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 4	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 3	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 16	92.36 2	90.69 9	76.15 4	93.08 2	82.75 4	92.19 6	90.71 3	80.45 6	89.27 6	87.91 9	90.82 12	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 8	82.09 6	93.85 1	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 23	91.80 6	91.63 5	75.80 7	91.99 4	81.23 8	92.54 2	89.18 6	80.89 4	87.99 16	87.91 9	89.70 45	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 6	85.08 5	90.95 7	91.58 7	92.03 1	75.53 12	91.15 5	80.10 14	92.27 5	88.34 11	80.80 5	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
SMA-MVS		87.56 7	90.17 7	84.52 9	91.71 3	90.57 9	90.77 8	75.19 13	90.67 7	80.50 13	86.59 17	88.86 8	78.09 15	89.92 1	89.41 1	90.84 11	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 8	89.70 8	84.86 8	91.26 6	91.10 8	90.90 6	75.65 8	89.21 9	81.25 7	91.12 8	88.93 7	78.82 10	87.42 20	86.23 30	91.28 3	93.90 13

HPM-MVS++		87.09 9	88.92 13	84.95 6	92.61 1	87.91 40	90.23 15	76.06 5	88.85 12	81.20 9	87.33 13	87.93 12	79.47 9	88.59 9	88.23 5	90.15 34	93.60 20

SD-MVS		86.96 10	89.45 9	84.05 14	90.13 19	89.23 23	89.77 18	74.59 14	89.17 10	80.70 10	89.93 11	89.67 5	78.47 12	87.57 19	86.79 23	90.67 18	93.76 16
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 11	89.23 10	84.14 12	89.78 26	88.67 30	90.59 10	73.46 26	88.99 11	80.52 12	91.26 7	88.65 9	79.91 8	86.96 29	86.22 31	90.59 20	93.83 14
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 12	88.91 14	84.41 10	90.66 11	90.10 13	90.78 7	75.64 9	87.38 16	78.72 18	90.68 10	86.82 17	80.15 7	87.13 25	86.45 29	90.51 21	93.83 14
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ACMMP_NAP		86.52 13	89.01 11	83.62 16	90.28 18	90.09 14	90.32 13	74.05 19	88.32 13	79.74 15	87.04 15	85.59 23	76.97 28	89.35 4	88.44 4	90.35 30	94.27 11

CNVR-MVS		86.36 14	88.19 17	84.23 11	91.33 5	89.84 15	90.34 11	75.56 10	87.36 17	78.97 17	81.19 29	86.76 18	78.74 11	89.30 5	88.58 2	90.45 27	94.33 10

HFP-MVS		86.15 15	87.95 18	84.06 13	90.80 9	89.20 24	89.62 19	74.26 16	87.52 14	80.63 11	86.82 16	84.19 29	78.22 14	87.58 18	87.19 17	90.81 13	93.13 25

SteuartSystems-ACMMP		85.99 16	88.31 16	83.27 20	90.73 10	89.84 15	90.27 14	74.31 15	84.56 29	75.88 31	87.32 14	85.04 24	77.31 23	89.01 7	88.46 3	91.14 4	93.96 12
Skip Steuart: Steuart Systems R&D Blog.
ACMMPR		85.52 17	87.53 20	83.17 21	90.13 19	89.27 21	89.30 20	73.97 20	86.89 19	77.14 25	86.09 18	83.18 32	77.74 19	87.42 20	87.20 16	90.77 14	92.63 26

MP-MVS		85.50 18	87.40 21	83.28 19	90.65 12	89.51 20	89.16 23	74.11 18	83.70 34	78.06 22	85.54 20	84.89 28	77.31 23	87.40 22	87.14 18	90.41 28	93.65 19
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
NCCC		85.34 19	86.59 25	83.88 15	91.48 4	88.88 25	89.79 17	75.54 11	86.67 20	77.94 23	76.55 35	84.99 25	78.07 16	88.04 13	87.68 13	90.46 26	93.31 21

DeepPCF-MVS	79.04 1	85.30 20	88.93 12	81.06 32	88.77 36	90.48 11	85.46 46	73.08 28	90.97 6	73.77 38	84.81 22	85.95 20	77.43 22	88.22 11	87.73 11	87.85 87	94.34 9

CSCG		85.28 21	87.68 19	82.49 24	89.95 24	91.99 5	88.82 24	71.20 37	86.41 21	79.63 16	79.26 30	88.36 10	73.94 41	86.64 31	86.67 26	91.40 2	94.41 8

MCST-MVS		85.13 22	86.62 24	83.39 17	90.55 14	89.82 17	89.29 21	73.89 22	84.38 30	76.03 30	79.01 32	85.90 21	78.47 12	87.81 17	86.11 33	92.11 1	93.29 22

TSAR-MVS + ACMM		85.10 23	88.81 15	80.77 35	89.55 29	88.53 32	88.59 27	72.55 30	87.39 15	71.90 43	90.95 9	87.55 13	74.57 36	87.08 27	86.54 27	87.47 97	93.67 17

train_agg		84.86 24	87.21 23	82.11 26	90.59 13	85.47 55	89.81 16	73.55 25	83.95 31	73.30 39	89.84 12	87.23 15	75.61 33	86.47 33	85.46 38	89.78 40	92.06 32

DeepC-MVS	78.47 2	84.81 25	86.03 29	83.37 18	89.29 32	90.38 12	88.61 26	76.50 1	86.25 22	77.22 24	75.12 41	80.28 45	77.59 21	88.39 10	88.17 6	91.02 6	93.66 18
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 26	86.43 27	82.77 23	89.48 30	88.13 39	88.64 25	73.93 21	84.92 24	76.77 27	81.94 27	83.50 31	77.29 25	86.92 30	86.49 28	90.49 22	93.14 24

MVS_0304		84.63 27	87.25 22	81.59 29	88.58 37	90.50 10	87.82 34	69.16 52	83.82 33	78.46 20	82.32 25	84.97 26	74.56 37	88.16 12	87.72 12	90.94 10	93.24 23

PGM-MVS		84.42 28	86.29 28	82.23 25	90.04 22	88.82 26	89.23 22	71.74 35	82.82 39	74.61 34	84.41 23	82.09 35	77.03 27	87.13 25	86.73 25	90.73 16	92.06 32

DeepC-MVS_fast	78.24 3	84.27 29	85.50 31	82.85 22	90.46 17	89.24 22	87.83 33	74.24 17	84.88 25	76.23 29	75.26 40	81.05 43	77.62 20	88.02 14	87.62 14	90.69 17	92.41 28
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 30	86.58 26	80.32 36	85.14 54	86.96 44	84.91 50	70.25 41	84.71 28	73.91 37	85.16 21	85.63 22	77.92 17	85.44 42	85.71 36	89.77 41	92.45 27

ACMMP		83.42 31	85.27 32	81.26 31	88.47 38	88.49 33	88.31 31	72.09 32	83.42 35	72.77 41	82.65 24	78.22 50	75.18 34	86.24 38	85.76 35	90.74 15	92.13 31
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 32	84.33 35	82.11 26	89.56 28	88.49 33	90.33 12	73.24 27	83.85 32	76.46 28	72.43 52	82.65 33	73.02 48	86.37 35	86.91 20	90.03 36	89.62 53

X-MVS		83.23 33	85.20 33	80.92 34	89.71 27	88.68 27	88.21 32	73.60 23	82.57 40	71.81 46	77.07 33	81.92 37	71.72 58	86.98 28	86.86 21	90.47 23	92.36 29

CDPH-MVS		82.64 34	85.03 34	79.86 39	89.41 31	88.31 36	88.32 30	71.84 34	80.11 46	67.47 67	82.09 26	81.44 41	71.85 56	85.89 41	86.15 32	90.24 32	91.25 38

