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.

SED-MVS		95.53 1	95.79 1	95.23 1	97.60 10	98.92 1	95.99 5	92.05 7	97.14 1	94.19 1	94.71 6	93.25 2	95.08 1	94.32 11	92.59 15	96.49 18	99.58 3

DPE-MVS		95.10 2	95.53 2	94.60 5	97.77 8	98.64 5	96.60 4	92.45 5	96.34 6	91.41 6	96.70 2	92.26 6	93.56 6	93.68 18	91.73 30	95.79 39	99.37 7
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
DVP-MVS		95.06 3	95.37 4	94.70 3	97.59 11	98.89 2	95.37 12	92.04 8	96.85 3	94.00 2	92.81 14	93.02 3	92.93 7	94.22 14	92.15 21	96.30 25	99.61 2
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
DVP-MVS++		95.03 4	95.03 5	95.03 2	97.91 6	98.84 3	95.80 6	91.88 10	96.65 5	93.15 3	93.79 8	90.11 12	95.03 2	94.20 16	92.39 16	96.44 22	99.22 10

MSP-MVS		95.00 5	95.47 3	94.45 6	96.78 19	98.11 10	95.72 8	90.91 14	96.68 4	91.57 5	96.98 1	89.47 14	94.76 3	95.24 3	92.15 21	96.98 7	99.64 1
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
CNVR-MVS		94.53 6	94.85 7	94.15 8	98.03 4	98.59 6	95.56 9	92.91 2	94.86 13	88.46 14	91.32 21	90.83 10	94.03 5	95.20 4	94.16 5	95.89 34	99.01 16

SF-MVS		94.40 7	94.15 12	94.70 3	98.25 3	98.24 8	96.86 3	93.46 1	94.87 12	90.26 9	95.96 3	88.42 17	92.76 10	92.29 31	90.84 43	96.62 13	98.44 26

APDe-MVS		94.31 8	94.30 10	94.33 7	97.57 12	98.06 12	95.79 7	91.98 9	95.50 9	92.19 4	95.25 4	87.97 20	92.93 7	93.01 24	91.02 41	95.52 41	99.29 8
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
MCST-MVS		94.10 9	94.77 8	93.31 10	98.31 2	98.34 7	95.43 10	92.54 4	94.41 16	83.05 31	91.38 19	90.97 9	92.24 14	95.05 6	94.02 6	98.31 1	99.20 11

HPM-MVS++		94.04 10	94.96 6	92.96 12	97.93 5	97.71 18	94.65 15	91.01 13	95.91 7	87.43 16	93.52 11	92.63 5	92.29 13	94.22 14	92.34 18	94.47 63	98.37 27

NCCC		93.59 11	94.00 14	93.10 11	97.90 7	97.93 14	95.40 11	92.39 6	94.47 15	84.94 21	91.21 22	89.32 15	92.53 11	93.90 17	92.98 12	95.44 43	98.22 30

SMA-MVS		93.47 12	94.29 11	92.52 14	97.72 9	97.77 17	94.46 18	90.19 17	94.96 11	87.15 17	90.15 26	90.99 8	91.49 17	94.31 12	93.33 10	94.10 69	98.53 24
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
APD-MVS		93.47 12	93.44 17	93.50 9	97.06 15	97.09 27	95.27 13	91.47 11	95.71 8	89.57 11	93.66 9	86.28 26	92.81 9	92.06 34	90.70 44	94.83 60	98.60 21
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
SD-MVS		93.36 14	94.33 9	92.22 16	94.68 43	97.89 16	94.56 16	90.89 15	94.80 14	90.04 10	93.53 10	90.14 11	89.78 23	92.74 27	92.17 19	93.35 109	99.07 14
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
TSAR-MVS + MP.		93.07 15	93.53 16	92.53 13	94.23 46	97.54 21	94.75 14	89.87 18	95.26 10	89.20 13	93.16 12	88.19 19	92.15 15	91.79 39	89.65 60	94.99 56	99.16 12
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
DPM-MVS		92.86 16	93.19 19	92.47 15	95.78 35	97.40 22	97.39 1	92.56 3	92.88 24	81.84 38	81.31 39	92.95 4	91.21 18	96.54 1	97.33 1	96.01 32	93.94 112

SteuartSystems-ACMMP		92.31 17	93.31 18	91.15 22	96.88 17	97.36 23	93.95 22	89.44 20	92.62 25	83.20 28	94.34 7	85.55 28	88.95 30	93.07 23	91.90 26	94.51 62	98.30 28
Skip Steuart: Steuart Systems R&D Blog.
ACMMP_NAP		92.16 18	92.91 22	91.28 21	96.95 16	97.36 23	93.66 23	89.23 22	93.33 19	83.71 26	90.53 23	86.84 23	90.39 20	93.30 22	91.56 32	93.74 81	97.43 46

HFP-MVS		92.02 19	92.13 24	91.89 19	97.16 14	96.46 39	93.57 24	87.60 25	93.79 18	88.17 15	93.15 13	83.94 38	91.19 19	90.81 49	89.83 55	93.66 85	96.94 62

train_agg		91.99 20	93.71 15	89.98 27	96.42 27	97.03 29	94.31 20	89.05 23	93.33 19	77.75 46	95.06 5	88.27 18	88.38 37	92.02 36	91.41 35	94.00 73	98.84 19

DeepC-MVS_fast	86.59 2	91.69 21	91.39 27	92.05 18	97.43 13	96.92 32	94.05 21	90.23 16	93.31 22	83.19 29	77.91 45	84.23 34	92.42 12	94.62 9	94.83 3	95.00 55	97.88 36
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TSAR-MVS + GP.		91.29 22	93.11 21	89.18 32	87.81 91	96.21 45	92.51 33	83.83 44	94.24 17	83.77 25	91.87 18	89.62 13	90.07 21	90.40 54	90.31 48	97.09 6	99.10 13

ACMMPR		91.15 23	91.44 26	90.81 23	96.61 21	96.25 43	93.09 25	87.08 28	93.32 21	84.78 22	92.08 17	82.10 44	89.71 24	90.24 55	89.82 56	93.61 90	96.30 76

DeepPCF-MVS	86.71 1	91.00 24	94.05 13	87.43 43	95.58 38	98.17 9	86.22 76	88.59 24	97.01 2	76.77 55	85.11 35	88.90 16	87.29 45	95.02 7	94.69 4	90.15 182	99.48 6

TSAR-MVS + ACMM		90.98 25	93.18 20	88.42 37	95.69 36	96.73 34	94.52 17	86.97 31	92.99 23	76.32 56	92.31 16	86.64 24	84.40 72	92.97 25	92.02 23	92.62 134	98.59 22

MP-MVS		90.81 26	91.45 25	90.06 26	96.59 22	96.33 42	92.46 34	87.19 27	90.27 39	82.54 34	91.38 19	84.88 31	88.27 38	90.58 52	89.30 65	93.30 111	97.44 44
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CP-MVS		90.57 27	90.68 29	90.44 24	96.13 29	95.90 51	92.77 30	86.86 32	92.12 29	84.19 23	89.18 29	82.37 42	89.43 27	89.65 67	88.43 76	93.27 112	97.13 54

MSLP-MVS++		90.33 28	88.82 39	92.10 17	96.52 25	95.93 47	94.35 19	86.26 33	88.37 54	89.24 12	75.94 53	82.60 41	89.71 24	89.45 70	92.17 19	96.51 17	97.24 51

CANet		89.98 29	90.42 33	89.47 31	94.13 47	98.05 13	91.76 39	83.27 47	90.87 36	81.90 37	72.32 61	84.82 32	88.42 35	94.52 10	93.78 8	97.34 4	98.58 23

