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	94.37 294	95.08 1	94.68 57	97.72 39	82.94 100	99.64 4	97.85 5	98.76 33	99.06 7

SMA-MVS		95.20 10	95.07 20	95.59 6	98.14 41	88.48 8	96.26 54	97.28 40	85.90 202	97.67 4	98.10 14	88.41 23	99.56 16	94.66 49	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	91.37 123	95.55 7	95.63 143	88.73 6	97.07 23	96.77 91	90.84 26	84.02 328	96.62 95	75.95 211	99.34 42	87.77 179	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	95.29 130	96.96 68	92.09 10	95.32 49	97.08 70	89.49 17	99.33 45	95.10 44	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	93.01 339	92.58 6	94.22 62	97.20 64	80.56 139	99.59 11	97.04 20	98.68 41	98.81 22

ACMMP_NAP		94.74 25	94.56 33	95.28 11	98.02 47	87.70 12	95.68 106	97.34 30	88.28 120	95.30 50	97.67 41	85.90 54	99.54 24	93.91 57	98.95 15	98.60 28

DPE-MVS		95.57 6	95.67 6	95.25 12	98.36 31	87.28 19	95.56 118	97.51 11	89.13 87	97.14 18	97.91 32	91.64 9	99.62 5	94.61 50	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	87.76 147	95.71 44	97.70 40	88.28 26	99.35 41	93.89 58	98.78 30	98.48 35

MCST-MVS		94.45 34	94.20 51	95.19 14	98.46 22	87.50 17	95.00 153	97.12 55	87.13 168	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	95.66 109	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	92.66 309	96.56 112	83.44 276	91.68 137	95.04 183	86.60 46	98.99 81	85.60 213	97.92 85	96.93 182

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	94.36 205	96.97 65	91.07 22	93.14 88	97.56 43	84.30 81	99.56 16	93.43 64	98.75 34	98.47 38

MSP-MVS		95.42 8	95.56 8	94.98 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 48	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 64	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 68	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 60	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	94.60 207	82.33 109	98.62 131	92.40 86	92.86 227	98.27 63

MP-MVS-pluss		94.21 45	94.00 59	94.85 26	98.17 39	86.65 32	94.82 166	97.17 49	86.26 194	92.83 97	97.87 34	85.57 59	99.56 16	94.37 53	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	94.60 207	82.33 109	98.62 131	92.40 86	92.86 227	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 69	99.49 30	91.99 105	98.56 54	98.47 38

X-MVStestdata		88.31 231	86.13 280	94.85 26	98.54 16	86.60 35	96.93 27	97.19 44	90.66 34	92.85 95	23.41 483	85.02 69	99.49 30	91.99 105	98.56 54	98.47 38

SteuartSystems-ACMMP		95.20 10	95.32 13	94.85 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 54	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	95.62 144	87.61 16	95.99 79	96.07 165	89.77 64	94.12 66	94.87 191	80.56 139	98.66 123	92.42 85	93.10 223	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	95.65 111	96.89 77	89.40 75	92.81 98	96.97 75	85.37 62	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 61	99.56 16	92.06 103	98.79 28	98.44 42
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
APD-MVS		94.24 43	94.07 56	94.75 40	98.06 45	86.90 24	95.88 90	96.94 71	85.68 209	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 65	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	95.30 128	97.78 4	87.45 158	93.26 84	97.33 56	84.62 78	99.51 28	90.75 131	98.57 53	98.32 54

DVP-MVS		95.67 5	96.02 4	94.64 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	91.83 132	97.17 67	83.96 85	99.55 20	91.44 119	98.64 49	98.43 43

PHI-MVS		93.89 60	93.65 74	94.62 45	96.84 84	86.43 40	96.69 37	97.49 12	85.15 233	93.56 81	96.28 105	85.60 58	99.31 47	92.45 83	98.79 28	98.12 80

TSAR-MVS + MP.		94.85 19	94.94 24	94.58 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 51	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	95.65 141	85.73 71	94.94 156	96.69 103	91.89 12	90.69 161	95.88 134	81.99 121	99.54 24	93.14 70	97.95 84	98.39 45

train_agg		93.44 75	93.08 85	94.52 48	97.53 67	86.49 38	94.07 225	96.78 89	81.86 320	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	94.06 227	96.66 104	80.09 351	92.77 100	96.63 94	86.62 44	99.04 68	87.40 186	98.66 45	98.17 73

3Dnovator	86.66 5	91.73 120	90.82 138	94.44 49	94.59 208	86.37 42	97.18 17	97.02 62	89.20 84	84.31 323	96.66 90	73.74 251	99.17 56	86.74 196	97.96 83	97.79 116

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 77	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	86.90 178	92.62 109	96.80 86	84.85 75	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	94.40 197	93.93 312	89.77 64	94.21 63	95.59 154	87.35 37	98.61 133	92.72 78	96.15 135	97.83 113

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	96.13 116	77.97 181	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		91.04 157		85.08 67	99.01 74		98.13 75	97.86 108

SymmetryMVS		92.81 96	92.31 100	94.32 58	96.15 107	86.20 49	96.30 47	94.43 290	91.65 17	92.68 105	96.13 116	77.97 181	98.84 105	90.75 131	94.72 167	97.92 102

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	90.15 176	97.03 74	81.44 129	99.51 28	90.85 130	95.74 142	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	94.89 159	96.99 63	89.02 93	89.56 185	97.37 55	82.51 106	99.38 35	92.20 95	98.30 61	97.57 131
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	95.62 144	85.92 60	96.08 69	96.33 129	89.86 55	93.89 74	94.66 204	82.11 116	98.50 139	92.33 91	92.82 230	98.27 63

fmvsm_l_conf0.5_n_3		94.80 23	95.01 21	94.15 63	95.64 142	85.08 81	96.09 68	97.36 28	90.98 24	97.09 20	98.12 10	84.98 73	98.94 91	97.07 17	97.80 92	98.43 43

EPNet		91.79 111	91.02 132	94.10 64	90.10 406	85.25 79	96.03 76	92.05 366	92.83 5	87.39 234	95.78 145	79.39 163	99.01 74	88.13 173	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 46	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	95.42 152	85.04 82	93.06 291	97.13 54	90.74 31	91.84 130	95.09 182	86.32 49	99.21 54	91.22 121	98.45 56	97.65 125
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		91.95 108	91.28 126	93.96 68	98.33 33	85.92 60	94.66 178	96.66 104	82.69 297	90.03 178	95.82 141	82.30 111	99.03 69	84.57 231	96.48 128	96.91 184

HPM-MVS_fast		93.40 80	93.22 82	93.94 69	98.36 31	84.83 86	97.15 18	96.80 88	85.77 206	92.47 113	97.13 69	82.38 107	99.07 64	90.51 136	98.40 58	97.92 102

test_fmvsmconf0.1_n		94.20 47	94.31 43	93.88 70	92.46 321	84.80 87	96.18 59	96.82 85	89.29 81	95.68 45	98.11 11	85.10 66	98.99 81	97.38 11	97.75 96	97.86 108

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	96.91 309	94.38 52	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	93.24 282	97.24 41	88.76 103	91.60 138	95.85 138	86.07 53	98.66 123	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	87.46 156	93.75 75	97.43 51	84.24 82	99.01 74	92.73 76	97.80 92	97.88 106

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	86.96 174	93.92 73	97.47 49	83.88 86	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	95.68 106	96.43 120	86.56 186	96.84 26	97.81 37	87.56 35	98.77 114	97.14 15	96.82 118	97.16 163

CSCG		93.23 85	93.05 86	93.76 77	98.04 46	84.07 112	96.22 56	97.37 27	84.15 257	90.05 177	95.66 151	87.77 29	99.15 60	89.91 143	98.27 62	98.07 82

GDP-MVS		92.04 106	91.46 120	93.75 78	94.55 214	84.69 90	95.60 117	96.56 112	87.83 144	93.07 91	95.89 133	73.44 255	98.65 125	90.22 139	96.03 137	97.91 104

BP-MVS1		92.48 101	92.07 104	93.72 79	94.50 217	84.39 105	95.90 89	94.30 297	90.39 38	92.67 107	95.94 129	74.46 234	98.65 125	93.14 70	97.35 104	98.13 77

test_fmvsmconf0.01_n		93.19 86	93.02 87	93.71 80	89.25 419	84.42 104	96.06 73	96.29 131	89.06 88	94.68 57	98.13 7	79.22 165	98.98 85	97.22 13	97.24 106	97.74 119

UA-Net		92.83 94	92.54 97	93.68 81	96.10 114	84.71 89	95.66 109	96.39 124	91.92 11	93.22 86	96.49 100	83.16 95	98.87 99	84.47 233	95.47 149	97.45 137

fmvsm_l_conf0.5_n_a		94.20 47	94.40 38	93.60 82	95.29 156	84.98 83	95.61 114	96.28 134	86.31 192	96.75 28	97.86 35	87.40 36	98.74 118	97.07 17	97.02 111	97.07 168

QAPM		89.51 188	88.15 215	93.59 83	94.92 180	84.58 92	96.82 34	96.70 102	78.43 378	83.41 345	96.19 111	73.18 260	99.30 48	77.11 346	96.54 125	96.89 185

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	97.17 156

fmvsm_s_conf0.5_n_9		94.99 16	95.50 9	93.44 85	96.51 99	82.25 183	95.76 101	96.92 73	93.37 3	97.63 7	98.43 1	84.82 76	99.16 59	98.15 1	97.92 85	98.90 14

KinetiMVS		91.82 110	91.30 124	93.39 86	94.72 196	83.36 137	95.45 121	96.37 126	90.33 40	92.17 118	96.03 123	72.32 272	98.75 115	87.94 176	96.34 130	98.07 82

casdiffmvs_mvg		92.96 93	92.83 91	93.35 87	94.59 208	83.40 135	95.00 153	96.34 128	90.30 43	92.05 121	96.05 120	83.43 89	98.15 175	92.07 100	95.67 143	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 99	96.74 96	88.02 133	96.23 33	97.84 36	83.36 93	98.83 108	97.49 8	97.34 105	97.25 149

EI-MVSNet-Vis-set		93.01 92	92.92 89	93.29 89	95.01 171	83.51 132	94.48 187	95.77 193	90.87 25	92.52 111	96.67 89	84.50 79	99.00 79	91.99 105	94.44 180	97.36 140

Vis-MVSNet		91.75 118	91.23 127	93.29 89	95.32 155	83.78 122	96.14 64	95.98 172	89.89 53	90.45 165	96.58 97	75.09 223	98.31 166	84.75 225	96.90 114	97.78 117
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	95.51 157	84.50 79	98.79 112	94.83 47	98.86 19	97.72 121

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	95.77 146	85.02 69	98.33 163	93.03 72	98.62 50	98.13 77

VNet		92.24 105	91.91 106	93.24 92	96.59 91	83.43 133	94.84 165	96.44 119	89.19 85	94.08 70	95.90 132	77.85 187	98.17 173	88.90 163	93.38 212	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	81.87 123	99.22 52	97.86 4	97.91 87	97.20 154

VDD-MVS		90.74 146	89.92 160	93.20 94	96.27 104	83.02 155	95.73 103	93.86 316	88.42 116	92.53 110	96.84 81	62.09 379	98.64 128	90.95 127	92.62 237	97.93 101

Elysia		90.12 165	89.10 183	93.18 96	93.16 291	84.05 114	95.22 137	96.27 135	85.16 231	90.59 162	94.68 200	64.64 362	98.37 156	86.38 202	95.77 140	97.12 165

StellarMVS		90.12 165	89.10 183	93.18 96	93.16 291	84.05 114	95.22 137	96.27 135	85.16 231	90.59 162	94.68 200	64.64 362	98.37 156	86.38 202	95.77 140	97.12 165

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 57	98.24 168	93.64 61	98.17 70	98.19 71

nrg030		91.08 140	90.39 144	93.17 98	93.07 298	86.91 23	96.41 42	96.26 139	88.30 119	88.37 210	94.85 194	82.19 115	97.64 235	91.09 122	82.95 362	94.96 270

MVSMamba_PlusPlus		93.44 75	93.54 76	93.14 100	96.58 93	83.05 153	96.06 73	96.50 117	84.42 254	94.09 67	95.56 156	85.01 72	98.69 122	94.96 45	98.66 45	97.67 124

EI-MVSNet-UG-set		92.74 97	92.62 96	93.12 101	94.86 185	83.20 142	94.40 197	95.74 196	90.71 33	92.05 121	96.60 96	84.00 84	98.99 81	91.55 117	93.63 202	97.17 156

test_fmvsmvis_n_1920		93.44 75	93.55 75	93.10 102	93.67 277	84.26 108	95.83 95	96.14 156	89.00 96	92.43 114	97.50 48	83.37 92	98.72 119	96.61 24	97.44 101	96.32 210

新几何1				93.10 102	97.30 75	84.35 107		95.56 213	71.09 446	91.26 147	96.24 106	82.87 102	98.86 101	79.19 325	98.10 76	96.07 226

OMC-MVS		91.23 132	90.62 143	93.08 104	96.27 104	84.07 112	93.52 264	95.93 178	86.95 175	89.51 186	96.13 116	78.50 175	98.35 160	85.84 211	92.90 226	96.83 192

OpenMVS	83.78 11	88.74 218	87.29 237	93.08 104	92.70 316	85.39 77	96.57 40	96.43 120	78.74 373	80.85 377	96.07 119	69.64 308	99.01 74	78.01 337	96.65 123	94.83 278

