High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

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

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

OPU-MVS				96.21 3	98.00 48	90.85 3	97.13 19				97.08 70	92.59 2	98.94 91	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 71	96.83 82	88.12 27	99.55 20	93.41 66	98.94 16	98.28 61

MM		95.10 14	94.91 26	95.68 5	96.09 115	88.34 9	96.68 38	94.37 297	95.08 1	94.68 57	97.72 39	82.94 99	99.64 4	97.85 5	98.76 33	99.06 7

SMA-MVS		95.20 10	95.07 20	95.59 6	98.14 41	88.48 8	96.26 54	97.28 40	85.90 205	97.67 4	98.10 14	88.41 23	99.56 16	94.66 48	99.19 1	98.71 25
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
3Dnovator+	87.14 4	92.42 103	91.37 125	95.55 7	95.63 142	88.73 6	97.07 23	96.77 91	90.84 26	84.02 330	96.62 95	75.95 212	99.34 42	87.77 181	97.68 97	98.59 29

TestfortrainingZip a		95.70 4	95.76 5	95.51 8	98.88 1	87.98 10	97.32 10	97.86 1	88.11 129	97.21 14	97.54 44	92.42 4	99.67 1	93.66 60	98.85 20	98.89 15

CNVR-MVS		95.40 9	95.37 11	95.50 9	98.11 42	88.51 7	95.29 131	96.96 68	92.09 10	95.32 49	97.08 70	89.49 17	99.33 45	95.10 43	98.85 20	98.66 26

MGCNet		94.18 50	93.80 64	95.34 10	94.91 182	87.62 15	95.97 82	93.01 347	92.58 6	94.22 62	97.20 64	80.56 138	99.59 11	97.04 20	98.68 41	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	88.28 120	95.30 50	97.67 41	85.90 54	99.54 24	93.91 56	98.95 15	98.60 28

DPE-MVS		95.57 6	95.67 6	95.25 12	98.36 31	87.28 19	95.56 119	97.51 11	89.13 87	97.14 18	97.91 32	91.64 9	99.62 5	94.61 49	99.17 2	98.86 16
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
SF-MVS		94.97 17	94.90 28	95.20 13	97.84 56	87.76 11	96.65 39	97.48 16	87.76 150	95.71 44	97.70 40	88.28 26	99.35 41	93.89 57	98.78 30	98.48 35

MCST-MVS		94.45 34	94.20 51	95.19 14	98.46 22	87.50 17	95.00 154	97.12 55	87.13 171	92.51 112	96.30 104	89.24 19	99.34 42	93.46 63	98.62 50	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 72	96.58 97	87.74 30	99.44 33	92.83 75	98.40 58	98.62 27

DPM-MVS		92.58 99	91.74 109	95.08 16	96.19 106	89.31 5	92.66 311	96.56 112	83.44 279	91.68 138	95.04 184	86.60 46	98.99 81	85.60 215	97.92 85	96.93 184

ZNCC-MVS		94.47 33	94.28 45	95.03 17	98.52 18	86.96 21	96.85 33	97.32 34	88.24 121	93.15 87	97.04 73	86.17 51	99.62 5	92.40 86	98.81 27	98.52 31

test_0728_SECOND				95.01 18	98.79 5	86.43 40	97.09 21	97.49 12					99.61 7	95.62 34	99.08 7	98.99 9

MTAPA		94.42 39	94.22 48	95.00 19	98.42 24	86.95 22	94.36 208	96.97 65	91.07 22	93.14 88	97.56 43	84.30 80	99.56 16	93.43 64	98.75 34	98.47 38

MSP-MVS		95.42 8	95.56 8	94.98 20	98.49 20	86.52 37	96.91 30	97.47 17	91.73 14	96.10 36	96.69 87	89.90 14	99.30 48	94.70 47	98.04 80	99.13 2
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
region2R		94.43 36	94.27 47	94.92 21	98.65 11	86.67 31	96.92 29	97.23 43	88.60 111	93.58 79	97.27 58	85.22 63	99.54 24	92.21 94	98.74 35	98.56 30

APDe-MVS		95.46 7	95.64 7	94.91 22	98.26 34	86.29 47	97.46 7	97.40 26	89.03 92	96.20 35	98.10 14	89.39 18	99.34 42	95.88 29	99.03 11	99.10 4
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
ACMMPR		94.43 36	94.28 45	94.91 22	98.63 12	86.69 29	96.94 25	97.32 34	88.63 108	93.53 82	97.26 60	85.04 67	99.54 24	92.35 89	98.78 30	98.50 32

GST-MVS		94.21 45	93.97 60	94.90 24	98.41 25	86.82 25	96.54 41	97.19 44	88.24 121	93.26 84	96.83 82	85.48 59	99.59 11	91.43 120	98.40 58	98.30 55

HFP-MVS		94.52 31	94.40 38	94.86 25	98.61 13	86.81 26	96.94 25	97.34 30	88.63 108	93.65 77	97.21 62	86.10 52	99.49 30	92.35 89	98.77 32	98.30 55

sasdasda		93.27 82	92.75 92	94.85 26	95.70 138	87.66 13	96.33 44	96.41 122	90.00 51	94.09 67	94.60 209	82.33 108	98.62 132	92.40 86	92.86 229	98.27 63

MP-MVS-pluss		94.21 45	94.00 59	94.85 26	98.17 39	86.65 32	94.82 167	97.17 49	86.26 197	92.83 97	97.87 34	85.57 58	99.56 16	94.37 52	98.92 17	98.34 48
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
canonicalmvs		93.27 82	92.75 92	94.85 26	95.70 138	87.66 13	96.33 44	96.41 122	90.00 51	94.09 67	94.60 209	82.33 108	98.62 132	92.40 86	92.86 229	98.27 63

XVS		94.45 34	94.32 41	94.85 26	98.54 16	86.60 35	96.93 27	97.19 44	90.66 34	92.85 95	97.16 68	85.02 68	99.49 30	91.99 105	98.56 54	98.47 38

X-MVStestdata		88.31 233	86.13 282	94.85 26	98.54 16	86.60 35	96.93 27	97.19 44	90.66 34	92.85 95	23.41 495	85.02 68	99.49 30	91.99 105	98.56 54	98.47 38

SteuartSystems-ACMMP		95.20 10	95.32 13	94.85 26	96.99 81	86.33 43	97.33 8	97.30 37	91.38 19	95.39 48	97.46 50	88.98 22	99.40 34	94.12 53	98.89 18	98.82 21
Skip Steuart: Steuart Systems R&D Blog.
MED-MVS test				94.84 32	98.88 1	85.89 64	97.32 10	97.86 1	88.11 129	97.21 14	97.54 44		99.67 1	95.27 40	98.85 20	98.95 11

MED-MVS		95.74 3	96.04 3	94.84 32	98.88 1	85.89 64	97.32 10	97.86 1	89.01 94	97.21 14	97.54 44	92.42 4	99.67 1	95.27 40	98.85 20	98.95 11

DVP-MVS++		95.98 1	96.36 1	94.82 34	97.78 60	86.00 53	98.29 1	97.49 12	90.75 29	97.62 8	98.06 22	92.59 2	99.61 7	95.64 32	99.02 12	98.86 16

ME-MVS		95.17 12	95.29 14	94.81 35	98.39 28	85.89 64	95.91 88	97.55 9	89.01 94	95.86 42	97.54 44	89.24 19	99.59 11	95.27 40	98.85 20	98.95 11

alignmvs		93.08 90	92.50 98	94.81 35	95.62 143	87.61 16	95.99 79	96.07 167	89.77 64	94.12 66	94.87 193	80.56 138	98.66 124	92.42 85	93.10 225	98.15 75

SED-MVS		95.91 2	96.28 2	94.80 37	98.77 8	85.99 55	97.13 19	97.44 21	90.31 41	97.71 2	98.07 20	92.31 6	99.58 14	95.66 30	99.13 3	98.84 19

DeepC-MVS_fast	89.43 2	94.04 53	93.79 65	94.80 37	97.48 70	86.78 27	95.65 112	96.89 77	89.40 75	92.81 98	96.97 75	85.37 61	99.24 51	90.87 129	98.69 39	98.38 47
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MP-MVS		94.25 42	94.07 56	94.77 39	98.47 21	86.31 45	96.71 36	96.98 64	89.04 90	91.98 123	97.19 65	85.43 60	99.56 16	92.06 103	98.79 28	98.44 42
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
APD-MVS		94.24 43	94.07 56	94.75 40	98.06 45	86.90 24	95.88 90	96.94 71	85.68 212	95.05 55	97.18 66	87.31 38	99.07 64	91.90 111	98.61 52	98.28 61
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CP-MVS		94.34 40	94.21 50	94.74 41	98.39 28	86.64 33	97.60 5	97.24 41	88.53 113	92.73 103	97.23 61	85.20 64	99.32 46	92.15 97	98.83 26	98.25 68

PGM-MVS		93.96 58	93.72 70	94.68 42	98.43 23	86.22 48	95.30 129	97.78 4	87.45 161	93.26 84	97.33 56	84.62 77	99.51 28	90.75 131	98.57 53	98.32 54

DVP-MVS		95.67 5	96.02 4	94.64 43	98.78 6	85.93 58	97.09 21	96.73 97	90.27 45	97.04 22	98.05 25	91.47 10	99.55 20	95.62 34	99.08 7	98.45 41
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
mPP-MVS		93.99 56	93.78 66	94.63 44	98.50 19	85.90 63	96.87 31	96.91 75	88.70 106	91.83 133	97.17 67	83.96 84	99.55 20	91.44 119	98.64 49	98.43 43

PHI-MVS		93.89 60	93.65 74	94.62 45	96.84 84	86.43 40	96.69 37	97.49 12	85.15 236	93.56 81	96.28 105	85.60 57	99.31 47	92.45 83	98.79 28	98.12 80

TSAR-MVS + MP.		94.85 19	94.94 24	94.58 46	98.25 35	86.33 43	96.11 67	96.62 107	88.14 126	96.10 36	96.96 76	89.09 21	98.94 91	94.48 50	98.68 41	98.48 35
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
CANet		93.54 69	93.20 83	94.55 47	95.65 140	85.73 71	94.94 157	96.69 103	91.89 12	90.69 164	95.88 136	81.99 120	99.54 24	93.14 70	97.95 84	98.39 45

train_agg		93.44 75	93.08 85	94.52 48	97.53 67	86.49 38	94.07 228	96.78 89	81.86 323	92.77 100	96.20 108	87.63 32	99.12 62	92.14 98	98.69 39	97.94 96

CDPH-MVS		92.83 94	92.30 101	94.44 49	97.79 58	86.11 52	94.06 230	96.66 104	80.09 354	92.77 100	96.63 94	86.62 44	99.04 68	87.40 188	98.66 45	98.17 73

3Dnovator	86.66 5	91.73 121	90.82 140	94.44 49	94.59 208	86.37 42	97.18 17	97.02 62	89.20 84	84.31 325	96.66 90	73.74 253	99.17 56	86.74 198	97.96 83	97.79 119

SR-MVS		94.23 44	94.17 54	94.43 51	98.21 38	85.78 69	96.40 43	96.90 76	88.20 124	94.33 61	97.40 53	84.75 76	99.03 69	93.35 67	97.99 82	98.48 35

HPM-MVS		94.02 54	93.88 61	94.43 51	98.39 28	85.78 69	97.25 15	97.07 60	86.90 181	92.62 109	96.80 86	84.85 74	99.17 56	92.43 84	98.65 48	98.33 50
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
TSAR-MVS + GP.		93.66 67	93.41 78	94.41 53	96.59 91	86.78 27	94.40 200	93.93 315	89.77 64	94.21 63	95.59 156	87.35 37	98.61 134	92.72 78	96.15 136	97.83 115

reproduce-ours		94.82 20	94.97 22	94.38 54	97.91 53	85.46 74	95.86 91	97.15 51	89.82 57	95.23 52	98.10 14	87.09 40	99.37 37	95.30 38	98.25 67	98.30 55

our_new_method		94.82 20	94.97 22	94.38 54	97.91 53	85.46 74	95.86 91	97.15 51	89.82 57	95.23 52	98.10 14	87.09 40	99.37 37	95.30 38	98.25 67	98.30 55

NormalMVS		93.46 72	93.16 84	94.37 56	98.40 26	86.20 49	96.30 47	96.27 135	91.65 17	92.68 105	96.13 118	77.97 182	98.84 105	90.75 131	98.26 63	98.07 82

test12				94.34 57	97.13 79	86.15 51		96.29 131		91.04 160		85.08 66	99.01 74		98.13 75	97.86 110

SymmetryMVS		92.81 96	92.31 100	94.32 58	96.15 107	86.20 49	96.30 47	94.43 293	91.65 17	92.68 105	96.13 118	77.97 182	98.84 105	90.75 131	94.72 167	97.92 104

ACMMP		93.24 84	92.88 90	94.30 59	98.09 44	85.33 78	96.86 32	97.45 20	88.33 117	90.15 179	97.03 74	81.44 128	99.51 28	90.85 130	95.74 143	98.04 89
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
reproduce_model		94.76 24	94.92 25	94.29 60	97.92 49	85.18 80	95.95 85	97.19 44	89.67 67	95.27 51	98.16 6	86.53 47	99.36 40	95.42 37	98.15 73	98.33 50

DeepC-MVS	88.79 3	93.31 81	92.99 88	94.26 61	96.07 117	85.83 67	94.89 160	96.99 63	89.02 93	89.56 188	97.37 55	82.51 105	99.38 35	92.20 95	98.30 61	97.57 134
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MGCFI-Net		93.03 91	92.63 95	94.23 62	95.62 143	85.92 60	96.08 69	96.33 129	89.86 55	93.89 74	94.66 206	82.11 115	98.50 140	92.33 91	92.82 232	98.27 63

fmvsm_l_conf0.5_n_3		94.80 23	95.01 21	94.15 63	95.64 141	85.08 81	96.09 68	97.36 28	90.98 24	97.09 20	98.12 10	84.98 72	98.94 91	97.07 17	97.80 92	98.43 43

EPNet		91.79 112	91.02 134	94.10 64	90.10 409	85.25 79	96.03 76	92.05 374	92.83 5	87.39 236	95.78 147	79.39 164	99.01 74	88.13 175	97.48 100	98.05 88
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
lecture		95.10 14	95.46 10	94.01 65	98.40 26	84.36 106	97.70 3	97.78 4	91.19 20	96.22 34	98.08 19	86.64 43	99.37 37	94.91 45	98.26 63	98.29 60

test_fmvsmconf_n		94.60 28	94.81 30	93.98 66	94.62 204	84.96 84	96.15 62	97.35 29	89.37 76	96.03 39	98.11 11	86.36 48	99.01 74	97.45 10	97.83 90	97.96 95

DELS-MVS		93.43 79	93.25 81	93.97 67	95.42 151	85.04 82	93.06 294	97.13 54	90.74 31	91.84 131	95.09 183	86.32 49	99.21 54	91.22 121	98.45 56	97.65 128
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
DP-MVS Recon		91.95 109	91.28 128	93.96 68	98.33 33	85.92 60	94.66 179	96.66 104	82.69 301	90.03 181	95.82 143	82.30 110	99.03 69	84.57 233	96.48 129	96.91 186

