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		88.94 1	90.98 1	86.56 1	92.53 7	95.09 1	88.55 5	76.83 6	94.16 1	86.57 1	90.85 5	87.07 1	86.18 1	86.36 7	85.08 13	88.67 25	98.21 3

DVP-MVS		88.07 2	90.73 2	84.97 4	91.98 10	95.01 2	87.86 10	76.88 5	93.90 2	85.15 2	90.11 7	86.90 2	79.46 12	86.26 10	84.67 18	88.50 32	98.25 2
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
DVP-MVS++		87.98 3	89.76 5	85.89 2	92.57 6	94.57 3	88.34 6	76.61 7	92.40 6	83.40 3	89.26 10	85.57 5	86.04 2	86.24 11	84.89 15	88.39 35	95.42 21

MSP-MVS		87.87 4	90.57 3	84.73 5	89.38 27	91.60 17	88.24 8	74.15 12	93.55 3	82.28 4	94.99 1	83.21 12	85.96 3	87.67 4	84.67 18	88.32 36	98.29 1
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
DPE-MVS		87.60 5	90.44 4	84.29 7	92.09 9	93.44 6	88.69 4	75.11 9	93.06 5	80.80 6	94.23 2	86.70 3	81.44 7	84.84 18	83.52 27	87.64 54	97.28 5
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
SF-MVS		87.30 6	88.71 6	85.64 3	94.57 1	94.55 4	91.01 1	79.94 1	89.15 12	79.85 7	92.37 3	83.29 11	79.75 9	83.52 26	82.72 33	88.75 24	95.37 24

APDe-MVS		86.37 7	88.41 8	84.00 9	91.43 15	91.83 16	88.34 6	74.67 10	91.19 7	81.76 5	91.13 4	81.94 19	80.07 8	83.38 27	82.58 35	87.69 52	96.78 11
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
CNVR-MVS		85.96 8	87.58 11	84.06 8	92.58 5	92.40 12	87.62 11	77.77 4	88.44 14	75.93 16	79.49 26	81.97 18	81.65 6	87.04 6	86.58 4	88.79 22	97.18 7

MCST-MVS		85.75 9	86.99 13	84.31 6	94.07 3	92.80 9	88.15 9	79.10 2	85.66 22	70.72 30	76.50 34	80.45 23	82.17 5	88.35 2	87.49 3	91.63 2	97.65 4

HPM-MVS++		85.64 10	88.43 7	82.39 12	92.65 4	90.24 26	85.83 17	74.21 11	90.68 9	75.63 17	86.77 13	84.15 8	78.68 16	86.33 8	85.26 10	87.32 63	95.60 18

DPM-MVS		85.41 11	86.72 17	83.89 10	91.66 13	91.92 15	90.49 2	78.09 3	86.90 18	73.95 21	74.52 36	82.01 17	79.29 13	90.24 1	90.65 1	89.86 6	90.78 80

SMA-MVS		85.24 12	88.27 9	81.72 15	91.74 12	90.71 20	86.71 13	73.16 19	90.56 10	74.33 20	83.07 18	85.88 4	77.16 21	86.28 9	85.58 7	87.23 68	95.77 14
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
APD-MVS		84.83 13	87.00 12	82.30 13	89.61 25	89.21 36	86.51 15	73.64 16	90.98 8	77.99 12	89.89 8	80.04 25	79.18 14	82.00 48	81.37 53	86.88 77	95.49 20
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
TSAR-MVS + MP.		84.39 14	86.58 18	81.83 14	88.09 39	86.47 74	85.63 19	73.62 17	90.13 11	79.24 9	89.67 9	82.99 13	77.72 19	81.22 53	80.92 63	86.68 82	94.66 29
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
SD-MVS		84.31 15	86.96 14	81.22 16	88.98 31	88.68 43	85.65 18	73.85 15	89.09 13	79.63 8	87.34 12	84.84 6	73.71 35	82.66 35	81.60 48	85.48 116	94.51 30
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
NCCC		84.16 16	85.46 22	82.64 11	92.34 8	90.57 23	86.57 14	76.51 8	86.85 19	72.91 24	77.20 32	78.69 27	79.09 15	84.64 20	84.88 16	88.44 33	95.41 22

MVS_0304		83.82 17	86.88 16	80.26 21	88.48 32	93.17 8	82.93 32	67.66 45	88.28 15	74.90 19	77.08 33	80.93 21	78.09 17	85.83 14	85.88 6	89.53 11	96.96 10

ACMMP_NAP		83.54 18	86.37 19	80.25 22	89.57 26	90.10 28	85.27 21	71.66 23	87.38 16	73.08 23	84.23 17	80.16 24	75.31 25	84.85 17	83.64 24	86.57 84	94.21 35

train_agg		83.35 19	86.93 15	79.17 27	89.70 24	88.41 47	85.60 20	72.89 21	86.31 20	66.58 42	90.48 6	82.24 16	73.06 41	83.10 31	82.64 34	87.21 72	95.30 25

DeepPCF-MVS	76.94 1	83.08 20	87.77 10	77.60 34	90.11 20	90.96 19	78.48 58	72.63 22	93.10 4	65.84 43	80.67 24	81.55 20	74.80 29	85.94 13	85.39 9	83.75 155	96.77 12

CSCG		82.90 21	84.52 24	81.02 18	91.85 11	93.43 7	87.14 12	74.01 14	81.96 32	76.14 14	70.84 38	82.49 14	69.71 70	82.32 41	85.18 12	87.26 67	95.40 23

SteuartSystems-ACMMP		82.51 22	85.35 23	79.20 26	90.25 18	89.39 34	84.79 22	70.95 25	82.86 28	68.32 38	86.44 14	77.19 28	73.07 40	83.63 25	83.64 24	87.82 47	94.34 32
Skip Steuart: Steuart Systems R&D Blog.
HFP-MVS		82.48 23	84.12 25	80.56 19	90.15 19	87.55 58	84.28 24	69.67 32	85.22 23	77.95 13	84.69 16	75.94 31	75.04 27	81.85 49	81.17 58	86.30 91	92.40 61

TSAR-MVS + GP.		82.27 24	85.98 20	77.94 32	80.72 71	88.25 50	81.12 44	67.71 44	87.10 17	73.31 22	85.23 15	83.68 9	76.64 23	80.43 61	81.47 51	88.15 42	95.66 17

DeepC-MVS_fast	75.41 2	81.69 25	82.10 32	81.20 17	91.04 17	87.81 57	83.42 27	74.04 13	83.77 26	71.09 28	66.88 49	72.44 38	79.48 11	85.08 15	84.97 14	88.12 43	93.78 42
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TSAR-MVS + ACMM		81.59 26	85.84 21	76.63 38	89.82 23	86.53 73	86.32 16	66.72 52	85.96 21	65.43 44	88.98 11	82.29 15	67.57 89	82.06 46	81.33 54	83.93 153	93.75 43

MP-MVS		80.94 27	83.49 27	77.96 31	88.48 32	88.16 51	82.82 33	69.34 34	80.79 38	69.67 34	82.35 21	77.13 29	71.60 56	80.97 58	80.96 62	85.87 102	94.06 38
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CANet		80.90 28	82.93 29	78.53 30	86.83 45	92.26 13	81.19 43	66.95 49	81.60 35	69.90 33	66.93 48	74.80 32	76.79 22	84.68 19	84.77 17	89.50 13	95.50 19

ACMMPR		80.62 29	82.98 28	77.87 33	88.41 34	87.05 65	83.02 29	69.18 35	83.91 25	68.35 37	82.89 19	73.64 35	72.16 50	80.78 59	81.13 59	86.10 96	91.43 70

