High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	courty.	delive.	electro	facade	kicker	meadow	office	pipes	playgr.	relief	relief.	terrace	terrai.

SED-MVS		79.21 1	84.74 2	72.75 2	78.66 3	81.96 2	82.94 6	58.16 6	86.82 3	67.66 1	88.29 4	86.15 3	66.42 3	80.41 5	78.65 6	82.65 20	90.92 2

RA-MVS		79.12 2	84.22 4	73.18 1	78.82 2	80.46 8	83.11 3	59.68 1	86.97 2	66.95 2	87.84 6	85.06 7	67.77 1	80.42 4	78.53 9	82.75 17	90.23 5

DVP-MVS		78.77 3	84.89 1	71.62 6	78.04 5	82.05 1	81.64 14	57.96 9	87.53 1	66.64 3	88.77 1	86.31 1	63.16 13	79.99 8	78.56 7	82.31 27	91.03 1
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
DVP-MVS++		78.76 4	84.44 3	72.14 4	76.63 10	81.93 3	82.92 7	58.10 7	85.86 6	66.53 4	87.86 5	86.16 2	66.45 2	80.46 3	78.53 9	82.19 32	90.29 4

MED-MVS		78.72 5	83.98 5	72.58 3	78.62 4	81.11 5	84.28 1	59.29 2	86.43 4	64.24 10	87.31 7	86.02 4	65.39 5	79.79 9	78.18 13	83.65 5	89.06 10

DPE-MVS		78.11 6	83.84 6	71.42 7	77.82 7	81.32 4	82.92 7	57.81 11	84.04 11	63.19 14	88.63 2	86.00 5	64.52 8	78.71 13	77.63 17	82.26 28	90.57 3
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MSP-MVS		77.82 7	83.46 7	71.24 11	75.26 21	80.22 9	82.95 5	57.85 10	85.90 5	64.79 6	88.54 3	83.43 10	66.24 4	78.21 19	78.56 7	80.34 51	89.39 8
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
aaEdge-Enhanced		77.69 8	83.11 8	71.36 8	77.52 8	80.15 11	82.75 9	57.21 15	84.71 10	62.22 22	87.31 7	85.76 6	65.28 6	78.00 20	76.77 25	83.21 9	89.06 10

APDe-MVS		77.58 9	82.93 9	71.35 9	77.86 6	80.55 7	83.38 2	57.61 12	85.57 7	61.11 26	86.10 10	82.98 11	64.76 7	78.29 17	76.78 24	83.40 7	90.20 6
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
SMA-MVS		77.32 10	82.51 10	71.26 10	75.43 19	80.19 10	82.22 11	58.26 5	84.83 9	64.36 8	78.19 18	83.46 9	63.61 11	81.00 1	80.28 1	83.66 4	89.62 7
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		77.13 11	81.70 11	71.79 5	79.32 1	80.76 6	82.96 4	57.49 13	82.82 12	64.79 6	83.69 13	84.46 8	62.83 16	77.13 29	75.21 35	83.35 8	87.85 19

ACMMP_NAP		76.15 12	81.17 12	70.30 14	74.09 25	79.47 13	81.59 16	57.09 18	81.38 14	63.89 12	79.02 16	80.48 22	62.24 20	80.05 7	79.12 4	82.94 14	88.64 12

HPM-MVS++		76.01 13	80.47 15	70.81 12	76.60 11	74.96 40	80.18 21	58.36 4	81.96 13	63.50 13	78.80 17	82.53 14	64.40 9	78.74 12	78.84 5	81.81 38	87.46 21

APD-MVS		75.80 14	80.90 14	69.86 18	75.42 20	78.48 19	81.43 17	57.44 14	80.45 17	59.32 33	85.28 11	80.82 21	63.96 10	76.89 31	76.08 31	81.58 43	88.30 15
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CNVR-MVS		75.62 15	79.91 17	70.61 13	75.76 14	78.82 17	81.66 13	57.12 17	79.77 19	63.04 15	70.69 28	81.15 19	62.99 14	80.23 6	79.54 3	83.11 11	89.16 9

SteuartSystems-ACMMP		75.23 16	79.60 18	70.13 16	76.81 9	78.92 15	81.74 12	57.99 8	75.30 32	59.83 32	75.69 21	78.45 27	60.48 32	80.58 2	79.77 2	83.94 3	88.52 13
Skip Steuart: Steuart Systems R&D Blog.
TSAR-MVS + MP.		75.22 17	80.06 16	69.56 19	74.61 23	72.74 53	80.59 18	55.70 27	80.80 16	62.65 18	86.25 9	82.92 12	62.07 22	76.89 31	75.66 34	81.77 40	85.19 37
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
HFP-MVS		74.87 18	78.86 23	70.21 15	73.99 26	77.91 21	80.36 20	56.63 20	78.41 22	64.27 9	74.54 23	77.75 32	62.96 15	78.70 14	77.82 15	83.02 12	86.91 24

CSCG		74.68 19	79.22 19	69.40 20	75.69 16	80.01 12	79.12 29	52.83 45	79.34 20	63.99 11	70.49 29	82.02 15	60.35 35	77.48 27	77.22 21	84.38 1	87.97 18

SD-MVS		74.43 20	78.87 21	69.26 22	74.39 24	73.70 49	79.06 30	55.24 29	81.04 15	62.71 17	80.18 15	82.61 13	61.70 24	75.43 44	73.92 46	82.44 26	85.22 36
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
MP-MVS		74.31 21	78.87 21	68.99 24	73.49 28	78.56 18	79.25 28	56.51 21	75.33 30	60.69 29	75.30 22	79.12 26	61.81 23	77.78 24	77.93 14	82.18 34	88.06 17
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
NCCC		74.27 22	77.83 27	70.13 16	75.70 15	77.41 26	80.51 19	57.09 18	78.25 23	62.28 21	65.54 41	78.26 28	62.18 21	79.13 10	78.51 11	83.01 13	87.68 20

DeepPCF-MVS	66.49 1	74.25 23	80.97 13	66.41 35	67.75 55	78.87 16	75.61 45	54.16 37	84.86 8	58.22 39	77.94 19	81.01 20	62.52 18	78.34 15	77.38 18	80.16 55	88.40 14

train_agg		73.89 24	78.25 25	68.80 26	75.25 22	72.27 56	79.75 23	56.05 24	74.87 35	58.97 34	81.83 14	79.76 24	61.05 28	77.39 28	76.01 32	81.71 41	85.61 34

DeepC-MVS	66.32 2	73.85 25	78.10 26	68.90 25	67.92 54	79.31 14	78.16 34	59.28 3	78.24 24	61.13 25	67.36 38	76.10 37	63.40 12	79.11 11	78.41 12	83.52 6	88.16 16
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ACMMPR		73.79 26	78.41 24	68.40 27	72.35 32	77.79 23	79.32 25	56.38 22	77.67 26	58.30 38	74.16 24	76.66 34	61.40 25	78.32 16	77.80 16	82.68 19	86.51 25

MCST-MVS		73.67 27	77.39 29	69.33 21	76.26 13	78.19 20	78.77 31	54.54 34	75.33 30	59.99 31	67.96 35	79.23 25	62.43 19	78.00 20	75.71 33	84.02 2	87.30 22

PGM-MVS		72.89 28	77.13 30	67.94 28	72.47 31	77.25 27	79.27 27	54.63 33	73.71 39	57.95 40	72.38 26	75.33 39	60.75 30	78.25 18	77.36 20	82.57 24	85.62 33

DPM-MVS		72.80 29	75.90 33	69.19 23	75.51 17	77.68 24	81.62 15	54.83 30	75.96 28	62.06 23	63.96 54	76.58 35	58.55 44	76.66 36	76.77 25	82.60 23	83.68 44

CP-MVS		72.63 30	76.95 31	67.59 29	70.67 41	75.53 38	77.95 36	56.01 25	75.65 29	58.82 35	69.16 33	76.48 36	60.46 33	77.66 25	77.20 22	81.65 42	86.97 23

TSAR-MVS + ACMM		72.56 31	79.07 20	64.96 44	73.24 29	73.16 52	78.50 32	48.80 71	79.34 20	55.32 47	85.04 12	81.49 18	58.57 43	75.06 47	73.75 47	75.35 130	85.61 34

MGCNet		72.45 32	77.44 28	66.61 33	71.08 39	77.81 22	76.74 39	49.30 65	73.12 42	61.17 24	73.70 25	78.08 29	58.78 41	76.75 35	76.52 28	82.61 22	86.14 28

DeepC-MVS_fast	65.08 3	72.00 33	76.11 32	67.21 31	68.93 50	77.46 25	76.54 41	54.35 35	74.92 34	58.64 37	65.18 43	74.04 47	62.62 17	77.92 22	77.02 23	82.16 35	86.21 26
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ACMMP		71.57 34	75.84 34	66.59 34	70.30 45	76.85 32	78.46 33	53.95 38	73.52 41	55.56 45	70.13 30	71.36 54	58.55 44	77.00 30	76.23 30	82.71 18	85.81 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
CDPH-MVS		71.47 35	75.82 35	66.41 35	72.97 30	77.15 28	78.14 35	54.71 31	69.88 53	53.07 71	70.98 27	74.83 41	56.95 58	76.22 37	76.57 27	82.62 21	85.09 38