HPM-MVS_fast		93.40 80	93.22 82	93.94 69	98.36 31	84.83 86	97.15 18	96.80 88	85.77 209	92.47 113	97.13 69	82.38 106	99.07 64	90.51 136	98.40 58	97.92 104

test_fmvsmconf0.1_n		94.20 47	94.31 43	93.88 70	92.46 323	84.80 87	96.18 59	96.82 85	89.29 81	95.68 45	98.11 11	85.10 65	98.99 81	97.38 11	97.75 96	97.86 110

SD-MVS		94.96 18	95.33 12	93.88 70	97.25 78	86.69 29	96.19 57	97.11 58	90.42 37	96.95 24	97.27 58	89.53 16	96.91 314	94.38 51	98.85 20	98.03 90
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
MVS_111021_HR		93.45 74	93.31 79	93.84 72	96.99 81	84.84 85	93.24 285	97.24 41	88.76 103	91.60 139	95.85 140	86.07 53	98.66 124	91.91 109	98.16 71	98.03 90

SR-MVS-dyc-post		93.82 62	93.82 63	93.82 73	97.92 49	84.57 93	96.28 51	96.76 92	87.46 159	93.75 75	97.43 51	84.24 81	99.01 74	92.73 76	97.80 92	97.88 108

test_prior				93.82 73	97.29 76	84.49 97		96.88 78					98.87 99			98.11 81

APD-MVS_3200maxsize		93.78 63	93.77 67	93.80 75	97.92 49	84.19 110	96.30 47	96.87 79	86.96 177	93.92 73	97.47 49	83.88 85	98.96 88	92.71 79	97.87 88	98.26 67

fmvsm_l_conf0.5_n		94.29 41	94.46 36	93.79 76	95.28 157	85.43 76	95.68 107	96.43 120	86.56 189	96.84 26	97.81 37	87.56 35	98.77 114	97.14 15	96.82 119	97.16 165

CSCG		93.23 85	93.05 86	93.76 77	98.04 46	84.07 112	96.22 56	97.37 27	84.15 260	90.05 180	95.66 153	87.77 29	99.15 60	89.91 145	98.27 62	98.07 82

GDP-MVS		92.04 107	91.46 122	93.75 78	94.55 214	84.69 90	95.60 118	96.56 112	87.83 147	93.07 91	95.89 135	73.44 257	98.65 126	90.22 139	96.03 138	97.91 106

BP-MVS1		92.48 101	92.07 104	93.72 79	94.50 217	84.39 105	95.90 89	94.30 300	90.39 38	92.67 107	95.94 131	74.46 236	98.65 126	93.14 70	97.35 104	98.13 77

test_fmvsmconf0.01_n		93.19 86	93.02 87	93.71 80	89.25 422	84.42 104	96.06 73	96.29 131	89.06 88	94.68 57	98.13 7	79.22 166	98.98 85	97.22 13	97.24 106	97.74 122

UA-Net		92.83 94	92.54 97	93.68 81	96.10 114	84.71 89	95.66 110	96.39 124	91.92 11	93.22 86	96.49 100	83.16 94	98.87 99	84.47 235	95.47 150	97.45 140

fmvsm_l_conf0.5_n_a		94.20 47	94.40 38	93.60 82	95.29 156	84.98 83	95.61 115	96.28 134	86.31 195	96.75 28	97.86 35	87.40 36	98.74 118	97.07 17	97.02 111	97.07 170

QAPM		89.51 190	88.15 217	93.59 83	94.92 180	84.58 92	96.82 34	96.70 102	78.43 381	83.41 348	96.19 111	73.18 262	99.30 48	77.11 357	96.54 126	96.89 187

test_fmvsm_n_1920		94.71 26	95.11 19	93.50 84	95.79 132	84.62 91	96.15 62	97.64 6	89.85 56	97.19 17	97.89 33	86.28 50	98.71 121	97.11 16	98.08 79	97.17 158

fmvsm_s_conf0.5_n_9		94.99 16	95.50 9	93.44 85	96.51 99	82.25 183	95.76 102	96.92 73	93.37 3	97.63 7	98.43 1	84.82 75	99.16 59	98.15 1	97.92 85	98.90 14

KinetiMVS		91.82 111	91.30 126	93.39 86	94.72 196	83.36 137	95.45 122	96.37 126	90.33 40	92.17 118	96.03 125	72.32 274	98.75 115	87.94 178	96.34 131	98.07 82

casdiffmvs_mvg		92.96 93	92.83 91	93.35 87	94.59 208	83.40 135	95.00 154	96.34 128	90.30 43	92.05 121	96.05 122	83.43 88	98.15 176	92.07 100	95.67 144	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 88	94.79 189	83.81 121	95.77 100	96.74 96	88.02 135	96.23 33	97.84 36	83.36 92	98.83 108	97.49 8	97.34 105	97.25 151

EI-MVSNet-Vis-set		93.01 92	92.92 89	93.29 89	95.01 171	83.51 132	94.48 188	95.77 194	90.87 25	92.52 111	96.67 89	84.50 78	99.00 79	91.99 105	94.44 180	97.36 143

Vis-MVSNet		91.75 119	91.23 129	93.29 89	95.32 155	83.78 122	96.14 64	95.98 173	89.89 53	90.45 168	96.58 97	75.09 225	98.31 167	84.75 227	96.90 115	97.78 120
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
balanced_conf03		93.98 57	94.22 48	93.26 91	96.13 109	83.29 139	96.27 53	96.52 115	89.82 57	95.56 47	95.51 159	84.50 78	98.79 112	94.83 46	98.86 19	97.72 124

SPE-MVS-test		94.02 54	94.29 44	93.24 92	96.69 87	83.24 140	97.49 6	96.92 73	92.14 9	92.90 93	95.77 148	85.02 68	98.33 164	93.03 72	98.62 50	98.13 77

VNet		92.24 105	91.91 107	93.24 92	96.59 91	83.43 133	94.84 166	96.44 119	89.19 85	94.08 70	95.90 134	77.85 188	98.17 174	88.90 165	93.38 214	98.13 77

fmvsm_s_conf0.5_n_10		94.43 36	94.84 29	93.20 94	95.73 135	83.19 143	95.99 79	97.31 36	91.08 21	97.67 4	98.11 11	81.87 122	99.22 52	97.86 4	97.91 87	97.20 156

VDD-MVS		90.74 148	89.92 162	93.20 94	96.27 104	83.02 155	95.73 104	93.86 319	88.42 116	92.53 110	96.84 81	62.09 387	98.64 129	90.95 127	92.62 239	97.93 103

Elysia		90.12 167	89.10 185	93.18 96	93.16 293	84.05 114	95.22 138	96.27 135	85.16 234	90.59 165	94.68 202	64.64 367	98.37 157	86.38 204	95.77 141	97.12 167

StellarMVS		90.12 167	89.10 185	93.18 96	93.16 293	84.05 114	95.22 138	96.27 135	85.16 234	90.59 165	94.68 202	64.64 367	98.37 157	86.38 204	95.77 141	97.12 167

CS-MVS		94.12 51	94.44 37	93.17 98	96.55 94	83.08 152	97.63 4	96.95 70	91.71 15	93.50 83	96.21 107	85.61 56	98.24 169	93.64 61	98.17 70	98.19 71

nrg030		91.08 143	90.39 146	93.17 98	93.07 300	86.91 23	96.41 42	96.26 139	88.30 119	88.37 213	94.85 196	82.19 114	97.64 238	91.09 122	82.95 365	94.96 272

MVSMamba_PlusPlus		93.44 75	93.54 76	93.14 100	96.58 93	83.05 153	96.06 73	96.50 117	84.42 257	94.09 67	95.56 158	85.01 71	98.69 123	94.96 44	98.66 45	97.67 127

EI-MVSNet-UG-set		92.74 97	92.62 96	93.12 101	94.86 185	83.20 142	94.40 200	95.74 197	90.71 33	92.05 121	96.60 96	84.00 83	98.99 81	91.55 117	93.63 204	97.17 158

test_fmvsmvis_n_1920		93.44 75	93.55 75	93.10 102	93.67 279	84.26 108	95.83 95	96.14 158	89.00 96	92.43 114	97.50 48	83.37 91	98.72 119	96.61 24	97.44 101	96.32 212

新几何1				93.10 102	97.30 75	84.35 107		95.56 215	71.09 457	91.26 148	96.24 106	82.87 101	98.86 101	79.19 336	98.10 76	96.07 228

OMC-MVS		91.23 135	90.62 145	93.08 104	96.27 104	84.07 112	93.52 267	95.93 179	86.95 178	89.51 189	96.13 118	78.50 176	98.35 161	85.84 213	92.90 228	96.83 194

OpenMVS	83.78 11	88.74 220	87.29 239	93.08 104	92.70 318	85.39 77	96.57 40	96.43 120	78.74 376	80.85 380	96.07 121	69.64 311	99.01 74	78.01 348	96.65 124	94.83 280

MAR-MVS		90.30 163	89.37 178	93.07 106	96.61 90	84.48 98	95.68 107	95.67 206	82.36 306	87.85 223	92.85 276	76.63 201	98.80 110	80.01 316	96.68 123	95.91 234
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
lupinMVS		90.92 144	90.21 150	93.03 107	93.86 264	83.88 119	92.81 305	93.86 319	79.84 357	91.76 135	94.29 223	77.92 185	98.04 197	90.48 137	97.11 107	97.17 158

Effi-MVS+		91.59 129	91.11 131	93.01 108	94.35 232	83.39 136	94.60 181	95.10 250	87.10 172	90.57 167	93.10 271	81.43 129	98.07 191	89.29 157	94.48 178	97.59 133

fmvsm_s_conf0.5_n_a		93.57 68	93.76 68	93.00 109	95.02 170	83.67 125	96.19 57	96.10 164	87.27 165	95.98 40	98.05 25	83.07 98	98.45 150	96.68 23	95.51 147	96.88 188

MVS_111021_LR		92.47 102	92.29 102	92.98 110	95.99 124	84.43 102	93.08 291	96.09 165	88.20 124	91.12 153	95.72 151	81.33 130	97.76 227	91.74 113	97.37 103	96.75 196

fmvsm_s_conf0.1_n_a		93.19 86	93.26 80	92.97 111	92.49 321	83.62 128	96.02 77	95.72 201	86.78 183	96.04 38	98.19 4	82.30 110	98.43 154	96.38 25	95.42 153	96.86 189

ETV-MVS		92.74 97	92.66 94	92.97 111	95.20 163	84.04 116	95.07 150	96.51 116	90.73 32	92.96 92	91.19 337	84.06 82	98.34 162	91.72 114	96.54 126	96.54 207

LFMVS		90.08 170	89.13 184	92.95 113	96.71 86	82.32 182	96.08 69	89.91 432	86.79 182	92.15 120	96.81 84	62.60 385	98.34 162	87.18 192	93.90 194	98.19 71

UGNet		89.95 177	88.95 193	92.95 113	94.51 216	83.31 138	95.70 106	95.23 243	89.37 76	87.58 230	93.94 239	64.00 375	98.78 113	83.92 242	96.31 132	96.74 197
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
jason		90.80 146	90.10 154	92.90 115	93.04 303	83.53 131	93.08 291	94.15 308	80.22 351	91.41 145	94.91 190	76.87 195	97.93 217	90.28 138	96.90 115	97.24 152
jason: jason.
DP-MVS		87.25 274	85.36 313	92.90 115	97.65 64	83.24 140	94.81 168	92.00 376	74.99 424	81.92 369	95.00 186	72.66 267	99.05 66	66.92 438	92.33 244	96.40 209

fmvsm_s_conf0.5_n_8		94.56 30	95.12 18	92.87 117	95.96 127	81.32 212	95.76 102	97.57 8	93.48 2	97.53 10	98.32 3	81.78 125	99.13 61	97.91 2	97.81 91	98.16 74

fmvsm_s_conf0.5_n		93.76 64	94.06 58	92.86 118	95.62 143	83.17 144	96.14 64	96.12 162	88.13 127	95.82 43	98.04 28	83.43 88	98.48 142	96.97 21	96.23 133	96.92 185

fmvsm_s_conf0.1_n		93.46 72	93.66 73	92.85 119	93.75 271	83.13 146	96.02 77	95.74 197	87.68 153	95.89 41	98.17 5	82.78 102	98.46 146	96.71 22	96.17 135	96.98 179

CANet_DTU		90.26 165	89.41 177	92.81 120	93.46 286	83.01 156	93.48 268	94.47 292	89.43 74	87.76 228	94.23 228	70.54 299	99.03 69	84.97 222	96.39 130	96.38 210

MVSFormer		91.68 127	91.30 126	92.80 121	93.86 264	83.88 119	95.96 83	95.90 183	84.66 253	91.76 135	94.91 190	77.92 185	97.30 279	89.64 153	97.11 107	97.24 152

PVSNet_Blended_VisFu		91.38 132	90.91 137	92.80 121	96.39 101	83.17 144	94.87 162	96.66 104	83.29 284	89.27 195	94.46 218	80.29 141	99.17 56	87.57 185	95.37 154	96.05 231

fmvsm_l_conf0.5_n_9		94.65 27	95.28 15	92.77 123	95.95 128	81.83 194	95.53 120	97.12 55	91.68 16	97.89 1	98.06 22	85.71 55	98.65 126	97.32 12	98.26 63	97.83 115

LuminaMVS		90.55 159	89.81 164	92.77 123	92.78 316	84.21 109	94.09 226	94.17 307	85.82 206	91.54 140	94.14 230	69.93 305	97.92 218	91.62 116	94.21 188	96.18 220

balanced_ft_v1		92.23 106	92.05 105	92.77 123	95.40 152	81.78 198	95.80 96	95.69 205	87.94 139	91.92 128	95.04 184	75.91 213	98.71 121	93.83 58	96.94 112	97.82 117

fmvsm_s_conf0.5_n_6		94.11 52	94.56 33	92.76 126	94.98 175	81.96 192	95.79 98	97.29 39	89.31 79	97.52 11	97.61 42	83.25 93	98.88 98	97.05 19	98.22 69	97.43 142

VDDNet		89.56 189	88.49 208	92.76 126	95.07 169	82.09 186	96.30 47	93.19 342	81.05 345	91.88 129	96.86 80	61.16 403	98.33 164	88.43 172	92.49 243	97.84 114

viewdifsd2359ckpt09		91.18 138	90.65 144	92.75 128	94.61 207	82.36 181	94.32 209	95.74 197	84.72 250	89.66 187	95.15 181	79.69 159	98.04 197	87.70 182	94.27 187	97.85 113

h-mvs33		90.80 146	90.15 153	92.75 128	96.01 120	82.66 169	95.43 123	95.53 219	89.80 60	93.08 89	95.64 154	75.77 214	99.00 79	92.07 100	78.05 422	96.60 202

casdiffmvs		92.51 100	92.43 99	92.74 130	94.41 227	81.98 190	94.54 185	96.23 143	89.57 70	91.96 125	96.17 112	82.58 104	98.01 204	90.95 127	95.45 152	98.23 69
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
test_yl		90.69 151	90.02 160	92.71 131	95.72 136	82.41 179	94.11 222	95.12 248	85.63 213	91.49 142	94.70 200	74.75 229	98.42 155	86.13 208	92.53 241	97.31 144

DCV-MVSNet		90.69 151	90.02 160	92.71 131	95.72 136	82.41 179	94.11 222	95.12 248	85.63 213	91.49 142	94.70 200	74.75 229	98.42 155	86.13 208	92.53 241	97.31 144