MAR-MVS		90.30 161	89.37 176	93.07 106	96.61 90	84.48 98	95.68 106	95.67 204	82.36 302	87.85 221	92.85 274	76.63 200	98.80 110	80.01 311	96.68 122	95.91 232
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		90.92 141	90.21 148	93.03 107	93.86 262	83.88 119	92.81 303	93.86 316	79.84 354	91.76 134	94.29 221	77.92 184	98.04 196	90.48 137	97.11 107	97.17 156

Effi-MVS+		91.59 126	91.11 129	93.01 108	94.35 232	83.39 136	94.60 180	95.10 248	87.10 169	90.57 164	93.10 269	81.43 130	98.07 190	89.29 155	94.48 178	97.59 130

fmvsm_s_conf0.5_n_a		93.57 68	93.76 68	93.00 109	95.02 170	83.67 125	96.19 57	96.10 162	87.27 162	95.98 40	98.05 25	83.07 99	98.45 149	96.68 23	95.51 146	96.88 186

MVS_111021_LR		92.47 102	92.29 102	92.98 110	95.99 124	84.43 102	93.08 288	96.09 163	88.20 124	91.12 152	95.72 149	81.33 131	97.76 224	91.74 113	97.37 103	96.75 194

fmvsm_s_conf0.1_n_a		93.19 86	93.26 80	92.97 111	92.49 319	83.62 128	96.02 77	95.72 200	86.78 180	96.04 38	98.19 4	82.30 111	98.43 153	96.38 25	95.42 152	96.86 187

ETV-MVS		92.74 97	92.66 94	92.97 111	95.20 163	84.04 116	95.07 149	96.51 116	90.73 32	92.96 92	91.19 335	84.06 83	98.34 161	91.72 114	96.54 125	96.54 205

LFMVS		90.08 168	89.13 182	92.95 113	96.71 86	82.32 182	96.08 69	89.91 423	86.79 179	92.15 120	96.81 84	62.60 377	98.34 161	87.18 190	93.90 194	98.19 71

UGNet		89.95 175	88.95 191	92.95 113	94.51 216	83.31 138	95.70 105	95.23 241	89.37 76	87.58 228	93.94 237	64.00 367	98.78 113	83.92 240	96.31 131	96.74 195
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		90.80 144	90.10 152	92.90 115	93.04 301	83.53 131	93.08 288	94.15 305	80.22 348	91.41 144	94.91 188	76.87 194	97.93 214	90.28 138	96.90 114	97.24 150
jason: jason.
DP-MVS		87.25 272	85.36 310	92.90 115	97.65 64	83.24 140	94.81 167	92.00 368	74.99 414	81.92 366	95.00 184	72.66 265	99.05 66	66.92 427	92.33 242	96.40 207

fmvsm_s_conf0.5_n_8		94.56 30	95.12 18	92.87 117	95.96 127	81.32 210	95.76 101	97.57 8	93.48 2	97.53 10	98.32 3	81.78 126	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	95.62 144	83.17 144	96.14 64	96.12 160	88.13 127	95.82 43	98.04 28	83.43 89	98.48 141	96.97 21	96.23 132	96.92 183

fmvsm_s_conf0.1_n		93.46 72	93.66 73	92.85 119	93.75 269	83.13 146	96.02 77	95.74 196	87.68 150	95.89 41	98.17 5	82.78 103	98.46 145	96.71 22	96.17 134	96.98 177

CANet_DTU		90.26 163	89.41 175	92.81 120	93.46 284	83.01 156	93.48 265	94.47 289	89.43 74	87.76 226	94.23 226	70.54 296	99.03 69	84.97 220	96.39 129	96.38 208

MVSFormer		91.68 124	91.30 124	92.80 121	93.86 262	83.88 119	95.96 83	95.90 182	84.66 250	91.76 134	94.91 188	77.92 184	97.30 276	89.64 151	97.11 107	97.24 150

PVSNet_Blended_VisFu		91.38 129	90.91 135	92.80 121	96.39 101	83.17 144	94.87 161	96.66 104	83.29 281	89.27 192	94.46 216	80.29 142	99.17 56	87.57 183	95.37 153	96.05 229

fmvsm_l_conf0.5_n_9		94.65 27	95.28 15	92.77 123	95.95 128	81.83 194	95.53 119	97.12 55	91.68 16	97.89 1	98.06 22	85.71 56	98.65 125	97.32 12	98.26 63	97.83 113

LuminaMVS		90.55 157	89.81 162	92.77 123	92.78 314	84.21 109	94.09 223	94.17 304	85.82 203	91.54 139	94.14 228	69.93 302	97.92 215	91.62 116	94.21 188	96.18 218

fmvsm_s_conf0.5_n_6		94.11 52	94.56 33	92.76 125	94.98 175	81.96 192	95.79 97	97.29 39	89.31 79	97.52 11	97.61 42	83.25 94	98.88 98	97.05 19	98.22 69	97.43 139

VDDNet		89.56 187	88.49 206	92.76 125	95.07 169	82.09 186	96.30 47	93.19 334	81.05 342	91.88 128	96.86 80	61.16 395	98.33 163	88.43 170	92.49 241	97.84 112

viewdifsd2359ckpt09		91.18 135	90.65 142	92.75 127	94.61 207	82.36 181	94.32 206	95.74 196	84.72 247	89.66 184	95.15 180	79.69 158	98.04 196	87.70 180	94.27 187	97.85 111

h-mvs33		90.80 144	90.15 151	92.75 127	96.01 120	82.66 169	95.43 122	95.53 217	89.80 60	93.08 89	95.64 152	75.77 212	99.00 79	92.07 100	78.05 419	96.60 200

casdiffmvs		92.51 100	92.43 99	92.74 129	94.41 227	81.98 190	94.54 184	96.23 143	89.57 70	91.96 125	96.17 112	82.58 105	98.01 201	90.95 127	95.45 151	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		90.69 149	90.02 158	92.71 130	95.72 136	82.41 179	94.11 219	95.12 246	85.63 210	91.49 141	94.70 198	74.75 227	98.42 154	86.13 206	92.53 239	97.31 141

DCV-MVSNet		90.69 149	90.02 158	92.71 130	95.72 136	82.41 179	94.11 219	95.12 246	85.63 210	91.49 141	94.70 198	74.75 227	98.42 154	86.13 206	92.53 239	97.31 141

PCF-MVS	84.11 10	87.74 246	86.08 284	92.70 132	94.02 251	84.43 102	89.27 401	95.87 187	73.62 428	84.43 315	94.33 218	78.48 177	98.86 101	70.27 401	94.45 179	94.81 279
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	92.69 133	96.05 119	82.00 188	96.31 46	96.71 100	92.27 8	96.68 30	98.39 2	85.32 63	98.92 94	97.20 14	98.16 71	97.17 156

SSM_0404		90.73 147	90.08 153	92.69 133	95.00 174	83.13 146	94.32 206	95.00 256	85.41 221	89.84 179	95.35 165	76.13 203	97.98 206	85.46 216	94.18 189	96.95 179

baseline		92.39 104	92.29 102	92.69 133	94.46 222	81.77 197	94.14 216	96.27 135	89.22 83	91.88 128	96.00 124	82.35 108	97.99 203	91.05 123	95.27 157	98.30 55

MSLP-MVS++		93.72 66	94.08 55	92.65 136	97.31 74	83.43 133	95.79 97	97.33 32	90.03 50	93.58 79	96.96 76	84.87 74	97.76 224	92.19 96	98.66 45	96.76 193

EC-MVSNet		93.44 75	93.71 71	92.63 137	95.21 162	82.43 176	97.27 14	96.71 100	90.57 36	92.88 94	95.80 142	83.16 95	98.16 174	93.68 59	98.14 74	97.31 141

ab-mvs		89.41 195	88.35 208	92.60 138	95.15 167	82.65 173	92.20 328	95.60 211	83.97 261	88.55 206	93.70 251	74.16 242	98.21 172	82.46 264	89.37 286	96.94 181

LS3D		87.89 241	86.32 273	92.59 139	96.07 117	82.92 159	95.23 135	94.92 264	75.66 406	82.89 352	95.98 126	72.48 269	99.21 54	68.43 415	95.23 158	95.64 246

Anonymous20240529		88.09 237	86.59 262	92.58 140	96.53 96	81.92 193	95.99 79	95.84 189	74.11 423	89.06 196	95.21 175	61.44 387	98.81 109	83.67 247	87.47 317	97.01 175

fmvsm_s_conf0.5_n_3		94.49 32	95.13 17	92.56 141	95.49 150	81.10 220	95.93 86	97.16 50	92.96 4	97.39 12	98.13 7	83.63 88	98.80 110	97.89 3	97.61 99	97.78 117

CPTT-MVS		91.99 107	91.80 107	92.55 142	98.24 37	81.98 190	96.76 35	96.49 118	81.89 319	90.24 169	96.44 102	78.59 173	98.61 133	89.68 149	97.85 89	97.06 169

viewdifsd2359ckpt13		91.20 134	90.75 140	92.54 143	94.30 236	82.13 185	94.03 229	95.89 184	85.60 212	90.20 171	95.36 164	79.69 158	97.90 218	87.85 178	93.86 195	97.61 127

114514_t		89.51 188	88.50 204	92.54 143	98.11 42	81.99 189	95.16 145	96.36 127	70.19 450	85.81 268	95.25 171	76.70 198	98.63 130	82.07 274	96.86 117	97.00 176

PAPM_NR		91.22 133	90.78 139	92.52 145	97.60 65	81.46 206	94.37 203	96.24 142	86.39 191	87.41 231	94.80 196	82.06 119	98.48 141	82.80 259	95.37 153	97.61 127

mamba_0408		89.06 208	87.92 222	92.50 146	94.76 190	82.66 169	79.84 470	94.64 282	85.18 226	88.96 198	95.00 184	76.00 208	97.98 206	83.74 244	93.15 220	96.85 188

DeepPCF-MVS	89.96 1	94.20 47	94.77 31	92.49 147	96.52 97	80.00 267	94.00 234	97.08 59	90.05 49	95.65 46	97.29 57	89.66 15	98.97 86	93.95 56	98.71 36	98.50 32

SSM_0407		90.47 159	89.80 163	92.46 148	94.76 190	82.66 169	93.98 236	95.00 256	85.41 221	88.96 198	95.35 165	76.13 203	97.88 219	85.46 216	93.15 220	96.85 188

IS-MVSNet		91.43 128	91.09 131	92.46 148	95.87 131	81.38 209	96.95 24	93.69 325	89.72 66	89.50 188	95.98 126	78.57 174	97.77 223	83.02 253	96.50 127	98.22 70

API-MVS		90.66 152	90.07 154	92.45 150	96.36 102	84.57 93	96.06 73	95.22 243	82.39 300	89.13 193	94.27 224	80.32 141	98.46 145	80.16 310	96.71 121	94.33 302

xiu_mvs_v1_base_debu		90.64 153	90.05 155	92.40 151	93.97 257	84.46 99	93.32 273	95.46 221	85.17 228	92.25 115	94.03 229	70.59 292	98.57 136	90.97 124	94.67 169	94.18 305

xiu_mvs_v1_base		90.64 153	90.05 155	92.40 151	93.97 257	84.46 99	93.32 273	95.46 221	85.17 228	92.25 115	94.03 229	70.59 292	98.57 136	90.97 124	94.67 169	94.18 305

xiu_mvs_v1_base_debi		90.64 153	90.05 155	92.40 151	93.97 257	84.46 99	93.32 273	95.46 221	85.17 228	92.25 115	94.03 229	70.59 292	98.57 136	90.97 124	94.67 169	94.18 305

fmvsm_s_conf0.5_n_2		93.47 71	93.83 62	92.39 154	95.36 153	81.19 216	95.20 142	96.56 112	90.37 39	97.13 19	98.03 29	77.47 190	98.96 88	97.79 6	96.58 124	97.03 172

viewmacassd2359aftdt		91.67 125	91.43 122	92.37 155	93.95 260	81.00 224	93.90 244	95.97 175	87.75 148	91.45 143	96.04 122	79.92 148	97.97 208	89.26 156	94.67 169	98.14 76

viewmanbaseed2359cas		91.78 114	91.58 114	92.37 155	94.32 234	81.07 221	93.76 250	95.96 176	87.26 163	91.50 140	95.88 134	80.92 137	97.97 208	89.70 148	94.92 163	98.07 82

fmvsm_s_conf0.1_n_2		93.16 88	93.42 77	92.37 155	94.62 204	81.13 218	95.23 135	95.89 184	90.30 43	96.74 29	98.02 30	76.14 202	98.95 90	97.64 7	96.21 133	97.03 172

AdaColmap		89.89 178	89.07 185	92.37 155	97.41 71	83.03 154	94.42 194	95.92 179	82.81 294	86.34 257	94.65 205	73.89 247	99.02 72	80.69 301	95.51 146	95.05 265

CNLPA		89.07 207	87.98 219	92.34 159	96.87 83	84.78 88	94.08 224	93.24 331	81.41 333	84.46 313	95.13 181	75.57 219	96.62 323	77.21 344	93.84 197	95.61 249

fmvsm_s_conf0.5_n_4		93.86 61	94.37 40	92.33 160	95.13 168	80.95 227	95.64 112	96.97 65	89.60 69	96.85 25	97.77 38	83.08 98	98.92 94	97.49 8	96.78 119	97.13 164

