81.38 215	96.95 24	93.69 339	89.72 69	89.50 198	95.98 131	78.57 180	97.77 230	83.02 261	96.50 128	98.22 72

API-MVS		90.66 159	90.07 161	92.45 155	96.36 103	84.57 94	96.06 73	95.22 249	82.39 310	89.13 203	94.27 233	80.32 143	98.46 147	80.16 320	96.71 122	94.33 311

xiu_mvs_v1_base_debu		90.64 160	90.05 162	92.40 156	93.97 263	84.46 100	93.32 279	95.46 227	85.17 236	92.25 116	94.03 238	70.59 302	98.57 138	90.97 125	94.67 172	94.18 315

xiu_mvs_v1_base		90.64 160	90.05 162	92.40 156	93.97 263	84.46 100	93.32 279	95.46 227	85.17 236	92.25 116	94.03 238	70.59 302	98.57 138	90.97 125	94.67 172	94.18 315

xiu_mvs_v1_base_debi		90.64 160	90.05 162	92.40 156	93.97 263	84.46 100	93.32 279	95.46 227	85.17 236	92.25 116	94.03 238	70.59 302	98.57 138	90.97 125	94.67 172	94.18 315

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	77.47 198	98.96 89	97.79 6	96.58 125	97.03 181

viewmacassd2359aftdt		91.67 130	91.43 126	92.37 160	93.95 266	81.00 230	93.90 250	95.97 180	87.75 155	91.45 146	96.04 127	79.92 150	97.97 214	89.26 162	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	87.26 170	91.50 143	95.88 139	80.92 139	97.97 214	89.70 154	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	76.14 210	98.95 91	97.64 7	96.21 134	97.03 181

AdaColmap		89.89 185	89.07 193	92.37 160	97.41 71	83.03 155	94.42 198	95.92 184	82.81 304	86.34 266	94.65 214	73.89 256	99.02 73	80.69 309	95.51 150	95.05 274

CNLPA		89.07 215	87.98 227	92.34 164	96.87 84	84.78 89	94.08 230	93.24 347	81.41 343	84.46 322	95.13 189	75.57 228	96.62 338	77.21 363	93.84 201	95.61 258

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	97.13 173

ET-MVSNet_ETH3D		87.51 268	85.91 300	92.32 166	93.70 282	83.93 118	92.33 329	90.94 417	84.16 265	72.09 469	92.52 297	69.90 313	95.85 390	89.20 163	88.36 314	97.17 165

E4		91.74 122	91.55 119	92.31 167	94.27 243	80.80 243	93.81 253	96.17 157	87.97 140	91.11 157	96.05 125	80.75 140	98.08 192	89.78 150	94.02 194	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	87.63 160	91.16 152	95.94 134	81.31 134	98.06 195	89.76 151	94.29 188	97.99 94

Anonymous202405211		87.68 255	86.13 288	92.31 167	96.66 89	80.74 245	94.87 164	91.49 401	80.47 357	89.46 199	95.44 167	54.72 454	98.23 171	82.19 278	89.89 286	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	87.63 160	91.16 152	95.95 133	81.30 135	98.06 195	89.76 151	94.29 188	97.99 94

CHOSEN 1792x2688		88.84 222	87.69 235	92.30 170	96.14 109	81.42 214	90.01 404	95.86 193	74.52 439	87.41 240	93.94 246	75.46 229	98.36 160	80.36 315	95.53 149	97.12 174

viewcassd2359sk11		91.79 114	91.62 113	92.29 172	94.62 206	80.88 237	93.70 263	96.18 156	87.38 167	91.13 155	95.85 143	81.62 130	98.06 195	89.71 153	94.40 184	97.94 99

HY-MVS	83.01 12	89.03 218	87.94 229	92.29 172	94.86 186	82.77 162	92.08 342	94.49 298	81.52 342	86.93 247	92.79 290	78.32 187	98.23 171	79.93 323	90.55 273	95.88 244

CDS-MVSNet		89.45 199	88.51 211	92.29 172	93.62 285	83.61 131	93.01 298	94.68 290	81.95 324	87.82 233	93.24 273	78.69 177	96.99 315	80.34 316	93.23 225	96.28 222
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PAPR		90.02 178	89.27 189	92.29 172	95.78 134	80.95 233	92.68 313	96.22 146	81.91 326	86.66 257	93.75 258	82.23 114	98.44 153	79.40 341	94.79 169	97.48 144

E3new		91.76 120	91.58 116	92.28 176	94.69 203	80.90 236	93.68 266	96.17 157	87.15 173	91.09 162	95.70 155	81.75 129	98.05 199	89.67 156	94.35 185	97.90 111

mvsmamba		90.33 167	89.69 173	92.25 177	95.17 165	81.64 205	95.27 135	93.36 345	84.88 249	89.51 196	94.27 233	69.29 328	97.42 268	89.34 160	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	88.06 134	91.11 157	96.16 116	79.92 150	98.03 203	90.00 143	93.80 203	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	88.06 134	91.11 157	96.16 116	79.92 150	98.03 203	90.00 143	93.80 203	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	88.06 134	91.11 157	96.16 116	79.92 150	98.03 203	90.00 143	93.80 203	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	88.06 134	91.11 157	96.16 116	79.92 150	98.03 203	90.00 143	93.80 203	97.94 99

PLC	84.53 7	89.06 216	88.03 225	92.15 182	97.27 78	82.69 169	94.29 214	95.44 232	79.71 366	84.01 338	94.18 236	76.68 207	98.75 116	77.28 362	93.41 219	95.02 275
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	95.25 246	86.15 205	89.76 193	95.60 159	83.42 91	98.32 167	87.37 194	93.25 224	97.56 139

patch_mono-2		93.74 65	94.32 41	92.01 184	97.54 66	78.37 328	93.40 275	97.19 44	88.02 138	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	78.89 377	90.45 171	95.92 136	82.65 105	98.84 106	80.68 310	98.26 62	96.14 229

UniMVSNet (Re)		89.80 188	89.07 193	92.01 184	93.60 286	84.52 97	94.78 173	97.47 16	89.26 86	86.44 263	92.32 303	82.10 119	97.39 279	84.81 231	80.84 407	94.12 319

MG-MVS		91.77 119	91.70 112	92.00 187	97.08 81	80.03 275	93.60 268	95.18 252	87.85 150	90.89 165	96.47 102	82.06 121	98.36 160	85.07 226	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	89.34 200	92.16 308	83.82 87	98.45 151	89.35 159	97.06 108	97.48 144

PVSNet_Blended		90.73 153	90.32 153	91.98 188	96.12 111	81.25 218	92.55 318	96.83 83	82.04 322	89.10 204	92.56 296	81.04 137	98.85 104	86.72 204	95.91 139	95.84 246

guyue		91.12 143	90.84 141	91.96 190	94.59 210	80.57 254	94.87 164	93.71 338	88.96 101	91.14 154	95.22 180	73.22 268	97.76 231	92.01 104	93.81 202	97.54 142

PS-MVSNAJ		91.18 140	90.92 138	91.96 190	95.26 161	82.60 176	92.09 341	95.70 207	86.27 201	91.84 132	92.46 298	79.70 159	98.99 82	89.08 164	95.86 141	94.29 312

TAMVS		89.21 209	88.29 220	91.96 190	93.71 280	82.62 175	93.30 283	94.19 312	82.22 316	87.78 234	93.94 246	78.83 174	96.95 318	77.70 358	92.98 233	96.32 219

SDMVSNet		90.19 171	89.61 176	91.93 193	96.00 122	83.09 152	92.89 305	95.98 177	88.73 108	86.85 253	95.20 184	72.09 285	97.08 306	88.90 169	89.85 288	95.63 256

FA-MVS(test-final)		89.66 191	88.91 201	91.93 193	94.57 214	80.27 260	91.36 362	94.74 287	84.87 250	89.82 190	92.61 295	74.72 239	98.47 146	83.97 247	93.53 213	97.04 180

MVS_Test		91.31 136	91.11 133	91.93 193	94.37 231	80.14 265	93.46 273	95.80 196	86.46 196	91.35 150	93.77 256	82.21 116	98.09 189	87.57 189	94.95 165	97.55 140

NR-MVSNet		88.58 232	87.47 241	91.93 193	93.04 307	84.16 112	94.77 174	96.25 142	89.05 94	80.04 402	93.29 271	79.02 172	97.05 311	81.71 293	80.05 417	94.59 295

HyFIR lowres test		88.09 245	86.81 258	91.93 193	96.00 122	80.63 247	90.01 404	95.79 197	73.42 451	87.68 236	92.10 314	73.86 257	97.96 216	80.75 308	91.70 256	97.19 164

GeoE		90.05 176	89.43 181	91.90 198	95.16 166	80.37 259	95.80 96	94.65 291	83.90 271	87.55 239	94.75 206	78.18 188	97.62 244	81.28 298	93.63 209	97.71 129

thisisatest0530		88.67 227	87.61 237	91.86 199	94.87 185	80.07 270	94.63 183	89.90 443	84.00 269	88.46 218	93.78 255	66.88 352	98.46 147	83.30 257	92.65 241	97.06 178

xiu_mvs_v2_base		91.13 142	90.89 140	91.86 199	94.97 177	82.42 180	92.24 334	95.64 215	86.11 209	91.74 139	93.14 277	79.67 164	98.89 98	89.06 165	95.46 154	94.28 313

DU-MVS		89.34 208	88.50 212	91.85 201	93.04 307	83.72 124	94.47 194	96.59 110	89.50 75	86.46 260	93.29 271	77.25 200	97.23 295	84.92 228	81.02 403	94.59 295

AstraMVS		90.69 155	90.30 154	91.84 202	93.81 271	79.85 283	94.76 175	92.39 370	88.96 101	91.01 164	95.87 142	70.69 300	97.94 219	92.49 82	92.70 240	97.73 127

OPM-MVS		90.12 172	89.56 177	91.82 203	93.14 299	83.90 119	94.16 221	95.74 201	88.96 101	87.86 229	95.43 169	72.48 278	97.91 222	88.10 181	90.18 280	93.65 353
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
HQP_MVS		90.60 163	90.19 156	91.82 203	94.70 201	82.73 166	95.85 93	96.22 146	90.81 27	86.91 249	94.86 201	74.23 247	98.12 179	88.15 177	89.99 282	94.63 292

UniMVSNet_NR-MVSNet		89.92 184	89.29 187	91.81 205	93.39 292	83.72 124	94.43 197	97.12 55	89.80 63	86.46 260	93.32 268	83.16 95	97.23 295	84.92 228	81.02 403	94.49 305

diffmvs		91.37 135	91.23 131	91.77 206	93.09 302	80.27 260	92.36 324	95.52 224	87.03 178	91.40 148	94.93 196	80.08 147	97.44 266	92.13 99	94.56 178	97.61 134
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	92.51 319	95.58 218	87.22 171	91.80 135	95.57 161	79.96 149	97.48 258	92.23 93	94.97 164	97.45 146

1112_ss		88.42 234	87.33 244	91.72 208	94.92 181	80.98 231	92.97 302	94.54 295	78.16 395	83.82 341	93.88 251	78.78 176	97.91 222	79.45 337	89.41 295	96.26 223

Fast-Effi-MVS+		89.41 203	88.64 207	91.71 209	94.74 195	80.81 242	93.54 269	95.10 256	83.11 294	86.82 255	90.67 368	79.74 158	97.75 235	80.51 313	93.55 211	96.57 212

WTY-MVS		89.60 193	88.92 200	91.67 210	95.47 151	81.15 223	92.38 323	94.78 285	83.11 294	89.06 206	94.32 228	78.67 178	96.61 341	81.57 294	90.89 269	97.24 158