PCF-MVS	84.11 10	87.74 248	86.08 286	92.70 133	94.02 253	84.43 102	89.27 410	95.87 188	73.62 439	84.43 317	94.33 220	78.48 178	98.86 101	70.27 412	94.45 179	94.81 281
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	92.69 134	96.05 119	82.00 188	96.31 46	96.71 100	92.27 8	96.68 30	98.39 2	85.32 62	98.92 94	97.20 14	98.16 71	97.17 158

SSM_0404		90.73 149	90.08 155	92.69 134	95.00 174	83.13 146	94.32 209	95.00 258	85.41 224	89.84 182	95.35 167	76.13 204	97.98 209	85.46 218	94.18 189	96.95 181

baseline		92.39 104	92.29 102	92.69 134	94.46 222	81.77 199	94.14 219	96.27 135	89.22 83	91.88 129	96.00 126	82.35 107	97.99 206	91.05 123	95.27 158	98.30 55

MSLP-MVS++		93.72 66	94.08 55	92.65 137	97.31 74	83.43 133	95.79 98	97.33 32	90.03 50	93.58 79	96.96 76	84.87 73	97.76 227	92.19 96	98.66 45	96.76 195

EC-MVSNet		93.44 75	93.71 71	92.63 138	95.21 162	82.43 176	97.27 14	96.71 100	90.57 36	92.88 94	95.80 144	83.16 94	98.16 175	93.68 59	98.14 74	97.31 144

ab-mvs		89.41 197	88.35 210	92.60 139	95.15 167	82.65 173	92.20 331	95.60 213	83.97 264	88.55 209	93.70 253	74.16 244	98.21 173	82.46 266	89.37 288	96.94 183

LS3D		87.89 243	86.32 275	92.59 140	96.07 117	82.92 159	95.23 136	94.92 267	75.66 416	82.89 355	95.98 128	72.48 271	99.21 54	68.43 426	95.23 159	95.64 248

Anonymous20240529		88.09 239	86.59 264	92.58 141	96.53 96	81.92 193	95.99 79	95.84 190	74.11 434	89.06 199	95.21 176	61.44 395	98.81 109	83.67 249	87.47 319	97.01 177

fmvsm_s_conf0.5_n_3		94.49 32	95.13 17	92.56 142	95.49 149	81.10 222	95.93 86	97.16 50	92.96 4	97.39 12	98.13 7	83.63 87	98.80 110	97.89 3	97.61 99	97.78 120

CPTT-MVS		91.99 108	91.80 108	92.55 143	98.24 37	81.98 190	96.76 35	96.49 118	81.89 322	90.24 172	96.44 102	78.59 174	98.61 134	89.68 151	97.85 89	97.06 171

viewdifsd2359ckpt13		91.20 137	90.75 142	92.54 144	94.30 238	82.13 185	94.03 232	95.89 185	85.60 215	90.20 174	95.36 166	79.69 159	97.90 221	87.85 180	93.86 195	97.61 130

114514_t		89.51 190	88.50 206	92.54 144	98.11 42	81.99 189	95.16 146	96.36 127	70.19 461	85.81 270	95.25 172	76.70 199	98.63 131	82.07 276	96.86 118	97.00 178

PAPM_NR		91.22 136	90.78 141	92.52 146	97.60 65	81.46 208	94.37 206	96.24 142	86.39 194	87.41 233	94.80 198	82.06 118	98.48 142	82.80 261	95.37 154	97.61 130

mamba_0408		89.06 210	87.92 224	92.50 147	94.76 190	82.66 169	79.84 482	94.64 285	85.18 229	88.96 201	95.00 186	76.00 209	97.98 209	83.74 246	93.15 222	96.85 190

DeepPCF-MVS	89.96 1	94.20 47	94.77 31	92.49 148	96.52 97	80.00 272	94.00 237	97.08 59	90.05 49	95.65 46	97.29 57	89.66 15	98.97 86	93.95 55	98.71 36	98.50 32

SSM_0407		90.47 161	89.80 165	92.46 149	94.76 190	82.66 169	93.98 239	95.00 258	85.41 224	88.96 201	95.35 167	76.13 204	97.88 222	85.46 218	93.15 222	96.85 190

IS-MVSNet		91.43 131	91.09 133	92.46 149	95.87 131	81.38 211	96.95 24	93.69 332	89.72 66	89.50 191	95.98 128	78.57 175	97.77 226	83.02 255	96.50 128	98.22 70

API-MVS		90.66 154	90.07 156	92.45 151	96.36 102	84.57 93	96.06 73	95.22 245	82.39 304	89.13 196	94.27 226	80.32 140	98.46 146	80.16 314	96.71 122	94.33 304

xiu_mvs_v1_base_debu		90.64 155	90.05 157	92.40 152	93.97 259	84.46 99	93.32 276	95.46 223	85.17 231	92.25 115	94.03 231	70.59 295	98.57 137	90.97 124	94.67 169	94.18 308

xiu_mvs_v1_base		90.64 155	90.05 157	92.40 152	93.97 259	84.46 99	93.32 276	95.46 223	85.17 231	92.25 115	94.03 231	70.59 295	98.57 137	90.97 124	94.67 169	94.18 308

xiu_mvs_v1_base_debi		90.64 155	90.05 157	92.40 152	93.97 259	84.46 99	93.32 276	95.46 223	85.17 231	92.25 115	94.03 231	70.59 295	98.57 137	90.97 124	94.67 169	94.18 308

fmvsm_s_conf0.5_n_2		93.47 71	93.83 62	92.39 155	95.36 153	81.19 218	95.20 143	96.56 112	90.37 39	97.13 19	98.03 29	77.47 191	98.96 88	97.79 6	96.58 125	97.03 174

viewmacassd2359aftdt		91.67 128	91.43 124	92.37 156	93.95 262	81.00 226	93.90 247	95.97 176	87.75 151	91.45 144	96.04 124	79.92 147	97.97 211	89.26 158	94.67 169	98.14 76

viewmanbaseed2359cas		91.78 115	91.58 114	92.37 156	94.32 235	81.07 223	93.76 253	95.96 177	87.26 166	91.50 141	95.88 136	80.92 136	97.97 211	89.70 150	94.92 163	98.07 82

fmvsm_s_conf0.1_n_2		93.16 88	93.42 77	92.37 156	94.62 204	81.13 220	95.23 136	95.89 185	90.30 43	96.74 29	98.02 30	76.14 203	98.95 90	97.64 7	96.21 134	97.03 174

AdaColmap		89.89 180	89.07 187	92.37 156	97.41 71	83.03 154	94.42 195	95.92 180	82.81 298	86.34 259	94.65 207	73.89 249	99.02 72	80.69 303	95.51 147	95.05 267

CNLPA		89.07 209	87.98 221	92.34 160	96.87 83	84.78 88	94.08 227	93.24 339	81.41 336	84.46 315	95.13 182	75.57 221	96.62 331	77.21 355	93.84 197	95.61 251

fmvsm_s_conf0.5_n_4		93.86 61	94.37 40	92.33 161	95.13 168	80.95 229	95.64 113	96.97 65	89.60 69	96.85 25	97.77 38	83.08 97	98.92 94	97.49 8	96.78 120	97.13 166

ET-MVSNet_ETH3D		87.51 262	85.91 294	92.32 162	93.70 278	83.93 117	92.33 325	90.94 408	84.16 259	72.09 459	92.52 289	69.90 306	95.85 382	89.20 159	88.36 306	97.17 158

E4		91.74 120	91.55 117	92.31 163	94.27 240	80.80 239	93.81 250	96.17 155	87.97 137	91.11 154	96.05 122	80.75 137	98.08 189	89.78 146	94.02 191	98.06 87

E2		91.79 112	91.61 112	92.31 163	94.49 218	80.86 235	93.74 255	96.19 148	87.63 156	91.16 149	95.94 131	81.31 131	98.06 192	89.76 147	94.29 185	97.99 92

Anonymous202405211		87.68 249	86.13 282	92.31 163	96.66 88	80.74 241	94.87 162	91.49 393	80.47 350	89.46 192	95.44 162	54.72 444	98.23 170	82.19 272	89.89 278	97.97 94

E3		91.78 115	91.61 112	92.30 166	94.48 219	80.86 235	93.73 256	96.19 148	87.63 156	91.16 149	95.95 130	81.30 132	98.06 192	89.76 147	94.29 185	97.99 92

CHOSEN 1792x2688		88.84 216	87.69 229	92.30 166	96.14 108	81.42 210	90.01 397	95.86 189	74.52 429	87.41 233	93.94 239	75.46 222	98.36 159	80.36 309	95.53 146	97.12 167

viewcassd2359sk11		91.79 112	91.62 111	92.29 168	94.62 204	80.88 233	93.70 260	96.18 154	87.38 163	91.13 152	95.85 140	81.62 127	98.06 192	89.71 149	94.40 181	97.94 96

HY-MVS	83.01 12	89.03 212	87.94 223	92.29 168	94.86 185	82.77 161	92.08 336	94.49 291	81.52 335	86.93 240	92.79 282	78.32 180	98.23 170	79.93 317	90.55 265	95.88 237

CDS-MVSNet		89.45 193	88.51 205	92.29 168	93.62 281	83.61 130	93.01 295	94.68 283	81.95 317	87.82 226	93.24 265	78.69 172	96.99 308	80.34 310	93.23 219	96.28 215
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PAPR		90.02 173	89.27 183	92.29 168	95.78 133	80.95 229	92.68 310	96.22 144	81.91 319	86.66 250	93.75 251	82.23 112	98.44 152	79.40 335	94.79 166	97.48 138

E3new		91.76 118	91.58 114	92.28 172	94.69 201	80.90 232	93.68 263	96.17 155	87.15 169	91.09 159	95.70 152	81.75 126	98.05 196	89.67 152	94.35 182	97.90 107

mvsmamba		90.33 162	89.69 168	92.25 173	95.17 164	81.64 201	95.27 134	93.36 337	84.88 243	89.51 189	94.27 226	69.29 320	97.42 264	89.34 156	96.12 137	97.68 126

E5new		91.71 122	91.55 117	92.20 174	94.33 233	80.62 245	94.41 196	96.19 148	88.06 131	91.11 154	96.16 113	79.92 147	98.03 200	90.00 140	93.80 199	97.94 96

E6new		91.71 122	91.55 117	92.20 174	94.32 235	80.62 245	94.41 196	96.19 148	88.06 131	91.11 154	96.16 113	79.92 147	98.03 200	90.00 140	93.80 199	97.94 96

E6		91.71 122	91.55 117	92.20 174	94.32 235	80.62 245	94.41 196	96.19 148	88.06 131	91.11 154	96.16 113	79.92 147	98.03 200	90.00 140	93.80 199	97.94 96

E5		91.71 122	91.55 117	92.20 174	94.33 233	80.62 245	94.41 196	96.19 148	88.06 131	91.11 154	96.16 113	79.92 147	98.03 200	90.00 140	93.80 199	97.94 96

PLC	84.53 7	89.06 210	88.03 219	92.15 178	97.27 77	82.69 168	94.29 211	95.44 228	79.71 359	84.01 331	94.18 229	76.68 200	98.75 115	77.28 354	93.41 213	95.02 268
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
EPP-MVSNet		91.70 126	91.56 116	92.13 179	95.88 129	80.50 252	97.33 8	95.25 242	86.15 200	89.76 186	95.60 155	83.42 90	98.32 166	87.37 190	93.25 218	97.56 135

patch_mono-2		93.74 65	94.32 41	92.01 180	97.54 66	78.37 321	93.40 272	97.19 44	88.02 135	94.99 56	97.21 62	88.35 24	98.44 152	94.07 54	98.09 77	99.23 1

原ACMM1				92.01 180	97.34 73	81.05 224		96.81 87	78.89 370	90.45 168	95.92 133	82.65 103	98.84 105	80.68 304	98.26 63	96.14 222

UniMVSNet (Re)		89.80 183	89.07 187	92.01 180	93.60 282	84.52 96	94.78 170	97.47 17	89.26 82	86.44 256	92.32 295	82.10 116	97.39 275	84.81 226	80.84 399	94.12 312

MG-MVS		91.77 117	91.70 110	92.00 183	97.08 80	80.03 270	93.60 265	95.18 246	87.85 146	90.89 162	96.47 101	82.06 118	98.36 159	85.07 221	97.04 110	97.62 129

EIA-MVS		91.95 109	91.94 106	91.98 184	95.16 165	80.01 271	95.36 124	96.73 97	88.44 114	89.34 193	92.16 300	83.82 86	98.45 150	89.35 155	97.06 109	97.48 138

PVSNet_Blended		90.73 149	90.32 148	91.98 184	96.12 110	81.25 214	92.55 315	96.83 83	82.04 315	89.10 197	92.56 288	81.04 134	98.85 103	86.72 200	95.91 139	95.84 239

guyue		91.12 141	90.84 139	91.96 186	94.59 208	80.57 250	94.87 162	93.71 331	88.96 97	91.14 151	95.22 173	73.22 261	97.76 227	92.01 104	93.81 198	97.54 137

PS-MVSNAJ		91.18 138	90.92 136	91.96 186	95.26 160	82.60 175	92.09 335	95.70 203	86.27 196	91.84 131	92.46 290	79.70 156	98.99 81	89.08 160	95.86 140	94.29 305

TAMVS		89.21 203	88.29 214	91.96 186	93.71 276	82.62 174	93.30 280	94.19 305	82.22 309	87.78 227	93.94 239	78.83 169	96.95 311	77.70 350	92.98 227	96.32 212

SDMVSNet		90.19 166	89.61 171	91.93 189	96.00 121	83.09 151	92.89 302	95.98 173	88.73 104	86.85 246	95.20 177	72.09 278	97.08 299	88.90 165	89.85 280	95.63 249

FA-MVS(test-final)		89.66 185	88.91 195	91.93 189	94.57 212	80.27 256	91.36 355	94.74 280	84.87 244	89.82 183	92.61 287	74.72 232	98.47 145	83.97 241	93.53 208	97.04 173

MVS_Test		91.31 134	91.11 131	91.93 189	94.37 228	80.14 261	93.46 270	95.80 192	86.46 192	91.35 147	93.77 249	82.21 113	98.09 187	87.57 185	94.95 162	97.55 136

NR-MVSNet		88.58 226	87.47 235	91.93 189	93.04 303	84.16 111	94.77 171	96.25 141	89.05 89	80.04 394	93.29 263	79.02 168	97.05 304	81.71 287	80.05 409	94.59 288

HyFIR lowres test		88.09 239	86.81 252	91.93 189	96.00 121	80.63 243	90.01 397	95.79 193	73.42 441	87.68 229	92.10 306	73.86 250	97.96 213	80.75 302	91.70 248	97.19 157

GeoE		90.05 171	89.43 176	91.90 194	95.16 165	80.37 255	95.80 96	94.65 284	83.90 265	87.55 232	94.75 199	78.18 181	97.62 240	81.28 292	93.63 204	97.71 125

thisisatest0530		88.67 221	87.61 231	91.86 195	94.87 184	80.07 266	94.63 180	89.90 433	84.00 263	88.46 211	93.78 248	66.88 344	98.46 146	83.30 251	92.65 234	97.06 171

xiu_mvs_v2_base		91.13 140	90.89 138	91.86 195	94.97 176	82.42 177	92.24 328	95.64 211	86.11 204	91.74 137	93.14 269	79.67 161	98.89 97	89.06 161	95.46 151	94.28 306