DeepC-MVS	74.46 3	80.30 30	81.05 35	79.42 24	87.42 41	88.50 45	83.23 28	73.27 18	82.78 29	71.01 29	62.86 60	69.93 51	74.80 29	84.30 21	84.20 21	86.79 80	94.77 27
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DELS-MVS		79.49 31	79.84 40	79.08 28	88.26 38	92.49 10	84.12 26	70.63 27	65.27 84	69.60 36	61.29 65	66.50 60	72.75 44	88.07 3	88.03 2	89.13 16	97.22 6
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
CP-MVS		79.44 32	81.51 34	77.02 37	86.95 43	85.96 84	82.00 35	68.44 41	81.82 33	67.39 39	77.43 30	73.68 34	71.62 55	79.56 73	79.58 77	85.73 106	92.51 58

PHI-MVS		79.43 33	84.06 26	74.04 59	86.15 48	91.57 18	80.85 47	68.90 38	82.22 31	51.81 102	78.10 28	74.28 33	70.39 67	84.01 24	84.00 22	86.14 95	94.24 33

PGM-MVS		79.42 34	81.84 33	76.60 39	88.38 36	86.69 69	82.97 31	65.75 58	80.39 39	64.94 46	81.95 23	72.11 43	71.41 59	80.45 60	80.55 69	86.18 93	90.76 83

CDPH-MVS		79.39 35	82.13 31	76.19 41	89.22 30	88.34 48	84.20 25	71.00 24	79.67 44	56.97 86	77.77 29	72.24 42	68.50 82	81.33 52	82.74 30	87.23 68	92.84 55

EPNet		79.28 36	82.25 30	75.83 43	88.31 37	90.14 27	79.43 54	68.07 42	81.76 34	61.26 67	77.26 31	70.08 50	70.06 68	82.43 39	82.00 39	87.82 47	92.09 64
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MSLP-MVS++		78.57 37	77.33 54	80.02 23	88.39 35	84.79 90	84.62 23	66.17 56	75.96 53	78.40 10	61.59 63	71.47 45	73.54 38	78.43 83	78.88 83	88.97 19	90.18 90

3Dnovator	70.49 5	78.42 38	76.77 59	80.35 20	91.43 15	90.27 25	81.84 37	70.79 26	72.10 60	71.95 25	50.02 110	67.86 57	77.47 20	82.89 32	84.24 20	88.61 28	89.99 93

HQP-MVS		78.26 39	80.91 36	75.17 49	85.67 50	84.33 96	83.01 30	69.38 33	79.88 42	55.83 87	79.85 25	64.90 67	70.81 61	82.46 37	81.78 43	86.30 91	93.18 49

X-MVS		78.16 40	80.55 37	75.38 47	87.99 40	86.27 79	81.05 45	68.98 36	78.33 46	61.07 70	75.25 35	72.27 39	67.52 91	80.03 66	80.52 70	85.66 113	91.20 74

3Dnovator+	70.16 6	77.87 41	77.29 55	78.55 29	89.25 29	88.32 49	80.09 50	67.95 43	74.89 58	71.83 26	52.05 103	70.68 48	76.27 24	82.27 42	82.04 37	85.92 99	90.77 82

sasdasda		77.65 42	79.59 41	75.39 45	81.52 63	89.83 32	81.32 41	60.74 114	80.05 40	66.72 40	68.43 42	65.09 63	74.72 31	78.87 77	82.73 31	87.32 63	92.16 62

canonicalmvs		77.65 42	79.59 41	75.39 45	81.52 63	89.83 32	81.32 41	60.74 114	80.05 40	66.72 40	68.43 42	65.09 63	74.72 31	78.87 77	82.73 31	87.32 63	92.16 62

ACMMP		77.61 44	79.59 41	75.30 48	85.87 49	85.58 85	81.42 39	67.38 48	79.38 45	62.61 55	78.53 27	65.79 62	68.80 81	78.56 82	78.50 88	85.75 103	90.80 79
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
QAPM		77.50 45	77.43 53	77.59 35	91.52 14	92.00 14	81.41 40	70.63 27	66.22 76	58.05 81	54.70 88	71.79 44	74.49 33	82.46 37	82.04 37	89.46 15	92.79 57

MVS_111021_HR		77.42 46	78.40 49	76.28 40	86.95 43	90.68 21	77.41 70	70.56 30	66.21 78	62.48 57	66.17 52	63.98 71	72.08 51	82.87 33	83.15 28	88.24 39	95.71 16

CLD-MVS		77.36 47	77.29 55	77.45 36	82.21 59	88.11 52	81.92 36	68.96 37	77.97 48	69.62 35	62.08 61	59.44 95	73.57 37	81.75 50	81.27 56	88.41 34	90.39 87
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MAR-MVS		77.19 48	78.37 50	75.81 44	89.87 22	90.58 22	79.33 55	65.56 60	77.62 50	58.33 80	59.24 73	67.98 55	74.83 28	82.37 40	83.12 29	86.95 75	87.67 118
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
MVSTER		76.92 49	79.92 39	73.42 63	74.98 123	82.97 104	78.15 63	63.41 78	78.02 47	64.41 48	67.54 46	72.80 37	71.05 60	83.29 30	83.73 23	88.53 31	91.12 75

PVSNet_BlendedMVS		76.84 50	78.47 47	74.95 51	82.37 57	89.90 30	75.45 82	65.45 61	74.99 56	70.66 31	63.07 58	58.27 103	67.60 86	84.24 22	81.70 45	88.18 40	97.10 8

PVSNet_Blended		76.84 50	78.47 47	74.95 51	82.37 57	89.90 30	75.45 82	65.45 61	74.99 56	70.66 31	63.07 58	58.27 103	67.60 86	84.24 22	81.70 45	88.18 40	97.10 8

ETV-MVS		76.25 52	80.22 38	71.63 78	78.23 93	87.95 56	72.75 102	60.27 120	77.50 51	57.73 82	71.53 37	66.60 59	73.16 39	80.99 57	81.23 57	87.63 55	95.73 15

AdaColmap		76.23 53	73.55 79	79.35 25	89.38 27	85.00 89	79.99 52	73.04 20	76.60 52	71.17 27	55.18 87	57.99 105	77.87 18	76.82 100	76.82 103	84.67 138	86.45 125

EC-MVSNet		76.05 54	78.87 44	72.77 67	78.87 88	86.63 70	77.50 69	57.04 146	75.34 54	61.68 64	64.20 55	69.56 52	73.96 34	82.12 44	80.65 67	87.57 56	93.57 45

CS-MVS		75.84 55	78.61 46	72.61 70	79.03 85	86.74 68	74.43 96	60.27 120	74.15 59	62.78 54	66.26 51	64.25 70	72.81 43	83.36 28	81.69 47	86.32 89	93.85 41

PCF-MVS	70.85 4	75.73 56	76.55 62	74.78 55	83.67 53	88.04 55	81.47 38	70.62 29	69.24 71	57.52 84	60.59 69	69.18 53	70.65 64	77.11 97	77.65 97	84.75 136	94.01 39
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
casdiffmvs_mvg		75.57 57	76.04 64	75.02 50	80.48 74	89.31 35	80.79 48	64.04 72	66.95 74	63.87 49	57.52 76	61.33 84	72.90 42	82.01 47	81.99 40	88.03 44	93.16 50
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
CPTT-MVS		75.43 58	77.13 57	73.44 61	81.43 65	82.55 110	80.96 46	64.35 66	77.95 49	61.39 66	69.20 41	70.94 47	69.38 77	73.89 132	73.32 146	83.14 165	92.06 65

MVS_Test		75.22 59	76.69 60	73.51 60	79.30 82	88.82 42	80.06 51	58.74 125	69.77 67	57.50 85	59.78 72	61.35 82	75.31 25	82.07 45	83.60 26	90.13 5	91.41 72

casdiffmvs		75.20 60	75.69 67	74.63 56	79.26 84	89.07 37	78.47 59	63.59 76	67.05 73	63.79 50	55.72 85	60.32 89	73.58 36	82.16 43	81.78 43	89.08 18	93.72 44
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		75.09 61	77.84 51	71.87 77	79.27 83	86.92 66	70.53 130	60.36 118	75.13 55	63.13 52	67.92 45	65.08 65	71.43 57	78.15 89	78.51 87	86.53 86	93.16 50