PGM-MVS		89.97 30	90.64 31	89.18 32	96.53 24	95.90 51	93.06 26	82.48 55	90.04 41	80.37 40	92.75 15	80.96 49	88.93 31	89.88 63	89.08 69	93.69 84	95.86 80

PHI-MVS		89.88 31	92.75 23	86.52 53	94.97 40	97.57 20	89.99 50	84.56 40	92.52 27	69.72 90	90.35 25	87.11 22	84.89 64	91.82 38	92.37 17	95.02 54	97.51 42

CSCG		89.81 32	89.69 34	89.96 28	96.55 23	97.90 15	92.89 28	87.06 29	88.74 51	86.17 18	78.24 44	86.53 25	84.75 67	87.82 91	90.59 45	92.32 139	98.01 33

X-MVS		89.73 33	90.65 30	88.66 35	96.44 26	95.93 47	92.26 36	86.98 30	90.73 37	76.32 56	89.56 28	82.05 45	86.51 51	89.98 61	89.60 61	93.43 104	96.72 71

EPNet		89.30 34	90.89 28	87.44 42	95.67 37	96.81 33	91.13 42	83.12 49	91.14 33	76.31 60	87.60 31	80.40 53	84.45 70	92.13 33	91.12 40	93.96 74	97.01 58
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DeepC-MVS	84.14 3	88.80 35	88.03 45	89.71 30	94.83 41	96.56 35	92.57 32	89.38 21	89.25 47	79.59 42	70.02 70	77.05 65	88.24 39	92.44 29	92.79 13	93.65 88	98.10 32
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CDPH-MVS		88.76 36	90.43 32	86.81 49	96.04 31	96.53 38	92.95 27	85.95 35	90.36 38	67.93 96	85.80 34	80.69 50	83.82 75	90.81 49	91.85 29	94.18 67	96.99 59

3Dnovator+	81.14 5	88.59 37	87.49 48	89.88 29	95.83 34	96.45 41	91.94 38	82.41 56	87.09 61	85.94 20	62.80 100	85.37 29	89.46 26	91.51 41	91.89 28	93.72 82	97.30 49

ACMMP		88.48 38	88.71 40	88.22 39	94.61 44	95.53 57	90.64 46	85.60 37	90.97 34	78.62 44	89.88 27	74.20 79	86.29 53	88.16 88	86.37 96	93.57 91	95.86 80
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
AdaColmap		88.46 39	85.75 64	91.62 20	96.25 28	95.35 62	90.71 44	91.08 12	90.22 40	86.17 18	74.33 57	73.67 82	92.00 16	86.31 109	85.82 105	93.52 94	94.53 99

MVS_0304		88.43 40	89.46 36	87.21 44	91.85 59	97.60 19	92.62 31	81.10 62	87.16 60	73.80 67	72.19 63	83.36 40	87.03 46	94.64 8	93.67 9	96.88 9	97.64 41

3Dnovator	80.58 8	88.20 41	86.53 54	90.15 25	96.86 18	96.46 39	91.97 37	83.06 50	85.16 66	83.66 27	62.28 103	82.15 43	88.98 29	90.99 46	92.65 14	96.38 24	96.03 77

CPTT-MVS		88.17 42	87.84 46	88.55 36	93.33 49	93.75 84	92.33 35	84.75 39	89.87 43	81.72 39	83.93 36	81.12 48	88.45 34	85.42 118	84.07 124	90.72 174	96.72 71

MVS_111021_HR		87.82 43	88.84 38	86.62 51	94.42 45	97.36 23	88.21 61	83.26 48	83.42 69	72.52 77	82.63 37	76.93 66	84.95 63	91.93 37	91.15 39	96.39 23	98.49 25

DELS-MVS		87.75 44	86.92 52	88.71 34	94.69 42	97.34 26	92.78 29	84.50 41	77.87 93	81.94 36	67.17 78	75.49 74	82.84 81	95.38 2	95.93 2	95.55 40	99.27 9
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
MVSTER		87.68 45	89.12 37	86.01 55	88.11 89	90.05 121	89.28 54	77.05 87	91.37 30	79.97 41	76.70 49	85.25 30	84.89 64	93.53 19	91.41 35	96.73 11	95.55 87

MVS_111021_LR		87.58 46	88.67 41	86.31 54	92.58 53	95.89 53	86.20 77	82.49 54	89.08 49	77.47 51	86.20 33	74.22 78	85.49 58	90.03 60	88.52 74	93.66 85	96.74 69

QAPM		87.06 47	86.46 55	87.75 40	96.63 20	97.09 27	91.71 40	82.62 53	80.58 82	71.28 82	66.04 85	84.24 33	87.01 47	89.93 62	89.91 54	97.26 5	97.44 44

PVSNet_BlendedMVS		86.98 48	87.05 50	86.90 46	93.03 50	96.98 30	86.57 73	81.82 58	89.78 44	82.78 32	71.54 64	66.07 115	80.73 93	93.46 20	91.97 24	96.45 20	99.53 4

PVSNet_Blended		86.98 48	87.05 50	86.90 46	93.03 50	96.98 30	86.57 73	81.82 58	89.78 44	82.78 32	71.54 64	66.07 115	80.73 93	93.46 20	91.97 24	96.45 20	99.53 4

ETV-MVS		86.94 50	89.49 35	83.95 71	87.28 98	95.61 56	83.58 105	76.37 94	92.59 26	73.20 69	80.35 40	76.42 69	87.38 44	92.20 32	90.45 47	95.90 33	98.83 20

CS-MVS-test		86.72 51	88.35 42	84.83 64	91.78 60	96.03 46	81.71 116	76.71 88	91.19 32	77.12 54	77.64 47	75.63 73	87.59 43	90.82 48	89.11 67	94.06 71	97.99 35

CS-MVS		86.70 52	87.61 47	85.65 56	91.33 64	95.64 55	84.73 93	76.64 90	88.68 52	77.78 45	74.87 54	72.86 86	89.09 28	92.89 26	90.18 51	94.31 66	98.16 31

EC-MVSNet		86.42 53	88.31 43	84.20 68	86.61 105	94.08 78	86.20 77	72.18 127	89.06 50	76.02 61	74.48 56	80.47 52	88.90 32	92.03 35	90.07 52	95.30 44	98.00 34

OMC-MVS		86.38 54	86.21 60	86.57 52	92.30 55	94.35 77	87.60 65	83.51 46	92.32 28	77.37 52	72.27 62	77.83 58	86.59 50	87.62 93	85.95 102	92.08 143	93.11 126

HQP-MVS		86.17 55	87.35 49	84.80 65	91.41 63	92.37 101	91.05 43	84.35 43	88.52 53	64.21 103	87.05 32	68.91 102	84.80 66	89.12 73	88.16 80	92.96 125	97.31 48

sasdasda		85.93 56	86.26 58	85.54 57	88.94 77	95.44 58	89.56 51	76.01 96	87.83 55	77.70 47	76.43 50	68.66 104	87.80 41	87.02 96	91.51 33	93.25 113	96.95 60

canonicalmvs		85.93 56	86.26 58	85.54 57	88.94 77	95.44 58	89.56 51	76.01 96	87.83 55	77.70 47	76.43 50	68.66 104	87.80 41	87.02 96	91.51 33	93.25 113	96.95 60