DU-MVS		89.34 202	88.50 206	91.85 197	93.04 303	83.72 123	94.47 191	96.59 109	89.50 71	86.46 253	93.29 263	77.25 193	97.23 288	84.92 223	81.02 395	94.59 288

AstraMVS		90.69 151	90.30 149	91.84 198	93.81 267	79.85 278	94.76 172	92.39 362	88.96 97	91.01 161	95.87 139	70.69 293	97.94 216	92.49 82	92.70 233	97.73 123

OPM-MVS		90.12 167	89.56 172	91.82 199	93.14 295	83.90 118	94.16 218	95.74 197	88.96 97	87.86 222	95.43 164	72.48 271	97.91 219	88.10 177	90.18 272	93.65 345
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
HQP_MVS		90.60 158	90.19 151	91.82 199	94.70 199	82.73 165	95.85 93	96.22 144	90.81 27	86.91 242	94.86 194	74.23 240	98.12 177	88.15 173	89.99 274	94.63 285

UniMVSNet_NR-MVSNet		89.92 179	89.29 181	91.81 201	93.39 288	83.72 123	94.43 194	97.12 55	89.80 60	86.46 253	93.32 260	83.16 94	97.23 288	84.92 223	81.02 395	94.49 298

diffmvs		91.37 133	91.23 129	91.77 202	93.09 298	80.27 256	92.36 321	95.52 220	87.03 174	91.40 146	94.93 189	80.08 144	97.44 262	92.13 99	94.56 175	97.61 130
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
diffmvs_AUTHOR		91.51 130	91.44 123	91.73 203	93.09 298	80.27 256	92.51 316	95.58 214	87.22 167	91.80 134	95.57 157	79.96 146	97.48 254	92.23 93	94.97 161	97.45 140

1112_ss		88.42 228	87.33 238	91.72 204	94.92 180	80.98 227	92.97 299	94.54 288	78.16 387	83.82 334	93.88 244	78.78 171	97.91 219	79.45 331	89.41 287	96.26 216

Fast-Effi-MVS+		89.41 197	88.64 201	91.71 205	94.74 193	80.81 238	93.54 266	95.10 250	83.11 288	86.82 248	90.67 360	79.74 155	97.75 231	80.51 307	93.55 206	96.57 205

WTY-MVS		89.60 187	88.92 194	91.67 206	95.47 150	81.15 219	92.38 320	94.78 278	83.11 288	89.06 199	94.32 221	78.67 173	96.61 334	81.57 288	90.89 261	97.24 152

TAPA-MVS	84.62 6	88.16 237	87.01 247	91.62 207	96.64 89	80.65 242	94.39 202	96.21 147	76.38 408	86.19 263	95.44 162	79.75 154	98.08 189	62.75 456	95.29 156	96.13 223
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
VPA-MVSNet		89.62 186	88.96 192	91.60 208	93.86 264	82.89 160	95.46 121	97.33 32	87.91 141	88.43 212	93.31 261	74.17 243	97.40 272	87.32 191	82.86 370	94.52 293

FE-MVS		87.40 267	86.02 288	91.57 209	94.56 213	79.69 285	90.27 384	93.72 330	80.57 348	88.80 205	91.62 326	65.32 360	98.59 136	74.97 380	94.33 184	96.44 208

XVG-OURS		89.40 199	88.70 200	91.52 210	94.06 251	81.46 208	91.27 360	96.07 167	86.14 201	88.89 204	95.77 148	68.73 329	97.26 285	87.39 189	89.96 276	95.83 240

hse-mvs2		89.88 181	89.34 179	91.51 211	94.83 187	81.12 221	93.94 241	93.91 318	89.80 60	93.08 89	93.60 254	75.77 214	97.66 235	92.07 100	77.07 429	95.74 244

TranMVSNet+NR-MVSNet		88.84 216	87.95 222	91.49 212	92.68 319	83.01 156	94.92 159	96.31 130	89.88 54	85.53 279	93.85 246	76.63 201	96.96 310	81.91 280	79.87 412	94.50 296

AUN-MVS		87.78 247	86.54 267	91.48 213	94.82 188	81.05 224	93.91 245	93.93 315	83.00 293	86.93 240	93.53 255	69.50 314	97.67 233	86.14 206	77.12 428	95.73 246

XVG-OURS-SEG-HR		89.95 177	89.45 174	91.47 214	94.00 257	81.21 217	91.87 340	96.06 169	85.78 208	88.55 209	95.73 150	74.67 233	97.27 283	88.71 169	89.64 285	95.91 234

MVS		87.44 265	86.10 285	91.44 215	92.61 320	83.62 128	92.63 312	95.66 208	67.26 467	81.47 372	92.15 301	77.95 184	98.22 172	79.71 320	95.48 149	92.47 395

viewdifsd2359ckpt07		91.11 142	91.02 134	91.41 216	94.21 244	78.37 321	92.91 301	95.71 202	87.50 158	90.32 171	95.88 136	80.27 142	97.99 206	88.78 168	93.55 206	97.86 110

F-COLMAP		87.95 242	86.80 253	91.40 217	96.35 103	80.88 233	94.73 174	95.45 226	79.65 360	82.04 367	94.61 208	71.13 285	98.50 140	76.24 367	91.05 259	94.80 282

dcpmvs_2		93.49 70	94.19 52	91.38 218	97.69 63	76.78 364	94.25 213	96.29 131	88.33 117	94.46 59	96.88 79	88.07 28	98.64 129	93.62 62	98.09 77	98.73 23

thisisatest0515		87.33 270	85.99 289	91.37 219	93.49 284	79.55 286	90.63 376	89.56 441	80.17 352	87.56 231	90.86 350	67.07 341	98.28 168	81.50 289	93.02 226	96.29 214

HQP-MVS		89.80 183	89.28 182	91.34 220	94.17 246	81.56 202	94.39 202	96.04 170	88.81 100	85.43 288	93.97 238	73.83 251	97.96 213	87.11 195	89.77 283	94.50 296

fmvsm_s_conf0.5_n_7		93.15 89	93.76 68	91.31 221	94.42 226	79.48 288	94.52 186	97.14 53	89.33 78	94.17 65	98.09 18	81.83 123	97.49 253	96.33 26	98.02 81	96.95 181

RRT-MVS		90.85 145	90.70 143	91.30 222	94.25 241	76.83 363	94.85 165	96.13 161	89.04 90	90.23 173	94.88 192	70.15 304	98.72 119	91.86 112	94.88 164	98.34 48

FMVSNet3		87.40 267	86.11 284	91.30 222	93.79 270	83.64 127	94.20 217	94.81 276	83.89 266	84.37 318	91.87 317	68.45 332	96.56 343	78.23 345	85.36 338	93.70 344

FMVSNet2		87.19 280	85.82 297	91.30 222	94.01 254	83.67 125	94.79 169	94.94 262	83.57 274	83.88 333	92.05 310	66.59 349	96.51 347	77.56 352	85.01 341	93.73 342

RPMNet		83.95 359	81.53 370	91.21 225	90.58 397	79.34 297	85.24 460	96.76 92	71.44 455	85.55 277	82.97 460	70.87 290	98.91 96	61.01 460	89.36 289	95.40 255

IB-MVS	80.51 15	85.24 336	83.26 354	91.19 226	92.13 332	79.86 277	91.75 344	91.29 398	83.28 285	80.66 384	88.49 409	61.28 397	98.46 146	80.99 298	79.46 416	95.25 261
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
CLD-MVS		89.47 192	88.90 196	91.18 227	94.22 243	82.07 187	92.13 333	96.09 165	87.90 142	85.37 294	92.45 291	74.38 238	97.56 245	87.15 193	90.43 267	93.93 322
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
LPG-MVS_test		89.45 193	88.90 196	91.12 228	94.47 220	81.49 206	95.30 129	96.14 158	86.73 185	85.45 285	95.16 179	69.89 307	98.10 179	87.70 182	89.23 292	93.77 338

LGP-MVS_train				91.12 228	94.47 220	81.49 206		96.14 158	86.73 185	85.45 285	95.16 179	69.89 307	98.10 179	87.70 182	89.23 292	93.77 338

ACMM	84.12 9	89.14 205	88.48 209	91.12 228	94.65 203	81.22 216	95.31 127	96.12 162	85.31 228	85.92 268	94.34 219	70.19 303	98.06 192	85.65 214	88.86 297	94.08 316
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tttt0517		88.61 223	87.78 228	91.11 231	94.96 177	77.81 339	95.35 125	89.69 436	85.09 238	88.05 220	94.59 211	66.93 342	98.48 142	83.27 252	92.13 246	97.03 174

GBi-Net		87.26 272	85.98 290	91.08 232	94.01 254	83.10 148	95.14 147	94.94 262	83.57 274	84.37 318	91.64 322	66.59 349	96.34 360	78.23 345	85.36 338	93.79 333

test1		87.26 272	85.98 290	91.08 232	94.01 254	83.10 148	95.14 147	94.94 262	83.57 274	84.37 318	91.64 322	66.59 349	96.34 360	78.23 345	85.36 338	93.79 333

FMVSNet1		85.85 321	84.11 341	91.08 232	92.81 314	83.10 148	95.14 147	94.94 262	81.64 330	82.68 357	91.64 322	59.01 419	96.34 360	75.37 374	83.78 354	93.79 333

Test_1112_low_res		87.65 251	86.51 268	91.08 232	94.94 179	79.28 301	91.77 343	94.30 300	76.04 414	83.51 344	92.37 293	77.86 187	97.73 232	78.69 340	89.13 294	96.22 217

PS-MVSNAJss		89.97 175	89.62 170	91.02 236	91.90 341	80.85 237	95.26 135	95.98 173	86.26 197	86.21 262	94.29 223	79.70 156	97.65 236	88.87 167	88.10 308	94.57 290

BH-RMVSNet		88.37 231	87.48 234	91.02 236	95.28 157	79.45 290	92.89 302	93.07 345	85.45 223	86.91 242	94.84 197	70.35 300	97.76 227	73.97 389	94.59 174	95.85 238

UniMVSNet_ETH3D		87.53 261	86.37 272	91.00 238	92.44 324	78.96 306	94.74 173	95.61 212	84.07 262	85.36 295	94.52 213	59.78 411	97.34 277	82.93 256	87.88 313	96.71 198

FIs		90.51 160	90.35 147	90.99 239	93.99 258	80.98 227	95.73 104	97.54 10	89.15 86	86.72 249	94.68 202	81.83 123	97.24 287	85.18 220	88.31 307	94.76 283

ACMP	84.23 8	89.01 214	88.35 210	90.99 239	94.73 194	81.27 213	95.07 150	95.89 185	86.48 190	83.67 339	94.30 222	69.33 316	97.99 206	87.10 197	88.55 299	93.72 343
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
Anonymous20231211		86.59 304	85.13 319	90.98 241	96.52 97	81.50 204	96.14 64	96.16 157	73.78 437	83.65 340	92.15 301	63.26 381	97.37 276	82.82 260	81.74 384	94.06 317

IMVS_0403		89.97 175	89.64 169	90.96 242	93.72 272	77.75 344	93.00 296	95.34 237	85.53 219	88.77 206	94.49 214	78.49 177	97.84 223	84.75 227	92.65 234	97.28 147

sss		88.93 215	88.26 216	90.94 243	94.05 252	80.78 240	91.71 345	95.38 232	81.55 334	88.63 208	93.91 243	75.04 226	95.47 401	82.47 265	91.61 249	96.57 205

IMVS_0407		89.85 182	89.51 173	90.88 244	93.72 272	77.75 344	93.07 293	95.34 237	85.53 219	88.34 214	94.49 214	77.69 189	97.60 241	84.75 227	92.65 234	97.28 147

viewmambaseed2359dif		90.04 172	89.78 166	90.83 245	92.85 313	77.92 333	92.23 329	95.01 254	81.90 320	90.20 174	95.45 161	79.64 163	97.34 277	87.52 187	93.17 220	97.23 155

sd_testset		88.59 225	87.85 227	90.83 245	96.00 121	80.42 254	92.35 323	94.71 281	88.73 104	86.85 246	95.20 177	67.31 336	96.43 354	79.64 323	89.85 280	95.63 249

PVSNet_BlendedMVS		89.98 174	89.70 167	90.82 247	96.12 110	81.25 214	93.92 243	96.83 83	83.49 278	89.10 197	92.26 298	81.04 134	98.85 103	86.72 200	87.86 314	92.35 402

cascas		86.43 312	84.98 322	90.80 248	92.10 334	80.92 231	90.24 388	95.91 182	73.10 444	83.57 343	88.39 410	65.15 362	97.46 258	84.90 225	91.43 251	94.03 319

ECVR-MVS		89.09 208	88.53 204	90.77 249	95.62 143	75.89 377	96.16 60	84.22 470	87.89 144	90.20 174	96.65 91	63.19 382	98.10 179	85.90 211	96.94 112	98.33 50

GA-MVS		86.61 302	85.27 316	90.66 250	91.33 364	78.71 310	90.40 383	93.81 325	85.34 227	85.12 298	89.57 391	61.25 398	97.11 297	80.99 298	89.59 286	96.15 221

thres600view7		87.65 251	86.67 259	90.59 251	96.08 116	78.72 308	94.88 161	91.58 389	87.06 173	88.08 218	92.30 296	68.91 326	98.10 179	70.05 419	91.10 254	94.96 272

thres400		87.62 256	86.64 260	90.57 252	95.99 124	78.64 311	94.58 182	91.98 378	86.94 179	88.09 216	91.77 318	69.18 322	98.10 179	70.13 416	91.10 254	94.96 272

baseline1		88.10 238	87.28 240	90.57 252	94.96 177	80.07 266	94.27 212	91.29 398	86.74 184	87.41 233	94.00 236	76.77 198	96.20 365	80.77 301	79.31 418	95.44 253

viewdifsd2359ckpt11		89.43 195	89.05 189	90.56 254	92.89 311	77.00 359	92.81 305	94.52 289	87.03 174	89.77 184	95.79 145	74.67 233	97.51 249	88.97 163	84.98 342	97.17 158

viewmsd2359difaftdt		89.43 195	89.05 189	90.56 254	92.89 311	77.00 359	92.81 305	94.52 289	87.03 174	89.77 184	95.79 145	74.67 233	97.51 249	88.97 163	84.98 342	97.17 158

usedtu_dtu_shiyan1		86.84 291	85.61 305	90.53 256	90.50 401	81.80 196	90.97 368	94.96 260	83.05 290	83.50 345	90.32 367	72.15 275	96.65 325	79.49 328	85.55 336	93.15 368

FE-MVSNET3		86.84 291	85.61 305	90.53 256	90.50 401	81.80 196	90.97 368	94.96 260	83.05 290	83.50 345	90.32 367	72.15 275	96.65 325	79.49 328	85.55 336	93.15 368

FC-MVSNet-test		90.27 164	90.18 152	90.53 256	93.71 276	79.85 278	95.77 100	97.59 7	89.31 79	86.27 260	94.67 205	81.93 121	97.01 307	84.26 237	88.09 310	94.71 284

