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.

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

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

SMA-MVS		87.56 7	90.17 7	84.52 9	91.71 3	90.57 9	90.77 8	75.19 13	90.67 7	80.50 13	86.59 17	88.86 8	78.09 15	89.92 1	89.41 1	90.84 10	95.19 5
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
NCCC		85.34 19	86.59 24	83.88 15	91.48 4	88.88 25	89.79 17	75.54 11	86.67 20	77.94 22	76.55 34	84.99 25	78.07 16	88.04 12	87.68 12	90.46 26	93.31 21

CNVR-MVS		86.36 14	88.19 17	84.23 11	91.33 5	89.84 14	90.34 11	75.56 10	87.36 17	78.97 17	81.19 28	86.76 18	78.74 11	89.30 5	88.58 2	90.45 27	94.33 10

SF-MVS		87.47 8	89.70 8	84.86 8	91.26 6	91.10 8	90.90 6	75.65 8	89.21 9	81.25 7	91.12 8	88.93 7	78.82 10	87.42 19	86.23 30	91.28 3	93.90 13

APDe-MVS		88.00 6	90.50 6	85.08 5	90.95 7	91.58 7	92.03 1	75.53 12	91.15 5	80.10 14	92.27 5	88.34 11	80.80 5	88.00 14	86.99 18	91.09 5	95.16 6
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
DPE-MVS		88.63 4	91.29 4	85.53 3	90.87 8	92.20 4	91.98 2	76.00 6	90.55 8	82.09 6	93.85 1	90.75 2	81.25 1	88.62 8	87.59 14	90.96 9	95.48 4
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
HFP-MVS		86.15 15	87.95 18	84.06 13	90.80 9	89.20 23	89.62 19	74.26 16	87.52 14	80.63 11	86.82 16	84.19 28	78.22 14	87.58 17	87.19 16	90.81 12	93.13 24

SteuartSystems-ACMMP		85.99 16	88.31 16	83.27 20	90.73 10	89.84 14	90.27 14	74.31 15	84.56 29	75.88 30	87.32 14	85.04 24	77.31 23	89.01 7	88.46 3	91.14 4	93.96 12
Skip Steuart: Steuart Systems R&D Blog.
APD-MVS		86.84 12	88.91 14	84.41 10	90.66 11	90.10 12	90.78 7	75.64 9	87.38 16	78.72 18	90.68 10	86.82 17	80.15 7	87.13 24	86.45 28	90.51 21	93.83 14
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MP-MVS		85.50 18	87.40 21	83.28 19	90.65 12	89.51 19	89.16 23	74.11 18	83.70 33	78.06 21	85.54 20	84.89 27	77.31 23	87.40 21	87.14 17	90.41 28	93.65 19
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
train_agg		84.86 24	87.21 22	82.11 26	90.59 13	85.47 55	89.81 16	73.55 25	83.95 31	73.30 38	89.84 12	87.23 15	75.61 33	86.47 33	85.46 38	89.78 40	92.06 31

MCST-MVS		85.13 22	86.62 23	83.39 17	90.55 14	89.82 16	89.29 21	73.89 22	84.38 30	76.03 29	79.01 31	85.90 21	78.47 12	87.81 16	86.11 33	92.11 1	93.29 22

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

DVP-MVS		88.67 3	91.62 2	85.22 4	90.47 16	92.36 2	90.69 9	76.15 4	93.08 2	82.75 4	92.19 6	90.71 3	80.45 6	89.27 6	87.91 9	90.82 11	95.84 2
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
DeepC-MVS_fast	78.24 3	84.27 28	85.50 30	82.85 22	90.46 17	89.24 21	87.83 33	74.24 17	84.88 25	76.23 28	75.26 39	81.05 43	77.62 20	88.02 13	87.62 13	90.69 16	92.41 27
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ACMMP_NAP		86.52 13	89.01 11	83.62 16	90.28 18	90.09 13	90.32 13	74.05 19	88.32 13	79.74 15	87.04 15	85.59 23	76.97 28	89.35 4	88.44 4	90.35 30	94.27 11

