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	courty.	delive.	electro	facade	kicker	meadow	office	pipes	playgr.	relief	relief.	terrace	terrai.

TSAR-MVS + MP.		98.49 11	98.78 10	98.15 21	98.14 53	99.17 35	99.34 8	97.18 32	98.44 6	95.72 22	97.84 19	99.28 14	98.87 7	99.05 1	98.05 30	99.66 2	99.60 7
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
DVP-MVS++		98.92 3	99.18 1	98.61 6	99.47 7	99.61 2	99.39 3	97.82 1	98.80 2	96.86 11	98.90 4	99.92 1	98.67 19	99.02 2	98.20 22	99.43 50	99.82 1

SED-MVS		98.90 4	99.07 2	98.69 5	99.38 20	99.61 2	99.33 10	97.80 4	98.25 11	97.60 5	98.87 6	99.89 3	98.67 19	99.02 2	98.26 20	99.36 67	99.61 6

DVP-MVS		98.86 6	98.97 5	98.75 4	99.43 14	99.63 1	99.25 15	97.81 2	98.62 3	97.69 4	97.59 23	99.90 2	98.93 5	98.99 4	98.42 13	99.37 65	99.62 4
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
SteuartSystems-ACMMP		98.38 16	98.71 13	97.99 25	99.34 22	99.46 13	99.34 8	97.33 27	97.31 40	94.25 33	98.06 16	99.17 21	98.13 34	98.98 5	98.46 11	99.55 18	99.54 11
Skip Steuart: Steuart Systems R&D Blog.
APDe-MVS		98.87 5	98.96 6	98.77 3	99.58 2	99.53 7	99.44 1	97.81 2	98.22 13	97.33 7	98.70 8	99.33 12	98.86 8	98.96 6	98.40 15	99.63 5	99.57 9
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
DeepPCF-MVS	95.28 2	97.00 42	98.35 24	95.42 63	97.30 66	98.94 54	94.82 151	96.03 41	98.24 12	92.11 55	95.80 44	98.64 35	95.51 117	98.95 7	98.66 6	96.78 226	99.20 46

SMA-MVS		98.66 9	98.89 9	98.39 11	99.60 1	99.41 15	99.00 24	97.63 15	97.78 21	95.83 21	98.33 14	99.83 4	98.85 10	98.93 8	98.56 7	99.41 56	99.40 21
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
CNVR-MVS		98.47 13	98.46 19	98.48 9	99.40 16	99.05 39	99.02 22	97.54 19	97.73 22	96.65 14	97.20 32	99.13 22	98.85 10	98.91 9	98.10 27	99.41 56	99.08 60

DPE-MVS		98.75 7	98.91 8	98.57 7	99.21 25	99.54 6	99.42 2	97.78 6	97.49 35	96.84 12	98.94 3	99.82 5	98.59 23	98.90 10	98.22 21	99.56 17	99.48 17
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
DeepC-MVS_fast	96.13 1	98.13 22	98.27 28	97.97 26	99.16 28	99.03 45	99.05 21	97.24 29	98.22 13	94.17 35	95.82 43	98.07 41	98.69 18	98.83 11	98.80 2	99.52 22	99.10 57
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ACMMP_NAP		98.20 20	98.49 16	97.85 27	99.50 6	99.40 16	99.26 14	97.64 14	97.47 37	92.62 50	97.59 23	99.09 24	98.71 17	98.82 12	97.86 43	99.40 59	99.19 47

MED-MVS		99.01 1	99.06 3	98.95 1	99.53 4	99.49 10	99.28 12	97.78 6	98.88 1	98.80 1	99.17 1	99.73 8	98.82 12	98.68 13	98.12 26	99.50 29	99.33 26

CS-MVS		96.87 46	97.41 43	96.24 48	97.42 63	99.48 11	97.30 60	91.83 88	97.17 44	93.02 44	94.80 56	94.45 70	98.16 33	98.61 14	97.85 44	99.69 1	99.50 13

MSP-MVS		98.73 8	98.93 7	98.50 8	99.44 13	99.57 4	99.36 4	97.65 12	98.14 15	96.51 17	98.49 10	99.65 10	98.67 19	98.60 15	98.42 13	99.40 59	99.63 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
MVSMamba_PlusPlus		96.66 52	97.63 37	95.52 60	94.94 108	99.02 47	97.77 52	92.59 70	97.73 22	89.99 97	95.56 47	94.81 67	98.43 28	98.58 16	98.53 8	99.40 59	99.16 52

ME-MVS		98.97 2	99.00 4	98.94 2	99.53 4	99.47 12	99.35 6	97.66 10	98.36 7	98.80 1	99.17 1	99.76 6	98.86 8	98.57 17	98.32 19	99.42 53	99.33 26

TSAR-MVS + ACMM		97.71 31	98.60 15	96.66 42	98.64 43	99.05 39	98.85 29	97.23 30	98.45 5	89.40 110	97.51 27	99.27 16	96.88 64	98.53 18	97.81 46	98.96 149	99.59 8

MGCNet		97.94 26	98.72 12	97.02 38	98.48 45	99.50 9	99.02 22	94.06 50	98.33 8	94.51 30	98.78 7	97.73 45	96.60 77	98.51 19	98.68 5	99.45 40	99.53 12

DELS-MVS		96.06 57	96.04 64	96.07 52	97.77 58	99.25 31	98.10 44	93.26 58	94.42 130	92.79 47	88.52 119	93.48 76	95.06 125	98.51 19	98.83 1	99.45 40	99.28 32
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
NCCC		98.10 23	98.05 33	98.17 20	99.38 20	99.05 39	99.00 24	97.53 20	98.04 17	95.12 27	94.80 56	99.18 20	98.58 24	98.49 21	97.78 47	99.39 62	98.98 77

APD-MVS		98.36 17	98.32 26	98.41 10	99.47 7	99.26 29	99.12 18	97.77 8	96.73 57	96.12 19	97.27 31	98.88 26	98.46 27	98.47 22	98.39 16	99.52 22	99.22 43
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
SPE-MVS-test		97.00 42	97.85 36	96.00 53	97.77 58	99.56 5	96.35 95	91.95 80	97.54 33	92.20 53	96.14 39	96.00 63	98.19 31	98.46 23	97.78 47	99.57 14	99.45 19

HPM-MVS++		98.34 18	98.47 18	98.18 18	99.46 10	99.15 36	99.10 19	97.69 9	97.67 28	94.93 29	97.62 22	99.70 9	98.60 22	98.45 24	97.46 56	99.31 75	99.26 37

IS_MVSNet		95.28 66	96.43 59	93.94 118	95.30 95	99.01 50	95.90 122	91.12 122	94.13 137	87.50 142	91.23 89	94.45 70	94.17 142	98.45 24	98.50 9	99.65 3	99.23 41

MP-MVS		98.09 24	98.30 27	97.84 28	99.34 22	99.19 34	99.23 16	97.40 22	97.09 48	93.03 43	97.58 25	98.85 27	98.57 25	98.44 26	97.69 49	99.48 31	99.23 41
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
ETV-MVS		96.31 55	97.47 42	94.96 75	94.79 113	98.78 67	96.08 109	91.41 116	96.16 74	90.50 83	95.76 45	96.20 59	97.39 49	98.42 27	97.82 45	99.57 14	99.18 50

X-MVS		97.84 27	98.19 30	97.42 32	99.40 16	99.35 20	99.06 20	97.25 28	97.38 39	90.85 73	96.06 40	98.72 32	98.53 26	98.41 28	98.15 25	99.46 36	99.28 32

EC-MVSNet		96.49 53	97.63 37	95.16 67	94.75 117	98.69 74	97.39 59	88.97 152	96.34 69	92.02 56	96.04 41	96.46 54	98.21 29	98.41 28	97.96 36	99.61 6	99.55 10

MCST-MVS		98.20 20	98.36 22	98.01 24	99.40 16	99.05 39	99.00 24	97.62 16	97.59 32	93.70 37	97.42 30	99.30 13	98.77 15	98.39 30	97.48 55	99.59 7	99.31 31

DeepC-MVS	94.87 4	96.76 51	96.50 57	97.05 37	98.21 51	99.28 27	98.67 31	97.38 23	97.31 40	90.36 89	89.19 110	93.58 75	98.19 31	98.31 31	98.50 9	99.51 27	99.36 23
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
test2506		94.32 98	93.00 143	95.87 54	96.16 79	99.39 18	96.96 66	92.80 67	95.22 111	94.47 31	91.55 87	70.45 240	95.25 122	98.29 32	97.98 33	99.59 7	98.10 158

ECVR-MVS		94.14 106	92.96 144	95.52 60	96.16 79	99.39 18	96.96 66	92.80 67	95.22 111	92.38 52	81.48 192	80.31 182	95.25 122	98.29 32	97.98 33	99.59 7	98.05 159