TAPA-MVS	84.62 6	88.16 243	87.01 253	91.62 211	96.64 90	80.65 246	94.39 205	96.21 149	76.38 418	86.19 270	95.44 167	79.75 157	98.08 192	62.75 466	95.29 159	96.13 230
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
VPA-MVSNet		89.62 192	88.96 198	91.60 212	93.86 268	82.89 161	95.46 121	97.33 32	87.91 145	88.43 219	93.31 269	74.17 250	97.40 276	87.32 195	82.86 378	94.52 300

FE-MVS		87.40 273	86.02 294	91.57 213	94.56 215	79.69 291	90.27 391	93.72 337	80.57 355	88.80 212	91.62 334	65.32 368	98.59 137	74.97 388	94.33 187	96.44 215

hybridnocas07		90.93 147	90.72 145	91.54 214	92.75 322	79.72 289	92.35 326	95.21 250	86.41 198	90.44 174	95.40 170	79.17 171	97.39 279	90.83 132	93.94 197	97.50 143

XVG-OURS		89.40 205	88.70 206	91.52 215	94.06 255	81.46 212	91.27 367	96.07 170	86.14 206	88.89 211	95.77 151	68.73 337	97.26 292	87.39 193	89.96 284	95.83 247

hse-mvs2		89.88 186	89.34 185	91.51 216	94.83 188	81.12 225	93.94 244	93.91 325	89.80 63	93.08 90	93.60 261	75.77 221	97.66 239	92.07 100	77.07 437	95.74 251

TranMVSNet+NR-MVSNet		88.84 222	87.95 228	91.49 217	92.68 325	83.01 157	94.92 161	96.31 131	89.88 57	85.53 286	93.85 253	76.63 208	96.96 317	81.91 286	79.87 420	94.50 303

AUN-MVS		87.78 253	86.54 273	91.48 218	94.82 189	81.05 228	93.91 248	93.93 322	83.00 299	86.93 247	93.53 263	69.50 322	97.67 237	86.14 210	77.12 436	95.73 253

XVG-OURS-SEG-HR		89.95 182	89.45 179	91.47 219	94.00 261	81.21 221	91.87 346	96.06 172	85.78 213	88.55 216	95.73 153	74.67 240	97.27 290	88.71 173	89.64 293	95.91 241

MVS		87.44 271	86.10 291	91.44 220	92.61 327	83.62 129	92.63 315	95.66 212	67.26 478	81.47 380	92.15 309	77.95 191	98.22 173	79.71 326	95.48 152	92.47 404

hybrid		90.69 155	90.45 150	91.43 221	92.67 326	79.42 299	92.28 333	95.21 250	85.15 241	90.39 175	95.37 172	78.93 173	97.32 285	90.27 141	93.74 207	97.55 140

viewdifsd2359ckpt07		91.11 144	91.02 136	91.41 222	94.21 248	78.37 328	92.91 304	95.71 206	87.50 162	90.32 176	95.88 139	80.27 145	97.99 209	88.78 172	93.55 211	97.86 114

F-COLMAP		87.95 248	86.80 259	91.40 223	96.35 104	80.88 237	94.73 177	95.45 230	79.65 367	82.04 375	94.61 215	71.13 292	98.50 141	76.24 375	91.05 267	94.80 289

dcpmvs_2		93.49 70	94.19 52	91.38 224	97.69 63	76.78 372	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		87.33 276	85.99 295	91.37 225	93.49 288	79.55 292	90.63 383	89.56 451	80.17 359	87.56 238	90.86 358	67.07 349	98.28 169	81.50 295	93.02 232	96.29 221

HQP-MVS		89.80 188	89.28 188	91.34 226	94.17 250	81.56 206	94.39 205	96.04 173	88.81 104	85.43 295	93.97 245	73.83 258	97.96 216	87.11 199	89.77 291	94.50 303

fmvsm_s_conf0.5_n_7		93.15 89	93.76 68	91.31 227	94.42 229	79.48 294	94.52 189	97.14 53	89.33 82	94.17 66	98.09 18	81.83 126	97.49 257	96.33 26	98.02 80	96.95 188

RRT-MVS		90.85 149	90.70 146	91.30 228	94.25 245	76.83 371	94.85 167	96.13 164	89.04 95	90.23 178	94.88 199	70.15 311	98.72 120	91.86 112	94.88 167	98.34 49

FMVSNet3		87.40 273	86.11 290	91.30 228	93.79 274	83.64 128	94.20 220	94.81 283	83.89 272	84.37 325	91.87 325	68.45 340	96.56 350	78.23 353	85.36 346	93.70 352

FMVSNet2		87.19 286	85.82 303	91.30 228	94.01 258	83.67 126	94.79 172	94.94 269	83.57 280	83.88 340	92.05 318	66.59 357	96.51 354	77.56 360	85.01 349	93.73 350

RPMNet		83.95 366	81.53 377	91.21 231	90.58 404	79.34 304	85.24 469	96.76 93	71.44 465	85.55 284	82.97 470	70.87 297	98.91 97	61.01 470	89.36 297	95.40 262

IB-MVS	80.51 15	85.24 342	83.26 361	91.19 232	92.13 339	79.86 282	91.75 350	91.29 407	83.28 291	80.66 392	88.49 417	61.28 407	98.46 147	80.99 304	79.46 424	95.25 268
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		89.47 198	88.90 202	91.18 233	94.22 247	82.07 190	92.13 339	96.09 168	87.90 146	85.37 301	92.45 299	74.38 245	97.56 249	87.15 197	90.43 275	93.93 330
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		89.45 199	88.90 202	91.12 234	94.47 223	81.49 210	95.30 130	96.14 161	86.73 189	85.45 292	95.16 186	69.89 314	98.10 181	87.70 186	89.23 300	93.77 346

LGP-MVS_train				91.12 234	94.47 223	81.49 210		96.14 161	86.73 189	85.45 292	95.16 186	69.89 314	98.10 181	87.70 186	89.23 300	93.77 346

ACMM	84.12 9	89.14 211	88.48 215	91.12 234	94.65 205	81.22 220	95.31 128	96.12 165	85.31 233	85.92 275	94.34 226	70.19 310	98.06 195	85.65 218	88.86 305	94.08 323
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tttt0517		88.61 229	87.78 234	91.11 237	94.96 178	77.81 347	95.35 126	89.69 446	85.09 244	88.05 227	94.59 218	66.93 350	98.48 143	83.27 258	92.13 253	97.03 181

GBi-Net		87.26 278	85.98 296	91.08 238	94.01 258	83.10 149	95.14 148	94.94 269	83.57 280	84.37 325	91.64 330	66.59 357	96.34 368	78.23 353	85.36 346	93.79 341

test1		87.26 278	85.98 296	91.08 238	94.01 258	83.10 149	95.14 148	94.94 269	83.57 280	84.37 325	91.64 330	66.59 357	96.34 368	78.23 353	85.36 346	93.79 341

FMVSNet1		85.85 327	84.11 348	91.08 238	92.81 319	83.10 149	95.14 148	94.94 269	81.64 337	82.68 365	91.64 330	59.01 429	96.34 368	75.37 382	83.78 362	93.79 341

Test_1112_low_res		87.65 257	86.51 274	91.08 238	94.94 180	79.28 308	91.77 349	94.30 307	76.04 424	83.51 351	92.37 301	77.86 194	97.73 236	78.69 348	89.13 302	96.22 224

PS-MVSNAJss		89.97 180	89.62 175	91.02 242	91.90 348	80.85 241	95.26 136	95.98 177	86.26 202	86.21 269	94.29 230	79.70 159	97.65 240	88.87 171	88.10 316	94.57 297

BH-RMVSNet		88.37 237	87.48 240	91.02 242	95.28 158	79.45 296	92.89 305	93.07 353	85.45 228	86.91 249	94.84 204	70.35 307	97.76 231	73.97 397	94.59 177	95.85 245

UniMVSNet_ETH3D		87.53 267	86.37 278	91.00 244	92.44 331	78.96 313	94.74 176	95.61 216	84.07 268	85.36 302	94.52 220	59.78 421	97.34 283	82.93 262	87.88 321	96.71 205

FIs		90.51 165	90.35 152	90.99 245	93.99 262	80.98 231	95.73 104	97.54 9	89.15 90	86.72 256	94.68 209	81.83 126	97.24 294	85.18 224	88.31 315	94.76 290

ACMP	84.23 8	89.01 220	88.35 216	90.99 245	94.73 196	81.27 217	95.07 151	95.89 189	86.48 194	83.67 346	94.30 229	69.33 324	97.99 209	87.10 201	88.55 307	93.72 351
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
Anonymous20231211		86.59 310	85.13 325	90.98 247	96.52 98	81.50 208	96.14 64	96.16 159	73.78 447	83.65 347	92.15 309	63.26 389	97.37 282	82.82 266	81.74 392	94.06 324

IMVS_0403		89.97 180	89.64 174	90.96 248	93.72 276	77.75 352	93.00 299	95.34 241	85.53 224	88.77 213	94.49 221	78.49 184	97.84 227	84.75 232	92.65 241	97.28 153

sss		88.93 221	88.26 222	90.94 249	94.05 256	80.78 244	91.71 351	95.38 236	81.55 341	88.63 215	93.91 250	75.04 233	95.47 409	82.47 271	91.61 257	96.57 212

IMVS_0407		89.85 187	89.51 178	90.88 250	93.72 276	77.75 352	93.07 296	95.34 241	85.53 224	88.34 221	94.49 221	77.69 196	97.60 245	84.75 232	92.65 241	97.28 153

dtuplus		89.78 190	89.43 181	90.85 251	92.83 318	77.91 341	92.32 331	94.97 266	82.33 314	90.20 179	95.53 163	78.56 181	97.38 281	85.15 225	92.95 234	97.24 158

viewmambaseed2359dif		90.04 177	89.78 171	90.83 252	92.85 317	77.92 340	92.23 335	95.01 260	81.90 327	90.20 179	95.45 166	79.64 166	97.34 283	87.52 191	93.17 226	97.23 162

sd_testset		88.59 231	87.85 233	90.83 252	96.00 122	80.42 258	92.35 326	94.71 288	88.73 108	86.85 253	95.20 184	67.31 344	96.43 362	79.64 329	89.85 288	95.63 256

PVSNet_BlendedMVS		89.98 179	89.70 172	90.82 254	96.12 111	81.25 218	93.92 246	96.83 83	83.49 284	89.10 204	92.26 306	81.04 137	98.85 104	86.72 204	87.86 322	92.35 411

cascas		86.43 318	84.98 328	90.80 255	92.10 341	80.92 235	90.24 395	95.91 186	73.10 454	83.57 350	88.39 418	65.15 370	97.46 262	84.90 230	91.43 259	94.03 326

ECVR-MVS		89.09 214	88.53 210	90.77 256	95.62 144	75.89 385	96.16 60	84.22 480	87.89 148	90.20 179	96.65 91	63.19 391	98.10 181	85.90 215	96.94 112	98.33 51

GA-MVS		86.61 308	85.27 322	90.66 257	91.33 371	78.71 317	90.40 390	93.81 332	85.34 232	85.12 305	89.57 399	61.25 408	97.11 304	80.99 304	89.59 294	96.15 228

thres600view7		87.65 257	86.67 265	90.59 258	96.08 117	78.72 315	94.88 163	91.58 397	87.06 177	88.08 225	92.30 304	68.91 334	98.10 181	70.05 429	91.10 262	94.96 279

thres400		87.62 262	86.64 266	90.57 259	95.99 125	78.64 318	94.58 185	91.98 386	86.94 183	88.09 223	91.77 326	69.18 330	98.10 181	70.13 426	91.10 262	94.96 279

baseline1		88.10 244	87.28 246	90.57 259	94.96 178	80.07 270	94.27 215	91.29 407	86.74 188	87.41 240	94.00 243	76.77 205	96.20 373	80.77 307	79.31 426	95.44 260

viewdifsd2359ckpt11		89.43 201	89.05 195	90.56 261	92.89 315	77.00 367	92.81 308	94.52 296	87.03 178	89.77 191	95.79 148	74.67 240	97.51 253	88.97 167	84.98 350	97.17 165

viewmsd2359difaftdt		89.43 201	89.05 195	90.56 261	92.89 315	77.00 367	92.81 308	94.52 296	87.03 178	89.77 191	95.79 148	74.67 240	97.51 253	88.97 167	84.98 350	97.17 165

usedtu_dtu_shiyan1		86.84 297	85.61 311	90.53 263	90.50 408	81.80 200	90.97 375	94.96 267	83.05 296	83.50 352	90.32 375	72.15 282	96.65 332	79.49 334	85.55 344	93.15 376