SD-MVS		86.96 10	89.45 9	84.05 14	90.13 19	89.23 22	89.77 18	74.59 14	89.17 10	80.70 10	89.93 11	89.67 5	78.47 12	87.57 18	86.79 22	90.67 17	93.76 16
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
ACMMPR		85.52 17	87.53 20	83.17 21	90.13 19	89.27 20	89.30 20	73.97 20	86.89 19	77.14 24	86.09 18	83.18 32	77.74 19	87.42 19	87.20 15	90.77 13	92.63 25

TPM-MVS					90.07 21	88.36 35	88.45 29			77.10 25	75.60 37	83.98 29	71.33 63			89.75 43	89.62 52
Ray Leroy Khuboni and Hongjun Xu: Textureless Resilient Propagation Matching in Multiple View Stereosis (TPM-MVS). SATNAC 2025
PGM-MVS		84.42 27	86.29 27	82.23 25	90.04 22	88.82 26	89.23 22	71.74 35	82.82 37	74.61 33	84.41 23	82.09 35	77.03 27	87.13 24	86.73 24	90.73 15	92.06 31

MSP-MVS		88.09 5	90.84 5	84.88 7	90.00 23	91.80 6	91.63 5	75.80 7	91.99 4	81.23 8	92.54 2	89.18 6	80.89 4	87.99 15	87.91 9	89.70 45	94.51 7
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
CSCG		85.28 21	87.68 19	82.49 24	89.95 24	91.99 5	88.82 24	71.20 37	86.41 21	79.63 16	79.26 29	88.36 10	73.94 41	86.64 31	86.67 25	91.40 2	94.41 8

mPP-MVS					89.90 25							81.29 42

TSAR-MVS + MP.		86.88 11	89.23 10	84.14 12	89.78 26	88.67 30	90.59 10	73.46 26	88.99 11	80.52 12	91.26 7	88.65 9	79.91 8	86.96 29	86.22 31	90.59 19	93.83 14
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
X-MVS		83.23 32	85.20 32	80.92 33	89.71 27	88.68 27	88.21 32	73.60 23	82.57 38	71.81 45	77.07 32	81.92 37	71.72 58	86.98 28	86.86 20	90.47 23	92.36 28

DPM-MVS		83.30 31	84.33 34	82.11 26	89.56 28	88.49 33	90.33 12	73.24 27	83.85 32	76.46 27	72.43 51	82.65 33	73.02 48	86.37 35	86.91 19	90.03 36	89.62 52

TSAR-MVS + ACMM		85.10 23	88.81 15	80.77 34	89.55 29	88.53 32	88.59 27	72.55 30	87.39 15	71.90 42	90.95 9	87.55 13	74.57 36	87.08 26	86.54 26	87.47 92	93.67 17

CP-MVS		84.74 26	86.43 26	82.77 23	89.48 30	88.13 39	88.64 25	73.93 21	84.92 24	76.77 26	81.94 26	83.50 30	77.29 25	86.92 30	86.49 27	90.49 22	93.14 23

CDPH-MVS		82.64 33	85.03 33	79.86 38	89.41 31	88.31 36	88.32 30	71.84 34	80.11 44	67.47 64	82.09 25	81.44 41	71.85 56	85.89 41	86.15 32	90.24 32	91.25 37

DeepC-MVS	78.47 2	84.81 25	86.03 28	83.37 18	89.29 32	90.38 11	88.61 26	76.50 1	86.25 22	77.22 23	75.12 40	80.28 45	77.59 21	88.39 10	88.17 6	91.02 6	93.66 18
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
AdaColmap		79.74 46	78.62 63	81.05 32	89.23 33	86.06 52	84.95 49	71.96 33	79.39 47	75.51 31	63.16 92	68.84 96	76.51 29	83.55 61	82.85 59	88.13 75	86.46 77

SR-MVS					88.99 34			73.57 24				87.54 14

EPNet		79.08 56	80.62 52	77.28 51	88.90 35	83.17 81	83.65 54	72.41 31	74.41 57	67.15 67	76.78 33	74.37 65	64.43 99	83.70 60	83.69 53	87.15 96	88.19 61
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DeepPCF-MVS	79.04 1	85.30 20	88.93 12	81.06 31	88.77 36	90.48 10	85.46 46	73.08 28	90.97 6	73.77 37	84.81 22	85.95 20	77.43 22	88.22 11	87.73 11	87.85 85	94.34 9