CP-MVS		98.32 19	98.34 25	98.29 14	99.34 22	99.30 25	99.15 17	97.35 24	97.49 35	95.58 24	97.72 21	98.62 36	98.82 12	98.29 32	97.67 50	99.51 27	99.28 32

test1111		93.94 115	92.78 145	95.29 66	96.14 81	99.42 14	96.79 77	92.85 66	95.08 117	91.39 61	80.69 198	79.86 186	95.00 126	98.28 35	98.00 32	99.58 11	98.11 157

SD-MVS		98.52 10	98.77 11	98.23 17	98.15 52	99.26 29	98.79 30	97.59 18	98.52 4	96.25 18	97.99 18	99.75 7	99.01 3	98.27 36	97.97 35	99.59 7	99.63 2
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
HFP-MVS		98.48 12	98.62 14	98.32 13	99.39 19	99.33 24	99.27 13	97.42 21	98.27 10	95.25 26	98.34 13	98.83 28	99.08 1	98.26 37	98.08 29	99.48 31	99.26 37

ACMMPR		98.40 14	98.49 16	98.28 15	99.41 15	99.40 16	99.36 4	97.35 24	98.30 9	95.02 28	97.79 20	98.39 39	99.04 2	98.26 37	98.10 27	99.50 29	99.22 43

CANet		96.84 48	97.20 45	96.42 43	97.92 56	99.24 33	98.60 33	93.51 55	97.11 47	93.07 40	91.16 90	97.24 48	96.21 95	98.24 39	98.05 30	99.22 96	99.35 24

PGM-MVS		97.81 28	98.11 31	97.46 31	99.55 3	99.34 23	99.32 11	94.51 48	96.21 73	93.07 40	98.05 17	97.95 44	98.82 12	98.22 40	97.89 42	99.48 31	99.09 59

Vis-MVSNet (Re-imp)		94.46 90	96.24 61	92.40 139	95.23 100	98.64 80	95.56 136	90.99 123	94.42 130	85.02 153	90.88 96	94.65 69	88.01 222	98.17 41	98.37 18	99.57 14	98.53 129

SF-MVS		98.39 15	98.45 20	98.33 12	99.45 11	99.05 39	98.27 40	97.65 12	97.73 22	97.02 10	98.18 15	99.25 17	98.11 35	98.15 42	97.62 51	99.45 40	99.19 47

UA-Net		93.96 112	95.95 65	91.64 148	96.06 82	98.59 85	95.29 139	90.00 133	91.06 189	82.87 161	90.64 97	98.06 42	86.06 233	98.14 43	98.20 22	99.58 11	96.96 194

PVSNet_Blended_VisFu		94.77 80	95.54 70	93.87 122	96.48 74	98.97 52	94.33 165	91.84 83	94.93 120	90.37 88	85.04 170	94.99 66	90.87 195	98.12 44	97.30 64	99.30 77	99.45 19

MVS_111021_HR		97.04 41	98.20 29	95.69 57	98.44 48	99.29 26	96.59 86	93.20 61	97.70 26	89.94 100	98.46 11	96.89 50	96.71 69	98.11 45	97.95 37	99.27 82	99.01 73

PVSNet_BlendedMVS		95.41 64	95.28 76	95.57 58	97.42 63	99.02 47	95.89 124	93.10 63	96.16 74	93.12 38	91.99 78	85.27 140	94.66 131	98.09 46	97.34 61	99.24 87	99.08 60

PVSNet_Blended		95.41 64	95.28 76	95.57 58	97.42 63	99.02 47	95.89 124	93.10 63	96.16 74	93.12 38	91.99 78	85.27 140	94.66 131	98.09 46	97.34 61	99.24 87	99.08 60

MVS_111021_LR		97.16 39	98.01 34	96.16 49	98.47 46	98.98 51	96.94 68	93.89 52	97.64 30	91.44 59	98.89 5	96.41 55	97.20 54	98.02 48	97.29 66	99.04 143	98.85 92

train_agg		97.65 32	98.06 32	97.18 35	98.94 34	98.91 59	98.98 28	97.07 34	96.71 58	90.66 80	97.43 29	99.08 25	98.20 30	97.96 49	97.14 68	99.22 96	99.19 47

3Dnovator+	93.91 7	97.23 38	97.22 44	97.24 34	98.89 38	98.85 64	98.26 41	93.25 60	97.99 18	95.56 25	90.01 104	98.03 43	98.05 37	97.91 50	98.43 12	99.44 47	99.35 24

3Dnovator	93.79 8	97.08 40	97.20 45	96.95 40	99.09 30	99.03 45	98.20 42	93.33 56	97.99 18	93.82 36	90.61 98	96.80 52	97.82 40	97.90 51	98.78 3	99.47 35	99.26 37

PHI-MVS		97.78 29	98.44 21	97.02 38	98.73 40	99.25 31	98.11 43	95.54 42	96.66 60	92.79 47	98.52 9	99.38 11	97.50 48	97.84 52	98.39 16	99.45 40	99.03 70

gg-mvs-nofinetune		86.17 232	88.57 199	83.36 243	93.44 166	98.15 121	96.58 87	72.05 263	74.12 265	49.23 271	64.81 258	90.85 89	89.90 214	97.83 53	96.84 89	98.97 147	97.41 181

CDPH-MVS		96.84 48	97.49 40	96.09 50	98.92 36	98.85 64	98.61 32	95.09 44	96.00 81	87.29 143	95.45 50	97.42 46	97.16 55	97.83 53	97.94 38	99.44 47	98.92 83

EIA-MVS		95.50 59	96.19 62	94.69 90	94.83 112	98.88 63	95.93 119	91.50 108	94.47 129	89.43 108	93.14 67	92.72 80	97.05 60	97.82 55	97.13 69	99.43 50	99.15 54

FA-MVS(training)		93.94 115	95.16 79	92.53 138	94.87 111	98.57 88	95.42 138	79.49 241	95.37 100	90.98 71	86.54 146	94.26 72	95.44 119	97.80 56	95.19 145	98.97 147	98.38 145

Casviewmamba		94.92 71	94.85 87	95.00 71	94.72 121	98.62 84	96.69 83	91.81 90	96.94 52	90.43 84	88.11 123	86.57 118	96.84 65	97.72 57	97.32 63	99.48 31	98.69 107

QAPM		96.78 50	97.14 48	96.36 45	99.05 31	99.14 37	98.02 46	93.26 58	97.27 42	90.84 76	91.16 90	97.31 47	97.64 46	97.70 58	98.20 22	99.33 69	99.18 50

CHOSEN 280x420		95.46 62	97.01 49	93.66 126	97.28 67	97.98 127	96.40 93	85.39 203	96.10 78	91.07 69	96.53 35	96.34 57	95.61 110	97.65 59	96.95 78	96.21 236	97.49 178

TSAR-MVS + GP.		97.45 34	98.36 22	96.39 44	95.56 89	98.93 56	97.74 53	93.31 57	97.61 31	94.24 34	98.44 12	99.19 19	98.03 38	97.60 60	97.41 58	99.44 47	99.33 26

MVSTER		94.89 73	95.07 83	94.68 91	94.71 123	96.68 160	97.00 64	90.57 128	95.18 113	93.05 42	95.21 51	86.41 122	93.72 154	97.59 61	95.88 123	99.00 144	98.50 132

Vis-MVSNet		92.77 143	95.00 85	90.16 171	94.10 156	98.79 66	94.76 154	88.26 159	92.37 174	79.95 176	88.19 122	91.58 84	84.38 243	97.59 61	97.58 53	99.52 22	98.91 86
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
hybridcas		94.67 83	94.44 94	94.94 76	94.66 128	98.57 88	96.76 79	91.72 99	96.60 61	90.57 81	86.88 136	85.79 133	96.53 80	97.55 63	97.07 70	99.43 50	98.62 119

LS3D		95.46 62	95.14 80	95.84 55	97.91 57	98.90 61	98.58 34	97.79 5	97.07 49	83.65 159	88.71 115	88.64 107	97.82 40	97.49 64	97.42 57	99.26 85	97.72 172

casdiffmvs_mvg		94.55 87	94.26 99	94.88 79	94.96 107	98.51 96	97.11 62	91.82 89	94.28 133	89.20 116	86.60 144	86.85 116	96.56 79	97.47 65	97.25 67	99.64 4	98.83 95
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
MSLP-MVS++		98.04 25	97.93 35	98.18 18	99.10 29	99.09 38	98.34 39	96.99 35	97.54 33	96.60 15	94.82 55	98.45 37	98.89 6	97.46 66	98.77 4	99.17 114	99.37 22

casdiffmvs		94.38 95	94.15 111	94.64 92	94.70 125	98.51 96	96.03 116	91.66 102	95.70 92	89.36 112	86.48 148	85.03 149	96.60 77	97.40 67	97.30 64	99.52 22	98.67 112
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
viewdifsd2359ckpt07		94.23 104	94.19 104	94.27 111	94.69 127	98.45 104	96.06 113	91.72 99	95.09 116	88.79 129	86.81 137	86.35 124	95.64 107	97.38 68	96.88 84	98.68 179	98.40 142

CANet_DTU		93.92 117	96.57 56	90.83 161	95.63 87	98.39 106	96.99 65	87.38 168	96.26 71	71.97 226	96.31 37	93.02 77	94.53 134	97.38 68	96.83 90	98.49 193	97.79 165