FE-MVSNET3		86.84 297	85.61 311	90.53 263	90.50 408	81.80 200	90.97 375	94.96 267	83.05 296	83.50 352	90.32 375	72.15 282	96.65 332	79.49 334	85.55 344	93.15 376

FC-MVSNet-test		90.27 169	90.18 157	90.53 263	93.71 280	79.85 283	95.77 100	97.59 6	89.31 83	86.27 267	94.67 212	81.93 124	97.01 314	84.26 242	88.09 318	94.71 291

PAPM		86.68 307	85.39 317	90.53 263	93.05 306	79.33 307	89.79 407	94.77 286	78.82 380	81.95 376	93.24 273	76.81 203	97.30 286	66.94 446	93.16 227	94.95 283

WR-MVS		88.38 236	87.67 236	90.52 267	93.30 294	80.18 263	93.26 286	95.96 181	88.57 116	85.47 291	92.81 288	76.12 213	96.91 321	81.24 299	82.29 383	94.47 308

SSM_04072		88.57 233	87.92 230	90.51 268	94.76 192	82.66 170	79.84 492	94.64 292	85.18 234	88.96 208	95.00 193	76.00 216	92.03 460	83.74 252	93.15 228	96.85 197

MVSTER		88.84 222	88.29 220	90.51 268	92.95 312	80.44 257	93.73 259	95.01 260	84.66 259	87.15 244	93.12 278	72.79 273	97.21 297	87.86 183	87.36 330	93.87 335

testdata				90.49 270	96.40 101	77.89 344		95.37 238	72.51 459	93.63 79	96.69 87	82.08 120	97.65 240	83.08 259	97.39 101	95.94 240

test1111		89.10 212	88.64 207	90.48 271	95.53 149	74.97 395	96.08 69	84.89 478	88.13 131	90.16 185	96.65 91	63.29 388	98.10 181	86.14 210	96.90 115	98.39 46

tt0805		86.92 294	85.74 309	90.48 271	92.22 335	79.98 278	95.63 114	94.88 277	83.83 274	84.74 314	92.80 289	57.61 436	97.67 237	85.48 221	84.42 355	93.79 341

jajsoiax		88.24 241	87.50 239	90.48 271	90.89 392	80.14 265	95.31 128	95.65 214	84.97 247	84.24 333	94.02 241	65.31 369	97.42 268	88.56 174	88.52 309	93.89 331

PatchMatch-RL		86.77 304	85.54 313	90.47 274	95.88 130	82.71 168	90.54 386	92.31 374	79.82 365	84.32 330	91.57 338	68.77 336	96.39 364	73.16 403	93.48 217	92.32 412

0.4-1-1-0.1		81.55 398	78.59 421	90.42 275	87.55 451	79.90 280	88.56 430	89.19 456	77.01 410	79.72 409	77.71 486	54.84 451	97.11 304	80.50 314	72.20 451	94.26 314

tfpn200view9		87.58 265	86.64 266	90.41 276	95.99 125	78.64 318	94.58 185	91.98 386	86.94 183	88.09 223	91.77 326	69.18 330	98.10 181	70.13 426	91.10 262	94.48 306

VPNet		88.20 242	87.47 241	90.39 277	93.56 287	79.46 295	94.04 234	95.54 222	88.67 111	86.96 246	94.58 219	69.33 324	97.15 299	84.05 246	80.53 412	94.56 298

ACMH	80.38 17	85.36 337	83.68 355	90.39 277	94.45 226	80.63 247	94.73 177	94.85 279	82.09 318	77.24 437	92.65 293	60.01 419	97.58 247	72.25 408	84.87 352	92.96 383
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
thres100view900		87.63 260	86.71 262	90.38 279	96.12 111	78.55 321	95.03 155	91.58 397	87.15 173	88.06 226	92.29 305	68.91 334	98.10 181	70.13 426	91.10 262	94.48 306

mvs_tets		88.06 247	87.28 246	90.38 279	90.94 388	79.88 281	95.22 139	95.66 212	85.10 243	84.21 334	93.94 246	63.53 386	97.40 276	88.50 175	88.40 313	93.87 335

1314		87.51 268	86.57 271	90.34 281	92.42 332	79.74 288	92.63 315	95.35 240	78.35 390	80.14 399	91.62 334	74.05 252	97.15 299	81.05 300	93.53 213	94.12 319

LTVRE_ROB	82.13 13	86.26 321	84.90 331	90.34 281	94.44 227	81.50 208	92.31 332	94.89 275	83.03 298	79.63 411	92.67 292	69.69 317	97.79 229	71.20 414	86.26 339	91.72 422
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		80.75 411	77.58 426	90.25 283	86.55 456	79.72 289	87.46 451	89.48 454	76.43 417	77.93 432	75.94 489	52.31 463	97.05 311	80.25 319	71.85 455	93.99 328

test_djsdf		89.03 218	88.64 207	90.21 284	90.74 399	79.28 308	95.96 83	95.90 187	84.66 259	85.33 303	92.94 283	74.02 253	97.30 286	89.64 157	88.53 308	94.05 325

v2v482		87.84 250	87.06 250	90.17 285	90.99 384	79.23 311	94.00 240	95.13 253	84.87 250	85.53 286	92.07 317	74.45 244	97.45 263	84.71 237	81.75 391	93.85 338

pmmvs4		85.43 335	83.86 353	90.16 286	90.02 419	82.97 159	90.27 391	92.67 365	75.93 425	80.73 390	91.74 328	71.05 293	95.73 398	78.85 347	83.46 369	91.78 421

V42		87.68 255	86.86 255	90.15 287	90.58 404	80.14 265	94.24 218	95.28 245	83.66 278	85.67 281	91.33 340	74.73 238	97.41 274	84.43 241	81.83 389	92.89 386

MSDG		84.86 350	83.09 364	90.14 288	93.80 272	80.05 272	89.18 420	93.09 352	78.89 377	78.19 428	91.91 323	65.86 367	97.27 290	68.47 435	88.45 311	93.11 378

sc_t1		81.53 399	78.67 420	90.12 289	90.78 396	78.64 318	93.91 248	90.20 432	68.42 474	80.82 389	89.88 392	46.48 478	96.76 326	76.03 378	71.47 456	94.96 279

anonymousdsp		87.84 250	87.09 249	90.12 289	89.13 430	80.54 255	94.67 181	95.55 220	82.05 320	83.82 341	92.12 311	71.47 290	97.15 299	87.15 197	87.80 325	92.67 393

thres200		87.21 284	86.24 285	90.12 289	95.36 154	78.53 322	93.26 286	92.10 380	86.42 197	88.00 228	91.11 351	69.24 329	98.00 208	69.58 430	91.04 268	93.83 340

CR-MVSNet		85.35 338	83.76 354	90.12 289	90.58 404	79.34 304	85.24 469	91.96 388	78.27 392	85.55 284	87.87 428	71.03 294	95.61 401	73.96 398	89.36 297	95.40 262

0.4-1-1-0.2		80.84 410	77.77 424	90.06 293	86.18 460	79.35 302	86.75 457	89.54 452	76.23 422	78.59 427	75.46 492	55.03 450	96.99 315	80.11 321	72.05 453	93.85 338

v1144		87.61 263	86.79 260	90.06 293	91.01 383	79.34 304	93.95 243	95.42 235	83.36 289	85.66 282	91.31 343	74.98 234	97.42 268	83.37 256	82.06 385	93.42 362

XXY-MVS		87.65 257	86.85 256	90.03 295	92.14 338	80.60 253	93.76 256	95.23 247	82.94 301	84.60 316	94.02 241	74.27 246	95.49 408	81.04 301	83.68 365	94.01 327

Vis-MVSNet (Re-imp)		89.59 194	89.44 180	90.03 295	95.74 135	75.85 386	95.61 115	90.80 421	87.66 159	87.83 232	95.40 170	76.79 204	96.46 359	78.37 349	96.73 121	97.80 122

test2506		87.21 284	86.28 283	90.02 297	95.62 144	73.64 411	96.25 55	71.38 504	87.89 148	90.45 171	96.65 91	55.29 448	98.09 189	86.03 214	96.94 112	98.33 51

BH-untuned		88.60 230	88.13 224	90.01 298	95.24 162	78.50 324	93.29 284	94.15 315	84.75 255	84.46 322	93.40 265	75.76 223	97.40 276	77.59 359	94.52 180	94.12 319

v1192		87.25 280	86.33 280	90.00 299	90.76 398	79.04 312	93.80 254	95.48 225	82.57 308	85.48 290	91.18 347	73.38 267	97.42 268	82.30 275	82.06 385	93.53 356

v7n		86.81 299	85.76 307	89.95 300	90.72 400	79.25 310	95.07 151	95.92 184	84.45 262	82.29 369	90.86 358	72.60 277	97.53 251	79.42 340	80.52 413	93.08 380

testing91		87.11 289	86.18 286	89.92 301	94.43 228	75.38 394	91.53 357	92.27 376	86.48 194	86.50 258	90.24 378	61.19 411	97.53 251	82.10 280	90.88 270	96.84 200

IMVS_0404		87.60 264	86.84 257	89.89 302	93.72 276	77.75 352	88.56 430	95.34 241	85.53 224	79.98 403	94.49 221	66.54 360	94.64 422	84.75 232	92.65 241	97.28 153

v8		87.50 270	86.71 262	89.89 302	91.37 368	79.40 300	94.50 190	95.38 236	84.81 253	83.60 349	91.33 340	76.05 214	97.42 268	82.84 265	80.51 414	92.84 388

v10		87.25 280	86.38 277	89.85 304	91.19 374	79.50 293	94.48 191	95.45 230	83.79 276	83.62 348	91.19 345	75.13 231	97.42 268	81.94 285	80.60 409	92.63 395

baseline2		86.50 314	85.39 317	89.84 305	91.12 379	76.70 374	91.88 345	88.58 458	82.35 313	79.95 404	90.95 356	73.42 265	97.63 243	80.27 318	89.95 285	95.19 269

pm-mvs1		86.61 308	85.54 313	89.82 306	91.44 363	80.18 263	95.28 134	94.85 279	83.84 273	81.66 378	92.62 294	72.45 280	96.48 356	79.67 328	78.06 429	92.82 389

TR-MVS		86.78 301	85.76 307	89.82 306	94.37 231	78.41 326	92.47 320	92.83 359	81.11 351	86.36 264	92.40 300	68.73 337	97.48 258	73.75 401	89.85 288	93.57 355

ACMH+	81.04 14	85.05 345	83.46 358	89.82 306	94.66 204	79.37 301	94.44 196	94.12 318	82.19 317	78.04 430	92.82 287	58.23 432	97.54 250	73.77 400	82.90 377	92.54 401