ACMMP		83.42 30	85.27 31	81.26 30	88.47 37	88.49 33	88.31 31	72.09 32	83.42 34	72.77 40	82.65 24	78.22 50	75.18 34	86.24 38	85.76 35	90.74 14	92.13 30
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
3Dnovator+	75.73 4	82.40 34	82.76 39	81.97 28	88.02 38	89.67 17	86.60 37	71.48 36	81.28 42	78.18 20	64.78 86	77.96 52	77.13 26	87.32 22	86.83 21	90.41 28	91.48 35

OPM-MVS		79.68 47	79.28 61	80.15 37	87.99 39	86.77 46	88.52 28	72.72 29	64.55 99	67.65 63	67.87 75	74.33 66	74.31 39	86.37 35	85.25 40	89.73 44	89.81 50
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
MAR-MVS		79.21 52	80.32 56	77.92 49	87.46 40	88.15 38	83.95 53	67.48 63	74.28 58	68.25 59	64.70 87	77.04 53	72.17 52	85.42 43	85.00 42	88.22 71	87.62 66
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
HQP-MVS		81.19 40	83.27 37	78.76 44	87.40 41	85.45 56	86.95 35	70.47 40	81.31 41	66.91 68	79.24 30	76.63 54	71.67 59	84.43 54	83.78 52	89.19 56	92.05 33

CANet		81.62 39	83.41 36	79.53 40	87.06 42	88.59 31	85.47 45	67.96 57	76.59 52	74.05 34	74.69 41	81.98 36	72.98 49	86.14 39	85.47 37	89.68 46	90.42 46

MSLP-MVS++		82.09 36	82.66 40	81.42 29	87.03 43	87.22 43	85.82 42	70.04 42	80.30 43	78.66 19	68.67 71	81.04 44	77.81 18	85.19 46	84.88 43	89.19 56	91.31 36

ACMM	72.26 8	78.86 57	78.13 65	79.71 39	86.89 44	83.40 76	86.02 40	70.50 39	75.28 54	71.49 49	63.01 93	69.26 90	73.57 43	84.11 56	83.98 48	89.76 42	87.84 64
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
XVS					86.63 45	88.68 27	85.00 47			71.81 45		81.92 37				90.47 23

X-MVStestdata					86.63 45	88.68 27	85.00 47			71.81 45		81.92 37				90.47 23

PHI-MVS		82.36 35	85.89 29	78.24 47	86.40 47	89.52 18	85.52 44	69.52 48	82.38 40	65.67 71	81.35 27	82.36 34	73.07 47	87.31 23	86.76 23	89.24 52	91.56 34

LGP-MVS_train		79.83 43	81.22 48	78.22 48	86.28 48	85.36 58	86.76 36	69.59 46	77.34 49	65.14 74	75.68 36	70.79 80	71.37 62	84.60 50	84.01 47	90.18 33	90.74 42

MVS_0304		81.73 38	83.86 35	79.26 41	86.22 49	89.18 24	86.41 38	67.15 64	75.28 54	70.75 52	74.59 42	83.49 31	74.42 38	87.05 27	86.34 29	90.58 20	91.08 39

CPTT-MVS		81.77 37	83.10 38	80.21 36	85.93 50	86.45 49	87.72 34	70.98 38	82.54 39	71.53 48	74.23 45	81.49 40	76.31 31	82.85 70	81.87 66	88.79 64	92.26 29

MVS_111021_HR		80.13 42	81.46 45	78.58 45	85.77 51	85.17 59	83.45 55	69.28 49	74.08 61	70.31 54	74.31 44	75.26 62	73.13 46	86.46 34	85.15 41	89.53 47	89.81 50

ACMP	73.23 7	79.79 44	80.53 53	78.94 42	85.61 52	85.68 53	85.61 43	69.59 46	77.33 50	71.00 51	74.45 43	69.16 91	71.88 54	83.15 67	83.37 55	89.92 37	90.57 45
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
UA-Net		74.47 77	77.80 67	70.59 92	85.33 53	85.40 57	73.54 144	65.98 73	60.65 130	56.00 110	72.11 52	79.15 46	54.63 169	83.13 68	82.25 63	88.04 79	81.92 126