ET-MVSNet_ETH3D		87.51 260	85.91 292	92.32 161	93.70 276	83.93 117	92.33 322	90.94 400	84.16 256	72.09 448	92.52 287	69.90 303	95.85 371	89.20 157	88.36 304	97.17 156

E4		91.74 119	91.55 117	92.31 162	94.27 238	80.80 237	93.81 247	96.17 153	87.97 135	91.11 153	96.05 120	80.75 138	98.08 188	89.78 144	94.02 191	98.06 87

E2		91.79 111	91.61 112	92.31 162	94.49 218	80.86 233	93.74 252	96.19 148	87.63 153	91.16 148	95.94 129	81.31 132	98.06 191	89.76 145	94.29 185	97.99 92

Anonymous202405211		87.68 247	86.13 280	92.31 162	96.66 88	80.74 239	94.87 161	91.49 385	80.47 347	89.46 189	95.44 160	54.72 434	98.23 169	82.19 270	89.89 276	97.97 94

E3		91.78 114	91.61 112	92.30 165	94.48 219	80.86 233	93.73 253	96.19 148	87.63 153	91.16 148	95.95 128	81.30 133	98.06 191	89.76 145	94.29 185	97.99 92

CHOSEN 1792x2688		88.84 214	87.69 227	92.30 165	96.14 108	81.42 208	90.01 388	95.86 188	74.52 419	87.41 231	93.94 237	75.46 220	98.36 158	80.36 306	95.53 145	97.12 165

viewcassd2359sk11		91.79 111	91.62 111	92.29 167	94.62 204	80.88 231	93.70 257	96.18 152	87.38 160	91.13 151	95.85 138	81.62 128	98.06 191	89.71 147	94.40 181	97.94 96

HY-MVS	83.01 12	89.03 210	87.94 221	92.29 167	94.86 185	82.77 161	92.08 333	94.49 288	81.52 332	86.93 238	92.79 280	78.32 179	98.23 169	79.93 312	90.55 263	95.88 235

CDS-MVSNet		89.45 191	88.51 203	92.29 167	93.62 279	83.61 130	93.01 292	94.68 280	81.95 313	87.82 224	93.24 263	78.69 171	96.99 303	80.34 307	93.23 217	96.28 213
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PAPR		90.02 171	89.27 181	92.29 167	95.78 133	80.95 227	92.68 308	96.22 144	81.91 315	86.66 248	93.75 249	82.23 113	98.44 151	79.40 324	94.79 166	97.48 135

E3new		91.76 117	91.58 114	92.28 171	94.69 201	80.90 230	93.68 260	96.17 153	87.15 166	91.09 156	95.70 150	81.75 127	98.05 195	89.67 150	94.35 182	97.90 105

mvsmamba		90.33 160	89.69 166	92.25 172	95.17 164	81.64 199	95.27 133	93.36 330	84.88 240	89.51 186	94.27 224	69.29 317	97.42 261	89.34 154	96.12 136	97.68 123

E6		91.71 121	91.55 117	92.20 173	94.32 234	80.62 243	94.41 195	96.19 148	88.06 131	91.11 153	96.16 113	79.92 148	98.03 199	90.00 140	93.80 199	97.94 96

E5		91.71 121	91.55 117	92.20 173	94.33 233	80.62 243	94.41 195	96.19 148	88.06 131	91.11 153	96.16 113	79.92 148	98.03 199	90.00 140	93.80 199	97.94 96

PLC	84.53 7	89.06 208	88.03 217	92.15 175	97.27 77	82.69 168	94.29 208	95.44 226	79.71 356	84.01 329	94.18 227	76.68 199	98.75 115	77.28 343	93.41 211	95.02 266
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
EPP-MVSNet		91.70 123	91.56 116	92.13 176	95.88 129	80.50 248	97.33 8	95.25 240	86.15 197	89.76 183	95.60 153	83.42 91	98.32 165	87.37 188	93.25 216	97.56 132

patch_mono-2		93.74 65	94.32 41	92.01 177	97.54 66	78.37 309	93.40 269	97.19 44	88.02 133	94.99 56	97.21 62	88.35 24	98.44 151	94.07 55	98.09 77	99.23 1

原ACMM1				92.01 177	97.34 73	81.05 222		96.81 87	78.89 367	90.45 165	95.92 131	82.65 104	98.84 105	80.68 302	98.26 63	96.14 220

UniMVSNet (Re)		89.80 181	89.07 185	92.01 177	93.60 280	84.52 96	94.78 169	97.47 17	89.26 82	86.44 254	92.32 293	82.10 117	97.39 272	84.81 224	80.84 396	94.12 309

MG-MVS		91.77 116	91.70 110	92.00 180	97.08 80	80.03 265	93.60 262	95.18 244	87.85 143	90.89 159	96.47 101	82.06 119	98.36 158	85.07 219	97.04 110	97.62 126

EIA-MVS		91.95 108	91.94 105	91.98 181	95.16 165	80.01 266	95.36 123	96.73 97	88.44 114	89.34 190	92.16 298	83.82 87	98.45 149	89.35 153	97.06 109	97.48 135

PVSNet_Blended		90.73 147	90.32 146	91.98 181	96.12 110	81.25 212	92.55 313	96.83 83	82.04 311	89.10 194	92.56 286	81.04 135	98.85 103	86.72 198	95.91 138	95.84 237

guyue		91.12 138	90.84 137	91.96 183	94.59 208	80.57 246	94.87 161	93.71 324	88.96 97	91.14 150	95.22 172	73.22 259	97.76 224	92.01 104	93.81 198	97.54 134

PS-MVSNAJ		91.18 135	90.92 134	91.96 183	95.26 160	82.60 175	92.09 332	95.70 202	86.27 193	91.84 130	92.46 288	79.70 155	98.99 81	89.08 158	95.86 139	94.29 303

TAMVS		89.21 201	88.29 212	91.96 183	93.71 274	82.62 174	93.30 277	94.19 302	82.22 305	87.78 225	93.94 237	78.83 168	96.95 306	77.70 339	92.98 225	96.32 210

SDMVSNet		90.19 164	89.61 169	91.93 186	96.00 121	83.09 151	92.89 300	95.98 172	88.73 104	86.85 244	95.20 176	72.09 275	97.08 295	88.90 163	89.85 278	95.63 247

FA-MVS(test-final)		89.66 183	88.91 193	91.93 186	94.57 212	80.27 252	91.36 349	94.74 277	84.87 241	89.82 180	92.61 285	74.72 230	98.47 144	83.97 239	93.53 206	97.04 171

MVS_Test		91.31 131	91.11 129	91.93 186	94.37 228	80.14 257	93.46 267	95.80 191	86.46 189	91.35 146	93.77 247	82.21 114	98.09 186	87.57 183	94.95 162	97.55 133

NR-MVSNet		88.58 224	87.47 233	91.93 186	93.04 301	84.16 111	94.77 170	96.25 141	89.05 89	80.04 391	93.29 261	79.02 167	97.05 300	81.71 285	80.05 406	94.59 286

HyFIR lowres test		88.09 237	86.81 250	91.93 186	96.00 121	80.63 241	90.01 388	95.79 192	73.42 430	87.68 227	92.10 304	73.86 248	97.96 210	80.75 300	91.70 246	97.19 155

GeoE		90.05 169	89.43 174	91.90 191	95.16 165	80.37 251	95.80 96	94.65 281	83.90 262	87.55 230	94.75 197	78.18 180	97.62 237	81.28 290	93.63 202	97.71 122

thisisatest0530		88.67 219	87.61 229	91.86 192	94.87 184	80.07 261	94.63 179	89.90 424	84.00 260	88.46 208	93.78 246	66.88 341	98.46 145	83.30 249	92.65 232	97.06 169

xiu_mvs_v2_base		91.13 137	90.89 136	91.86 192	94.97 176	82.42 177	92.24 325	95.64 209	86.11 201	91.74 136	93.14 267	79.67 160	98.89 97	89.06 159	95.46 150	94.28 304

DU-MVS		89.34 200	88.50 204	91.85 194	93.04 301	83.72 123	94.47 190	96.59 109	89.50 71	86.46 251	93.29 261	77.25 192	97.23 285	84.92 221	81.02 392	94.59 286

AstraMVS		90.69 149	90.30 147	91.84 195	93.81 265	79.85 272	94.76 171	92.39 354	88.96 97	91.01 158	95.87 137	70.69 290	97.94 213	92.49 82	92.70 231	97.73 120

OPM-MVS		90.12 165	89.56 170	91.82 196	93.14 293	83.90 118	94.16 215	95.74 196	88.96 97	87.86 220	95.43 162	72.48 269	97.91 216	88.10 175	90.18 270	93.65 340
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
HQP_MVS		90.60 156	90.19 149	91.82 196	94.70 199	82.73 165	95.85 93	96.22 144	90.81 27	86.91 240	94.86 192	74.23 238	98.12 176	88.15 171	89.99 272	94.63 283

UniMVSNet_NR-MVSNet		89.92 177	89.29 179	91.81 198	93.39 286	83.72 123	94.43 193	97.12 55	89.80 60	86.46 251	93.32 258	83.16 95	97.23 285	84.92 221	81.02 392	94.49 296

diffmvs		91.37 130	91.23 127	91.77 199	93.09 296	80.27 252	92.36 319	95.52 218	87.03 171	91.40 145	94.93 187	80.08 145	97.44 259	92.13 99	94.56 175	97.61 127
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		91.51 127	91.44 121	91.73 200	93.09 296	80.27 252	92.51 314	95.58 212	87.22 164	91.80 133	95.57 155	79.96 147	97.48 251	92.23 93	94.97 161	97.45 137

1112_ss		88.42 226	87.33 236	91.72 201	94.92 180	80.98 225	92.97 296	94.54 285	78.16 384	83.82 332	93.88 242	78.78 170	97.91 216	79.45 320	89.41 285	96.26 214

Fast-Effi-MVS+		89.41 195	88.64 199	91.71 202	94.74 193	80.81 236	93.54 263	95.10 248	83.11 285	86.82 246	90.67 358	79.74 154	97.75 228	80.51 305	93.55 204	96.57 203

WTY-MVS		89.60 185	88.92 192	91.67 203	95.47 151	81.15 217	92.38 318	94.78 275	83.11 285	89.06 196	94.32 219	78.67 172	96.61 326	81.57 286	90.89 259	97.24 150

TAPA-MVS	84.62 6	88.16 235	87.01 245	91.62 204	96.64 89	80.65 240	94.39 199	96.21 147	76.38 399	86.19 261	95.44 160	79.75 153	98.08 188	62.75 445	95.29 155	96.13 221
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
VPA-MVSNet		89.62 184	88.96 190	91.60 205	93.86 262	82.89 160	95.46 120	97.33 32	87.91 138	88.43 209	93.31 259	74.17 241	97.40 269	87.32 189	82.86 367	94.52 291

FE-MVS		87.40 265	86.02 286	91.57 206	94.56 213	79.69 276	90.27 375	93.72 323	80.57 345	88.80 202	91.62 324	65.32 357	98.59 135	74.97 369	94.33 184	96.44 206

XVG-OURS		89.40 197	88.70 198	91.52 207	94.06 249	81.46 206	91.27 354	96.07 165	86.14 198	88.89 201	95.77 146	68.73 326	97.26 282	87.39 187	89.96 274	95.83 238

hse-mvs2		89.88 179	89.34 177	91.51 208	94.83 187	81.12 219	93.94 238	93.91 315	89.80 60	93.08 89	93.60 252	75.77 212	97.66 232	92.07 100	77.07 426	95.74 242

TranMVSNet+NR-MVSNet		88.84 214	87.95 220	91.49 209	92.68 317	83.01 156	94.92 158	96.31 130	89.88 54	85.53 277	93.85 244	76.63 200	96.96 305	81.91 278	79.87 409	94.50 294

AUN-MVS		87.78 245	86.54 265	91.48 210	94.82 188	81.05 222	93.91 242	93.93 312	83.00 289	86.93 238	93.53 253	69.50 311	97.67 230	86.14 204	77.12 425	95.73 244

XVG-OURS-SEG-HR		89.95 175	89.45 172	91.47 211	94.00 255	81.21 215	91.87 337	96.06 167	85.78 205	88.55 206	95.73 148	74.67 231	97.27 280	88.71 167	89.64 283	95.91 232

MVS		87.44 263	86.10 283	91.44 212	92.61 318	83.62 128	92.63 310	95.66 206	67.26 456	81.47 369	92.15 299	77.95 183	98.22 171	79.71 314	95.48 148	92.47 384

viewdifsd2359ckpt07		91.11 139	91.02 132	91.41 213	94.21 242	78.37 309	92.91 299	95.71 201	87.50 155	90.32 168	95.88 134	80.27 143	97.99 203	88.78 166	93.55 204	97.86 108

F-COLMAP		87.95 240	86.80 251	91.40 214	96.35 103	80.88 231	94.73 173	95.45 224	79.65 357	82.04 364	94.61 206	71.13 282	98.50 139	76.24 356	91.05 257	94.80 280

dcpmvs_2		93.49 70	94.19 52	91.38 215	97.69 63	76.78 352	94.25 210	96.29 131	88.33 117	94.46 59	96.88 79	88.07 28	98.64 128	93.62 62	98.09 77	98.73 23

thisisatest0515		87.33 268	85.99 287	91.37 216	93.49 282	79.55 277	90.63 369	89.56 432	80.17 349	87.56 229	90.86 348	67.07 338	98.28 167	81.50 287	93.02 224	96.29 212