MAR-MVS		85.65 58	86.30 57	84.88 63	95.51 39	95.89 53	86.50 75	76.71 88	89.23 48	68.59 93	70.93 68	74.49 76	88.55 33	89.40 71	90.30 49	93.42 105	93.88 117
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
PCF-MVS	82.38 4	85.52 59	84.41 69	86.81 49	91.51 62	96.23 44	90.27 47	89.81 19	77.87 93	70.67 86	69.20 72	77.86 56	85.55 57	85.92 114	86.38 95	93.03 122	97.43 46
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
CLD-MVS		85.43 60	84.24 72	86.83 48	87.69 94	93.16 92	90.01 49	82.72 52	87.17 59	79.28 43	71.43 67	65.81 118	86.02 54	87.33 95	86.96 89	95.25 50	97.83 38
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
OpenMVS	77.91 11	85.09 61	83.42 76	87.03 45	96.12 30	96.55 37	89.36 53	81.59 60	79.19 89	75.20 63	55.84 131	79.04 55	84.45 70	88.47 82	89.35 64	95.48 42	95.48 88

MGCFI-Net		85.07 62	85.99 61	83.99 69	88.81 80	95.23 67	89.06 56	75.74 99	87.40 58	70.72 85	75.99 52	68.44 106	86.51 51	86.83 100	91.24 37	93.11 120	96.78 68

TSAR-MVS + COLMAP		84.93 63	85.79 63	83.92 72	90.90 66	93.57 88	89.25 55	82.00 57	91.29 31	61.66 112	88.25 30	59.46 138	86.71 49	89.79 64	87.09 86	93.01 123	91.09 148

TAPA-MVS	80.99 7	84.83 64	84.42 68	85.31 60	91.89 58	93.73 86	88.53 60	82.80 51	89.99 42	69.78 89	71.53 66	75.03 75	85.47 59	86.26 110	84.54 119	93.39 107	89.90 158
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PLC	81.02 6	84.81 65	81.81 95	88.31 38	93.77 48	90.35 116	88.80 58	84.47 42	86.76 62	82.17 35	66.56 81	71.01 94	88.41 36	85.48 116	84.28 122	92.26 141	88.21 171
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
EIA-MVS		84.75 66	86.43 56	82.79 77	86.88 101	95.36 61	82.84 112	76.39 93	87.61 57	71.03 83	74.33 57	71.12 93	85.16 60	89.69 66	88.70 73	94.40 64	98.23 29

CNLPA		84.72 67	82.14 89	87.73 41	92.85 52	93.83 83	84.70 94	85.07 38	90.90 35	83.16 30	56.28 127	71.53 90	88.14 40	84.19 123	84.00 128	92.48 136	94.26 106

MVS_Test		84.60 68	85.13 67	83.99 69	88.17 87	95.27 66	88.21 61	73.15 118	84.31 68	70.55 87	68.67 76	68.78 103	86.99 48	91.71 40	91.90 26	96.84 10	95.27 93

casdiffmvs_mvg		83.97 69	82.62 85	85.54 57	87.71 92	94.38 76	88.93 57	80.11 66	77.34 97	77.57 50	63.01 99	65.95 117	84.96 62	90.69 51	90.23 50	93.95 75	96.74 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
casdiffmvs		83.84 70	82.65 84	85.22 61	87.25 99	94.62 73	86.01 81	79.62 67	79.48 86	77.59 49	61.92 106	64.34 122	85.57 56	90.55 53	90.51 46	95.26 48	97.14 53
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
baseline		83.83 71	84.38 70	83.18 76	86.65 103	94.59 74	85.79 84	73.78 115	85.83 64	72.94 70	69.28 71	70.80 96	83.45 78	86.80 101	87.59 82	96.47 19	95.77 84

diffmvs		83.69 72	83.17 80	84.31 66	85.45 117	93.92 79	86.89 68	78.62 70	82.71 75	75.95 62	66.78 80	63.90 125	83.84 74	87.90 90	89.16 66	95.10 53	97.82 39
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
CANet_DTU		83.33 73	86.59 53	79.53 101	88.88 79	94.87 70	86.63 72	68.85 150	85.45 65	50.54 158	77.86 46	69.94 99	85.62 55	92.63 28	90.88 42	96.63 12	94.46 100

DI_MVS_plusplus_trai		83.32 74	82.53 87	84.25 67	86.26 111	93.66 87	90.23 48	77.16 86	77.05 101	74.06 66	53.74 140	74.33 77	83.61 77	91.40 43	89.82 56	94.17 68	97.73 40

baseline1		82.63 75	82.02 90	83.34 75	88.30 86	91.89 105	88.03 64	80.86 63	75.05 108	65.96 98	64.27 92	72.20 88	80.01 97	91.32 44	89.56 62	96.90 8	89.85 159

PVSNet_Blended_VisFu		82.55 76	83.70 75	81.21 88	89.66 70	95.15 69	82.41 113	77.36 85	72.53 127	73.64 68	61.15 109	77.19 64	70.35 155	91.31 45	89.72 59	93.84 77	98.85 18

ET-MVSNet_ETH3D		82.37 77	85.68 65	78.51 111	62.90 215	94.66 71	87.06 67	73.57 116	83.13 71	61.52 114	78.37 43	76.01 71	89.99 22	84.14 124	89.03 70	96.03 31	94.42 101

PMMVS		82.26 78	85.48 66	78.51 111	85.92 114	91.92 104	78.30 144	70.77 135	86.30 63	61.11 116	82.46 38	70.88 95	84.70 68	88.05 89	84.78 115	90.24 181	93.98 110

ACMP	79.58 9	82.23 79	81.82 94	82.71 78	88.15 88	90.95 113	85.23 89	78.52 72	81.70 77	72.52 77	78.41 42	60.63 133	80.48 95	82.88 135	83.44 132	91.37 160	94.70 97
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
CHOSEN 280x420		82.15 80	85.87 62	77.80 116	86.54 107	93.42 90	81.74 115	59.96 193	78.99 91	63.99 104	74.50 55	83.95 37	80.99 88	89.53 69	85.01 110	93.56 93	95.71 86

LGP-MVS_train		82.12 81	82.57 86	81.59 84	89.26 74	90.23 119	88.76 59	78.05 73	81.26 79	61.64 113	79.52 41	62.11 128	79.59 99	85.20 119	84.68 117	92.27 140	95.02 95

FMVSNet3		81.93 82	81.98 91	81.88 83	79.49 154	87.02 137	88.15 63	72.57 121	83.02 72	72.63 74	56.55 123	73.48 83	82.34 84	91.49 42	91.20 38	96.07 27	91.13 147

test2506		81.91 83	81.78 97	82.06 82	89.09 75	95.32 63	84.61 96	77.54 81	74.61 112	68.77 92	63.80 96	67.53 109	77.09 108	90.19 57	89.01 71	95.27 45	92.00 139

thisisatest0530		81.67 84	84.27 71	78.63 107	85.53 115	93.88 82	81.77 114	73.84 112	81.35 78	63.85 106	68.79 74	77.64 60	73.02 136	88.73 80	85.73 106	93.76 80	93.80 121

tttt0517		81.51 85	84.12 74	78.47 113	85.33 119	93.74 85	81.42 119	73.84 112	81.21 80	63.59 107	68.73 75	77.46 63	73.02 136	88.47 82	85.73 106	93.63 89	93.49 125

FA-MVS(training)		81.41 86	81.98 91	80.76 94	87.58 95	94.59 74	83.09 107	61.18 190	79.80 85	74.74 64	58.46 115	69.76 100	82.12 85	88.90 76	87.00 87	95.83 37	95.33 90

