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.

DVP-MVS++		89.14 1	91.86 1	85.97 1	92.55 2	92.38 1	91.69 4	76.31 3	93.31 1	83.11 3	92.44 5	91.18 1	81.17 2	89.55 2	87.93 8	91.01 7	96.21 1

SED-MVS		88.85 2	91.59 3	85.67 2	90.54 15	92.29 3	91.71 3	76.40 2	92.41 3	83.24 2	92.50 4	90.64 4	81.10 3	89.53 3	88.02 7	91.00 8	95.73 3

DVP-MVS		88.67 3	91.62 2	85.22 4	90.47 17	92.36 2	90.69 10	76.15 4	93.08 2	82.75 4	92.19 7	90.71 3	80.45 7	89.27 6	87.91 9	90.82 13	95.84 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
DPE-MVS		88.63 4	91.29 4	85.53 3	90.87 8	92.20 4	91.98 2	76.00 6	90.55 9	82.09 6	93.85 2	90.75 2	81.25 1	88.62 8	87.59 15	90.96 9	95.48 4
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MSP-MVS		88.09 5	90.84 5	84.88 7	90.00 24	91.80 6	91.63 5	75.80 7	91.99 4	81.23 8	92.54 3	89.18 7	80.89 4	87.99 16	87.91 9	89.70 46	94.51 7
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
APDe-MVS		88.00 6	90.50 7	85.08 5	90.95 7	91.58 7	92.03 1	75.53 12	91.15 5	80.10 14	92.27 6	88.34 12	80.80 6	88.00 15	86.99 19	91.09 5	95.16 6
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
ME-MVS		87.91 7	90.84 5	84.49 10	90.52 16	91.48 8	91.13 6	75.02 14	90.82 7	78.42 21	94.25 1	90.29 5	80.86 5	87.82 17	86.80 23	90.95 10	94.45 8

SMA-MVS		87.56 8	90.17 8	84.52 9	91.71 3	90.57 10	90.77 9	75.19 13	90.67 8	80.50 13	86.59 18	88.86 9	78.09 16	89.92 1	89.41 1	90.84 12	95.19 5
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
SF-MVS		87.47 9	89.70 9	84.86 8	91.26 6	91.10 9	90.90 7	75.65 8	89.21 10	81.25 7	91.12 9	88.93 8	78.82 11	87.42 21	86.23 31	91.28 3	93.90 14

HPM-MVS++		87.09 10	88.92 14	84.95 6	92.61 1	87.91 41	90.23 16	76.06 5	88.85 13	81.20 9	87.33 14	87.93 13	79.47 10	88.59 9	88.23 5	90.15 35	93.60 21

SD-MVS		86.96 11	89.45 10	84.05 15	90.13 20	89.23 24	89.77 19	74.59 15	89.17 11	80.70 10	89.93 12	89.67 6	78.47 13	87.57 20	86.79 24	90.67 19	93.76 17
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.		86.88 12	89.23 11	84.14 13	89.78 27	88.67 31	90.59 11	73.46 27	88.99 12	80.52 12	91.26 8	88.65 10	79.91 9	86.96 30	86.22 32	90.59 21	93.83 15
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
APD-MVS		86.84 13	88.91 15	84.41 11	90.66 11	90.10 14	90.78 8	75.64 9	87.38 17	78.72 18	90.68 11	86.82 18	80.15 8	87.13 26	86.45 30	90.51 22	93.83 15
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ACMMP_NAP		86.52 14	89.01 12	83.62 17	90.28 19	90.09 15	90.32 14	74.05 20	88.32 14	79.74 15	87.04 16	85.59 24	76.97 29	89.35 4	88.44 4	90.35 31	94.27 12

CNVR-MVS		86.36 15	88.19 18	84.23 12	91.33 5	89.84 16	90.34 12	75.56 10	87.36 18	78.97 17	81.19 30	86.76 19	78.74 12	89.30 5	88.58 2	90.45 28	94.33 11

HFP-MVS		86.15 16	87.95 19	84.06 14	90.80 9	89.20 25	89.62 20	74.26 17	87.52 15	80.63 11	86.82 17	84.19 30	78.22 15	87.58 19	87.19 17	90.81 14	93.13 26

SteuartSystems-ACMMP		85.99 17	88.31 17	83.27 21	90.73 10	89.84 16	90.27 15	74.31 16	84.56 30	75.88 32	87.32 15	85.04 25	77.31 24	89.01 7	88.46 3	91.14 4	93.96 13
Skip Steuart: Steuart Systems R&D Blog.
ACMMPR		85.52 18	87.53 21	83.17 22	90.13 20	89.27 22	89.30 21	73.97 21	86.89 20	77.14 26	86.09 19	83.18 33	77.74 20	87.42 21	87.20 16	90.77 15	92.63 27

MP-MVS		85.50 19	87.40 22	83.28 20	90.65 12	89.51 21	89.16 24	74.11 19	83.70 35	78.06 23	85.54 21	84.89 29	77.31 24	87.40 23	87.14 18	90.41 29	93.65 20
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
NCCC		85.34 20	86.59 26	83.88 16	91.48 4	88.88 26	89.79 18	75.54 11	86.67 21	77.94 24	76.55 36	84.99 26	78.07 17	88.04 13	87.68 13	90.46 27	93.31 22

DeepPCF-MVS	79.04 1	85.30 21	88.93 13	81.06 33	88.77 37	90.48 12	85.46 47	73.08 29	90.97 6	73.77 39	84.81 23	85.95 21	77.43 23	88.22 11	87.73 11	87.85 96	94.34 10

CSCG		85.28 22	87.68 20	82.49 25	89.95 25	91.99 5	88.82 25	71.20 38	86.41 22	79.63 16	79.26 31	88.36 11	73.94 42	86.64 32	86.67 27	91.40 2	94.41 9

MCST-MVS		85.13 23	86.62 25	83.39 18	90.55 14	89.82 18	89.29 22	73.89 23	84.38 31	76.03 31	79.01 33	85.90 22	78.47 13	87.81 18	86.11 34	92.11 1	93.29 23

TSAR-MVS + ACMM		85.10 24	88.81 16	80.77 36	89.55 30	88.53 33	88.59 28	72.55 31	87.39 16	71.90 44	90.95 10	87.55 14	74.57 37	87.08 28	86.54 28	87.47 106	93.67 18

train_agg		84.86 25	87.21 24	82.11 27	90.59 13	85.47 57	89.81 17	73.55 26	83.95 32	73.30 40	89.84 13	87.23 16	75.61 34	86.47 34	85.46 39	89.78 41	92.06 33

DeepC-MVS	78.47 2	84.81 26	86.03 30	83.37 19	89.29 33	90.38 13	88.61 27	76.50 1	86.25 23	77.22 25	75.12 42	80.28 46	77.59 22	88.39 10	88.17 6	91.02 6	93.66 19
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CP-MVS		84.74 27	86.43 28	82.77 24	89.48 31	88.13 40	88.64 26	73.93 22	84.92 25	76.77 28	81.94 28	83.50 32	77.29 26	86.92 31	86.49 29	90.49 23	93.14 25

MGCNet		84.63 28	87.25 23	81.59 30	88.58 38	90.50 11	87.82 35	69.16 53	83.82 34	78.46 20	82.32 26	84.97 27	74.56 38	88.16 12	87.72 12	90.94 11	93.24 24

PGM-MVS		84.42 29	86.29 29	82.23 26	90.04 23	88.82 27	89.23 23	71.74 36	82.82 40	74.61 35	84.41 24	82.09 36	77.03 28	87.13 26	86.73 26	90.73 17	92.06 33

DeepC-MVS_fast	78.24 3	84.27 30	85.50 32	82.85 23	90.46 18	89.24 23	87.83 34	74.24 18	84.88 26	76.23 30	75.26 41	81.05 44	77.62 21	88.02 14	87.62 14	90.69 18	92.41 29
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.		83.69 31	86.58 27	80.32 37	85.14 55	86.96 45	84.91 51	70.25 42	84.71 29	73.91 38	85.16 22	85.63 23	77.92 18	85.44 43	85.71 37	89.77 42	92.45 28