TSAR-MVS + GP.		83.69 29	86.58 25	80.32 35	85.14 54	86.96 44	84.91 50	70.25 41	84.71 28	73.91 36	85.16 21	85.63 22	77.92 17	85.44 42	85.71 36	89.77 41	92.45 26

LS3D		74.08 79	73.39 94	74.88 68	85.05 55	82.62 85	79.71 75	68.66 52	72.82 66	58.80 93	57.61 124	61.31 121	71.07 65	80.32 102	78.87 115	86.00 134	80.18 143

QAPM		78.47 59	80.22 57	76.43 58	85.03 56	86.75 47	80.62 66	66.00 72	73.77 63	65.35 73	65.54 82	78.02 51	72.69 50	83.71 59	83.36 56	88.87 62	90.41 47

OpenMVS	70.44 10	76.15 70	76.82 78	75.37 65	85.01 57	84.79 61	78.99 84	62.07 121	71.27 69	67.88 62	57.91 123	72.36 73	70.15 67	82.23 75	81.41 71	88.12 76	87.78 65

CLD-MVS		79.35 50	81.23 47	77.16 53	85.01 57	86.92 45	85.87 41	60.89 132	80.07 46	75.35 32	72.96 48	73.21 70	68.43 79	85.41 44	84.63 44	87.41 93	85.44 88
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
3Dnovator	73.76 5	79.75 45	80.52 54	78.84 43	84.94 59	87.35 41	84.43 52	65.54 75	78.29 48	73.97 35	63.00 94	75.62 61	74.07 40	85.00 47	85.34 39	90.11 35	89.04 55

PCF-MVS	73.28 6	79.42 49	80.41 55	78.26 46	84.88 60	88.17 37	86.08 39	69.85 43	75.23 56	68.43 58	68.03 74	78.38 48	71.76 57	81.26 88	80.65 88	88.56 67	91.18 38
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
DELS-MVS		79.15 55	81.07 50	76.91 55	83.54 61	87.31 42	84.45 51	64.92 80	69.98 70	69.34 56	71.62 55	76.26 55	69.84 68	86.57 32	85.90 34	89.39 49	89.88 49
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
OMC-MVS		80.26 41	82.59 41	77.54 50	83.04 62	85.54 54	83.25 56	65.05 79	87.32 18	72.42 41	72.04 53	78.97 47	73.30 45	83.86 57	81.60 70	88.15 74	88.83 57

EC-MVSNet		79.44 48	81.35 46	77.22 52	82.95 63	84.67 63	81.31 60	63.65 92	72.47 68	68.75 57	73.15 47	78.33 49	75.99 32	86.06 40	83.96 49	90.67 17	90.79 41

PLC	68.99 11	75.68 71	75.31 83	76.12 60	82.94 64	81.26 95	79.94 71	66.10 70	77.15 51	66.86 69	59.13 113	68.53 98	73.73 42	80.38 101	79.04 111	87.13 100	81.68 128
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CNLPA		77.20 65	77.54 69	76.80 56	82.63 65	84.31 65	79.77 73	64.64 81	85.17 23	73.18 39	56.37 130	69.81 87	74.53 37	81.12 91	78.69 116	86.04 132	87.29 69

ACMH+	66.54 13	71.36 100	70.09 118	72.85 79	82.59 66	81.13 97	78.56 87	68.04 55	61.55 123	52.52 132	51.50 172	54.14 157	68.56 78	78.85 124	79.50 106	86.82 108	83.94 108

ACMH	65.37 14	70.71 104	70.00 119	71.54 84	82.51 67	82.47 86	77.78 95	68.13 54	56.19 158	46.06 168	54.30 142	51.20 184	68.68 77	80.66 97	80.72 81	86.07 128	84.45 105
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EIA-MVS		75.64 72	76.60 79	74.53 72	82.43 68	83.84 70	78.32 91	62.28 120	65.96 89	63.28 83	68.95 67	67.54 101	71.61 60	82.55 72	81.63 69	89.24 52	85.72 82

sasdasda		79.16 53	82.37 42	75.41 63	82.33 69	86.38 50	80.80 63	63.18 98	82.90 35	67.34 65	72.79 49	76.07 57	69.62 69	83.46 64	84.41 45	89.20 54	90.60 43

canonicalmvs		79.16 53	82.37 42	75.41 63	82.33 69	86.38 50	80.80 63	63.18 98	82.90 35	67.34 65	72.79 49	76.07 57	69.62 69	83.46 64	84.41 45	89.20 54	90.60 43