OPM-MVS		81.34 87	78.18 113	85.02 62	91.27 65	91.78 106	90.66 45	83.62 45	62.39 156	65.91 99	63.35 97	64.33 123	85.03 61	87.77 92	85.88 104	93.66 85	91.75 143
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
baseline2		81.21 88	83.36 79	78.70 105	83.22 132	92.71 94	80.32 125	74.25 111	80.39 83	63.94 105	68.89 73	68.44 106	74.67 122	89.61 68	86.68 93	95.83 37	96.81 67

IS_MVSNet		80.92 89	84.14 73	77.16 119	87.43 96	93.90 81	80.44 121	74.64 105	75.05 108	61.10 117	65.59 87	76.89 67	67.39 163	90.88 47	90.05 53	91.95 147	96.62 74

ACMM	78.09 10	80.91 90	78.39 110	83.86 73	89.61 73	87.71 134	85.16 90	80.67 65	79.04 90	74.18 65	63.82 95	60.84 132	82.59 82	84.33 121	83.59 131	90.96 168	89.39 164
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EPP-MVSNet		80.82 91	82.79 82	78.52 109	86.31 110	92.37 101	79.83 128	74.51 106	73.79 119	64.46 102	67.01 79	80.63 51	74.33 125	85.63 115	84.35 121	91.68 153	95.79 83

CostFormer		80.72 92	81.81 95	79.44 103	86.50 108	91.65 107	84.31 98	59.84 194	80.86 81	72.69 72	62.46 102	73.74 80	79.93 98	82.58 139	84.50 120	93.37 108	96.90 65

GBi-Net		80.72 92	80.49 99	81.00 91	78.18 158	86.19 151	86.73 69	72.57 121	83.02 72	72.63 74	56.55 123	73.48 83	80.99 88	86.57 103	86.83 90	94.89 57	90.77 151

test1		80.72 92	80.49 99	81.00 91	78.18 158	86.19 151	86.73 69	72.57 121	83.02 72	72.63 74	56.55 123	73.48 83	80.99 88	86.57 103	86.83 90	94.89 57	90.77 151

UGNet		80.71 95	83.09 81	77.93 115	87.02 100	92.71 94	80.28 126	76.53 91	73.83 118	71.35 81	70.07 69	73.71 81	58.93 183	87.39 94	86.97 88	93.48 101	96.94 62
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
CHOSEN 1792x2688		80.23 96	79.16 107	81.48 86	91.97 56	96.56 35	86.18 79	75.40 102	76.17 104	61.32 115	37.43 201	61.08 131	76.52 114	92.35 30	91.64 31	97.46 3	98.86 17

thres100view900		79.83 97	77.79 117	82.21 79	88.42 83	93.54 89	87.07 66	81.11 61	70.15 134	61.01 118	56.65 121	51.22 155	81.78 86	89.77 65	85.95 102	93.84 77	97.26 50

Effi-MVS+		79.80 98	80.04 101	79.52 102	85.53 115	93.31 91	85.28 87	70.68 137	74.15 114	58.79 127	62.03 105	60.51 134	83.37 79	88.41 84	86.09 101	93.49 100	95.80 82

ECVR-MVS		79.76 99	78.27 111	81.50 85	89.09 75	95.32 63	84.61 96	77.54 81	74.61 112	65.38 100	50.22 152	56.31 149	77.09 108	90.19 57	89.01 71	95.27 45	92.25 134

DCV-MVSNet		79.76 99	79.17 106	80.44 97	84.65 123	84.51 175	84.20 99	72.36 126	75.17 107	70.81 84	66.21 84	66.56 112	80.99 88	82.89 134	84.56 118	89.65 187	94.30 105

FC-MVSNet-train		79.54 101	78.20 112	81.09 90	86.55 106	88.63 130	79.96 127	78.53 71	70.90 132	68.24 94	65.87 86	56.45 148	80.29 96	86.20 112	84.08 123	92.97 124	95.31 92

test-LLR		79.52 102	83.42 76	74.97 128	81.79 137	91.26 108	76.17 165	70.57 138	77.71 95	52.14 145	66.26 82	77.47 61	73.10 132	87.02 96	87.16 84	96.05 29	97.02 56

FMVSNet2		79.24 103	78.14 114	80.53 96	78.18 158	86.19 151	86.73 69	71.91 128	72.97 122	70.48 88	50.63 150	66.56 112	80.99 88	90.10 59	89.77 58	94.89 57	90.77 151

TESTMET0.1,1		79.15 104	83.42 76	74.18 134	79.81 152	91.26 108	76.17 165	67.83 163	77.71 95	52.14 145	66.26 82	77.47 61	73.10 132	87.02 96	87.16 84	96.05 29	97.02 56

tfpn200view9		79.05 105	77.21 121	81.18 89	88.42 83	92.55 99	85.12 91	77.94 75	70.15 134	61.01 118	56.65 121	51.22 155	81.11 87	88.23 85	84.80 114	93.50 99	96.90 65

test1111		78.99 106	77.77 118	80.42 98	88.64 81	95.31 65	83.39 106	77.67 79	72.76 125	61.91 110	49.58 155	55.59 151	75.67 119	90.23 56	89.09 68	95.23 51	91.83 142

PatchMatch-RL		78.75 107	76.47 128	81.41 87	88.53 82	91.10 110	78.09 145	77.51 84	77.33 98	71.98 79	64.38 91	48.10 168	82.55 83	84.06 125	82.35 141	89.78 184	87.97 173

LS3D		78.72 108	75.79 133	82.15 80	91.91 57	89.39 126	83.66 103	85.88 36	76.81 102	59.22 126	57.67 118	58.53 142	83.72 76	82.07 144	81.63 152	88.50 195	84.39 184

thres200		78.69 109	76.71 124	80.99 93	88.35 85	92.56 97	86.03 80	77.94 75	66.27 141	60.66 120	56.08 128	51.11 157	79.45 100	88.23 85	85.54 109	93.52 94	97.20 52

Anonymous20231211		78.61 110	75.57 136	82.15 80	84.43 127	90.26 117	84.08 101	77.68 78	71.09 130	72.90 71	39.24 195	66.21 114	84.23 73	82.15 142	84.04 125	89.61 188	96.03 77

IB-MVS	74.10 12	78.52 111	78.51 109	78.52 109	90.15 68	95.39 60	71.95 185	77.53 83	74.95 110	77.25 53	58.93 113	55.92 150	58.37 185	79.01 169	87.89 81	95.88 35	97.47 43
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
EPNet_dtu		78.49 112	81.96 93	74.45 133	92.57 54	88.74 129	82.98 108	78.83 69	83.28 70	44.64 189	77.40 48	67.73 108	53.98 194	85.44 117	84.91 111	93.71 83	86.22 179
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
thres400		78.39 113	76.39 129	80.73 95	88.02 90	92.94 93	84.77 92	78.88 68	65.20 149	59.70 124	55.20 134	50.85 158	79.45 100	88.81 77	84.81 113	93.57 91	96.91 64

UA-Net		78.30 114	80.92 98	75.25 127	87.42 97	92.48 100	79.54 131	75.49 101	60.47 160	60.52 121	68.44 77	84.08 36	57.54 187	88.54 81	88.45 75	90.96 168	83.97 186

Vis-MVSNet (Re-imp)		78.28 115	82.68 83	73.16 145	86.64 104	92.68 96	78.07 146	74.48 107	74.05 115	53.47 138	64.22 93	76.52 68	54.28 190	88.96 75	88.29 78	92.03 145	94.00 109

MSDG		78.11 116	73.17 149	83.86 73	91.78 60	86.83 139	85.25 88	86.02 34	72.84 124	69.69 91	51.43 147	54.00 153	77.61 104	81.95 147	82.27 143	92.83 130	82.91 191