HQP-MVS		89.80 181	89.28 180	91.34 217	94.17 244	81.56 200	94.39 199	96.04 168	88.81 100	85.43 286	93.97 236	73.83 249	97.96 210	87.11 193	89.77 281	94.50 294

fmvsm_s_conf0.5_n_7		93.15 89	93.76 68	91.31 218	94.42 226	79.48 279	94.52 185	97.14 53	89.33 78	94.17 65	98.09 18	81.83 124	97.49 250	96.33 26	98.02 81	96.95 179

RRT-MVS		90.85 143	90.70 141	91.30 219	94.25 239	76.83 351	94.85 164	96.13 159	89.04 90	90.23 170	94.88 190	70.15 301	98.72 119	91.86 112	94.88 164	98.34 48

FMVSNet3		87.40 265	86.11 282	91.30 219	93.79 268	83.64 127	94.20 214	94.81 273	83.89 263	84.37 316	91.87 315	68.45 329	96.56 332	78.23 334	85.36 335	93.70 339

FMVSNet2		87.19 278	85.82 295	91.30 219	94.01 252	83.67 125	94.79 168	94.94 259	83.57 271	83.88 331	92.05 308	66.59 346	96.51 336	77.56 341	85.01 338	93.73 337

RPMNet		83.95 356	81.53 367	91.21 222	90.58 395	79.34 285	85.24 448	96.76 92	71.44 444	85.55 275	82.97 455	70.87 287	98.91 96	61.01 449	89.36 287	95.40 253

IB-MVS	80.51 15	85.24 333	83.26 351	91.19 223	92.13 330	79.86 271	91.75 340	91.29 390	83.28 282	80.66 381	88.49 406	61.28 389	98.46 145	80.99 296	79.46 413	95.25 259
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		89.47 190	88.90 194	91.18 224	94.22 241	82.07 187	92.13 330	96.09 163	87.90 139	85.37 292	92.45 289	74.38 236	97.56 242	87.15 191	90.43 265	93.93 318
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		89.45 191	88.90 194	91.12 225	94.47 220	81.49 204	95.30 128	96.14 156	86.73 182	85.45 283	95.16 178	69.89 304	98.10 178	87.70 180	89.23 290	93.77 333

LGP-MVS_train				91.12 225	94.47 220	81.49 204		96.14 156	86.73 182	85.45 283	95.16 178	69.89 304	98.10 178	87.70 180	89.23 290	93.77 333

ACMM	84.12 9	89.14 203	88.48 207	91.12 225	94.65 203	81.22 214	95.31 126	96.12 160	85.31 225	85.92 266	94.34 217	70.19 300	98.06 191	85.65 212	88.86 295	94.08 313
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tttt0517		88.61 221	87.78 226	91.11 228	94.96 177	77.81 327	95.35 124	89.69 427	85.09 235	88.05 218	94.59 209	66.93 339	98.48 141	83.27 250	92.13 244	97.03 172

GBi-Net		87.26 270	85.98 288	91.08 229	94.01 252	83.10 148	95.14 146	94.94 259	83.57 271	84.37 316	91.64 320	66.59 346	96.34 349	78.23 334	85.36 335	93.79 328

test1		87.26 270	85.98 288	91.08 229	94.01 252	83.10 148	95.14 146	94.94 259	83.57 271	84.37 316	91.64 320	66.59 346	96.34 349	78.23 334	85.36 335	93.79 328

FMVSNet1		85.85 318	84.11 338	91.08 229	92.81 312	83.10 148	95.14 146	94.94 259	81.64 327	82.68 354	91.64 320	59.01 411	96.34 349	75.37 363	83.78 351	93.79 328

Test_1112_low_res		87.65 249	86.51 266	91.08 229	94.94 179	79.28 289	91.77 339	94.30 297	76.04 404	83.51 342	92.37 291	77.86 186	97.73 229	78.69 329	89.13 292	96.22 215

PS-MVSNAJss		89.97 173	89.62 168	91.02 233	91.90 339	80.85 235	95.26 134	95.98 172	86.26 194	86.21 260	94.29 221	79.70 155	97.65 233	88.87 165	88.10 306	94.57 288

BH-RMVSNet		88.37 229	87.48 232	91.02 233	95.28 157	79.45 281	92.89 300	93.07 337	85.45 220	86.91 240	94.84 195	70.35 297	97.76 224	73.97 378	94.59 174	95.85 236

UniMVSNet_ETH3D		87.53 259	86.37 270	91.00 235	92.44 322	78.96 294	94.74 172	95.61 210	84.07 259	85.36 293	94.52 211	59.78 403	97.34 274	82.93 254	87.88 311	96.71 196

FIs		90.51 158	90.35 145	90.99 236	93.99 256	80.98 225	95.73 103	97.54 10	89.15 86	86.72 247	94.68 200	81.83 124	97.24 284	85.18 218	88.31 305	94.76 281

ACMP	84.23 8	89.01 212	88.35 208	90.99 236	94.73 194	81.27 211	95.07 149	95.89 184	86.48 187	83.67 337	94.30 220	69.33 313	97.99 203	87.10 195	88.55 297	93.72 338
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
Anonymous20231211		86.59 301	85.13 316	90.98 238	96.52 97	81.50 202	96.14 64	96.16 155	73.78 426	83.65 338	92.15 299	63.26 373	97.37 273	82.82 258	81.74 381	94.06 314

IMVS_0403		89.97 173	89.64 167	90.96 239	93.72 270	77.75 332	93.00 293	95.34 235	85.53 216	88.77 203	94.49 212	78.49 176	97.84 220	84.75 225	92.65 232	97.28 144

sss		88.93 213	88.26 214	90.94 240	94.05 250	80.78 238	91.71 341	95.38 230	81.55 331	88.63 205	93.91 241	75.04 224	95.47 390	82.47 263	91.61 247	96.57 203

IMVS_0407		89.85 180	89.51 171	90.88 241	93.72 270	77.75 332	93.07 290	95.34 235	85.53 216	88.34 211	94.49 212	77.69 188	97.60 238	84.75 225	92.65 232	97.28 144

viewmambaseed2359dif		90.04 170	89.78 164	90.83 242	92.85 311	77.92 321	92.23 326	95.01 252	81.90 316	90.20 171	95.45 159	79.64 162	97.34 274	87.52 185	93.17 218	97.23 153

sd_testset		88.59 223	87.85 225	90.83 242	96.00 121	80.42 250	92.35 320	94.71 278	88.73 104	86.85 244	95.20 176	67.31 333	96.43 343	79.64 316	89.85 278	95.63 247

PVSNet_BlendedMVS		89.98 172	89.70 165	90.82 244	96.12 110	81.25 212	93.92 240	96.83 83	83.49 275	89.10 194	92.26 296	81.04 135	98.85 103	86.72 198	87.86 312	92.35 391

cascas		86.43 309	84.98 319	90.80 245	92.10 332	80.92 229	90.24 379	95.91 181	73.10 433	83.57 341	88.39 407	65.15 359	97.46 255	84.90 223	91.43 249	94.03 316

ECVR-MVS		89.09 206	88.53 202	90.77 246	95.62 144	75.89 365	96.16 60	84.22 458	87.89 141	90.20 171	96.65 91	63.19 374	98.10 178	85.90 209	96.94 112	98.33 50

GA-MVS		86.61 299	85.27 313	90.66 247	91.33 362	78.71 298	90.40 374	93.81 320	85.34 224	85.12 296	89.57 388	61.25 390	97.11 294	80.99 296	89.59 284	96.15 219

thres600view7		87.65 249	86.67 257	90.59 248	96.08 116	78.72 296	94.88 160	91.58 381	87.06 170	88.08 216	92.30 294	68.91 323	98.10 178	70.05 408	91.10 252	94.96 270

thres400		87.62 254	86.64 258	90.57 249	95.99 124	78.64 299	94.58 181	91.98 370	86.94 176	88.09 214	91.77 316	69.18 319	98.10 178	70.13 405	91.10 252	94.96 270

baseline1		88.10 236	87.28 238	90.57 249	94.96 177	80.07 261	94.27 209	91.29 390	86.74 181	87.41 231	94.00 234	76.77 197	96.20 354	80.77 299	79.31 415	95.44 251

viewdifsd2359ckpt11		89.43 193	89.05 187	90.56 251	92.89 309	77.00 347	92.81 303	94.52 286	87.03 171	89.77 181	95.79 143	74.67 231	97.51 246	88.97 161	84.98 339	97.17 156

viewmsd2359difaftdt		89.43 193	89.05 187	90.56 251	92.89 309	77.00 347	92.81 303	94.52 286	87.03 171	89.77 181	95.79 143	74.67 231	97.51 246	88.97 161	84.98 339	97.17 156

FE-MVSNET3		86.84 289	85.61 303	90.53 253	90.50 399	81.80 196	90.97 362	94.96 258	83.05 287	83.50 343	90.32 365	72.15 273	96.65 320	79.49 318	85.55 334	93.15 363

FC-MVSNet-test		90.27 162	90.18 150	90.53 253	93.71 274	79.85 272	95.77 99	97.59 7	89.31 79	86.27 258	94.67 203	81.93 122	97.01 302	84.26 235	88.09 308	94.71 282

PAPM		86.68 298	85.39 308	90.53 253	93.05 300	79.33 288	89.79 391	94.77 276	78.82 370	81.95 365	93.24 263	76.81 195	97.30 276	66.94 425	93.16 219	94.95 274

WR-MVS		88.38 228	87.67 228	90.52 256	93.30 288	80.18 255	93.26 280	95.96 176	88.57 112	85.47 282	92.81 278	76.12 205	96.91 309	81.24 291	82.29 372	94.47 299

SSM_04072		88.57 225	87.92 222	90.51 257	94.76 190	82.66 169	79.84 470	94.64 282	85.18 226	88.96 198	95.00 184	76.00 208	92.03 439	83.74 244	93.15 220	96.85 188

MVSTER		88.84 214	88.29 212	90.51 257	92.95 306	80.44 249	93.73 253	95.01 252	84.66 250	87.15 235	93.12 268	72.79 264	97.21 287	87.86 177	87.36 320	93.87 323

testdata				90.49 259	96.40 100	77.89 324		95.37 232	72.51 438	93.63 78	96.69 87	82.08 118	97.65 233	83.08 251	97.39 102	95.94 231

test1111		89.10 204	88.64 199	90.48 260	95.53 149	74.97 375	96.08 69	84.89 456	88.13 127	90.16 175	96.65 91	63.29 372	98.10 178	86.14 204	96.90 114	98.39 45

tt0805		86.92 286	85.74 301	90.48 260	92.22 326	79.98 268	95.63 113	94.88 267	83.83 265	84.74 305	92.80 279	57.61 418	97.67 230	85.48 215	84.42 344	93.79 328

jajsoiax		88.24 233	87.50 231	90.48 260	90.89 383	80.14 257	95.31 126	95.65 208	84.97 238	84.24 324	94.02 232	65.31 358	97.42 261	88.56 168	88.52 299	93.89 319

PatchMatch-RL		86.77 295	85.54 304	90.47 263	95.88 129	82.71 167	90.54 372	92.31 358	79.82 355	84.32 321	91.57 328	68.77 325	96.39 345	73.16 384	93.48 210	92.32 392

tfpn200view9		87.58 257	86.64 258	90.41 264	95.99 124	78.64 299	94.58 181	91.98 370	86.94 176	88.09 214	91.77 316	69.18 319	98.10 178	70.13 405	91.10 252	94.48 297

VPNet		88.20 234	87.47 233	90.39 265	93.56 281	79.46 280	94.04 228	95.54 216	88.67 107	86.96 237	94.58 210	69.33 313	97.15 289	84.05 238	80.53 401	94.56 289

ACMH	80.38 17	85.36 328	83.68 345	90.39 265	94.45 223	80.63 241	94.73 173	94.85 269	82.09 307	77.24 417	92.65 283	60.01 401	97.58 240	72.25 389	84.87 341	92.96 369
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
thres100view900		87.63 252	86.71 254	90.38 267	96.12 110	78.55 302	95.03 152	91.58 381	87.15 166	88.06 217	92.29 295	68.91 323	98.10 178	70.13 405	91.10 252	94.48 297

mvs_tets		88.06 239	87.28 238	90.38 267	90.94 379	79.88 270	95.22 137	95.66 206	85.10 234	84.21 325	93.94 237	63.53 370	97.40 269	88.50 169	88.40 303	93.87 323

1314		87.51 260	86.57 263	90.34 269	92.42 323	79.74 275	92.63 310	95.35 234	78.35 379	80.14 388	91.62 324	74.05 243	97.15 289	81.05 292	93.53 206	94.12 309

LTVRE_ROB	82.13 13	86.26 312	84.90 322	90.34 269	94.44 224	81.50 202	92.31 324	94.89 265	83.03 288	79.63 398	92.67 282	69.69 307	97.79 222	71.20 394	86.26 329	91.72 402
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
test_djsdf		89.03 210	88.64 199	90.21 271	90.74 390	79.28 289	95.96 83	95.90 182	84.66 250	85.33 294	92.94 273	74.02 244	97.30 276	89.64 151	88.53 298	94.05 315

v2v482		87.84 242	87.06 242	90.17 272	90.99 375	79.23 292	94.00 234	95.13 245	84.87 241	85.53 277	92.07 307	74.45 235	97.45 256	84.71 230	81.75 380	93.85 326

pmmvs4		85.43 326	83.86 343	90.16 273	90.02 409	82.97 158	90.27 375	92.67 349	75.93 405	80.73 379	91.74 318	71.05 283	95.73 379	78.85 328	83.46 358	91.78 401