X-MVS		71.18 36	75.66 36	65.96 39	71.71 34	76.96 29	77.26 38	55.88 26	72.75 44	54.48 64	64.39 48	74.47 42	54.19 87	77.84 23	77.37 19	82.21 31	85.85 31

HQP-MVS		70.88 37	75.02 37	66.05 38	71.69 35	74.47 45	77.51 37	53.17 42	72.89 43	54.88 53	70.03 31	70.48 57	57.26 52	76.02 39	75.01 38	81.78 39	86.21 26

TSAR-MVS + GP.		69.71 38	73.92 39	64.80 46	68.27 52	70.56 64	71.90 55	50.75 55	71.38 48	57.46 42	68.68 34	75.42 38	60.10 36	73.47 55	73.99 45	80.32 52	83.97 42

3Dnovator+	62.63 4	69.51 39	72.62 42	65.88 40	68.21 53	76.47 34	73.50 54	52.74 46	70.85 49	58.65 36	55.97 109	69.95 58	61.11 27	76.80 33	75.09 36	81.09 46	83.23 48

OPM-MVS		69.33 40	71.05 50	67.32 30	72.34 33	75.70 37	79.57 24	56.34 23	55.21 103	53.81 68	59.51 93	68.96 65	59.67 37	77.61 26	76.44 29	82.19 32	83.88 43
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
PHI-MVS		69.27 41	74.84 38	62.76 54	66.83 58	74.83 41	73.88 52	49.32 64	70.61 50	50.93 84	69.62 32	74.84 40	57.25 53	75.53 43	74.32 43	78.35 78	84.17 41

LGP-MVS_train		68.87 42	72.03 45	65.18 43	69.33 48	74.03 48	76.67 40	53.88 39	68.46 54	52.05 78	63.21 58	63.89 100	56.31 63	75.99 40	74.43 42	82.83 16	84.18 40

CANet		68.77 43	73.01 40	63.83 48	68.30 51	75.19 39	73.73 53	47.90 73	63.86 61	54.84 57	67.51 37	74.36 45	57.62 48	74.22 52	73.57 50	80.56 49	82.36 50

CPTT-MVS		68.76 44	73.01 40	63.81 49	65.42 67	73.66 50	76.39 43	52.08 47	72.61 45	50.33 86	60.73 85	72.65 50	59.43 38	73.32 56	72.12 53	79.19 67	85.99 29

ACMP	61.42 5	68.72 45	71.37 47	65.64 41	69.06 49	74.45 46	75.88 44	53.30 41	68.10 55	55.74 44	61.53 79	62.29 108	56.97 57	74.70 50	74.23 44	82.88 15	84.31 39
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MSLP-MVS++		68.17 46	70.72 53	65.19 42	69.41 47	70.64 63	74.99 47	45.76 85	70.20 52	60.17 30	56.42 107	73.01 48	61.14 26	72.80 59	70.54 64	79.70 58	81.42 56

MAR-MVS		68.04 47	70.74 52	64.90 45	71.68 36	76.33 35	74.63 49	50.48 59	63.81 62	55.52 46	54.88 116	69.90 59	57.39 51	75.42 45	74.79 40	79.71 57	80.03 62
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
AdaColmap		67.89 48	68.85 65	66.77 32	73.73 27	74.30 47	75.28 46	53.58 40	70.24 51	57.59 41	51.19 136	59.19 125	60.74 31	75.33 46	73.72 48	79.69 60	77.96 83

MVSMamba_PlusPlus		67.64 49	71.37 47	63.30 50	66.37 63	72.40 55	70.80 62	48.42 72	62.82 65	54.87 55	63.02 61	70.51 56	59.13 40	75.59 42	73.57 50	80.21 53	81.67 54

MVS_111021_HR		67.62 50	70.39 54	64.39 47	69.77 46	70.45 66	71.44 60	51.72 51	60.77 72	55.06 50	62.14 73	66.40 90	58.13 47	76.13 38	74.79 40	80.19 54	82.04 53

ACMM	60.30 7	67.58 51	68.82 66	66.13 37	70.59 42	72.01 58	76.54 41	54.26 36	65.64 59	54.78 58	50.35 139	61.72 114	58.74 42	75.79 41	75.03 37	81.88 36	81.17 57
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PCF-MVS	59.98 8	67.32 52	71.04 51	62.97 53	64.77 70	74.49 44	74.78 48	49.54 61	67.44 56	54.39 67	58.35 101	72.81 49	55.79 70	71.54 68	69.24 75	78.57 71	83.41 46
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
CLD-MVS		67.02 53	71.57 46	61.71 56	71.01 40	74.81 42	71.62 58	38.91 195	71.86 47	60.70 28	64.97 45	67.88 74	51.88 114	76.77 34	74.98 39	76.11 118	69.75 156
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
EC-MVSNet		67.01 54	70.27 57	63.21 51	67.21 56	70.47 65	69.01 78	46.96 78	59.16 82	53.23 70	64.01 52	69.71 62	60.37 34	74.92 48	71.24 59	82.50 25	82.41 49

3Dnovator	60.86 6	66.99 55	70.32 55	63.11 52	66.63 59	74.52 43	71.56 59	45.76 85	67.37 57	55.00 52	54.31 121	68.19 70	58.49 46	73.97 53	73.63 49	81.22 45	80.23 61

Casviewmamba		66.44 56	70.12 58	62.15 55	66.40 62	71.79 59	71.67 57	47.32 75	64.01 60	51.09 83	64.00 53	69.72 61	57.04 55	72.83 58	69.10 78	79.37 62	79.41 66

CS-MVS		65.88 57	69.71 61	61.41 58	61.76 102	68.14 81	67.65 85	44.00 115	59.14 83	52.69 72	65.19 42	68.13 71	60.90 29	74.74 49	71.58 55	81.46 44	81.04 58

DELS-MVS		65.87 58	70.30 56	60.71 70	64.05 78	72.68 54	70.90 61	45.43 89	57.49 96	49.05 92	64.43 47	68.66 66	55.11 78	74.31 51	73.02 52	79.70 58	81.51 55
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
sasdasda		65.62 59	72.06 43	58.11 88	63.94 79	71.05 61	64.49 127	43.18 145	74.08 36	47.35 98	64.17 50	71.97 51	51.17 119	71.87 64	70.74 61	78.51 74	80.56 59

canonicalmvs		65.62 59	72.06 43	58.11 88	63.94 79	71.05 61	64.49 127	43.18 145	74.08 36	47.35 98	64.17 50	71.97 51	51.17 119	71.87 64	70.74 61	78.51 74	80.56 59

viewdifsd2359ckpt09		65.38 61	68.69 68	61.53 57	62.15 96	71.64 60	71.84 56	47.45 74	58.95 84	51.79 80	61.73 78	65.71 95	57.08 54	72.17 62	70.82 60	78.87 68	79.79 63

QAPM		65.27 62	69.49 63	60.35 71	65.43 66	72.20 57	65.69 115	47.23 76	63.46 63	49.14 90	53.56 122	71.04 55	57.01 56	72.60 61	71.41 57	77.62 89	82.14 52

casdiffmvs_mvg		65.26 63	69.48 64	60.33 72	62.99 94	69.34 69	69.80 76	45.27 91	63.38 64	51.11 82	65.12 44	69.75 60	53.51 95	71.74 66	68.86 82	79.33 63	78.19 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
SPE-MVS-test		65.18 64	68.70 67	61.07 60	61.92 99	68.06 88	67.09 96	45.18 93	58.47 88	52.02 79	65.76 39	66.44 89	59.24 39	72.71 60	70.05 69	80.98 47	79.40 67

OMC-MVS		65.16 65	71.35 49	57.94 93	52.95 183	68.82 74	69.00 79	38.28 204	79.89 18	55.20 48	62.76 64	68.31 68	56.14 67	71.30 70	68.70 84	76.06 122	79.67 64

EPNet		65.14 66	69.54 62	60.00 75	66.61 60	67.67 96	67.53 87	55.32 28	62.67 68	46.22 111	67.74 36	65.93 93	48.07 142	72.17 62	72.12 53	76.28 114	78.47 75
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
hybridcas		64.37 67	68.25 69	59.84 77	63.43 83	68.95 72	70.14 73	43.11 150	62.73 67	49.21 89	62.50 70	69.22 64	54.64 83	70.95 76	66.48 130	78.51 74	76.90 102

viewcassd2359sk11		64.22 68	67.08 72	60.87 65	63.08 85	68.05 90	70.51 68	43.92 122	59.80 75	55.05 51	62.49 71	66.89 82	55.09 79	69.39 102	66.19 137	77.60 90	76.77 107

E2		64.19 69	67.06 73	60.84 67	63.07 86	68.02 91	70.44 69	43.88 123	59.94 74	55.15 49	62.73 65	66.97 81	55.01 80	69.18 105	65.98 141	77.53 94	76.63 110

E3new		64.18 70	67.01 75	60.89 63	63.07 86	68.08 86	70.57 66	43.95 119	59.33 79	54.87 55	61.94 77	66.76 85	55.16 76	69.60 98	66.42 133	77.70 86	76.92 99