HyFIR lowres test		78.08 117	76.81 122	79.56 100	90.77 67	94.64 72	82.97 109	69.85 143	69.81 136	59.53 125	33.52 207	64.66 119	78.97 102	88.77 79	88.38 77	95.27 45	97.86 37

GeoE		78.04 118	77.52 120	78.65 106	84.51 125	90.84 114	80.94 120	69.24 148	72.86 123	66.06 97	53.45 141	60.46 135	77.37 105	84.20 122	84.85 112	93.78 79	96.00 79

test-mter		77.90 119	82.44 88	72.60 150	78.52 156	90.24 118	73.85 178	65.31 178	76.37 103	51.29 149	65.58 88	75.94 72	71.36 146	85.98 113	86.26 97	95.26 48	96.71 73

thres600view7		77.66 120	75.67 134	79.98 99	87.71 92	92.56 97	83.79 102	77.94 75	64.41 151	58.69 128	54.32 139	50.54 159	78.23 103	88.23 85	83.06 135	93.52 94	96.55 75

MS-PatchMatch		77.47 121	76.48 127	78.63 107	89.89 69	90.42 115	85.42 86	69.53 145	70.79 133	60.43 122	50.05 153	70.62 98	70.66 152	86.71 102	82.54 138	95.86 36	84.23 185

Fast-Effi-MVS+		77.37 122	76.68 125	78.17 114	82.84 134	89.94 122	81.47 118	68.01 159	72.99 121	60.26 123	55.07 135	53.20 154	82.99 80	86.47 108	86.12 100	93.46 102	92.98 129

dmvs_re		77.25 123	75.86 132	78.86 104	81.08 143	89.36 127	84.15 100	80.73 64	73.02 120	55.58 134	58.33 116	48.97 164	75.32 120	83.92 128	86.25 98	96.29 26	91.20 146

Vis-MVSNet		77.24 124	79.99 104	74.02 135	84.62 124	93.92 79	80.33 124	72.55 124	62.58 155	55.25 136	64.45 90	69.49 101	57.00 188	88.78 78	88.21 79	94.36 65	92.54 131
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MDTV_nov1_ep13		77.20 125	80.04 101	73.90 137	82.22 135	90.14 120	79.25 135	61.52 188	78.63 92	56.98 129	65.52 89	72.80 87	73.05 134	80.93 155	83.20 133	90.36 178	89.05 167

EPMVS		77.16 126	79.08 108	74.92 129	86.73 102	91.98 103	78.62 140	55.44 202	79.43 87	56.59 131	61.24 108	70.73 97	76.97 111	80.59 158	81.43 158	95.15 52	88.17 172

tpm cat1		76.93 127	76.19 131	77.79 117	85.08 122	88.58 131	82.96 110	59.33 195	75.72 106	72.64 73	51.25 148	64.41 121	75.74 118	77.90 177	80.10 174	90.97 167	95.35 89

PatchmatchNet		76.85 128	80.03 103	73.15 146	84.08 129	91.04 112	77.76 150	55.85 201	79.43 87	52.74 143	62.08 104	76.02 70	74.56 123	79.92 163	81.41 159	93.92 76	90.29 156
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
IterMVS-LS		76.80 129	76.33 130	77.35 118	84.07 130	84.11 176	81.54 117	68.52 152	66.17 142	61.74 111	57.84 117	64.31 124	74.88 121	83.48 131	86.21 99	93.34 110	92.16 136
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CDS-MVSNet		76.57 130	76.78 123	76.32 122	80.94 145	89.75 123	82.94 111	72.64 120	59.01 166	62.95 109	58.60 114	62.67 127	66.91 165	86.26 110	87.20 83	91.57 155	93.97 111
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
SCA		76.41 131	79.90 105	72.35 154	84.26 128	85.24 166	75.57 172	54.56 204	79.95 84	52.72 144	64.22 93	77.84 57	73.73 129	80.48 159	81.37 160	93.25 113	90.20 157

tpmrst		76.27 132	77.65 119	74.66 131	86.13 113	89.53 125	79.31 134	54.91 203	77.19 100	56.27 132	55.87 130	64.58 120	77.25 106	80.85 156	80.21 171	94.07 70	95.32 91

dps		75.76 133	75.02 138	76.63 121	84.51 125	88.12 132	77.51 151	58.33 197	75.91 105	71.98 79	57.37 119	57.85 143	76.81 113	77.89 178	78.40 183	90.63 175	89.63 161

CR-MVSNet		74.84 134	77.91 115	71.26 167	81.77 139	85.52 162	78.32 142	54.14 206	74.05 115	51.09 152	50.00 154	71.38 92	70.77 150	86.48 106	84.03 126	91.46 159	93.92 114

Effi-MVS+-dtu		74.57 135	74.60 142	74.53 132	81.38 141	86.74 141	80.39 123	67.70 164	67.36 140	53.06 139	59.86 111	57.50 144	75.84 117	80.19 161	78.62 181	88.79 194	91.95 141

RPSCF		74.27 136	73.24 148	75.48 126	81.01 144	80.18 198	76.24 164	72.37 125	74.84 111	68.24 94	72.47 60	67.39 110	73.89 126	71.05 202	69.38 209	81.14 214	77.37 203

FMVSNet1		74.26 137	71.95 154	76.95 120	74.28 190	83.94 178	83.61 104	69.99 141	57.08 172	65.08 101	42.39 184	57.41 145	76.98 110	86.57 103	86.83 90	91.77 152	89.42 162

GA-MVS		73.62 138	74.52 143	72.58 151	79.93 150	89.29 128	78.02 147	71.67 131	60.79 159	42.68 193	54.41 138	49.07 163	70.07 156	89.39 72	86.55 94	93.13 119	92.12 137

Fast-Effi-MVS+-dtu		73.56 139	75.32 137	71.50 163	80.35 147	86.83 139	79.72 129	58.07 198	67.64 139	44.83 186	60.28 110	54.07 152	73.59 131	81.90 149	82.30 142	92.46 137	94.18 107

tpm		73.50 140	74.85 139	71.93 157	83.19 133	86.84 138	78.61 141	55.91 200	65.64 144	48.90 165	56.30 126	61.09 130	72.31 138	79.10 168	80.61 170	92.68 132	94.35 104

RPMNet		73.46 141	77.85 116	68.34 177	81.71 140	85.52 162	73.83 179	50.54 213	74.05 115	46.10 180	53.03 144	71.91 89	66.31 167	83.55 129	82.18 145	91.55 157	94.71 96

USDC		73.43 142	72.31 152	74.73 130	80.86 146	86.21 149	80.42 122	71.83 130	71.69 129	46.94 173	59.60 112	42.58 189	76.47 115	82.66 138	81.22 163	91.88 149	82.24 197

pmmvs4		73.38 143	71.53 157	75.55 125	75.95 176	85.24 166	77.25 155	71.59 132	71.03 131	63.10 108	49.09 160	44.22 179	73.73 129	82.04 145	80.18 172	91.68 153	88.89 169

UniMVSNet_NR-MVSNet		73.11 144	72.59 150	73.71 140	76.90 167	86.58 145	77.01 156	75.82 98	65.59 145	48.82 166	50.97 149	48.42 166	71.61 142	79.19 167	83.03 136	92.11 142	94.37 102

FMVSNet5		72.83 145	73.89 146	71.59 161	67.42 208	76.28 206	75.88 169	63.74 182	77.27 99	54.59 137	53.32 142	71.48 91	73.85 127	81.95 147	81.69 150	94.06 71	75.20 207