OpenMVS	67.62 8	74.92 62	73.91 77	76.09 42	90.10 21	90.38 24	78.01 64	66.35 54	66.09 79	62.80 53	46.33 135	64.55 69	71.77 54	79.92 68	80.88 64	87.52 58	89.20 102

viewcassd2359sk11		74.75 63	74.61 74	74.90 53	79.62 76	88.96 40	78.47 59	64.08 70	63.51 89	65.27 45	57.02 78	57.89 107	72.25 48	80.30 64	81.57 49	89.72 7	93.04 52

viewmanbaseed2359cas		74.53 64	74.69 73	74.35 57	79.37 80	88.90 41	78.96 56	64.07 71	63.67 86	62.19 58	56.95 79	58.42 101	72.04 52	80.08 65	81.92 41	89.47 14	92.91 54

diffmvs		74.32 65	75.42 68	73.04 65	75.60 119	87.27 61	78.20 62	62.96 83	68.66 72	61.89 60	59.79 71	59.84 92	71.80 53	78.30 86	79.87 72	87.80 49	94.23 34
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
MGCFI-Net		74.26 66	78.69 45	69.10 94	80.64 72	87.32 60	73.21 101	59.20 123	79.76 43	50.18 112	68.10 44	64.86 68	64.65 102	78.28 87	80.83 65	86.69 81	91.69 69

MVS_111021_LR		74.26 66	75.95 65	72.27 72	79.43 79	85.04 88	72.71 103	65.27 63	70.92 63	63.58 51	69.32 40	60.31 90	69.43 75	77.01 98	77.15 100	83.22 162	91.93 67

viewdifsd2359ckpt13		74.11 68	74.06 76	74.18 58	79.34 81	89.07 37	78.31 61	64.25 68	62.52 92	62.06 59	55.80 83	56.70 113	72.29 47	80.35 63	81.47 51	88.80 21	92.47 60

OMC-MVS		74.03 69	75.82 66	71.95 75	79.56 77	80.98 124	75.35 84	63.21 79	84.48 24	61.83 61	61.54 64	66.89 58	69.41 76	76.60 102	74.07 136	82.34 175	86.15 129

DI_MVS_pp		73.94 70	74.85 70	72.88 66	76.57 111	86.80 67	80.41 49	61.47 103	62.35 94	59.44 78	47.91 118	68.12 54	72.24 49	82.84 34	81.50 50	87.15 74	94.42 31

diffmvs_AUTHOR		73.73 71	74.73 71	72.56 71	75.05 122	87.15 64	77.82 67	62.29 93	66.22 76	61.10 69	57.92 74	59.72 93	71.43 57	78.25 88	79.68 75	87.71 51	94.17 36

EIA-MVS		73.48 72	76.05 63	70.47 84	78.12 94	87.21 62	71.78 112	60.63 116	69.66 68	55.56 91	64.86 54	60.69 86	69.53 73	77.35 96	78.59 84	87.22 70	94.01 39

TSAR-MVS + COLMAP		73.09 73	76.86 58	68.71 97	74.97 124	82.49 111	74.51 93	61.83 98	83.16 27	49.31 115	82.22 22	51.62 140	68.94 80	78.76 81	75.52 121	82.67 170	84.23 147

viewmacassd2359aftdt		73.00 74	72.63 85	73.44 61	78.70 89	88.45 46	78.52 57	63.49 77	57.74 115	60.15 76	52.57 102	57.01 111	70.69 63	78.85 79	81.29 55	89.10 17	92.48 59

baseline		72.89 75	74.46 75	71.07 79	75.99 115	87.50 59	74.57 88	60.49 117	70.72 64	57.60 83	60.63 68	60.97 85	70.79 62	75.27 116	76.33 109	86.94 76	89.79 96

CANet_DTU		72.84 76	76.63 61	68.43 102	76.81 108	86.62 72	75.54 81	54.71 172	72.06 61	43.54 138	67.11 47	58.46 99	72.40 46	81.13 56	80.82 66	87.57 56	90.21 89

OPM-MVS		72.74 77	70.93 98	74.85 54	85.30 51	84.34 95	82.82 33	69.79 31	49.96 149	55.39 93	54.09 95	60.14 91	70.04 69	80.38 62	79.43 78	85.74 105	88.20 114
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
CHOSEN 1792x2688		72.55 78	71.98 88	73.22 64	86.57 46	92.41 11	75.63 78	66.77 51	62.08 95	52.32 99	30.27 204	50.74 143	66.14 94	86.22 12	85.41 8	91.90 1	96.75 13

viewmambaseed2359dif		72.54 79	72.88 84	72.13 73	74.78 125	86.45 75	77.24 72	61.65 102	62.61 91	61.83 61	55.85 81	57.51 109	70.64 65	75.71 111	77.90 96	86.65 83	94.16 37

CostFormer		72.18 80	73.90 78	70.18 86	79.47 78	86.19 82	76.94 74	48.62 193	66.07 80	60.40 75	54.14 94	65.82 61	67.98 83	75.84 110	76.41 108	87.67 53	92.83 56

ACMP	68.86 7	72.15 81	72.25 86	72.03 74	80.96 67	80.87 126	77.93 65	64.13 69	69.29 69	60.79 73	64.04 56	53.54 133	63.91 105	73.74 135	75.27 122	84.45 145	88.98 104
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LGP-MVS_train		72.02 82	73.18 82	70.67 83	82.13 60	80.26 132	79.58 53	63.04 81	70.09 65	51.98 100	65.06 53	55.62 121	62.49 115	75.97 109	76.32 110	84.80 135	88.93 105

PVSNet_Blended_VisFu		71.76 83	73.54 80	69.69 89	79.01 86	87.16 63	72.05 109	61.80 99	56.46 120	59.66 77	53.88 96	62.48 74	59.08 138	81.17 54	78.90 82	86.53 86	94.74 28

baseline1		71.47 84	72.02 87	70.82 81	80.56 73	84.51 92	76.61 75	66.93 50	56.22 122	48.66 116	55.40 86	60.43 88	62.55 114	83.35 29	80.99 60	89.60 9	83.28 155

TAPA-MVS	67.10 9	71.45 85	73.47 81	69.10 94	77.04 106	80.78 127	73.81 97	62.10 94	80.80 37	51.28 103	60.91 66	63.80 73	67.98 83	74.59 122	72.42 160	82.37 174	80.97 169
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
ET-MVSNet_ETH3D		71.38 86	74.70 72	67.51 109	51.61 219	88.06 54	77.29 71	60.95 113	63.61 87	48.36 118	66.60 50	60.67 87	79.55 10	73.56 136	80.58 68	87.30 66	89.80 95

CNLPA		71.37 87	70.27 103	72.66 69	80.79 70	81.33 120	71.07 125	65.75 58	82.36 30	64.80 47	42.46 147	56.49 114	72.70 45	73.00 143	70.52 179	80.84 188	85.76 135

baseline2		71.22 88	73.01 83	69.13 93	75.76 117	86.34 78	71.23 120	62.78 89	62.62 90	52.85 98	57.32 77	54.31 128	63.27 110	79.74 71	79.31 79	88.89 20	91.43 70

Effi-MVS+		70.42 89	71.23 95	69.47 90	78.04 95	85.24 87	75.57 80	58.88 124	59.56 104	48.47 117	52.73 101	54.94 124	69.69 71	78.34 85	77.06 101	86.18 93	90.73 84