E3		64.18 70	67.01 75	60.89 63	63.07 86	68.07 87	70.57 66	43.94 120	59.32 80	54.88 53	61.95 75	66.78 84	55.16 76	69.60 98	66.43 132	77.70 86	76.92 99

casdiffmvs		64.09 72	68.13 70	59.37 81	61.81 100	68.32 78	68.48 83	44.45 103	61.95 69	49.12 91	63.04 60	69.67 63	53.83 91	70.46 82	66.06 138	78.55 72	77.43 87
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
E4		64.06 73	66.79 82	60.87 65	63.03 91	68.11 83	70.61 65	44.00 115	58.24 91	54.56 61	61.00 84	66.64 86	55.22 74	69.80 94	66.69 120	77.81 82	77.07 96

E6new		64.03 74	66.63 85	60.99 61	63.04 89	68.16 79	70.80 62	44.14 106	57.66 94	54.63 59	60.32 87	66.05 91	55.49 71	70.14 90	67.09 107	77.85 80	76.94 97

E6		64.03 74	66.63 85	60.99 61	63.04 89	68.16 79	70.80 62	44.14 106	57.66 94	54.63 59	60.32 87	66.05 91	55.49 71	70.14 90	67.09 107	77.85 80	76.94 97

E5new		64.00 76	66.77 83	60.77 68	63.02 92	68.11 83	70.42 70	43.97 117	58.41 89	54.52 62	61.10 81	66.52 87	54.97 81	69.61 96	66.52 126	77.74 83	77.09 94

E5		64.00 76	66.77 83	60.77 68	63.02 92	68.11 83	70.42 70	43.97 117	58.41 89	54.52 62	61.10 81	66.52 87	54.97 81	69.61 96	66.52 126	77.74 83	77.09 94

casdiffseed414692147		63.90 78	66.17 93	61.24 59	64.92 69	69.27 70	70.00 75	46.18 82	58.66 86	51.43 81	55.30 113	62.51 105	56.20 66	70.93 77	68.62 86	78.73 69	77.90 84

viewdifsd2359ckpt13		63.83 79	67.03 74	60.10 74	62.56 95	68.92 73	69.73 77	43.49 137	57.96 92	52.16 77	61.09 83	65.39 96	55.20 75	70.36 86	67.48 103	77.48 95	78.00 82

viewmanbaseed2359cas		63.67 80	67.42 71	59.30 83	61.34 105	67.42 102	70.01 74	40.50 180	59.53 77	52.60 73	62.56 69	67.34 80	54.44 86	70.33 87	66.93 113	76.91 104	77.82 86

PVSNet_Blended_VisFu		63.65 81	66.92 78	59.83 78	60.03 117	73.44 51	66.33 102	48.95 67	52.20 126	50.81 85	56.07 108	60.25 121	53.56 93	73.23 57	70.01 70	79.30 64	83.24 47

viewmacassd2359aftdt		63.43 82	66.95 77	59.32 82	61.27 108	67.48 100	70.15 72	40.54 177	57.82 93	52.27 76	60.49 86	66.81 83	54.58 85	70.67 80	67.39 105	77.08 103	78.02 81

Effi-MVS+		63.28 83	65.96 95	60.17 73	64.26 74	68.06 88	68.78 81	45.71 87	54.08 108	46.64 105	55.92 110	63.13 104	55.94 68	70.38 85	71.43 56	79.68 61	78.70 72

ETV-MVS		63.23 84	66.08 94	59.91 76	63.13 84	68.13 82	67.62 86	44.62 100	53.39 113	46.23 110	58.74 98	58.19 128	57.45 50	73.60 54	71.38 58	80.39 50	79.13 68

MVS_111021_LR		63.05 85	66.43 89	59.10 84	61.33 106	63.77 142	65.87 112	43.58 133	60.20 73	53.70 69	62.09 74	62.38 107	55.84 69	70.24 88	68.08 90	74.30 139	78.28 78

OpenMVS	57.13 9	62.81 86	65.75 97	59.39 80	66.47 61	69.52 68	64.26 130	43.07 151	61.34 71	50.19 87	47.29 157	64.41 99	54.60 84	70.18 89	68.62 86	77.73 85	78.89 71

CNLPA		62.78 87	66.31 90	58.65 86	58.47 129	68.41 77	65.98 109	41.22 171	78.02 25	56.04 43	46.65 160	59.50 124	57.50 49	69.67 95	65.27 153	72.70 181	76.67 109

TSAR-MVS + COLMAP		62.65 88	69.90 59	54.19 128	46.31 222	66.73 109	65.49 117	41.36 168	76.57 27	46.31 109	76.80 20	56.68 134	53.27 102	69.50 100	66.65 122	72.40 188	76.36 118

GeoE		62.43 89	64.79 105	59.68 79	64.15 77	67.17 105	68.80 80	44.42 104	55.65 102	47.38 97	51.54 133	62.51 105	54.04 90	69.99 92	68.07 91	79.28 65	78.57 73

MVS_Test		62.40 90	66.23 91	57.94 93	59.77 121	64.77 132	66.50 101	41.76 162	57.26 98	49.33 88	62.68 66	67.47 79	53.50 97	68.57 116	66.25 134	76.77 106	76.58 112

onestephybrid01		62.35 91	66.85 80	57.10 102	59.33 124	65.58 121	67.18 92	43.71 129	57.48 97	48.34 94	62.61 67	67.84 75	50.93 121	69.40 101	66.88 116	73.15 173	78.12 80

viewmamba		62.28 92	66.90 79	56.89 105	58.53 128	64.79 131	67.28 89	43.17 147	59.60 76	48.15 95	63.20 59	67.57 78	50.82 122	69.05 109	66.77 117	73.41 165	77.32 88

DI_MVS_pp		61.88 93	65.17 102	58.06 90	60.05 116	65.26 125	66.03 106	44.22 105	55.75 101	46.73 103	54.64 119	68.12 72	54.13 89	69.13 107	66.66 121	77.18 99	76.61 111

diffmvs_AUTHOR		61.79 94	66.80 81	55.95 114	56.69 156	63.92 140	67.27 90	41.28 169	59.32 80	46.43 108	63.31 57	68.30 69	50.56 125	68.30 120	66.06 138	73.48 163	78.36 76

viewdifsd2359ckpt07		61.71 95	65.49 99	57.31 100	62.12 97	65.52 122	68.53 82	38.21 206	56.37 99	48.07 96	61.11 80	65.85 94	52.82 104	68.34 119	64.46 166	74.08 142	76.80 104

diffmvs		61.64 96	66.55 88	55.90 115	56.63 157	63.71 143	67.13 95	41.27 170	59.49 78	46.70 104	63.93 55	68.01 73	50.46 126	67.30 147	65.51 148	73.24 172	77.87 85
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
PVSNet_BlendedMVS		61.63 97	64.82 103	57.91 95	57.21 150	67.55 98	63.47 134	46.08 83	54.72 105	52.46 74	58.59 99	60.73 117	51.82 115	70.46 82	65.20 155	76.44 111	76.50 116

PVSNet_Blended		61.63 97	64.82 103	57.91 95	57.21 150	67.55 98	63.47 134	46.08 83	54.72 105	52.46 74	58.59 99	60.73 117	51.82 115	70.46 82	65.20 155	76.44 111	76.50 116

EIA-MVS		61.53 99	63.79 112	58.89 85	63.82 81	67.61 97	65.35 118	42.15 159	49.98 134	45.66 114	57.47 105	56.62 135	56.59 61	70.91 78	69.15 76	79.78 56	74.80 130

MGCFI-Net		61.46 100	69.72 60	51.83 148	61.00 109	66.16 116	56.50 172	40.73 175	73.98 38	35.18 166	64.23 49	71.42 53	42.45 172	69.22 104	64.01 170	75.09 133	79.03 70

hybridnocas07		61.04 101	66.19 92	55.03 120	55.86 161	62.77 151	66.02 107	39.98 187	58.77 85	47.07 100	63.48 56	67.60 77	48.61 135	68.22 125	65.32 151	72.62 185	77.17 92

TAPA-MVS	54.74 10	60.85 102	66.61 87	54.12 130	47.38 217	65.33 123	65.35 118	36.51 222	75.16 33	48.82 93	54.70 118	63.51 102	53.31 101	68.36 118	64.97 160	73.37 167	74.27 133
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
hybrid		60.72 103	65.86 96	54.73 122	55.25 167	62.37 154	65.92 110	39.45 190	58.64 87	46.85 102	62.81 63	67.76 76	48.44 137	67.71 138	65.01 159	72.46 187	76.72 108

viewmambaseed2359dif		60.40 104	64.15 109	56.03 113	57.79 135	63.53 144	65.91 111	41.64 163	54.98 104	46.47 107	60.16 90	64.71 98	50.76 123	66.25 166	62.83 186	73.61 162	76.57 114

dtuplus		60.38 105	64.02 110	56.13 112	58.12 131	63.10 145	66.05 105	41.59 165	54.56 107	46.60 106	59.27 94	64.90 97	50.72 124	66.90 156	63.35 180	73.68 161	76.05 120

Fast-Effi-MVS+		60.36 106	63.35 115	56.87 106	58.70 125	65.86 118	65.08 121	37.11 217	53.00 118	45.36 116	52.12 130	56.07 141	56.27 64	71.28 71	69.42 74	78.71 70	75.69 124