ACMMP		83.42 32	85.27 33	81.26 32	88.47 39	88.49 34	88.31 32	72.09 33	83.42 36	72.77 42	82.65 25	78.22 51	75.18 35	86.24 39	85.76 36	90.74 16	92.13 32
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
DPM-MVS		83.30 33	84.33 36	82.11 27	89.56 29	88.49 34	90.33 13	73.24 28	83.85 33	76.46 29	72.43 53	82.65 34	73.02 49	86.37 36	86.91 20	90.03 37	89.62 54

X-MVS		83.23 34	85.20 34	80.92 35	89.71 28	88.68 28	88.21 33	73.60 24	82.57 41	71.81 47	77.07 34	81.92 38	71.72 59	86.98 29	86.86 21	90.47 24	92.36 30

CDPH-MVS		82.64 35	85.03 35	79.86 40	89.41 32	88.31 37	88.32 31	71.84 35	80.11 47	67.47 76	82.09 27	81.44 42	71.85 57	85.89 42	86.15 33	90.24 33	91.25 39

3Dnovator+	75.73 4	82.40 36	82.76 40	81.97 29	88.02 40	89.67 19	86.60 39	71.48 37	81.28 45	78.18 22	64.78 105	77.96 53	77.13 27	87.32 24	86.83 22	90.41 29	91.48 37

PHI-MVS		82.36 37	85.89 31	78.24 48	86.40 49	89.52 20	85.52 45	69.52 49	82.38 43	65.67 84	81.35 29	82.36 35	73.07 48	87.31 25	86.76 25	89.24 53	91.56 36

MSLP-MVS++		82.09 38	82.66 41	81.42 31	87.03 45	87.22 44	85.82 43	70.04 43	80.30 46	78.66 19	68.67 80	81.04 45	77.81 19	85.19 47	84.88 44	89.19 57	91.31 38

CPTT-MVS		81.77 39	83.10 39	80.21 38	85.93 51	86.45 50	87.72 36	70.98 39	82.54 42	71.53 50	74.23 46	81.49 41	76.31 32	82.85 72	81.87 68	88.79 66	92.26 31

CANet		81.62 40	83.41 37	79.53 42	87.06 44	88.59 32	85.47 46	67.96 59	76.59 55	74.05 36	74.69 43	81.98 37	72.98 50	86.14 40	85.47 38	89.68 47	90.42 47

HQP-MVS		81.19 41	83.27 38	78.76 45	87.40 43	85.45 58	86.95 37	70.47 41	81.31 44	66.91 81	79.24 32	76.63 55	71.67 61	84.43 55	83.78 53	89.19 57	92.05 35

OMC-MVS		80.26 42	82.59 42	77.54 51	83.04 63	85.54 56	83.25 57	65.05 81	87.32 19	72.42 43	72.04 55	78.97 48	73.30 46	83.86 58	81.60 73	88.15 77	88.83 59

MVS_111021_HR		80.13 43	81.46 47	78.58 46	85.77 52	85.17 61	83.45 56	69.28 50	74.08 63	70.31 59	74.31 45	75.26 64	73.13 47	86.46 35	85.15 42	89.53 48	89.81 52

LGP-MVS_train		79.83 44	81.22 50	78.22 49	86.28 50	85.36 60	86.76 38	69.59 47	77.34 52	65.14 88	75.68 38	70.79 97	71.37 64	84.60 51	84.01 48	90.18 34	90.74 43

ACMP	73.23 7	79.79 45	80.53 55	78.94 43	85.61 53	85.68 55	85.61 44	69.59 47	77.33 53	71.00 54	74.45 44	69.16 108	71.88 55	83.15 68	83.37 56	89.92 38	90.57 46
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
3Dnovator	73.76 5	79.75 46	80.52 56	78.84 44	84.94 60	87.35 42	84.43 53	65.54 76	78.29 51	73.97 37	63.00 113	75.62 63	74.07 41	85.00 48	85.34 40	90.11 36	89.04 57

AdaColmap		79.74 47	78.62 65	81.05 34	89.23 34	86.06 53	84.95 50	71.96 34	79.39 50	75.51 33	63.16 111	68.84 113	76.51 30	83.55 62	82.85 60	88.13 78	86.46 84

OPM-MVS		79.68 48	79.28 63	80.15 39	87.99 41	86.77 47	88.52 29	72.72 30	64.55 118	67.65 75	67.87 86	74.33 68	74.31 40	86.37 36	85.25 41	89.73 45	89.81 52
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
EC-MVSNet		79.44 49	81.35 48	77.22 53	82.95 64	84.67 65	81.31 73	63.65 94	72.47 70	68.75 66	73.15 48	78.33 50	75.99 33	86.06 41	83.96 50	90.67 19	90.79 42

PCF-MVS	73.28 6	79.42 50	80.41 57	78.26 47	84.88 61	88.17 38	86.08 40	69.85 44	75.23 58	68.43 68	68.03 85	78.38 49	71.76 58	81.26 94	80.65 91	88.56 69	91.18 40
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
CLD-MVS		79.35 51	81.23 49	77.16 54	85.01 58	86.92 46	85.87 42	60.89 147	80.07 49	75.35 34	72.96 49	73.21 73	68.43 92	85.41 45	84.63 45	87.41 107	85.44 105
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
CS-MVS		79.22 52	81.11 51	77.01 55	81.36 78	84.03 69	80.35 80	63.25 99	73.43 67	70.37 58	74.10 47	76.03 60	76.40 31	86.32 38	83.95 51	90.34 32	89.93 50

MAR-MVS		79.21 53	80.32 58	77.92 50	87.46 42	88.15 39	83.95 54	67.48 65	74.28 60	68.25 69	64.70 106	77.04 54	72.17 53	85.42 44	85.00 43	88.22 74	87.62 69
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
sasdasda		79.16 54	82.37 43	75.41 73	82.33 70	86.38 51	80.80 76	63.18 105	82.90 38	67.34 77	72.79 50	76.07 58	69.62 74	83.46 65	84.41 46	89.20 55	90.60 44

canonicalmvs		79.16 54	82.37 43	75.41 73	82.33 70	86.38 51	80.80 76	63.18 105	82.90 38	67.34 77	72.79 50	76.07 58	69.62 74	83.46 65	84.41 46	89.20 55	90.60 44

DELS-MVS		79.15 56	81.07 52	76.91 56	83.54 62	87.31 43	84.45 52	64.92 82	69.98 80	69.34 65	71.62 57	76.26 56	69.84 71	86.57 33	85.90 35	89.39 50	89.88 51
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
EPNet		79.08 57	80.62 54	77.28 52	88.90 36	83.17 85	83.65 55	72.41 32	74.41 59	67.15 80	76.78 35	74.37 67	64.43 117	83.70 61	83.69 54	87.15 110	88.19 63
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
ACMM	72.26 8	78.86 58	78.13 68	79.71 41	86.89 46	83.40 80	86.02 41	70.50 40	75.28 57	71.49 51	63.01 112	69.26 107	73.57 44	84.11 57	83.98 49	89.76 43	87.84 66
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
SPE-MVS-test		78.79 59	80.72 53	76.53 58	81.11 83	83.88 72	79.69 90	63.72 93	73.80 64	69.95 62	75.40 40	76.17 57	74.85 36	84.50 54	82.78 61	89.87 40	88.54 61

QAPM		78.47 60	80.22 59	76.43 59	85.03 57	86.75 48	80.62 79	66.00 73	73.77 65	65.35 87	65.54 101	78.02 52	72.69 51	83.71 60	83.36 57	88.87 63	90.41 48

TSAR-MVS + COLMAP		78.34 61	81.64 46	74.48 86	80.13 104	85.01 62	81.73 69	65.93 75	84.75 28	61.68 101	85.79 20	66.27 125	71.39 63	82.91 71	80.78 82	86.01 149	85.98 86

MVS_111021_LR		78.13 62	79.85 61	76.13 61	81.12 82	81.50 105	80.28 82	65.25 79	76.09 56	71.32 52	76.49 37	72.87 75	72.21 52	82.79 73	81.29 75	86.59 133	87.91 65

casdiffmvs_mvg		77.79 63	79.55 62	75.73 63	81.56 75	84.70 64	82.12 59	64.26 89	74.27 61	67.93 72	70.83 62	74.66 66	69.19 87	83.33 67	81.94 67	89.29 52	87.14 75
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
TAPA-MVS	71.42 9	77.69 64	80.05 60	74.94 78	80.68 95	84.52 67	81.36 72	63.14 108	84.77 27	64.82 90	68.72 78	75.91 61	71.86 56	81.62 80	79.55 113	87.80 98	85.24 110
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
viewdifsd2359ckpt09		77.36 65	78.39 67	76.16 60	79.98 105	85.78 54	82.78 58	65.29 78	70.87 78	68.68 67	68.99 73	70.81 96	71.70 60	82.68 74	81.86 69	88.56 69	87.71 68