EPP-MVSNet		95.27 67	96.18 63	94.20 115	94.88 110	98.64 80	94.97 145	90.70 126	95.34 102	89.67 103	91.66 85	93.84 73	95.42 120	97.32 70	97.00 74	99.58 11	99.47 18

ACMMP		97.37 36	97.48 41	97.25 33	98.88 39	99.28 27	98.47 37	96.86 37	97.04 50	92.15 54	97.57 26	96.05 62	97.67 43	97.27 71	95.99 119	99.46 36	99.14 56
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
OpenMVS	92.33 11	95.50 59	95.22 78	95.82 56	98.98 32	98.97 52	97.67 54	93.04 65	94.64 124	89.18 117	84.44 176	94.79 68	96.79 66	97.23 72	97.61 52	99.24 87	98.88 88

gm-plane-assit		83.26 247	85.29 243	80.89 248	89.52 212	89.89 259	70.26 268	78.24 243	77.11 263	58.01 268	74.16 231	66.90 254	90.63 205	97.20 73	96.05 116	98.66 183	95.68 222

CPTT-MVS		97.78 29	97.54 39	98.05 23	98.91 37	99.05 39	99.00 24	96.96 36	97.14 46	95.92 20	95.50 48	98.78 30	98.99 4	97.20 73	96.07 114	98.54 190	99.04 69

AdaColmap		97.53 33	96.93 51	98.24 16	99.21 25	98.77 68	98.47 37	97.34 26	96.68 59	96.52 16	95.11 53	96.12 60	98.72 16	97.19 75	96.24 108	99.17 114	98.39 144

OMC-MVS		97.00 42	96.92 52	97.09 36	98.69 41	98.66 77	97.85 50	95.02 45	98.09 16	94.47 31	93.15 66	96.90 49	97.38 50	97.16 76	96.82 91	99.13 121	97.65 173

PLC	94.95 3	97.37 36	96.77 54	98.07 22	98.97 33	98.21 116	97.94 49	96.85 38	97.66 29	97.58 6	93.33 65	96.84 51	98.01 39	97.13 77	96.20 110	99.09 128	98.01 160
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
DPM-MVS		96.86 47	96.82 53	96.91 41	98.08 54	98.20 117	98.52 36	97.20 31	97.24 43	91.42 60	91.84 82	98.45 37	97.25 53	97.07 78	97.40 59	98.95 150	97.55 176

baseline1		94.59 86	94.47 92	94.72 89	95.16 102	97.97 128	96.07 111	91.94 81	94.86 121	89.98 98	91.60 86	85.87 131	95.64 107	97.07 78	96.90 79	99.52 22	97.06 193

EPNet		96.27 56	96.97 50	95.46 62	98.47 46	98.28 109	97.41 57	93.67 53	95.86 87	92.86 46	97.51 27	93.79 74	91.76 176	97.03 80	97.03 72	98.61 186	99.28 32
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tttt0517		94.52 89	95.44 74	93.44 130	94.51 139	98.68 75	94.61 158	90.72 124	95.61 97	86.84 147	93.78 61	89.26 101	94.74 128	97.02 81	94.86 154	99.20 109	98.87 90

thisisatest0530		94.54 88	95.47 71	93.46 129	94.51 139	98.65 79	94.66 155	90.72 124	95.69 94	86.90 146	93.80 60	89.44 98	94.74 128	96.98 82	94.86 154	99.19 111	98.85 92

E2		94.88 74	94.85 87	94.91 77	94.58 132	98.59 85	96.16 102	91.80 91	95.88 85	91.04 70	90.11 103	86.91 115	96.68 71	96.91 83	96.85 86	99.19 111	98.70 106

viewcassd2359sk11		94.63 84	94.45 93	94.84 82	94.58 132	98.57 88	96.13 105	91.79 92	95.32 103	90.67 79	88.73 114	86.13 125	96.65 72	96.82 84	96.87 85	99.21 102	98.68 109

tfpn200view9		93.64 128	92.57 150	94.89 78	95.33 93	98.94 54	96.82 73	92.31 72	92.63 165	88.29 132	87.21 133	78.01 197	97.12 58	96.82 84	95.85 124	99.45 40	98.56 126

thres600view7		93.49 133	92.37 161	94.79 85	95.42 90	98.93 56	96.58 87	92.31 72	93.04 158	87.88 139	86.62 143	76.94 209	97.09 59	96.82 84	95.63 131	99.45 40	98.63 117

thres200		93.62 129	92.54 151	94.88 79	95.36 92	98.93 56	96.75 80	92.31 72	92.84 161	88.28 134	86.99 135	77.81 204	97.13 56	96.82 84	95.92 120	99.45 40	98.49 134

viewdifsd2359ckpt13		94.14 106	94.00 113	94.30 110	94.55 136	98.55 93	95.71 132	91.76 96	95.03 118	88.12 137	87.34 128	85.15 144	96.39 89	96.81 88	96.60 95	99.24 87	98.50 132

MAR-MVS		95.50 59	95.60 68	95.39 64	98.67 42	98.18 120	95.89 124	89.81 139	94.55 126	91.97 57	92.99 68	90.21 94	97.30 52	96.79 89	97.49 54	98.72 175	98.99 75
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
FMVSNet2		93.30 136	93.36 137	93.22 134	91.34 189	95.86 185	96.22 97	88.24 160	95.15 114	89.92 101	81.64 190	89.36 99	94.40 137	96.77 90	96.98 77	99.21 102	97.79 165

thres400		93.56 131	92.43 158	94.87 81	95.40 91	98.91 59	96.70 82	92.38 71	92.93 160	88.19 136	86.69 140	77.35 206	97.13 56	96.75 91	95.85 124	99.42 53	98.56 126

CNLPA		96.90 45	96.28 60	97.64 30	98.56 44	98.63 82	96.85 72	96.60 39	97.73 22	97.08 9	89.78 106	96.28 58	97.80 42	96.73 92	96.63 94	98.94 152	98.14 156

viewmanbaseed2359cas		94.31 99	94.25 101	94.38 106	94.72 121	98.59 85	96.09 108	91.84 83	95.35 101	87.92 138	87.86 125	85.54 135	96.45 88	96.71 93	97.04 71	99.26 85	98.67 112

CHOSEN 1792x2688		92.66 145	92.49 154	92.85 136	97.13 68	98.89 62	95.90 122	88.50 158	95.32 103	83.31 160	71.99 243	88.96 105	94.10 144	96.69 94	96.49 98	98.15 204	99.10 57

UGNet		94.92 71	96.63 55	92.93 135	96.03 83	98.63 82	94.53 160	91.52 106	96.23 72	90.03 96	92.87 71	96.10 61	86.28 232	96.68 95	96.60 95	99.16 117	99.32 30
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
Effi-MVS+		92.93 142	93.86 119	91.86 144	94.07 157	98.09 125	95.59 135	85.98 187	94.27 134	79.54 180	91.12 93	81.81 178	96.71 69	96.67 96	96.06 115	99.27 82	98.98 77

E3new		94.34 96	93.98 116	94.75 87	94.56 134	98.56 91	96.13 105	91.78 94	94.54 128	90.22 90	87.24 131	85.36 139	96.62 74	96.61 97	96.90 79	99.22 96	98.68 109

E3		94.33 97	93.99 115	94.73 88	94.56 134	98.56 91	96.14 103	91.78 94	94.55 126	90.05 95	87.23 132	85.39 137	96.61 76	96.61 97	96.90 79	99.21 102	98.68 109

viewdifsd2359ckpt09		94.40 94	94.26 99	94.57 93	94.51 139	98.50 102	95.96 118	91.72 99	95.31 107	89.37 111	88.33 120	85.88 130	96.64 73	96.61 97	96.57 97	99.20 109	98.60 122

Effi-MVS+-dtu		91.78 154	93.59 127	89.68 179	92.44 179	97.11 147	94.40 164	84.94 212	92.43 170	75.48 207	91.09 94	83.75 166	93.55 157	96.61 97	95.47 136	97.24 222	98.67 112

TAPA-MVS	94.18 5	96.38 54	96.49 58	96.25 46	98.26 50	98.66 77	98.00 47	94.96 46	97.17 44	89.48 107	92.91 70	96.35 56	97.53 47	96.59 101	95.90 122	99.28 79	97.82 164
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PatchMatch-RL		94.69 82	94.41 95	95.02 70	97.63 62	98.15 121	94.50 163	91.99 78	95.32 103	91.31 62	95.47 49	83.44 168	96.02 101	96.56 102	95.23 143	98.69 178	96.67 202

Fast-Effi-MVS+		91.87 151	92.08 165	91.62 150	92.91 173	97.21 146	94.93 146	84.60 216	93.61 145	81.49 170	83.50 181	78.95 189	96.62 74	96.55 103	96.22 109	99.16 117	98.51 131

FC-MVSNet-train		93.85 120	93.91 117	93.78 124	94.94 108	96.79 157	94.29 166	91.13 121	93.84 142	88.26 135	90.40 99	85.23 143	94.65 133	96.54 104	95.31 140	99.38 63	99.28 32