Effi-MVS+-dtu		60.34 107	62.32 119	58.03 92	64.31 72	67.44 101	65.99 108	42.26 156	49.55 137	42.00 135	48.92 147	59.79 123	56.27 64	68.07 130	67.03 109	77.35 97	75.45 126

LS3D		60.20 108	61.70 120	58.45 87	64.18 75	67.77 93	67.19 91	48.84 70	61.67 70	41.27 139	45.89 172	51.81 157	54.18 88	68.78 111	66.50 129	75.03 134	69.48 163

FA-MVS(training)		60.00 109	63.14 117	56.33 110	59.50 122	64.30 137	65.15 120	38.75 201	56.20 100	45.77 112	53.08 123	56.45 136	52.10 112	69.04 110	67.67 99	76.69 107	75.27 129

DCV-MVSNet		59.49 110	64.00 111	54.23 127	61.81 100	64.33 136	61.42 140	43.77 125	52.85 121	38.94 153	55.62 112	62.15 112	43.24 169	69.39 102	67.66 100	76.22 116	75.97 121

viewdifsd2359ckpt11		59.45 111	63.57 113	54.65 125	57.17 152	62.71 152	64.67 125	38.99 192	52.96 119	42.12 133	58.97 96	62.23 109	51.18 117	67.35 145	63.98 171	73.75 154	76.80 104

viewmsd2359difaftdt		59.45 111	63.57 113	54.65 125	57.17 152	62.71 152	64.67 125	38.99 192	52.96 119	42.12 133	58.97 96	62.22 110	51.18 117	67.35 145	63.98 171	73.75 154	76.80 104

EPP-MVSNet		59.39 113	65.45 100	52.32 145	60.96 110	67.70 95	58.42 158	44.75 98	49.71 136	27.23 212	59.03 95	62.20 111	43.34 166	70.71 79	69.13 77	79.25 66	79.63 65

ACMH+	53.71 12	59.26 114	60.28 133	58.06 90	64.17 76	68.46 76	67.51 88	50.93 54	52.46 124	35.83 165	40.83 217	45.12 216	52.32 109	69.88 93	69.00 81	77.59 92	76.21 119

PLC	52.09 14	59.21 115	62.47 118	55.41 119	53.24 181	64.84 130	64.47 129	40.41 183	65.92 58	44.53 120	46.19 168	55.69 142	55.33 73	68.24 124	65.30 152	74.50 137	71.09 147
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
v10		59.17 116	60.60 129	57.50 98	57.95 133	66.73 109	67.09 96	44.11 108	46.85 166	45.42 115	48.18 153	51.07 159	53.63 92	67.84 134	66.59 125	76.79 105	76.92 99

CANet_DTU		58.88 117	64.68 106	52.12 146	55.77 162	66.75 108	63.92 131	37.04 218	53.32 114	37.45 161	59.81 91	61.81 113	44.43 160	68.25 122	67.47 104	74.12 141	75.33 127

v1144		58.88 117	60.16 137	57.39 99	58.03 132	67.26 103	67.14 94	44.46 102	45.17 178	44.33 121	47.81 154	49.92 168	53.20 103	67.77 136	66.62 124	77.15 100	76.58 112

v8		58.88 117	60.57 131	56.92 104	57.35 144	65.69 120	66.69 100	42.64 153	47.89 161	45.77 112	49.04 144	52.98 152	52.77 105	67.51 142	65.57 147	76.26 115	75.30 128

v2v482		58.69 120	60.12 140	57.03 103	57.16 154	66.05 117	67.17 93	43.52 135	46.33 170	45.19 117	49.46 143	51.02 160	52.51 107	67.30 147	66.03 140	76.61 108	74.62 131

v1192		58.51 121	59.66 144	57.17 101	57.82 134	67.72 94	66.21 104	44.83 97	44.15 187	43.49 124	46.68 159	47.94 180	53.55 94	67.39 144	66.51 128	77.13 101	77.20 91

UA-Net		58.50 122	64.68 106	51.30 151	66.97 57	67.13 106	53.68 201	45.65 88	49.51 139	31.58 187	62.91 62	68.47 67	35.85 218	68.20 126	67.28 106	74.03 145	69.24 167

Vis-MVSNet		58.48 123	65.70 98	50.06 158	53.40 180	67.20 104	60.24 148	43.32 142	48.83 148	30.23 193	62.38 72	61.61 115	40.35 181	71.03 73	69.77 71	72.82 177	79.11 69
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MSDG		58.46 124	58.97 155	57.85 97	66.27 65	66.23 115	67.72 84	42.33 155	53.43 112	43.68 123	43.39 199	45.35 212	49.75 129	68.66 114	67.77 96	77.38 96	67.96 172

FC-MVSNet-train		58.40 125	63.15 116	52.85 141	64.29 73	61.84 157	55.98 179	46.47 80	53.06 116	34.96 169	61.95 75	56.37 139	39.49 186	68.67 113	68.36 89	75.92 124	71.81 144

ET-MVSNet_ETH3D		58.38 126	61.57 121	54.67 124	42.15 238	65.26 125	65.70 113	43.82 124	48.84 147	42.34 130	59.76 92	47.76 183	56.68 60	67.02 154	68.60 88	77.33 98	73.73 139

IB-MVS	54.11 11	58.36 127	60.70 128	55.62 117	58.67 126	68.02 91	61.56 137	43.15 148	46.09 172	44.06 122	44.24 189	50.99 162	48.71 134	66.70 158	70.33 65	77.60 90	78.50 74
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
IterMVS-LS		58.30 128	61.39 122	54.71 123	59.92 119	58.40 194	59.42 150	43.64 131	48.71 151	40.25 146	57.53 104	58.55 127	52.15 111	65.42 178	65.34 150	72.85 175	75.77 122
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ACMH	52.42 13	58.24 129	59.56 149	56.70 108	66.34 64	69.59 67	66.71 99	49.12 66	46.08 173	28.90 200	42.67 211	41.20 235	52.60 106	71.39 69	70.28 66	76.51 110	75.72 123
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v144192		58.23 130	59.40 151	56.87 106	57.56 136	66.89 107	65.70 113	45.01 95	44.06 188	42.88 126	46.61 161	48.09 179	53.49 98	66.94 155	65.90 144	76.61 108	77.29 89

MS-PatchMatch		58.19 131	60.20 136	55.85 116	65.17 68	64.16 138	64.82 122	41.48 167	50.95 129	42.17 132	45.38 178	56.42 137	48.08 141	68.30 120	66.70 119	73.39 166	69.46 165

IS_MVSNet		57.95 132	64.26 108	50.60 153	61.62 104	65.25 127	57.18 165	45.42 90	50.79 130	26.49 218	57.81 103	60.05 122	34.51 222	71.24 72	70.20 68	78.36 77	74.44 132

v1921920		57.89 133	59.02 154	56.58 109	57.55 137	66.66 113	64.72 124	44.70 99	43.55 192	42.73 127	46.17 169	46.93 198	53.51 95	66.78 157	65.75 146	76.29 113	77.28 90

Anonymous20231211		57.71 134	60.79 126	54.13 129	61.68 103	65.81 119	60.81 145	43.70 130	51.97 127	39.67 148	34.82 237	63.59 101	43.31 167	68.55 117	66.63 123	75.59 125	74.13 135

v1240		57.55 135	58.63 158	56.29 111	57.30 147	66.48 114	63.77 132	44.56 101	42.77 203	42.48 129	45.64 175	46.28 205	53.46 99	66.32 164	65.80 145	76.16 117	77.13 93

MVSTER		57.19 136	61.11 124	52.62 143	50.82 203	58.79 187	61.55 138	37.86 214	48.81 149	41.31 138	57.43 106	52.10 155	48.60 136	68.19 127	66.75 118	75.56 126	75.68 125

UGNet		57.03 137	65.25 101	47.44 188	46.54 221	66.73 109	56.30 174	43.28 143	50.06 133	32.99 179	62.57 68	63.26 103	33.31 227	68.25 122	67.58 101	72.20 191	78.29 77
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
EG-PatchMatch MVS		56.98 138	58.24 162	55.50 118	64.66 71	68.62 75	61.48 139	43.63 132	38.44 238	41.44 136	38.05 229	46.18 207	43.95 162	71.71 67	70.61 63	77.87 79	74.08 136

V42		56.97 139	60.14 138	53.28 135	48.16 212	62.78 150	66.30 103	37.93 213	47.44 163	42.68 128	48.19 152	52.59 154	51.90 113	67.46 143	65.94 143	72.72 179	76.55 115

UniMVSNet_NR-MVSNet		56.94 140	61.14 123	52.05 147	60.02 118	65.21 128	57.44 163	52.93 44	49.37 140	24.31 228	54.62 120	50.54 163	39.04 188	68.69 112	68.84 83	78.53 73	70.72 149

HyFIR lowres test		56.87 141	58.60 159	54.84 121	56.62 158	69.27 70	64.77 123	42.21 157	45.66 176	37.50 160	33.08 241	57.47 133	53.33 100	65.46 177	67.94 92	74.60 136	71.35 146

thisisatest0530		56.68 142	59.68 143	53.19 137	52.97 182	60.96 167	59.41 151	40.51 178	48.26 157	41.06 141	52.67 126	46.30 204	49.78 127	67.66 140	67.83 94	75.39 128	74.07 137