ACMM	66.70 10	70.42 89	68.49 113	72.67 68	82.85 54	77.76 155	77.70 68	64.76 65	64.61 85	60.74 74	49.29 112	53.97 131	65.86 95	74.97 118	75.57 119	84.13 152	83.29 154
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
FMVSNet3		70.41 91	71.89 90	68.68 98	70.89 149	79.42 139	75.63 78	60.97 110	65.32 81	51.06 104	47.37 123	62.05 76	64.90 99	82.49 36	82.27 36	88.64 27	84.34 146

PMMVS		70.37 92	75.06 69	64.90 124	71.46 143	81.88 112	64.10 164	55.64 158	71.31 62	46.69 122	70.69 39	58.56 96	69.53 73	79.03 76	75.63 117	81.96 179	88.32 113

MS-PatchMatch		70.34 93	69.00 109	71.91 76	85.20 52	85.35 86	77.84 66	61.77 100	58.01 113	55.40 92	41.26 154	58.34 102	61.69 118	81.70 51	78.29 89	89.56 10	80.02 172

FA-MVS(training)		70.24 94	71.77 91	68.45 101	77.52 102	86.03 83	73.33 99	49.12 192	63.55 88	55.77 88	48.91 115	56.26 115	67.78 85	77.60 91	79.62 76	87.19 73	90.40 86

test2506		69.26 95	70.79 99	67.48 110	78.64 90	86.40 76	72.22 107	62.75 90	58.05 111	45.24 128	50.76 106	54.93 125	58.05 144	79.82 69	79.70 73	87.96 45	85.90 133

GBi-Net		69.21 96	70.40 101	67.81 106	69.49 154	78.65 145	74.54 89	60.97 110	65.32 81	51.06 104	47.37 123	62.05 76	63.43 107	77.49 92	78.22 90	87.37 60	83.73 149

test1		69.21 96	70.40 101	67.81 106	69.49 154	78.65 145	74.54 89	60.97 110	65.32 81	51.06 104	47.37 123	62.05 76	63.43 107	77.49 92	78.22 90	87.37 60	83.73 149

viewdifsd2359ckpt11		69.15 98	68.30 115	70.14 87	73.44 134	82.79 106	72.24 105	61.20 106	54.59 137	61.70 63	53.16 97	52.89 137	67.57 89	71.81 157	72.73 157	84.66 139	90.10 91

viewmsd2359difaftdt		69.14 99	68.29 116	70.13 88	73.44 134	82.79 106	72.24 105	61.20 106	54.60 136	61.68 64	53.16 97	52.87 138	67.58 88	71.82 155	72.73 157	84.66 139	90.10 91

DCV-MVSNet		69.13 100	69.07 108	69.21 92	77.65 99	77.52 157	74.68 87	57.85 135	54.92 132	55.34 94	55.74 84	55.56 122	66.35 93	75.05 117	76.56 106	83.35 159	88.13 115

IB-MVS	64.48 11	69.02 101	68.97 110	69.09 96	81.75 62	89.01 39	64.50 162	64.91 64	56.65 118	62.59 56	47.89 119	45.23 156	51.99 166	69.18 181	81.88 42	88.77 23	92.93 53
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
GeoE		68.96 102	69.32 106	68.54 99	76.61 110	83.12 103	71.78 112	56.87 148	60.21 102	54.86 95	45.95 136	54.79 127	64.27 103	74.59 122	75.54 120	86.84 79	91.01 77

FC-MVSNet-train		68.83 103	68.29 116	69.47 90	78.35 92	79.94 133	64.72 161	66.38 53	54.96 131	54.51 96	56.75 80	47.91 150	66.91 92	75.57 115	75.75 115	85.92 99	87.12 120

PLC	64.00 12	68.54 104	66.66 129	70.74 82	80.28 75	74.88 176	72.64 104	63.70 75	69.26 70	55.71 89	47.24 126	55.31 123	70.42 66	72.05 154	70.67 177	81.66 182	77.19 180
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
Anonymous20231211		68.44 105	66.37 132	70.86 80	77.58 100	83.49 101	75.15 85	61.89 97	52.54 142	58.50 79	28.89 206	56.78 112	69.29 78	74.96 120	76.61 104	82.73 168	91.36 73

HyFIR lowres test		68.39 106	68.28 118	68.52 100	80.85 68	88.11 52	71.08 124	58.09 130	54.87 134	47.80 121	27.55 210	55.80 119	64.97 98	79.11 75	79.14 81	88.31 37	93.35 46

thisisatest0530		68.38 107	70.98 97	65.35 120	72.61 137	84.42 93	68.21 143	57.98 131	59.77 103	50.80 107	54.63 89	58.48 98	57.92 146	76.99 99	77.47 98	84.60 141	85.07 139

test-LLR		68.23 108	71.61 93	64.28 131	71.37 144	81.32 121	63.98 167	61.03 108	58.62 107	42.96 143	52.74 99	61.65 80	57.74 149	75.64 113	78.09 93	88.61 28	93.21 47

FMVSNet2		68.06 109	68.57 112	67.45 111	69.49 154	78.65 145	74.54 89	60.23 122	56.29 121	49.64 114	42.13 150	57.08 110	63.43 107	81.15 55	80.99 60	87.37 60	83.73 149

tttt0517		67.99 110	70.61 100	64.94 123	71.94 142	83.96 99	67.62 147	57.98 131	59.30 105	49.90 113	54.50 92	57.98 106	57.92 146	76.48 103	77.47 98	84.24 148	84.58 143

ECVR-MVS		67.93 111	68.49 113	67.28 113	78.64 90	86.40 76	72.22 107	62.75 90	58.05 111	44.06 136	40.92 158	48.20 148	58.05 144	79.82 69	79.70 73	87.96 45	86.32 128

dmvs_re		67.60 112	67.21 126	68.06 104	74.07 127	79.01 141	73.31 100	68.74 39	58.27 109	42.07 149	49.72 111	43.96 159	60.66 124	76.79 101	78.04 95	89.51 12	84.69 142

Fast-Effi-MVS+		67.59 113	67.56 123	67.62 108	73.67 130	81.14 123	71.12 123	54.79 171	58.88 106	50.61 109	46.70 133	47.05 152	69.12 79	76.06 108	76.44 107	86.43 88	86.65 123

EPP-MVSNet		67.58 114	71.10 96	63.48 137	75.71 118	83.35 102	66.85 153	57.83 136	53.02 141	41.15 153	55.82 82	67.89 56	56.01 155	74.40 125	72.92 154	83.33 160	90.30 88

UGNet		67.57 115	71.69 92	62.76 144	69.88 152	82.58 109	66.43 157	58.64 126	54.71 135	51.87 101	61.74 62	62.01 79	45.46 189	74.78 121	74.99 123	84.24 148	91.02 76
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
tpm cat1		67.47 116	67.05 127	67.98 105	76.63 109	81.51 118	74.49 94	47.65 198	61.18 97	61.12 68	42.51 146	53.02 136	64.74 101	70.11 175	71.50 166	83.22 162	89.49 98

TESTMET0.1,1		67.38 117	71.61 93	62.45 147	66.05 177	81.32 121	63.98 167	55.36 163	58.62 107	42.96 143	52.74 99	61.65 80	57.74 149	75.64 113	78.09 93	88.61 28	93.21 47

IS_MVSNet		67.29 118	71.98 88	61.82 151	76.92 107	84.32 97	65.90 160	58.22 128	55.75 126	39.22 162	54.51 91	62.47 75	45.99 187	78.83 80	78.52 86	84.70 137	89.47 99

tpmrst		67.15 119	68.12 120	66.03 117	76.21 113	80.98 124	71.27 119	45.05 204	60.69 100	50.63 108	46.95 131	54.15 130	65.30 96	71.80 158	71.77 164	87.72 50	90.48 85

thres100view900		67.14 120	66.09 135	68.38 103	77.70 97	83.84 100	74.52 92	66.33 55	49.16 153	43.40 140	43.24 139	41.34 164	62.59 113	79.31 74	75.92 114	85.73 106	89.81 94

test1111		66.72 121	67.80 121	65.45 119	77.42 104	86.63 70	69.69 134	62.98 82	55.29 128	39.47 159	40.12 163	47.11 151	55.70 156	79.96 67	80.00 71	87.47 59	85.49 138