ETV-MVS		77.32 66	78.81 64	75.58 69	82.24 72	83.64 78	79.98 83	64.02 90	69.64 86	63.90 95	70.89 61	69.94 103	73.41 45	85.39 46	83.91 52	89.92 38	88.31 62

CNLPA		77.20 67	77.54 73	76.80 57	82.63 66	84.31 68	79.77 87	64.64 83	85.17 24	73.18 41	56.37 149	69.81 104	74.53 39	81.12 97	78.69 130	86.04 148	87.29 72

casdiffmvs		76.76 68	78.46 66	74.77 80	80.32 100	83.73 77	80.65 78	63.24 101	73.58 66	66.11 83	69.39 72	74.09 69	69.49 83	82.52 76	79.35 119	88.84 65	86.52 83
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
E2		76.70 69	77.54 73	75.73 63	80.76 87	83.07 88	81.91 65	63.15 107	72.42 71	71.09 53	70.03 67	72.22 78	69.53 80	80.57 110	78.80 129	87.91 92	85.64 98

viewcassd2359sk11		76.64 70	77.43 78	75.72 65	80.75 88	83.07 88	81.95 64	63.20 104	72.02 74	70.88 55	69.50 70	72.02 80	69.58 79	80.68 108	78.98 125	87.97 89	85.74 93

PVSNet_Blended_VisFu		76.57 71	77.90 69	75.02 77	80.56 96	86.58 49	79.24 95	66.18 70	64.81 115	68.18 70	65.61 99	71.45 85	67.05 97	84.16 56	81.80 70	88.90 61	90.92 41

MGCFI-Net		76.55 72	81.71 45	70.52 111	81.71 74	84.62 66	75.02 138	62.17 132	82.91 37	53.58 145	72.78 52	75.87 62	61.75 140	82.96 70	82.61 63	88.86 64	90.26 49

E3new		76.51 73	77.22 83	75.69 66	80.74 89	83.07 88	81.99 61	63.23 102	71.18 76	70.52 57	68.77 76	71.75 82	69.61 76	80.73 103	79.18 120	88.03 87	85.85 90

E3		76.51 73	77.21 84	75.69 66	80.74 89	83.06 91	81.98 62	63.22 103	71.17 77	70.55 56	68.77 76	71.76 81	69.61 76	80.73 103	79.18 120	88.03 87	85.84 92

viewmanbaseed2359cas		76.36 75	77.87 70	74.60 83	79.81 106	82.88 96	81.69 70	61.02 145	72.14 73	67.97 71	69.61 69	72.45 76	69.53 80	81.53 83	79.83 104	87.57 104	86.65 82

viewdifsd2359ckpt13		76.26 76	77.31 82	75.03 76	80.14 102	83.77 76	81.58 71	62.80 116	70.34 79	67.83 74	68.06 84	70.93 93	70.20 69	81.46 85	79.88 102	87.63 103	86.71 81

E4		76.24 77	76.77 91	75.61 68	80.69 93	83.05 92	81.98 62	63.25 99	69.47 87	70.06 60	67.40 90	71.46 84	69.59 78	80.73 103	79.37 117	88.10 83	85.95 87

E5		76.23 78	76.79 90	75.58 69	80.69 93	83.05 92	82.00 60	63.37 97	69.73 83	70.01 61	67.77 88	71.43 87	69.37 85	80.50 111	79.13 122	88.04 85	85.92 88

PVSNet_BlendedMVS		76.21 79	77.52 75	74.69 81	79.46 111	83.79 74	77.50 114	64.34 87	69.88 81	71.88 45	68.54 81	70.42 99	67.05 97	83.48 63	79.63 107	87.89 94	86.87 77

PVSNet_Blended		76.21 79	77.52 75	74.69 81	79.46 111	83.79 74	77.50 114	64.34 87	69.88 81	71.88 45	68.54 81	70.42 99	67.05 97	83.48 63	79.63 107	87.89 94	86.87 77

OpenMVS	70.44 10	76.15 81	76.82 89	75.37 75	85.01 58	84.79 63	78.99 99	62.07 133	71.27 75	67.88 73	57.91 142	72.36 77	70.15 70	82.23 78	81.41 74	88.12 79	87.78 67

E6new		76.06 82	76.54 93	75.51 71	80.71 91	83.10 86	81.74 67	63.03 110	68.89 89	69.71 63	66.73 96	70.84 94	69.76 72	80.88 101	79.61 109	88.11 81	85.72 95

E6		76.06 82	76.54 93	75.51 71	80.71 91	83.10 86	81.74 67	63.03 110	68.89 89	69.71 63	66.73 96	70.84 94	69.76 72	80.88 101	79.61 109	88.11 81	85.72 95

viewmacassd2359aftdt		75.85 84	77.01 87	74.49 85	79.69 108	82.87 97	81.77 66	61.06 143	69.37 88	67.26 79	66.73 96	71.63 83	69.48 84	81.51 84	80.20 97	87.69 100	86.77 80

PLC	68.99 11	75.68 85	75.31 99	76.12 62	82.94 65	81.26 110	79.94 85	66.10 71	77.15 54	66.86 82	59.13 132	68.53 115	73.73 43	80.38 115	79.04 123	87.13 114	81.68 147
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
EIA-MVS		75.64 86	76.60 92	74.53 84	82.43 69	83.84 73	78.32 107	62.28 131	65.96 106	63.28 99	68.95 74	67.54 120	71.61 62	82.55 75	81.63 72	89.24 53	85.72 95

MVS_Test		75.37 87	77.13 86	73.31 91	79.07 114	81.32 108	79.98 83	60.12 159	69.72 84	64.11 94	70.53 64	73.22 72	68.90 88	80.14 122	79.48 115	87.67 101	85.50 103

Effi-MVS+		75.28 88	76.20 95	74.20 87	81.15 81	83.24 83	81.11 74	63.13 109	66.37 102	60.27 107	64.30 109	68.88 112	70.93 68	81.56 82	81.69 71	88.61 67	87.35 70

DI_MVS_pp		75.13 89	76.12 96	73.96 88	78.18 120	81.55 103	80.97 75	62.54 126	68.59 92	65.13 89	61.43 116	74.81 65	69.32 86	81.01 99	79.59 111	87.64 102	85.89 89

diffmvs_AUTHOR		74.91 90	77.47 77	71.92 97	75.60 148	80.50 119	79.48 93	60.02 161	72.41 72	64.39 92	70.63 63	73.27 71	66.55 106	79.97 124	78.34 135	85.46 161	87.17 74

diffmvs		74.86 91	77.37 80	71.93 96	75.62 146	80.35 123	79.42 94	60.15 158	72.81 69	64.63 91	71.51 58	73.11 74	66.53 109	79.02 138	77.98 139	85.25 167	86.83 79
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		74.55 92	76.09 97	72.75 93	79.51 110	81.32 108	80.29 81	58.44 178	68.61 91	65.63 85	68.17 83	71.24 90	67.64 95	80.13 123	77.62 146	84.96 173	85.56 100

UA-Net		74.47 93	77.80 71	70.59 110	85.33 54	85.40 59	73.54 163	65.98 74	60.65 150	56.00 128	72.11 54	79.15 47	54.63 191	83.13 69	82.25 65	88.04 85	81.92 145

GeoE		74.23 94	74.84 103	73.52 89	80.42 99	81.46 106	79.77 87	61.06 143	67.23 99	63.67 96	59.56 129	68.74 114	67.90 93	80.25 120	79.37 117	88.31 71	87.26 73

LS3D		74.08 95	73.39 113	74.88 79	85.05 56	82.62 99	79.71 89	68.66 54	72.82 68	58.80 111	57.61 143	61.31 140	71.07 67	80.32 116	78.87 128	86.00 150	80.18 162

EPP-MVSNet		74.00 96	77.41 79	70.02 119	80.53 97	83.91 71	74.99 139	62.68 124	65.06 113	49.77 167	68.68 79	72.09 79	63.06 125	82.49 77	80.73 83	89.12 59	88.91 58