CostFormer		56.57 143	59.13 153	53.60 132	57.52 139	61.12 164	66.94 98	35.95 225	53.44 111	44.68 119	55.87 111	54.44 145	48.21 139	60.37 201	58.33 209	68.27 213	70.33 154

tttt0517		56.53 144	59.59 145	52.95 140	52.66 185	60.99 166	59.21 153	40.51 178	47.89 161	40.40 144	52.50 129	46.04 208	49.78 127	67.75 137	67.83 94	75.15 131	74.17 134

ECVR-MVS		56.44 145	60.74 127	51.42 150	60.39 114	64.55 134	58.69 156	48.87 68	53.91 109	26.76 215	45.55 177	53.43 150	37.71 199	70.96 74	69.49 72	76.08 119	67.32 179

Fast-Effi-MVS+-dtu		56.30 146	59.29 152	52.82 142	58.64 127	64.89 129	65.56 116	32.89 245	45.80 175	35.04 168	45.89 172	54.14 146	49.41 130	67.16 150	66.45 131	75.37 129	70.69 151

baseline2		55.89 147	57.82 165	53.64 131	57.36 143	61.09 165	59.75 149	40.45 181	47.38 164	41.26 140	51.23 135	46.90 199	48.11 140	65.63 175	64.38 167	74.90 135	68.16 171

TranMVSNet+NR-MVSNet		55.87 148	60.14 138	50.88 152	59.46 123	63.82 141	57.93 160	52.98 43	48.94 146	20.52 236	52.87 125	47.33 190	36.81 209	69.12 108	69.03 80	77.56 93	69.89 155

CHOSEN 1792x2688		55.85 149	58.01 163	53.33 134	57.26 149	62.82 149	63.29 136	41.55 166	46.65 168	38.34 154	34.55 238	53.50 148	52.43 108	67.10 152	67.56 102	67.13 217	73.92 138

test2506		55.82 150	59.57 148	51.46 149	60.39 114	64.55 134	58.69 156	48.87 68	53.91 109	26.99 213	48.97 145	41.72 234	37.71 199	70.96 74	69.49 72	76.08 119	67.37 177

v7n		55.67 151	57.46 170	53.59 133	56.06 159	65.29 124	61.06 143	43.26 144	40.17 222	37.99 157	40.79 218	45.27 215	47.09 146	67.67 139	66.21 135	76.08 119	76.82 103

GA-MVS		55.67 151	58.33 160	52.58 144	55.23 168	63.09 146	61.08 142	40.15 186	42.95 198	37.02 163	52.61 127	47.68 184	47.51 144	65.92 171	65.35 149	74.49 138	70.68 152

v148		55.58 153	57.61 169	53.20 136	54.59 173	61.86 156	61.18 141	38.70 202	44.30 186	42.25 131	47.53 155	50.24 166	48.73 133	65.15 179	62.61 190	73.79 149	71.61 145

DU-MVS		55.41 154	59.59 145	50.54 155	54.60 171	62.97 147	57.44 163	51.80 49	48.62 154	24.31 228	51.99 131	47.00 195	39.04 188	68.11 128	67.75 97	76.03 123	70.72 149

NR-MVSNet		55.35 155	59.46 150	50.56 154	61.33 106	62.97 147	57.91 161	51.80 49	48.62 154	20.59 235	51.99 131	44.73 222	34.10 225	68.58 115	68.64 85	77.66 88	70.67 153

test1111		55.24 156	59.98 141	49.71 159	59.80 120	64.10 139	56.48 173	49.34 63	52.27 125	21.56 233	44.49 187	51.96 156	35.93 217	70.59 81	69.07 79	75.13 132	67.40 175

GBi-Net		55.20 157	60.25 134	49.31 162	52.42 186	61.44 159	57.03 166	44.04 111	49.18 143	30.47 189	48.28 149	58.19 128	38.22 194	68.05 131	66.96 110	73.69 157	69.65 157

test1		55.20 157	60.25 134	49.31 162	52.42 186	61.44 159	57.03 166	44.04 111	49.18 143	30.47 189	48.28 149	58.19 128	38.22 194	68.05 131	66.96 110	73.69 157	69.65 157

baseline		55.19 159	60.88 125	48.55 175	49.87 207	58.10 205	58.70 155	34.75 231	52.82 122	39.48 152	60.18 89	60.86 116	45.41 154	61.05 197	60.74 200	63.10 231	72.41 142

UniMVSNet (Re)		55.15 160	60.39 132	49.03 168	55.31 164	64.59 133	55.77 180	50.63 56	48.66 153	20.95 234	51.47 134	50.40 164	34.41 224	67.81 135	67.89 93	77.11 102	71.88 143

FMVSNet2		55.04 161	59.95 142	49.31 162	52.42 186	61.44 159	57.03 166	44.08 110	49.55 137	30.40 192	46.89 158	58.84 126	38.22 194	67.07 153	66.21 135	73.69 157	69.65 157

FMVSNet3		54.78 162	59.58 147	49.17 165	52.37 189	61.31 163	56.72 171	44.04 111	49.18 143	30.47 189	48.28 149	58.19 128	38.09 197	65.48 176	65.20 155	73.31 169	69.45 166

pmmvs4		54.66 163	56.07 174	53.00 139	54.63 170	57.08 212	60.43 147	44.10 109	51.69 128	40.55 143	46.55 164	44.79 221	45.95 152	62.54 190	63.66 176	72.36 189	66.20 194

baseline1		54.48 164	58.69 156	49.57 160	60.63 113	58.29 202	55.70 181	44.95 96	49.20 142	29.62 196	54.77 117	54.75 144	35.29 219	67.15 151	64.08 168	71.21 200	62.58 224

FMVSNet1		54.08 165	58.68 157	48.71 172	50.90 202	61.35 162	56.73 170	43.94 120	45.91 174	29.32 199	42.72 207	56.26 140	37.70 201	68.05 131	66.96 110	73.69 157	69.50 162

thisisatest0515		53.85 166	56.84 173	50.37 156	50.25 206	58.17 203	55.99 178	39.90 188	41.88 210	38.16 156	45.91 171	45.30 213	44.58 159	66.15 169	66.89 114	73.36 168	73.57 140

Baseline_NR-MVSNet		53.50 167	57.89 164	48.37 179	54.60 171	59.25 183	56.10 175	51.84 48	49.32 141	17.92 243	45.38 178	47.68 184	36.93 206	68.11 128	65.95 142	72.84 176	69.57 161

IterMVS		53.45 168	57.12 171	49.17 165	49.23 209	60.93 168	59.05 154	34.63 233	44.53 181	33.22 177	51.09 138	51.01 161	48.38 138	62.43 192	60.79 199	70.54 205	69.05 168
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tpm cat1		53.30 169	53.41 195	53.17 138	58.16 130	59.15 184	63.73 133	38.27 205	50.73 131	46.98 101	45.57 176	44.00 228	49.20 131	55.90 241	54.02 240	62.65 234	64.50 210

anonymousdsp		52.84 170	57.78 166	47.06 190	40.24 249	58.95 186	53.70 199	33.54 241	36.51 246	32.69 182	43.88 192	45.40 211	47.97 143	67.17 149	70.28 66	74.22 140	82.29 51

UniMVSNet_ETH3D		52.62 171	55.98 175	48.70 173	51.04 200	60.71 169	56.87 169	46.74 79	42.52 205	26.96 214	42.50 212	45.95 209	37.87 198	66.22 167	65.15 158	72.74 178	68.78 170

tfpn200view9		52.53 172	55.51 178	49.06 167	57.31 145	60.24 171	55.42 186	43.77 125	42.85 201	27.81 208	43.00 205	45.06 218	37.32 203	66.38 161	64.54 162	72.71 180	66.54 187

CDS-MVSNet		52.42 173	57.06 172	47.02 191	53.92 178	58.30 201	55.50 184	46.47 80	42.52 205	29.38 198	49.50 142	52.85 153	28.49 238	66.70 158	66.89 114	68.34 212	62.63 223
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
thres200		52.39 174	55.37 181	48.90 169	57.39 142	60.18 172	55.60 182	43.73 127	42.93 199	27.41 210	43.35 200	45.09 217	36.61 212	66.36 162	63.92 175	72.66 182	65.78 199

thres400		52.38 175	55.51 178	48.74 171	57.49 140	60.10 174	55.45 185	43.54 134	42.90 200	26.72 216	43.34 201	45.03 220	36.61 212	66.20 168	64.53 163	72.66 182	66.43 190

IterMVS-SCA-FT		52.18 176	57.75 167	45.68 202	51.01 201	62.06 155	55.10 189	34.75 231	44.85 179	32.86 181	51.13 137	51.22 158	48.74 132	62.47 191	61.51 195	51.61 260	71.02 148

dmvs_re		52.07 177	55.11 183	48.54 176	57.27 148	51.93 228	57.73 162	43.13 149	43.65 190	26.57 217	44.52 186	50.00 167	36.53 214	66.58 160	62.15 192	69.97 207	66.91 184