EPMVS		66.21 122	67.49 124	64.73 125	75.81 116	84.20 98	68.94 139	44.37 208	61.55 96	48.07 120	49.21 114	54.87 126	62.88 111	71.82 155	71.40 170	88.28 38	79.37 175

EPNet_dtu		66.17 123	70.13 104	61.54 153	81.04 66	77.39 159	68.87 140	62.50 92	69.78 66	33.51 190	63.77 57	56.22 116	37.65 203	72.20 151	72.18 163	85.69 109	79.38 174
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
IterMVS-LS		66.08 124	66.56 131	65.51 118	73.67 130	74.88 176	70.89 127	53.55 179	50.42 147	48.32 119	50.59 108	55.66 120	61.83 117	73.93 131	74.42 132	84.82 134	86.01 131
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tfpn200view9		65.90 125	64.96 139	67.00 114	77.70 97	81.58 116	71.71 115	62.94 86	49.16 153	43.40 140	43.24 139	41.34 164	61.42 120	76.24 105	74.63 128	84.84 131	88.52 111

thres200		65.58 126	64.74 141	66.56 115	77.52 102	81.61 114	73.44 98	62.95 84	46.23 165	42.45 147	42.76 141	41.18 166	58.12 142	76.24 105	75.59 118	84.89 129	89.58 97

MSDG		65.57 127	61.57 164	70.24 85	82.02 61	76.47 164	74.46 95	68.73 40	56.52 119	50.33 110	38.47 169	41.10 168	62.42 116	72.12 152	72.94 153	83.47 158	73.37 194

Vis-MVSNet		65.53 128	69.83 105	60.52 157	70.80 150	84.59 91	66.37 159	55.47 162	48.40 156	40.62 157	57.67 75	58.43 100	45.37 190	77.49 92	76.24 111	84.47 144	85.99 132
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
PatchmatchNet		65.43 129	67.71 122	62.78 143	73.49 132	82.83 105	66.42 158	45.40 203	60.40 101	45.27 127	49.22 113	57.60 108	60.01 130	70.61 167	71.38 171	86.08 97	81.91 166
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MDTV_nov1_ep13		65.21 130	67.28 125	62.79 142	70.91 148	81.72 113	69.28 138	49.50 191	58.08 110	43.94 137	50.50 109	56.02 117	58.86 139	70.72 166	73.37 144	84.24 148	80.52 171

thres400		65.18 131	64.44 143	66.04 116	76.40 112	82.63 108	71.52 117	64.27 67	44.93 171	40.69 156	41.86 151	40.79 170	58.12 142	77.67 90	74.64 127	85.26 119	88.56 110

tpm		64.85 132	66.02 136	63.48 137	74.52 126	78.38 148	70.98 126	44.99 206	51.61 144	43.28 142	47.66 121	53.18 134	60.57 125	70.58 169	71.30 173	86.54 85	89.45 100

UA-Net		64.62 133	68.23 119	60.42 158	77.53 101	81.38 119	60.08 186	57.47 141	47.01 160	44.75 132	60.68 67	71.32 46	41.84 197	73.27 138	72.25 162	80.83 189	71.68 199

Effi-MVS+-dtu		64.58 134	64.08 144	65.16 121	73.04 136	75.17 175	70.68 129	56.23 152	54.12 139	44.71 133	47.42 122	51.10 141	63.82 106	68.08 184	66.32 196	82.47 173	86.38 126

GA-MVS		64.55 135	65.76 138	63.12 139	69.68 153	81.56 117	69.59 135	58.16 129	45.23 170	35.58 182	47.01 130	41.82 163	59.41 134	79.62 72	78.54 85	86.32 89	86.56 124

LS3D		64.54 136	62.14 160	67.34 112	80.85 68	75.79 170	69.99 131	65.87 57	60.77 99	44.35 134	42.43 148	45.95 155	65.01 97	69.88 176	68.69 186	77.97 203	71.43 201

CDS-MVSNet		64.22 137	65.89 137	62.28 149	70.05 151	80.59 128	69.91 133	57.98 131	43.53 175	46.58 123	48.22 117	50.76 142	46.45 184	75.68 112	76.08 112	82.70 169	86.34 127
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
dps		64.08 138	63.22 148	65.08 122	75.27 121	79.65 136	66.68 155	46.63 202	56.94 116	55.67 90	43.96 138	43.63 161	64.00 104	69.50 180	69.82 181	82.25 176	79.02 176

test-mter		64.06 139	69.24 107	58.01 172	59.07 206	77.40 158	59.13 189	48.11 196	55.64 127	39.18 163	51.56 105	58.54 97	55.38 158	73.52 137	76.00 113	87.22 70	92.05 66

SCA		63.90 140	66.67 128	60.66 156	73.75 128	71.78 191	59.87 187	43.66 209	61.13 98	45.03 130	51.64 104	59.45 94	57.92 146	70.96 164	70.80 175	83.71 156	80.92 170

thres600view7		63.77 141	63.14 149	64.51 127	75.49 120	81.61 114	69.59 135	62.95 84	43.96 174	38.90 164	41.09 155	40.24 175	55.25 159	76.24 105	71.54 165	84.89 129	87.30 119

v2v482		63.68 142	62.85 154	64.65 126	68.01 163	80.46 130	71.90 110	57.60 138	44.26 172	42.82 145	39.80 165	38.62 180	61.56 119	73.06 141	74.86 125	86.03 98	88.90 107

FMVSNet1		63.48 143	63.07 150	63.97 133	65.31 182	76.37 166	71.77 114	57.90 134	43.32 176	45.66 125	35.06 192	49.43 145	58.57 140	77.49 92	78.22 90	84.59 142	81.60 168

v8		63.44 144	62.58 156	64.43 128	68.28 162	78.07 150	71.82 111	54.85 169	46.70 163	45.20 129	39.40 166	40.91 169	60.54 126	72.85 145	74.39 133	85.92 99	85.76 135

pmmvs4		63.14 145	62.46 157	63.94 134	66.03 178	76.40 165	66.82 154	57.60 138	56.74 117	50.26 111	40.81 159	37.51 183	59.26 136	71.75 159	71.48 167	83.68 157	82.53 160

Fast-Effi-MVS+-dtu		63.05 146	64.72 142	61.11 154	71.21 147	76.81 163	70.72 128	43.13 213	52.51 143	35.34 183	46.55 134	46.36 153	61.40 121	71.57 161	71.44 168	84.84 131	87.79 117

v1144		63.00 147	62.39 158	63.70 136	67.72 166	80.27 131	71.23 120	56.40 149	42.51 177	40.81 155	38.12 173	37.73 181	60.42 128	74.46 124	74.55 130	85.64 114	89.12 103

v10		63.00 147	62.22 159	63.90 135	67.88 165	77.78 154	71.59 116	54.34 173	45.37 169	42.76 146	38.53 168	38.93 178	61.05 123	74.39 126	74.52 131	85.75 103	86.04 130

V42		62.86 149	62.97 151	62.74 145	60.84 200	78.99 143	71.46 118	57.13 145	46.85 161	44.28 135	38.87 167	40.73 172	57.63 151	72.60 149	74.14 134	85.09 124	88.63 109

gg-mvs-nofinetune		62.34 150	66.19 134	57.86 174	76.15 114	88.61 44	71.18 122	41.24 221	25.74 223	13.16 225	22.91 218	63.97 72	54.52 161	85.06 16	85.25 11	90.92 3	91.78 68

CR-MVSNet		62.31 151	64.75 140	59.47 164	68.63 160	71.29 194	67.53 148	43.18 211	55.83 124	41.40 150	41.04 156	55.85 118	57.29 152	72.76 146	73.27 148	78.77 200	83.23 156