EI-MVSNet		89.10 212	88.86 204	89.80 309	91.84 350	78.30 331	93.70 263	95.01 260	85.73 215	87.15 244	95.28 177	79.87 156	97.21 297	83.81 250	87.36 330	93.88 334

gbinet_0.2-2-1-0.02		82.59 380	80.19 392	89.77 310	85.23 471	80.05 272	91.59 356	93.52 341	77.60 399	79.78 408	82.87 472	63.26 389	96.45 360	78.93 345	68.97 466	92.81 390

usedtu_blend_shiyan5		82.39 385	79.93 399	89.75 311	85.12 472	80.08 268	92.36 324	93.26 346	74.29 442	79.00 419	82.72 473	64.29 380	96.60 345	79.60 330	68.75 470	92.55 398

v144192		87.19 286	86.35 279	89.74 312	90.64 402	78.24 333	93.92 246	95.43 233	81.93 325	85.51 288	91.05 354	74.21 249	97.45 263	82.86 264	81.56 393	93.53 356

COLMAP_ROB	80.39 16	83.96 365	82.04 374	89.74 312	95.28 158	79.75 287	94.25 216	92.28 375	75.17 432	78.02 431	93.77 256	58.60 431	97.84 227	65.06 457	85.92 340	91.63 424
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
SCA		86.32 320	85.18 324	89.73 314	92.15 337	76.60 375	91.12 371	91.69 393	83.53 283	85.50 289	88.81 411	66.79 353	96.48 356	76.65 368	90.35 277	96.12 231

blend_shiyan4		81.94 388	79.35 407	89.70 315	85.52 467	80.08 268	91.29 365	93.82 329	77.12 408	79.31 415	82.94 471	54.81 452	96.60 345	79.60 330	69.78 461	92.41 407

IterMVS-LS		88.36 238	87.91 232	89.70 315	93.80 272	78.29 332	93.73 259	95.08 258	85.73 215	84.75 313	91.90 324	79.88 155	96.92 320	83.83 249	82.51 379	93.89 331
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
blended_shiyan8		82.79 375	80.49 385	89.69 317	85.50 468	79.83 285	91.38 360	93.82 329	77.14 405	79.39 414	83.73 463	64.95 374	96.63 335	79.75 325	68.77 469	92.62 397

testing11		86.44 317	85.35 320	89.69 317	94.29 242	75.40 393	91.30 364	90.53 427	84.76 254	85.06 307	90.13 384	58.95 430	97.45 263	82.08 281	91.09 266	96.21 226

testing99		86.72 305	85.73 310	89.69 317	94.23 246	74.91 397	91.35 363	90.97 415	86.14 206	86.36 264	90.22 379	59.41 424	97.48 258	82.24 277	90.66 272	96.69 207

v1921920		86.97 293	86.06 293	89.69 317	90.53 407	78.11 336	93.80 254	95.43 233	81.90 327	85.33 303	91.05 354	72.66 274	97.41 274	82.05 283	81.80 390	93.53 356

icg_test_0407_2		89.15 210	88.97 197	89.68 321	93.72 276	77.75 352	88.26 436	95.34 241	85.53 224	88.34 221	94.49 221	77.69 196	93.99 435	84.75 232	92.65 241	97.28 153

blended_shiyan6		82.78 376	80.48 386	89.67 322	85.53 466	79.76 286	91.37 361	93.82 329	77.14 405	79.30 416	83.73 463	64.96 373	96.63 335	79.68 327	68.75 470	92.63 395

VortexMVS		88.42 234	88.01 226	89.63 323	93.89 267	78.82 314	93.82 252	95.47 226	86.67 191	84.53 320	91.99 320	72.62 276	96.65 332	89.02 166	84.09 359	93.41 363

Fast-Effi-MVS+-dtu		87.44 271	86.72 261	89.63 323	92.04 342	77.68 357	94.03 235	93.94 321	85.81 212	82.42 368	91.32 342	70.33 308	97.06 309	80.33 317	90.23 279	94.14 318

v1240		86.78 301	85.85 302	89.56 325	90.45 411	77.79 349	93.61 267	95.37 238	81.65 336	85.43 295	91.15 349	71.50 289	97.43 267	81.47 296	82.05 387	93.47 360

Effi-MVS+-dtu		88.65 228	88.35 216	89.54 326	93.33 293	76.39 379	94.47 194	94.36 305	87.70 156	85.43 295	89.56 400	73.45 263	97.26 292	85.57 220	91.28 261	94.97 276

wanda-best-256-512		82.44 382	80.07 394	89.53 327	85.12 472	79.44 297	90.49 387	93.75 335	76.97 411	79.00 419	82.72 473	64.29 380	96.61 341	79.56 332	68.75 470	92.55 398

FE-blended-shiyan7		82.44 382	80.07 394	89.53 327	85.12 472	79.44 297	90.49 387	93.75 335	76.97 411	79.00 419	82.72 473	64.29 380	96.61 341	79.56 332	68.75 470	92.55 398

AllTest		83.42 372	81.39 378	89.52 329	95.01 172	77.79 349	93.12 290	90.89 419	77.41 401	76.12 446	93.34 266	54.08 457	97.51 253	68.31 437	84.27 357	93.26 366

TestCases				89.52 329	95.01 172	77.79 349		90.89 419	77.41 401	76.12 446	93.34 266	54.08 457	97.51 253	68.31 437	84.27 357	93.26 366

mvs_anonymous		89.37 207	89.32 186	89.51 331	93.47 289	74.22 404	91.65 354	94.83 281	82.91 302	85.45 292	93.79 254	81.23 136	96.36 367	86.47 206	94.09 193	97.94 99

XVG-ACMP-BASELINE		86.00 323	84.84 333	89.45 332	91.20 373	78.00 338	91.70 352	95.55 220	85.05 245	82.97 362	92.25 307	54.49 455	97.48 258	82.93 262	87.45 329	92.89 386

testing222		84.84 351	83.32 359	89.43 333	94.15 253	75.94 384	91.09 372	89.41 455	84.90 248	85.78 278	89.44 401	52.70 462	96.28 371	70.80 420	91.57 258	96.07 235

MVP-Stereo		85.97 324	84.86 332	89.32 334	90.92 390	82.19 187	92.11 340	94.19 312	78.76 382	78.77 426	91.63 333	68.38 341	96.56 350	75.01 387	93.95 196	89.20 464
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PatchmatchNet		85.85 327	84.70 335	89.29 335	91.76 354	75.54 390	88.49 432	91.30 406	81.63 338	85.05 308	88.70 415	71.71 286	96.24 372	74.61 393	89.05 303	96.08 234
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v148		87.04 291	86.32 281	89.21 336	90.94 388	77.26 363	93.71 262	94.43 300	84.84 252	84.36 328	90.80 362	76.04 215	97.05 311	82.12 279	79.60 423	93.31 365

tfpnnormal		84.72 353	83.23 362	89.20 337	92.79 320	80.05 272	94.48 191	95.81 195	82.38 311	81.08 386	91.21 344	69.01 333	96.95 318	61.69 468	80.59 410	90.58 450

cl22		86.78 301	85.98 296	89.18 338	92.34 333	77.62 358	90.84 379	94.13 317	81.33 345	83.97 339	90.15 383	73.96 254	96.60 345	84.19 243	82.94 374	93.33 364

BH-w/o		87.57 266	87.05 251	89.12 339	94.90 184	77.90 343	92.41 321	93.51 342	82.89 303	83.70 345	91.34 339	75.75 224	97.07 308	75.49 380	93.49 215	92.39 409

WR-MVS_H		87.80 252	87.37 243	89.10 340	93.23 295	78.12 335	95.61 115	97.30 37	87.90 146	83.72 344	92.01 319	79.65 165	96.01 382	76.36 372	80.54 411	93.16 374

miper_enhance_ethall		86.90 295	86.18 286	89.06 341	91.66 359	77.58 359	90.22 397	94.82 282	79.16 373	84.48 321	89.10 405	79.19 170	96.66 331	84.06 245	82.94 374	92.94 384

c3_l		87.14 288	86.50 275	89.04 342	92.20 336	77.26 363	91.22 370	94.70 289	82.01 323	84.34 329	90.43 373	78.81 175	96.61 341	83.70 254	81.09 400	93.25 368

miper_ehance_all_eth		87.22 283	86.62 269	89.02 343	92.13 339	77.40 361	90.91 378	94.81 283	81.28 346	84.32 330	90.08 386	79.26 168	96.62 338	83.81 250	82.94 374	93.04 381

gg-mvs-nofinetune		81.77 392	79.37 406	88.99 344	90.85 394	77.73 356	86.29 461	79.63 491	74.88 437	83.19 361	69.05 502	60.34 416	96.11 377	75.46 381	94.64 176	93.11 378

ETVMVS		84.43 358	82.92 368	88.97 345	94.37 231	74.67 398	91.23 369	88.35 460	83.37 288	86.06 273	89.04 406	55.38 446	95.67 400	67.12 444	91.34 260	96.58 211

pmmvs6		83.42 372	81.60 376	88.87 346	88.01 446	77.87 345	94.96 158	94.24 311	74.67 438	78.80 425	91.09 352	60.17 418	96.49 355	77.06 367	75.40 443	92.23 414

test_cas_vis1_n_1920		88.83 225	88.85 205	88.78 347	91.15 378	76.72 373	93.85 251	94.93 273	83.23 293	92.81 99	96.00 129	61.17 412	94.45 423	91.67 115	94.84 168	95.17 270

MIMVSNet		82.59 380	80.53 383	88.76 348	91.51 361	78.32 330	86.57 460	90.13 435	79.32 369	80.70 391	88.69 416	52.98 461	93.07 451	66.03 452	88.86 305	94.90 284

cl____		86.52 313	85.78 304	88.75 349	92.03 343	76.46 377	90.74 380	94.30 307	81.83 332	83.34 357	90.78 363	75.74 226	96.57 348	81.74 291	81.54 394	93.22 370

DIV-MVS_self_test		86.53 312	85.78 304	88.75 349	92.02 344	76.45 378	90.74 380	94.30 307	81.83 332	83.34 357	90.82 361	75.75 224	96.57 348	81.73 292	81.52 395	93.24 369

CP-MVSNet		87.63 260	87.26 248	88.74 351	93.12 300	76.59 376	95.29 132	96.58 111	88.43 119	83.49 354	92.98 282	75.28 230	95.83 391	78.97 344	81.15 399	93.79 341

eth_miper_zixun_eth		86.50 314	85.77 306	88.68 352	91.94 345	75.81 387	90.47 389	94.89 275	82.05 320	84.05 336	90.46 372	75.96 218	96.77 325	82.76 268	79.36 425	93.46 361

CHOSEN 280x420		85.15 343	83.99 351	88.65 353	92.47 329	78.40 327	79.68 494	92.76 362	74.90 436	81.41 382	89.59 398	69.85 316	95.51 405	79.92 324	95.29 159	92.03 417

PS-CasMVS		87.32 277	86.88 254	88.63 354	92.99 310	76.33 381	95.33 127	96.61 109	88.22 127	83.30 359	93.07 280	73.03 271	95.79 395	78.36 350	81.00 405	93.75 348

TransMVSNet (Re)		84.43 358	83.06 366	88.54 355	91.72 355	78.44 325	95.18 145	92.82 361	82.73 306	79.67 410	92.12 311	73.49 262	95.96 384	71.10 418	68.73 474	91.21 437

tt0320-xc		79.63 425	76.66 434	88.52 356	91.03 382	78.72 315	93.00 299	89.53 453	66.37 480	76.11 448	87.11 439	46.36 480	95.32 413	72.78 405	67.67 475	91.51 429

EG-PatchMatch MVS		82.37 386	80.34 388	88.46 357	90.27 413	79.35 302	92.80 311	94.33 306	77.14 405	73.26 465	90.18 382	47.47 475	96.72 327	70.25 423	87.32 332	89.30 461

PEN-MVS		86.80 300	86.27 284	88.40 358	92.32 334	75.71 389	95.18 145	96.38 126	87.97 140	82.82 364	93.15 276	73.39 266	95.92 386	76.15 376	79.03 428	93.59 354