GBi-Net		93.81 123	94.18 105	93.38 131	91.34 189	95.86 185	96.22 97	88.68 154	95.23 108	90.40 85	86.39 150	91.16 85	94.40 137	96.52 105	96.30 103	99.21 102	97.79 165

test1		93.81 123	94.18 105	93.38 131	91.34 189	95.86 185	96.22 97	88.68 154	95.23 108	90.40 85	86.39 150	91.16 85	94.40 137	96.52 105	96.30 103	99.21 102	97.79 165

FMVSNet1		91.54 159	90.93 182	92.26 141	90.35 199	95.27 208	95.22 141	87.16 172	91.37 186	87.62 141	75.45 220	83.84 165	94.43 135	96.52 105	96.30 103	98.82 164	97.74 171

MVS_Test		94.82 76	95.66 67	93.84 123	94.79 113	98.35 107	96.49 90	89.10 151	96.12 77	87.09 145	92.58 73	90.61 91	96.48 84	96.51 108	96.89 83	99.11 125	98.54 128

GG-mvs-BLEND		66.17 262	94.91 86	32.63 267	1.32 276	96.64 161	91.40 221	0.85 274	94.39 132	2.20 278	90.15 102	95.70 64	2.27 273	96.39 109	95.44 137	97.78 212	95.68 222

thres100view900		93.55 132	92.47 157	94.81 84	95.33 93	98.74 69	96.78 78	92.30 75	92.63 165	88.29 132	87.21 133	78.01 197	96.78 67	96.38 110	95.92 120	99.38 63	98.40 142

baseline2		93.01 141	94.17 108	91.64 148	92.83 175	97.49 135	93.40 179	87.53 166	93.67 144	86.07 149	91.83 83	86.58 117	91.36 181	96.38 110	95.06 148	98.67 180	98.20 154

test-mter		90.95 164	93.54 130	87.93 208	90.28 200	96.80 154	91.44 220	82.68 229	92.15 179	74.37 218	89.57 107	88.23 112	90.88 194	96.37 112	94.31 174	97.93 211	97.37 182

LGP-MVS_train		94.12 108	94.62 89	93.53 127	96.44 75	97.54 133	97.40 58	91.84 83	94.66 123	81.09 172	95.70 46	83.36 169	95.10 124	96.36 113	95.71 130	99.32 71	99.03 70

DI_MVS_pp		94.01 111	93.63 125	94.44 101	94.54 138	98.26 112	97.51 56	90.63 127	95.88 85	89.34 113	80.54 200	89.36 99	95.48 118	96.33 114	96.27 106	99.17 114	98.78 100

E6new		93.85 120	93.39 133	94.39 104	94.50 142	98.53 94	95.93 119	91.41 116	93.47 150	88.81 127	85.51 165	84.16 161	96.46 86	96.32 115	96.99 75	99.21 102	98.78 100

E6		93.85 120	93.39 133	94.39 104	94.50 142	98.53 94	95.93 119	91.41 116	93.47 150	88.81 127	85.51 165	84.16 161	96.46 86	96.32 115	96.99 75	99.21 102	98.78 100

TSAR-MVS + COLMAP		94.79 78	94.51 91	95.11 68	96.50 73	97.54 133	97.99 48	94.54 47	97.81 20	85.88 150	96.73 34	81.28 181	96.99 61	96.29 117	95.21 144	98.76 174	96.73 201

OPM-MVS		93.61 130	92.43 158	95.00 71	96.94 70	97.34 141	97.78 51	94.23 49	89.64 203	85.53 151	88.70 116	82.81 174	96.28 93	96.28 118	95.00 152	99.24 87	97.22 186
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
anonymousdsp		88.90 199	91.00 181	86.44 232	88.74 231	95.97 180	90.40 233	82.86 227	88.77 210	67.33 247	81.18 195	81.44 180	90.22 210	96.23 119	94.27 175	99.12 123	99.16 52

E4		93.88 119	93.38 135	94.48 98	94.50 142	98.51 96	96.08 109	91.74 98	93.42 154	88.84 126	85.51 165	84.38 155	96.49 83	96.22 120	96.90 79	99.22 96	98.69 107

GeoE		92.52 147	92.64 149	92.39 140	93.96 158	97.76 130	96.01 117	85.60 198	93.23 155	83.94 156	81.56 191	84.80 150	95.63 109	96.22 120	95.83 126	99.19 111	99.07 64

ACMM	92.75 10	94.41 93	93.84 121	95.09 69	96.41 76	96.80 154	94.88 150	93.54 54	96.41 67	90.16 91	92.31 76	83.11 171	96.32 92	96.22 120	94.65 159	99.22 96	97.35 183
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CSCG		97.44 35	97.18 47	97.75 29	99.47 7	99.52 8	98.55 35	95.41 43	97.69 27	95.72 22	94.29 59	95.53 65	98.10 36	96.20 123	97.38 60	99.24 87	99.62 4

test-LLR		91.62 157	93.56 128	89.35 186	93.31 169	96.57 163	92.02 208	87.06 173	92.34 175	75.05 214	90.20 100	88.64 107	90.93 191	96.19 124	94.07 178	97.75 214	96.90 197

TESTMET0.1,1		91.07 163	93.56 128	88.17 198	90.43 196	96.57 163	92.02 208	82.83 228	92.34 175	75.05 214	90.20 100	88.64 107	90.93 191	96.19 124	94.07 178	97.75 214	96.90 197

diffmvs_AUTHOR		94.09 109	93.86 119	94.36 107	94.60 131	98.31 108	96.29 96	91.51 107	96.39 68	88.49 131	87.35 127	83.32 170	96.16 98	96.17 126	96.64 93	99.10 126	98.82 97

E5new		93.95 113	93.42 131	94.57 93	94.50 142	98.51 96	96.18 100	91.84 83	93.55 148	89.12 120	85.80 162	84.38 155	96.53 80	96.16 127	96.85 86	99.23 94	98.67 112

E5		93.95 113	93.42 131	94.57 93	94.50 142	98.51 96	96.18 100	91.84 83	93.55 148	89.12 120	85.80 162	84.38 155	96.53 80	96.16 127	96.85 86	99.23 94	98.67 112

MSDG		94.82 76	93.73 123	96.09 50	98.34 49	97.43 138	97.06 63	96.05 40	95.84 88	90.56 82	86.30 155	89.10 104	95.55 115	96.13 129	95.61 132	99.00 144	95.73 221

casdiffseed414692147		93.07 140	92.06 166	94.25 113	94.46 147	98.28 109	95.61 134	91.28 120	92.74 163	88.58 130	82.11 188	80.19 184	96.25 94	96.05 130	96.49 98	99.32 71	98.57 124

diffmvs		94.31 99	94.21 103	94.42 102	94.64 129	98.28 109	96.36 94	91.56 104	96.77 56	88.89 125	88.97 111	84.23 160	96.01 102	96.05 130	96.41 101	99.05 141	98.79 99
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
viewmacassd2359aftdt		93.65 127	93.29 138	94.07 117	94.61 130	98.51 96	96.04 115	91.75 97	93.61 145	86.56 148	84.89 171	84.41 154	96.17 96	95.97 132	97.03 72	99.28 79	98.63 117

FMVSNet3		93.79 125	94.17 108	93.35 133	91.21 192	95.99 178	96.62 84	88.68 154	95.23 108	90.40 85	86.39 150	91.16 85	94.11 143	95.96 133	96.67 92	99.07 132	97.79 165

EPNet_dtu		92.45 148	95.02 84	89.46 183	98.02 55	95.47 201	94.79 152	92.62 69	94.97 119	70.11 237	94.76 58	92.61 81	84.07 247	95.94 134	95.56 133	97.15 223	95.82 220
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CDS-MVSNet		92.77 143	93.60 126	91.80 146	92.63 177	96.80 154	95.24 140	89.14 150	90.30 200	84.58 154	86.76 138	90.65 90	90.42 207	95.89 135	96.49 98	98.79 171	98.32 150
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
HyFIR lowres test		92.03 149	91.55 174	92.58 137	97.13 68	98.72 71	94.65 156	86.54 178	93.58 147	82.56 163	67.75 254	90.47 92	95.67 106	95.87 136	95.54 134	98.91 156	98.93 82

GA-MVS		89.28 192	90.75 185	87.57 215	91.77 183	96.48 165	92.29 200	87.58 165	90.61 197	65.77 251	84.48 175	76.84 210	89.46 216	95.84 137	93.68 190	98.52 191	97.34 184