UniMVSNet_NR-MVSNet		62.30 152	63.51 147	60.89 155	69.48 157	77.83 153	64.07 165	63.94 73	50.03 148	31.17 195	44.82 137	41.12 167	51.37 169	71.02 163	74.81 126	85.30 118	84.95 140

v1192		62.25 153	61.64 163	62.96 140	66.88 171	79.72 135	69.96 132	55.77 156	41.58 182	39.42 160	37.05 178	35.96 194	60.50 127	74.30 129	74.09 135	85.24 120	88.76 108

Vis-MVSNet (Re-imp)		62.25 153	68.74 111	54.68 189	73.70 129	78.74 144	56.51 195	57.49 140	55.22 129	26.86 203	54.56 90	61.35 82	31.06 205	73.10 140	74.90 124	82.49 172	83.31 153

CHOSEN 280x420		62.23 155	66.57 130	57.17 180	59.88 203	68.92 200	61.20 183	42.28 215	54.17 138	39.57 158	47.78 120	64.97 66	62.68 112	73.85 133	69.52 184	77.43 204	86.75 122

PatchMatch-RL		62.22 156	60.69 170	64.01 132	68.74 159	75.75 171	59.27 188	60.35 119	56.09 123	53.80 97	47.06 129	36.45 189	64.80 100	68.22 183	67.22 190	77.10 206	74.02 189

v144192		62.05 157	61.46 165	62.73 146	66.59 175	79.87 134	69.30 137	55.88 154	41.50 184	39.41 161	37.23 176	36.45 189	59.62 132	72.69 148	73.51 141	85.61 115	88.93 105

v148		62.00 158	61.19 167	62.96 140	67.46 169	79.49 138	67.87 144	57.66 137	42.30 178	45.02 131	38.20 172	38.89 179	54.77 160	69.83 177	72.60 159	84.96 125	87.01 121

IterMVS		61.87 159	63.55 146	59.90 160	67.29 170	72.20 188	67.34 151	48.56 194	47.48 159	37.86 171	47.07 128	48.27 146	54.08 162	72.12 152	73.71 139	84.30 147	83.99 148
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1921920		61.66 160	61.10 168	62.31 148	66.32 176	79.57 137	68.41 142	55.49 161	41.03 185	38.69 165	36.64 184	35.27 197	59.60 133	73.23 139	73.41 143	85.37 117	88.51 112

ACMH	59.42 14	61.59 161	59.22 180	64.36 130	78.92 87	78.26 149	67.65 146	67.48 47	39.81 190	30.98 197	38.25 171	34.59 200	61.37 122	70.55 170	73.47 142	79.74 195	79.59 173
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMH+	60.36 13	61.16 162	58.38 182	64.42 129	77.37 105	74.35 181	68.45 141	62.81 88	45.86 167	38.48 166	35.71 187	37.35 184	59.81 131	67.24 186	69.80 183	79.58 196	78.32 178

v1240		61.09 163	60.55 172	61.72 152	65.92 180	79.28 140	67.16 152	54.91 168	39.79 191	38.10 168	36.08 186	34.64 199	59.15 137	72.86 144	73.36 145	85.10 122	87.84 116

NR-MVSNet		61.08 164	62.09 161	59.90 160	71.96 141	75.87 168	63.60 171	61.96 95	49.31 151	27.95 200	42.76 141	33.85 204	48.82 176	74.35 127	74.05 137	85.13 121	84.45 144

DU-MVS		60.87 165	61.82 162	59.76 162	66.69 172	75.87 168	64.07 165	61.96 95	49.31 151	31.17 195	42.76 141	36.95 186	51.37 169	69.67 178	73.20 151	83.30 161	84.95 140

UniMVSNet (Re)		60.62 166	62.93 153	57.92 173	67.64 167	77.90 152	61.75 180	61.24 105	49.83 150	29.80 199	42.57 144	40.62 173	43.36 193	70.49 171	73.27 148	83.76 154	85.81 134

PatchT		60.46 167	63.85 145	56.51 183	65.95 179	75.68 172	47.34 209	41.39 218	53.89 140	41.40 150	37.84 174	50.30 144	57.29 152	72.76 146	73.27 148	85.67 110	83.23 156

TranMVSNet+NR-MVSNet		60.38 168	61.30 166	59.30 166	68.34 161	75.57 174	63.38 174	63.78 74	46.74 162	27.73 201	42.56 145	36.84 187	47.66 179	70.36 172	74.59 129	84.91 128	82.46 161

IterMVS-SCA-FT		60.21 169	62.97 151	57.00 181	66.64 174	71.84 189	67.53 148	46.93 201	47.56 158	36.77 176	46.85 132	48.21 147	52.51 165	70.36 172	72.40 161	71.63 219	83.53 152

pmmvs5		59.72 170	60.24 174	59.11 168	62.77 194	77.33 160	63.17 175	54.00 176	40.21 189	37.23 172	40.41 160	35.99 193	51.75 167	72.55 150	72.74 156	85.72 108	82.45 162

USDC		59.69 171	60.03 176	59.28 167	64.04 187	71.84 189	63.15 176	55.36 163	54.90 133	35.02 184	48.34 116	29.79 216	58.16 141	70.60 168	71.33 172	79.99 193	73.42 193

Baseline_NR-MVSNet		59.47 172	60.28 173	58.54 171	66.69 172	73.90 182	61.63 181	62.90 87	49.15 155	26.87 202	35.18 191	37.62 182	48.20 177	69.67 178	73.61 140	84.92 126	82.82 159

thisisatest0515		59.37 173	60.68 171	57.84 175	64.39 186	75.65 173	58.56 191	53.86 177	41.55 183	42.12 148	40.40 161	39.59 176	47.09 182	71.69 160	73.79 138	81.02 187	82.08 165

pm-mvs1		59.21 174	59.58 179	58.77 170	67.97 164	77.07 162	64.12 163	57.20 143	34.73 207	36.86 173	35.34 189	40.54 174	43.34 194	74.32 128	73.30 147	83.13 166	81.77 167

tfpnnormal		58.97 175	56.48 190	61.89 150	71.27 146	76.21 167	66.65 156	61.76 101	32.90 210	36.41 177	27.83 209	29.14 217	50.64 173	73.06 141	73.05 152	84.58 143	83.15 158

FMVSNet5		58.86 176	60.24 174	57.25 179	52.66 218	66.25 206	63.77 170	52.86 184	57.85 114	37.92 170	36.12 185	52.22 139	51.37 169	70.88 165	71.43 169	84.92 126	66.91 210

TAMVS		58.86 176	60.91 169	56.47 184	62.38 196	77.57 156	58.97 190	52.98 182	38.76 194	36.17 178	42.26 149	47.94 149	46.45 184	70.23 174	70.79 176	81.86 180	78.82 177

EG-PatchMatch MVS		58.73 178	58.03 185	59.55 163	72.32 138	80.49 129	63.44 173	55.55 160	32.49 211	38.31 167	28.87 207	37.22 185	42.84 195	74.30 129	75.70 116	84.84 131	77.14 181

RPMNet		58.63 179	62.80 155	53.76 194	67.59 168	71.29 194	54.60 198	38.13 223	55.83 124	35.70 181	41.58 153	53.04 135	47.89 178	66.10 188	67.38 188	78.65 202	84.40 145

ADS-MVSNet		58.40 180	59.16 181	57.52 177	65.80 181	74.57 180	60.26 184	40.17 222	50.51 146	38.01 169	40.11 164	44.72 157	59.36 135	64.91 193	66.55 194	81.53 183	72.72 197

UniMVSNet_ETH3D		57.83 181	56.46 191	59.43 165	63.24 191	73.22 185	67.70 145	55.58 159	36.17 202	36.84 174	32.64 196	35.14 198	51.50 168	65.81 189	69.81 182	81.73 181	82.44 163