PatchT		72.66 146	76.58 126	68.09 179	79.02 155	86.09 155	59.81 207	51.78 211	72.00 128	51.09 152	46.84 164	66.70 111	70.77 150	86.48 106	84.03 126	96.07 27	93.92 114

ACMH	71.22 14	72.65 147	70.13 162	75.59 124	86.19 112	86.14 154	75.76 170	77.63 80	54.79 180	46.16 179	53.28 143	47.28 170	77.24 107	78.91 170	81.18 164	90.57 176	89.33 165
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
IterMVS		72.43 148	74.05 144	70.55 171	80.34 148	81.17 192	77.44 152	61.00 192	63.57 154	46.82 175	55.88 129	59.09 141	65.03 169	83.15 132	83.83 129	92.67 133	91.65 144
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ACMH+	72.14 13	72.38 149	69.34 169	75.93 123	85.21 120	84.89 170	76.96 159	76.04 95	59.76 161	51.63 148	50.37 151	48.69 165	76.90 112	76.06 186	78.69 179	88.85 193	86.90 177

DU-MVS		72.19 150	71.35 158	73.17 144	75.95 176	86.02 156	77.01 156	74.42 108	65.39 147	48.82 166	49.10 158	42.81 187	71.61 142	78.67 171	83.10 134	91.22 163	94.37 102

IterMVS-SCA-FT		72.18 151	73.96 145	70.11 173	80.15 149	81.11 193	77.42 153	61.09 191	63.67 153	46.73 176	55.77 132	59.15 140	63.95 172	82.83 136	83.70 130	91.31 161	91.49 145

UniMVSNet (Re)		72.12 152	72.28 153	71.93 157	76.77 168	87.38 136	75.73 171	73.51 117	65.76 143	50.24 160	48.65 161	46.49 171	63.85 173	80.10 162	82.47 139	91.49 158	95.13 94

ADS-MVSNet		72.11 153	73.72 147	70.24 172	81.24 142	86.59 144	74.75 175	50.56 212	72.58 126	49.17 163	55.40 133	61.46 129	73.80 128	76.01 187	78.14 184	91.93 148	85.86 180

gg-mvs-nofinetune		72.10 154	74.79 140	68.97 176	83.31 131	95.22 68	85.66 85	48.77 214	35.68 216	22.17 223	30.49 210	77.73 59	76.37 116	94.30 13	93.03 11	97.55 2	97.05 55

TAMVS		72.06 155	71.76 156	72.41 153	76.68 169	88.12 132	74.82 174	68.09 157	53.52 185	56.91 130	52.94 145	56.93 147	66.91 165	81.37 152	82.44 140	91.07 165	86.99 176

v2v482		71.73 156	69.80 164	73.99 136	75.88 180	86.66 143	79.58 130	71.90 129	57.58 170	50.41 159	45.35 168	43.24 185	73.05 134	79.69 164	82.18 145	93.08 121	93.87 118

test0.0.03 1		71.70 157	74.68 141	68.23 178	81.79 137	83.81 179	68.64 189	70.57 138	68.81 138	43.47 190	62.77 101	60.09 137	51.77 201	82.48 140	81.67 151	93.16 117	83.13 189

V42		71.58 158	70.11 163	73.30 143	75.66 183	86.68 142	79.17 137	69.92 142	59.29 165	52.80 142	44.36 172	45.66 173	68.83 157	79.48 166	81.49 155	93.44 103	93.82 120

NR-MVSNet		71.47 159	71.11 159	71.90 159	77.73 163	86.02 156	76.88 160	74.42 108	65.39 147	46.09 181	49.10 158	39.87 202	64.27 171	81.40 151	82.24 144	91.99 146	93.75 122

v8		71.42 160	69.69 165	73.43 142	76.45 172	85.12 169	79.53 132	67.47 167	59.34 164	52.90 141	44.60 170	45.82 172	71.05 148	79.56 165	81.45 157	93.17 116	91.96 140

TranMVSNet+NR-MVSNet		71.12 161	70.24 161	72.15 155	76.01 175	84.80 172	76.55 162	75.65 100	61.99 157	45.29 184	48.42 162	43.07 186	67.55 161	78.28 174	82.83 137	91.85 150	92.29 132

v10		70.97 162	69.44 166	72.75 147	75.90 179	84.58 174	79.43 133	66.45 172	58.07 168	49.93 161	43.87 178	43.68 180	71.91 140	82.04 145	81.70 149	92.89 128	92.11 138

v1144		70.93 163	69.42 168	72.70 148	75.48 184	86.26 147	79.22 136	69.39 147	55.61 177	48.05 171	43.47 179	42.55 190	71.51 144	82.11 143	81.74 148	92.56 135	94.17 108

thisisatest0515		70.62 164	71.94 155	69.07 175	76.48 171	85.59 161	68.03 190	68.02 158	59.70 162	52.94 140	52.19 146	50.36 160	58.10 186	83.15 132	81.63 152	90.87 171	90.99 149

Baseline_NR-MVSNet		70.61 165	68.87 172	72.65 149	75.95 176	80.49 196	75.92 168	74.75 104	65.10 150	48.78 168	41.28 190	44.28 178	68.45 158	78.67 171	79.64 175	92.04 144	92.62 130

v148		70.34 166	68.46 175	72.54 152	76.04 174	86.38 146	74.83 173	72.73 119	55.88 176	55.26 135	43.32 181	43.49 181	64.52 170	76.93 184	80.11 173	91.85 150	93.11 126

v1192		70.32 167	68.77 173	72.12 156	74.76 186	85.62 160	78.73 138	68.53 151	55.08 179	46.34 178	42.39 184	40.67 197	71.90 141	82.27 141	81.53 154	92.43 138	93.86 119

v144192		70.10 168	68.55 174	71.90 159	74.55 187	85.67 159	77.81 148	68.22 156	54.65 181	46.91 174	42.76 182	41.27 194	70.95 149	80.48 159	81.11 168	92.96 125	93.90 116

pmmvs5		70.01 169	69.31 170	70.82 170	75.80 182	86.26 147	72.94 180	67.91 160	53.84 184	47.22 172	47.31 163	41.47 193	67.61 160	83.93 127	81.93 147	93.42 105	90.42 155

COLMAP_ROB	66.31 15	69.91 170	66.61 180	73.76 138	86.44 109	82.76 183	76.59 161	76.46 92	63.82 152	50.92 156	45.60 167	49.13 162	65.87 168	74.96 192	74.45 199	86.30 204	75.57 206
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
v1921920		69.85 171	68.38 176	71.58 162	74.35 188	85.39 164	77.78 149	67.88 162	54.64 182	45.39 183	42.11 187	39.97 201	71.10 147	81.68 150	81.17 166	92.96 125	93.69 124

pm-mvs1		69.62 172	68.07 178	71.44 164	77.21 165	85.32 165	76.11 167	71.05 133	46.55 205	51.17 151	41.83 188	48.20 167	61.81 179	84.00 126	81.14 167	91.28 162	89.42 162

UniMVSNet_ETH3D		69.49 173	65.86 182	73.72 139	76.51 170	85.88 158	78.65 139	70.52 140	48.08 202	55.71 133	37.64 198	40.56 198	71.38 145	75.05 191	81.49 155	89.57 190	92.29 132

tfpnnormal		69.29 174	65.58 183	73.62 141	79.87 151	84.82 171	76.97 158	75.12 103	45.29 206	49.03 164	35.57 205	37.20 210	68.02 159	82.70 137	81.24 162	92.69 131	92.20 135

v1240		69.28 175	67.82 179	71.00 169	74.09 192	85.13 168	76.54 163	67.28 169	53.17 186	44.70 187	41.55 189	39.38 203	70.51 154	81.29 153	81.18 164	92.88 129	93.02 128