3Dnovator+	75.73 4	82.40 35	82.76 39	81.97 28	88.02 39	89.67 18	86.60 38	71.48 36	81.28 44	78.18 21	64.78 95	77.96 52	77.13 26	87.32 23	86.83 22	90.41 28	91.48 36

PHI-MVS		82.36 36	85.89 30	78.24 47	86.40 48	89.52 19	85.52 44	69.52 48	82.38 42	65.67 75	81.35 28	82.36 34	73.07 47	87.31 24	86.76 24	89.24 52	91.56 35

MSLP-MVS++		82.09 37	82.66 40	81.42 30	87.03 44	87.22 43	85.82 42	70.04 42	80.30 45	78.66 19	68.67 75	81.04 44	77.81 18	85.19 46	84.88 43	89.19 56	91.31 37

CPTT-MVS		81.77 38	83.10 38	80.21 37	85.93 50	86.45 49	87.72 35	70.98 38	82.54 41	71.53 49	74.23 45	81.49 40	76.31 31	82.85 71	81.87 67	88.79 65	92.26 30

CANet		81.62 39	83.41 36	79.53 41	87.06 43	88.59 31	85.47 45	67.96 58	76.59 54	74.05 35	74.69 42	81.98 36	72.98 49	86.14 39	85.47 37	89.68 46	90.42 46

HQP-MVS		81.19 40	83.27 37	78.76 44	87.40 42	85.45 56	86.95 36	70.47 40	81.31 43	66.91 72	79.24 31	76.63 54	71.67 59	84.43 54	83.78 52	89.19 56	92.05 34

OMC-MVS		80.26 41	82.59 41	77.54 50	83.04 62	85.54 54	83.25 56	65.05 79	87.32 18	72.42 42	72.04 54	78.97 47	73.30 45	83.86 57	81.60 71	88.15 75	88.83 58

MVS_111021_HR		80.13 42	81.46 46	78.58 45	85.77 51	85.17 59	83.45 55	69.28 49	74.08 62	70.31 55	74.31 44	75.26 63	73.13 46	86.46 34	85.15 41	89.53 47	89.81 51

LGP-MVS_train		79.83 43	81.22 49	78.22 48	86.28 49	85.36 58	86.76 37	69.59 46	77.34 51	65.14 78	75.68 37	70.79 87	71.37 62	84.60 50	84.01 47	90.18 33	90.74 42

ACMP	73.23 7	79.79 44	80.53 54	78.94 42	85.61 52	85.68 53	85.61 43	69.59 46	77.33 52	71.00 52	74.45 43	69.16 98	71.88 54	83.15 67	83.37 55	89.92 37	90.57 45
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
3Dnovator	73.76 5	79.75 45	80.52 55	78.84 43	84.94 59	87.35 41	84.43 52	65.54 75	78.29 50	73.97 36	63.00 103	75.62 62	74.07 40	85.00 47	85.34 39	90.11 35	89.04 56

AdaColmap		79.74 46	78.62 64	81.05 33	89.23 33	86.06 52	84.95 49	71.96 33	79.39 49	75.51 32	63.16 101	68.84 103	76.51 29	83.55 61	82.85 59	88.13 76	86.46 82

OPM-MVS		79.68 47	79.28 62	80.15 38	87.99 40	86.77 46	88.52 28	72.72 29	64.55 108	67.65 66	67.87 80	74.33 67	74.31 39	86.37 35	85.25 40	89.73 44	89.81 51
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
EC-MVSNet		79.44 48	81.35 47	77.22 52	82.95 63	84.67 63	81.31 64	63.65 92	72.47 69	68.75 58	73.15 47	78.33 49	75.99 32	86.06 40	83.96 49	90.67 18	90.79 41

PCF-MVS	73.28 6	79.42 49	80.41 56	78.26 46	84.88 60	88.17 37	86.08 39	69.85 43	75.23 57	68.43 59	68.03 79	78.38 48	71.76 57	81.26 92	80.65 89	88.56 68	91.18 39
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
CLD-MVS		79.35 50	81.23 48	77.16 53	85.01 57	86.92 45	85.87 41	60.89 137	80.07 48	75.35 33	72.96 48	73.21 72	68.43 83	85.41 44	84.63 44	87.41 98	85.44 95
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 51	81.11 50	77.01 54	81.36 77	84.03 67	80.35 71	63.25 96	73.43 66	70.37 54	74.10 46	76.03 59	76.40 30	86.32 37	83.95 50	90.34 31	89.93 49

MAR-MVS		79.21 52	80.32 57	77.92 49	87.46 41	88.15 38	83.95 53	67.48 64	74.28 59	68.25 60	64.70 96	77.04 53	72.17 52	85.42 43	85.00 42	88.22 72	87.62 67
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 53	82.37 42	75.41 64	82.33 69	86.38 50	80.80 67	63.18 99	82.90 37	67.34 68	72.79 49	76.07 57	69.62 70	83.46 64	84.41 45	89.20 54	90.60 43

canonicalmvs		79.16 53	82.37 42	75.41 64	82.33 69	86.38 50	80.80 67	63.18 99	82.90 37	67.34 68	72.79 49	76.07 57	69.62 70	83.46 64	84.41 45	89.20 54	90.60 43

DELS-MVS		79.15 55	81.07 51	76.91 55	83.54 61	87.31 42	84.45 51	64.92 80	69.98 75	69.34 57	71.62 56	76.26 55	69.84 69	86.57 32	85.90 34	89.39 49	89.88 50
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 56	80.62 53	77.28 51	88.90 35	83.17 83	83.65 54	72.41 31	74.41 58	67.15 71	76.78 34	74.37 66	64.43 107	83.70 60	83.69 53	87.15 101	88.19 62
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
ACMM	72.26 8	78.86 57	78.13 66	79.71 40	86.89 45	83.40 78	86.02 40	70.50 39	75.28 56	71.49 50	63.01 102	69.26 97	73.57 43	84.11 56	83.98 48	89.76 42	87.84 65
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
SPE-MVS-test		78.79 58	80.72 52	76.53 57	81.11 82	83.88 70	79.69 80	63.72 91	73.80 63	69.95 56	75.40 39	76.17 56	74.85 35	84.50 53	82.78 60	89.87 39	88.54 60

QAPM		78.47 59	80.22 58	76.43 58	85.03 56	86.75 47	80.62 70	66.00 72	73.77 64	65.35 77	65.54 91	78.02 51	72.69 50	83.71 59	83.36 56	88.87 62	90.41 47

TSAR-MVS + COLMAP		78.34 60	81.64 45	74.48 77	80.13 96	85.01 60	81.73 60	65.93 74	84.75 27	61.68 91	85.79 19	66.27 115	71.39 61	82.91 70	80.78 80	86.01 138	85.98 84

MVS_111021_LR		78.13 61	79.85 60	76.13 59	81.12 81	81.50 96	80.28 72	65.25 77	76.09 55	71.32 51	76.49 36	72.87 74	72.21 51	82.79 72	81.29 73	86.59 123	87.91 64

casdiffmvs_mvg		77.79 62	79.55 61	75.73 61	81.56 74	84.70 62	82.12 57	64.26 87	74.27 60	67.93 63	70.83 61	74.66 65	69.19 78	83.33 66	81.94 66	89.29 51	87.14 73
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 63	80.05 59	74.94 69	80.68 87	84.52 65	81.36 63	63.14 101	84.77 26	64.82 80	68.72 73	75.91 60	71.86 55	81.62 78	79.55 109	87.80 89	85.24 100
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
ETV-MVS		77.32 64	78.81 63	75.58 63	82.24 71	83.64 76	79.98 73	64.02 88	69.64 80	63.90 85	70.89 60	69.94 93	73.41 44	85.39 45	83.91 51	89.92 37	88.31 61