ETV-MVS		77.32 64	78.81 62	75.58 62	82.24 71	83.64 74	79.98 69	64.02 88	69.64 75	63.90 79	70.89 59	69.94 86	73.41 44	85.39 45	83.91 51	89.92 37	88.31 60

MSDG		71.52 97	69.87 120	73.44 77	82.21 72	79.35 115	79.52 77	64.59 82	66.15 87	61.87 84	53.21 157	56.09 147	65.85 97	78.94 123	78.50 118	86.60 117	76.85 166

casdiffmvs_mvg		77.79 62	79.55 60	75.73 61	81.56 73	84.70 62	82.12 57	64.26 87	74.27 59	67.93 61	70.83 60	74.66 64	69.19 74	83.33 66	81.94 65	89.29 51	87.14 71
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
test2506		71.72 94	72.95 98	70.29 95	81.49 74	83.27 77	75.74 109	67.59 61	68.19 78	49.81 145	61.15 98	49.73 192	58.82 135	84.76 48	82.94 57	88.27 69	80.63 137

ECVR-MVS		72.20 90	73.91 90	70.20 97	81.49 74	83.27 77	75.74 109	67.59 61	68.19 78	49.31 149	55.77 132	62.00 119	58.82 135	84.76 48	82.94 57	88.27 69	80.41 141

CS-MVS		79.22 51	81.11 49	77.01 54	81.36 76	84.03 66	80.35 67	63.25 96	73.43 65	70.37 53	74.10 46	76.03 59	76.40 30	86.32 37	83.95 50	90.34 31	89.93 48

IS_MVSNet		73.33 83	77.34 74	68.65 115	81.29 77	83.47 75	74.45 126	63.58 94	65.75 91	48.49 151	67.11 79	70.61 81	54.63 169	84.51 52	83.58 54	89.48 48	86.34 78

test1111		71.56 96	73.44 93	69.38 108	81.16 78	82.95 82	74.99 120	67.68 59	66.89 83	46.33 165	55.19 138	60.91 122	57.99 143	84.59 51	82.70 61	88.12 76	80.85 134

Effi-MVS+		75.28 74	76.20 80	74.20 74	81.15 79	83.24 79	81.11 61	63.13 101	66.37 85	60.27 89	64.30 90	68.88 95	70.93 66	81.56 79	81.69 68	88.61 65	87.35 67

MVS_111021_LR		78.13 61	79.85 59	76.13 59	81.12 80	81.50 91	80.28 68	65.25 77	76.09 53	71.32 50	76.49 35	72.87 72	72.21 51	82.79 71	81.29 72	86.59 118	87.91 63

CS-MVS-test		78.79 58	80.72 51	76.53 57	81.11 81	83.88 69	79.69 76	63.72 91	73.80 62	69.95 55	75.40 38	76.17 56	74.85 35	84.50 53	82.78 60	89.87 39	88.54 59

FC-MVSNet-train		72.60 88	75.07 84	69.71 103	81.10 82	78.79 122	73.74 143	65.23 78	66.10 88	53.34 125	70.36 62	63.40 115	56.92 153	81.44 81	80.96 77	87.93 81	84.46 104

MS-PatchMatch		70.17 111	70.49 115	69.79 102	80.98 83	77.97 134	77.51 97	58.95 155	62.33 117	55.22 114	53.14 158	65.90 107	62.03 115	79.08 121	77.11 141	84.08 158	77.91 158

Anonymous202405211			72.16 106		80.85 84	81.85 88	76.88 105	65.40 76	62.89 114		46.35 189	67.99 100	62.05 114	81.15 90	80.38 92	85.97 135	84.50 103

TAPA-MVS	71.42 9	77.69 63	80.05 58	74.94 67	80.68 85	84.52 64	81.36 59	63.14 100	84.77 26	64.82 76	68.72 69	75.91 60	71.86 55	81.62 77	79.55 105	87.80 87	85.24 91
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PVSNet_Blended_VisFu		76.57 67	77.90 66	75.02 66	80.56 86	86.58 48	79.24 80	66.18 69	64.81 96	68.18 60	65.61 80	71.45 75	67.05 83	84.16 55	81.80 67	88.90 60	90.92 40