Baseline_NR-MVSNet		87.07 290	86.63 268	88.40 358	91.44 363	77.87 345	94.23 219	92.57 367	84.12 267	85.74 280	92.08 315	77.25 200	96.04 378	82.29 276	79.94 418	91.30 435

UBG		85.51 333	84.57 340	88.35 360	94.21 248	71.78 436	90.07 402	89.66 448	82.28 315	85.91 276	89.01 407	61.30 406	97.06 309	76.58 371	92.06 254	96.22 224

D2MVS		85.90 325	85.09 326	88.35 360	90.79 395	77.42 360	91.83 348	95.70 207	80.77 354	80.08 401	90.02 388	66.74 355	96.37 365	81.88 287	87.97 320	91.26 436

pmmvs5		84.21 361	82.84 371	88.34 362	88.95 432	76.94 369	92.41 321	91.91 390	75.63 427	80.28 396	91.18 347	64.59 377	95.57 402	77.09 366	83.47 368	92.53 402

tt0320		80.13 417	77.41 427	88.29 363	90.50 408	78.02 337	93.10 293	90.71 424	66.06 483	76.75 441	86.97 440	49.56 470	95.40 410	71.65 409	71.41 457	91.46 432

LCM-MVSNet-Re		88.30 240	88.32 219	88.27 364	94.71 200	72.41 431	93.15 289	90.98 414	87.77 153	79.25 417	91.96 321	78.35 186	95.75 396	83.04 260	95.62 147	96.65 208

CostFormer		85.77 330	84.94 330	88.26 365	91.16 377	72.58 429	89.47 415	91.04 413	76.26 421	86.45 262	89.97 390	70.74 299	96.86 324	82.35 274	87.07 335	95.34 266

ITE_SJBPF				88.24 366	91.88 349	77.05 366		92.92 356	85.54 222	80.13 400	93.30 270	57.29 437	96.20 373	72.46 407	84.71 353	91.49 430

PVSNet	78.82 18	85.55 332	84.65 336	88.23 367	94.72 198	71.93 432	87.12 454	92.75 363	78.80 381	84.95 310	90.53 370	64.43 378	96.71 329	74.74 390	93.86 199	96.06 237

IterMVS-SCA-FT		85.45 334	84.53 341	88.18 368	91.71 356	76.87 370	90.19 399	92.65 366	85.40 231	81.44 381	90.54 369	66.79 353	95.00 419	81.04 301	81.05 401	92.66 394

EPNet_dtu		86.49 316	85.94 299	88.14 369	90.24 414	72.82 421	94.11 225	92.20 378	86.66 192	79.42 413	92.36 302	73.52 261	95.81 393	71.26 413	93.66 208	95.80 249
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Patchmtry		82.71 378	80.93 382	88.06 370	90.05 418	76.37 380	84.74 474	91.96 388	72.28 462	81.32 384	87.87 428	71.03 294	95.50 407	68.97 432	80.15 416	92.32 412

test_vis1_n_1920		89.39 206	89.84 168	88.04 371	92.97 311	72.64 426	94.71 179	96.03 175	86.18 204	91.94 128	96.56 99	61.63 401	95.74 397	93.42 65	95.11 163	95.74 251

DTE-MVSNet		86.11 322	85.48 315	87.98 372	91.65 360	74.92 396	94.93 160	95.75 200	87.36 168	82.26 370	93.04 281	72.85 272	95.82 392	74.04 396	77.46 434	93.20 372

PMMVS		85.71 331	84.96 329	87.95 373	88.90 433	77.09 365	88.68 428	90.06 437	72.32 461	86.47 259	90.76 364	72.15 282	94.40 426	81.78 290	93.49 215	92.36 410

GG-mvs-BLEND				87.94 374	89.73 425	77.91 341	87.80 442	78.23 496		80.58 393	83.86 461	59.88 420	95.33 412	71.20 414	92.22 252	90.60 449

MonoMVSNet		86.89 296	86.55 272	87.92 375	89.46 428	73.75 408	94.12 223	93.10 351	87.82 152	85.10 306	90.76 364	69.59 319	94.94 420	86.47 206	82.50 380	95.07 273

reproduce_monomvs		86.37 319	85.87 301	87.87 376	93.66 284	73.71 409	93.44 274	95.02 259	88.61 114	82.64 367	91.94 322	57.88 434	96.68 330	89.96 147	79.71 422	93.22 370

pmmvs-eth3d		80.97 408	78.72 419	87.74 377	84.99 475	79.97 279	90.11 401	91.65 395	75.36 429	73.51 463	86.03 449	59.45 423	93.96 438	75.17 384	72.21 450	89.29 463

MS-PatchMatch		85.05 345	84.16 346	87.73 378	91.42 366	78.51 323	91.25 368	93.53 340	77.50 400	80.15 398	91.58 336	61.99 398	95.51 405	75.69 379	94.35 185	89.16 465

mmtdpeth		85.04 347	84.15 347	87.72 379	93.11 301	75.74 388	94.37 209	92.83 359	84.98 246	89.31 201	86.41 446	61.61 403	97.14 302	92.63 81	62.11 486	90.29 451

test_0402		81.30 404	79.17 412	87.67 380	93.19 296	78.17 334	92.98 301	91.71 391	75.25 431	76.02 449	90.31 377	59.23 425	96.37 365	50.22 491	83.63 366	88.47 474

IterMVS		84.88 349	83.98 352	87.60 381	91.44 363	76.03 383	90.18 400	92.41 369	83.24 292	81.06 387	90.42 374	66.60 356	94.28 430	79.46 336	80.98 406	92.48 403
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Patchmatch-test		81.37 402	79.30 408	87.58 382	90.92 390	74.16 406	80.99 487	87.68 465	70.52 469	76.63 443	88.81 411	71.21 291	92.76 455	60.01 475	86.93 336	95.83 247

EPMVS		83.90 368	82.70 372	87.51 383	90.23 415	72.67 424	88.62 429	81.96 486	81.37 344	85.01 309	88.34 419	66.31 361	94.45 423	75.30 383	87.12 333	95.43 261

ADS-MVSNet2		81.66 395	79.71 403	87.50 384	91.35 369	74.19 405	83.33 480	88.48 459	72.90 456	82.24 371	85.77 453	64.98 371	93.20 449	64.57 459	83.74 363	95.12 271

OurMVSNet-221017-0		85.35 338	84.64 338	87.49 385	90.77 397	72.59 428	94.01 238	94.40 303	84.72 256	79.62 412	93.17 275	61.91 399	96.72 327	81.99 284	81.16 397	93.16 374

tpm2		84.08 363	82.94 367	87.48 386	91.39 367	71.27 441	89.23 419	90.37 429	71.95 463	84.64 315	89.33 402	67.30 345	96.55 352	75.17 384	87.09 334	94.63 292

RPSCF		85.07 344	84.27 343	87.48 386	92.91 314	70.62 450	91.69 353	92.46 368	76.20 423	82.67 366	95.22 180	63.94 384	97.29 289	77.51 361	85.80 341	94.53 299

myMVS_eth3d28		85.80 329	85.26 323	87.42 388	94.73 196	69.92 456	90.60 384	90.95 416	87.21 172	86.06 273	90.04 387	59.47 422	96.02 380	74.89 389	93.35 223	96.33 218

FE-MVSNET2		81.82 391	79.99 397	87.34 389	84.74 476	77.36 362	92.72 312	94.55 294	82.09 318	73.79 462	86.46 443	57.80 435	94.45 423	74.65 391	73.10 445	90.20 452

WBMVS		84.97 348	84.18 345	87.34 389	94.14 254	71.62 440	90.20 398	92.35 371	81.61 339	84.06 335	90.76 364	61.82 400	96.52 353	78.93 345	83.81 361	93.89 331

miper_lstm_enhance		85.27 341	84.59 339	87.31 391	91.28 372	74.63 399	87.69 447	94.09 319	81.20 350	81.36 383	89.85 394	74.97 235	94.30 429	81.03 303	79.84 421	93.01 382

FMVSNet5		81.52 400	79.60 404	87.27 392	91.17 375	77.95 339	91.49 358	92.26 377	76.87 413	76.16 445	87.91 427	51.67 464	92.34 458	67.74 441	81.16 397	91.52 428

USDC		82.76 377	81.26 380	87.26 393	91.17 375	74.55 400	89.27 417	93.39 344	78.26 393	75.30 453	92.08 315	54.43 456	96.63 335	71.64 410	85.79 342	90.61 447

test-LLR		85.87 326	85.41 316	87.25 394	90.95 386	71.67 438	89.55 411	89.88 444	83.41 286	84.54 318	87.95 425	67.25 346	95.11 416	81.82 288	93.37 221	94.97 276

test-mter		84.54 357	83.64 356	87.25 394	90.95 386	71.67 438	89.55 411	89.88 444	79.17 372	84.54 318	87.95 425	55.56 443	95.11 416	81.82 288	93.37 221	94.97 276

JIA-IIPM		81.04 405	78.98 417	87.25 394	88.64 434	73.48 413	81.75 486	89.61 450	73.19 453	82.05 374	73.71 496	66.07 366	95.87 389	71.18 416	84.60 354	92.41 407

TDRefinement		79.81 421	77.34 428	87.22 397	79.24 493	75.48 391	93.12 290	92.03 383	76.45 416	75.01 454	91.58 336	49.19 471	96.44 361	70.22 425	69.18 465	89.75 457

tpmvs		83.35 374	82.07 373	87.20 398	91.07 381	71.00 447	88.31 435	91.70 392	78.91 375	80.49 395	87.18 437	69.30 327	97.08 306	68.12 440	83.56 367	93.51 359

ppachtmachnet_test		81.84 390	80.07 394	87.15 399	88.46 438	74.43 403	89.04 423	92.16 379	75.33 430	77.75 434	88.99 408	66.20 363	95.37 411	65.12 456	77.60 432	91.65 423

dmvs_re		84.20 362	83.22 363	87.14 400	91.83 352	77.81 347	90.04 403	90.19 433	84.70 258	81.49 379	89.17 404	64.37 379	91.13 471	71.58 411	85.65 343	92.46 405

tpm cat1		81.96 387	80.27 389	87.01 401	91.09 380	71.02 446	87.38 452	91.53 400	66.25 481	80.17 397	86.35 448	68.22 342	96.15 376	69.16 431	82.29 383	93.86 337

test_fmvs1_n		87.03 292	87.04 252	86.97 402	89.74 424	71.86 433	94.55 187	94.43 300	78.47 387	91.95 127	95.50 165	51.16 466	93.81 439	93.02 73	94.56 178	95.26 267

OpenMVS_ROB	74.94 19	79.51 426	77.03 433	86.93 403	87.00 453	76.23 382	92.33 329	90.74 423	68.93 473	74.52 458	88.23 422	49.58 469	96.62 338	57.64 481	84.29 356	87.94 477

SixPastTwentyTwo		83.91 367	82.90 369	86.92 404	90.99 384	70.67 449	93.48 271	91.99 385	85.54 222	77.62 436	92.11 313	60.59 415	96.87 323	76.05 377	77.75 431	93.20 372

ADS-MVSNet		81.56 397	79.78 400	86.90 405	91.35 369	71.82 434	83.33 480	89.16 457	72.90 456	82.24 371	85.77 453	64.98 371	93.76 440	64.57 459	83.74 363	95.12 271

PatchT		82.68 379	81.27 379	86.89 406	90.09 417	70.94 448	84.06 477	90.15 434	74.91 435	85.63 283	83.57 465	69.37 323	94.87 421	65.19 454	88.50 310	94.84 286

tpm		84.73 352	84.02 350	86.87 407	90.33 412	68.90 459	89.06 422	89.94 441	80.85 353	85.75 279	89.86 393	68.54 339	95.97 383	77.76 357	84.05 360	95.75 250