CNLPA		77.20 65	77.54 71	76.80 56	82.63 65	84.31 66	79.77 77	64.64 81	85.17 23	73.18 40	56.37 139	69.81 94	74.53 38	81.12 95	78.69 121	86.04 137	87.29 70

casdiffmvs		76.76 66	78.46 65	74.77 71	80.32 92	83.73 75	80.65 69	63.24 97	73.58 65	66.11 74	69.39 70	74.09 68	69.49 75	82.52 74	79.35 114	88.84 64	86.52 81
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
viewcassd2359sk11		76.64 67	77.43 75	75.72 62	80.75 86	83.07 84	81.95 58	63.20 98	72.02 72	70.88 53	69.50 68	72.02 78	69.58 72	80.68 101	78.98 117	87.97 81	85.74 87

PVSNet_Blended_VisFu		76.57 68	77.90 67	75.02 68	80.56 88	86.58 48	79.24 85	66.18 69	64.81 105	68.18 61	65.61 89	71.45 80	67.05 87	84.16 55	81.80 68	88.90 60	90.92 40

MGCFI-Net		76.55 69	81.71 44	70.52 101	81.71 73	84.62 64	75.02 128	62.17 123	82.91 36	53.58 133	72.78 51	75.87 61	61.75 130	82.96 69	82.61 62	88.86 63	90.26 48

viewmanbaseed2359cas		76.36 70	77.87 68	74.60 74	79.81 97	82.88 87	81.69 61	61.02 135	72.14 71	67.97 62	69.61 67	72.45 75	69.53 73	81.53 81	79.83 102	87.57 95	86.65 80

viewdifsd2359ckpt13		76.26 71	77.31 79	75.03 67	80.14 94	83.77 74	81.58 62	62.80 107	70.34 74	67.83 65	68.06 78	70.93 86	70.20 67	81.46 83	79.88 100	87.63 94	86.71 79

PVSNet_BlendedMVS		76.21 72	77.52 72	74.69 72	79.46 101	83.79 72	77.50 104	64.34 85	69.88 76	71.88 44	68.54 76	70.42 89	67.05 87	83.48 62	79.63 105	87.89 85	86.87 75

PVSNet_Blended		76.21 72	77.52 72	74.69 72	79.46 101	83.79 72	77.50 104	64.34 85	69.88 76	71.88 44	68.54 76	70.42 89	67.05 87	83.48 62	79.63 105	87.89 85	86.87 75

OpenMVS	70.44 10	76.15 74	76.82 84	75.37 66	85.01 57	84.79 61	78.99 89	62.07 124	71.27 73	67.88 64	57.91 132	72.36 76	70.15 68	82.23 76	81.41 72	88.12 77	87.78 66

viewmacassd2359aftdt		75.85 75	77.01 82	74.49 76	79.69 99	82.87 88	81.77 59	61.06 133	69.37 81	67.26 70	66.73 88	71.63 79	69.48 76	81.51 82	80.20 95	87.69 91	86.77 78

PLC	68.99 11	75.68 76	75.31 89	76.12 60	82.94 64	81.26 100	79.94 75	66.10 70	77.15 53	66.86 73	59.13 122	68.53 105	73.73 42	80.38 106	79.04 115	87.13 105	81.68 137
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
EIA-MVS		75.64 77	76.60 85	74.53 75	82.43 68	83.84 71	78.32 97	62.28 122	65.96 96	63.28 89	68.95 71	67.54 110	71.61 60	82.55 73	81.63 70	89.24 52	85.72 89

MVS_Test		75.37 78	77.13 81	73.31 82	79.07 104	81.32 99	79.98 73	60.12 149	69.72 78	64.11 84	70.53 63	73.22 71	68.90 79	80.14 113	79.48 111	87.67 92	85.50 93

Effi-MVS+		75.28 79	76.20 86	74.20 78	81.15 80	83.24 81	81.11 65	63.13 102	66.37 92	60.27 97	64.30 99	68.88 102	70.93 66	81.56 80	81.69 69	88.61 66	87.35 68

DI_MVS_pp		75.13 80	76.12 87	73.96 79	78.18 110	81.55 94	80.97 66	62.54 117	68.59 82	65.13 79	61.43 106	74.81 64	69.32 77	81.01 97	79.59 107	87.64 93	85.89 85

diffmvs_AUTHOR		74.91 81	77.47 74	71.92 87	75.60 138	80.50 109	79.48 83	60.02 151	72.41 70	64.39 82	70.63 62	73.27 70	66.55 96	79.97 114	78.34 126	85.46 149	87.17 72

diffmvs		74.86 82	77.37 77	71.93 86	75.62 136	80.35 113	79.42 84	60.15 148	72.81 68	64.63 81	71.51 57	73.11 73	66.53 99	79.02 128	77.98 130	85.25 153	86.83 77
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
UA-Net		74.47 83	77.80 69	70.59 100	85.33 53	85.40 57	73.54 153	65.98 73	60.65 139	56.00 118	72.11 53	79.15 46	54.63 178	83.13 68	82.25 64	88.04 80	81.92 135

GeoE		74.23 84	74.84 93	73.52 80	80.42 91	81.46 97	79.77 77	61.06 133	67.23 89	63.67 86	59.56 119	68.74 104	67.90 84	80.25 111	79.37 113	88.31 69	87.26 71

LS3D		74.08 85	73.39 103	74.88 70	85.05 55	82.62 90	79.71 79	68.66 53	72.82 67	58.80 101	57.61 133	61.31 130	71.07 65	80.32 107	78.87 120	86.00 139	80.18 152

EPP-MVSNet		74.00 86	77.41 76	70.02 109	80.53 89	83.91 69	74.99 129	62.68 115	65.06 103	49.77 155	68.68 74	72.09 77	63.06 115	82.49 75	80.73 81	89.12 58	88.91 57

FA-MVS(training)		73.66 87	74.95 92	72.15 85	78.63 108	80.46 111	78.92 91	54.79 182	69.71 79	65.37 76	62.04 104	66.89 113	67.10 86	80.72 99	79.87 101	88.10 79	84.97 105

DCV-MVSNet		73.65 88	75.78 88	71.16 92	80.19 93	79.27 123	77.45 106	61.68 130	66.73 91	58.72 102	65.31 92	69.96 92	62.19 120	81.29 91	80.97 77	86.74 116	86.91 74

viewmambaseed2359dif		73.61 89	75.14 90	71.84 88	75.87 131	79.69 118	78.99 89	60.42 144	68.19 84	64.15 83	67.85 81	71.20 84	66.55 96	77.41 147	75.78 160	85.04 156	85.85 86

IS_MVSNet		73.33 90	77.34 78	68.65 124	81.29 78	83.47 77	74.45 135	63.58 94	65.75 98	48.49 160	67.11 87	70.61 88	54.63 178	84.51 52	83.58 54	89.48 48	86.34 83

CANet_DTU		73.29 91	76.96 83	69.00 121	77.04 122	82.06 92	79.49 82	56.30 179	67.85 87	53.29 135	71.12 59	70.37 91	61.81 129	81.59 79	80.96 78	86.09 132	84.73 109

Fast-Effi-MVS+		73.11 92	73.66 100	72.48 84	77.72 116	80.88 106	78.55 94	58.83 165	65.19 102	60.36 96	59.98 116	62.42 127	71.22 64	81.66 77	80.61 91	88.20 73	84.88 108