EPNet_dtu		52.05 178	58.26 161	44.81 211	54.10 176	50.09 235	52.01 214	40.82 174	53.03 117	27.41 210	54.90 115	57.96 132	26.72 240	62.97 187	62.70 189	67.78 215	66.19 195
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
thres100view900		52.04 179	54.81 186	48.80 170	57.31 145	59.33 180	55.30 187	42.92 152	42.85 201	27.81 208	43.00 205	45.06 218	36.99 205	64.74 181	63.51 177	72.47 186	65.21 204

COLMAP_ROB	46.52 15	51.99 180	54.86 185	48.63 174	49.13 210	61.73 158	60.53 146	36.57 221	53.14 115	32.95 180	37.10 230	38.68 246	40.49 180	65.72 173	63.08 182	72.11 192	64.60 209
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
TransMVSNet (Re)		51.92 181	55.38 180	47.88 185	60.95 111	59.90 175	53.95 196	45.14 94	39.47 226	24.85 225	43.87 193	46.51 203	29.15 234	67.55 141	65.23 154	73.26 171	65.16 205

thres600view7		51.91 182	55.14 182	48.14 181	57.43 141	60.18 172	54.60 191	43.73 127	42.61 204	25.20 223	43.10 204	44.47 225	35.19 220	66.36 162	63.28 181	72.66 182	66.01 197

usedtu_dtu_shiyan1		51.41 183	55.78 176	46.30 198	47.91 215	59.47 178	52.99 206	42.13 160	48.17 158	24.88 224	40.95 216	48.18 178	35.95 216	64.48 183	64.49 164	73.94 147	64.75 207

pmmvs-eth3d		51.33 184	52.25 215	50.26 157	50.82 203	54.65 218	56.03 177	43.45 141	43.51 193	37.20 162	39.20 225	39.04 245	42.28 173	61.85 195	62.78 187	71.78 195	64.72 208

USDC		51.11 185	53.71 190	48.08 183	44.76 230	55.99 216	53.01 205	40.90 172	52.49 123	36.14 164	44.67 185	33.66 257	43.27 168	63.23 186	61.10 197	70.39 206	64.82 206

pm-mvs1		51.02 186	55.55 177	45.73 201	54.16 175	58.52 189	50.92 216	42.56 154	40.32 220	25.67 222	43.66 196	50.34 165	30.06 232	65.85 172	63.97 173	70.99 203	66.21 193

SCA		50.99 187	53.22 199	48.40 178	51.07 199	56.78 213	50.25 218	39.05 191	48.31 156	41.38 137	49.54 141	46.70 202	46.00 151	58.31 220	56.28 221	62.65 234	56.60 242

0.4-1-1-0.1		50.59 188	53.51 193	47.17 189	46.63 220	58.96 185	54.24 193	36.39 223	43.20 195	33.94 176	44.77 184	49.55 169	40.04 185	57.50 226	56.17 223	71.80 194	64.43 211

CR-MVSNet		50.47 189	52.61 207	47.98 184	49.03 211	52.94 223	48.27 225	38.86 197	44.41 182	39.59 149	44.34 188	44.65 224	46.63 148	58.97 214	60.31 201	65.48 223	62.66 221

dps		50.42 190	51.20 222	49.51 161	55.88 160	56.07 215	53.73 197	38.89 196	43.66 189	40.36 145	45.66 174	37.63 250	45.23 155	59.05 212	56.18 222	62.94 232	60.16 232

blend_shiyan4		50.41 191	53.51 193	46.79 194	44.79 229	58.47 190	52.51 207	36.99 219	41.74 211	34.13 172	42.68 208	49.24 171	38.37 191	58.53 219	56.69 220	73.96 146	67.20 180

Vis-MVSNet (Re-imp)		50.37 192	57.73 168	41.80 226	57.53 138	54.35 219	45.70 240	45.24 92	49.80 135	13.43 251	58.23 102	56.42 137	20.11 253	62.96 188	63.36 179	68.76 211	58.96 236

MDTV_nov1_ep13		50.32 193	52.43 212	47.86 186	49.87 207	54.70 217	58.10 159	34.29 235	45.59 177	37.71 158	47.44 156	47.42 188	41.86 175	58.07 223	55.21 233	65.34 225	58.56 237

tfpnnormal		50.16 194	52.19 216	47.78 187	56.86 155	58.37 196	54.15 194	44.01 114	38.35 240	25.94 221	36.10 233	37.89 248	34.50 223	65.93 170	63.42 178	71.26 199	65.28 203

usedtu_blend_shiyan5		50.12 195	53.15 200	46.58 195	41.54 241	58.31 197	53.69 200	38.00 209	38.58 234	34.13 172	42.68 208	49.24 171	38.37 191	59.28 208	56.77 216	73.78 150	67.20 180

0.3-1-1-0.015		50.11 196	52.80 203	46.98 192	46.15 224	58.39 195	53.96 195	35.90 226	42.52 205	34.13 172	43.69 195	49.24 171	40.30 182	56.60 234	55.53 229	71.41 198	63.65 215

PatchMatch-RL		50.11 196	51.56 219	48.43 177	46.23 223	51.94 227	50.21 219	38.62 203	46.62 169	37.51 159	42.43 213	39.38 243	52.24 110	60.98 198	59.56 204	65.76 222	60.01 234

0.4-1-1-0.2		49.99 198	52.69 205	46.83 193	45.99 225	58.16 204	53.71 198	35.75 227	42.13 208	34.14 171	44.08 190	49.28 170	40.24 184	56.44 236	55.24 232	71.18 202	63.49 217

FE-MVSNET3		49.99 198	53.11 201	46.34 197	41.54 241	58.31 197	52.24 211	38.00 209	38.58 234	34.13 172	42.68 208	49.24 171	38.37 191	59.28 208	56.77 216	73.78 150	66.92 182

PatchmatchNet		49.92 200	51.29 220	48.32 180	51.83 193	51.86 229	53.38 204	37.63 216	47.90 160	40.83 142	48.54 148	45.30 213	45.19 156	56.86 229	53.99 242	61.08 240	54.57 245
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
TDRefinement		49.31 201	52.44 211	45.67 203	30.44 264	59.42 179	59.24 152	39.78 189	48.76 150	31.20 188	35.73 234	29.90 264	42.81 171	64.24 184	62.59 191	70.55 204	66.43 190

test-LLR		49.28 202	50.29 226	48.10 182	55.26 165	47.16 245	49.52 220	43.48 139	39.22 227	31.98 183	43.65 197	47.93 181	41.29 178	56.80 230	55.36 230	67.08 218	61.94 225

CMPMVS	37.70 17	49.24 203	52.71 204	45.19 206	45.97 226	51.23 231	47.44 231	29.31 250	43.04 197	44.69 118	34.45 239	48.35 177	43.64 163	62.59 189	59.82 203	60.08 241	69.48 163
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
blended_shiyan6		49.22 204	52.60 208	45.26 205	41.68 239	58.46 192	52.42 208	38.16 207	38.60 232	28.50 206	40.28 220	47.09 192	36.76 211	59.62 205	57.25 215	74.06 143	66.92 182

blended_shiyan8		49.21 205	52.59 209	45.27 204	41.67 240	58.47 190	52.41 209	38.16 207	38.60 232	28.53 205	40.26 221	47.07 193	36.78 210	59.62 205	57.26 214	74.06 143	66.88 185

PEN-MVS		49.21 205	54.32 188	43.24 221	54.33 174	59.26 182	47.04 233	51.37 53	41.67 212	9.97 260	46.22 167	41.80 233	22.97 249	60.52 199	64.03 169	73.73 156	66.75 186

PMMVS		49.20 207	54.28 189	43.28 220	34.13 258	45.70 253	48.98 223	26.09 259	46.31 171	34.92 170	55.22 114	53.47 149	47.48 145	59.43 207	59.04 207	68.05 214	60.77 229

gg-mvs-nofinetune		49.07 208	52.56 210	45.00 210	61.99 98	59.78 176	53.55 203	41.63 164	31.62 256	12.08 254	29.56 251	53.28 151	29.57 233	66.27 165	64.49 164	71.19 201	62.92 219

wanda-best-256-512		49.05 209	52.38 213	45.17 208	41.54 241	58.31 197	52.24 211	38.00 209	38.58 234	28.56 203	40.23 222	47.00 195	36.88 207	59.28 208	56.77 216	73.78 150	66.45 188

FE-blended-shiyan7		49.05 209	52.38 213	45.17 208	41.54 241	58.31 197	52.24 211	38.00 209	38.58 234	28.56 203	40.23 222	47.00 195	36.88 207	59.28 208	56.77 216	73.78 150	66.45 188

gbinet_0.2-2-1-0.02		48.89 211	52.69 205	44.45 214	39.54 252	59.33 180	52.39 210	38.76 200	35.41 247	26.17 220	39.15 226	47.39 189	36.41 215	60.29 203	57.58 212	73.45 164	69.65 157

tpm		48.82 212	51.27 221	45.96 200	54.10 176	47.35 244	56.05 176	30.23 249	46.70 167	43.21 125	52.54 128	47.55 187	37.28 204	54.11 246	50.50 251	54.90 253	60.12 233

WR-MVS		48.78 213	55.06 184	41.45 227	55.50 163	60.40 170	43.77 248	49.99 60	41.92 209	8.10 266	45.24 181	45.56 210	17.47 254	61.57 196	64.60 161	73.85 148	66.14 196