MIMVSNet		88.99 198	91.07 180	86.57 231	86.78 240	95.62 194	91.20 226	75.40 257	90.65 196	76.57 199	84.05 178	82.44 177	91.01 190	95.84 137	95.38 138	98.48 194	93.50 247

pm-mvs1		89.19 195	89.02 195	89.38 185	90.40 197	95.74 192	92.05 206	88.10 162	86.13 241	77.70 191	73.72 234	79.44 188	88.97 219	95.81 139	94.51 170	99.08 130	97.78 170

sasdasda		95.25 68	95.45 72	95.00 71	95.27 97	98.72 71	96.89 69	89.82 137	96.51 63	90.84 76	93.72 62	86.01 127	97.66 44	95.78 140	97.94 38	99.54 19	99.50 13

canonicalmvs		95.25 68	95.45 72	95.00 71	95.27 97	98.72 71	96.89 69	89.82 137	96.51 63	90.84 76	93.72 62	86.01 127	97.66 44	95.78 140	97.94 38	99.54 19	99.50 13

baseline		94.83 75	95.82 66	93.68 125	94.75 117	97.80 129	96.51 89	88.53 157	97.02 51	89.34 113	92.93 69	92.18 82	94.69 130	95.78 140	96.08 113	98.27 202	98.97 81

Fast-Effi-MVS+-dtu		91.19 162	93.64 124	88.33 196	92.19 181	96.46 166	93.99 169	81.52 236	92.59 167	71.82 227	92.17 77	85.54 135	91.68 177	95.73 143	94.64 160	98.80 169	98.34 147

ACMP	92.88 9	94.43 91	94.38 96	94.50 97	96.01 84	97.69 131	95.85 129	92.09 77	95.74 90	89.12 120	95.14 52	82.62 176	94.77 127	95.73 143	94.67 158	99.14 120	99.06 65
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
FC-MVSNet-test		91.63 156	93.82 122	89.08 187	92.02 182	96.40 169	93.26 182	87.26 169	93.72 143	77.26 194	88.61 118	89.86 96	85.50 236	95.72 145	95.02 150	99.16 117	97.44 180

MGCFI-Net		95.12 70	95.39 75	94.79 85	95.24 99	98.68 75	96.80 76	89.72 141	96.48 65	90.11 93	93.64 64	85.86 132	97.36 51	95.69 146	97.92 41	99.53 21	99.49 16

hybrid		94.23 104	94.23 102	94.24 114	94.70 125	98.20 117	95.66 133	91.43 113	96.94 52	89.13 118	89.47 108	84.64 152	95.59 113	95.56 147	96.20 110	98.95 150	98.57 124

tfpnnormal		88.50 202	87.01 224	90.23 169	91.36 188	95.78 191	92.74 189	90.09 132	83.65 250	76.33 202	71.46 246	69.58 246	91.84 174	95.54 148	94.02 180	99.06 137	99.03 70

DCV-MVSNet		94.76 81	95.12 82	94.35 108	95.10 105	95.81 189	96.46 91	89.49 145	96.33 70	90.16 91	92.55 74	90.26 93	95.83 104	95.52 149	96.03 117	99.06 137	99.33 26

CLD-MVS		94.79 78	94.36 97	95.30 65	95.21 101	97.46 136	97.23 61	92.24 76	96.43 66	91.77 58	92.69 72	84.31 158	96.06 99	95.52 149	95.03 149	99.31 75	99.06 65
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
HQP-MVS		94.43 91	94.57 90	94.27 111	96.41 76	97.23 145	96.89 69	93.98 51	95.94 83	83.68 158	95.01 54	84.46 153	95.58 114	95.47 151	94.85 157	99.07 132	99.00 74

PMMVS		94.61 85	95.56 69	93.50 128	94.30 153	96.74 158	94.91 147	89.56 144	95.58 98	87.72 140	96.15 38	92.86 78	96.06 99	95.47 151	95.02 150	98.43 199	97.09 189

thisisatest0515		90.12 180	92.06 166	87.85 209	90.03 203	96.17 174	87.83 242	87.45 167	91.71 183	77.15 195	85.40 168	84.01 164	85.74 235	95.41 153	93.30 197	98.88 158	98.43 138

IterMVS-SCA-FT		90.24 176	92.48 156	87.63 213	92.85 174	94.30 232	93.79 171	81.47 237	92.66 164	69.95 238	84.66 174	88.38 110	89.99 212	95.39 154	94.34 173	97.74 216	97.63 174

onestephybrid01		94.30 101	94.16 110	94.46 99	94.74 120	98.25 113	95.77 131	91.59 103	96.57 62	90.06 94	88.08 124	85.68 134	95.53 116	95.37 155	96.41 101	99.07 132	98.74 105

IterMVS-LS		92.56 146	93.18 139	91.84 145	93.90 159	94.97 215	94.99 144	86.20 182	94.18 136	82.68 162	85.81 161	87.36 114	94.43 135	95.31 156	96.02 118	98.87 160	98.60 122
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS		90.20 177	92.43 158	87.61 214	92.82 176	94.31 231	94.11 167	81.54 235	92.97 159	69.90 239	84.71 173	88.16 113	89.96 213	95.25 157	94.17 176	97.31 221	97.46 179
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CR-MVSNet		90.16 179	91.96 169	88.06 202	93.32 168	95.95 182	93.36 180	75.99 255	92.40 172	75.19 211	83.18 182	85.37 138	92.05 171	95.21 158	94.56 166	98.47 195	97.08 191

PatchT		89.13 196	91.71 170	86.11 235	92.92 172	95.59 197	83.64 255	75.09 258	91.87 181	75.19 211	82.63 185	85.06 148	92.05 171	95.21 158	94.56 166	97.76 213	97.08 191

Anonymous202405211			92.18 163		95.04 106	98.20 117	96.14 103	91.79 92	93.93 138		74.60 226	88.38 110	96.48 84	95.17 160	95.82 128	99.00 144	99.15 54

hybridnocas07		94.25 103	94.18 105	94.33 109	94.75 117	98.23 115	95.86 128	91.49 109	96.88 54	89.13 118	89.37 109	84.73 151	95.73 105	95.14 161	96.27 106	99.05 141	98.62 119

test0.0.03 1		91.97 150	93.91 117	89.72 176	93.31 169	96.40 169	91.34 223	87.06 173	93.86 140	81.67 168	91.15 92	89.16 103	86.02 234	95.08 162	95.09 146	98.91 156	96.64 204

MS-PatchMatch		91.82 153	92.51 152	91.02 157	95.83 86	96.88 150	95.05 143	84.55 218	93.85 141	82.01 165	82.51 186	91.71 83	90.52 206	95.07 163	93.03 201	98.13 205	94.52 230

usedtu_dtu_shiyan1		90.61 169	91.45 176	89.62 181	85.03 248	96.03 177	93.51 176	89.17 149	93.13 157	79.51 181	81.79 189	84.24 159	91.63 178	95.06 164	93.79 189	98.88 158	96.12 212

USDC		90.69 167	90.52 186	90.88 160	94.17 155	96.43 167	95.82 130	86.76 175	93.92 139	76.27 203	86.49 147	74.30 223	93.67 156	95.04 165	93.36 194	98.61 186	94.13 237

PCF-MVS	93.95 6	95.65 58	95.14 80	96.25 46	97.73 61	98.73 70	97.59 55	97.13 33	92.50 169	89.09 123	89.85 105	96.65 53	96.90 63	94.97 166	94.89 153	99.08 130	98.38 145
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Anonymous20231211		93.49 133	92.33 162	94.84 82	94.78 115	98.00 126	96.11 107	91.85 82	94.86 121	90.91 72	74.69 225	89.18 102	96.73 68	94.82 167	95.51 135	98.67 180	99.24 40

viewmamba		94.27 102	94.15 111	94.42 102	94.77 116	98.24 114	95.87 127	91.46 111	97.44 38	88.99 124	88.77 113	85.11 146	96.34 91	94.77 168	96.19 112	99.07 132	98.53 129

dmvs_re		91.84 152	91.60 173	92.12 143	91.60 185	97.26 143	95.14 142	91.96 79	91.02 190	80.98 173	86.56 145	77.96 199	93.84 151	94.71 169	95.08 147	99.22 96	98.62 119

FMVSNet5		90.36 174	90.93 182	89.70 177	87.99 234	92.25 245	92.03 207	83.51 223	92.20 178	84.13 155	85.59 164	86.48 119	92.43 168	94.61 170	94.52 169	98.13 205	90.85 256

ACMH	90.77 13	91.51 160	91.63 172	91.38 152	95.62 88	96.87 152	91.76 212	89.66 142	91.58 184	78.67 185	86.73 139	78.12 193	93.77 153	94.59 171	94.54 168	98.78 172	98.98 77
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
TAMVS		90.54 172	90.87 184	90.16 171	91.48 187	96.61 162	93.26 182	86.08 185	87.71 222	81.66 169	83.11 184	84.04 163	90.42 207	94.54 172	94.60 163	98.04 209	95.48 226

TransMVSNet (Re)		87.73 218	86.79 226	88.83 190	90.76 193	94.40 229	91.33 224	89.62 143	84.73 247	75.41 209	72.73 239	71.41 236	86.80 228	94.53 173	93.93 182	99.06 137	95.83 218

pmmvs5		87.83 216	88.09 204	87.51 218	89.59 211	95.48 200	89.75 237	84.73 214	86.07 243	71.44 229	80.57 199	70.09 244	90.74 202	94.47 174	92.87 205	98.82 164	97.10 188