V42		87.68 247	86.86 247	90.15 274	90.58 395	80.14 257	94.24 212	95.28 239	83.66 269	85.67 272	91.33 330	74.73 229	97.41 267	84.43 234	81.83 378	92.89 372

MSDG		84.86 341	83.09 354	90.14 275	93.80 266	80.05 263	89.18 404	93.09 336	78.89 367	78.19 409	91.91 313	65.86 356	97.27 280	68.47 414	88.45 301	93.11 364

sc_t1		81.53 382	78.67 403	90.12 276	90.78 387	78.64 299	93.91 242	90.20 413	68.42 453	80.82 378	89.88 381	46.48 457	96.76 314	76.03 359	71.47 441	94.96 270

anonymousdsp		87.84 242	87.09 241	90.12 276	89.13 420	80.54 247	94.67 177	95.55 214	82.05 309	83.82 332	92.12 301	71.47 280	97.15 289	87.15 191	87.80 315	92.67 378

thres200		87.21 276	86.24 277	90.12 276	95.36 153	78.53 303	93.26 280	92.10 364	86.42 190	88.00 219	91.11 341	69.24 318	98.00 202	69.58 409	91.04 258	93.83 327

CR-MVSNet		85.35 329	83.76 344	90.12 276	90.58 395	79.34 285	85.24 448	91.96 372	78.27 381	85.55 275	87.87 417	71.03 284	95.61 382	73.96 379	89.36 287	95.40 253

v1144		87.61 255	86.79 252	90.06 280	91.01 374	79.34 285	93.95 237	95.42 229	83.36 280	85.66 273	91.31 333	74.98 225	97.42 261	83.37 248	82.06 374	93.42 349

XXY-MVS		87.65 249	86.85 248	90.03 281	92.14 329	80.60 245	93.76 250	95.23 241	82.94 291	84.60 307	94.02 232	74.27 237	95.49 389	81.04 293	83.68 354	94.01 317

Vis-MVSNet (Re-imp)		89.59 186	89.44 173	90.03 281	95.74 134	75.85 366	95.61 114	90.80 404	87.66 152	87.83 223	95.40 163	76.79 196	96.46 341	78.37 330	96.73 120	97.80 115

test2506		87.21 276	86.28 275	90.02 283	95.62 144	73.64 391	96.25 55	71.38 481	87.89 141	90.45 165	96.65 91	55.29 430	98.09 186	86.03 208	96.94 112	98.33 50

BH-untuned		88.60 222	88.13 216	90.01 284	95.24 161	78.50 305	93.29 278	94.15 305	84.75 246	84.46 313	93.40 255	75.76 214	97.40 269	77.59 340	94.52 177	94.12 309

v1192		87.25 272	86.33 272	90.00 285	90.76 389	79.04 293	93.80 248	95.48 219	82.57 298	85.48 281	91.18 337	73.38 258	97.42 261	82.30 267	82.06 374	93.53 343

v7n		86.81 290	85.76 299	89.95 286	90.72 391	79.25 291	95.07 149	95.92 179	84.45 253	82.29 358	90.86 348	72.60 268	97.53 244	79.42 323	80.52 402	93.08 366

testing91		87.11 281	86.18 278	89.92 287	94.43 225	75.38 374	91.53 346	92.27 360	86.48 187	86.50 249	90.24 367	61.19 393	97.53 244	82.10 272	90.88 260	96.84 191

IMVS_0404		87.60 256	86.84 249	89.89 288	93.72 270	77.75 332	88.56 413	95.34 235	85.53 216	79.98 392	94.49 212	66.54 349	94.64 403	84.75 225	92.65 232	97.28 144

v8		87.50 262	86.71 254	89.89 288	91.37 359	79.40 282	94.50 186	95.38 230	84.81 244	83.60 340	91.33 330	76.05 206	97.42 261	82.84 257	80.51 403	92.84 374

v10		87.25 272	86.38 269	89.85 290	91.19 365	79.50 278	94.48 187	95.45 224	83.79 267	83.62 339	91.19 335	75.13 222	97.42 261	81.94 277	80.60 398	92.63 380

baseline2		86.50 305	85.39 308	89.84 291	91.12 370	76.70 354	91.88 336	88.58 436	82.35 303	79.95 393	90.95 346	73.42 256	97.63 236	80.27 309	89.95 275	95.19 260

pm-mvs1		86.61 299	85.54 304	89.82 292	91.44 354	80.18 255	95.28 132	94.85 269	83.84 264	81.66 367	92.62 284	72.45 271	96.48 338	79.67 315	78.06 418	92.82 375

TR-MVS		86.78 292	85.76 299	89.82 292	94.37 228	78.41 307	92.47 315	92.83 343	81.11 341	86.36 255	92.40 290	68.73 326	97.48 251	73.75 382	89.85 278	93.57 342

ACMH+	81.04 14	85.05 336	83.46 348	89.82 292	94.66 202	79.37 283	94.44 192	94.12 308	82.19 306	78.04 411	92.82 277	58.23 414	97.54 243	73.77 381	82.90 366	92.54 381

EI-MVSNet		89.10 204	88.86 196	89.80 295	91.84 341	78.30 312	93.70 257	95.01 252	85.73 207	87.15 235	95.28 169	79.87 152	97.21 287	83.81 242	87.36 320	93.88 322

v144192		87.19 278	86.35 271	89.74 296	90.64 393	78.24 314	93.92 240	95.43 227	81.93 314	85.51 279	91.05 344	74.21 240	97.45 256	82.86 256	81.56 382	93.53 343

COLMAP_ROB	80.39 16	83.96 355	82.04 364	89.74 296	95.28 157	79.75 274	94.25 210	92.28 359	75.17 412	78.02 412	93.77 247	58.60 413	97.84 220	65.06 436	85.92 330	91.63 404
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
SCA		86.32 311	85.18 315	89.73 298	92.15 328	76.60 355	91.12 358	91.69 377	83.53 274	85.50 280	88.81 400	66.79 342	96.48 338	76.65 349	90.35 267	96.12 222

blend_shiyan4		81.94 372	79.35 391	89.70 299	85.52 451	80.08 260	91.29 352	93.82 319	77.12 393	79.31 401	82.94 456	54.81 432	96.60 328	79.60 317	69.78 446	92.41 387

IterMVS-LS		88.36 230	87.91 224	89.70 299	93.80 266	78.29 313	93.73 253	95.08 250	85.73 207	84.75 304	91.90 314	79.88 151	96.92 308	83.83 241	82.51 368	93.89 319
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
testing11		86.44 308	85.35 311	89.69 301	94.29 237	75.40 373	91.30 351	90.53 408	84.76 245	85.06 298	90.13 373	58.95 412	97.45 256	82.08 273	91.09 256	96.21 217

testing99		86.72 296	85.73 302	89.69 301	94.23 240	74.91 377	91.35 350	90.97 398	86.14 198	86.36 255	90.22 368	59.41 406	97.48 251	82.24 269	90.66 262	96.69 198

v1921920		86.97 285	86.06 285	89.69 301	90.53 398	78.11 317	93.80 248	95.43 227	81.90 316	85.33 294	91.05 344	72.66 265	97.41 267	82.05 275	81.80 379	93.53 343

icg_test_0407_2		89.15 202	88.97 189	89.68 304	93.72 270	77.75 332	88.26 418	95.34 235	85.53 216	88.34 211	94.49 212	77.69 188	93.99 415	84.75 225	92.65 232	97.28 144

VortexMVS		88.42 226	88.01 218	89.63 305	93.89 261	78.82 295	93.82 246	95.47 220	86.67 184	84.53 311	91.99 310	72.62 267	96.65 320	89.02 160	84.09 348	93.41 350

Fast-Effi-MVS+-dtu		87.44 263	86.72 253	89.63 305	92.04 333	77.68 337	94.03 229	93.94 311	85.81 204	82.42 357	91.32 332	70.33 298	97.06 298	80.33 308	90.23 269	94.14 308

v1240		86.78 292	85.85 294	89.56 307	90.45 401	77.79 329	93.61 261	95.37 232	81.65 326	85.43 286	91.15 339	71.50 279	97.43 260	81.47 288	82.05 376	93.47 347

Effi-MVS+-dtu		88.65 220	88.35 208	89.54 308	93.33 287	76.39 359	94.47 190	94.36 295	87.70 149	85.43 286	89.56 389	73.45 254	97.26 282	85.57 214	91.28 251	94.97 267

AllTest		83.42 362	81.39 368	89.52 309	95.01 171	77.79 329	93.12 284	90.89 402	77.41 388	76.12 426	93.34 256	54.08 437	97.51 246	68.31 416	84.27 346	93.26 353

TestCases				89.52 309	95.01 171	77.79 329		90.89 402	77.41 388	76.12 426	93.34 256	54.08 437	97.51 246	68.31 416	84.27 346	93.26 353

mvs_anonymous		89.37 199	89.32 178	89.51 311	93.47 283	74.22 384	91.65 344	94.83 271	82.91 292	85.45 283	93.79 245	81.23 134	96.36 348	86.47 200	94.09 190	97.94 96

XVG-ACMP-BASELINE		86.00 314	84.84 324	89.45 312	91.20 364	78.00 319	91.70 342	95.55 214	85.05 236	82.97 351	92.25 297	54.49 435	97.48 251	82.93 254	87.45 319	92.89 372

testing222		84.84 342	83.32 349	89.43 313	94.15 247	75.94 364	91.09 359	89.41 434	84.90 239	85.78 269	89.44 390	52.70 442	96.28 352	70.80 400	91.57 248	96.07 226

MVP-Stereo		85.97 315	84.86 323	89.32 314	90.92 381	82.19 184	92.11 331	94.19 302	78.76 372	78.77 408	91.63 323	68.38 330	96.56 332	75.01 368	93.95 193	89.20 443
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PatchmatchNet		85.85 318	84.70 326	89.29 315	91.76 345	75.54 370	88.49 414	91.30 389	81.63 328	85.05 299	88.70 404	71.71 276	96.24 353	74.61 374	89.05 293	96.08 225
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v148		87.04 283	86.32 273	89.21 316	90.94 379	77.26 343	93.71 256	94.43 290	84.84 243	84.36 319	90.80 352	76.04 207	97.05 300	82.12 271	79.60 412	93.31 352

tfpnnormal		84.72 344	83.23 352	89.20 317	92.79 313	80.05 263	94.48 187	95.81 190	82.38 301	81.08 375	91.21 334	69.01 322	96.95 306	61.69 447	80.59 399	90.58 429

cl22		86.78 292	85.98 288	89.18 318	92.34 324	77.62 338	90.84 365	94.13 307	81.33 335	83.97 330	90.15 372	73.96 245	96.60 328	84.19 236	82.94 363	93.33 351

BH-w/o		87.57 258	87.05 243	89.12 319	94.90 183	77.90 323	92.41 316	93.51 327	82.89 293	83.70 336	91.34 329	75.75 215	97.07 297	75.49 361	93.49 208	92.39 389

WR-MVS_H		87.80 244	87.37 235	89.10 320	93.23 289	78.12 316	95.61 114	97.30 37	87.90 139	83.72 335	92.01 309	79.65 161	96.01 363	76.36 353	80.54 400	93.16 361

miper_enhance_ethall		86.90 287	86.18 278	89.06 321	91.66 350	77.58 339	90.22 381	94.82 272	79.16 363	84.48 312	89.10 394	79.19 166	96.66 319	84.06 237	82.94 363	92.94 370

c3_l		87.14 280	86.50 267	89.04 322	92.20 327	77.26 343	91.22 357	94.70 279	82.01 312	84.34 320	90.43 363	78.81 169	96.61 326	83.70 246	81.09 389	93.25 355

miper_ehance_all_eth		87.22 275	86.62 261	89.02 323	92.13 330	77.40 341	90.91 364	94.81 273	81.28 336	84.32 321	90.08 375	79.26 164	96.62 323	83.81 242	82.94 363	93.04 367

gg-mvs-nofinetune		81.77 376	79.37 390	88.99 324	90.85 385	77.73 336	86.29 440	79.63 469	74.88 417	83.19 350	69.05 472	60.34 398	96.11 358	75.46 362	94.64 173	93.11 364

ETVMVS		84.43 349	82.92 358	88.97 325	94.37 228	74.67 378	91.23 356	88.35 438	83.37 279	86.06 264	89.04 395	55.38 428	95.67 381	67.12 423	91.34 250	96.58 202

pmmvs6		83.42 362	81.60 366	88.87 326	88.01 435	77.87 325	94.96 155	94.24 301	74.67 418	78.80 407	91.09 342	60.17 400	96.49 337	77.06 348	75.40 432	92.23 394

test_cas_vis1_n_1920		88.83 217	88.85 197	88.78 327	91.15 369	76.72 353	93.85 245	94.93 263	83.23 284	92.81 98	96.00 124	61.17 394	94.45 404	91.67 115	94.84 165	95.17 261

MIMVSNet		82.59 368	80.53 373	88.76 328	91.51 352	78.32 311	86.57 439	90.13 416	79.32 359	80.70 380	88.69 405	52.98 441	93.07 431	66.03 431	88.86 295	94.90 275

cl____		86.52 304	85.78 296	88.75 329	92.03 334	76.46 357	90.74 366	94.30 297	81.83 322	83.34 347	90.78 353	75.74 217	96.57 330	81.74 283	81.54 383	93.22 357