UGNet		72.78 93	77.67 70	67.07 146	71.65 175	83.24 81	75.20 122	63.62 93	64.93 104	56.72 114	71.82 55	73.30 69	49.02 191	81.02 96	80.70 87	86.22 129	88.67 59
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 94	77.20 80	67.59 136	74.19 151	84.01 68	76.61 116	61.69 129	60.62 140	50.61 150	70.25 65	71.31 83	55.57 173	83.85 58	82.28 63	86.90 110	88.08 63
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
FC-MVSNet-train		72.60 95	75.07 91	69.71 112	81.10 83	78.79 129	73.74 152	65.23 78	66.10 95	53.34 134	70.36 64	63.40 124	56.92 162	81.44 85	80.96 78	87.93 83	84.46 113

viewdifsd2359ckpt11		72.49 96	74.10 96	70.61 97	75.87 131	78.53 133	76.92 109	58.16 168	65.69 99	61.34 94	67.21 84	68.35 107	66.51 100	77.91 140	75.60 162	84.86 161	85.43 96

viewmsd2359difaftdt		72.49 96	74.10 96	70.61 97	75.87 131	78.53 133	76.92 109	58.16 168	65.69 99	61.33 95	67.21 84	68.34 108	66.51 100	77.91 140	75.60 162	84.86 161	85.42 97

ET-MVSNet_ETH3D		72.46 98	74.19 95	70.44 102	62.50 210	81.17 101	79.90 76	62.46 120	64.52 109	57.52 110	71.49 58	59.15 140	72.08 53	78.61 133	81.11 75	88.16 74	83.29 123

ECVR-MVS		72.20 99	73.91 99	70.20 106	81.49 75	83.27 79	75.74 117	67.59 62	68.19 84	49.31 158	55.77 141	62.00 128	58.82 144	84.76 48	82.94 57	88.27 70	80.41 150

MVSTER		72.06 100	74.24 94	69.51 115	70.39 186	75.97 161	76.91 112	57.36 175	64.64 107	61.39 93	68.86 72	63.76 122	63.46 112	81.44 85	79.70 104	87.56 96	85.31 99

Anonymous20231211		71.90 101	72.48 112	71.21 91	80.14 94	81.53 95	76.92 109	62.89 105	64.46 110	58.94 99	43.80 202	70.98 85	62.22 119	80.70 100	80.19 97	86.18 130	85.73 88

Effi-MVS+-dtu		71.82 102	71.86 117	71.78 89	78.77 105	80.47 110	78.55 94	61.67 131	60.68 138	55.49 119	58.48 126	65.48 117	68.85 80	76.92 153	75.55 165	87.35 99	85.46 94

test2506		71.72 103	72.95 107	70.29 104	81.49 75	83.27 79	75.74 117	67.59 62	68.19 84	49.81 154	61.15 107	49.73 201	58.82 144	84.76 48	82.94 57	88.27 70	80.63 146

IterMVS-LS		71.69 104	72.82 110	70.37 103	77.54 118	76.34 158	75.13 126	60.46 143	61.53 133	57.57 109	64.89 94	67.33 111	66.04 104	77.09 152	77.37 143	85.48 148	85.18 101
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
test1111		71.56 105	73.44 102	69.38 117	81.16 79	82.95 85	74.99 129	67.68 60	66.89 90	46.33 174	55.19 147	60.91 131	57.99 152	84.59 51	82.70 61	88.12 77	80.85 143

MSDG		71.52 106	69.87 129	73.44 81	82.21 72	79.35 122	79.52 81	64.59 82	66.15 94	61.87 90	53.21 166	56.09 156	65.85 105	78.94 129	78.50 123	86.60 122	76.85 175

thisisatest0530		71.48 107	73.01 106	69.70 113	73.83 156	78.62 131	74.53 134	59.12 159	64.13 111	58.63 103	64.60 97	58.63 142	64.27 108	80.28 109	80.17 98	87.82 88	84.64 111

tttt0517		71.41 108	72.95 107	69.60 114	73.70 158	78.70 130	74.42 138	59.12 159	63.89 115	58.35 106	64.56 98	58.39 144	64.27 108	80.29 108	80.17 98	87.74 90	84.69 110

ACMH+	66.54 13	71.36 109	70.09 127	72.85 83	82.59 66	81.13 102	78.56 93	68.04 56	61.55 132	52.52 141	51.50 181	54.14 166	68.56 82	78.85 130	79.50 110	86.82 113	83.94 117

IB-MVS	66.94 12	71.21 110	71.66 118	70.68 95	79.18 103	82.83 89	72.61 159	61.77 128	59.66 144	63.44 88	53.26 164	59.65 138	59.16 143	76.78 156	82.11 65	87.90 84	87.33 69
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		70.78 111	73.37 104	67.76 129	72.95 163	78.00 138	75.15 123	62.72 110	64.13 111	51.44 143	58.37 127	69.02 99	57.59 154	81.33 88	80.72 82	86.70 117	82.02 129

test1		70.78 111	73.37 104	67.76 129	72.95 163	78.00 138	75.15 123	62.72 110	64.13 111	51.44 143	58.37 127	69.02 99	57.59 154	81.33 88	80.72 82	86.70 117	82.02 129

ACMH	65.37 14	70.71 113	70.00 128	71.54 90	82.51 67	82.47 91	77.78 101	68.13 55	56.19 167	46.06 177	54.30 151	51.20 193	68.68 81	80.66 102	80.72 82	86.07 133	84.45 114
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
UniMVSNet_NR-MVSNet		70.59 114	72.19 113	68.72 122	77.72 116	80.72 107	73.81 150	69.65 45	61.99 128	43.23 187	60.54 112	57.50 147	58.57 146	79.56 120	81.07 76	89.34 50	83.97 115

FMVSNet3		70.49 115	72.90 109	67.67 134	72.88 166	77.98 141	74.96 132	62.72 110	64.13 111	51.44 143	58.37 127	69.02 99	57.43 157	79.43 123	79.57 108	86.59 123	81.81 136

baseline		70.45 116	74.09 98	66.20 155	70.95 183	75.67 162	74.26 142	53.57 184	68.33 83	58.42 104	69.87 66	71.45 80	61.55 131	74.84 167	74.76 170	78.42 190	83.72 120

FMVSNet2		70.39 117	72.67 111	67.72 132	72.95 163	78.00 138	75.15 123	62.69 114	63.29 119	51.25 147	55.64 142	68.49 106	57.59 154	80.91 98	80.35 94	86.70 117	82.02 129

v8		70.23 118	69.86 130	70.67 96	74.69 146	79.82 117	78.79 92	59.18 158	58.80 148	58.20 107	55.00 148	57.33 148	66.31 103	77.51 145	76.71 152	86.82 113	83.88 118

v10		70.22 119	69.76 132	70.74 93	74.79 145	80.30 115	79.22 86	59.81 153	57.71 155	56.58 116	54.22 157	55.31 159	66.95 90	78.28 136	77.47 140	87.12 107	85.07 103

MS-PatchMatch		70.17 120	70.49 124	69.79 111	80.98 84	77.97 143	77.51 103	58.95 162	62.33 126	55.22 122	53.14 167	65.90 116	62.03 123	79.08 127	77.11 147	84.08 167	77.91 167

baseline1		70.10 121	72.17 114	67.69 133	79.74 98	76.80 153	73.91 146	64.38 84	62.74 124	48.30 162	64.94 93	64.08 121	54.17 180	81.46 83	78.92 118	85.66 145	76.22 177

v2v482		70.05 122	69.46 136	70.74 93	74.62 147	80.32 114	79.00 88	60.62 140	57.41 157	56.89 113	55.43 146	55.14 161	66.39 102	77.25 149	77.14 146	86.90 110	83.57 122

v1144		69.93 123	69.36 137	70.61 97	74.89 144	80.93 103	79.11 87	60.64 139	55.97 169	55.31 121	53.85 159	54.14 166	66.54 98	78.10 138	77.44 141	87.14 104	85.09 102

baseline2		69.69 124	70.27 126	69.01 120	75.72 135	77.13 151	73.82 149	58.94 163	61.35 134	57.09 112	61.68 105	57.17 150	61.99 124	78.10 138	76.58 154	86.48 126	79.85 154