FA-MVS(training)		73.66 97	74.95 102	72.15 95	78.63 118	80.46 121	78.92 101	54.79 197	69.71 85	65.37 86	62.04 114	66.89 123	67.10 96	80.72 106	79.87 103	88.10 83	84.97 115

DCV-MVSNet		73.65 98	75.78 98	71.16 102	80.19 101	79.27 133	77.45 116	61.68 139	66.73 101	58.72 112	65.31 102	69.96 102	62.19 130	81.29 93	80.97 79	86.74 126	86.91 76

viewmambaseed2359dif		73.61 99	75.14 100	71.84 98	75.87 141	79.69 128	78.99 99	60.42 154	68.19 94	64.15 93	67.85 87	71.20 91	66.55 106	77.41 158	75.78 172	85.04 170	85.85 90

IS_MVSNet		73.33 100	77.34 81	68.65 134	81.29 79	83.47 79	74.45 145	63.58 96	65.75 108	48.49 172	67.11 95	70.61 98	54.63 191	84.51 53	83.58 55	89.48 49	86.34 85

CANet_DTU		73.29 101	76.96 88	69.00 131	77.04 132	82.06 101	79.49 92	56.30 193	67.85 97	53.29 147	71.12 60	70.37 101	61.81 139	81.59 81	80.96 80	86.09 143	84.73 119

Fast-Effi-MVS+		73.11 102	73.66 110	72.48 94	77.72 126	80.88 116	78.55 104	58.83 176	65.19 112	60.36 106	59.98 126	62.42 137	71.22 66	81.66 79	80.61 93	88.20 75	84.88 118

UGNet		72.78 103	77.67 72	67.07 156	71.65 185	83.24 83	75.20 132	63.62 95	64.93 114	56.72 124	71.82 56	73.30 70	49.02 205	81.02 98	80.70 89	86.22 139	88.67 60
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
Vis-MVSNet		72.77 104	77.20 85	67.59 146	74.19 161	84.01 70	76.61 126	61.69 138	60.62 151	50.61 162	70.25 66	71.31 89	55.57 185	83.85 59	82.28 64	86.90 119	88.08 64
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
FC-MVSNet-train		72.60 105	75.07 101	69.71 122	81.10 84	78.79 139	73.74 162	65.23 80	66.10 105	53.34 146	70.36 65	63.40 134	56.92 172	81.44 87	80.96 80	87.93 91	84.46 123

viewdifsd2359ckpt11		72.49 106	74.10 106	70.61 107	75.87 141	78.53 143	76.92 119	58.16 180	65.69 109	61.34 104	67.21 92	68.35 117	66.51 110	77.91 150	75.60 174	84.86 176	85.43 106

viewmsd2359difaftdt		72.49 106	74.10 106	70.61 107	75.87 141	78.53 143	76.92 119	58.16 180	65.69 109	61.33 105	67.21 92	68.34 118	66.51 110	77.91 150	75.60 174	84.86 176	85.42 107

ET-MVSNet_ETH3D		72.46 108	74.19 105	70.44 112	62.50 222	81.17 111	79.90 86	62.46 129	64.52 119	57.52 120	71.49 59	59.15 150	72.08 54	78.61 143	81.11 77	88.16 76	83.29 133

ECVR-MVS		72.20 109	73.91 109	70.20 116	81.49 76	83.27 81	75.74 127	67.59 63	68.19 94	49.31 170	55.77 151	62.00 138	58.82 154	84.76 49	82.94 58	88.27 72	80.41 160

MVSTER		72.06 110	74.24 104	69.51 125	70.39 196	75.97 172	76.91 122	57.36 187	64.64 117	61.39 103	68.86 75	63.76 132	63.46 122	81.44 87	79.70 106	87.56 105	85.31 109

Anonymous20231211		71.90 111	72.48 122	71.21 101	80.14 102	81.53 104	76.92 119	62.89 114	64.46 120	58.94 109	43.80 216	70.98 92	62.22 129	80.70 107	80.19 99	86.18 140	85.73 94

Effi-MVS+-dtu		71.82 112	71.86 127	71.78 99	78.77 115	80.47 120	78.55 104	61.67 140	60.68 149	55.49 129	58.48 136	65.48 127	68.85 89	76.92 164	75.55 177	87.35 108	85.46 104

test2506		71.72 113	72.95 117	70.29 114	81.49 76	83.27 81	75.74 127	67.59 63	68.19 94	49.81 166	61.15 117	49.73 215	58.82 154	84.76 49	82.94 58	88.27 72	80.63 156

IterMVS-LS		71.69 114	72.82 120	70.37 113	77.54 128	76.34 169	75.13 136	60.46 153	61.53 144	57.57 119	64.89 104	67.33 121	66.04 114	77.09 163	77.37 154	85.48 160	85.18 111
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
test1111		71.56 115	73.44 112	69.38 127	81.16 80	82.95 94	74.99 139	67.68 61	66.89 100	46.33 187	55.19 157	60.91 141	57.99 162	84.59 52	82.70 62	88.12 79	80.85 153

MSDG		71.52 116	69.87 139	73.44 90	82.21 73	79.35 132	79.52 91	64.59 84	66.15 104	61.87 100	53.21 176	56.09 169	65.85 115	78.94 139	78.50 132	86.60 132	76.85 188

thisisatest0530		71.48 117	73.01 116	69.70 123	73.83 166	78.62 141	74.53 144	59.12 170	64.13 121	58.63 113	64.60 107	58.63 152	64.27 118	80.28 118	80.17 100	87.82 97	84.64 121

tttt0517		71.41 118	72.95 117	69.60 124	73.70 168	78.70 140	74.42 148	59.12 170	63.89 125	58.35 116	64.56 108	58.39 156	64.27 118	80.29 117	80.17 100	87.74 99	84.69 120

ACMH+	66.54 13	71.36 119	70.09 137	72.85 92	82.59 67	81.13 112	78.56 103	68.04 57	61.55 143	52.52 153	51.50 193	54.14 180	68.56 91	78.85 140	79.50 114	86.82 122	83.94 127

IB-MVS	66.94 12	71.21 120	71.66 128	70.68 105	79.18 113	82.83 98	72.61 169	61.77 137	59.66 155	63.44 98	53.26 174	59.65 148	59.16 153	76.78 167	82.11 66	87.90 93	87.33 71
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
GBi-Net		70.78 121	73.37 114	67.76 139	72.95 173	78.00 148	75.15 133	62.72 119	64.13 121	51.44 155	58.37 137	69.02 109	57.59 164	81.33 90	80.72 84	86.70 127	82.02 139

test1		70.78 121	73.37 114	67.76 139	72.95 173	78.00 148	75.15 133	62.72 119	64.13 121	51.44 155	58.37 137	69.02 109	57.59 164	81.33 90	80.72 84	86.70 127	82.02 139

ACMH	65.37 14	70.71 123	70.00 138	71.54 100	82.51 68	82.47 100	77.78 111	68.13 56	56.19 178	46.06 190	54.30 161	51.20 207	68.68 90	80.66 109	80.72 84	86.07 144	84.45 124
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
UniMVSNet_NR-MVSNet		70.59 124	72.19 123	68.72 132	77.72 126	80.72 117	73.81 160	69.65 46	61.99 138	43.23 201	60.54 122	57.50 159	58.57 156	79.56 130	81.07 78	89.34 51	83.97 125

FMVSNet3		70.49 125	72.90 119	67.67 144	72.88 176	77.98 151	74.96 142	62.72 119	64.13 121	51.44 155	58.37 137	69.02 109	57.43 167	79.43 133	79.57 112	86.59 133	81.81 146

baseline		70.45 126	74.09 108	66.20 165	70.95 193	75.67 173	74.26 152	53.57 199	68.33 93	58.42 114	69.87 68	71.45 85	61.55 141	74.84 179	74.76 182	78.42 205	83.72 130