Patchmatch-RL test		81.67 394	79.96 398	86.81 408	85.42 469	71.23 442	82.17 485	87.50 466	78.47 387	77.19 438	82.50 477	70.81 298	93.48 444	82.66 269	72.89 448	95.71 254

test_vis1_n		86.56 311	86.49 276	86.78 409	88.51 435	72.69 423	94.68 180	93.78 334	79.55 368	90.70 166	95.31 176	48.75 472	93.28 447	93.15 69	93.99 195	94.38 310

testing3-2		86.72 305	86.71 262	86.74 410	96.11 114	65.92 472	93.39 276	89.65 449	89.46 76	87.84 231	92.79 290	59.17 427	97.60 245	81.31 297	90.72 271	96.70 206

test_fmvs1		87.34 275	87.56 238	86.68 411	90.59 403	71.80 435	94.01 238	94.04 320	78.30 391	91.97 125	95.22 180	56.28 441	93.71 441	92.89 74	94.71 171	94.52 300

MDA-MVSNet-bldmvs		78.85 431	76.31 436	86.46 412	89.76 423	73.88 407	88.79 426	90.42 428	79.16 373	59.18 491	88.33 420	60.20 417	94.04 433	62.00 467	68.96 467	91.48 431

mvs5depth		80.98 407	79.15 413	86.45 413	84.57 477	73.29 416	87.79 443	91.67 394	80.52 356	82.20 373	89.72 396	55.14 449	95.93 385	73.93 399	66.83 477	90.12 454

tpmrst		85.35 338	84.99 327	86.43 414	90.88 393	67.88 465	88.71 427	91.43 404	80.13 360	86.08 272	88.80 413	73.05 270	96.02 380	82.48 270	83.40 371	95.40 262

TESTMET0.1,1		83.74 370	82.85 370	86.42 415	89.96 420	71.21 443	89.55 411	87.88 462	77.41 401	83.37 356	87.31 433	56.71 439	93.65 443	80.62 311	92.85 238	94.40 309

our_test_3		81.93 389	80.46 387	86.33 416	88.46 438	73.48 413	88.46 433	91.11 409	76.46 415	76.69 442	88.25 421	66.89 351	94.36 427	68.75 433	79.08 427	91.14 439

lessismore_v0				86.04 417	88.46 438	68.78 460		80.59 489		73.01 467	90.11 385	55.39 445	96.43 362	75.06 386	65.06 481	92.90 385

TinyColmap		79.76 422	77.69 425	85.97 418	91.71 356	73.12 417	89.55 411	90.36 430	75.03 433	72.03 470	90.19 381	46.22 481	96.19 375	63.11 463	81.03 402	88.59 473

KD-MVS_2432*1600		78.50 432	76.02 440	85.93 419	86.22 458	74.47 401	84.80 472	92.33 372	79.29 370	76.98 439	85.92 450	53.81 459	93.97 436	67.39 442	57.42 491	89.36 459

miper_refine_blended		78.50 432	76.02 440	85.93 419	86.22 458	74.47 401	84.80 472	92.33 372	79.29 370	76.98 439	85.92 450	53.81 459	93.97 436	67.39 442	57.42 491	89.36 459

K. test v3		81.59 396	80.15 393	85.91 421	89.89 422	69.42 458	92.57 317	87.71 464	85.56 221	73.44 464	89.71 397	55.58 442	95.52 404	77.17 364	69.76 462	92.78 391

SSC-MVS3.2		84.60 356	84.19 344	85.85 422	92.74 323	68.07 462	88.15 438	93.81 332	87.42 166	83.76 343	91.07 353	62.91 393	95.73 398	74.56 394	83.24 372	93.75 348

mvsany_test1		85.42 336	85.30 321	85.77 423	87.95 448	75.41 392	87.61 450	80.97 488	76.82 414	88.68 214	95.83 145	77.44 199	90.82 474	85.90 215	86.51 337	91.08 443

MIMVSNet1		79.38 427	77.28 429	85.69 424	86.35 457	73.67 410	91.61 355	92.75 363	78.11 396	72.64 468	88.12 423	48.16 473	91.97 464	60.32 472	77.49 433	91.43 433

UWE-MVS		83.69 371	83.09 364	85.48 425	93.06 305	65.27 477	90.92 377	86.14 470	79.90 363	86.26 268	90.72 367	57.17 438	95.81 393	71.03 419	92.62 246	95.35 265

UnsupCasMVSNet_eth		80.07 418	78.27 423	85.46 426	85.24 470	72.63 427	88.45 434	94.87 278	82.99 300	71.64 473	88.07 424	56.34 440	91.75 466	73.48 402	63.36 484	92.01 418

CL-MVSNet_self_test		81.74 393	80.53 383	85.36 427	85.96 461	72.45 430	90.25 393	93.07 353	81.24 348	79.85 407	87.29 434	70.93 296	92.52 456	66.95 445	69.23 464	91.11 441

MDA-MVSNet_test_wron		79.21 429	77.19 431	85.29 428	88.22 443	72.77 422	85.87 463	90.06 437	74.34 440	62.62 488	87.56 431	66.14 364	91.99 463	66.90 449	73.01 446	91.10 442

YYNet1		79.22 428	77.20 430	85.28 429	88.20 444	72.66 425	85.87 463	90.05 439	74.33 441	62.70 486	87.61 430	66.09 365	92.03 460	66.94 446	72.97 447	91.15 438

WB-MVSnew		83.77 369	83.28 360	85.26 430	91.48 362	71.03 445	91.89 344	87.98 461	78.91 375	84.78 312	90.22 379	69.11 332	94.02 434	64.70 458	90.44 274	90.71 445

dp		81.47 401	80.23 390	85.17 431	89.92 421	65.49 475	86.74 458	90.10 436	76.30 420	81.10 385	87.12 438	62.81 394	95.92 386	68.13 439	79.88 419	94.09 322

UnsupCasMVSNet_bld		76.23 442	73.27 446	85.09 432	83.79 479	72.92 419	85.65 466	93.47 343	71.52 464	68.84 479	79.08 484	49.77 468	93.21 448	66.81 450	60.52 488	89.13 467

usedtu_dtu_shiyan2		74.72 444	71.30 449	84.98 433	77.78 495	70.58 451	91.85 347	90.76 422	67.24 479	68.06 481	82.17 478	37.13 490	92.78 454	60.69 471	66.03 478	91.59 427

SD_0403		84.71 354	84.65 336	84.92 434	92.95 312	65.95 471	92.07 343	93.23 348	83.82 275	79.03 418	93.73 259	73.90 255	92.91 453	63.02 465	90.05 281	95.89 243

Anonymous20231206		81.03 406	79.77 402	84.82 435	87.85 449	70.26 453	91.42 359	92.08 381	73.67 448	77.75 434	89.25 403	62.43 396	93.08 450	61.50 469	82.00 388	91.12 440

FE-MVSNET		78.19 434	76.03 439	84.69 436	83.70 480	73.31 415	90.58 385	90.00 440	77.11 409	71.91 471	85.47 455	55.53 444	91.94 465	59.69 476	70.24 459	88.83 469

test0.0.03 1		82.41 384	81.69 375	84.59 437	88.23 442	72.89 420	90.24 395	87.83 463	83.41 286	79.86 406	89.78 395	67.25 346	88.99 484	65.18 455	83.42 370	91.90 420

CMPMVS	59.16 21	80.52 412	79.20 411	84.48 438	83.98 478	67.63 468	89.95 406	93.84 328	64.79 485	66.81 482	91.14 350	57.93 433	95.17 414	76.25 374	88.10 316	90.65 446
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
CVMVSNet		84.69 355	84.79 334	84.37 439	91.84 350	64.92 478	93.70 263	91.47 403	66.19 482	86.16 271	95.28 177	67.18 348	93.33 446	80.89 306	90.42 276	94.88 285

PVSNet_0	73.20 20	77.22 438	74.83 444	84.37 439	90.70 401	71.10 444	83.09 482	89.67 447	72.81 458	73.93 461	83.13 467	60.79 414	93.70 442	68.54 434	50.84 497	88.30 475

LF4IMVS		80.37 415	79.07 415	84.27 441	86.64 454	69.87 457	89.39 416	91.05 412	76.38 418	74.97 455	90.00 389	47.85 474	94.25 431	74.55 395	80.82 408	88.69 471

Anonymous20240521		80.44 414	79.21 410	84.11 442	85.75 464	67.89 464	92.86 307	93.23 348	75.61 428	75.59 452	87.47 432	50.03 467	94.33 428	71.14 417	81.21 396	90.12 454

PM-MVS		78.11 435	76.12 438	84.09 443	83.54 481	70.08 454	88.97 424	85.27 477	79.93 362	74.73 457	86.43 445	34.70 493	93.48 444	79.43 339	72.06 452	88.72 470

dtuonly		84.33 360	84.48 342	83.87 444	86.63 455	63.54 483	86.79 456	91.48 402	78.02 397	83.20 360	93.56 262	69.53 321	94.11 432	79.08 343	92.02 255	93.97 329

test_fmvs2		83.98 364	84.03 349	83.83 445	87.16 452	67.53 469	93.93 245	92.89 357	77.62 398	86.89 252	93.53 263	47.18 476	92.02 462	90.54 136	86.51 337	91.93 419

testgi		80.94 409	80.20 391	83.18 446	87.96 447	66.29 470	91.28 366	90.70 425	83.70 277	78.12 429	92.84 285	51.37 465	90.82 474	63.34 462	82.46 381	92.43 406

KD-MVS_self_test		80.20 416	79.24 409	83.07 447	85.64 465	65.29 476	91.01 374	93.93 322	78.71 384	76.32 444	86.40 447	59.20 426	92.93 452	72.59 406	69.35 463	91.00 444

testing3		80.46 413	79.59 405	83.06 448	93.44 291	64.64 479	93.33 278	85.47 475	84.34 264	79.93 405	90.84 360	44.35 484	92.39 457	57.06 483	87.56 326	92.16 416

ambc				83.06 448	79.99 491	63.51 484	77.47 495	92.86 358		74.34 460	84.45 460	28.74 494	95.06 418	73.06 404	68.89 468	90.61 447

test20.03		79.95 420	79.08 414	82.55 450	85.79 463	67.74 467	91.09 372	91.08 410	81.23 349	74.48 459	89.96 391	61.63 401	90.15 476	60.08 473	76.38 439	89.76 456

MVStest1		72.91 447	69.70 452	82.54 451	78.14 494	73.05 418	88.21 437	86.21 469	60.69 489	64.70 484	90.53 370	46.44 479	85.70 492	58.78 479	53.62 494	88.87 468

test_vis1_rt		77.96 436	76.46 435	82.48 452	85.89 462	71.74 437	90.25 393	78.89 492	71.03 468	71.30 474	81.35 480	42.49 486	91.05 472	84.55 239	82.37 382	84.65 481

EU-MVSNet		81.32 403	80.95 381	82.42 453	88.50 437	63.67 482	93.32 279	91.33 405	64.02 486	80.57 394	92.83 286	61.21 410	92.27 459	76.34 373	80.38 415	91.32 434

myMVS_eth3d		79.67 423	78.79 418	82.32 454	91.92 346	64.08 480	89.75 409	87.40 467	81.72 334	78.82 423	87.20 435	45.33 482	91.29 469	59.09 478	87.84 323	91.60 425

ttmdpeth		76.55 440	74.64 445	82.29 455	82.25 486	67.81 466	89.76 408	85.69 473	70.35 470	75.76 450	91.69 329	46.88 477	89.77 478	66.16 451	63.23 485	89.30 461

dtuonlycased		79.67 423	79.05 416	81.54 456	88.34 441	68.44 461	88.96 425	90.65 426	78.48 386	73.21 466	85.88 452	63.18 392	91.00 473	70.40 421	72.32 449	85.19 480

pmmvs3		71.81 450	68.71 453	81.11 457	75.86 497	70.42 452	86.74 458	83.66 481	58.95 492	68.64 480	80.89 482	36.93 491	89.52 480	63.10 464	63.59 483	83.39 482