DU-MVS		69.63 125	70.91 121	68.13 128	75.99 127	79.54 119	73.81 150	69.20 50	61.20 136	43.23 187	58.52 124	53.50 173	58.57 146	79.22 125	80.45 92	87.97 81	83.97 115

UniMVSNet (Re)		69.53 126	71.90 116	66.76 151	76.42 125	80.93 103	72.59 160	68.03 57	61.75 131	41.68 192	58.34 130	57.23 149	53.27 183	79.53 121	80.62 90	88.57 67	84.90 107

v1192		69.50 127	68.83 143	70.29 104	74.49 148	80.92 105	78.55 94	60.54 141	55.04 175	54.21 124	52.79 173	52.33 186	66.92 91	77.88 142	77.35 144	87.04 108	85.51 92

HyFIR lowres test		69.47 128	68.94 142	70.09 108	76.77 124	82.93 86	76.63 115	60.17 147	59.00 147	54.03 127	40.54 212	65.23 118	67.89 85	76.54 159	78.30 127	85.03 157	80.07 153

v144192		69.34 129	68.68 147	70.12 107	74.06 152	80.54 108	78.08 100	60.54 141	54.99 177	54.13 126	52.92 171	52.80 184	66.73 94	77.13 151	76.72 151	87.15 101	85.63 90

TranMVSNet+NR-MVSNet		69.25 130	70.81 122	67.43 137	77.23 121	79.46 121	73.48 155	69.66 44	60.43 141	39.56 195	58.82 123	53.48 175	55.74 171	79.59 118	81.21 74	88.89 61	82.70 125

CHOSEN 1792x2688		69.20 131	69.26 138	69.13 118	76.86 123	78.93 125	77.27 107	60.12 149	61.86 130	54.42 123	42.54 206	61.61 129	66.91 92	78.55 134	78.14 129	79.23 188	83.23 124

v1921920		69.03 132	68.32 151	69.86 110	74.03 153	80.37 112	77.55 102	60.25 146	54.62 179	53.59 132	52.36 177	51.50 192	66.75 93	77.17 150	76.69 153	86.96 109	85.56 91

CostFormer		68.92 133	69.58 134	68.15 127	75.98 129	76.17 160	78.22 99	51.86 196	65.80 97	61.56 92	63.57 100	62.83 125	61.85 127	70.40 199	68.67 197	79.42 186	79.62 158

FMVSNet1		68.84 134	70.47 125	66.94 148	71.35 180	77.68 146	74.71 133	62.35 121	56.93 160	49.94 153	50.01 187	64.59 119	57.07 159	81.33 88	80.72 82	86.25 128	82.00 132

NR-MVSNet		68.79 135	70.56 123	66.71 153	77.48 119	79.54 119	73.52 154	69.20 50	61.20 136	39.76 194	58.52 124	50.11 199	51.37 187	80.26 110	80.71 86	88.97 59	83.59 121

V42		68.76 136	69.63 133	67.74 131	64.93 206	78.01 137	78.30 98	56.48 178	58.65 149	56.30 117	54.26 155	57.03 151	64.85 106	77.47 146	77.01 148	85.60 146	84.96 106

v1240		68.64 137	67.89 157	69.51 115	73.89 155	80.26 116	76.73 114	59.97 152	53.43 187	53.08 136	51.82 180	50.84 195	66.62 95	76.79 155	76.77 150	86.78 115	85.34 98

Fast-Effi-MVS+-dtu		68.34 138	69.47 135	67.01 147	75.15 140	77.97 143	77.12 108	55.40 181	57.87 150	46.68 172	56.17 140	60.39 132	62.36 118	76.32 160	76.25 158	85.35 152	81.34 139

GA-MVS		68.14 139	69.17 140	66.93 149	73.77 157	78.50 135	74.45 135	58.28 167	55.11 174	48.44 161	60.08 114	53.99 169	61.50 132	78.43 135	77.57 137	85.13 154	80.54 147

tfpn200view9		68.11 140	68.72 146	67.40 138	77.83 114	78.93 125	74.28 140	62.81 106	56.64 162	46.82 170	52.65 174	53.47 176	56.59 163	80.41 103	78.43 124	86.11 131	80.52 148

EPNet_dtu		68.08 141	71.00 120	64.67 163	79.64 100	68.62 195	75.05 127	63.30 95	66.36 93	45.27 181	67.40 83	66.84 114	43.64 200	75.37 163	74.98 169	81.15 180	77.44 170
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
thres200		67.98 142	68.55 149	67.30 141	77.89 113	78.86 127	74.18 144	62.75 108	56.35 165	46.48 173	52.98 170	53.54 172	56.46 164	80.41 103	77.97 131	86.05 135	79.78 156

thres400		67.95 143	68.62 148	67.17 143	77.90 111	78.59 132	74.27 141	62.72 110	56.34 166	45.77 179	53.00 169	53.35 179	56.46 164	80.21 112	78.43 124	85.91 142	80.43 149

pmmvs4		67.89 144	67.39 162	68.48 125	71.60 177	73.57 176	74.45 135	60.98 136	64.65 106	57.97 108	54.95 149	51.73 191	61.88 126	73.78 173	75.11 167	83.99 169	77.91 167

v148		67.85 145	67.53 158	68.23 126	73.25 161	77.57 149	74.26 142	57.36 175	55.70 170	57.45 111	53.53 160	55.42 158	61.96 125	75.23 164	73.92 173	85.08 155	81.32 140

Vis-MVSNet (Re-imp)		67.83 146	73.52 101	61.19 179	78.37 109	76.72 155	66.80 185	62.96 103	65.50 101	34.17 206	67.19 86	69.68 95	39.20 209	79.39 124	79.44 112	85.68 144	76.73 176

PatchMatch-RL		67.78 147	66.65 167	69.10 119	73.01 162	72.69 179	68.49 175	61.85 127	62.93 122	60.20 98	56.83 138	50.42 197	69.52 74	75.62 162	74.46 172	81.51 177	73.62 194

thres600view7		67.68 148	68.43 150	66.80 150	77.90 111	78.86 127	73.84 148	62.75 108	56.07 168	44.70 185	52.85 172	52.81 183	55.58 172	80.41 103	77.77 133	86.05 135	80.28 151

COLMAP_ROB	62.73 15	67.66 149	66.76 166	68.70 123	80.49 90	77.98 141	75.29 121	62.95 104	63.62 117	49.96 152	47.32 197	50.72 196	58.57 146	76.87 154	75.50 166	84.94 159	75.33 186
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
CDS-MVSNet		67.65 150	69.83 131	65.09 159	75.39 139	76.55 156	74.42 138	63.75 90	53.55 185	49.37 157	59.41 120	62.45 126	44.44 198	79.71 117	79.82 103	83.17 173	77.36 171
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
RPSCF		67.64 151	71.25 119	63.43 172	61.86 212	70.73 186	67.26 180	50.86 201	74.20 61	58.91 100	67.49 82	69.33 96	64.10 110	71.41 186	68.45 201	77.61 192	77.17 172

thres100view900		67.60 152	68.02 153	67.12 145	77.83 114	77.75 145	73.90 147	62.52 118	56.64 162	46.82 170	52.65 174	53.47 176	55.92 168	78.77 131	77.62 136	85.72 143	79.23 160