FMVSNet2		70.39 127	72.67 121	67.72 142	72.95 173	78.00 148	75.15 133	62.69 123	63.29 129	51.25 159	55.64 152	68.49 116	57.59 164	80.91 100	80.35 96	86.70 127	82.02 139

v8		70.23 128	69.86 140	70.67 106	74.69 156	79.82 127	78.79 102	59.18 169	58.80 159	58.20 117	55.00 158	57.33 160	66.31 113	77.51 156	76.71 163	86.82 122	83.88 128

v10		70.22 129	69.76 142	70.74 103	74.79 155	80.30 125	79.22 96	59.81 163	57.71 166	56.58 126	54.22 167	55.31 172	66.95 100	78.28 146	77.47 151	87.12 116	85.07 113

MS-PatchMatch		70.17 130	70.49 134	69.79 121	80.98 85	77.97 153	77.51 113	58.95 173	62.33 136	55.22 132	53.14 177	65.90 126	62.03 133	79.08 137	77.11 158	84.08 182	77.91 180

baseline1		70.10 131	72.17 124	67.69 143	79.74 107	76.80 163	73.91 156	64.38 86	62.74 134	48.30 174	64.94 103	64.08 131	54.17 193	81.46 85	78.92 126	85.66 156	76.22 191

v2v482		70.05 132	69.46 146	70.74 103	74.62 157	80.32 124	79.00 98	60.62 150	57.41 168	56.89 123	55.43 156	55.14 174	66.39 112	77.25 160	77.14 157	86.90 119	83.57 132

v1144		69.93 133	69.36 147	70.61 107	74.89 154	80.93 113	79.11 97	60.64 149	55.97 180	55.31 131	53.85 169	54.14 180	66.54 108	78.10 148	77.44 152	87.14 113	85.09 112

baseline2		69.69 134	70.27 136	69.01 130	75.72 145	77.13 161	73.82 159	58.94 174	61.35 145	57.09 122	61.68 115	57.17 162	61.99 134	78.10 148	76.58 165	86.48 136	79.85 164

DU-MVS		69.63 135	70.91 131	68.13 138	75.99 137	79.54 129	73.81 160	69.20 51	61.20 147	43.23 201	58.52 134	53.50 187	58.57 156	79.22 135	80.45 94	87.97 89	83.97 125

UniMVSNet (Re)		69.53 136	71.90 126	66.76 161	76.42 135	80.93 113	72.59 170	68.03 58	61.75 142	41.68 206	58.34 140	57.23 161	53.27 197	79.53 131	80.62 92	88.57 68	84.90 117

v1192		69.50 137	68.83 153	70.29 114	74.49 158	80.92 115	78.55 104	60.54 151	55.04 187	54.21 134	52.79 183	52.33 200	66.92 101	77.88 152	77.35 155	87.04 117	85.51 102

HyFIR lowres test		69.47 138	68.94 152	70.09 118	76.77 134	82.93 95	76.63 125	60.17 157	59.00 158	54.03 137	40.54 226	65.23 128	67.89 94	76.54 170	78.30 136	85.03 171	80.07 163

v144192		69.34 139	68.68 157	70.12 117	74.06 162	80.54 118	78.08 110	60.54 151	54.99 189	54.13 136	52.92 181	52.80 198	66.73 104	77.13 162	76.72 162	87.15 110	85.63 99

TranMVSNet+NR-MVSNet		69.25 140	70.81 132	67.43 147	77.23 131	79.46 131	73.48 165	69.66 45	60.43 152	39.56 209	58.82 133	53.48 189	55.74 183	79.59 128	81.21 76	88.89 62	82.70 135

CHOSEN 1792x2688		69.20 141	69.26 148	69.13 128	76.86 133	78.93 135	77.27 117	60.12 159	61.86 140	54.42 133	42.54 220	61.61 139	66.91 102	78.55 144	78.14 138	79.23 203	83.23 134

v1921920		69.03 142	68.32 161	69.86 120	74.03 163	80.37 122	77.55 112	60.25 156	54.62 191	53.59 144	52.36 189	51.50 206	66.75 103	77.17 161	76.69 164	86.96 118	85.56 100

CostFormer		68.92 143	69.58 144	68.15 137	75.98 139	76.17 171	78.22 109	51.86 211	65.80 107	61.56 102	63.57 110	62.83 135	61.85 137	70.40 212	68.67 209	79.42 201	79.62 168

FMVSNet1		68.84 144	70.47 135	66.94 158	71.35 190	77.68 156	74.71 143	62.35 130	56.93 171	49.94 165	50.01 199	64.59 129	57.07 169	81.33 90	80.72 84	86.25 138	82.00 142

NR-MVSNet		68.79 145	70.56 133	66.71 163	77.48 129	79.54 129	73.52 164	69.20 51	61.20 147	39.76 208	58.52 134	50.11 213	51.37 201	80.26 119	80.71 88	88.97 60	83.59 131

V42		68.76 146	69.63 143	67.74 141	64.93 218	78.01 147	78.30 108	56.48 191	58.65 160	56.30 127	54.26 165	57.03 163	64.85 116	77.47 157	77.01 159	85.60 157	84.96 116

v1240		68.64 147	67.89 168	69.51 125	73.89 165	80.26 126	76.73 124	59.97 162	53.43 199	53.08 148	51.82 192	50.84 209	66.62 105	76.79 166	76.77 161	86.78 125	85.34 108

Fast-Effi-MVS+-dtu		68.34 148	69.47 145	67.01 157	75.15 150	77.97 153	77.12 118	55.40 195	57.87 161	46.68 185	56.17 150	60.39 142	62.36 128	76.32 171	76.25 170	85.35 164	81.34 149

GA-MVS		68.14 149	69.17 150	66.93 159	73.77 167	78.50 145	74.45 145	58.28 179	55.11 186	48.44 173	60.08 124	53.99 183	61.50 142	78.43 145	77.57 148	85.13 168	80.54 157

tfpn200view9		68.11 150	68.72 156	67.40 148	77.83 124	78.93 135	74.28 150	62.81 115	56.64 173	46.82 183	52.65 186	53.47 190	56.59 173	80.41 112	78.43 133	86.11 141	80.52 158

EPNet_dtu		68.08 151	71.00 130	64.67 173	79.64 109	68.62 210	75.05 137	63.30 98	66.36 103	45.27 194	67.40 90	66.84 124	43.64 215	75.37 174	74.98 181	81.15 195	77.44 183
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
thres200		67.98 152	68.55 159	67.30 151	77.89 123	78.86 137	74.18 154	62.75 117	56.35 176	46.48 186	52.98 180	53.54 186	56.46 174	80.41 112	77.97 140	86.05 146	79.78 166

thres400		67.95 153	68.62 158	67.17 153	77.90 121	78.59 142	74.27 151	62.72 119	56.34 177	45.77 192	53.00 179	53.35 193	56.46 174	80.21 121	78.43 133	85.91 153	80.43 159

pmmvs4		67.89 154	67.39 173	68.48 135	71.60 187	73.57 190	74.45 145	60.98 146	64.65 116	57.97 118	54.95 159	51.73 205	61.88 136	73.78 185	75.11 179	83.99 184	77.91 180

v148		67.85 155	67.53 169	68.23 136	73.25 171	77.57 159	74.26 152	57.36 187	55.70 181	57.45 121	53.53 170	55.42 171	61.96 135	75.23 176	73.92 185	85.08 169	81.32 150

Vis-MVSNet (Re-imp)		67.83 156	73.52 111	61.19 193	78.37 119	76.72 165	66.80 199	62.96 112	65.50 111	34.17 220	67.19 94	69.68 105	39.20 224	79.39 134	79.44 116	85.68 155	76.73 190

PatchMatch-RL		67.78 157	66.65 178	69.10 129	73.01 172	72.69 193	68.49 189	61.85 136	62.93 132	60.20 108	56.83 148	50.42 211	69.52 82	75.62 173	74.46 184	81.51 192	73.62 209

thres600view7		67.68 158	68.43 160	66.80 160	77.90 121	78.86 137	73.84 158	62.75 117	56.07 179	44.70 199	52.85 182	52.81 197	55.58 184	80.41 112	77.77 143	86.05 146	80.28 161