Syy-MVS		80.07 418	79.78 400	80.94 458	91.92 346	59.93 491	89.75 409	87.40 467	81.72 334	78.82 423	87.20 435	66.29 362	91.29 469	47.06 496	87.84 323	91.60 425

UWE-MVS-28		78.98 430	78.38 422	80.80 459	88.18 445	60.66 490	90.65 382	78.51 493	78.84 379	77.93 432	90.93 357	59.08 428	89.02 483	50.96 489	90.33 278	92.72 392

new-patchmatchnet		76.41 441	75.17 443	80.13 460	82.65 485	59.61 492	87.66 448	91.08 410	78.23 394	69.85 477	83.22 466	54.76 453	91.63 468	64.14 461	64.89 482	89.16 465

mvsany_test3		74.95 443	73.26 447	80.02 461	74.61 498	63.16 485	85.53 467	78.42 494	74.16 443	74.89 456	86.46 443	36.02 492	89.09 482	82.39 273	66.91 476	87.82 478

test_fmvs3		77.67 437	77.16 432	79.22 462	79.52 492	61.14 487	92.34 328	91.64 396	73.98 445	78.86 422	86.59 442	27.38 497	87.03 486	88.12 180	75.97 441	89.50 458

DSMNet-mixed		76.94 439	76.29 437	78.89 463	83.10 483	56.11 500	87.78 444	79.77 490	60.65 490	75.64 451	88.71 414	61.56 404	88.34 485	60.07 474	89.29 299	92.21 415

EGC-MVSNET		61.97 459	56.37 464	78.77 464	89.63 426	73.50 412	89.12 421	82.79 483	0.21 545	1.24 546	84.80 458	39.48 487	90.04 477	44.13 498	75.94 442	72.79 497

ArgMatch-SfM		70.39 451	67.69 455	78.49 465	81.44 487	60.73 488	84.71 475	75.65 503	68.09 476	66.71 483	86.79 441	20.42 503	86.05 491	71.50 412	53.87 493	88.67 472

new_pmnet		72.15 448	70.13 451	78.20 466	82.95 484	65.68 473	83.91 478	82.40 485	62.94 488	64.47 485	79.82 483	42.85 485	86.26 490	57.41 482	74.44 444	82.65 486

MVS-HIRNet		73.70 446	72.20 448	78.18 467	91.81 353	56.42 499	82.94 483	82.58 484	55.24 493	68.88 478	66.48 504	55.32 447	95.13 415	58.12 480	88.42 312	83.01 484

LCM-MVSNet		66.00 456	62.16 461	77.51 468	64.51 513	58.29 494	83.87 479	90.90 418	48.17 498	54.69 494	73.31 497	16.83 507	86.75 487	65.47 453	61.67 487	87.48 479

APD_test1		69.04 452	66.26 458	77.36 469	80.51 490	62.79 486	85.46 468	83.51 482	54.11 495	59.14 492	84.79 459	23.40 500	89.61 479	55.22 484	70.24 459	79.68 491

test_f		71.95 449	70.87 450	75.21 470	74.21 501	59.37 493	85.07 471	85.82 472	65.25 484	70.42 476	83.13 467	23.62 498	82.93 498	78.32 351	71.94 454	83.33 483

ANet_high		58.88 463	54.22 468	72.86 471	56.50 518	56.67 496	80.75 488	86.00 471	73.09 455	37.39 509	64.63 508	22.17 501	79.49 502	43.51 499	23.96 512	82.43 487

test_vis3_rt		65.12 457	62.60 459	72.69 472	71.44 503	60.71 489	87.17 453	65.55 506	63.80 487	53.22 495	65.65 507	14.54 508	89.44 481	76.65 368	65.38 480	67.91 504

LoFTR		57.22 466	52.62 470	71.00 473	72.03 502	48.57 506	72.00 503	70.08 505	44.40 503	40.92 507	76.42 488	8.12 512	82.76 499	42.28 502	47.33 500	81.66 488

FPMVS		64.63 458	62.55 460	70.88 474	70.80 504	56.71 495	84.42 476	84.42 479	51.78 496	49.57 496	81.61 479	23.49 499	81.48 500	40.61 504	76.25 440	74.46 496

dmvs_testset		74.57 445	75.81 442	70.86 475	87.72 450	40.47 515	87.05 455	77.90 498	82.75 305	71.15 475	85.47 455	67.98 343	84.12 496	45.26 497	76.98 438	88.00 476

DenseAffine		56.77 467	52.17 471	70.54 476	74.27 499	53.25 502	77.23 496	50.43 514	49.87 497	47.26 501	77.37 487	7.99 513	79.10 503	50.35 490	34.79 506	79.28 492

N_pmnet		68.89 453	68.44 454	70.23 477	89.07 431	28.79 524	88.06 439	19.50 524	69.47 472	71.86 472	84.93 457	61.24 409	91.75 466	54.70 485	77.15 435	90.15 453

testf1		59.54 461	56.11 465	69.85 478	69.28 505	56.61 497	80.37 489	76.55 501	42.58 505	45.68 502	75.61 490	11.26 509	84.18 494	43.20 500	60.44 489	68.75 502

APD_test2		59.54 461	56.11 465	69.85 478	69.28 505	56.61 497	80.37 489	76.55 501	42.58 505	45.68 502	75.61 490	11.26 509	84.18 494	43.20 500	60.44 489	68.75 502

WB-MVS		67.92 454	67.49 456	69.21 480	81.09 488	41.17 514	88.03 440	78.00 497	73.50 450	62.63 487	83.11 469	63.94 384	86.52 488	25.66 512	51.45 496	79.94 490

PMMVS2		59.60 460	56.40 463	69.21 480	68.83 507	46.58 507	73.02 502	77.48 499	55.07 494	49.21 497	72.95 498	17.43 506	80.04 501	49.32 493	44.33 501	80.99 489

SSC-MVS		67.06 455	66.56 457	68.56 482	80.54 489	40.06 516	87.77 445	77.37 500	72.38 460	61.75 489	82.66 476	63.37 387	86.45 489	24.48 513	48.69 499	79.16 493

Gipuma		57.99 465	54.91 467	67.24 483	88.51 435	65.59 474	52.21 510	90.33 431	43.58 504	42.84 505	51.18 513	20.29 504	85.07 493	34.77 506	70.45 458	51.05 512
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
RoMa-SfM		53.80 468	49.39 472	67.06 484	67.87 509	48.86 504	75.04 497	38.06 520	47.23 500	47.40 500	78.96 485	7.40 514	76.66 505	48.89 494	33.62 507	75.64 495

DKM		50.92 472	46.13 476	65.30 485	66.27 511	45.98 509	73.05 501	31.91 522	45.08 501	42.04 506	75.01 494	4.95 522	73.81 507	47.90 495	28.96 509	76.09 494

MatchFormer		51.11 471	46.66 475	64.46 486	67.11 510	43.39 512	70.54 504	63.67 508	33.19 509	37.22 510	70.30 500	6.67 517	78.17 504	30.29 509	40.94 503	71.81 500

PMVS	47.18 22	52.22 470	48.46 474	63.48 487	45.72 522	46.20 508	73.41 500	78.31 495	41.03 507	30.06 514	65.68 506	6.05 518	83.43 497	30.04 510	65.86 479	60.80 506
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
dongtai		58.82 464	58.24 462	60.56 488	83.13 482	45.09 511	82.32 484	48.22 516	67.61 477	61.70 490	69.15 501	38.75 488	76.05 506	32.01 508	41.31 502	60.55 507

PDCNetPlus		48.34 474	45.15 477	57.91 489	61.43 515	41.85 513	65.98 505	38.30 519	47.59 499	37.96 508	71.85 499	10.18 511	66.85 512	52.94 487	20.14 523	65.03 505

MVE	39.65 23	43.39 476	38.59 482	57.77 490	56.52 517	48.77 505	55.38 508	58.64 511	29.33 513	28.96 515	52.65 512	4.68 525	64.62 513	28.11 511	33.07 508	59.93 508
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		50.52 473	48.47 473	56.66 491	52.26 521	18.98 529	41.51 516	81.40 487	10.10 519	44.59 504	75.01 494	28.51 495	68.16 508	53.54 486	49.31 498	82.83 485

DeepMVS_CX				56.31 492	74.23 500	51.81 503		56.67 512	44.85 502	48.54 498	75.16 493	27.87 496	58.74 515	40.92 503	52.22 495	58.39 510

ELoFTR		40.15 479	35.08 483	55.36 493	41.27 529	28.17 526	47.70 512	43.76 517	29.15 514	30.35 513	65.97 505	2.17 532	66.90 511	34.51 507	20.83 522	71.00 501

kuosan		53.51 469	53.30 469	54.13 494	76.06 496	45.36 510	80.11 491	48.36 515	59.63 491	54.84 493	63.43 509	37.41 489	62.07 514	20.73 515	39.10 504	54.96 511

PMatch-SfM		38.18 480	33.34 484	52.72 495	43.67 524	28.18 525	52.96 509	16.29 528	29.70 512	31.24 512	68.56 503	1.08 544	57.70 516	38.73 505	17.80 525	72.30 499

MASt3R-SfM		45.78 475	43.96 478	51.24 496	45.04 523	29.83 523	57.88 507	38.83 518	31.88 511	47.48 499	81.30 481	7.16 515	51.15 518	49.56 492	36.51 505	72.74 498

GLUNet-SfM		31.36 482	26.25 487	46.70 497	35.51 531	24.89 527	33.71 521	36.36 521	19.08 515	23.78 518	52.69 511	3.82 530	56.26 517	19.75 517	11.56 534	58.95 509

E-PMN		43.23 477	42.29 479	46.03 498	65.58 512	37.41 517	73.51 499	64.62 507	33.99 508	28.47 516	47.87 514	19.90 505	67.91 509	22.23 514	24.45 510	32.77 518

EMVS		42.07 478	41.12 480	44.92 499	63.45 514	35.56 519	73.65 498	63.48 509	33.05 510	26.88 517	45.45 515	21.27 502	67.14 510	19.80 516	23.02 514	32.06 519

ALIKED-LG		28.00 483	26.54 486	32.41 500	58.12 516	31.80 520	47.26 513	21.21 523	14.15 516	19.16 520	41.93 517	6.72 516	35.73 519	5.96 526	24.32 511	29.69 520

ALIKED-MNN		26.28 484	24.57 489	31.39 501	56.22 519	31.73 521	45.54 514	19.13 526	11.12 517	17.11 522	39.35 519	5.01 521	34.53 520	5.54 528	22.12 516	27.92 521

ALIKED-NN		26.07 485	24.75 488	30.02 502	55.08 520	30.61 522	44.20 515	19.22 525	10.98 518	17.98 521	40.71 518	5.39 520	32.83 521	5.59 527	23.63 513	26.63 522

tmp_tt		35.64 481	39.24 481	24.84 503	14.87 548	23.90 528	62.71 506	51.51 513	6.58 527	36.66 511	62.08 510	44.37 483	30.34 523	52.40 488	22.00 517	20.27 524

wuyk23d		21.27 487	20.48 490	23.63 504	68.59 508	36.41 518	49.57 511	6.85 540	9.37 520	7.89 529	4.46 545	4.03 529	31.37 522	17.47 518	16.07 527	3.12 540

SP-LightGlue		20.24 488	20.15 492	20.49 505	43.51 525	12.27 537	38.68 518	14.56 531	7.54 524	12.90 526	30.07 523	4.75 523	14.38 527	7.60 522	21.75 518	34.82 513