DIV-MVS_self_test		86.53 303	85.78 296	88.75 329	92.02 335	76.45 358	90.74 366	94.30 297	81.83 322	83.34 347	90.82 351	75.75 215	96.57 330	81.73 284	81.52 384	93.24 356

CP-MVSNet		87.63 252	87.26 240	88.74 331	93.12 294	76.59 356	95.29 130	96.58 110	88.43 115	83.49 344	92.98 272	75.28 221	95.83 372	78.97 326	81.15 388	93.79 328

eth_miper_zixun_eth		86.50 305	85.77 298	88.68 332	91.94 336	75.81 367	90.47 373	94.89 265	82.05 309	84.05 327	90.46 362	75.96 210	96.77 313	82.76 260	79.36 414	93.46 348

CHOSEN 280x420		85.15 334	83.99 341	88.65 333	92.47 320	78.40 308	79.68 472	92.76 346	74.90 416	81.41 371	89.59 387	69.85 306	95.51 386	79.92 313	95.29 155	92.03 397

PS-CasMVS		87.32 269	86.88 246	88.63 334	92.99 304	76.33 361	95.33 125	96.61 108	88.22 123	83.30 349	93.07 270	73.03 262	95.79 376	78.36 331	81.00 394	93.75 335

TransMVSNet (Re)		84.43 349	83.06 356	88.54 335	91.72 346	78.44 306	95.18 143	92.82 345	82.73 296	79.67 397	92.12 301	73.49 253	95.96 365	71.10 398	68.73 453	91.21 416

tt0320-xc		79.63 405	76.66 414	88.52 336	91.03 373	78.72 296	93.00 293	89.53 433	66.37 457	76.11 428	87.11 428	46.36 459	95.32 394	72.78 386	67.67 454	91.51 408

EG-PatchMatch MVS		82.37 370	80.34 376	88.46 337	90.27 403	79.35 284	92.80 306	94.33 296	77.14 392	73.26 445	90.18 371	47.47 454	96.72 315	70.25 402	87.32 322	89.30 440

PEN-MVS		86.80 291	86.27 276	88.40 338	92.32 325	75.71 369	95.18 143	96.38 125	87.97 135	82.82 353	93.15 266	73.39 257	95.92 367	76.15 357	79.03 417	93.59 341

Baseline_NR-MVSNet		87.07 282	86.63 260	88.40 338	91.44 354	77.87 325	94.23 213	92.57 351	84.12 258	85.74 271	92.08 305	77.25 192	96.04 359	82.29 268	79.94 407	91.30 414

UBG		85.51 324	84.57 331	88.35 340	94.21 242	71.78 416	90.07 386	89.66 429	82.28 304	85.91 267	89.01 396	61.30 388	97.06 298	76.58 352	92.06 245	96.22 215

D2MVS		85.90 316	85.09 317	88.35 340	90.79 386	77.42 340	91.83 338	95.70 202	80.77 344	80.08 390	90.02 377	66.74 344	96.37 346	81.88 279	87.97 310	91.26 415

pmmvs5		84.21 351	82.84 361	88.34 342	88.95 422	76.94 349	92.41 316	91.91 374	75.63 407	80.28 385	91.18 337	64.59 364	95.57 383	77.09 347	83.47 357	92.53 382

mamv4		90.92 141	91.78 108	88.33 343	95.67 140	70.75 429	92.92 298	96.02 171	81.90 316	88.11 213	95.34 167	85.88 55	96.97 304	95.22 43	95.01 160	97.26 148

tt0320		80.13 398	77.41 407	88.29 344	90.50 399	78.02 318	93.10 287	90.71 406	66.06 460	76.75 421	86.97 429	49.56 449	95.40 391	71.65 390	71.41 442	91.46 411

LCM-MVSNet-Re		88.30 232	88.32 211	88.27 345	94.71 198	72.41 411	93.15 283	90.98 397	87.77 146	79.25 402	91.96 311	78.35 178	95.75 377	83.04 252	95.62 144	96.65 199

CostFormer		85.77 321	84.94 321	88.26 346	91.16 368	72.58 409	89.47 399	91.04 396	76.26 402	86.45 253	89.97 379	70.74 289	96.86 312	82.35 266	87.07 325	95.34 257

ITE_SJBPF				88.24 347	91.88 340	77.05 346		92.92 340	85.54 214	80.13 389	93.30 260	57.29 419	96.20 354	72.46 388	84.71 342	91.49 409

PVSNet	78.82 18	85.55 323	84.65 327	88.23 348	94.72 196	71.93 412	87.12 435	92.75 347	78.80 371	84.95 301	90.53 360	64.43 365	96.71 317	74.74 371	93.86 195	96.06 228

IterMVS-SCA-FT		85.45 325	84.53 332	88.18 349	91.71 347	76.87 350	90.19 383	92.65 350	85.40 223	81.44 370	90.54 359	66.79 342	95.00 400	81.04 293	81.05 390	92.66 379

EPNet_dtu		86.49 307	85.94 291	88.14 350	90.24 404	72.82 401	94.11 219	92.20 362	86.66 185	79.42 400	92.36 292	73.52 252	95.81 374	71.26 393	93.66 201	95.80 240
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Patchmtry		82.71 366	80.93 372	88.06 351	90.05 408	76.37 360	84.74 453	91.96 372	72.28 441	81.32 373	87.87 417	71.03 284	95.50 388	68.97 411	80.15 405	92.32 392

test_vis1_n_1920		89.39 198	89.84 161	88.04 352	92.97 305	72.64 406	94.71 175	96.03 170	86.18 196	91.94 127	96.56 99	61.63 383	95.74 378	93.42 65	95.11 159	95.74 242

DTE-MVSNet		86.11 313	85.48 306	87.98 353	91.65 351	74.92 376	94.93 157	95.75 195	87.36 161	82.26 359	93.04 271	72.85 263	95.82 373	74.04 377	77.46 423	93.20 359

PMMVS		85.71 322	84.96 320	87.95 354	88.90 423	77.09 345	88.68 411	90.06 418	72.32 440	86.47 250	90.76 354	72.15 273	94.40 407	81.78 282	93.49 208	92.36 390

GG-mvs-BLEND				87.94 355	89.73 415	77.91 322	87.80 424	78.23 474		80.58 382	83.86 448	59.88 402	95.33 393	71.20 394	92.22 243	90.60 428

MonoMVSNet		86.89 288	86.55 264	87.92 356	89.46 418	73.75 388	94.12 217	93.10 335	87.82 145	85.10 297	90.76 354	69.59 309	94.94 401	86.47 200	82.50 369	95.07 264

reproduce_monomvs		86.37 310	85.87 293	87.87 357	93.66 278	73.71 389	93.44 268	95.02 251	88.61 110	82.64 356	91.94 312	57.88 416	96.68 318	89.96 142	79.71 411	93.22 357

pmmvs-eth3d		80.97 391	78.72 402	87.74 358	84.99 454	79.97 269	90.11 385	91.65 379	75.36 409	73.51 443	86.03 437	59.45 405	93.96 418	75.17 365	72.21 438	89.29 442

MS-PatchMatch		85.05 336	84.16 336	87.73 359	91.42 357	78.51 304	91.25 355	93.53 326	77.50 387	80.15 387	91.58 326	61.99 380	95.51 386	75.69 360	94.35 182	89.16 444

mmtdpeth		85.04 338	84.15 337	87.72 360	93.11 295	75.74 368	94.37 203	92.83 343	84.98 237	89.31 191	86.41 434	61.61 385	97.14 292	92.63 81	62.11 464	90.29 430

test_0402		81.30 387	79.17 396	87.67 361	93.19 290	78.17 315	92.98 295	91.71 375	75.25 411	76.02 429	90.31 366	59.23 407	96.37 346	50.22 467	83.63 355	88.47 452

IterMVS		84.88 340	83.98 342	87.60 362	91.44 354	76.03 363	90.18 384	92.41 353	83.24 283	81.06 376	90.42 364	66.60 345	94.28 411	79.46 319	80.98 395	92.48 383
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Patchmatch-test		81.37 385	79.30 392	87.58 363	90.92 381	74.16 386	80.99 465	87.68 443	70.52 448	76.63 423	88.81 400	71.21 281	92.76 434	60.01 453	86.93 326	95.83 238

EPMVS		83.90 358	82.70 362	87.51 364	90.23 405	72.67 404	88.62 412	81.96 464	81.37 334	85.01 300	88.34 408	66.31 350	94.45 404	75.30 364	87.12 323	95.43 252

ADS-MVSNet2		81.66 379	79.71 387	87.50 365	91.35 360	74.19 385	83.33 458	88.48 437	72.90 435	82.24 360	85.77 440	64.98 360	93.20 429	64.57 438	83.74 352	95.12 262

OurMVSNet-221017-0		85.35 329	84.64 329	87.49 366	90.77 388	72.59 408	94.01 232	94.40 293	84.72 247	79.62 399	93.17 265	61.91 381	96.72 315	81.99 276	81.16 386	93.16 361

tpm2		84.08 353	82.94 357	87.48 367	91.39 358	71.27 421	89.23 403	90.37 410	71.95 442	84.64 306	89.33 391	67.30 334	96.55 334	75.17 365	87.09 324	94.63 283

RPSCF		85.07 335	84.27 333	87.48 367	92.91 308	70.62 431	91.69 343	92.46 352	76.20 403	82.67 355	95.22 172	63.94 368	97.29 279	77.51 342	85.80 331	94.53 290

myMVS_eth3d28		85.80 320	85.26 314	87.42 369	94.73 194	69.92 436	90.60 370	90.95 399	87.21 165	86.06 264	90.04 376	59.47 404	96.02 361	74.89 370	93.35 215	96.33 209

FE-MVSNET2		81.82 375	79.99 382	87.34 370	84.74 455	77.36 342	92.72 307	94.55 284	82.09 307	73.79 442	86.46 431	57.80 417	94.45 404	74.65 372	73.10 434	90.20 431

WBMVS		84.97 339	84.18 335	87.34 370	94.14 248	71.62 420	90.20 382	92.35 355	81.61 329	84.06 326	90.76 354	61.82 382	96.52 335	78.93 327	83.81 350	93.89 319

miper_lstm_enhance		85.27 332	84.59 330	87.31 372	91.28 363	74.63 379	87.69 429	94.09 309	81.20 340	81.36 372	89.85 383	74.97 226	94.30 410	81.03 295	79.84 410	93.01 368

FMVSNet5		81.52 383	79.60 388	87.27 373	91.17 366	77.95 320	91.49 347	92.26 361	76.87 395	76.16 425	87.91 416	51.67 443	92.34 437	67.74 420	81.16 386	91.52 407

USDC		82.76 365	81.26 370	87.26 374	91.17 366	74.55 380	89.27 401	93.39 329	78.26 382	75.30 433	92.08 305	54.43 436	96.63 322	71.64 391	85.79 332	90.61 426

test-LLR		85.87 317	85.41 307	87.25 375	90.95 377	71.67 418	89.55 395	89.88 425	83.41 277	84.54 309	87.95 414	67.25 335	95.11 397	81.82 280	93.37 213	94.97 267

test-mter		84.54 348	83.64 346	87.25 375	90.95 377	71.67 418	89.55 395	89.88 425	79.17 362	84.54 309	87.95 414	55.56 425	95.11 397	81.82 280	93.37 213	94.97 267

JIA-IIPM		81.04 388	78.98 400	87.25 375	88.64 424	73.48 393	81.75 464	89.61 431	73.19 432	82.05 363	73.71 468	66.07 355	95.87 370	71.18 396	84.60 343	92.41 387

TDRefinement		79.81 402	77.34 408	87.22 378	79.24 471	75.48 371	93.12 284	92.03 367	76.45 398	75.01 434	91.58 326	49.19 450	96.44 342	70.22 404	69.18 450	89.75 436

tpmvs		83.35 364	82.07 363	87.20 379	91.07 372	71.00 427	88.31 417	91.70 376	78.91 365	80.49 384	87.18 426	69.30 316	97.08 295	68.12 419	83.56 356	93.51 346

ppachtmachnet_test		81.84 374	80.07 381	87.15 380	88.46 428	74.43 383	89.04 407	92.16 363	75.33 410	77.75 414	88.99 397	66.20 352	95.37 392	65.12 435	77.60 421	91.65 403

dmvs_re		84.20 352	83.22 353	87.14 381	91.83 343	77.81 327	90.04 387	90.19 414	84.70 249	81.49 368	89.17 393	64.37 366	91.13 450	71.58 392	85.65 333	92.46 385

tpm cat1		81.96 371	80.27 377	87.01 382	91.09 371	71.02 426	87.38 433	91.53 384	66.25 458	80.17 386	86.35 436	68.22 331	96.15 357	69.16 410	82.29 372	93.86 325

test_fmvs1_n		87.03 284	87.04 244	86.97 383	89.74 414	71.86 413	94.55 183	94.43 290	78.47 376	91.95 126	95.50 158	51.16 445	93.81 419	93.02 73	94.56 175	95.26 258

OpenMVS_ROB	74.94 19	79.51 406	77.03 413	86.93 384	87.00 441	76.23 362	92.33 322	90.74 405	68.93 452	74.52 438	88.23 411	49.58 448	96.62 323	57.64 459	84.29 345	87.94 455

SixPastTwentyTwo		83.91 357	82.90 359	86.92 385	90.99 375	70.67 430	93.48 265	91.99 369	85.54 214	77.62 416	92.11 303	60.59 397	96.87 311	76.05 358	77.75 420	93.20 359