COLMAP_ROB	62.73 15	67.66 159	66.76 177	68.70 133	80.49 98	77.98 151	75.29 131	62.95 113	63.62 127	49.96 164	47.32 211	50.72 210	58.57 156	76.87 165	75.50 178	84.94 174	75.33 200
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
CDS-MVSNet		67.65 160	69.83 141	65.09 169	75.39 149	76.55 166	74.42 148	63.75 92	53.55 197	49.37 169	59.41 130	62.45 136	44.44 213	79.71 127	79.82 105	83.17 188	77.36 184
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
RPSCF		67.64 161	71.25 129	63.43 185	61.86 224	70.73 201	67.26 194	50.86 216	74.20 62	58.91 110	67.49 89	69.33 106	64.10 120	71.41 198	68.45 214	77.61 207	77.17 185

thres100view900		67.60 162	68.02 164	67.12 155	77.83 124	77.75 155	73.90 157	62.52 127	56.64 173	46.82 183	52.65 186	53.47 190	55.92 180	78.77 141	77.62 146	85.72 154	79.23 170

Baseline_NR-MVSNet		67.53 163	68.77 155	66.09 166	75.99 137	74.75 183	72.43 171	68.41 55	61.33 146	38.33 213	51.31 194	54.13 182	56.03 179	79.22 135	78.19 137	85.37 163	82.45 137

thisisatest0515		67.40 164	68.78 154	65.80 167	70.02 198	75.24 179	69.36 185	57.37 186	54.94 190	53.67 143	55.53 155	54.85 176	58.00 161	78.19 147	78.91 127	86.39 137	83.78 129

USDC		67.36 165	67.90 167	66.74 162	71.72 183	75.23 180	71.58 174	60.28 155	67.45 98	50.54 163	60.93 118	45.20 228	62.08 131	76.56 169	74.50 183	84.25 180	75.38 199

EG-PatchMatch MVS		67.24 166	66.94 175	67.60 145	78.73 116	81.35 107	73.28 167	59.49 165	46.89 224	51.42 158	43.65 217	53.49 188	55.50 186	81.38 89	80.66 90	87.15 110	81.17 151

dmvs_re		67.22 167	67.92 166	66.40 164	75.94 140	70.55 203	74.97 141	63.87 91	57.07 170	44.75 197	54.29 162	56.72 165	54.65 190	79.53 131	77.51 150	84.20 181	79.78 166

UniMVSNet_ETH3D		67.18 168	67.03 174	67.36 149	74.44 159	78.12 146	74.07 155	66.38 68	52.22 204	46.87 182	48.64 205	51.84 204	56.96 170	77.29 159	78.53 131	85.42 162	82.59 136

v7n		67.05 169	66.94 175	67.17 153	72.35 178	78.97 134	73.26 168	58.88 175	51.16 211	50.90 160	48.21 207	50.11 213	60.96 145	77.70 153	77.38 153	86.68 130	85.05 114

IterMVS-SCA-FT		66.89 170	69.22 149	64.17 176	71.30 191	75.64 174	71.33 175	53.17 203	57.63 167	49.08 171	60.72 120	60.05 146	63.09 124	74.99 178	73.92 185	77.07 211	81.57 148

IterMVS		66.36 171	68.30 162	64.10 177	69.48 203	74.61 184	73.41 166	50.79 217	57.30 169	48.28 175	60.64 121	59.92 147	60.85 149	74.14 183	72.66 193	81.80 191	78.82 173
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
FE-MVSNET3		66.26 172	68.15 163	64.06 178	67.01 208	76.52 167	70.61 179	61.10 141	61.86 140	44.86 195	49.77 202	56.69 166	53.97 194	77.58 155	77.88 141	86.80 124	76.78 189

TDRefinement		66.09 173	65.03 191	67.31 150	69.73 200	76.75 164	75.33 129	64.55 85	60.28 153	49.72 168	45.63 214	42.83 232	60.46 150	75.75 172	75.95 171	84.08 182	78.04 179

pm-mvs1		65.62 174	67.42 171	63.53 184	73.66 169	76.39 168	69.66 182	60.87 148	49.73 216	43.97 200	51.24 195	57.00 164	48.16 206	79.89 125	77.84 142	84.85 178	79.82 165

tpm cat1		65.41 175	63.81 200	67.28 152	75.61 147	72.88 192	75.32 130	52.85 205	62.97 131	63.66 97	53.24 175	53.29 195	61.83 138	65.54 226	64.14 228	74.43 223	74.60 202

SCA		65.40 176	66.58 179	64.02 179	70.65 194	73.37 191	67.35 193	53.46 201	63.66 126	54.14 135	60.84 119	60.20 145	61.50 142	69.96 214	68.14 215	77.01 212	69.91 215

anonymousdsp		65.28 177	67.98 165	62.13 189	58.73 233	73.98 189	67.10 196	50.69 218	48.41 219	47.66 181	54.27 163	52.75 199	61.45 144	76.71 168	80.20 97	87.13 114	89.53 56

PMMVS		65.06 178	69.17 150	60.26 198	55.25 240	63.43 227	66.71 200	43.01 236	62.41 135	50.64 161	69.44 71	67.04 122	63.29 123	74.36 182	73.54 188	82.68 189	73.99 208

CR-MVSNet		64.83 179	65.54 184	64.01 180	70.64 195	69.41 205	65.97 204	52.74 206	57.81 163	52.65 150	54.27 163	56.31 168	60.92 146	72.20 194	73.09 190	81.12 196	75.69 196

blend_shiyan4		64.82 180	65.21 187	64.37 175	65.04 215	74.06 187	70.30 180	55.30 196	55.39 183	53.88 140	52.71 184	58.58 153	56.43 176	69.45 218	68.13 217	85.30 165	78.14 177

TransMVSNet (Re)		64.74 181	65.66 183	63.66 183	77.40 130	75.33 178	69.86 181	62.67 125	47.63 221	41.21 207	50.01 199	52.33 200	45.31 211	79.57 129	77.69 145	85.49 159	77.07 187

test-LLR		64.42 182	64.36 196	64.49 174	75.02 152	63.93 224	66.61 201	61.96 134	54.41 192	47.77 178	57.46 144	60.25 143	55.20 187	70.80 205	69.33 204	80.40 199	74.38 204

MDTV_nov1_ep13		64.37 183	65.24 186	63.37 186	68.94 205	70.81 200	72.40 172	50.29 220	60.10 154	53.91 139	60.07 125	59.15 150	57.21 168	69.43 219	67.30 219	77.47 208	69.78 217

usedtu_blend_shiyan5		64.27 184	64.70 193	63.77 181	59.06 229	74.03 188	71.65 173	56.37 192	51.17 210	53.88 140	52.71 184	58.58 153	56.43 176	70.13 213	68.14 215	85.26 166	78.14 177

tfpnnormal		64.27 184	63.64 202	65.02 170	75.84 144	75.61 175	71.24 177	62.52 127	47.79 220	42.97 203	42.65 219	44.49 229	52.66 199	78.77 141	76.86 160	84.88 175	79.29 169

PatchmatchNet		64.21 186	64.65 194	63.69 182	71.29 192	68.66 209	69.63 183	51.70 213	63.04 130	53.77 142	59.83 128	58.34 157	60.23 151	68.54 222	66.06 224	75.56 218	68.08 222
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
dps		64.00 187	62.99 204	65.18 168	73.29 170	72.07 196	68.98 188	53.07 204	57.74 165	58.41 115	55.55 154	47.74 221	60.89 148	69.53 217	67.14 221	76.44 215	71.19 213

pmmvs-eth3d		63.52 188	62.44 211	64.77 172	66.82 212	70.12 204	69.41 184	59.48 166	54.34 195	52.71 149	46.24 213	44.35 230	56.93 171	72.37 189	73.77 187	83.30 186	75.91 193

WR-MVS		63.03 189	67.40 172	57.92 208	75.14 151	77.60 158	60.56 223	66.10 71	54.11 196	23.88 232	53.94 168	53.58 185	34.50 229	73.93 184	77.71 144	87.35 108	80.94 152

blended_shiyan6		62.98 190	63.66 201	62.19 188	59.20 228	74.17 186	69.04 187	56.52 190	51.09 212	47.91 177	48.11 208	55.02 175	54.98 189	70.43 211	68.59 211	85.51 158	78.20 176

PEN-MVS		62.96 191	65.77 182	59.70 201	73.98 164	75.45 176	63.39 215	67.61 62	52.49 202	25.49 231	53.39 171	49.12 217	40.85 221	71.94 196	77.26 156	86.86 121	80.72 155

TinyColmap		62.84 192	61.03 217	64.96 171	69.61 201	71.69 197	68.48 190	59.76 164	55.41 182	47.69 180	47.33 210	34.20 242	62.76 127	74.52 180	72.59 194	81.44 193	71.47 212