PAPM		86.68 301	85.39 311	90.53 256	93.05 302	79.33 300	89.79 400	94.77 279	78.82 373	81.95 368	93.24 265	76.81 196	97.30 279	66.94 436	93.16 221	94.95 276

WR-MVS		88.38 230	87.67 230	90.52 260	93.30 290	80.18 259	93.26 283	95.96 177	88.57 112	85.47 284	92.81 280	76.12 206	96.91 314	81.24 293	82.29 375	94.47 301

SSM_04072		88.57 227	87.92 224	90.51 261	94.76 190	82.66 169	79.84 482	94.64 285	85.18 229	88.96 201	95.00 186	76.00 209	92.03 451	83.74 246	93.15 222	96.85 190

MVSTER		88.84 216	88.29 214	90.51 261	92.95 308	80.44 253	93.73 256	95.01 254	84.66 253	87.15 237	93.12 270	72.79 266	97.21 290	87.86 179	87.36 322	93.87 327

testdata				90.49 263	96.40 100	77.89 336		95.37 234	72.51 449	93.63 78	96.69 87	82.08 117	97.65 236	83.08 253	97.39 102	95.94 233

test1111		89.10 206	88.64 201	90.48 264	95.53 148	74.97 387	96.08 69	84.89 468	88.13 127	90.16 178	96.65 91	63.29 380	98.10 179	86.14 206	96.90 115	98.39 45

tt0805		86.92 288	85.74 303	90.48 264	92.22 328	79.98 273	95.63 114	94.88 270	83.83 268	84.74 307	92.80 281	57.61 426	97.67 233	85.48 217	84.42 347	93.79 333

jajsoiax		88.24 235	87.50 233	90.48 264	90.89 385	80.14 261	95.31 127	95.65 210	84.97 241	84.24 326	94.02 234	65.31 361	97.42 264	88.56 170	88.52 301	93.89 323

PatchMatch-RL		86.77 298	85.54 307	90.47 267	95.88 129	82.71 167	90.54 379	92.31 366	79.82 358	84.32 323	91.57 330	68.77 328	96.39 356	73.16 395	93.48 212	92.32 403

0.4-1-1-0.1		81.55 390	78.59 412	90.42 268	87.55 443	79.90 275	88.56 422	89.19 446	77.01 400	79.72 400	77.71 473	54.84 441	97.11 297	80.50 308	72.20 442	94.26 307

tfpn200view9		87.58 259	86.64 260	90.41 269	95.99 124	78.64 311	94.58 182	91.98 378	86.94 179	88.09 216	91.77 318	69.18 322	98.10 179	70.13 416	91.10 254	94.48 299

VPNet		88.20 236	87.47 235	90.39 270	93.56 283	79.46 289	94.04 231	95.54 218	88.67 107	86.96 239	94.58 212	69.33 316	97.15 292	84.05 240	80.53 404	94.56 291

ACMH	80.38 17	85.36 331	83.68 348	90.39 270	94.45 223	80.63 243	94.73 174	94.85 272	82.09 311	77.24 428	92.65 285	60.01 409	97.58 243	72.25 400	84.87 344	92.96 375
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
thres100view900		87.63 254	86.71 256	90.38 272	96.12 110	78.55 314	95.03 153	91.58 389	87.15 169	88.06 219	92.29 297	68.91 326	98.10 179	70.13 416	91.10 254	94.48 299

mvs_tets		88.06 241	87.28 240	90.38 272	90.94 381	79.88 276	95.22 138	95.66 208	85.10 237	84.21 327	93.94 239	63.53 378	97.40 272	88.50 171	88.40 305	93.87 327

1314		87.51 262	86.57 265	90.34 274	92.42 325	79.74 283	92.63 312	95.35 236	78.35 382	80.14 391	91.62 326	74.05 245	97.15 292	81.05 294	93.53 208	94.12 312

LTVRE_ROB	82.13 13	86.26 315	84.90 325	90.34 274	94.44 224	81.50 204	92.31 327	94.89 268	83.03 292	79.63 402	92.67 284	69.69 310	97.79 225	71.20 405	86.26 331	91.72 413
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		80.75 403	77.58 417	90.25 276	86.55 447	79.72 284	87.46 443	89.48 444	76.43 407	77.93 423	75.94 474	52.31 453	97.05 304	80.25 313	71.85 446	93.99 321

test_djsdf		89.03 212	88.64 201	90.21 277	90.74 392	79.28 301	95.96 83	95.90 183	84.66 253	85.33 296	92.94 275	74.02 246	97.30 279	89.64 153	88.53 300	94.05 318

v2v482		87.84 244	87.06 244	90.17 278	90.99 377	79.23 304	94.00 237	95.13 247	84.87 244	85.53 279	92.07 309	74.45 237	97.45 259	84.71 232	81.75 383	93.85 330

pmmvs4		85.43 329	83.86 346	90.16 279	90.02 412	82.97 158	90.27 384	92.67 357	75.93 415	80.73 382	91.74 320	71.05 286	95.73 390	78.85 339	83.46 361	91.78 412

V42		87.68 249	86.86 249	90.15 280	90.58 397	80.14 261	94.24 215	95.28 241	83.66 272	85.67 274	91.33 332	74.73 231	97.41 270	84.43 236	81.83 381	92.89 378

MSDG		84.86 344	83.09 357	90.14 281	93.80 268	80.05 268	89.18 413	93.09 344	78.89 370	78.19 419	91.91 315	65.86 359	97.27 283	68.47 425	88.45 303	93.11 370

sc_t1		81.53 391	78.67 411	90.12 282	90.78 389	78.64 311	93.91 245	90.20 422	68.42 464	80.82 381	89.88 384	46.48 468	96.76 319	76.03 370	71.47 447	94.96 272

anonymousdsp		87.84 244	87.09 243	90.12 282	89.13 423	80.54 251	94.67 178	95.55 216	82.05 313	83.82 334	92.12 303	71.47 283	97.15 292	87.15 193	87.80 317	92.67 384

thres200		87.21 278	86.24 279	90.12 282	95.36 153	78.53 315	93.26 283	92.10 372	86.42 193	88.00 221	91.11 343	69.24 321	98.00 205	69.58 420	91.04 260	93.83 332

CR-MVSNet		85.35 332	83.76 347	90.12 282	90.58 397	79.34 297	85.24 460	91.96 380	78.27 384	85.55 277	87.87 420	71.03 287	95.61 393	73.96 390	89.36 289	95.40 255

0.4-1-1-0.2		80.84 402	77.77 415	90.06 286	86.18 451	79.35 295	86.75 448	89.54 442	76.23 412	78.59 418	75.46 477	55.03 440	96.99 308	80.11 315	72.05 444	93.85 330

v1144		87.61 257	86.79 254	90.06 286	91.01 376	79.34 297	93.95 240	95.42 231	83.36 283	85.66 275	91.31 335	74.98 227	97.42 264	83.37 250	82.06 377	93.42 354

XXY-MVS		87.65 251	86.85 250	90.03 288	92.14 331	80.60 249	93.76 253	95.23 243	82.94 295	84.60 309	94.02 234	74.27 239	95.49 400	81.04 295	83.68 357	94.01 320

Vis-MVSNet (Re-imp)		89.59 188	89.44 175	90.03 288	95.74 134	75.85 378	95.61 115	90.80 412	87.66 155	87.83 225	95.40 165	76.79 197	96.46 352	78.37 341	96.73 121	97.80 118

test2506		87.21 278	86.28 277	90.02 290	95.62 143	73.64 403	96.25 55	71.38 493	87.89 144	90.45 168	96.65 91	55.29 438	98.09 187	86.03 210	96.94 112	98.33 50

BH-untuned		88.60 224	88.13 218	90.01 291	95.24 161	78.50 317	93.29 281	94.15 308	84.75 249	84.46 315	93.40 257	75.76 216	97.40 272	77.59 351	94.52 177	94.12 312

v1192		87.25 274	86.33 274	90.00 292	90.76 391	79.04 305	93.80 251	95.48 221	82.57 302	85.48 283	91.18 339	73.38 260	97.42 264	82.30 269	82.06 377	93.53 348

v7n		86.81 293	85.76 301	89.95 293	90.72 393	79.25 303	95.07 150	95.92 180	84.45 256	82.29 361	90.86 350	72.60 270	97.53 247	79.42 334	80.52 405	93.08 372

testing91		87.11 283	86.18 280	89.92 294	94.43 225	75.38 386	91.53 350	92.27 368	86.48 190	86.50 251	90.24 370	61.19 401	97.53 247	82.10 274	90.88 262	96.84 193

IMVS_0404		87.60 258	86.84 251	89.89 295	93.72 272	77.75 344	88.56 422	95.34 237	85.53 219	79.98 395	94.49 214	66.54 352	94.64 414	84.75 227	92.65 234	97.28 147

v8		87.50 264	86.71 256	89.89 295	91.37 361	79.40 293	94.50 187	95.38 232	84.81 247	83.60 342	91.33 332	76.05 207	97.42 264	82.84 259	80.51 406	92.84 380

v10		87.25 274	86.38 271	89.85 297	91.19 367	79.50 287	94.48 188	95.45 226	83.79 270	83.62 341	91.19 337	75.13 224	97.42 264	81.94 279	80.60 401	92.63 386

baseline2		86.50 308	85.39 311	89.84 298	91.12 372	76.70 366	91.88 339	88.58 448	82.35 307	79.95 396	90.95 348	73.42 258	97.63 239	80.27 312	89.95 277	95.19 262

pm-mvs1		86.61 302	85.54 307	89.82 299	91.44 356	80.18 259	95.28 133	94.85 272	83.84 267	81.66 370	92.62 286	72.45 273	96.48 349	79.67 322	78.06 421	92.82 381

TR-MVS		86.78 295	85.76 301	89.82 299	94.37 228	78.41 319	92.47 317	92.83 351	81.11 344	86.36 257	92.40 292	68.73 329	97.48 254	73.75 393	89.85 280	93.57 347

ACMH+	81.04 14	85.05 339	83.46 351	89.82 299	94.66 202	79.37 294	94.44 193	94.12 311	82.19 310	78.04 421	92.82 279	58.23 422	97.54 246	73.77 392	82.90 369	92.54 392

EI-MVSNet		89.10 206	88.86 198	89.80 302	91.84 343	78.30 324	93.70 260	95.01 254	85.73 210	87.15 237	95.28 170	79.87 153	97.21 290	83.81 244	87.36 322	93.88 326

usedtu_blend_shiyan5		82.39 377	79.93 391	89.75 303	85.12 462	80.08 264	92.36 321	93.26 338	74.29 432	79.00 410	82.72 462	64.29 372	96.60 338	79.60 324	68.75 460	92.55 389

v144192		87.19 280	86.35 273	89.74 304	90.64 395	78.24 326	93.92 243	95.43 229	81.93 318	85.51 281	91.05 346	74.21 242	97.45 259	82.86 258	81.56 385	93.53 348

COLMAP_ROB	80.39 16	83.96 358	82.04 367	89.74 304	95.28 157	79.75 282	94.25 213	92.28 367	75.17 422	78.02 422	93.77 249	58.60 421	97.84 223	65.06 447	85.92 332	91.63 415
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
SCA		86.32 314	85.18 318	89.73 306	92.15 330	76.60 367	91.12 364	91.69 385	83.53 277	85.50 282	88.81 403	66.79 345	96.48 349	76.65 360	90.35 269	96.12 224

blend_shiyan4		81.94 380	79.35 399	89.70 307	85.52 458	80.08 264	91.29 358	93.82 322	77.12 398	79.31 406	82.94 461	54.81 442	96.60 338	79.60 324	69.78 452	92.41 398

IterMVS-LS		88.36 232	87.91 226	89.70 307	93.80 268	78.29 325	93.73 256	95.08 252	85.73 210	84.75 306	91.90 316	79.88 152	96.92 313	83.83 243	82.51 371	93.89 323
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
blended_shiyan8		82.79 368	80.49 378	89.69 309	85.50 459	79.83 280	91.38 353	93.82 322	77.14 395	79.39 405	83.73 453	64.95 366	96.63 328	79.75 319	68.77 459	92.62 388

testing11		86.44 311	85.35 314	89.69 309	94.29 239	75.40 385	91.30 357	90.53 417	84.76 248	85.06 300	90.13 376	58.95 420	97.45 259	82.08 275	91.09 258	96.21 219

testing99		86.72 299	85.73 304	89.69 309	94.23 242	74.91 389	91.35 356	90.97 406	86.14 201	86.36 257	90.22 371	59.41 414	97.48 254	82.24 271	90.66 264	96.69 200

v1921920		86.97 287	86.06 287	89.69 309	90.53 400	78.11 329	93.80 251	95.43 229	81.90 320	85.33 296	91.05 346	72.66 267	97.41 270	82.05 277	81.80 382	93.53 348

icg_test_0407_2		89.15 204	88.97 191	89.68 313	93.72 272	77.75 344	88.26 428	95.34 237	85.53 219	88.34 214	94.49 214	77.69 189	93.99 426	84.75 227	92.65 234	97.28 147

blended_shiyan6		82.78 369	80.48 379	89.67 314	85.53 457	79.76 281	91.37 354	93.82 322	77.14 395	79.30 407	83.73 453	64.96 365	96.63 328	79.68 321	68.75 460	92.63 386

VortexMVS		88.42 228	88.01 220	89.63 315	93.89 263	78.82 307	93.82 249	95.47 222	86.67 187	84.53 313	91.99 312	72.62 269	96.65 325	89.02 162	84.09 351	93.41 355

Fast-Effi-MVS+-dtu		87.44 265	86.72 255	89.63 315	92.04 335	77.68 349	94.03 232	93.94 314	85.81 207	82.42 360	91.32 334	70.33 301	97.06 302	80.33 311	90.23 271	94.14 311

v1240		86.78 295	85.85 296	89.56 317	90.45 404	77.79 341	93.61 264	95.37 234	81.65 329	85.43 288	91.15 341	71.50 282	97.43 263	81.47 290	82.05 379	93.47 352

Effi-MVS+-dtu		88.65 222	88.35 210	89.54 318	93.33 289	76.39 371	94.47 191	94.36 298	87.70 152	85.43 288	89.56 392	73.45 256	97.26 285	85.57 216	91.28 253	94.97 269

wanda-best-256-512		82.44 374	80.07 386	89.53 319	85.12 462	79.44 291	90.49 380	93.75 328	76.97 401	79.00 410	82.72 462	64.29 372	96.61 334	79.56 326	68.75 460	92.55 389

FE-blended-shiyan7		82.44 374	80.07 386	89.53 319	85.12 462	79.44 291	90.49 380	93.75 328	76.97 401	79.00 410	82.72 462	64.29 372	96.61 334	79.56 326	68.75 460	92.55 389

AllTest		83.42 365	81.39 371	89.52 321	95.01 171	77.79 341	93.12 287	90.89 410	77.41 391	76.12 437	93.34 258	54.08 447	97.51 249	68.31 427	84.27 349	93.26 358

TestCases				89.52 321	95.01 171	77.79 341		90.89 410	77.41 391	76.12 437	93.34 258	54.08 447	97.51 249	68.31 427	84.27 349	93.26 358

mvs_anonymous		89.37 201	89.32 180	89.51 323	93.47 285	74.22 396	91.65 348	94.83 274	82.91 296	85.45 285	93.79 247	81.23 133	96.36 359	86.47 202	94.09 190	97.94 96