ADS-MVSNet		81.56 381	79.78 384	86.90 386	91.35 360	71.82 414	83.33 458	89.16 435	72.90 435	82.24 360	85.77 440	64.98 360	93.76 420	64.57 438	83.74 352	95.12 262

PatchT		82.68 367	81.27 369	86.89 387	90.09 407	70.94 428	84.06 455	90.15 415	74.91 415	85.63 274	83.57 450	69.37 312	94.87 402	65.19 433	88.50 300	94.84 277

tpm		84.73 343	84.02 340	86.87 388	90.33 402	68.90 439	89.06 406	89.94 422	80.85 343	85.75 270	89.86 382	68.54 328	95.97 364	77.76 338	84.05 349	95.75 241

Patchmatch-RL test		81.67 378	79.96 383	86.81 389	85.42 452	71.23 422	82.17 463	87.50 444	78.47 376	77.19 418	82.50 458	70.81 288	93.48 424	82.66 261	72.89 437	95.71 245

test_vis1_n		86.56 302	86.49 268	86.78 390	88.51 425	72.69 403	94.68 176	93.78 322	79.55 358	90.70 160	95.31 168	48.75 451	93.28 427	93.15 69	93.99 192	94.38 301

testing3-2		86.72 296	86.71 254	86.74 391	96.11 113	65.92 451	93.39 270	89.65 430	89.46 72	87.84 222	92.79 280	59.17 409	97.60 238	81.31 289	90.72 261	96.70 197

test_fmvs1		87.34 267	87.56 230	86.68 392	90.59 394	71.80 415	94.01 232	94.04 310	78.30 380	91.97 124	95.22 172	56.28 423	93.71 421	92.89 74	94.71 168	94.52 291

MDA-MVSNet-bldmvs		78.85 411	76.31 416	86.46 393	89.76 413	73.88 387	88.79 409	90.42 409	79.16 363	59.18 468	88.33 409	60.20 399	94.04 413	62.00 446	68.96 451	91.48 410

mvs5depth		80.98 390	79.15 397	86.45 394	84.57 456	73.29 396	87.79 425	91.67 378	80.52 346	82.20 362	89.72 385	55.14 431	95.93 366	73.93 380	66.83 456	90.12 433

tpmrst		85.35 329	84.99 318	86.43 395	90.88 384	67.88 444	88.71 410	91.43 387	80.13 350	86.08 263	88.80 402	73.05 261	96.02 361	82.48 262	83.40 360	95.40 253

TESTMET0.1,1		83.74 360	82.85 360	86.42 396	89.96 410	71.21 423	89.55 395	87.88 440	77.41 388	83.37 346	87.31 422	56.71 421	93.65 423	80.62 303	92.85 229	94.40 300

our_test_3		81.93 373	80.46 375	86.33 397	88.46 428	73.48 393	88.46 415	91.11 392	76.46 397	76.69 422	88.25 410	66.89 340	94.36 408	68.75 412	79.08 416	91.14 418

lessismore_v0				86.04 398	88.46 428	68.78 440		80.59 467		73.01 446	90.11 374	55.39 427	96.43 343	75.06 367	65.06 459	92.90 371

TinyColmap		79.76 403	77.69 406	85.97 399	91.71 347	73.12 397	89.55 395	90.36 411	75.03 413	72.03 449	90.19 370	46.22 460	96.19 356	63.11 442	81.03 391	88.59 451

KD-MVS_2432*1600		78.50 412	76.02 420	85.93 400	86.22 444	74.47 381	84.80 451	92.33 356	79.29 360	76.98 419	85.92 438	53.81 439	93.97 416	67.39 421	57.42 469	89.36 438

miper_refine_blended		78.50 412	76.02 420	85.93 400	86.22 444	74.47 381	84.80 451	92.33 356	79.29 360	76.98 419	85.92 438	53.81 439	93.97 416	67.39 421	57.42 469	89.36 438

K. test v3		81.59 380	80.15 380	85.91 402	89.89 412	69.42 438	92.57 312	87.71 442	85.56 213	73.44 444	89.71 386	55.58 424	95.52 385	77.17 345	69.76 447	92.78 376

SSC-MVS3.2		84.60 347	84.19 334	85.85 403	92.74 315	68.07 441	88.15 420	93.81 320	87.42 159	83.76 334	91.07 343	62.91 375	95.73 379	74.56 375	83.24 361	93.75 335

mvsany_test1		85.42 327	85.30 312	85.77 404	87.95 437	75.41 372	87.61 432	80.97 466	76.82 396	88.68 204	95.83 140	77.44 191	90.82 452	85.90 209	86.51 327	91.08 422

MIMVSNet1		79.38 407	77.28 409	85.69 405	86.35 443	73.67 390	91.61 345	92.75 347	78.11 385	72.64 447	88.12 412	48.16 452	91.97 443	60.32 450	77.49 422	91.43 412

UWE-MVS		83.69 361	83.09 354	85.48 406	93.06 299	65.27 456	90.92 363	86.14 448	79.90 353	86.26 259	90.72 357	57.17 420	95.81 374	71.03 399	92.62 237	95.35 256

UnsupCasMVSNet_eth		80.07 399	78.27 405	85.46 407	85.24 453	72.63 407	88.45 416	94.87 268	82.99 290	71.64 452	88.07 413	56.34 422	91.75 445	73.48 383	63.36 462	92.01 398

CL-MVSNet_self_test		81.74 377	80.53 373	85.36 408	85.96 446	72.45 410	90.25 377	93.07 337	81.24 338	79.85 396	87.29 423	70.93 286	92.52 435	66.95 424	69.23 449	91.11 420

MDA-MVSNet_test_wron		79.21 409	77.19 411	85.29 409	88.22 432	72.77 402	85.87 442	90.06 418	74.34 420	62.62 465	87.56 420	66.14 353	91.99 442	66.90 428	73.01 435	91.10 421

YYNet1		79.22 408	77.20 410	85.28 410	88.20 433	72.66 405	85.87 442	90.05 420	74.33 421	62.70 463	87.61 419	66.09 354	92.03 439	66.94 425	72.97 436	91.15 417

WB-MVSnew		83.77 359	83.28 350	85.26 411	91.48 353	71.03 425	91.89 335	87.98 439	78.91 365	84.78 303	90.22 368	69.11 321	94.02 414	64.70 437	90.44 264	90.71 424

dp		81.47 384	80.23 378	85.17 412	89.92 411	65.49 454	86.74 437	90.10 417	76.30 401	81.10 374	87.12 427	62.81 376	95.92 367	68.13 418	79.88 408	94.09 312

UnsupCasMVSNet_bld		76.23 422	73.27 426	85.09 413	83.79 458	72.92 399	85.65 445	93.47 328	71.52 443	68.84 458	79.08 463	49.77 447	93.21 428	66.81 429	60.52 466	89.13 446

SD_0403		84.71 345	84.65 327	84.92 414	92.95 306	65.95 450	92.07 334	93.23 332	83.82 266	79.03 403	93.73 250	73.90 246	92.91 433	63.02 444	90.05 271	95.89 234

Anonymous20231206		81.03 389	79.77 386	84.82 415	87.85 438	70.26 433	91.42 348	92.08 365	73.67 427	77.75 414	89.25 392	62.43 378	93.08 430	61.50 448	82.00 377	91.12 419

FE-MVSNET		78.19 414	76.03 419	84.69 416	83.70 459	73.31 395	90.58 371	90.00 421	77.11 394	71.91 450	85.47 442	55.53 426	91.94 444	59.69 454	70.24 444	88.83 448

test0.0.03 1		82.41 369	81.69 365	84.59 417	88.23 431	72.89 400	90.24 379	87.83 441	83.41 277	79.86 395	89.78 384	67.25 335	88.99 462	65.18 434	83.42 359	91.90 400

CMPMVS	59.16 21	80.52 393	79.20 395	84.48 418	83.98 457	67.63 447	89.95 390	93.84 318	64.79 462	66.81 460	91.14 340	57.93 415	95.17 395	76.25 355	88.10 306	90.65 425
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
CVMVSNet		84.69 346	84.79 325	84.37 419	91.84 341	64.92 457	93.70 257	91.47 386	66.19 459	86.16 262	95.28 169	67.18 337	93.33 426	80.89 298	90.42 266	94.88 276

PVSNet_0	73.20 20	77.22 418	74.83 424	84.37 419	90.70 392	71.10 424	83.09 460	89.67 428	72.81 437	73.93 441	83.13 452	60.79 396	93.70 422	68.54 413	50.84 474	88.30 453

LF4IMVS		80.37 396	79.07 399	84.27 421	86.64 442	69.87 437	89.39 400	91.05 395	76.38 399	74.97 435	90.00 378	47.85 453	94.25 412	74.55 376	80.82 397	88.69 450

Anonymous20240521		80.44 395	79.21 394	84.11 422	85.75 449	67.89 443	92.86 302	93.23 332	75.61 408	75.59 432	87.47 421	50.03 446	94.33 409	71.14 397	81.21 385	90.12 433

PM-MVS		78.11 415	76.12 418	84.09 423	83.54 460	70.08 434	88.97 408	85.27 455	79.93 352	74.73 437	86.43 433	34.70 471	93.48 424	79.43 322	72.06 439	88.72 449

test_fmvs2		83.98 354	84.03 339	83.83 424	87.16 440	67.53 448	93.93 239	92.89 341	77.62 386	86.89 243	93.53 253	47.18 455	92.02 441	90.54 134	86.51 327	91.93 399

testgi		80.94 392	80.20 379	83.18 425	87.96 436	66.29 449	91.28 353	90.70 407	83.70 268	78.12 410	92.84 275	51.37 444	90.82 452	63.34 441	82.46 370	92.43 386

KD-MVS_self_test		80.20 397	79.24 393	83.07 426	85.64 450	65.29 455	91.01 361	93.93 312	78.71 374	76.32 424	86.40 435	59.20 408	92.93 432	72.59 387	69.35 448	91.00 423

testing3		80.46 394	79.59 389	83.06 427	93.44 285	64.64 458	93.33 272	85.47 453	84.34 255	79.93 394	90.84 350	44.35 463	92.39 436	57.06 461	87.56 316	92.16 396

ambc				83.06 427	79.99 469	63.51 462	77.47 473	92.86 342		74.34 440	84.45 447	28.74 472	95.06 399	73.06 385	68.89 452	90.61 426

test20.03		79.95 401	79.08 398	82.55 429	85.79 448	67.74 446	91.09 359	91.08 393	81.23 339	74.48 439	89.96 380	61.63 383	90.15 454	60.08 451	76.38 428	89.76 435

MVStest1		72.91 426	69.70 431	82.54 430	78.14 472	73.05 398	88.21 419	86.21 447	60.69 466	64.70 461	90.53 360	46.44 458	85.70 469	58.78 457	53.62 471	88.87 447

test_vis1_rt		77.96 416	76.46 415	82.48 431	85.89 447	71.74 417	90.25 377	78.89 470	71.03 447	71.30 453	81.35 460	42.49 465	91.05 451	84.55 232	82.37 371	84.65 458

EU-MVSNet		81.32 386	80.95 371	82.42 432	88.50 427	63.67 461	93.32 273	91.33 388	64.02 463	80.57 383	92.83 276	61.21 392	92.27 438	76.34 354	80.38 404	91.32 413

myMVS_eth3d		79.67 404	78.79 401	82.32 433	91.92 337	64.08 459	89.75 393	87.40 445	81.72 324	78.82 405	87.20 424	45.33 461	91.29 448	59.09 456	87.84 313	91.60 405

ttmdpeth		76.55 420	74.64 425	82.29 434	82.25 465	67.81 445	89.76 392	85.69 451	70.35 449	75.76 430	91.69 319	46.88 456	89.77 456	66.16 430	63.23 463	89.30 440

pmmvs3		71.81 429	68.71 432	81.11 435	75.86 474	70.42 432	86.74 437	83.66 459	58.95 469	68.64 459	80.89 461	36.93 469	89.52 458	63.10 443	63.59 461	83.39 459

Syy-MVS		80.07 399	79.78 384	80.94 436	91.92 337	59.93 468	89.75 393	87.40 445	81.72 324	78.82 405	87.20 424	66.29 351	91.29 448	47.06 469	87.84 313	91.60 405

UWE-MVS-28		78.98 410	78.38 404	80.80 437	88.18 434	60.66 467	90.65 368	78.51 471	78.84 369	77.93 413	90.93 347	59.08 410	89.02 461	50.96 466	90.33 268	92.72 377

new-patchmatchnet		76.41 421	75.17 423	80.13 438	82.65 464	59.61 469	87.66 430	91.08 393	78.23 383	69.85 456	83.22 451	54.76 433	91.63 447	64.14 440	64.89 460	89.16 444

mvsany_test3		74.95 423	73.26 427	80.02 439	74.61 475	63.16 463	85.53 446	78.42 472	74.16 422	74.89 436	86.46 431	36.02 470	89.09 460	82.39 265	66.91 455	87.82 456

test_fmvs3		77.67 417	77.16 412	79.22 440	79.52 470	61.14 465	92.34 321	91.64 380	73.98 424	78.86 404	86.59 430	27.38 475	87.03 464	88.12 174	75.97 430	89.50 437

DSMNet-mixed		76.94 419	76.29 417	78.89 441	83.10 462	56.11 477	87.78 426	79.77 468	60.65 467	75.64 431	88.71 403	61.56 386	88.34 463	60.07 452	89.29 289	92.21 395