EPP-MVSNet		74.00 80	77.41 72	70.02 100	80.53 87	83.91 68	74.99 120	62.68 113	65.06 94	49.77 146	68.68 70	72.09 74	63.06 107	82.49 74	80.73 80	89.12 58	88.91 56

COLMAP_ROB	62.73 15	67.66 140	66.76 157	68.70 114	80.49 88	77.98 132	75.29 113	62.95 103	63.62 108	49.96 143	47.32 188	50.72 187	58.57 137	76.87 145	75.50 157	84.94 152	75.33 177
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
GeoE		74.23 78	74.84 86	73.52 76	80.42 89	81.46 92	79.77 73	61.06 130	67.23 82	63.67 80	59.56 110	68.74 97	67.90 80	80.25 106	79.37 109	88.31 68	87.26 70

casdiffmvs		76.76 66	78.46 64	74.77 69	80.32 90	83.73 73	80.65 65	63.24 97	73.58 64	66.11 70	69.39 66	74.09 67	69.49 72	82.52 73	79.35 110	88.84 63	86.52 76
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
DCV-MVSNet		73.65 82	75.78 82	71.16 86	80.19 91	79.27 116	77.45 100	61.68 127	66.73 84	58.72 94	65.31 83	69.96 85	62.19 112	81.29 87	80.97 76	86.74 111	86.91 72

Anonymous20231211		71.90 92	72.48 103	71.21 85	80.14 92	81.53 90	76.92 103	62.89 104	64.46 101	58.94 91	43.80 193	70.98 79	62.22 111	80.70 96	80.19 95	86.18 125	85.73 81

TSAR-MVS + COLMAP		78.34 60	81.64 44	74.48 73	80.13 93	85.01 60	81.73 58	65.93 74	84.75 27	61.68 85	85.79 19	66.27 106	71.39 61	82.91 69	80.78 79	86.01 133	85.98 79

baseline1		70.10 112	72.17 105	67.69 124	79.74 94	76.80 144	73.91 137	64.38 84	62.74 115	48.30 153	64.94 84	64.08 112	54.17 171	81.46 80	78.92 113	85.66 140	76.22 168

EPNet_dtu		68.08 132	71.00 111	64.67 154	79.64 95	68.62 186	75.05 119	63.30 95	66.36 86	45.27 172	67.40 77	66.84 105	43.64 191	75.37 154	74.98 160	81.15 170	77.44 161
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet_BlendedMVS		76.21 68	77.52 70	74.69 70	79.46 96	83.79 71	77.50 98	64.34 85	69.88 71	71.88 43	68.54 72	70.42 82	67.05 83	83.48 62	79.63 101	87.89 83	86.87 73

PVSNet_Blended		76.21 68	77.52 70	74.69 70	79.46 96	83.79 71	77.50 98	64.34 85	69.88 71	71.88 43	68.54 72	70.42 82	67.05 83	83.48 62	79.63 101	87.89 83	86.87 73

IB-MVS	66.94 12	71.21 101	71.66 109	70.68 89	79.18 98	82.83 84	72.61 150	61.77 125	59.66 135	63.44 82	53.26 155	59.65 129	59.16 134	76.78 147	82.11 64	87.90 82	87.33 68
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
MVS_Test		75.37 73	77.13 76	73.31 78	79.07 99	81.32 94	79.98 69	60.12 143	69.72 73	64.11 78	70.53 61	73.22 69	68.90 75	80.14 108	79.48 107	87.67 89	85.50 86

Effi-MVS+-dtu		71.82 93	71.86 108	71.78 83	78.77 100	80.47 104	78.55 88	61.67 128	60.68 129	55.49 111	58.48 117	65.48 108	68.85 76	76.92 144	75.55 156	87.35 94	85.46 87