Baseline_NR-MVSNet		67.53 153	68.77 145	66.09 156	75.99 127	74.75 172	72.43 161	68.41 54	61.33 135	38.33 199	51.31 182	54.13 168	56.03 167	79.22 125	78.19 128	85.37 151	82.45 127

thisisatest0515		67.40 154	68.78 144	65.80 157	70.02 188	75.24 168	69.36 172	57.37 174	54.94 178	53.67 131	55.53 145	54.85 162	58.00 151	78.19 137	78.91 119	86.39 127	83.78 119

USDC		67.36 155	67.90 156	66.74 152	71.72 173	75.23 169	71.58 163	60.28 145	67.45 88	50.54 151	60.93 108	45.20 214	62.08 121	76.56 158	74.50 171	84.25 165	75.38 185

EG-PatchMatch MVS		67.24 156	66.94 164	67.60 135	78.73 106	81.35 98	73.28 157	59.49 155	46.89 209	51.42 146	43.65 203	53.49 174	55.50 174	81.38 87	80.66 88	87.15 101	81.17 141

dmvs_re		67.22 157	67.92 155	66.40 154	75.94 130	70.55 188	74.97 131	63.87 89	57.07 159	44.75 183	54.29 152	56.72 153	54.65 177	79.53 121	77.51 139	84.20 166	79.78 156

UniMVSNet_ETH3D		67.18 158	67.03 163	67.36 139	74.44 149	78.12 136	74.07 145	66.38 67	52.22 192	46.87 169	48.64 192	51.84 190	56.96 160	77.29 148	78.53 122	85.42 150	82.59 126

v7n		67.05 159	66.94 164	67.17 143	72.35 168	78.97 124	73.26 158	58.88 164	51.16 198	50.90 148	48.21 194	50.11 199	60.96 135	77.70 143	77.38 142	86.68 120	85.05 104

IterMVS-SCA-FT		66.89 160	69.22 139	64.17 165	71.30 181	75.64 163	71.33 164	53.17 188	57.63 156	49.08 159	60.72 110	60.05 136	63.09 114	74.99 166	73.92 173	77.07 196	81.57 138

IterMVS		66.36 161	68.30 152	64.10 166	69.48 193	74.61 173	73.41 156	50.79 202	57.30 158	48.28 163	60.64 111	59.92 137	60.85 139	74.14 171	72.66 181	81.80 176	78.82 163
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TDRefinement		66.09 162	65.03 179	67.31 140	69.73 190	76.75 154	75.33 119	64.55 83	60.28 142	49.72 156	45.63 200	42.83 217	60.46 140	75.75 161	75.95 159	84.08 167	78.04 166

pm-mvs1		65.62 163	67.42 160	63.53 171	73.66 159	76.39 157	69.66 169	60.87 138	49.73 202	43.97 186	51.24 183	57.00 152	48.16 192	79.89 115	77.84 132	84.85 163	79.82 155

tpm cat1		65.41 164	63.81 187	67.28 142	75.61 137	72.88 178	75.32 120	52.85 190	62.97 121	63.66 87	53.24 165	53.29 181	61.83 128	65.54 211	64.14 213	74.43 208	74.60 188

SCA		65.40 165	66.58 168	64.02 167	70.65 184	73.37 177	67.35 179	53.46 186	63.66 116	54.14 125	60.84 109	60.20 135	61.50 132	69.96 200	68.14 202	77.01 197	69.91 200

anonymousdsp		65.28 166	67.98 154	62.13 175	58.73 219	73.98 175	67.10 182	50.69 203	48.41 205	47.66 168	54.27 153	52.75 185	61.45 134	76.71 157	80.20 95	87.13 105	89.53 55

PMMVS		65.06 167	69.17 140	60.26 184	55.25 225	63.43 212	66.71 186	43.01 221	62.41 125	50.64 149	69.44 69	67.04 112	63.29 113	74.36 170	73.54 176	82.68 174	73.99 193

CR-MVSNet		64.83 168	65.54 173	64.01 168	70.64 185	69.41 190	65.97 190	52.74 191	57.81 152	52.65 138	54.27 153	56.31 155	60.92 136	72.20 182	73.09 178	81.12 181	75.69 182

TransMVSNet (Re)		64.74 169	65.66 172	63.66 170	77.40 120	75.33 167	69.86 168	62.67 116	47.63 207	41.21 193	50.01 187	52.33 186	45.31 197	79.57 119	77.69 135	85.49 147	77.07 174

test-LLR		64.42 170	64.36 183	64.49 164	75.02 142	63.93 209	66.61 187	61.96 125	54.41 180	47.77 165	57.46 134	60.25 133	55.20 175	70.80 193	69.33 192	80.40 184	74.38 190

MDTV_nov1_ep13		64.37 171	65.24 175	63.37 173	68.94 195	70.81 185	72.40 162	50.29 205	60.10 143	53.91 129	60.07 115	59.15 140	57.21 158	69.43 204	67.30 204	77.47 193	69.78 202

tfpnnormal		64.27 172	63.64 188	65.02 160	75.84 134	75.61 164	71.24 166	62.52 118	47.79 206	42.97 189	42.65 205	44.49 215	52.66 185	78.77 131	76.86 149	84.88 160	79.29 159

PatchmatchNet		64.21 173	64.65 181	63.69 169	71.29 182	68.66 194	69.63 170	51.70 198	63.04 120	53.77 130	59.83 118	58.34 145	60.23 141	68.54 207	66.06 209	75.56 203	68.08 207
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
dps		64.00 174	62.99 190	65.18 158	73.29 160	72.07 181	68.98 174	53.07 189	57.74 154	58.41 105	55.55 144	47.74 207	60.89 138	69.53 203	67.14 206	76.44 200	71.19 198

pmmvs-eth3d		63.52 175	62.44 197	64.77 162	66.82 201	70.12 189	69.41 171	59.48 156	54.34 183	52.71 137	46.24 199	44.35 216	56.93 161	72.37 177	73.77 175	83.30 171	75.91 179

WR-MVS		63.03 176	67.40 161	57.92 194	75.14 141	77.60 148	60.56 208	66.10 70	54.11 184	23.88 217	53.94 158	53.58 171	34.50 214	73.93 172	77.71 134	87.35 99	80.94 142

PEN-MVS		62.96 177	65.77 171	59.70 187	73.98 154	75.45 165	63.39 201	67.61 61	52.49 190	25.49 216	53.39 161	49.12 203	40.85 206	71.94 184	77.26 145	86.86 112	80.72 145

TinyColmap		62.84 178	61.03 203	64.96 161	69.61 191	71.69 182	68.48 176	59.76 154	55.41 171	47.69 167	47.33 196	34.20 227	62.76 117	74.52 168	72.59 182	81.44 178	71.47 197

CP-MVSNet		62.68 179	65.49 174	59.40 190	71.84 171	75.34 166	62.87 203	67.04 65	52.64 189	27.19 214	53.38 162	48.15 205	41.40 204	71.26 187	75.68 161	86.07 133	82.00 132

gg-mvs-nofinetune		62.55 180	65.05 178	59.62 188	78.72 107	77.61 147	70.83 167	53.63 183	39.71 222	22.04 223	36.36 216	64.32 120	47.53 193	81.16 93	79.03 116	85.00 158	77.17 172

CVMVSNet		62.55 180	65.89 169	58.64 192	66.95 199	69.15 192	66.49 189	56.29 180	52.46 191	32.70 207	59.27 121	58.21 146	50.09 189	71.77 185	71.39 186	79.31 187	78.99 162