CP-MVSNet		48.37 214	53.53 192	42.34 223	51.35 196	58.01 206	46.56 235	50.54 57	41.62 213	10.61 256	46.53 165	40.68 239	23.18 247	58.71 217	61.83 193	71.81 193	67.36 178

pmmvs6		48.35 215	51.64 218	44.51 213	51.92 192	57.94 208	49.44 222	42.17 158	34.45 249	24.62 227	28.87 255	46.90 199	29.07 236	64.60 182	63.08 182	69.83 208	65.68 200

PS-CasMVS		48.18 216	53.25 198	42.27 224	51.26 197	57.94 208	46.51 236	50.52 58	41.30 214	10.56 257	45.35 180	40.34 241	23.04 248	58.66 218	61.79 194	71.74 196	67.38 176

PatchT		48.08 217	51.03 223	44.64 212	42.96 235	50.12 234	40.36 256	35.09 229	43.17 196	39.59 149	42.00 214	39.96 242	46.63 148	58.97 214	60.31 201	63.21 230	62.66 221

tpmrst		48.08 217	49.88 231	45.98 199	52.71 184	48.11 241	53.62 202	33.70 240	48.70 152	39.74 147	48.96 146	46.23 206	40.29 183	50.14 257	49.28 253	55.80 249	57.71 239

DTE-MVSNet		48.03 219	53.28 197	41.91 225	54.64 169	57.50 210	44.63 247	51.66 52	41.02 216	7.97 267	46.26 166	40.90 236	20.24 252	60.45 200	62.89 185	72.33 190	63.97 212

WR-MVS_H		47.65 220	53.67 191	40.63 233	51.45 194	59.74 177	44.71 246	49.37 62	40.69 218	7.61 268	46.04 170	44.34 227	17.32 255	57.79 224	61.18 196	73.30 170	65.86 198

MDTV_nov1_ep13_2view		47.62 221	49.72 232	45.18 207	48.05 213	53.70 221	54.90 190	33.80 239	39.90 225	29.79 195	38.85 227	41.89 232	39.17 187	58.99 213	55.55 228	65.34 225	59.17 235

SixPastTwentyTwo		47.55 222	50.25 228	44.41 215	47.30 218	54.31 220	47.81 228	40.36 184	33.76 250	19.93 238	43.75 194	32.77 259	42.07 174	59.82 204	60.94 198	68.98 209	66.37 192

dtuonly		47.41 223	53.02 202	40.88 231	39.20 253	46.62 251	54.26 192	25.80 260	44.41 182	26.35 219	45.20 182	53.69 147	44.32 161	60.37 201	57.56 213	55.34 250	63.26 218

TinyColmap		47.08 224	47.56 240	46.52 196	42.35 237	53.44 222	51.77 215	40.70 176	43.44 194	31.92 185	29.78 250	23.72 270	45.04 158	61.99 194	59.54 205	67.35 216	61.03 228

pmmvs5		47.07 225	51.02 224	42.46 222	45.18 228	51.47 230	48.23 227	33.09 244	38.17 241	28.62 202	46.60 162	43.48 229	30.74 230	58.28 221	58.63 208	68.92 210	60.48 230

LTVRE_ROB	44.17 16	47.06 226	50.15 229	43.44 218	51.39 195	58.42 193	42.90 250	43.51 136	22.27 267	14.85 248	41.94 215	34.57 255	45.43 153	62.28 193	62.77 188	62.56 236	68.83 169
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		46.41 227	48.72 235	43.72 216	47.77 216	52.94 223	46.02 239	33.92 237	44.41 182	31.82 186	36.89 231	37.42 252	37.41 202	53.88 247	54.02 240	65.37 224	61.47 227

RPSCF		46.41 227	54.42 187	37.06 243	25.70 271	45.14 254	45.39 242	20.81 264	62.79 66	35.10 167	44.92 183	55.60 143	43.56 164	56.12 238	52.45 246	51.80 259	63.91 213

CVMVSNet		46.38 229	52.01 217	39.81 235	42.40 236	50.26 233	46.15 237	37.68 215	40.03 224	15.09 247	46.56 163	47.56 186	33.72 226	56.50 235	55.65 227	63.80 229	67.53 173

TESTMET0.1,1		46.09 230	50.29 226	41.18 229	36.91 256	47.16 245	49.52 220	20.32 265	39.22 227	31.98 183	43.65 197	47.93 181	41.29 178	56.80 230	55.36 230	67.08 218	61.94 225

dtuonlycased		45.76 231	49.64 233	41.23 228	39.65 251	57.99 207	55.53 183	26.40 258	40.07 223	17.92 243	28.95 254	49.18 175	45.13 157	53.73 248	52.03 247	62.75 233	65.55 201

FE-MVSNET2		45.69 232	49.95 230	40.72 232	40.11 250	56.16 214	46.59 234	41.89 161	36.97 245	13.66 250	29.00 253	37.59 251	28.96 237	63.26 185	63.93 174	73.13 174	62.72 220

test-mter		45.30 233	50.37 225	39.38 236	33.65 260	46.99 247	47.59 229	18.59 266	38.75 230	28.00 207	43.28 202	46.82 201	41.50 177	57.28 227	55.78 226	66.93 220	63.70 214

gm-plane-assit		44.74 234	45.95 242	43.33 219	60.88 112	46.79 250	36.97 261	32.24 248	24.15 264	11.79 255	29.26 252	32.97 258	46.64 147	65.09 180	62.95 184	71.45 197	60.42 231

EPMVS		44.66 235	47.86 239	40.92 230	47.97 214	44.70 255	47.58 230	33.27 242	48.11 159	29.58 197	49.65 140	44.38 226	34.65 221	51.71 251	47.90 255	52.49 258	48.57 258

PM-MVS		44.55 236	48.13 238	40.37 234	32.85 262	46.82 249	46.11 238	29.28 251	40.48 219	29.99 194	39.98 224	34.39 256	41.80 176	56.08 239	53.88 244	62.19 237	65.31 202

TAMVS		44.02 237	49.18 234	37.99 241	47.03 219	45.97 252	45.04 243	28.47 253	39.11 229	20.23 237	43.22 203	48.52 176	28.49 238	58.15 222	57.95 211	58.71 243	51.36 248

MIMVSNet		43.79 238	48.53 236	38.27 239	41.46 245	48.97 238	50.81 217	32.88 246	44.55 180	22.07 231	32.05 242	47.15 191	24.76 243	58.73 216	56.09 225	57.63 248	52.14 246

test0.0.03 1		43.15 239	46.95 241	38.72 238	55.26 165	50.56 232	42.48 251	43.48 139	38.16 242	15.11 246	35.07 236	44.69 223	16.47 256	55.95 240	54.34 239	59.54 242	49.87 256

Anonymous20231206		42.28 240	45.89 243	38.07 240	51.96 191	48.98 237	43.66 249	38.81 199	38.74 231	14.32 249	26.74 257	40.90 236	20.94 250	56.64 233	54.67 237	58.71 243	54.59 244

MVS-HIRNet		42.24 241	41.15 255	43.51 217	44.06 234	40.74 258	35.77 264	35.35 228	35.38 248	38.34 154	25.63 260	38.55 247	43.48 165	50.77 253	47.03 257	64.07 227	49.98 254

MDA-MVSNet-bldmvs		41.36 242	43.15 253	39.27 237	28.74 266	52.68 225	44.95 245	40.84 173	32.89 252	18.13 242	31.61 245	22.09 271	38.97 190	50.45 256	56.11 224	64.01 228	56.23 243

FMVSNet5		40.96 243	45.81 244	35.29 248	34.30 257	44.55 256	47.28 232	28.84 252	40.76 217	21.62 232	29.85 249	42.44 230	24.77 242	57.53 225	55.00 234	54.93 252	50.56 252

CHOSEN 280x420		40.80 244	45.05 247	35.84 247	32.95 261	29.57 268	44.98 244	23.71 263	37.54 243	18.42 241	31.36 246	47.07 193	46.41 150	56.71 232	54.65 238	48.55 263	58.47 238

ADS-MVSNet		40.67 245	43.38 252	37.50 242	44.36 232	39.79 262	42.09 253	32.67 247	44.34 185	28.87 201	40.76 219	40.37 240	30.22 231	48.34 262	45.87 260	46.81 264	44.21 262

EU-MVSNet		40.63 246	45.65 245	34.78 249	39.11 254	46.94 248	40.02 257	34.03 236	33.50 251	10.37 258	35.57 235	37.80 249	23.65 246	51.90 250	50.21 252	61.49 239	63.62 216

pmnet_mix02		40.48 247	43.80 250	36.61 244	45.79 227	40.45 260	42.12 252	33.18 243	40.30 221	24.11 230	38.76 228	37.11 253	24.30 244	52.97 249	46.66 259	50.17 261	50.33 253

test20.03		40.38 248	44.20 249	35.92 246	53.73 179	49.05 236	38.54 258	43.49 137	32.55 253	9.54 261	27.88 256	39.12 244	12.24 262	56.28 237	54.69 236	57.96 247	49.83 257

FE-MVSNET		39.75 249	44.50 248	34.21 250	32.01 263	48.77 239	37.71 260	38.94 194	30.91 258	6.25 271	26.24 259	32.10 261	23.68 245	57.28 227	59.53 206	66.68 221	56.64 241