XVG-ACMP-BASELINE		86.00 317	84.84 327	89.45 324	91.20 366	78.00 331	91.70 346	95.55 216	85.05 239	82.97 354	92.25 299	54.49 445	97.48 254	82.93 256	87.45 321	92.89 378

testing222		84.84 345	83.32 352	89.43 325	94.15 249	75.94 376	91.09 365	89.41 445	84.90 242	85.78 271	89.44 393	52.70 452	96.28 363	70.80 411	91.57 250	96.07 228

MVP-Stereo		85.97 318	84.86 326	89.32 326	90.92 383	82.19 184	92.11 334	94.19 305	78.76 375	78.77 417	91.63 325	68.38 333	96.56 343	75.01 379	93.95 193	89.20 455
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PatchmatchNet		85.85 321	84.70 329	89.29 327	91.76 347	75.54 382	88.49 424	91.30 397	81.63 331	85.05 301	88.70 407	71.71 279	96.24 364	74.61 385	89.05 295	96.08 227
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v148		87.04 285	86.32 275	89.21 328	90.94 381	77.26 355	93.71 259	94.43 293	84.84 246	84.36 321	90.80 354	76.04 208	97.05 304	82.12 273	79.60 415	93.31 357

tfpnnormal		84.72 347	83.23 355	89.20 329	92.79 315	80.05 268	94.48 188	95.81 191	82.38 305	81.08 378	91.21 336	69.01 325	96.95 311	61.69 458	80.59 402	90.58 441

cl22		86.78 295	85.98 290	89.18 330	92.34 326	77.62 350	90.84 372	94.13 310	81.33 338	83.97 332	90.15 375	73.96 247	96.60 338	84.19 238	82.94 366	93.33 356

BH-w/o		87.57 260	87.05 245	89.12 331	94.90 183	77.90 335	92.41 318	93.51 334	82.89 297	83.70 338	91.34 331	75.75 217	97.07 301	75.49 372	93.49 210	92.39 400

WR-MVS_H		87.80 246	87.37 237	89.10 332	93.23 291	78.12 328	95.61 115	97.30 37	87.90 142	83.72 337	92.01 311	79.65 162	96.01 374	76.36 364	80.54 403	93.16 366

miper_enhance_ethall		86.90 289	86.18 280	89.06 333	91.66 352	77.58 351	90.22 390	94.82 275	79.16 366	84.48 314	89.10 397	79.19 167	96.66 324	84.06 239	82.94 366	92.94 376

c3_l		87.14 282	86.50 269	89.04 334	92.20 329	77.26 355	91.22 363	94.70 282	82.01 316	84.34 322	90.43 365	78.81 170	96.61 334	83.70 248	81.09 392	93.25 360

miper_ehance_all_eth		87.22 277	86.62 263	89.02 335	92.13 332	77.40 353	90.91 371	94.81 276	81.28 339	84.32 323	90.08 378	79.26 165	96.62 331	83.81 244	82.94 366	93.04 373

gg-mvs-nofinetune		81.77 384	79.37 398	88.99 336	90.85 387	77.73 348	86.29 452	79.63 481	74.88 427	83.19 353	69.05 484	60.34 406	96.11 369	75.46 373	94.64 173	93.11 370

ETVMVS		84.43 352	82.92 361	88.97 337	94.37 228	74.67 390	91.23 362	88.35 450	83.37 282	86.06 266	89.04 398	55.38 436	95.67 392	67.12 434	91.34 252	96.58 204

pmmvs6		83.42 365	81.60 369	88.87 338	88.01 438	77.87 337	94.96 156	94.24 304	74.67 428	78.80 416	91.09 344	60.17 408	96.49 348	77.06 359	75.40 435	92.23 405

test_cas_vis1_n_1920		88.83 219	88.85 199	88.78 339	91.15 371	76.72 365	93.85 248	94.93 266	83.23 287	92.81 98	96.00 126	61.17 402	94.45 415	91.67 115	94.84 165	95.17 263

MIMVSNet		82.59 373	80.53 376	88.76 340	91.51 354	78.32 323	86.57 451	90.13 425	79.32 362	80.70 383	88.69 408	52.98 451	93.07 442	66.03 442	88.86 297	94.90 277

cl____		86.52 307	85.78 298	88.75 341	92.03 336	76.46 369	90.74 373	94.30 300	81.83 325	83.34 350	90.78 355	75.74 219	96.57 341	81.74 285	81.54 386	93.22 362

DIV-MVS_self_test		86.53 306	85.78 298	88.75 341	92.02 337	76.45 370	90.74 373	94.30 300	81.83 325	83.34 350	90.82 353	75.75 217	96.57 341	81.73 286	81.52 387	93.24 361

CP-MVSNet		87.63 254	87.26 242	88.74 343	93.12 296	76.59 368	95.29 131	96.58 110	88.43 115	83.49 347	92.98 274	75.28 223	95.83 383	78.97 337	81.15 391	93.79 333

eth_miper_zixun_eth		86.50 308	85.77 300	88.68 344	91.94 338	75.81 379	90.47 382	94.89 268	82.05 313	84.05 329	90.46 364	75.96 211	96.77 318	82.76 262	79.36 417	93.46 353

CHOSEN 280x420		85.15 337	83.99 344	88.65 345	92.47 322	78.40 320	79.68 484	92.76 354	74.90 426	81.41 374	89.59 390	69.85 309	95.51 397	79.92 318	95.29 156	92.03 408

PS-CasMVS		87.32 271	86.88 248	88.63 346	92.99 306	76.33 373	95.33 126	96.61 108	88.22 123	83.30 352	93.07 272	73.03 264	95.79 387	78.36 342	81.00 397	93.75 340

TransMVSNet (Re)		84.43 352	83.06 359	88.54 347	91.72 348	78.44 318	95.18 144	92.82 353	82.73 300	79.67 401	92.12 303	73.49 255	95.96 376	71.10 409	68.73 464	91.21 428

tt0320-xc		79.63 416	76.66 425	88.52 348	91.03 375	78.72 308	93.00 296	89.53 443	66.37 469	76.11 439	87.11 431	46.36 470	95.32 405	72.78 397	67.67 465	91.51 420

EG-PatchMatch MVS		82.37 378	80.34 381	88.46 349	90.27 406	79.35 295	92.80 308	94.33 299	77.14 395	73.26 456	90.18 374	47.47 465	96.72 320	70.25 413	87.32 324	89.30 452

PEN-MVS		86.80 294	86.27 278	88.40 350	92.32 327	75.71 381	95.18 144	96.38 125	87.97 137	82.82 356	93.15 268	73.39 259	95.92 378	76.15 368	79.03 420	93.59 346

Baseline_NR-MVSNet		87.07 284	86.63 262	88.40 350	91.44 356	77.87 337	94.23 216	92.57 359	84.12 261	85.74 273	92.08 307	77.25 193	96.04 370	82.29 270	79.94 410	91.30 426

UBG		85.51 327	84.57 334	88.35 352	94.21 244	71.78 428	90.07 395	89.66 438	82.28 308	85.91 269	89.01 399	61.30 396	97.06 302	76.58 363	92.06 247	96.22 217

D2MVS		85.90 319	85.09 320	88.35 352	90.79 388	77.42 352	91.83 342	95.70 203	80.77 347	80.08 393	90.02 380	66.74 347	96.37 357	81.88 281	87.97 312	91.26 427

pmmvs5		84.21 354	82.84 364	88.34 354	88.95 425	76.94 361	92.41 318	91.91 382	75.63 417	80.28 388	91.18 339	64.59 369	95.57 394	77.09 358	83.47 360	92.53 393

tt0320		80.13 409	77.41 418	88.29 355	90.50 401	78.02 330	93.10 290	90.71 415	66.06 472	76.75 432	86.97 432	49.56 460	95.40 402	71.65 401	71.41 448	91.46 423

LCM-MVSNet-Re		88.30 234	88.32 213	88.27 356	94.71 198	72.41 423	93.15 286	90.98 405	87.77 149	79.25 408	91.96 313	78.35 179	95.75 388	83.04 254	95.62 145	96.65 201

CostFormer		85.77 324	84.94 324	88.26 357	91.16 370	72.58 421	89.47 408	91.04 404	76.26 411	86.45 255	89.97 382	70.74 292	96.86 317	82.35 268	87.07 327	95.34 259

ITE_SJBPF				88.24 358	91.88 342	77.05 358		92.92 348	85.54 217	80.13 392	93.30 262	57.29 427	96.20 365	72.46 399	84.71 345	91.49 421

PVSNet	78.82 18	85.55 326	84.65 330	88.23 359	94.72 196	71.93 424	87.12 446	92.75 355	78.80 374	84.95 303	90.53 362	64.43 370	96.71 322	74.74 382	93.86 195	96.06 230

IterMVS-SCA-FT		85.45 328	84.53 335	88.18 360	91.71 349	76.87 362	90.19 392	92.65 358	85.40 226	81.44 373	90.54 361	66.79 345	95.00 411	81.04 295	81.05 393	92.66 385

EPNet_dtu		86.49 310	85.94 293	88.14 361	90.24 407	72.82 413	94.11 222	92.20 370	86.66 188	79.42 404	92.36 294	73.52 254	95.81 385	71.26 404	93.66 203	95.80 242
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Patchmtry		82.71 371	80.93 375	88.06 362	90.05 411	76.37 372	84.74 465	91.96 380	72.28 452	81.32 376	87.87 420	71.03 287	95.50 399	68.97 422	80.15 408	92.32 403

test_vis1_n_1920		89.39 200	89.84 163	88.04 363	92.97 307	72.64 418	94.71 176	96.03 172	86.18 199	91.94 127	96.56 99	61.63 391	95.74 389	93.42 65	95.11 160	95.74 244

DTE-MVSNet		86.11 316	85.48 309	87.98 364	91.65 353	74.92 388	94.93 158	95.75 196	87.36 164	82.26 362	93.04 273	72.85 265	95.82 384	74.04 388	77.46 426	93.20 364

PMMVS		85.71 325	84.96 323	87.95 365	88.90 426	77.09 357	88.68 420	90.06 427	72.32 451	86.47 252	90.76 356	72.15 275	94.40 418	81.78 284	93.49 210	92.36 401

GG-mvs-BLEND				87.94 366	89.73 418	77.91 334	87.80 434	78.23 486		80.58 385	83.86 451	59.88 410	95.33 404	71.20 405	92.22 245	90.60 440

MonoMVSNet		86.89 290	86.55 266	87.92 367	89.46 421	73.75 400	94.12 220	93.10 343	87.82 148	85.10 299	90.76 356	69.59 312	94.94 412	86.47 202	82.50 372	95.07 266

reproduce_monomvs		86.37 313	85.87 295	87.87 368	93.66 280	73.71 401	93.44 271	95.02 253	88.61 110	82.64 359	91.94 314	57.88 424	96.68 323	89.96 144	79.71 414	93.22 362

pmmvs-eth3d		80.97 400	78.72 410	87.74 369	84.99 465	79.97 274	90.11 394	91.65 387	75.36 419	73.51 454	86.03 440	59.45 413	93.96 429	75.17 376	72.21 441	89.29 454

MS-PatchMatch		85.05 339	84.16 339	87.73 370	91.42 359	78.51 316	91.25 361	93.53 333	77.50 390	80.15 390	91.58 328	61.99 388	95.51 397	75.69 371	94.35 182	89.16 456

mmtdpeth		85.04 341	84.15 340	87.72 371	93.11 297	75.74 380	94.37 206	92.83 351	84.98 240	89.31 194	86.41 437	61.61 393	97.14 295	92.63 81	62.11 476	90.29 442

test_0402		81.30 396	79.17 404	87.67 372	93.19 292	78.17 327	92.98 298	91.71 383	75.25 421	76.02 440	90.31 369	59.23 415	96.37 357	50.22 479	83.63 358	88.47 464

IterMVS		84.88 343	83.98 345	87.60 373	91.44 356	76.03 375	90.18 393	92.41 361	83.24 286	81.06 379	90.42 366	66.60 348	94.28 422	79.46 330	80.98 398	92.48 394
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Patchmatch-test		81.37 394	79.30 400	87.58 374	90.92 383	74.16 398	80.99 477	87.68 455	70.52 459	76.63 434	88.81 403	71.21 284	92.76 446	60.01 465	86.93 328	95.83 240

EPMVS		83.90 361	82.70 365	87.51 375	90.23 408	72.67 416	88.62 421	81.96 476	81.37 337	85.01 302	88.34 411	66.31 353	94.45 415	75.30 375	87.12 325	95.43 254

ADS-MVSNet2		81.66 387	79.71 395	87.50 376	91.35 362	74.19 397	83.33 470	88.48 449	72.90 446	82.24 363	85.77 443	64.98 363	93.20 440	64.57 449	83.74 355	95.12 264

OurMVSNet-221017-0		85.35 332	84.64 332	87.49 377	90.77 390	72.59 420	94.01 235	94.40 296	84.72 250	79.62 403	93.17 267	61.91 389	96.72 320	81.99 278	81.16 389	93.16 366

tpm2		84.08 356	82.94 360	87.48 378	91.39 360	71.27 433	89.23 412	90.37 419	71.95 453	84.64 308	89.33 394	67.30 337	96.55 345	75.17 376	87.09 326	94.63 285

RPSCF		85.07 338	84.27 336	87.48 378	92.91 310	70.62 442	91.69 347	92.46 360	76.20 413	82.67 358	95.22 173	63.94 376	97.29 282	77.51 353	85.80 333	94.53 292

myMVS_eth3d28		85.80 323	85.26 317	87.42 380	94.73 194	69.92 448	90.60 377	90.95 407	87.21 168	86.06 266	90.04 379	59.47 412	96.02 372	74.89 381	93.35 217	96.33 211

FE-MVSNET2		81.82 383	79.99 389	87.34 381	84.74 466	77.36 354	92.72 309	94.55 287	82.09 311	73.79 453	86.46 434	57.80 425	94.45 415	74.65 383	73.10 437	90.20 443

WBMVS		84.97 342	84.18 338	87.34 381	94.14 250	71.62 432	90.20 391	92.35 363	81.61 332	84.06 328	90.76 356	61.82 390	96.52 346	78.93 338	83.81 353	93.89 323

miper_lstm_enhance		85.27 335	84.59 333	87.31 383	91.28 365	74.63 391	87.69 439	94.09 312	81.20 343	81.36 375	89.85 386	74.97 228	94.30 421	81.03 297	79.84 413	93.01 374

FMVSNet5		81.52 392	79.60 396	87.27 384	91.17 368	77.95 332	91.49 351	92.26 369	76.87 403	76.16 436	87.91 419	51.67 454	92.34 449	67.74 431	81.16 389	91.52 419

USDC		82.76 370	81.26 373	87.26 385	91.17 368	74.55 392	89.27 410	93.39 336	78.26 385	75.30 444	92.08 307	54.43 446	96.63 328	71.64 402	85.79 334	90.61 438

test-LLR		85.87 320	85.41 310	87.25 386	90.95 379	71.67 430	89.55 404	89.88 434	83.41 280	84.54 311	87.95 417	67.25 338	95.11 408	81.82 282	93.37 215	94.97 269

test-mter		84.54 351	83.64 349	87.25 386	90.95 379	71.67 430	89.55 404	89.88 434	79.17 365	84.54 311	87.95 417	55.56 433	95.11 408	81.82 282	93.37 215	94.97 269