CMPMVS	47.78 17	62.49 182	62.52 195	62.46 174	70.01 189	70.66 187	62.97 202	51.84 197	51.98 194	56.71 115	42.87 204	53.62 170	57.80 153	72.23 180	70.37 189	75.45 205	75.91 179
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs6		62.41 183	62.88 191	61.87 176	71.38 179	75.18 171	67.76 178	59.45 157	41.64 217	42.52 191	37.33 214	52.91 182	46.87 194	77.67 144	76.26 157	83.23 172	79.18 161

tpm		62.41 183	63.15 189	61.55 178	72.24 169	63.79 211	71.31 165	46.12 219	57.82 151	55.33 120	59.90 117	54.74 163	53.63 181	67.24 210	64.29 212	70.65 218	74.25 192

PS-CasMVS		62.38 185	65.06 177	59.25 191	71.73 172	75.21 170	62.77 204	66.99 66	51.94 196	26.96 215	52.00 179	47.52 208	41.06 205	71.16 190	75.60 162	85.97 140	81.97 134

pmmvs5		62.37 186	64.04 185	60.42 182	65.03 204	71.67 183	67.17 181	52.70 193	50.30 199	44.80 182	54.23 156	51.19 194	49.37 190	72.88 176	73.48 177	83.45 170	74.55 189

tpmrst		62.00 187	62.35 198	61.58 177	71.62 176	64.14 208	69.07 173	48.22 215	62.21 127	53.93 128	58.26 131	55.30 160	55.81 170	63.22 216	62.62 215	70.85 217	70.70 199

PatchT		61.97 188	64.04 185	59.55 189	60.49 214	67.40 198	56.54 215	48.65 211	56.69 161	52.65 138	51.10 184	52.14 189	60.92 136	72.20 182	73.09 178	78.03 191	75.69 182

DTE-MVSNet		61.85 189	64.96 180	58.22 193	74.32 150	74.39 174	61.01 207	67.85 59	51.76 197	21.91 224	53.28 163	48.17 204	37.74 211	72.22 181	76.44 155	86.52 125	78.49 164

SixPastTwentyTwo		61.84 190	62.45 196	61.12 180	69.20 194	72.20 180	62.03 205	57.40 173	46.54 210	38.03 201	57.14 137	41.72 219	58.12 150	69.67 202	71.58 185	81.94 175	78.30 165

WR-MVS_H		61.83 191	65.87 170	57.12 197	71.72 173	76.87 152	61.45 206	66.19 68	51.97 195	22.92 221	53.13 168	52.30 188	33.80 215	71.03 191	75.00 168	86.65 121	80.78 144

LTVRE_ROB	59.44 16	61.82 192	62.64 194	60.87 181	72.83 167	77.19 150	64.37 197	58.97 161	33.56 227	28.00 213	52.59 176	42.21 218	63.93 111	74.52 168	76.28 156	77.15 195	82.13 128
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		61.71 193	62.88 191	60.34 183	69.51 192	69.41 190	63.48 200	49.23 207	57.81 152	45.64 180	50.51 185	50.12 198	53.13 184	68.17 209	68.49 200	81.07 182	75.62 184

TESTMET0.1,1		61.10 194	64.36 183	57.29 196	57.53 220	63.93 209	66.61 187	36.22 225	54.41 180	47.77 165	57.46 134	60.25 133	55.20 175	70.80 193	69.33 192	80.40 184	74.38 190

test-mter		60.84 195	64.62 182	56.42 199	55.99 223	64.18 207	65.39 192	34.23 226	54.39 182	46.21 176	57.40 136	59.49 139	55.86 169	71.02 192	69.65 191	80.87 183	76.20 178

PM-MVS		60.48 196	60.94 204	59.94 185	58.85 217	66.83 201	64.27 198	51.39 199	55.03 176	48.03 164	50.00 189	40.79 221	58.26 149	69.20 205	67.13 207	78.84 189	77.60 169

MDTV_nov1_ep13_2view		60.16 197	60.51 205	59.75 186	65.39 203	69.05 193	68.00 177	48.29 213	51.99 193	45.95 178	48.01 195	49.64 202	53.39 182	68.83 206	66.52 208	77.47 193	69.55 203

EPMVS		60.00 198	61.97 199	57.71 195	68.46 196	63.17 215	64.54 196	48.23 214	63.30 118	44.72 184	60.19 113	56.05 157	50.85 188	65.27 214	62.02 216	69.44 220	63.81 214

TAMVS		59.58 199	62.81 193	55.81 201	66.03 202	65.64 206	63.86 199	48.74 210	49.95 201	37.07 203	54.77 150	58.54 143	44.44 198	72.29 179	71.79 183	74.70 207	66.66 209

test0.0.03 1		58.80 200	61.58 201	55.56 202	75.02 142	68.45 196	59.58 212	61.96 125	52.74 188	29.57 210	49.75 190	54.56 164	31.46 217	71.19 188	69.77 190	75.75 201	64.57 212

CHOSEN 280x420		58.70 201	61.88 200	54.98 204	55.45 224	50.55 228	64.92 194	40.36 222	55.21 172	38.13 200	48.31 193	63.76 122	63.03 116	73.73 174	68.58 199	68.00 223	73.04 195

MIMVSNet		58.52 202	61.34 202	55.22 203	60.76 213	67.01 200	66.81 184	49.02 209	56.43 164	38.90 197	40.59 211	54.54 165	40.57 207	73.16 175	71.65 184	75.30 206	66.00 210

FMVSNet5		57.24 203	60.02 206	53.99 207	56.45 222	62.74 216	65.27 193	47.03 216	55.14 173	39.55 196	40.88 209	53.42 178	41.83 201	72.35 178	71.10 188	73.79 210	64.50 213

gm-plane-assit		57.00 204	57.62 211	56.28 200	76.10 126	62.43 218	47.62 226	46.57 217	33.84 226	23.24 219	37.52 213	40.19 222	59.61 142	79.81 116	77.55 138	84.55 164	72.03 196

FC-MVSNet-test		56.90 205	65.20 176	47.21 216	66.98 198	63.20 214	49.11 225	58.60 166	59.38 146	11.50 232	65.60 90	56.68 154	24.66 224	71.17 189	71.36 187	72.38 214	69.02 205

Anonymous20231206		56.36 206	57.80 210	54.67 205	70.08 187	66.39 202	60.46 209	57.54 172	49.50 204	29.30 211	33.86 219	46.64 209	35.18 213	70.44 197	68.88 196	75.47 204	68.88 206

ADS-MVSNet		55.94 207	58.01 208	53.54 209	62.48 211	58.48 221	59.12 213	46.20 218	59.65 145	42.88 190	52.34 178	53.31 180	46.31 195	62.00 218	60.02 219	64.23 225	60.24 221

pmnet_mix02		55.30 208	57.01 212	53.30 210	64.14 207	59.09 220	58.39 214	50.24 206	53.47 186	38.68 198	49.75 190	45.86 212	40.14 208	65.38 213	60.22 218	68.19 222	65.33 211

EU-MVSNet		54.63 209	58.69 207	49.90 213	56.99 221	62.70 217	56.41 216	50.64 204	45.95 212	23.14 220	50.42 186	46.51 210	36.63 212	65.51 212	64.85 211	75.57 202	74.91 187

MVS-HIRNet		54.41 210	52.10 218	57.11 198	58.99 216	56.10 224	49.68 224	49.10 208	46.18 211	52.15 142	33.18 220	46.11 211	56.10 166	63.19 217	59.70 220	76.64 199	60.25 220

testgi		54.39 211	57.86 209	50.35 212	71.59 178	67.24 199	54.95 217	53.25 187	43.36 214	23.78 218	44.64 201	47.87 206	24.96 222	70.45 196	68.66 198	73.60 211	62.78 217

test20.03		53.93 212	56.28 213	51.19 211	72.19 170	65.83 203	53.20 220	61.08 132	42.74 215	22.08 222	37.07 215	45.76 213	24.29 225	70.44 197	69.04 194	74.31 209	63.05 216