EGC-MVSNET		61.97 437	56.37 442	78.77 442	89.63 416	73.50 392	89.12 405	82.79 461	0.21 488	1.24 489	84.80 445	39.48 466	90.04 455	44.13 471	75.94 431	72.79 470

new_pmnet		72.15 427	70.13 430	78.20 443	82.95 463	65.68 452	83.91 456	82.40 463	62.94 465	64.47 462	79.82 462	42.85 464	86.26 468	57.41 460	74.44 433	82.65 463

MVS-HIRNet		73.70 425	72.20 428	78.18 444	91.81 344	56.42 476	82.94 461	82.58 462	55.24 470	68.88 457	66.48 473	55.32 429	95.13 396	58.12 458	88.42 302	83.01 461

LCM-MVSNet		66.00 434	62.16 439	77.51 445	64.51 485	58.29 471	83.87 457	90.90 401	48.17 474	54.69 471	73.31 469	16.83 484	86.75 465	65.47 432	61.67 465	87.48 457

APD_test1		69.04 430	66.26 436	77.36 446	80.51 468	62.79 464	85.46 447	83.51 460	54.11 472	59.14 469	84.79 446	23.40 478	89.61 457	55.22 462	70.24 444	79.68 467

test_f		71.95 428	70.87 429	75.21 447	74.21 477	59.37 470	85.07 450	85.82 450	65.25 461	70.42 455	83.13 452	23.62 476	82.93 475	78.32 332	71.94 440	83.33 460

ANet_high		58.88 441	54.22 446	72.86 448	56.50 488	56.67 473	80.75 466	86.00 449	73.09 434	37.39 480	64.63 476	22.17 479	79.49 478	43.51 472	23.96 482	82.43 464

test_vis3_rt		65.12 435	62.60 437	72.69 449	71.44 478	60.71 466	87.17 434	65.55 482	63.80 464	53.22 472	65.65 475	14.54 485	89.44 459	76.65 349	65.38 458	67.91 473

FPMVS		64.63 436	62.55 438	70.88 450	70.80 479	56.71 472	84.42 454	84.42 457	51.78 473	49.57 473	81.61 459	23.49 477	81.48 476	40.61 476	76.25 429	74.46 469

dmvs_testset		74.57 424	75.81 422	70.86 451	87.72 439	40.47 486	87.05 436	77.90 476	82.75 295	71.15 454	85.47 442	67.98 332	84.12 473	45.26 470	76.98 427	88.00 454

N_pmnet		68.89 431	68.44 433	70.23 452	89.07 421	28.79 491	88.06 421	19.50 491	69.47 451	71.86 451	84.93 444	61.24 391	91.75 445	54.70 463	77.15 424	90.15 432

testf1		59.54 439	56.11 443	69.85 453	69.28 480	56.61 474	80.37 467	76.55 479	42.58 477	45.68 476	75.61 464	11.26 486	84.18 471	43.20 473	60.44 467	68.75 471

APD_test2		59.54 439	56.11 443	69.85 453	69.28 480	56.61 474	80.37 467	76.55 479	42.58 477	45.68 476	75.61 464	11.26 486	84.18 471	43.20 473	60.44 467	68.75 471

WB-MVS		67.92 432	67.49 434	69.21 455	81.09 466	41.17 485	88.03 422	78.00 475	73.50 429	62.63 464	83.11 454	63.94 368	86.52 466	25.66 481	51.45 473	79.94 466

PMMVS2		59.60 438	56.40 441	69.21 455	68.83 482	46.58 481	73.02 477	77.48 477	55.07 471	49.21 474	72.95 470	17.43 483	80.04 477	49.32 468	44.33 477	80.99 465

SSC-MVS		67.06 433	66.56 435	68.56 457	80.54 467	40.06 487	87.77 427	77.37 478	72.38 439	61.75 466	82.66 457	63.37 371	86.45 467	24.48 482	48.69 476	79.16 468

Gipuma		57.99 443	54.91 445	67.24 458	88.51 425	65.59 453	52.21 480	90.33 412	43.58 476	42.84 479	51.18 480	20.29 481	85.07 470	34.77 477	70.45 443	51.05 479
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	47.18 22	52.22 445	48.46 449	63.48 459	45.72 490	46.20 482	73.41 476	78.31 473	41.03 479	30.06 482	65.68 474	6.05 488	83.43 474	30.04 479	65.86 457	60.80 474
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
dongtai		58.82 442	58.24 440	60.56 460	83.13 461	45.09 484	82.32 462	48.22 490	67.61 455	61.70 467	69.15 471	38.75 467	76.05 479	32.01 478	41.31 478	60.55 475

MVE	39.65 23	43.39 447	38.59 453	57.77 461	56.52 487	48.77 480	55.38 479	58.64 486	29.33 482	28.96 483	52.65 479	4.68 489	64.62 483	28.11 480	33.07 480	59.93 476
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		50.52 446	48.47 448	56.66 462	52.26 489	18.98 493	41.51 482	81.40 465	10.10 483	44.59 478	75.01 467	28.51 473	68.16 480	53.54 464	49.31 475	82.83 462

DeepMVS_CX				56.31 463	74.23 476	51.81 479		56.67 487	44.85 475	48.54 475	75.16 466	27.87 474	58.74 485	40.92 475	52.22 472	58.39 477

kuosan		53.51 444	53.30 447	54.13 464	76.06 473	45.36 483	80.11 469	48.36 489	59.63 468	54.84 470	63.43 477	37.41 468	62.07 484	20.73 484	39.10 479	54.96 478

E-PMN		43.23 448	42.29 450	46.03 465	65.58 484	37.41 488	73.51 475	64.62 483	33.99 480	28.47 484	47.87 481	19.90 482	67.91 481	22.23 483	24.45 481	32.77 480

EMVS		42.07 449	41.12 451	44.92 466	63.45 486	35.56 490	73.65 474	63.48 484	33.05 481	26.88 485	45.45 482	21.27 480	67.14 482	19.80 485	23.02 483	32.06 481

tmp_tt		35.64 450	39.24 452	24.84 467	14.87 491	23.90 492	62.71 478	51.51 488	6.58 485	36.66 481	62.08 478	44.37 462	30.34 487	52.40 465	22.00 484	20.27 482

wuyk23d		21.27 452	20.48 455	23.63 468	68.59 483	36.41 489	49.57 481	6.85 492	9.37 484	7.89 486	4.46 488	4.03 490	31.37 486	17.47 486	16.07 485	3.12 483

test123		8.76 454	11.22 457	1.39 469	0.85 493	0.97 494	85.76 444	0.35 494	0.54 487	2.45 488	8.14 487	0.60 491	0.48 488	2.16 488	0.17 487	2.71 484

testmvs		8.92 453	11.52 456	1.12 470	1.06 492	0.46 495	86.02 441	0.65 493	0.62 486	2.74 487	9.52 486	0.31 492	0.45 489	2.38 487	0.39 486	2.46 485

mmdepth		0.00 457	0.00 460	0.00 471	0.00 494	0.00 496	0.00 483	0.00 495	0.00 489	0.00 490	0.00 489	0.00 493	0.00 490	0.00 489	0.00 488	0.00 486

monomultidepth		0.00 457	0.00 460	0.00 471	0.00 494	0.00 496	0.00 483	0.00 495	0.00 489	0.00 490	0.00 489	0.00 493	0.00 490	0.00 489	0.00 488	0.00 486

test_blank		0.00 457	0.00 460	0.00 471	0.00 494	0.00 496	0.00 483	0.00 495	0.00 489	0.00 490	0.00 489	0.00 493	0.00 490	0.00 489	0.00 488	0.00 486

uanet_test		0.00 457	0.00 460	0.00 471	0.00 494	0.00 496	0.00 483	0.00 495	0.00 489	0.00 490	0.00 489	0.00 493	0.00 490	0.00 489	0.00 488	0.00 486

DCPMVS		0.00 457	0.00 460	0.00 471	0.00 494	0.00 496	0.00 483	0.00 495	0.00 489	0.00 490	0.00 489	0.00 493	0.00 490	0.00 489	0.00 488	0.00 486

cdsmvs_eth3d_5k		22.14 451	29.52 454	0.00 471	0.00 494	0.00 496	0.00 483	95.76 194	0.00 489	0.00 490	94.29 221	75.66 218	0.00 490	0.00 489	0.00 488	0.00 486

pcd_1.5k_mvsjas		6.64 456	8.86 459	0.00 471	0.00 494	0.00 496	0.00 483	0.00 495	0.00 489	0.00 490	0.00 489	79.70 155	0.00 490	0.00 489	0.00 488	0.00 486

sosnet-low-res		0.00 457	0.00 460	0.00 471	0.00 494	0.00 496	0.00 483	0.00 495	0.00 489	0.00 490	0.00 489	0.00 493	0.00 490	0.00 489	0.00 488	0.00 486

sosnet		0.00 457	0.00 460	0.00 471	0.00 494	0.00 496	0.00 483	0.00 495	0.00 489	0.00 490	0.00 489	0.00 493	0.00 490	0.00 489	0.00 488	0.00 486

uncertanet		0.00 457	0.00 460	0.00 471	0.00 494	0.00 496	0.00 483	0.00 495	0.00 489	0.00 490	0.00 489	0.00 493	0.00 490	0.00 489	0.00 488	0.00 486

Regformer		0.00 457	0.00 460	0.00 471	0.00 494	0.00 496	0.00 483	0.00 495	0.00 489	0.00 490	0.00 489	0.00 493	0.00 490	0.00 489	0.00 488	0.00 486

ab-mvs-re		7.82 455	10.43 458	0.00 471	0.00 494	0.00 496	0.00 483	0.00 495	0.00 489	0.00 490	93.88 242	0.00 493	0.00 490	0.00 489	0.00 488	0.00 486

uanet		0.00 457	0.00 460	0.00 471	0.00 494	0.00 496	0.00 483	0.00 495	0.00 489	0.00 490	0.00 489	0.00 493	0.00 490	0.00 489	0.00 488	0.00 486

TestfortrainingZip							97.32 10

WAC-MVS						64.08 459								59.14 455

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

PC_three_1452									82.47 299	97.09 20	97.07 72	92.72 1	98.04 196	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					0.00 494

eth-test					0.00 494

ZD-MVS					98.15 40	86.62 34		97.07 60	83.63 270	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	87.46 156	93.75 75	97.43 51	82.94 100		92.73 76	97.80 92	97.88 106

IU-MVS					98.77 8	86.00 53		96.84 82	81.26 337	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	86.13 200	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	95.21 140	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																96.12 222

test_part2					98.55 15	87.22 20				96.40 31

sam_mvs1												71.70 277				96.12 222

sam_mvs												70.60 291

MTGPA								96.97 65

test_post1							88.00 423				9.81 485	69.31 315	95.53 384	76.65 349

test_post											10.29 484	70.57 295	95.91 369

patchmatchnet-post											83.76 449	71.53 278	96.48 338

MTMP							96.16 60	60.64 485

gm-plane-assit					89.60 417	68.00 442			77.28 391		88.99 397		97.57 241	79.44 321

test9_res														91.91 109	98.71 36	98.07 82

TEST9					97.53 67	86.49 38	94.07 225	96.78 89	81.61 329	92.77 100	96.20 108	87.71 31	99.12 62

test_8					97.49 69	86.30 46	94.02 231	96.76 92	81.86 320	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	94.11 219

test_prior2							94.12 217		87.67 151	92.63 108	96.39 103	86.62 44		91.50 118	98.67 44

旧先验2							93.36 271		71.25 445	94.37 60			97.13 293	86.74 196

新几何2							93.11 286

旧先验1					96.79 85	81.81 195		95.67 204			96.81 84	86.69 42			97.66 98	96.97 178

无先验							93.28 279	96.26 139	73.95 425				99.05 66	80.56 304		96.59 201

原ACMM2							92.94 297

test222					96.55 94	81.70 198	92.22 327	95.01 252	68.36 454	90.20 171	96.14 115	80.26 144			97.80 92	96.05 229

testdata2													98.75 115	78.30 333

segment_acmp												87.16 39

testdata1							92.15 329		87.94 137

plane_prior7					94.70 199	82.74 164

plane_prior6					94.52 215	82.75 162						74.23 238

plane_prior5								96.22 144					98.12 176	88.15 171	89.99 272	94.63 283

plane_prior4											94.86 192

plane_prior3						82.75 162			90.26 47	86.91 240

plane_prior2							95.85 93		90.81 27

plane_prior1					94.59 208

plane_prior						82.73 165	95.21 140		89.66 68						89.88 277

n2								0.00 495

nn								0.00 495

door-mid								85.49 452

test11								96.57 111

door								85.33 454

HQP5-MVS						81.56 200

HQP-NCC					94.17 244		94.39 199		88.81 100	85.43 286

ACMP_Plane					94.17 244		94.39 199		88.81 100	85.43 286

BP-MVS														87.11 193

HQP4-MVS										85.43 286			97.96 210			94.51 293

HQP3-MVS								96.04 168							89.77 281

HQP2-MVS												73.83 249

NP-MVS					94.37 228	82.42 177					93.98 235

MDTV_nov1_ep13_2view						55.91 478	87.62 431		73.32 431	84.59 308		70.33 298		74.65 372		95.50 250

MDTV_nov1_ep13			83.56 347		91.69 349	69.93 435	87.75 428	91.54 383	78.60 375	84.86 302	88.90 399	69.54 310	96.03 360	70.25 402	88.93 294

ACMMP++_ref															87.47 317

ACMMP++															88.01 309

Test By Simon												80.02 146

High-resolution multi-view benchmark