JIA-IIPM		81.04 397	78.98 408	87.25 386	88.64 427	73.48 405	81.75 476	89.61 440	73.19 443	82.05 366	73.71 480	66.07 358	95.87 381	71.18 407	84.60 346	92.41 398

TDRefinement		79.81 413	77.34 419	87.22 389	79.24 482	75.48 383	93.12 287	92.03 375	76.45 406	75.01 445	91.58 328	49.19 461	96.44 353	70.22 415	69.18 456	89.75 448

tpmvs		83.35 367	82.07 366	87.20 390	91.07 374	71.00 439	88.31 427	91.70 384	78.91 368	80.49 387	87.18 429	69.30 319	97.08 299	68.12 430	83.56 359	93.51 351

ppachtmachnet_test		81.84 382	80.07 386	87.15 391	88.46 431	74.43 395	89.04 416	92.16 371	75.33 420	77.75 425	88.99 400	66.20 355	95.37 403	65.12 446	77.60 424	91.65 414

dmvs_re		84.20 355	83.22 356	87.14 392	91.83 345	77.81 339	90.04 396	90.19 423	84.70 252	81.49 371	89.17 396	64.37 371	91.13 462	71.58 403	85.65 335	92.46 396

tpm cat1		81.96 379	80.27 382	87.01 393	91.09 373	71.02 438	87.38 444	91.53 392	66.25 470	80.17 389	86.35 439	68.22 334	96.15 368	69.16 421	82.29 375	93.86 329

test_fmvs1_n		87.03 286	87.04 246	86.97 394	89.74 417	71.86 425	94.55 184	94.43 293	78.47 379	91.95 126	95.50 160	51.16 456	93.81 430	93.02 73	94.56 175	95.26 260

OpenMVS_ROB	74.94 19	79.51 417	77.03 424	86.93 395	87.00 445	76.23 374	92.33 325	90.74 414	68.93 463	74.52 449	88.23 414	49.58 459	96.62 331	57.64 471	84.29 348	87.94 467

SixPastTwentyTwo		83.91 360	82.90 362	86.92 396	90.99 377	70.67 441	93.48 268	91.99 377	85.54 217	77.62 427	92.11 305	60.59 405	96.87 316	76.05 369	77.75 423	93.20 364

ADS-MVSNet		81.56 389	79.78 392	86.90 397	91.35 362	71.82 426	83.33 470	89.16 447	72.90 446	82.24 363	85.77 443	64.98 363	93.76 431	64.57 449	83.74 355	95.12 264

PatchT		82.68 372	81.27 372	86.89 398	90.09 410	70.94 440	84.06 467	90.15 424	74.91 425	85.63 276	83.57 455	69.37 315	94.87 413	65.19 444	88.50 302	94.84 279

tpm		84.73 346	84.02 343	86.87 399	90.33 405	68.90 451	89.06 415	89.94 431	80.85 346	85.75 272	89.86 385	68.54 331	95.97 375	77.76 349	84.05 352	95.75 243

Patchmatch-RL test		81.67 386	79.96 390	86.81 400	85.42 460	71.23 434	82.17 475	87.50 456	78.47 379	77.19 429	82.50 466	70.81 291	93.48 435	82.66 263	72.89 440	95.71 247

test_vis1_n		86.56 305	86.49 270	86.78 401	88.51 428	72.69 415	94.68 177	93.78 327	79.55 361	90.70 163	95.31 169	48.75 462	93.28 438	93.15 69	93.99 192	94.38 303

testing3-2		86.72 299	86.71 256	86.74 402	96.11 113	65.92 463	93.39 273	89.65 439	89.46 72	87.84 224	92.79 282	59.17 417	97.60 241	81.31 291	90.72 263	96.70 199

test_fmvs1		87.34 269	87.56 232	86.68 403	90.59 396	71.80 427	94.01 235	94.04 313	78.30 383	91.97 124	95.22 173	56.28 431	93.71 432	92.89 74	94.71 168	94.52 293

MDA-MVSNet-bldmvs		78.85 422	76.31 427	86.46 404	89.76 416	73.88 399	88.79 418	90.42 418	79.16 366	59.18 480	88.33 412	60.20 407	94.04 424	62.00 457	68.96 457	91.48 422

mvs5depth		80.98 399	79.15 405	86.45 405	84.57 467	73.29 408	87.79 435	91.67 386	80.52 349	82.20 365	89.72 388	55.14 439	95.93 377	73.93 391	66.83 467	90.12 445

tpmrst		85.35 332	84.99 321	86.43 406	90.88 386	67.88 456	88.71 419	91.43 395	80.13 353	86.08 265	88.80 405	73.05 263	96.02 372	82.48 264	83.40 363	95.40 255

TESTMET0.1,1		83.74 363	82.85 363	86.42 407	89.96 413	71.21 435	89.55 404	87.88 452	77.41 391	83.37 349	87.31 425	56.71 429	93.65 434	80.62 305	92.85 231	94.40 302

our_test_3		81.93 381	80.46 380	86.33 408	88.46 431	73.48 405	88.46 425	91.11 400	76.46 405	76.69 433	88.25 413	66.89 343	94.36 419	68.75 423	79.08 419	91.14 430

lessismore_v0				86.04 409	88.46 431	68.78 452		80.59 479		73.01 457	90.11 377	55.39 435	96.43 354	75.06 378	65.06 471	92.90 377

TinyColmap		79.76 414	77.69 416	85.97 410	91.71 349	73.12 409	89.55 404	90.36 420	75.03 423	72.03 460	90.19 373	46.22 471	96.19 367	63.11 453	81.03 394	88.59 463

KD-MVS_2432*1600		78.50 423	76.02 431	85.93 411	86.22 449	74.47 393	84.80 463	92.33 364	79.29 363	76.98 430	85.92 441	53.81 449	93.97 427	67.39 432	57.42 481	89.36 450

miper_refine_blended		78.50 423	76.02 431	85.93 411	86.22 449	74.47 393	84.80 463	92.33 364	79.29 363	76.98 430	85.92 441	53.81 449	93.97 427	67.39 432	57.42 481	89.36 450

K. test v3		81.59 388	80.15 385	85.91 413	89.89 415	69.42 450	92.57 314	87.71 454	85.56 216	73.44 455	89.71 389	55.58 432	95.52 396	77.17 356	69.76 453	92.78 382

SSC-MVS3.2		84.60 350	84.19 337	85.85 414	92.74 317	68.07 453	88.15 430	93.81 325	87.42 162	83.76 336	91.07 345	62.91 383	95.73 390	74.56 386	83.24 364	93.75 340

mvsany_test1		85.42 330	85.30 315	85.77 415	87.95 440	75.41 384	87.61 442	80.97 478	76.82 404	88.68 207	95.83 142	77.44 192	90.82 464	85.90 211	86.51 329	91.08 434

MIMVSNet1		79.38 418	77.28 420	85.69 416	86.35 448	73.67 402	91.61 349	92.75 355	78.11 388	72.64 458	88.12 415	48.16 463	91.97 455	60.32 462	77.49 425	91.43 424

UWE-MVS		83.69 364	83.09 357	85.48 417	93.06 301	65.27 468	90.92 370	86.14 460	79.90 356	86.26 261	90.72 359	57.17 428	95.81 385	71.03 410	92.62 239	95.35 258

UnsupCasMVSNet_eth		80.07 410	78.27 414	85.46 418	85.24 461	72.63 419	88.45 426	94.87 271	82.99 294	71.64 463	88.07 416	56.34 430	91.75 457	73.48 394	63.36 474	92.01 409

CL-MVSNet_self_test		81.74 385	80.53 376	85.36 419	85.96 452	72.45 422	90.25 386	93.07 345	81.24 341	79.85 399	87.29 426	70.93 289	92.52 447	66.95 435	69.23 455	91.11 432

MDA-MVSNet_test_wron		79.21 420	77.19 422	85.29 420	88.22 435	72.77 414	85.87 454	90.06 427	74.34 430	62.62 477	87.56 423	66.14 356	91.99 454	66.90 439	73.01 438	91.10 433

YYNet1		79.22 419	77.20 421	85.28 421	88.20 436	72.66 417	85.87 454	90.05 429	74.33 431	62.70 475	87.61 422	66.09 357	92.03 451	66.94 436	72.97 439	91.15 429

WB-MVSnew		83.77 362	83.28 353	85.26 422	91.48 355	71.03 437	91.89 338	87.98 451	78.91 368	84.78 305	90.22 371	69.11 324	94.02 425	64.70 448	90.44 266	90.71 436

dp		81.47 393	80.23 383	85.17 423	89.92 414	65.49 466	86.74 449	90.10 426	76.30 410	81.10 377	87.12 430	62.81 384	95.92 378	68.13 429	79.88 411	94.09 315

UnsupCasMVSNet_bld		76.23 433	73.27 437	85.09 424	83.79 469	72.92 411	85.65 457	93.47 335	71.52 454	68.84 469	79.08 472	49.77 458	93.21 439	66.81 440	60.52 478	89.13 458

usedtu_dtu_shiyan2		74.72 435	71.30 440	84.98 425	77.78 484	70.58 443	91.85 341	90.76 413	67.24 468	68.06 471	82.17 467	37.13 480	92.78 445	60.69 461	66.03 468	91.59 418

SD_0403		84.71 348	84.65 330	84.92 426	92.95 308	65.95 462	92.07 337	93.23 340	83.82 269	79.03 409	93.73 252	73.90 248	92.91 444	63.02 455	90.05 273	95.89 236

Anonymous20231206		81.03 398	79.77 394	84.82 427	87.85 441	70.26 445	91.42 352	92.08 373	73.67 438	77.75 425	89.25 395	62.43 386	93.08 441	61.50 459	82.00 380	91.12 431

FE-MVSNET		78.19 425	76.03 430	84.69 428	83.70 470	73.31 407	90.58 378	90.00 430	77.11 399	71.91 461	85.47 445	55.53 434	91.94 456	59.69 466	70.24 450	88.83 460

test0.0.03 1		82.41 376	81.69 368	84.59 429	88.23 434	72.89 412	90.24 388	87.83 453	83.41 280	79.86 398	89.78 387	67.25 338	88.99 474	65.18 445	83.42 362	91.90 411

CMPMVS	59.16 21	80.52 404	79.20 403	84.48 430	83.98 468	67.63 459	89.95 399	93.84 321	64.79 474	66.81 472	91.14 342	57.93 423	95.17 406	76.25 366	88.10 308	90.65 437
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
CVMVSNet		84.69 349	84.79 328	84.37 431	91.84 343	64.92 469	93.70 260	91.47 394	66.19 471	86.16 264	95.28 170	67.18 340	93.33 437	80.89 300	90.42 268	94.88 278

PVSNet_0	73.20 20	77.22 429	74.83 435	84.37 431	90.70 394	71.10 436	83.09 472	89.67 437	72.81 448	73.93 452	83.13 457	60.79 404	93.70 433	68.54 424	50.84 486	88.30 465

LF4IMVS		80.37 407	79.07 407	84.27 433	86.64 446	69.87 449	89.39 409	91.05 403	76.38 408	74.97 446	90.00 381	47.85 464	94.25 423	74.55 387	80.82 400	88.69 462

Anonymous20240521		80.44 406	79.21 402	84.11 434	85.75 455	67.89 455	92.86 304	93.23 340	75.61 418	75.59 443	87.47 424	50.03 457	94.33 420	71.14 408	81.21 388	90.12 445

PM-MVS		78.11 426	76.12 429	84.09 435	83.54 471	70.08 446	88.97 417	85.27 467	79.93 355	74.73 448	86.43 436	34.70 483	93.48 435	79.43 333	72.06 443	88.72 461

test_fmvs2		83.98 357	84.03 342	83.83 436	87.16 444	67.53 460	93.93 242	92.89 349	77.62 389	86.89 245	93.53 255	47.18 466	92.02 453	90.54 134	86.51 329	91.93 410

testgi		80.94 401	80.20 384	83.18 437	87.96 439	66.29 461	91.28 359	90.70 416	83.70 271	78.12 420	92.84 277	51.37 455	90.82 464	63.34 452	82.46 373	92.43 397

KD-MVS_self_test		80.20 408	79.24 401	83.07 438	85.64 456	65.29 467	91.01 367	93.93 315	78.71 377	76.32 435	86.40 438	59.20 416	92.93 443	72.59 398	69.35 454	91.00 435

testing3		80.46 405	79.59 397	83.06 439	93.44 287	64.64 470	93.33 275	85.47 465	84.34 258	79.93 397	90.84 352	44.35 474	92.39 448	57.06 473	87.56 318	92.16 407

ambc				83.06 439	79.99 480	63.51 474	77.47 485	92.86 350		74.34 451	84.45 450	28.74 484	95.06 410	73.06 396	68.89 458	90.61 438

test20.03		79.95 412	79.08 406	82.55 441	85.79 454	67.74 458	91.09 365	91.08 401	81.23 342	74.48 450	89.96 383	61.63 391	90.15 466	60.08 463	76.38 431	89.76 447

MVStest1		72.91 438	69.70 443	82.54 442	78.14 483	73.05 410	88.21 429	86.21 459	60.69 478	64.70 473	90.53 362	46.44 469	85.70 481	58.78 469	53.62 483	88.87 459

test_vis1_rt		77.96 427	76.46 426	82.48 443	85.89 453	71.74 429	90.25 386	78.89 482	71.03 458	71.30 464	81.35 469	42.49 476	91.05 463	84.55 234	82.37 374	84.65 470

EU-MVSNet		81.32 395	80.95 374	82.42 444	88.50 430	63.67 473	93.32 276	91.33 396	64.02 475	80.57 386	92.83 278	61.21 400	92.27 450	76.34 365	80.38 407	91.32 425

myMVS_eth3d		79.67 415	78.79 409	82.32 445	91.92 339	64.08 471	89.75 402	87.40 457	81.72 327	78.82 414	87.20 427	45.33 472	91.29 460	59.09 468	87.84 315	91.60 416

ttmdpeth		76.55 431	74.64 436	82.29 446	82.25 476	67.81 457	89.76 401	85.69 463	70.35 460	75.76 441	91.69 321	46.88 467	89.77 468	66.16 441	63.23 475	89.30 452

pmmvs3		71.81 441	68.71 444	81.11 447	75.86 486	70.42 444	86.74 449	83.66 471	58.95 481	68.64 470	80.89 470	36.93 481	89.52 470	63.10 454	63.59 473	83.39 471

Syy-MVS		80.07 410	79.78 392	80.94 448	91.92 339	59.93 480	89.75 402	87.40 457	81.72 327	78.82 414	87.20 427	66.29 354	91.29 460	47.06 481	87.84 315	91.60 416

UWE-MVS-28		78.98 421	78.38 413	80.80 449	88.18 437	60.66 479	90.65 375	78.51 483	78.84 372	77.93 423	90.93 349	59.08 418	89.02 473	50.96 478	90.33 270	92.72 383

new-patchmatchnet		76.41 432	75.17 434	80.13 450	82.65 475	59.61 481	87.66 440	91.08 401	78.23 386	69.85 467	83.22 456	54.76 443	91.63 459	64.14 451	64.89 472	89.16 456

mvsany_test3		74.95 434	73.26 438	80.02 451	74.61 487	63.16 475	85.53 458	78.42 484	74.16 433	74.89 447	86.46 434	36.02 482	89.09 472	82.39 267	66.91 466	87.82 468

test_fmvs3		77.67 428	77.16 423	79.22 452	79.52 481	61.14 477	92.34 324	91.64 388	73.98 435	78.86 413	86.59 433	27.38 487	87.03 476	88.12 176	75.97 433	89.50 449