SP-SuperGlue		20.22 489	20.18 491	20.36 506	43.26 526	12.27 537	38.71 517	14.77 530	7.64 523	13.04 525	30.21 522	4.73 524	14.21 529	7.59 523	21.65 519	34.59 514

SP-DiffGlue		20.02 490	19.96 493	20.21 507	19.64 545	13.14 536	30.51 522	15.49 529	8.39 521	19.98 519	43.75 516	5.48 519	13.72 530	13.75 519	22.65 515	33.78 516

SP-MNN		19.61 491	19.42 494	20.19 508	42.15 527	11.42 543	38.15 519	14.24 532	6.55 528	11.64 528	29.88 525	4.16 527	14.56 526	7.09 525	20.92 521	34.58 515

SP-NN		19.44 492	19.37 495	19.67 509	41.70 528	11.48 542	37.75 520	13.72 534	6.86 525	11.86 527	29.97 524	4.23 526	14.25 528	7.13 524	21.07 520	33.30 517

XFeat-MNN		17.43 493	16.95 496	18.86 510	16.90 546	11.28 544	27.31 523	17.08 527	8.08 522	15.61 524	35.73 520	4.06 528	22.95 524	10.20 520	17.59 526	22.35 523

XFeat-NN		15.96 494	15.86 497	16.25 511	15.78 547	9.87 547	25.17 524	13.83 533	6.76 526	15.68 523	34.83 521	3.61 531	19.28 525	9.22 521	17.90 524	19.58 525

SIFT-NN		12.98 495	13.18 498	12.37 512	36.49 530	16.03 530	22.41 525	7.69 536	4.89 529	7.41 530	20.48 527	1.69 533	11.46 532	1.88 531	15.70 528	9.61 527

SIFT-MNN		12.44 496	12.55 499	12.11 513	34.55 532	15.21 531	20.91 526	7.74 535	4.86 530	6.54 532	20.09 528	1.51 534	11.47 531	1.88 531	14.87 530	9.64 526

SIFT-NN-NCMNet		12.12 497	12.25 500	11.75 514	32.82 534	14.83 532	20.73 527	7.58 537	4.72 532	6.60 531	19.53 529	1.49 535	11.15 534	1.74 533	15.02 529	9.28 528

SIFT-NCM-Cal		11.58 498	11.64 501	11.40 515	33.45 533	14.10 533	19.75 529	6.89 538	4.68 535	4.55 539	18.60 534	1.34 539	11.28 533	1.53 539	13.95 531	8.82 532

SIFT-NN-CMatch		11.26 499	11.31 503	11.13 516	30.21 538	13.40 535	18.43 530	6.79 541	4.71 533	6.47 533	19.53 529	1.43 537	10.72 536	1.71 534	12.49 533	9.26 529

SIFT-ConvMatch		10.91 501	10.94 506	10.84 517	32.07 535	13.57 534	17.23 533	6.35 542	4.71 533	5.18 536	18.94 532	1.30 540	10.76 535	1.65 537	11.02 536	8.19 533

SIFT-NN-UMatch		11.06 500	11.19 505	10.66 518	28.66 540	12.16 539	19.79 528	6.86 539	4.73 531	5.21 535	19.47 531	1.46 536	10.70 537	1.71 534	12.79 532	9.13 530

SIFT-UMatch		10.58 502	10.73 507	10.15 519	31.05 536	11.65 541	18.01 531	5.92 544	4.65 536	4.72 537	18.93 533	1.25 542	10.62 538	1.66 536	10.39 537	8.16 534

SIFT-CM-Cal		10.08 504	10.13 510	9.92 520	30.71 537	11.88 540	15.35 535	5.44 545	4.59 537	4.72 537	18.04 537	1.26 541	10.19 539	1.46 541	9.60 538	7.69 535

SIFT-NN-PointCN		10.26 503	10.46 508	9.65 521	27.18 541	9.89 546	17.89 532	6.17 543	4.40 539	5.65 534	18.29 535	1.43 537	10.09 540	1.61 538	11.55 535	8.99 531

SIFT-UM-Cal		9.80 505	10.00 511	9.22 522	30.05 539	10.15 545	16.31 534	4.85 547	4.54 538	4.19 540	18.23 536	1.19 543	9.95 541	1.52 540	9.11 540	7.57 536

SIFT-PCN-Cal		8.65 509	8.88 513	7.98 523	26.74 542	7.47 549	13.90 537	4.61 548	4.09 541	3.82 541	15.86 538	1.01 545	8.94 542	1.34 542	8.52 541	7.53 537

SIFT-PointCN		8.76 507	9.03 512	7.96 524	26.50 543	7.60 548	14.94 536	5.08 546	4.10 540	3.74 542	15.46 539	0.94 546	8.92 543	1.33 543	9.14 539	7.37 538

SIFT-NCMNet		7.46 511	7.71 515	6.72 525	25.03 544	6.86 550	11.42 538	2.98 549	4.05 542	3.38 543	13.68 540	0.84 547	7.65 544	1.13 544	6.87 542	5.66 539

test123		8.76 507	11.22 504	1.39 526	0.85 550	0.97 551	85.76 465	0.35 551	0.54 544	2.45 545	8.14 544	0.60 548	0.48 545	2.16 530	0.17 544	2.71 541

testmvs		8.92 506	11.52 502	1.12 527	1.06 549	0.46 552	86.02 462	0.65 550	0.62 543	2.74 544	9.52 543	0.31 549	0.45 546	2.38 529	0.39 543	2.46 542

mmdepth		0.00 513	0.00 516	0.00 528	0.00 551	0.00 553	0.00 539	0.00 552	0.00 546	0.00 547	0.00 546	0.00 550	0.00 547	0.00 545	0.00 545	0.00 543

monomultidepth		0.00 513	0.00 516	0.00 528	0.00 551	0.00 553	0.00 539	0.00 552	0.00 546	0.00 547	0.00 546	0.00 550	0.00 547	0.00 545	0.00 545	0.00 543

test_blank		0.00 513	0.00 516	0.00 528	0.00 551	0.00 553	0.00 539	0.00 552	0.00 546	0.00 547	0.00 546	0.00 550	0.00 547	0.00 545	0.00 545	0.00 543

uanet_test		0.00 513	0.00 516	0.00 528	0.00 551	0.00 553	0.00 539	0.00 552	0.00 546	0.00 547	0.00 546	0.00 550	0.00 547	0.00 545	0.00 545	0.00 543

DCPMVS		0.00 513	0.00 516	0.00 528	0.00 551	0.00 553	0.00 539	0.00 552	0.00 546	0.00 547	0.00 546	0.00 550	0.00 547	0.00 545	0.00 545	0.00 543

cdsmvs_eth3d_5k		22.14 486	29.52 485	0.00 528	0.00 551	0.00 553	0.00 539	95.76 199	0.00 546	0.00 547	94.29 230	75.66 227	0.00 547	0.00 545	0.00 545	0.00 543

pcd_1.5k_mvsjas		6.64 512	8.86 514	0.00 528	0.00 551	0.00 553	0.00 539	0.00 552	0.00 546	0.00 547	0.00 546	79.70 159	0.00 547	0.00 545	0.00 545	0.00 543

sosnet-low-res		0.00 513	0.00 516	0.00 528	0.00 551	0.00 553	0.00 539	0.00 552	0.00 546	0.00 547	0.00 546	0.00 550	0.00 547	0.00 545	0.00 545	0.00 543

sosnet		0.00 513	0.00 516	0.00 528	0.00 551	0.00 553	0.00 539	0.00 552	0.00 546	0.00 547	0.00 546	0.00 550	0.00 547	0.00 545	0.00 545	0.00 543

uncertanet		0.00 513	0.00 516	0.00 528	0.00 551	0.00 553	0.00 539	0.00 552	0.00 546	0.00 547	0.00 546	0.00 550	0.00 547	0.00 545	0.00 545	0.00 543

Regformer		0.00 513	0.00 516	0.00 528	0.00 551	0.00 553	0.00 539	0.00 552	0.00 546	0.00 547	0.00 546	0.00 550	0.00 547	0.00 545	0.00 545	0.00 543

ab-mvs-re		7.82 510	10.43 509	0.00 528	0.00 551	0.00 553	0.00 539	0.00 552	0.00 546	0.00 547	93.88 251	0.00 550	0.00 547	0.00 545	0.00 545	0.00 543

uanet		0.00 513	0.00 516	0.00 528	0.00 551	0.00 553	0.00 539	0.00 552	0.00 546	0.00 547	0.00 546	0.00 550	0.00 547	0.00 545	0.00 545	0.00 543

WAC-MVS						64.08 480								59.14 477

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

PC_three_1452									82.47 309	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					0.00 551

eth-test					0.00 551

ZD-MVS					98.15 40	86.62 34		97.07 60	83.63 279	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	87.46 163	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	81.26 347	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	86.13 208	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																96.12 231

test_part2					98.55 15	87.22 20				96.40 31

sam_mvs1												71.70 287				96.12 231

sam_mvs												70.60 301

MTGPA								96.97 65

test_post1							88.00 441				9.81 542	69.31 326	95.53 403	76.65 368

test_post											10.29 541	70.57 305	95.91 388

patchmatchnet-post											83.76 462	71.53 288	96.48 356

MTMP							96.16 60	60.64 510

gm-plane-assit					89.60 427	68.00 463			77.28 404		88.99 408		97.57 248	79.44 338

test9_res														91.91 109	98.71 35	98.07 84

TEST9					97.53 67	86.49 38	94.07 231	96.78 90	81.61 339	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	81.86 330	92.70 105	96.20 110	87.63 33	99.02 73

agg_prior2														90.54 136	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		87.67 158	92.63 109	96.39 105	86.62 45		91.50 118	98.67 43

旧先验2							93.36 277		71.25 466	94.37 61			97.13 303	86.74 202

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	96.97 187

无先验							93.28 285	96.26 140	73.95 446				99.05 67	80.56 312		96.59 210

原ACMM2							92.94 303

test222					96.55 95	81.70 204	92.22 336	95.01 260	68.36 475	90.20 179	96.14 120	80.26 146			97.80 91	96.05 238

testdata2													98.75 116	78.30 352

segment_acmp												87.16 40

testdata1							92.15 338		87.94 142

plane_prior7					94.70 201	82.74 165

plane_prior6					94.52 217	82.75 163						74.23 247

plane_prior5								96.22 146					98.12 179	88.15 177	89.99 282	94.63 292

plane_prior4											94.86 201

plane_prior3						82.75 163			90.26 50	86.91 249

plane_prior2							95.85 93		90.81 27

plane_prior1					94.59 210

plane_prior						82.73 166	95.21 142		89.66 71						89.88 287

n2								0.00 552

nn								0.00 552

door-mid								85.49 474

test11								96.57 112

door								85.33 476

HQP5-MVS						81.56 206

HQP-NCC					94.17 250		94.39 205		88.81 104	85.43 295

ACMP_Plane					94.17 250		94.39 205		88.81 104	85.43 295

BP-MVS														87.11 199

HQP4-MVS										85.43 295			97.96 216			94.51 302

HQP3-MVS								96.04 173							89.77 291

HQP2-MVS												73.83 258

NP-MVS					94.37 231	82.42 180					93.98 244

MDTV_nov1_ep13_2view						55.91 501	87.62 449		73.32 452	84.59 317		70.33 308		74.65 391		95.50 259

MDTV_nov1_ep13			83.56 357		91.69 358	69.93 455	87.75 446	91.54 399	78.60 385	84.86 311	88.90 410	69.54 320	96.03 379	70.25 423	88.93 304

ACMMP++_ref															87.47 327

ACMMP++															88.01 319

Test By Simon												80.02 148

High-resolution multi-view benchmark