CP-MVSNet		62.68 193	65.49 185	59.40 204	71.84 181	75.34 177	62.87 217	67.04 66	52.64 201	27.19 229	53.38 172	48.15 219	41.40 219	71.26 199	75.68 173	86.07 144	82.00 142

gg-mvs-nofinetune		62.55 194	65.05 190	59.62 202	78.72 117	77.61 157	70.83 178	53.63 198	39.71 237	22.04 238	36.36 230	64.32 130	47.53 207	81.16 95	79.03 124	85.00 172	77.17 185

CVMVSNet		62.55 194	65.89 180	58.64 206	66.95 210	69.15 207	66.49 203	56.29 194	52.46 203	32.70 221	59.27 131	58.21 158	50.09 203	71.77 197	71.39 198	79.31 202	78.99 172

CMPMVS	47.78 17	62.49 196	62.52 209	62.46 187	70.01 199	70.66 202	62.97 216	51.84 212	51.98 206	56.71 125	42.87 218	53.62 184	57.80 163	72.23 192	70.37 201	75.45 220	75.91 193
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs6		62.41 197	62.88 205	61.87 190	71.38 189	75.18 182	67.76 192	59.45 167	41.64 232	42.52 205	37.33 228	52.91 196	46.87 208	77.67 154	76.26 169	83.23 187	79.18 171

tpm		62.41 197	63.15 203	61.55 192	72.24 179	63.79 226	71.31 176	46.12 234	57.82 162	55.33 130	59.90 127	54.74 177	53.63 195	67.24 225	64.29 227	70.65 233	74.25 207

PS-CasMVS		62.38 199	65.06 189	59.25 205	71.73 182	75.21 181	62.77 218	66.99 67	51.94 208	26.96 230	52.00 191	47.52 222	41.06 220	71.16 202	75.60 174	85.97 151	81.97 144

pmmvs5		62.37 200	64.04 198	60.42 196	65.03 216	71.67 198	67.17 195	52.70 208	50.30 213	44.80 196	54.23 166	51.19 208	49.37 204	72.88 188	73.48 189	83.45 185	74.55 203

tpmrst		62.00 201	62.35 212	61.58 191	71.62 186	64.14 223	69.07 186	48.22 230	62.21 137	53.93 138	58.26 141	55.30 173	55.81 182	63.22 231	62.62 230	70.85 232	70.70 214

PatchT		61.97 202	64.04 198	59.55 203	60.49 226	67.40 213	56.54 230	48.65 226	56.69 172	52.65 150	51.10 196	52.14 203	60.92 146	72.20 194	73.09 190	78.03 206	75.69 196

DTE-MVSNet		61.85 203	64.96 192	58.22 207	74.32 160	74.39 185	61.01 222	67.85 60	51.76 209	21.91 239	53.28 173	48.17 218	37.74 226	72.22 193	76.44 167	86.52 135	78.49 174

SixPastTwentyTwo		61.84 204	62.45 210	61.12 194	69.20 204	72.20 195	62.03 220	57.40 185	46.54 225	38.03 215	57.14 147	41.72 234	58.12 160	69.67 216	71.58 197	81.94 190	78.30 175

WR-MVS_H		61.83 205	65.87 181	57.12 211	71.72 183	76.87 162	61.45 221	66.19 69	51.97 207	22.92 236	53.13 178	52.30 202	33.80 230	71.03 203	75.00 180	86.65 131	80.78 154

LTVRE_ROB	59.44 16	61.82 206	62.64 208	60.87 195	72.83 177	77.19 160	64.37 211	58.97 172	33.56 242	28.00 228	52.59 188	42.21 233	63.93 121	74.52 180	76.28 168	77.15 210	82.13 138
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
RPMNet		61.71 207	62.88 205	60.34 197	69.51 202	69.41 205	63.48 214	49.23 222	57.81 163	45.64 193	50.51 197	50.12 212	53.13 198	68.17 224	68.49 213	81.07 197	75.62 198

TESTMET0.1,1		61.10 208	64.36 196	57.29 210	57.53 234	63.93 224	66.61 201	36.22 240	54.41 192	47.77 178	57.46 144	60.25 143	55.20 187	70.80 205	69.33 204	80.40 199	74.38 204

test-mter		60.84 209	64.62 195	56.42 214	55.99 238	64.18 222	65.39 206	34.23 241	54.39 194	46.21 189	57.40 146	59.49 149	55.86 181	71.02 204	69.65 203	80.87 198	76.20 192

PM-MVS		60.48 210	60.94 218	59.94 199	58.85 231	66.83 216	64.27 212	51.39 214	55.03 188	48.03 176	50.00 201	40.79 236	58.26 159	69.20 220	67.13 222	78.84 204	77.60 182

MDTV_nov1_ep13_2view		60.16 211	60.51 220	59.75 200	65.39 214	69.05 208	68.00 191	48.29 228	51.99 205	45.95 191	48.01 209	49.64 216	53.39 196	68.83 221	66.52 223	77.47 208	69.55 218

EPMVS		60.00 212	61.97 213	57.71 209	68.46 206	63.17 230	64.54 210	48.23 229	63.30 128	44.72 198	60.19 123	56.05 170	50.85 202	65.27 229	62.02 231	69.44 235	63.81 229

TAMVS		59.58 213	62.81 207	55.81 216	66.03 213	65.64 221	63.86 213	48.74 225	49.95 215	37.07 217	54.77 160	58.54 155	44.44 213	72.29 191	71.79 195	74.70 222	66.66 224

test0.0.03 1		58.80 214	61.58 215	55.56 217	75.02 152	68.45 211	59.58 227	61.96 134	52.74 200	29.57 225	49.75 203	54.56 178	31.46 232	71.19 200	69.77 202	75.75 216	64.57 227

FE-MVSNET2		58.78 215	60.53 219	56.73 213	57.08 235	72.23 194	62.74 219	59.35 168	47.17 222	30.52 223	34.62 233	43.62 231	44.57 212	75.24 175	76.57 166	86.11 141	74.30 206

CHOSEN 280x420		58.70 216	61.88 214	54.98 219	55.45 239	50.55 243	64.92 208	40.36 237	55.21 184	38.13 214	48.31 206	63.76 132	63.03 126	73.73 186	68.58 212	68.00 238	73.04 210

MIMVSNet		58.52 217	61.34 216	55.22 218	60.76 225	67.01 215	66.81 198	49.02 224	56.43 175	38.90 211	40.59 225	54.54 179	40.57 222	73.16 187	71.65 196	75.30 221	66.00 225

FMVSNet5		57.24 218	60.02 221	53.99 222	56.45 237	62.74 231	65.27 207	47.03 231	55.14 185	39.55 210	40.88 223	53.42 192	41.83 216	72.35 190	71.10 200	73.79 225	64.50 228

gm-plane-assit		57.00 219	57.62 226	56.28 215	76.10 136	62.43 233	47.62 241	46.57 232	33.84 241	23.24 234	37.52 227	40.19 237	59.61 152	79.81 126	77.55 149	84.55 179	72.03 211

FC-MVSNet-test		56.90 220	65.20 188	47.21 231	66.98 209	63.20 229	49.11 240	58.60 177	59.38 157	11.50 247	65.60 100	56.68 167	24.66 239	71.17 201	71.36 199	72.38 229	69.02 220

Anonymous20231206		56.36 221	57.80 225	54.67 220	70.08 197	66.39 217	60.46 224	57.54 184	49.50 218	29.30 226	33.86 234	46.64 223	35.18 228	70.44 209	68.88 208	75.47 219	68.88 221

ADS-MVSNet		55.94 222	58.01 223	53.54 224	62.48 223	58.48 236	59.12 228	46.20 233	59.65 156	42.88 204	52.34 190	53.31 194	46.31 209	62.00 233	60.02 234	64.23 240	60.24 236

pmnet_mix02		55.30 223	57.01 227	53.30 225	64.14 219	59.09 235	58.39 229	50.24 221	53.47 198	38.68 212	49.75 203	45.86 226	40.14 223	65.38 228	60.22 233	68.19 237	65.33 226

EU-MVSNet		54.63 224	58.69 222	49.90 228	56.99 236	62.70 232	56.41 231	50.64 219	45.95 227	23.14 235	50.42 198	46.51 224	36.63 227	65.51 227	64.85 226	75.57 217	74.91 201