MDA-MVSNet-bldmvs		53.37 213	53.01 217	53.79 208	43.67 230	67.95 197	59.69 211	57.92 171	43.69 213	32.41 208	41.47 207	27.89 233	52.38 186	56.97 225	65.99 210	76.68 198	67.13 208

FE-MVSNET		52.98 214	55.99 214	49.47 214	49.71 226	65.83 203	54.09 218	56.91 177	40.70 219	16.86 230	32.90 221	40.15 223	37.83 210	69.80 201	73.04 180	81.41 179	69.49 204

FPMVS		51.87 215	50.00 220	54.07 206	66.83 200	57.25 222	60.25 210	50.91 200	50.25 200	34.36 205	36.04 217	32.02 229	41.49 203	58.98 222	56.07 222	70.56 219	59.36 222

MIMVSNet1		49.27 216	53.25 216	44.62 218	44.61 228	61.52 219	53.61 219	52.18 194	41.62 218	18.68 227	28.14 226	41.58 220	25.50 220	68.46 208	69.04 194	73.15 212	62.37 218

pmmvs3		47.65 217	49.08 222	45.99 217	44.61 228	54.79 225	50.04 222	31.95 229	33.91 225	29.90 209	30.37 222	33.53 228	46.31 195	63.50 215	63.67 214	73.14 213	63.77 215

N_pmnet		47.35 218	50.13 219	44.11 219	59.98 215	51.64 227	51.86 221	44.80 220	49.58 203	20.76 225	40.65 210	40.05 224	29.64 218	59.84 220	55.15 223	57.63 226	54.00 224

new-patchmatchnet		46.97 219	49.47 221	44.05 220	62.82 209	56.55 223	45.35 227	52.01 195	42.47 216	17.04 229	35.73 218	35.21 226	21.84 228	61.27 219	54.83 224	65.26 224	60.26 219

GG-mvs-BLEND		46.86 220	67.51 159	22.75 226	0.05 238	76.21 159	64.69 195	0.04 234	61.90 129	0.09 239	55.57 143	71.32 82	0.08 234	70.54 195	67.19 205	71.58 215	69.86 201

PMVS	39.38 18	46.06 221	43.30 224	49.28 215	62.93 208	38.75 230	41.88 228	53.50 185	33.33 228	35.46 204	28.90 225	31.01 230	33.04 216	58.61 224	54.63 225	68.86 221	57.88 223
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
WB-MVS		40.01 222	45.06 223	34.13 222	58.84 218	53.28 226	28.60 231	58.10 170	32.93 229	4.65 237	40.92 208	28.33 232	7.26 231	58.86 223	56.09 221	47.36 229	44.98 226

new_pmnet		38.40 223	42.64 225	33.44 223	37.54 233	45.00 229	36.60 229	32.72 228	40.27 220	12.72 231	29.89 223	28.90 231	24.78 223	53.17 226	52.90 226	56.31 227	48.34 225

Gipuma		36.38 224	35.80 226	37.07 221	45.76 227	33.90 231	29.81 230	48.47 212	39.91 221	18.02 228	8.00 234	8.14 238	25.14 221	59.29 221	61.02 217	55.19 228	40.31 227
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMMVS2		25.60 225	29.75 227	20.76 227	28.00 234	30.93 232	23.10 233	29.18 230	23.14 231	1.46 238	18.23 230	16.54 235	5.08 232	40.22 227	41.40 228	37.76 230	37.79 229

test_method		22.26 226	25.94 228	17.95 228	3.24 237	7.17 237	23.83 232	7.27 232	37.35 224	20.44 226	21.87 229	39.16 225	18.67 229	34.56 228	20.84 232	34.28 231	20.64 233

E-PMN		21.77 227	18.24 230	25.89 224	40.22 231	19.58 234	12.46 236	39.87 223	18.68 233	6.71 234	9.57 231	4.31 241	22.36 227	19.89 232	27.28 230	33.73 232	28.34 231

EMVS		20.98 228	17.15 231	25.44 225	39.51 232	19.37 235	12.66 235	39.59 224	19.10 232	6.62 235	9.27 232	4.40 240	22.43 226	17.99 233	24.40 231	31.81 233	25.53 232

MVE	19.12 19	20.47 229	23.27 229	17.20 229	12.66 236	25.41 233	10.52 237	34.14 227	14.79 234	6.53 236	8.79 233	4.68 239	16.64 230	29.49 230	41.63 227	22.73 235	38.11 228
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		0.09 230	0.15 232	0.02 231	0.01 239	0.02 239	0.05 240	0.01 235	0.11 235	0.01 240	0.26 236	0.01 242	0.06 236	0.10 234	0.10 233	0.01 237	0.43 235

test123		0.09 230	0.14 233	0.02 231	0.00 240	0.02 239	0.02 241	0.01 235	0.09 236	0.00 241	0.30 235	0.00 243	0.08 234	0.03 235	0.09 234	0.01 237	0.45 234

uanet_test		0.00 232	0.00 234	0.00 233	0.00 240	0.00 241	0.00 242	0.00 237	0.00 237	0.00 241	0.00 237	0.00 243	0.00 237	0.00 236	0.00 235	0.00 239	0.00 236

sosnet-low-res		0.00 232	0.00 234	0.00 233	0.00 240	0.00 241	0.00 242	0.00 237	0.00 237	0.00 241	0.00 237	0.00 243	0.00 237	0.00 236	0.00 235	0.00 239	0.00 236

sosnet		0.00 232	0.00 234	0.00 233	0.00 240	0.00 241	0.00 242	0.00 237	0.00 237	0.00 241	0.00 237	0.00 243	0.00 237	0.00 236	0.00 235	0.00 239	0.00 236

TPM-MVS					90.07 21	88.36 35	88.45 29			77.10 26	75.60 38	83.98 30	71.33 63			89.75 43	89.62 53
Ray Leroy Khuboni and Hongjun Xu: Textureless Resilient Propagation Matching in Multiple View Stereosis (TPM-MVS). SATNAC 2025
RE-MVS-def										46.24 175

9.14												86.88 16

SR-MVS					88.99 34			73.57 24				87.54 14

Anonymous202405211			72.16 115		80.85 85	81.85 93	76.88 113	65.40 76	62.89 123		46.35 198	67.99 109	62.05 122	81.15 94	80.38 93	85.97 140	84.50 112

our_test_3					67.93 197	70.99 184	66.89 183

ambc			53.42 215		64.99 205	63.36 213	49.96 223		47.07 208	37.12 202	28.97 224	16.36 236	41.82 202	75.10 165	67.34 203	71.55 216	75.72 181

MTAPA										83.48 1		86.45 19

MTMP										82.66 5		84.91 27

Patchmatch-RL test							2.85 239

tmp_tt				14.50 230	14.68 235	7.17 237	10.46 238	2.21 233	37.73 223	28.71 212	25.26 227	16.98 234	4.37 233	31.49 229	29.77 229	26.56 234

XVS					86.63 46	88.68 27	85.00 47			71.81 46		81.92 37				90.47 23

X-MVStestdata					86.63 46	88.68 27	85.00 47			71.81 46		81.92 37				90.47 23

mPP-MVS					89.90 25							81.29 42

NP-MVS									80.10 47

Patchmtry						65.80 205	65.97 190	52.74 191		52.65 138

DeepMVS_CX						18.74 236	18.55 234	8.02 231	26.96 230	7.33 233	23.81 228	13.05 237	25.99 219	25.17 231		22.45 236	36.25 230

High-resolution multi-view benchmark