FC-MVSNet-test		39.65 250	48.35 237	29.49 255	44.43 231	39.28 264	30.23 268	40.44 182	43.59 191	3.12 275	53.00 124	42.03 231	10.02 270	55.09 243	54.77 235	48.66 262	50.71 251

testgi		38.71 251	43.64 251	32.95 251	52.30 190	48.63 240	35.59 265	35.05 230	31.58 257	9.03 265	30.29 247	40.75 238	11.19 268	55.30 242	53.47 245	54.53 255	45.48 260

FPMVS		38.36 252	40.41 256	35.97 245	38.92 255	39.85 261	45.50 241	25.79 261	41.13 215	18.70 240	30.10 248	24.56 268	31.86 229	49.42 259	46.80 258	55.04 251	51.03 249

GG-mvs-BLEND		36.62 253	53.39 196	17.06 263	0.01 282	58.61 188	48.63 224	0.01 276	47.13 165	0.02 282	43.98 191	60.64 119	0.03 278	54.92 245	51.47 249	53.64 256	56.99 240

usedtu_dtu_shiyan2		36.29 254	39.77 257	32.23 252	19.53 272	48.11 241	41.99 254	36.59 220	23.95 265	12.80 252	22.03 264	32.26 260	20.73 251	50.69 255	50.64 250	61.72 238	50.72 250

MIMVSNet1		35.51 255	41.41 254	28.63 256	27.53 268	43.36 257	38.09 259	33.82 238	32.01 254	6.77 269	21.63 265	35.43 254	11.97 264	55.05 244	53.99 242	53.59 257	48.36 259

pmmvs3		35.10 256	38.47 258	31.17 254	26.37 270	40.47 259	34.51 266	18.09 267	24.75 263	16.88 245	23.05 262	26.69 266	32.69 228	50.73 254	51.60 248	58.46 246	51.98 247

PMVS	27.84 18	33.81 257	35.28 262	32.09 253	34.13 258	24.81 270	32.51 267	26.48 257	26.41 261	19.37 239	23.76 261	24.02 269	25.18 241	50.78 252	47.24 256	54.89 254	49.95 255
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
new-patchmatchnet		33.24 258	37.20 259	28.62 257	44.32 233	38.26 265	29.68 269	36.05 224	31.97 255	6.33 270	26.59 258	27.33 265	11.12 269	50.08 258	41.05 264	44.23 265	45.15 261

N_pmnet		32.67 259	36.85 260	27.79 258	40.55 248	32.13 266	35.80 263	26.79 256	37.24 244	9.10 263	32.02 243	30.94 263	16.30 257	47.22 263	41.21 263	38.21 268	37.21 263

WB-MVS		29.70 260	35.40 261	23.05 260	40.96 247	39.59 263	18.79 272	40.20 185	25.26 262	1.88 278	33.33 240	21.97 272	3.36 272	48.69 261	44.60 261	33.11 270	34.39 265

Gipuma		25.87 261	26.91 264	24.66 259	28.98 265	20.17 271	20.46 270	34.62 234	29.55 259	9.10 263	4.91 276	5.31 279	15.76 259	49.37 260	49.10 254	39.03 266	29.95 267
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
new_pmnet		23.19 262	28.17 263	17.37 261	17.03 273	24.92 269	19.66 271	16.16 269	27.05 260	4.42 272	20.77 266	19.20 273	12.19 263	37.71 265	36.38 265	34.77 269	31.17 266

PMMVS2		15.84 263	19.68 265	11.35 265	15.74 274	16.95 272	13.31 273	17.64 268	16.08 269	0.36 279	13.12 269	11.47 275	1.69 275	28.82 266	27.24 267	19.38 274	24.09 269

E-PMN		15.09 264	13.19 268	17.30 262	27.80 267	12.62 274	7.81 276	27.54 254	14.62 271	3.19 273	6.89 273	2.52 284	15.09 260	15.93 269	20.22 268	22.38 271	19.53 270

EMVS		14.49 265	12.45 269	16.87 264	27.02 269	12.56 275	8.13 275	27.19 255	15.05 270	3.14 274	6.69 274	2.67 283	15.08 261	14.60 271	18.05 269	20.67 272	17.56 273

MVE	12.28 19	13.53 266	15.72 266	10.96 266	7.39 275	15.71 273	6.05 277	23.73 262	10.29 273	3.01 276	5.77 275	3.41 282	11.91 265	20.11 267	29.79 266	13.67 275	24.98 268
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		12.44 267	14.66 267	9.85 267	1.30 278	3.32 278	13.00 274	3.21 270	22.42 266	10.22 259	14.13 267	25.64 267	11.43 267	19.75 268	11.61 271	19.96 273	5.79 275

VLMVS_CLIP		6.73 268	10.38 270	2.46 269	3.99 276	4.43 277	1.10 280	0.52 272	9.66 274	0.13 280	13.65 268	7.20 276	6.06 271	10.97 273	8.87 272	2.96 278	17.92 272

MVS_clip		2.93 269	4.95 271	0.58 270	0.38 280	0.87 280	0.22 283	0.07 274	4.80 275	0.01 283	7.77 272	4.35 280	2.40 273	4.36 274	3.82 273	0.40 280	8.69 274

VLMVS		1.68 270	2.72 272	0.47 271	0.61 279	0.83 281	0.31 282	0.04 275	3.10 276	0.10 281	2.87 277	3.58 281	1.27 276	1.63 275	1.33 274	0.51 279	5.67 276

MVS_baseline		0.92 271	1.62 273	0.10 272	0.03 281	0.03 282	0.01 284	0.00 277	1.26 277	0.00 284	2.32 278	1.37 285	0.57 277	0.42 276	0.44 275	0.00 281	5.51 277

testmvs		0.01 272	0.02 274	0.00 273	0.00 283	0.00 283	0.01 284	0.00 277	0.01 278	0.00 284	0.03 280	0.00 286	0.01 279	0.01 277	0.01 276	0.00 281	0.06 279

test123		0.01 272	0.02 274	0.00 273	0.00 283	0.00 283	0.00 286	0.00 277	0.01 278	0.00 284	0.04 279	0.00 286	0.01 279	0.00 278	0.01 276	0.00 281	0.07 278

uanet_test		0.00 274	0.00 276	0.00 273	0.00 283	0.00 283	0.00 286	0.00 277	0.00 280	0.00 284	0.00 281	0.00 286	0.00 281	0.00 278	0.00 278	0.00 281	0.00 280

sosnet-low-res		0.00 274	0.00 276	0.00 273	0.00 283	0.00 283	0.00 286	0.00 277	0.00 280	0.00 284	0.00 281	0.00 286	0.00 281	0.00 278	0.00 278	0.00 281	0.00 280

sosnet		0.00 274	0.00 276	0.00 273	0.00 283	0.00 283	0.00 286	0.00 277	0.00 280	0.00 284	0.00 281	0.00 286	0.00 281	0.00 278	0.00 278	0.00 281	0.00 280

ACM-MVS					76.60 11	76.13 36	80.06 22		73.64 40	60.95 27	65.55 40	77.63 33	56.51 62			80.91 48	85.93 30

PatchmatchNet2					41.42 246	31.97 267	36.73 262
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet1												31.00 262	16.29 258	46.93 264	41.22 262	38.25 267	37.14 264
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet3										9.17 262	31.94 244
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
TestfortrainingZip							82.75 9	57.21 15		62.96 16						83.21 9

TPM-MVS					75.48 18	76.70 33	79.31 26			62.34 20	64.71 46	77.88 31	56.94 59			81.88 36	83.68 44
Ray Leroy Khuboni and Hongjun Xu: Textureless Resilient Propagation Matching in Multiple View Stereosis (TPM-MVS). SATNAC 2025
RE-MVS-def										33.01 178

9.14												81.81 16

SR-MVS					71.46 38			54.67 32				81.54 17

Anonymous202405211			60.60 129		63.44 82	66.71 112	61.00 144	47.23 76	50.62 132		36.85 232	60.63 120	43.03 170	69.17 106	67.72 98	75.41 127	72.54 141

our_test_3					51.15 198	57.31 211	55.12 188

ambc			45.54 246		50.66 205	52.63 226	40.99 255		38.36 239	24.67 226	22.62 263	13.94 274	29.14 235	65.71 174	58.06 210	58.60 245	67.43 174

MTAPA										65.14 5		80.20 23

MTMP										62.63 19		78.04 30

Patchmatch-RL test							1.04 281

tmp_tt				5.40 268	3.97 277	2.35 279	3.26 279	0.44 273	17.56 268	12.09 253	11.48 271	7.14 277	1.98 274	15.68 270	15.49 270	10.69 276

XVS					70.49 43	76.96 29	74.36 50			54.48 64		74.47 42				82.24 29

X-MVStestdata					70.49 43	76.96 29	74.36 50			54.48 64		74.47 42				82.24 29

mPP-MVS					71.67 37							74.36 45

NP-MVS									72.00 46

Patchmtry						47.61 243	48.27 225	38.86 197		39.59 149

DeepMVS_CX						6.95 276	5.98 278	2.25 271	11.73 272	2.07 277	11.85 270	5.43 278	11.75 266	11.40 272		8.10 277	18.38 271

High-resolution multi-view benchmark