DSMNet-mixed		76.94 430	76.29 428	78.89 453	83.10 473	56.11 489	87.78 436	79.77 480	60.65 479	75.64 442	88.71 406	61.56 394	88.34 475	60.07 464	89.29 291	92.21 406

EGC-MVSNET		61.97 449	56.37 454	78.77 454	89.63 419	73.50 404	89.12 414	82.79 473	0.21 500	1.24 501	84.80 448	39.48 477	90.04 467	44.13 483	75.94 434	72.79 482

new_pmnet		72.15 439	70.13 442	78.20 455	82.95 474	65.68 464	83.91 468	82.40 475	62.94 477	64.47 474	79.82 471	42.85 475	86.26 480	57.41 472	74.44 436	82.65 475

MVS-HIRNet		73.70 437	72.20 439	78.18 456	91.81 346	56.42 488	82.94 473	82.58 474	55.24 482	68.88 468	66.48 485	55.32 437	95.13 407	58.12 470	88.42 304	83.01 473

LCM-MVSNet		66.00 446	62.16 451	77.51 457	64.51 497	58.29 483	83.87 469	90.90 409	48.17 486	54.69 483	73.31 481	16.83 496	86.75 477	65.47 443	61.67 477	87.48 469

APD_test1		69.04 442	66.26 448	77.36 458	80.51 479	62.79 476	85.46 459	83.51 472	54.11 484	59.14 481	84.79 449	23.40 490	89.61 469	55.22 474	70.24 450	79.68 479

test_f		71.95 440	70.87 441	75.21 459	74.21 489	59.37 482	85.07 462	85.82 462	65.25 473	70.42 466	83.13 457	23.62 488	82.93 487	78.32 343	71.94 445	83.33 472

ANet_high		58.88 453	54.22 458	72.86 460	56.50 500	56.67 485	80.75 478	86.00 461	73.09 445	37.39 492	64.63 488	22.17 491	79.49 490	43.51 484	23.96 494	82.43 476

test_vis3_rt		65.12 447	62.60 449	72.69 461	71.44 490	60.71 478	87.17 445	65.55 494	63.80 476	53.22 484	65.65 487	14.54 497	89.44 471	76.65 360	65.38 470	67.91 485

FPMVS		64.63 448	62.55 450	70.88 462	70.80 491	56.71 484	84.42 466	84.42 469	51.78 485	49.57 485	81.61 468	23.49 489	81.48 488	40.61 488	76.25 432	74.46 481

dmvs_testset		74.57 436	75.81 433	70.86 463	87.72 442	40.47 498	87.05 447	77.90 488	82.75 299	71.15 465	85.47 445	67.98 335	84.12 485	45.26 482	76.98 430	88.00 466

N_pmnet		68.89 443	68.44 445	70.23 464	89.07 424	28.79 503	88.06 431	19.50 503	69.47 462	71.86 462	84.93 447	61.24 399	91.75 457	54.70 475	77.15 427	90.15 444

testf1		59.54 451	56.11 455	69.85 465	69.28 492	56.61 486	80.37 479	76.55 491	42.58 489	45.68 488	75.61 475	11.26 498	84.18 483	43.20 485	60.44 479	68.75 483

APD_test2		59.54 451	56.11 455	69.85 465	69.28 492	56.61 486	80.37 479	76.55 491	42.58 489	45.68 488	75.61 475	11.26 498	84.18 483	43.20 485	60.44 479	68.75 483

WB-MVS		67.92 444	67.49 446	69.21 467	81.09 477	41.17 497	88.03 432	78.00 487	73.50 440	62.63 476	83.11 459	63.94 376	86.52 478	25.66 493	51.45 485	79.94 478

PMMVS2		59.60 450	56.40 453	69.21 467	68.83 494	46.58 493	73.02 489	77.48 489	55.07 483	49.21 486	72.95 482	17.43 495	80.04 489	49.32 480	44.33 489	80.99 477

SSC-MVS		67.06 445	66.56 447	68.56 469	80.54 478	40.06 499	87.77 437	77.37 490	72.38 450	61.75 478	82.66 465	63.37 379	86.45 479	24.48 494	48.69 488	79.16 480

Gipuma		57.99 455	54.91 457	67.24 470	88.51 428	65.59 465	52.21 492	90.33 421	43.58 488	42.84 491	51.18 492	20.29 493	85.07 482	34.77 489	70.45 449	51.05 491
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	47.18 22	52.22 457	48.46 461	63.48 471	45.72 502	46.20 494	73.41 488	78.31 485	41.03 491	30.06 494	65.68 486	6.05 500	83.43 486	30.04 491	65.86 469	60.80 486
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
dongtai		58.82 454	58.24 452	60.56 472	83.13 472	45.09 496	82.32 474	48.22 502	67.61 466	61.70 479	69.15 483	38.75 478	76.05 491	32.01 490	41.31 490	60.55 487

MVE	39.65 23	43.39 459	38.59 465	57.77 473	56.52 499	48.77 492	55.38 491	58.64 498	29.33 494	28.96 495	52.65 491	4.68 501	64.62 495	28.11 492	33.07 492	59.93 488
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		50.52 458	48.47 460	56.66 474	52.26 501	18.98 505	41.51 494	81.40 477	10.10 495	44.59 490	75.01 479	28.51 485	68.16 492	53.54 476	49.31 487	82.83 474

DeepMVS_CX				56.31 475	74.23 488	51.81 491		56.67 499	44.85 487	48.54 487	75.16 478	27.87 486	58.74 497	40.92 487	52.22 484	58.39 489

kuosan		53.51 456	53.30 459	54.13 476	76.06 485	45.36 495	80.11 481	48.36 501	59.63 480	54.84 482	63.43 489	37.41 479	62.07 496	20.73 496	39.10 491	54.96 490

E-PMN		43.23 460	42.29 462	46.03 477	65.58 496	37.41 500	73.51 487	64.62 495	33.99 492	28.47 496	47.87 493	19.90 494	67.91 493	22.23 495	24.45 493	32.77 492

EMVS		42.07 461	41.12 463	44.92 478	63.45 498	35.56 502	73.65 486	63.48 496	33.05 493	26.88 497	45.45 494	21.27 492	67.14 494	19.80 497	23.02 495	32.06 493

tmp_tt		35.64 462	39.24 464	24.84 479	14.87 503	23.90 504	62.71 490	51.51 500	6.58 497	36.66 493	62.08 490	44.37 473	30.34 499	52.40 477	22.00 496	20.27 494

wuyk23d		21.27 464	20.48 467	23.63 480	68.59 495	36.41 501	49.57 493	6.85 504	9.37 496	7.89 498	4.46 500	4.03 502	31.37 498	17.47 498	16.07 497	3.12 495

test123		8.76 466	11.22 469	1.39 481	0.85 505	0.97 506	85.76 456	0.35 506	0.54 499	2.45 500	8.14 499	0.60 503	0.48 500	2.16 500	0.17 499	2.71 496

testmvs		8.92 465	11.52 468	1.12 482	1.06 504	0.46 507	86.02 453	0.65 505	0.62 498	2.74 499	9.52 498	0.31 504	0.45 501	2.38 499	0.39 498	2.46 497

mmdepth		0.00 469	0.00 472	0.00 483	0.00 506	0.00 508	0.00 495	0.00 507	0.00 501	0.00 502	0.00 501	0.00 505	0.00 502	0.00 501	0.00 500	0.00 498

monomultidepth		0.00 469	0.00 472	0.00 483	0.00 506	0.00 508	0.00 495	0.00 507	0.00 501	0.00 502	0.00 501	0.00 505	0.00 502	0.00 501	0.00 500	0.00 498

test_blank		0.00 469	0.00 472	0.00 483	0.00 506	0.00 508	0.00 495	0.00 507	0.00 501	0.00 502	0.00 501	0.00 505	0.00 502	0.00 501	0.00 500	0.00 498

uanet_test		0.00 469	0.00 472	0.00 483	0.00 506	0.00 508	0.00 495	0.00 507	0.00 501	0.00 502	0.00 501	0.00 505	0.00 502	0.00 501	0.00 500	0.00 498

DCPMVS		0.00 469	0.00 472	0.00 483	0.00 506	0.00 508	0.00 495	0.00 507	0.00 501	0.00 502	0.00 501	0.00 505	0.00 502	0.00 501	0.00 500	0.00 498

cdsmvs_eth3d_5k		22.14 463	29.52 466	0.00 483	0.00 506	0.00 508	0.00 495	95.76 195	0.00 501	0.00 502	94.29 223	75.66 220	0.00 502	0.00 501	0.00 500	0.00 498

pcd_1.5k_mvsjas		6.64 468	8.86 471	0.00 483	0.00 506	0.00 508	0.00 495	0.00 507	0.00 501	0.00 502	0.00 501	79.70 156	0.00 502	0.00 501	0.00 500	0.00 498

sosnet-low-res		0.00 469	0.00 472	0.00 483	0.00 506	0.00 508	0.00 495	0.00 507	0.00 501	0.00 502	0.00 501	0.00 505	0.00 502	0.00 501	0.00 500	0.00 498

sosnet		0.00 469	0.00 472	0.00 483	0.00 506	0.00 508	0.00 495	0.00 507	0.00 501	0.00 502	0.00 501	0.00 505	0.00 502	0.00 501	0.00 500	0.00 498

uncertanet		0.00 469	0.00 472	0.00 483	0.00 506	0.00 508	0.00 495	0.00 507	0.00 501	0.00 502	0.00 501	0.00 505	0.00 502	0.00 501	0.00 500	0.00 498

Regformer		0.00 469	0.00 472	0.00 483	0.00 506	0.00 508	0.00 495	0.00 507	0.00 501	0.00 502	0.00 501	0.00 505	0.00 502	0.00 501	0.00 500	0.00 498

ab-mvs-re		7.82 467	10.43 470	0.00 483	0.00 506	0.00 508	0.00 495	0.00 507	0.00 501	0.00 502	93.88 244	0.00 505	0.00 502	0.00 501	0.00 500	0.00 498

uanet		0.00 469	0.00 472	0.00 483	0.00 506	0.00 508	0.00 495	0.00 507	0.00 501	0.00 502	0.00 501	0.00 505	0.00 502	0.00 501	0.00 500	0.00 498

TestfortrainingZip							97.32 10

WAC-MVS						64.08 471								59.14 467

FOURS1					98.86 4	85.54 73	98.29 1	97.49 12	89.79 63	96.29 32

PC_three_1452									82.47 303	97.09 20	97.07 72	92.72 1	98.04 197	92.70 80	99.02 12	98.86 16

test_one_0601					98.58 14	85.83 67		97.44 21	91.05 23	96.78 27	98.06 22	91.45 13

eth-test2					0.00 506

eth-test					0.00 506

ZD-MVS					98.15 40	86.62 34		97.07 60	83.63 273	94.19 64	96.91 78	87.57 34	99.26 50	91.99 105	98.44 57

RE-MVS-def			93.68 72		97.92 49	84.57 93	96.28 51	96.76 92	87.46 159	93.75 75	97.43 51	82.94 99		92.73 76	97.80 92	97.88 108

IU-MVS					98.77 8	86.00 53		96.84 82	81.26 340	97.26 13				95.50 36	99.13 3	99.03 8

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

test_241102_ONE					98.77 8	85.99 55		97.44 21	90.26 47	97.71 2	97.96 31	92.31 6	99.38 35

9.14			94.47 35		97.79 58		96.08 69	97.44 21	86.13 203	95.10 54	97.40 53	88.34 25	99.22 52	93.25 68	98.70 38

save fliter					97.85 55	85.63 72	95.21 141	96.82 85	89.44 73

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

test0726					98.78 6	85.93 58	97.19 16	97.47 17	90.27 45	97.64 6	98.13 7	91.47 10

GSMVS																96.12 224

test_part2					98.55 15	87.22 20				96.40 31

sam_mvs1												71.70 280				96.12 224

sam_mvs												70.60 294

MTGPA								96.97 65

test_post1							88.00 433				9.81 497	69.31 318	95.53 395	76.65 360

test_post											10.29 496	70.57 298	95.91 380

patchmatchnet-post											83.76 452	71.53 281	96.48 349

MTMP							96.16 60	60.64 497

gm-plane-assit					89.60 420	68.00 454			77.28 394		88.99 400		97.57 244	79.44 332

test9_res														91.91 109	98.71 36	98.07 82

TEST9					97.53 67	86.49 38	94.07 228	96.78 89	81.61 332	92.77 100	96.20 108	87.71 31	99.12 62

test_8					97.49 69	86.30 46	94.02 234	96.76 92	81.86 323	92.70 104	96.20 108	87.63 32	99.02 72

agg_prior2														90.54 134	98.68 41	98.27 63

agg_prior					97.38 72	85.92 60		96.72 99		92.16 119			98.97 86

test_prior4						85.96 57	94.11 222

test_prior2							94.12 220		87.67 154	92.63 108	96.39 103	86.62 44		91.50 118	98.67 44

旧先验2							93.36 274		71.25 456	94.37 60			97.13 296	86.74 198

新几何2							93.11 289

旧先验1					96.79 85	81.81 195		95.67 206			96.81 84	86.69 42			97.66 98	96.97 180

无先验							93.28 282	96.26 139	73.95 436				99.05 66	80.56 306		96.59 203

原ACMM2							92.94 300

test222					96.55 94	81.70 200	92.22 330	95.01 254	68.36 465	90.20 174	96.14 117	80.26 143			97.80 92	96.05 231

testdata2													98.75 115	78.30 344

segment_acmp												87.16 39

testdata1							92.15 332		87.94 139

plane_prior7					94.70 199	82.74 164

plane_prior6					94.52 215	82.75 162						74.23 240

plane_prior5								96.22 144					98.12 177	88.15 173	89.99 274	94.63 285

plane_prior4											94.86 194

plane_prior3						82.75 162			90.26 47	86.91 242

plane_prior2							95.85 93		90.81 27

plane_prior1					94.59 208

plane_prior						82.73 165	95.21 141		89.66 68						89.88 279

n2								0.00 507

nn								0.00 507

door-mid								85.49 464

test11								96.57 111

door								85.33 466

HQP5-MVS						81.56 202

HQP-NCC					94.17 246		94.39 202		88.81 100	85.43 288

ACMP_Plane					94.17 246		94.39 202		88.81 100	85.43 288

BP-MVS														87.11 195

HQP4-MVS										85.43 288			97.96 213			94.51 295

HQP3-MVS								96.04 170							89.77 283

HQP2-MVS												73.83 251

NP-MVS					94.37 228	82.42 177					93.98 237

MDTV_nov1_ep13_2view						55.91 490	87.62 441		73.32 442	84.59 310		70.33 301		74.65 383		95.50 252

MDTV_nov1_ep13			83.56 350		91.69 351	69.93 447	87.75 438	91.54 391	78.60 378	84.86 304	88.90 402	69.54 313	96.03 371	70.25 413	88.93 296

ACMMP++_ref															87.47 319

ACMMP++															88.01 311

Test By Simon												80.02 145

High-resolution multi-view benchmark