CVMVSNet		68.95 176	70.79 160	66.79 185	79.69 153	83.75 180	72.05 184	70.90 134	56.20 174	36.30 205	54.94 137	59.22 139	54.03 193	78.33 173	78.65 180	87.77 200	84.44 183

MIMVSNet		68.66 177	69.43 167	67.76 180	64.92 212	84.68 173	74.16 176	54.10 208	60.85 158	51.27 150	39.47 194	49.48 161	67.48 162	84.86 120	85.57 108	94.63 61	81.10 198

TDRefinement		67.82 178	64.91 189	71.22 168	82.08 136	81.45 188	77.42 153	73.79 114	59.62 163	48.35 170	42.35 186	42.40 191	60.87 181	74.69 193	74.64 198	84.83 208	79.20 201

anonymousdsp		67.61 179	68.94 171	66.04 186	71.44 204	83.97 177	66.45 194	63.53 184	50.54 195	42.42 194	49.39 156	45.63 174	62.84 176	77.99 176	81.34 161	89.59 189	93.75 122

TinyColmap		67.16 180	63.51 196	71.42 165	77.94 161	79.54 202	72.80 181	69.78 144	56.58 173	45.52 182	44.53 171	33.53 215	74.45 124	76.91 185	77.06 190	88.03 199	76.41 204

FC-MVSNet-test		67.04 181	72.47 151	60.70 203	76.92 166	81.41 189	61.52 204	69.45 146	65.58 146	26.74 219	61.79 107	60.40 136	41.17 210	77.60 180	77.78 186	88.41 196	82.70 193

TransMVSNet (Re)		66.87 182	64.30 191	69.88 174	78.32 157	81.35 191	73.88 177	74.34 110	43.19 210	45.20 185	40.12 192	42.37 192	55.97 189	80.85 156	79.15 176	91.56 156	83.06 190

CMPMVS	50.59 17	66.74 183	62.72 200	71.42 165	85.40 118	89.72 124	72.69 182	70.72 136	51.24 191	51.75 147	38.91 196	44.40 176	63.74 174	70.84 203	71.52 203	84.19 209	72.45 211
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
v7n		66.43 184	65.51 184	67.51 181	71.63 203	83.10 181	70.89 188	65.02 179	50.13 198	44.68 188	39.59 193	38.77 204	62.57 177	77.59 181	78.91 177	90.29 180	90.44 154

EG-PatchMatch MVS		66.23 185	65.20 186	67.43 182	77.74 162	86.20 150	72.51 183	63.68 183	43.95 208	43.44 191	36.22 204	45.43 175	54.04 192	81.00 154	80.95 169	93.15 118	82.67 194

WR-MVS		64.98 186	66.59 181	63.09 196	74.34 189	82.68 184	64.98 200	69.17 149	54.42 183	36.18 206	44.32 173	44.35 177	44.65 204	73.60 194	77.83 185	89.21 192	88.96 168

gm-plane-assit		64.86 187	68.15 177	61.02 202	76.44 173	68.29 215	41.60 220	53.37 209	34.68 218	26.19 221	33.22 208	57.09 146	71.97 139	95.12 5	93.97 7	96.54 16	94.66 98

CP-MVSNet		64.84 188	64.97 187	64.69 191	72.09 199	81.04 194	66.66 193	67.53 166	52.45 188	37.40 201	44.00 177	38.37 206	53.54 196	72.26 198	76.93 191	90.94 170	89.75 160

MDTV_nov1_ep13_2view		64.72 189	64.94 188	64.46 192	71.14 205	81.94 187	67.53 191	54.54 205	55.92 175	43.29 192	44.02 176	43.27 184	59.87 182	71.85 200	74.77 197	90.36 178	82.82 192

MVS-HIRNet		64.63 190	64.03 195	65.33 188	75.01 185	82.84 182	58.54 211	52.10 210	55.42 178	49.29 162	29.83 213	43.48 182	66.97 164	78.28 174	78.81 178	90.07 183	79.52 200

pmnet_mix02		64.58 191	64.11 194	65.12 189	74.16 191	80.17 199	63.24 202	67.91 160	57.87 169	41.69 195	45.86 166	40.99 196	53.97 195	69.92 206	71.67 202	89.77 185	82.29 196

LTVRE_ROB	63.07 16	64.49 192	63.16 199	66.04 186	77.47 164	82.64 185	70.98 187	65.02 179	34.01 219	29.61 215	49.12 157	35.58 214	70.57 153	75.10 190	78.45 182	82.60 212	87.24 175
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
PEN-MVS		64.35 193	64.29 192	64.42 193	72.67 195	79.83 200	66.97 192	68.24 155	51.21 192	35.29 208	44.09 174	38.51 205	52.36 199	71.06 201	77.65 187	90.99 166	87.68 174

pmmvs6		64.24 194	61.77 204	67.12 183	72.39 198	81.39 190	71.33 186	65.95 177	36.05 215	48.48 169	30.55 209	43.45 183	58.75 184	77.88 179	76.36 194	85.83 205	86.70 178

pmmvs-eth3d		64.24 194	61.96 202	66.90 184	66.35 209	76.04 208	66.09 196	66.31 174	52.59 187	50.94 155	37.61 199	32.79 217	62.43 178	75.78 188	75.48 196	89.27 191	83.39 188

PS-CasMVS		64.22 196	64.19 193	64.25 194	71.86 201	80.67 195	66.42 195	67.43 168	50.64 194	36.48 203	42.60 183	37.46 209	52.56 198	71.98 199	76.69 193	90.76 172	89.29 166

WR-MVS_H		64.14 197	65.36 185	62.71 198	72.47 197	82.33 186	65.13 197	66.99 170	51.81 190	36.47 204	43.33 180	42.77 188	43.99 206	72.41 197	75.99 195	91.20 164	88.86 170

SixPastTwentyTwo		63.75 198	63.42 197	64.13 195	72.91 194	80.34 197	61.29 205	63.90 181	49.58 199	40.42 197	54.99 136	37.13 211	60.90 180	68.46 207	70.80 204	85.37 207	82.65 195

PM-MVS		63.52 199	62.51 201	64.70 190	64.79 214	76.08 207	65.07 198	62.08 186	58.13 167	46.56 177	44.98 169	31.31 218	62.89 175	72.58 196	69.93 208	86.81 202	84.55 182

DTE-MVSNet		63.26 200	63.41 198	63.08 197	72.59 196	78.56 203	65.03 199	68.28 154	50.53 196	32.38 212	44.03 175	37.79 208	49.48 202	70.83 204	76.73 192	90.73 173	85.42 181

testgi		63.11 201	64.88 190	61.05 201	75.83 181	78.51 204	60.42 206	66.20 175	48.77 200	34.56 209	56.96 120	40.35 199	40.95 211	77.46 182	77.22 189	88.37 198	74.86 209

GG-mvs-BLEND		62.08 202	88.31 43	31.46 217	0.16 229	98.10 11	91.57 41	0.09 225	85.07 67	0.21 230	73.90 59	83.74 39	0.19 227	88.98 74	89.39 63	96.58 14	99.02 15

Anonymous20231206		62.05 203	61.83 203	62.30 200	72.09 199	77.84 205	63.10 203	67.62 165	50.20 197	36.68 202	29.59 214	37.05 212	43.90 207	77.33 183	77.31 188	90.41 177	83.49 187