TransMVSNet (Re)		57.83 181	56.90 188	58.91 169	72.26 139	74.69 179	63.57 172	61.42 104	32.30 212	32.65 191	33.97 194	35.96 194	39.17 201	73.84 134	72.84 155	84.37 146	74.69 187

MIMVSNet		57.78 183	59.71 178	55.53 186	54.79 214	77.10 161	63.89 169	45.02 205	46.59 164	36.79 175	28.36 208	40.77 171	45.84 188	74.97 118	76.58 105	86.87 78	73.60 192

CMPMVS	43.63 17	57.67 184	55.43 192	60.28 159	72.01 140	79.00 142	62.77 177	53.23 181	41.77 181	45.42 126	30.74 203	39.03 177	53.01 164	64.81 195	64.65 202	75.26 211	68.03 208
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test0.0.03 1		57.35 185	59.89 177	54.38 192	71.37 144	73.45 184	52.71 201	61.03 108	46.11 166	26.33 204	41.73 152	44.08 158	29.72 207	71.43 162	70.90 174	85.10 122	71.56 200

v7n		57.04 186	56.64 189	57.52 177	62.85 193	74.75 178	61.76 179	51.80 187	35.58 206	36.02 180	32.33 198	33.61 205	50.16 174	67.73 185	70.34 180	82.51 171	82.12 164

pmmvs-eth3d		55.20 187	53.95 196	56.65 182	57.34 212	67.77 202	57.54 193	53.74 178	40.93 186	41.09 154	31.19 202	29.10 218	49.07 175	65.54 190	67.28 189	81.14 185	75.81 182

COLMAP_ROB	51.17 15	55.13 188	52.90 201	57.73 176	73.47 133	67.21 204	62.13 178	55.82 155	47.83 157	34.39 186	31.60 200	34.24 201	44.90 191	63.88 200	62.52 209	75.67 209	63.02 218
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
RPSCF		55.07 189	58.06 184	51.57 196	48.87 223	58.95 220	53.68 200	41.26 220	62.42 93	45.88 124	54.38 93	54.26 129	53.75 163	57.15 211	53.53 221	66.01 221	65.75 212

gm-plane-assit		54.99 190	57.99 186	51.49 198	69.27 158	54.42 224	32.32 227	42.59 214	21.18 227	13.71 223	23.61 215	43.84 160	60.21 129	87.09 5	86.55 5	90.81 4	89.28 101

anonymousdsp		54.99 190	57.24 187	52.36 195	53.82 216	71.75 192	51.49 202	48.14 195	33.74 208	33.66 189	38.34 170	36.13 192	47.54 180	64.53 197	70.60 178	79.53 197	85.59 137

CVMVSNet		54.92 192	58.16 183	51.13 199	62.61 195	68.44 201	55.45 197	52.38 185	42.28 179	21.45 211	47.10 127	46.10 154	37.96 202	64.42 198	63.81 203	76.92 207	75.01 186

GG-mvs-BLEND		54.54 193	77.58 52	27.67 223	0.03 238	90.09 29	77.20 73	0.02 234	66.83 75	0.05 239	59.90 70	73.33 36	0.04 234	78.40 84	79.30 80	88.65 26	95.20 26

MDTV_nov1_ep13_2view		54.47 194	54.61 193	54.30 193	60.50 201	73.82 183	57.92 192	43.38 210	39.43 193	32.51 192	33.23 195	34.05 202	47.26 181	62.36 201	66.21 197	84.24 148	73.19 195

pmmvs6		54.20 195	53.54 197	54.97 187	63.22 192	72.98 186	60.17 185	52.32 186	26.77 222	34.30 187	23.29 217	36.23 191	40.33 200	68.77 182	68.76 185	79.47 198	78.00 179

pmnet_mix02		53.92 196	53.30 198	54.65 191	61.89 197	71.33 193	54.54 199	54.17 175	40.38 187	34.65 185	34.76 193	30.68 215	40.44 199	60.97 203	63.71 204	82.19 177	71.24 202

MVS-HIRNet		53.86 197	53.02 199	54.85 188	60.30 202	72.36 187	44.63 217	42.20 216	39.45 192	43.47 139	21.66 221	34.00 203	55.47 157	65.42 191	67.16 191	83.02 167	71.08 203

TDRefinement		52.70 198	51.02 207	54.66 190	57.41 211	65.06 210	61.47 182	54.94 166	44.03 173	33.93 188	30.13 205	27.57 219	46.17 186	61.86 202	62.48 210	74.01 215	66.06 211

TinyColmap		52.66 199	50.09 210	55.65 185	59.72 204	64.02 214	57.15 194	52.96 183	40.28 188	32.51 192	32.42 197	20.97 228	56.65 154	63.95 199	65.15 201	74.91 212	63.87 216

Anonymous20231206		52.23 200	52.80 202	51.56 197	64.70 185	69.41 198	51.01 203	58.60 127	36.63 199	22.44 210	21.80 220	31.42 211	30.52 206	66.79 187	67.83 187	82.10 178	75.73 183

PEN-MVS		51.04 201	52.94 200	48.82 202	61.45 199	66.00 207	48.68 206	57.20 143	36.87 197	15.36 219	36.98 179	32.72 206	28.77 211	57.63 210	66.37 195	81.44 184	74.00 190

WR-MVS		51.02 202	54.56 194	46.90 208	63.84 188	69.23 199	44.78 216	56.38 150	38.19 195	14.19 221	37.38 175	36.82 188	22.39 218	60.14 205	66.20 198	79.81 194	73.95 191

CP-MVSNet		50.57 203	52.60 204	48.21 205	58.77 208	65.82 208	48.17 207	56.29 151	37.41 196	16.59 216	37.14 177	31.95 208	29.21 208	56.60 213	63.71 204	80.22 191	75.56 184

PS-CasMVS		50.17 204	52.02 205	48.02 206	58.60 209	65.54 209	48.04 208	56.19 153	36.42 201	16.42 218	35.68 188	31.33 212	28.85 210	56.42 215	63.54 206	80.01 192	75.18 185

PM-MVS		50.11 205	50.38 209	49.80 200	47.23 225	62.08 217	50.91 204	44.84 207	41.90 180	36.10 179	35.22 190	26.05 223	46.83 183	57.64 209	55.42 220	72.90 216	74.32 188

DTE-MVSNet		49.82 206	51.92 206	47.37 207	61.75 198	64.38 212	45.89 215	57.33 142	36.11 203	12.79 226	36.87 180	31.93 209	25.73 215	58.01 208	65.22 200	80.75 190	70.93 204

WR-MVS_H		49.62 207	52.63 203	46.11 211	58.80 207	67.58 203	46.14 214	54.94 166	36.51 200	13.63 224	36.75 182	35.67 196	22.10 219	56.43 214	62.76 208	81.06 186	72.73 196

LTVRE_ROB	47.26 16	49.41 208	49.91 211	48.82 202	64.76 184	69.79 197	49.05 205	47.12 200	20.36 229	16.52 217	36.65 183	26.96 220	50.76 172	60.47 204	63.16 207	64.73 222	72.00 198
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
SixPastTwentyTwo		49.11 209	49.22 212	48.99 201	58.54 210	64.14 213	47.18 210	47.75 197	31.15 214	24.42 206	41.01 157	26.55 221	44.04 192	54.76 218	58.70 215	71.99 218	68.21 206

testgi		48.51 210	50.53 208	46.16 210	64.78 183	67.15 205	41.54 220	54.81 170	29.12 217	17.03 215	32.07 199	31.98 207	20.15 222	65.26 192	67.00 192	78.67 201	61.10 222

N_pmnet		47.67 211	47.00 215	48.45 204	54.72 215	62.78 215	46.95 211	51.25 188	36.01 204	26.09 205	26.59 212	25.93 224	35.50 204	55.67 217	59.01 213	76.22 208	63.04 217