TinyColmap		89.42 187	88.58 198	90.40 168	93.80 163	95.45 202	93.96 170	86.54 178	92.24 177	76.49 200	80.83 196	70.44 241	93.37 159	94.45 175	93.30 197	98.26 203	93.37 249

RPMNet		90.19 178	92.03 168	88.05 203	93.46 165	95.95 182	93.41 178	74.59 260	92.40 172	75.91 205	84.22 177	86.41 122	92.49 167	94.42 176	93.85 186	98.44 197	96.96 194

testgi		89.42 187	91.50 175	87.00 223	92.40 180	95.59 197	89.15 239	85.27 207	92.78 162	72.42 224	91.75 84	76.00 216	84.09 246	94.38 177	93.82 188	98.65 184	96.15 210

LTVRE_ROB	87.32 16	87.55 219	88.25 202	86.73 229	90.66 194	95.80 190	93.05 185	84.77 213	83.35 251	60.32 263	83.12 183	67.39 253	93.32 160	94.36 178	94.86 154	98.28 201	98.87 90
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
dtuonly		90.46 173	91.17 178	89.63 180	91.72 184	95.69 193	94.51 162	87.20 171	90.71 195	73.98 220	81.33 193	86.42 121	94.02 147	94.30 179	93.91 183	96.36 235	95.83 218

CVMVSNet		89.77 184	91.66 171	87.56 216	93.21 171	95.45 202	91.94 211	89.22 148	89.62 204	69.34 243	83.99 179	85.90 129	84.81 241	94.30 179	95.28 141	96.85 225	97.09 189

COLMAP_ROB	90.49 14	93.27 137	92.71 148	93.93 119	97.75 60	97.44 137	96.07 111	93.17 62	95.40 99	83.86 157	83.76 180	88.72 106	93.87 149	94.25 181	94.11 177	98.87 160	95.28 228
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
NR-MVSNet		89.34 190	88.66 197	90.13 174	90.40 197	95.61 195	93.04 186	89.91 134	91.22 187	78.96 183	77.72 213	68.90 249	89.16 218	94.24 182	93.95 181	99.32 71	98.99 75

EG-PatchMatch MVS		86.68 227	87.24 220	86.02 236	90.58 195	96.26 172	91.08 227	81.59 234	84.96 246	69.80 241	71.35 247	75.08 220	84.23 244	94.24 182	93.35 195	98.82 164	95.46 227

v7n		86.43 229	86.52 233	86.33 233	87.91 235	94.93 217	90.15 235	83.05 225	86.57 237	70.21 236	71.48 245	66.78 256	87.72 223	94.19 184	92.96 202	98.92 154	98.76 104

viewmambaseed2359dif		93.92 117	93.38 135	94.54 96	94.55 136	98.15 121	96.41 92	91.47 110	95.10 115	89.58 105	86.64 141	85.10 147	96.17 96	94.08 185	95.77 129	99.09 128	98.84 94

dtuplus		93.75 126	93.15 141	94.46 99	94.41 151	98.12 124	96.06 113	91.45 112	94.25 135	89.32 115	85.82 160	85.24 142	96.38 90	93.99 186	95.83 126	99.12 123	98.78 100

ET-MVSNet_ETH3D		93.34 135	94.33 98	92.18 142	83.26 253	97.66 132	96.72 81	89.89 136	95.62 96	87.17 144	96.00 42	83.69 167	96.99 61	93.78 187	95.34 139	99.06 137	98.18 155

UniMVSNet (Re)		90.03 182	89.61 191	90.51 167	89.97 205	96.12 175	92.32 198	89.26 147	90.99 191	80.95 174	78.25 210	75.08 220	91.14 187	93.78 187	93.87 185	99.41 56	99.21 45

v1192		87.51 220	87.31 218	87.74 211	89.04 225	94.87 220	92.07 205	85.03 209	88.49 214	70.32 234	72.65 240	70.35 242	91.21 186	93.59 189	92.80 206	98.78 172	98.42 140

v10		88.00 209	87.96 208	88.05 203	89.44 213	94.68 222	92.36 197	83.35 224	89.37 205	72.96 223	73.98 232	72.79 230	91.35 182	93.59 189	92.88 204	98.81 167	98.42 140

pmmvs6		85.98 238	84.89 247	87.25 220	88.83 229	94.35 230	89.36 238	85.30 206	78.51 262	75.44 208	62.71 260	75.41 217	87.65 224	93.58 191	92.40 214	96.89 224	97.29 185

pmmvs4		90.55 171	89.91 189	91.30 155	90.26 201	94.95 216	92.73 190	87.94 163	93.44 153	85.35 152	82.28 187	76.09 215	93.02 165	93.56 192	92.26 217	98.51 192	96.77 200

v1144		87.92 214	87.79 212	88.07 200	89.27 217	95.15 211	92.17 203	85.62 197	88.52 213	71.52 228	73.80 233	72.40 232	91.06 189	93.54 193	92.80 206	98.81 167	98.33 148

ACMH+	90.88 12	91.41 161	91.13 179	91.74 147	95.11 104	96.95 149	93.13 184	89.48 146	92.42 171	79.93 177	85.13 169	78.02 195	93.82 152	93.49 194	93.88 184	98.94 152	97.99 161

Anonymous20231206		83.84 246	85.19 245	82.26 247	87.38 238	92.87 240	85.49 250	83.65 221	86.07 243	63.44 258	68.42 251	69.01 248	75.45 257	93.34 195	92.44 213	98.12 207	94.20 236

v1921920		87.31 224	87.13 222	87.52 217	88.87 228	94.72 221	91.96 210	84.59 217	88.28 215	69.86 240	72.50 241	70.03 245	91.10 188	93.33 196	92.61 211	98.71 176	98.44 135

v1240		86.89 226	86.75 228	87.06 222	88.75 230	94.65 224	91.30 225	84.05 219	87.49 226	68.94 244	71.96 244	68.86 250	90.65 204	93.33 196	92.72 210	98.67 180	98.24 152

FE-MVSNET2		81.81 249	81.15 252	82.57 246	75.40 265	92.39 244	86.04 248	83.61 222	81.61 256	68.16 246	55.75 263	59.22 267	83.77 248	93.31 198	91.54 222	98.45 196	94.24 235

WR-MVS_H		87.93 212	87.85 211	88.03 205	89.62 209	95.58 199	90.47 232	85.55 199	87.20 228	76.83 198	74.42 229	72.67 231	86.37 231	93.22 199	93.04 200	99.33 69	98.83 95

TDRefinement		89.07 197	88.15 203	90.14 173	95.16 102	96.88 150	95.55 137	90.20 131	89.68 202	76.42 201	76.67 217	74.30 223	84.85 240	93.11 200	91.91 219	98.64 185	94.47 231

MVS-HIRNet		85.36 240	86.89 225	83.57 242	90.13 202	94.51 226	83.57 256	72.61 262	88.27 216	71.22 231	68.97 250	81.81 178	88.91 220	93.08 201	91.94 218	94.97 256	89.64 259

CP-MVSNet		87.89 215	87.27 219	88.62 192	89.30 216	95.06 212	90.60 231	85.78 191	87.43 227	75.98 204	74.60 226	68.14 252	90.76 200	93.07 202	93.60 191	99.30 77	98.98 77

PS-CasMVS		87.33 223	86.68 232	88.10 199	89.22 223	94.93 217	90.35 234	85.70 192	86.44 240	74.01 219	73.43 236	66.59 258	90.04 211	92.92 203	93.52 192	99.28 79	98.91 86

UniMVSNet_NR-MVSNet		90.35 175	89.96 188	90.80 162	89.66 208	95.83 188	92.48 194	90.53 129	90.96 192	79.57 178	79.33 204	77.14 207	93.21 163	92.91 204	94.50 171	99.37 65	99.05 67

SixPastTwentyTwo		88.37 205	89.47 192	87.08 221	90.01 204	95.93 184	87.41 243	85.32 204	90.26 201	70.26 235	86.34 154	71.95 233	90.93 191	92.89 205	91.72 220	98.55 189	97.22 186

v144192		87.40 222	87.20 221	87.64 212	88.89 226	94.88 219	91.65 215	84.70 215	87.80 221	71.17 232	73.20 238	70.91 238	90.75 201	92.69 206	92.49 212	98.71 176	98.43 138

viewdifsd2359ckpt11		93.27 137	92.72 146	93.91 120	94.46 147	97.42 139	94.91 147	91.42 114	95.74 90	89.57 106	87.34 128	82.87 173	95.61 110	92.62 207	94.62 161	97.49 219	98.44 135

viewmsd2359difaftdt		93.27 137	92.72 146	93.91 120	94.46 147	97.42 139	94.91 147	91.42 114	95.69 94	89.59 104	87.34 128	82.90 172	95.60 112	92.62 207	94.62 161	97.49 219	98.44 135

PM-MVS		84.72 243	84.47 248	85.03 238	84.67 249	91.57 255	86.27 247	82.31 233	87.65 223	70.62 233	76.54 219	56.41 270	88.75 221	92.59 209	89.85 231	97.54 218	96.66 203