N_pmnet		60.52 204	58.83 207	62.50 199	68.97 207	75.61 209	59.72 209	66.47 171	51.90 189	41.26 196	35.42 206	35.63 213	52.25 200	67.07 210	70.08 207	86.35 203	76.10 205

EU-MVSNet		58.73 205	60.92 205	56.17 206	66.17 211	72.39 212	58.85 210	61.24 189	48.47 201	27.91 217	46.70 165	40.06 200	39.07 212	68.27 208	70.34 206	83.77 210	80.23 199

test20.03		57.93 206	59.22 206	56.44 205	71.84 202	73.78 211	53.55 214	65.96 176	43.02 211	28.46 216	37.50 200	38.17 207	30.41 216	75.25 189	74.42 200	88.41 196	72.37 212

MDA-MVSNet-bldmvs		54.99 207	52.66 211	57.71 204	52.74 220	74.87 210	55.61 212	68.41 153	43.65 209	32.54 210	37.93 197	22.11 225	54.11 191	48.85 218	67.34 210	82.85 211	73.88 210

new-patchmatchnet		53.91 208	52.69 210	55.33 208	64.83 213	70.90 213	52.24 215	61.75 187	41.09 212	30.82 213	29.90 212	28.22 220	36.69 213	61.52 212	65.08 211	85.64 206	72.14 213

MIMVSNet1		52.76 209	53.95 209	51.38 210	41.96 223	70.79 214	53.56 213	63.03 185	39.36 213	27.83 218	22.73 219	33.07 216	34.47 215	70.49 205	72.69 201	87.41 201	68.51 214

pmmvs3		52.59 210	52.43 212	52.78 209	54.53 219	64.49 217	50.07 216	46.89 217	35.31 217	30.19 214	27.27 216	26.96 222	53.02 197	67.28 209	70.54 205	81.96 213	75.20 207

new_pmnet		50.32 211	51.36 213	49.11 211	49.19 221	64.89 216	48.66 218	47.99 216	47.55 203	26.27 220	29.51 215	28.66 219	44.89 203	61.12 213	62.74 213	77.66 215	65.03 215

FPMVS		50.25 212	45.67 215	55.58 207	70.48 206	60.12 218	59.78 208	59.33 195	46.66 204	37.94 199	30.22 211	27.51 221	35.94 214	50.98 217	47.90 217	70.02 217	56.31 216

test_method		47.92 213	55.39 208	39.21 214	19.90 227	49.24 221	39.29 221	34.65 222	57.37 171	32.54 210	25.11 217	41.02 195	44.31 205	66.58 211	57.57 215	64.59 220	90.82 150

PMVS	36.83 18	40.62 214	36.39 217	45.56 212	58.40 216	33.20 224	32.62 223	56.02 199	28.25 221	37.92 200	22.29 220	26.15 223	25.29 218	48.49 219	43.82 220	63.13 221	52.53 219
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
WB-MVS		39.74 215	42.23 216	36.84 215	66.24 210	50.82 220	26.18 226	66.39 173	31.14 220	4.85 228	37.06 202	24.28 224	7.95 224	54.48 214	54.23 216	49.46 224	43.61 220

Gipuma		35.20 216	33.96 218	36.65 216	43.30 222	32.51 225	26.96 225	48.31 215	38.87 214	20.08 224	8.08 222	7.41 229	26.44 217	53.60 215	58.43 214	54.81 222	38.79 222
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMMVS2		32.52 217	33.92 219	30.88 218	34.15 226	44.70 223	27.79 224	39.69 221	22.21 222	4.31 229	15.73 221	14.13 227	12.45 223	40.11 220	47.00 218	66.88 218	53.54 217

E-PMN		21.42 218	17.56 221	25.94 219	36.25 225	19.02 228	11.56 227	43.72 219	15.25 224	6.99 226	8.04 223	4.53 231	21.77 220	16.13 223	26.16 222	35.34 225	33.77 223

MVE	25.07 19	21.25 219	23.51 220	18.62 221	15.07 228	29.77 227	10.67 229	34.60 223	12.51 225	9.46 225	7.84 224	3.82 232	14.38 222	27.45 222	42.42 221	27.56 227	40.74 221
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		20.61 220	16.32 222	25.62 220	36.41 224	18.93 229	11.51 228	43.75 218	15.65 223	6.53 227	7.56 225	4.68 230	22.03 219	14.56 224	23.10 223	33.51 226	29.77 224

testmvs		0.76 221	1.23 223	0.21 222	0.05 230	0.21 230	0.38 231	0.09 225	0.94 226	0.05 231	2.13 227	0.08 233	0.60 226	0.82 225	0.77 224	0.11 228	3.62 226

test123		0.67 222	1.11 224	0.16 223	0.01 231	0.14 231	0.20 232	0.04 227	0.77 227	0.02 232	2.15 226	0.02 234	0.61 225	0.23 226	0.72 225	0.07 229	3.76 225

uanet_test		0.00 223	0.00 225	0.00 224	0.00 232	0.00 232	0.00 233	0.00 228	0.00 228	0.00 233	0.00 228	0.00 235	0.00 228	0.00 227	0.00 226	0.00 230	0.00 227

sosnet-low-res		0.00 223	0.00 225	0.00 224	0.00 232	0.00 232	0.00 233	0.00 228	0.00 228	0.00 233	0.00 228	0.00 235	0.00 228	0.00 227	0.00 226	0.00 230	0.00 227

sosnet		0.00 223	0.00 225	0.00 224	0.00 232	0.00 232	0.00 233	0.00 228	0.00 228	0.00 233	0.00 228	0.00 235	0.00 228	0.00 227	0.00 226	0.00 230	0.00 227

TPM-MVS					98.35 1	98.66 4	96.92 2			83.78 24	90.39 24	94.36 1	94.48 4			96.58 14	93.94 112
Ray Leroy Khuboni and Hongjun Xu: Textureless Resilient Propagation Matching in Multiple View Stereosis (TPM-MVS). SATNAC 2025
RE-MVS-def										39.41 198

9.14												91.16 7

SR-MVS					96.04 31			87.51 26				87.60 21

Anonymous202405211			75.59 135		85.13 121	91.06 111	84.62 95	77.96 74	69.47 137		40.79 191	63.84 126	84.57 69	83.55 129	84.69 116	89.69 186	95.75 85

our_test_3					73.80 193	79.57 201	64.47 201

ambc			50.35 214		55.61 218	59.93 219	48.73 217		44.08 207	35.81 207	24.01 218	10.64 228	41.57 209	72.83 195	63.35 212	74.99 216	77.61 202

MTAPA										91.14 7		85.84 27

MTMP										90.95 8		84.13 35

Patchmatch-RL test							8.17 230

tmp_tt				39.78 213	56.31 217	31.71 226	35.84 222	15.08 224	82.57 76	50.83 157	63.07 98	47.51 169	15.28 221	52.23 216	44.24 219	65.35 219

XVS					89.65 71	95.93 47	85.97 82			76.32 56		82.05 45				93.51 97

X-MVStestdata					89.65 71	95.93 47	85.97 82			76.32 56		82.05 45				93.51 97

mPP-MVS					95.90 33							80.22 54

NP-MVS									89.55 46

Patchmtry						87.41 135	78.32 142	54.14 206		51.09 152

DeepMVS_CX						48.96 222	43.77 219	40.58 220	50.93 193	24.67 222	36.95 203	20.18 226	41.60 208	38.92 221		52.37 223	53.31 218

High-resolution multi-view benchmark