FC-MVSNet-test		47.24 212	54.37 195	38.93 218	59.49 205	58.25 222	34.48 226	53.36 180	45.66 168	6.66 232	50.62 107	42.02 162	16.62 226	58.39 207	61.21 211	62.99 223	64.40 215

test20.03		47.23 213	48.69 213	45.53 212	63.28 190	64.39 211	41.01 221	56.93 147	29.16 216	15.21 220	23.90 214	30.76 214	17.51 225	64.63 196	65.26 199	79.21 199	62.71 219

EU-MVSNet		44.84 214	47.85 214	41.32 217	49.26 222	56.59 223	43.07 218	47.64 199	33.03 209	13.82 222	36.78 181	30.99 213	24.37 216	53.80 219	55.57 219	69.78 220	68.21 206

FE-MVSNET		44.36 215	46.68 216	41.65 214	37.55 228	61.05 218	42.06 219	54.34 173	27.09 220	9.86 231	20.55 222	25.56 225	28.72 212	60.12 206	66.83 193	77.36 205	65.56 213

MDA-MVSNet-bldmvs		44.15 216	42.27 221	46.34 209	38.34 227	62.31 216	46.28 212	55.74 157	29.83 215	20.98 212	27.11 211	16.45 234	41.98 196	41.11 226	57.47 216	74.72 213	61.65 221

new-patchmatchnet		42.21 217	42.97 218	41.33 216	53.05 217	59.89 219	39.38 222	49.61 190	28.26 219	12.10 227	22.17 219	21.54 227	19.22 223	50.96 220	56.04 218	74.61 214	61.92 220

pmmvs3		41.86 218	42.29 220	41.36 215	39.80 226	52.66 225	38.93 224	35.85 227	23.40 226	20.22 213	19.30 223	20.84 229	40.56 198	55.98 216	58.79 214	72.80 217	65.03 214

MIMVSNet1		40.84 219	43.46 217	37.79 219	32.14 229	58.92 221	39.24 223	50.83 189	27.00 221	11.29 228	16.76 228	26.53 222	17.75 224	57.14 212	61.12 212	75.46 210	56.78 223

FPMVS		39.11 220	36.39 222	42.28 213	55.97 213	45.94 227	46.23 213	41.57 217	35.73 205	22.61 208	23.46 216	19.82 230	28.32 213	43.57 223	40.67 225	58.96 225	45.54 225

new_pmnet		33.19 221	35.52 223	30.47 221	27.55 233	45.31 228	29.29 228	30.92 228	29.00 218	9.88 230	18.77 224	17.64 232	26.77 214	44.07 222	45.98 223	58.41 226	47.87 224

PMVS	27.44 18	32.08 222	29.07 226	35.60 220	48.33 224	24.79 231	26.97 229	41.34 219	20.45 228	22.50 209	17.11 227	18.64 231	20.44 221	41.99 225	38.06 226	54.02 227	42.44 226
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
WB-MVS		30.42 223	32.63 225	27.84 222	51.51 220	41.64 229	17.75 232	55.06 165	20.11 230	2.46 237	26.13 213	16.63 233	3.90 232	44.91 221	44.54 224	36.34 231	34.48 228

test_method		28.15 224	34.48 224	20.76 225	6.76 237	21.18 233	21.03 230	18.41 231	36.77 198	17.52 214	15.67 229	31.63 210	24.05 217	41.03 227	26.69 229	36.82 230	68.38 205

Gipuma		24.91 225	24.61 227	25.26 224	31.47 230	21.59 232	18.06 231	37.53 224	25.43 224	10.03 229	4.18 234	4.25 238	14.85 227	43.20 224	47.03 222	39.62 229	26.55 231
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMMVS2		20.45 226	22.31 228	18.27 228	20.52 234	26.73 230	14.85 234	28.43 230	13.69 231	0.79 238	10.35 230	9.10 235	3.83 233	27.64 229	32.87 227	41.17 228	35.81 227

E-PMN		15.08 227	11.65 230	19.08 226	28.73 231	12.31 236	6.95 237	36.87 226	10.71 233	3.63 235	5.13 231	2.22 241	13.81 229	11.34 232	18.50 231	24.49 233	21.32 232

EMVS		14.40 228	10.71 231	18.70 227	28.15 232	12.09 237	7.06 236	36.89 225	11.00 232	3.56 236	4.95 232	2.27 240	13.91 228	10.13 233	16.06 232	22.63 234	18.51 233

MVE	15.98 19	14.37 229	16.36 229	12.04 230	7.72 236	20.24 234	5.90 238	29.05 229	8.28 234	3.92 234	4.72 233	2.42 239	9.57 230	18.89 231	31.46 228	16.07 236	28.53 230
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		0.05 230	0.08 232	0.01 231	0.00 239	0.01 239	0.03 240	0.01 235	0.05 235	0.00 240	0.14 236	0.01 242	0.03 236	0.05 234	0.05 233	0.01 237	0.24 235

test123		0.05 230	0.08 232	0.01 231	0.00 239	0.01 239	0.01 241	0.00 236	0.05 235	0.00 240	0.16 235	0.00 243	0.04 234	0.02 235	0.05 233	0.00 238	0.26 234

uanet_test		0.00 232	0.00 234	0.00 233	0.00 239	0.00 241	0.00 242	0.00 236	0.00 237	0.00 240	0.00 237	0.00 243	0.00 237	0.00 236	0.00 235	0.00 238	0.00 236

sosnet-low-res		0.00 232	0.00 234	0.00 233	0.00 239	0.00 241	0.00 242	0.00 236	0.00 237	0.00 240	0.00 237	0.00 243	0.00 237	0.00 236	0.00 235	0.00 238	0.00 236

sosnet		0.00 232	0.00 234	0.00 233	0.00 239	0.00 241	0.00 242	0.00 236	0.00 237	0.00 240	0.00 237	0.00 243	0.00 237	0.00 236	0.00 235	0.00 238	0.00 236

TPM-MVS					94.34 2	93.91 5	89.34 3			75.49 18	82.52 20	83.34 10	83.53 4			89.62 8	90.78 80
Ray Leroy Khuboni and Hongjun Xu: Textureless Resilient Propagation Matching in Multiple View Stereosis (TPM-MVS). SATNAC 2025
RE-MVS-def										31.47 194

9.14												84.47 7

SR-MVS					86.33 47			67.54 46				80.78 22

Anonymous202405211			66.35 133		78.00 96	84.41 94	74.85 86	63.18 80	51.00 145		31.37 201	53.73 132	69.67 72	76.28 104	76.84 102	83.21 164	90.85 78

our_test_3					63.32 189	71.07 196	55.90 196

ambc			42.30 219		50.36 221	49.51 226	35.47 225		32.04 213	23.53 207	17.36 225	8.95 236	29.06 209	64.88 194	56.26 217	61.29 224	67.12 209

MTAPA										78.32 11		79.42 26

MTMP										76.04 15		76.65 30

Patchmatch-RL test							2.17 239

tmp_tt				16.09 229	13.07 235	8.12 238	13.61 235	2.08 233	55.09 130	30.10 198	40.26 162	22.83 226	5.35 231	29.91 228	25.25 230	32.33 232

XVS					82.43 55	86.27 79	75.70 76			61.07 70		72.27 39				85.67 110

X-MVStestdata					82.43 55	86.27 79	75.70 76			61.07 70		72.27 39				85.67 110

mPP-MVS					86.96 42							70.61 49

NP-MVS									81.60 35

Patchmtry						78.06 151	67.53 148	43.18 211		41.40 150

DeepMVS_CX						19.81 235	17.01 233	10.02 232	23.61 225	5.85 233	17.21 226	8.03 237	21.13 220	22.60 230		21.42 235	30.01 229

High-resolution multi-view benchmark