DU-MVS		89.67 185	88.84 196	90.63 165	89.26 218	95.61 195	92.48 194	89.91 134	91.22 187	79.57 178	77.72 213	71.18 237	93.21 163	92.53 210	94.57 165	99.35 68	99.05 67

Baseline_NR-MVSNet		89.27 193	88.01 207	90.73 164	89.26 218	93.71 238	92.71 191	89.78 140	90.73 193	81.28 171	73.53 235	72.85 229	92.30 170	92.53 210	93.84 187	99.07 132	98.88 88

IB-MVS	89.56 15	91.71 155	92.50 153	90.79 163	95.94 85	98.44 105	87.05 245	91.38 119	93.15 156	92.98 45	84.78 172	85.14 145	78.27 253	92.47 212	94.44 172	99.10 126	99.08 60
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
WR-MVS		87.93 212	88.09 204	87.75 210	89.26 218	95.28 206	90.81 229	86.69 176	88.90 207	75.29 210	74.31 230	73.72 226	85.19 239	92.26 213	93.32 196	99.27 82	98.81 98

EU-MVSNet		85.62 239	87.65 217	83.24 244	88.54 232	92.77 243	87.12 244	85.32 204	86.71 236	64.54 254	78.52 206	75.11 219	78.35 252	92.25 214	92.28 216	95.58 248	95.93 214

pmmvs-eth3d		84.33 244	82.94 250	85.96 237	84.16 250	90.94 256	86.55 246	83.79 220	84.25 248	75.85 206	70.64 248	56.43 269	87.44 227	92.20 215	90.41 228	97.97 210	95.68 222

TranMVSNet+NR-MVSNet		89.23 194	88.48 200	90.11 175	89.07 224	95.25 209	92.91 187	90.43 130	90.31 199	77.10 196	76.62 218	71.57 235	91.83 175	92.12 216	94.59 164	99.32 71	98.92 83

v8		88.21 208	87.94 210	88.51 193	89.62 209	95.01 214	92.31 199	84.99 210	88.94 206	74.70 216	75.03 222	73.51 227	90.67 203	92.11 217	92.74 209	98.80 169	98.24 152

v2v482		88.25 207	87.71 215	88.88 189	89.23 222	95.28 206	92.10 204	87.89 164	88.69 211	73.31 222	75.32 221	71.64 234	91.89 173	92.10 218	92.92 203	98.86 162	97.99 161

V42		88.31 206	87.95 209	88.73 191	89.44 213	95.34 205	92.23 202	87.21 170	88.83 208	74.49 217	74.89 224	73.43 228	90.41 209	92.08 219	92.77 208	98.60 188	98.33 148

test20.03		82.92 248	85.52 238	79.90 251	87.75 236	91.84 254	82.80 257	82.99 226	82.65 255	60.32 263	78.90 205	70.50 239	67.10 262	92.05 220	90.89 223	98.44 197	91.80 254

MDTV_nov1_ep13		91.57 158	93.18 139	89.70 177	93.39 167	96.97 148	93.53 175	80.91 238	95.70 92	81.86 166	92.40 75	89.93 95	93.25 162	91.97 221	90.80 224	95.25 253	94.46 232

test_method		72.96 258	78.68 257	66.28 261	50.17 273	64.90 271	75.45 267	50.90 270	87.89 218	62.54 259	62.98 259	68.34 251	70.45 260	91.90 222	82.41 260	88.19 267	92.35 251

MIMVSNet1		80.03 252	80.93 253	78.97 252	72.46 267	90.73 257	80.81 262	82.44 231	80.39 258	63.64 256	57.57 262	64.93 260	76.37 255	91.66 223	91.55 221	98.07 208	89.70 258

RPSCF		94.05 110	94.00 113	94.12 116	96.20 78	96.41 168	96.61 85	91.54 105	95.83 89	89.73 102	96.94 33	92.80 79	95.35 121	91.63 224	90.44 227	95.27 252	93.94 241

UniMVSNet_ETH3D		88.47 204	86.00 236	91.35 154	91.55 186	96.29 171	92.53 193	88.81 153	85.58 245	82.33 164	67.63 255	66.87 255	94.04 146	91.49 225	95.24 142	98.84 163	98.92 83

ambc			73.83 262		76.23 264	85.13 265	82.27 258		84.16 249	65.58 253	52.82 265	23.31 277	73.55 259	91.41 226	85.26 248	92.97 263	94.70 229

PEN-MVS		87.22 225	86.50 234	88.07 200	88.88 227	94.44 227	90.99 228	86.21 180	86.53 238	73.66 221	74.97 223	66.56 259	89.42 217	91.20 227	93.48 193	99.24 87	98.31 151

FE-MVSNET		79.15 254	80.25 254	77.87 255	69.65 268	89.30 261	81.34 261	82.42 232	79.49 261	59.18 267	59.18 261	59.41 266	77.03 254	91.12 228	90.65 225	97.57 217	92.63 250

SCA		90.92 165	93.04 142	88.45 194	93.72 164	97.33 142	92.77 188	76.08 254	96.02 80	78.26 190	91.96 80	90.86 88	93.99 148	90.98 229	90.04 230	95.88 241	94.06 240

v148		87.51 220	86.79 226	88.36 195	89.39 215	95.21 210	89.84 236	88.20 161	87.61 225	77.56 192	73.38 237	70.32 243	86.80 228	90.70 230	92.31 215	98.37 200	97.98 163

DTE-MVSNet		86.67 228	86.09 235	87.35 219	88.45 233	94.08 234	90.65 230	86.05 186	86.13 241	72.19 225	74.58 228	66.77 257	87.61 225	90.31 231	93.12 199	99.13 121	97.62 175

EPMVS		90.88 166	92.12 164	89.44 184	94.71 123	97.24 144	93.55 174	76.81 248	95.89 84	81.77 167	91.49 88	86.47 120	93.87 149	90.21 232	90.07 229	95.92 240	93.49 248

pmmvs3		79.16 253	80.12 256	78.05 254	79.36 254	86.59 264	78.13 265	73.87 261	76.42 264	57.51 269	70.59 249	57.02 268	84.66 242	90.10 233	88.32 235	94.75 258	91.77 255

PatchmatchNet		90.56 170	92.49 154	88.31 197	93.83 162	96.86 153	92.42 196	76.50 251	95.96 82	78.31 189	91.96 80	89.66 97	93.48 158	90.04 234	89.20 233	95.32 250	93.73 246
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MDTV_nov1_ep13_2view		86.30 230	88.27 201	84.01 241	87.71 237	94.67 223	88.08 241	76.78 249	90.59 198	68.66 245	80.46 201	80.12 185	87.58 226	89.95 235	88.20 236	95.25 253	93.90 243

tpm		87.95 211	89.44 193	86.21 234	92.53 178	94.62 225	91.40 221	76.36 252	91.46 185	69.80 241	87.43 126	75.14 218	91.55 179	89.85 236	90.60 226	95.61 247	96.96 194

new_pmnet		81.53 250	82.68 251	80.20 249	83.47 252	89.47 260	82.21 259	78.36 242	87.86 219	60.14 265	67.90 253	69.43 247	82.03 250	89.22 237	87.47 242	94.99 255	87.39 261

ADS-MVSNet		89.80 183	91.33 177	88.00 206	94.43 150	96.71 159	92.29 200	74.95 259	96.07 79	77.39 193	88.67 117	86.09 126	93.26 161	88.44 238	89.57 232	95.68 246	93.81 244

N_pmnet		84.80 241	85.10 246	84.45 240	89.25 221	92.86 241	84.04 253	86.21 180	88.78 209	66.73 250	72.41 242	74.87 222	85.21 238	88.32 239	86.45 244	95.30 251	92.04 253

CMPMVS	65.18 17	84.76 242	83.10 249	86.69 230	95.29 96	95.05 213	88.37 240	85.51 201	80.27 259	71.31 230	68.37 252	73.85 225	85.25 237	87.72 240	87.75 239	94.38 260	88.70 260
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
dps		90.11 181	89.37 194	90.98 158	93.89 160	96.21 173	93.49 177	77.61 246	91.95 180	92.74 49	88.85 112	78.77 191	92.37 169	87.71 241	87.71 241	95.80 244	94.38 233