MVS-HIRNet		54.41 225	52.10 233	57.11 212	58.99 230	56.10 239	49.68 239	49.10 223	46.18 226	52.15 154	33.18 235	46.11 225	56.10 178	63.19 232	59.70 235	76.64 214	60.25 235

testgi		54.39 226	57.86 224	50.35 227	71.59 188	67.24 214	54.95 232	53.25 202	43.36 229	23.78 233	44.64 215	47.87 220	24.96 237	70.45 208	68.66 210	73.60 226	62.78 232

test20.03		53.93 227	56.28 228	51.19 226	72.19 180	65.83 218	53.20 235	61.08 142	42.74 230	22.08 237	37.07 229	45.76 227	24.29 240	70.44 209	69.04 206	74.31 224	63.05 231

MDA-MVSNet-bldmvs		53.37 228	53.01 232	53.79 223	43.67 245	67.95 212	59.69 226	57.92 183	43.69 228	32.41 222	41.47 221	27.89 248	52.38 200	56.97 240	65.99 225	76.68 213	67.13 223

FE-MVSNET		52.98 229	55.99 229	49.47 229	49.71 241	65.83 218	54.09 233	56.91 189	40.70 234	16.86 245	32.90 236	40.15 238	37.83 225	69.80 215	73.04 192	81.41 194	69.49 219

FPMVS		51.87 230	50.00 235	54.07 221	66.83 211	57.25 237	60.25 225	50.91 215	50.25 214	34.36 219	36.04 231	32.02 244	41.49 218	58.98 237	56.07 237	70.56 234	59.36 237

MIMVSNet1		49.27 231	53.25 231	44.62 233	44.61 243	61.52 234	53.61 234	52.18 209	41.62 233	18.68 242	28.14 241	41.58 235	25.50 235	68.46 223	69.04 206	73.15 227	62.37 233

pmmvs3		47.65 232	49.08 237	45.99 232	44.61 243	54.79 240	50.04 237	31.95 244	33.91 240	29.90 224	30.37 237	33.53 243	46.31 209	63.50 230	63.67 229	73.14 228	63.77 230

N_pmnet		47.35 233	50.13 234	44.11 234	59.98 227	51.64 242	51.86 236	44.80 235	49.58 217	20.76 240	40.65 224	40.05 239	29.64 233	59.84 235	55.15 238	57.63 241	54.00 239

new-patchmatchnet		46.97 234	49.47 236	44.05 235	62.82 221	56.55 238	45.35 242	52.01 210	42.47 231	17.04 244	35.73 232	35.21 241	21.84 243	61.27 234	54.83 239	65.26 239	60.26 234

GG-mvs-BLEND		46.86 235	67.51 170	22.75 241	0.05 253	76.21 170	64.69 209	0.04 249	61.90 139	0.09 254	55.57 153	71.32 88	0.08 249	70.54 207	67.19 220	71.58 230	69.86 216

PMVS	39.38 18	46.06 236	43.30 239	49.28 230	62.93 220	38.75 245	41.88 243	53.50 200	33.33 243	35.46 218	28.90 240	31.01 245	33.04 231	58.61 239	54.63 240	68.86 236	57.88 238
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
WB-MVS		40.01 237	45.06 238	34.13 237	58.84 232	53.28 241	28.60 246	58.10 182	32.93 244	4.65 252	40.92 222	28.33 247	7.26 246	58.86 238	56.09 236	47.36 244	44.98 241

new_pmnet		38.40 238	42.64 240	33.44 238	37.54 248	45.00 244	36.60 244	32.72 243	40.27 235	12.72 246	29.89 238	28.90 246	24.78 238	53.17 241	52.90 241	56.31 242	48.34 240

Gipuma		36.38 239	35.80 241	37.07 236	45.76 242	33.90 246	29.81 245	48.47 227	39.91 236	18.02 243	8.00 249	8.14 253	25.14 236	59.29 236	61.02 232	55.19 243	40.31 242
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMMVS2		25.60 240	29.75 242	20.76 242	28.00 249	30.93 247	23.10 248	29.18 245	23.14 246	1.46 253	18.23 245	16.54 250	5.08 247	40.22 242	41.40 243	37.76 245	37.79 244

test_method		22.26 241	25.94 243	17.95 243	3.24 252	7.17 252	23.83 247	7.27 247	37.35 239	20.44 241	21.87 244	39.16 240	18.67 244	34.56 243	20.84 247	34.28 246	20.64 248

E-PMN		21.77 242	18.24 245	25.89 239	40.22 246	19.58 249	12.46 251	39.87 238	18.68 248	6.71 249	9.57 246	4.31 256	22.36 242	19.89 247	27.28 245	33.73 247	28.34 246

EMVS		20.98 243	17.15 246	25.44 240	39.51 247	19.37 250	12.66 250	39.59 239	19.10 247	6.62 250	9.27 247	4.40 255	22.43 241	17.99 248	24.40 246	31.81 248	25.53 247

MVE	19.12 19	20.47 244	23.27 244	17.20 244	12.66 251	25.41 248	10.52 252	34.14 242	14.79 249	6.53 251	8.79 248	4.68 254	16.64 245	29.49 245	41.63 242	22.73 250	38.11 243
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		0.09 245	0.15 247	0.02 246	0.01 254	0.02 254	0.05 255	0.01 250	0.11 250	0.01 255	0.26 251	0.01 257	0.06 251	0.10 249	0.10 248	0.01 252	0.43 250

test123		0.09 245	0.14 248	0.02 246	0.00 255	0.02 254	0.02 256	0.01 250	0.09 251	0.00 256	0.30 250	0.00 258	0.08 249	0.03 250	0.09 249	0.01 252	0.45 249

uanet_test		0.00 247	0.00 249	0.00 248	0.00 255	0.00 256	0.00 257	0.00 252	0.00 252	0.00 256	0.00 252	0.00 258	0.00 252	0.00 251	0.00 250	0.00 254	0.00 251

sosnet-low-res		0.00 247	0.00 249	0.00 248	0.00 255	0.00 256	0.00 257	0.00 252	0.00 252	0.00 256	0.00 252	0.00 258	0.00 252	0.00 251	0.00 250	0.00 254	0.00 251

sosnet		0.00 247	0.00 249	0.00 248	0.00 255	0.00 256	0.00 257	0.00 252	0.00 252	0.00 256	0.00 252	0.00 258	0.00 252	0.00 251	0.00 250	0.00 254	0.00 251

TPM-MVS					90.07 22	88.36 36	88.45 30			77.10 27	75.60 39	83.98 31	71.33 65			89.75 44	89.62 54
Ray Leroy Khuboni and Hongjun Xu: Textureless Resilient Propagation Matching in Multiple View Stereosis (TPM-MVS). SATNAC 2025
RE-MVS-def										46.24 188

9.14												86.88 17

SR-MVS					88.99 35			73.57 25				87.54 15

Anonymous202405211			72.16 125		80.85 86	81.85 102	76.88 123	65.40 77	62.89 133		46.35 212	67.99 119	62.05 132	81.15 96	80.38 95	85.97 151	84.50 122

our_test_3					67.93 207	70.99 199	66.89 197

ambc			53.42 230		64.99 217	63.36 228	49.96 238		47.07 223	37.12 216	28.97 239	16.36 251	41.82 217	75.10 177	67.34 218	71.55 231	75.72 195

MTAPA										83.48 1		86.45 20

MTMP										82.66 5		84.91 28

Patchmatch-RL test							2.85 254

tmp_tt				14.50 245	14.68 250	7.17 252	10.46 253	2.21 248	37.73 238	28.71 227	25.26 242	16.98 249	4.37 248	31.49 244	29.77 244	26.56 249

XVS					86.63 47	88.68 28	85.00 48			71.81 47		81.92 38				90.47 24

X-MVStestdata					86.63 47	88.68 28	85.00 48			71.81 47		81.92 38				90.47 24

mPP-MVS					89.90 26							81.29 43

NP-MVS									80.10 48

Patchmtry						65.80 220	65.97 204	52.74 206		52.65 150

DeepMVS_CX						18.74 251	18.55 249	8.02 246	26.96 245	7.33 248	23.81 243	13.05 252	25.99 234	25.17 246		22.45 251	36.25 245

High-resolution multi-view benchmark