CostFormer		90.69 167	90.48 187	90.93 159	94.18 154	96.08 176	94.03 168	78.20 244	93.47 150	89.96 99	90.97 95	80.30 183	93.72 154	87.66 242	88.75 234	95.51 249	96.12 212

pmnet_mix02		86.12 233	87.12 223	84.96 239	89.82 206	94.12 233	84.88 252	86.63 177	91.78 182	65.60 252	80.76 197	76.98 208	86.61 230	87.29 243	84.80 249	96.21 236	94.09 238

tpmrst		88.86 201	89.62 190	87.97 207	94.33 152	95.98 179	92.62 192	76.36 252	94.62 125	76.94 197	85.98 159	82.80 175	92.80 166	86.90 244	87.15 243	94.77 257	93.93 242

gbinet_0.2-2-1-0.02		86.23 231	85.66 237	86.89 224	78.33 255	92.17 248	91.62 219	85.96 189	86.51 239	79.33 182	78.13 211	77.66 205	89.55 215	85.60 245	82.66 254	96.56 234	96.87 199

blended_shiyan6		86.10 234	85.52 238	86.79 226	77.63 258	92.20 247	91.66 214	85.46 202	86.86 230	78.43 186	78.30 209	76.71 211	90.80 199	85.37 246	82.98 252	96.74 227	96.18 208

blended_shiyan8		86.10 234	85.44 240	86.88 225	77.65 257	92.22 246	91.69 213	85.52 200	86.88 229	78.82 184	78.06 212	76.43 214	90.85 196	85.36 247	82.97 253	96.74 227	96.14 211

wanda-best-256-512		86.03 236	85.37 241	86.79 226	77.63 258	92.14 250	91.64 216	85.67 193	86.75 231	78.43 186	78.36 207	76.66 212	90.81 197	85.19 248	82.63 255	96.58 230	95.88 215

FE-blended-shiyan7		86.03 236	85.37 241	86.79 226	77.63 258	92.14 250	91.64 216	85.67 193	86.74 233	78.43 186	78.36 207	76.66 212	90.81 197	85.19 248	82.63 255	96.58 230	95.88 215

usedtu_blend_shiyan5		87.98 210	86.70 230	89.47 182	77.63 258	92.14 250	94.53 160	85.67 193	86.74 233	91.16 63	86.06 156	77.89 200	91.22 183	85.19 248	82.63 255	96.58 230	96.25 206

FE-MVSNET3		87.75 217	86.69 231	88.99 188	77.63 258	92.14 250	91.64 216	85.67 193	86.75 231	91.16 63	86.06 156	77.89 200	91.22 183	85.19 248	82.63 255	96.58 230	96.18 208

tpm cat1		88.90 199	87.78 213	90.22 170	93.88 161	95.39 204	93.79 171	78.11 245	92.55 168	89.43 108	81.31 194	79.84 187	91.40 180	84.95 252	86.34 246	94.68 259	94.09 238

blend_shiyan4		88.50 202	86.74 229	90.54 166	85.31 247	92.15 249	93.79 171	85.10 208	87.64 224	91.16 63	86.06 156	77.89 200	91.22 183	84.59 253	82.60 259	96.67 229	96.25 206

new-patchmatchnet		78.49 255	78.19 258	78.84 253	84.13 251	90.06 258	77.11 266	80.39 239	79.57 260	59.64 266	66.01 256	55.65 271	75.62 256	84.55 254	80.70 262	96.14 238	90.77 257

0.4-1-1-0.1		89.64 186	88.08 206	91.46 151	86.21 241	94.41 228	94.79 152	86.20 182	88.54 212	91.15 67	86.64 141	78.03 194	94.36 140	84.47 255	88.05 237	96.08 239	96.40 205

0.4-1-1-0.2		89.32 191	87.66 216	91.26 156	86.11 242	93.97 236	94.54 159	85.98 187	87.83 220	91.12 68	86.40 149	78.02 195	94.06 145	84.03 256	87.73 240	95.75 245	95.62 225

usedtu_dtu_shiyan2		75.82 257	75.29 260	76.44 257	65.25 270	87.28 262	82.09 260	76.55 250	68.86 266	66.94 248	48.90 266	60.22 264	74.42 258	83.98 257	83.40 251	93.39 261	94.38 233

0.3-1-1-0.015		89.40 189	87.72 214	91.36 153	86.10 243	94.08 234	94.62 157	86.10 184	88.02 217	91.16 63	86.39 150	77.89 200	94.30 141	83.93 258	87.88 238	95.88 241	95.86 217

Gipuma		68.35 260	66.71 263	70.27 258	74.16 266	68.78 270	63.93 271	71.77 264	83.34 252	54.57 270	34.37 268	31.88 274	68.69 261	83.30 259	85.53 247	88.48 266	79.78 265
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
dtuonlycased		84.27 245	85.21 244	83.17 245	85.99 245	92.85 242	83.74 254	82.59 230	86.74 233	66.76 249	77.36 215	78.74 192	84.13 245	83.16 260	83.81 250	95.83 243	93.80 245

tmp_tt				66.88 260	86.07 244	73.86 269	68.22 269	33.38 271	96.88 54	80.67 175	88.23 121	78.82 190	49.78 267	82.68 261	77.47 264	83.19 270

WB-MVS		69.22 259	76.91 259	60.24 263	85.80 246	79.37 267	56.86 273	84.96 211	81.50 257	18.16 276	76.85 216	61.07 261	34.23 270	82.46 262	81.81 261	81.43 271	75.31 268

DeepMVS_CX						86.86 263	79.50 263	70.43 265	90.73 193	63.66 255	80.36 202	60.83 262	79.68 251	76.23 263		89.46 265	86.53 262

MDA-MVSNet-bldmvs		80.11 251	80.24 255	79.94 250	77.01 263	93.21 239	78.86 264	85.94 190	82.71 254	60.86 260	79.71 203	51.77 272	83.71 249	75.60 264	86.37 245	93.28 262	92.35 251

FPMVS		75.84 256	74.59 261	77.29 256	86.92 239	83.89 266	85.01 251	80.05 240	82.91 253	60.61 262	65.25 257	60.41 263	63.86 263	75.60 264	73.60 266	87.29 268	80.47 264

PMMVS2		64.36 263	65.94 265	62.52 262	67.37 269	77.44 268	64.39 270	69.32 268	61.47 268	34.59 272	46.09 267	41.03 273	48.02 269	74.56 266	78.23 263	91.43 264	82.76 263

PMVS	63.12 18	67.27 261	66.39 264	68.30 259	77.98 256	60.24 272	59.53 272	76.82 247	66.65 267	60.74 261	54.39 264	59.82 265	51.24 266	73.92 267	70.52 267	83.48 269	79.17 266
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	50.86 19	49.54 266	51.43 266	47.33 266	44.14 274	59.20 273	36.45 276	60.59 269	41.47 271	31.14 273	29.58 269	17.06 278	48.52 268	62.22 268	74.63 265	63.12 274	75.87 267
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		50.67 264	47.85 267	53.96 264	64.13 272	50.98 275	38.06 274	69.51 266	51.40 270	24.60 274	29.46 271	24.39 276	56.07 265	48.17 269	59.70 268	71.40 272	70.84 269

EMVS		49.98 265	46.76 268	53.74 265	64.96 271	51.29 274	37.81 275	69.35 267	51.83 269	22.69 275	29.57 270	25.06 275	57.28 264	44.81 270	56.11 269	70.32 273	68.64 270

testmvs		12.09 267	16.94 269	6.42 268	3.15 275	6.08 276	9.51 278	3.84 272	21.46 272	5.31 277	27.49 272	6.76 279	10.89 271	17.06 271	15.01 270	5.84 275	24.75 271

test123		9.58 268	13.53 270	4.97 269	1.31 277	5.47 277	8.32 279	2.95 273	18.14 273	2.03 279	20.82 273	2.34 280	10.60 272	10.00 272	14.16 271	4.60 276	23.77 272

uanet_test		0.00 269	0.00 271	0.00 270	0.00 278	0.00 278	0.00 280	0.00 275	0.00 274	0.00 280	0.00 274	0.00 281	0.00 274	0.00 273	0.00 272	0.00 277	0.00 273

sosnet-low-res		0.00 269	0.00 271	0.00 270	0.00 278	0.00 278	0.00 280	0.00 275	0.00 274	0.00 280	0.00 274	0.00 281	0.00 274	0.00 273	0.00 272	0.00 277	0.00 273

sosnet		0.00 269	0.00 271	0.00 270	0.00 278	0.00 278	0.00 280	0.00 275	0.00 274	0.00 280	0.00 274	0.00 281	0.00 274	0.00 273	0.00 272	0.00 277	0.00 273

TestfortrainingZip							99.35 6	97.66 10		98.71 3						99.42 53

TPM-MVS					98.94 34	98.47 103	98.04 45			92.62 50	96.51 36	98.76 31	95.94 103			98.92 154	97.55 176
Ray Leroy Khuboni and Hongjun Xu: Textureless Resilient Propagation Matching in Multiple View Stereosis (TPM-MVS). SATNAC 2025
RE-MVS-def										63.50 257

9.14												99.28 14

SR-MVS					99.45 11			97.61 17				99.20 18

our_test_3					89.78 207	93.84 237	85.59 249

MTAPA										96.83 13		99.12 23

MTMP										97.18 8		98.83 28

Patchmatch-RL test							34.61 277

XVS					96.60 71	99.35 20	96.82 73			90.85 73		98.72 32				99.46 36

X-MVStestdata					96.60 71	99.35 20	96.82 73			90.85 73		98.72 32				99.46 36

mPP-MVS					99.21 25							98.29 40

NP-MVS									95.32 103

Patchmtry						95.96 181	93.36 180	75.99 255		75.19 211

High-resolution multi-view benchmark