EG-PatchMatch MVS		67.24 147	66.94 155	67.60 126	78.73 101	81.35 93	73.28 148	59.49 148	46.89 200	51.42 137	43.65 194	53.49 165	55.50 165	81.38 83	80.66 87	87.15 96	81.17 132

gg-mvs-nofinetune		62.55 171	65.05 169	59.62 179	78.72 102	77.61 138	70.83 158	53.63 173	39.71 212	22.04 214	36.36 207	64.32 111	47.53 184	81.16 89	79.03 112	85.00 151	77.17 163

FA-MVS(training)		73.66 81	74.95 85	72.15 81	78.63 103	80.46 105	78.92 85	54.79 172	69.71 74	65.37 72	62.04 95	66.89 104	67.10 82	80.72 95	79.87 98	88.10 78	84.97 96

Vis-MVSNet (Re-imp)		67.83 137	73.52 92	61.19 170	78.37 104	76.72 146	66.80 176	62.96 102	65.50 92	34.17 197	67.19 78	69.68 88	39.20 200	79.39 118	79.44 108	85.68 139	76.73 167

DI_MVS_plusplus_trai		75.13 75	76.12 81	73.96 75	78.18 105	81.55 89	80.97 62	62.54 115	68.59 76	65.13 75	61.43 97	74.81 63	69.32 73	81.01 93	79.59 103	87.64 90	85.89 80

thres600view7		67.68 139	68.43 141	66.80 141	77.90 106	78.86 120	73.84 139	62.75 106	56.07 159	44.70 176	52.85 163	52.81 174	55.58 163	80.41 98	77.77 127	86.05 130	80.28 142

thres400		67.95 134	68.62 139	67.17 134	77.90 106	78.59 125	74.27 132	62.72 108	56.34 157	45.77 170	53.00 160	53.35 170	56.46 155	80.21 107	78.43 119	85.91 137	80.43 140

thres200		67.98 133	68.55 140	67.30 132	77.89 108	78.86 120	74.18 135	62.75 106	56.35 156	46.48 164	52.98 161	53.54 163	56.46 155	80.41 98	77.97 125	86.05 130	79.78 147

thres100view900		67.60 143	68.02 144	67.12 136	77.83 109	77.75 136	73.90 138	62.52 116	56.64 153	46.82 161	52.65 165	53.47 167	55.92 159	78.77 125	77.62 130	85.72 138	79.23 151

tfpn200view9		68.11 131	68.72 137	67.40 129	77.83 109	78.93 118	74.28 131	62.81 105	56.64 153	46.82 161	52.65 165	53.47 167	56.59 154	80.41 98	78.43 119	86.11 126	80.52 139

Fast-Effi-MVS+		73.11 85	73.66 91	72.48 80	77.72 111	80.88 101	78.55 88	58.83 158	65.19 93	60.36 88	59.98 107	62.42 118	71.22 64	81.66 76	80.61 90	88.20 72	84.88 99

UniMVSNet_NR-MVSNet		70.59 105	72.19 104	68.72 113	77.72 111	80.72 102	73.81 141	69.65 45	61.99 119	43.23 178	60.54 103	57.50 138	58.57 137	79.56 114	81.07 75	89.34 50	83.97 106

IterMVS-LS		71.69 95	72.82 101	70.37 94	77.54 113	76.34 149	75.13 118	60.46 138	61.53 124	57.57 101	64.89 85	67.33 102	66.04 96	77.09 143	77.37 137	85.48 143	85.18 92
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
NR-MVSNet		68.79 126	70.56 114	66.71 144	77.48 114	79.54 112	73.52 145	69.20 50	61.20 127	39.76 185	58.52 115	50.11 190	51.37 178	80.26 105	80.71 85	88.97 59	83.59 112

TransMVSNet (Re)		64.74 160	65.66 163	63.66 161	77.40 115	75.33 158	69.86 159	62.67 114	47.63 198	41.21 184	50.01 178	52.33 177	45.31 188	79.57 113	77.69 129	85.49 142	77.07 165

TranMVSNet+NR-MVSNet		69.25 121	70.81 113	67.43 128	77.23 116	79.46 114	73.48 146	69.66 44	60.43 132	39.56 186	58.82 114	53.48 166	55.74 162	79.59 112	81.21 73	88.89 61	82.70 116

CANet_DTU		73.29 84	76.96 77	69.00 112	77.04 117	82.06 87	79.49 78	56.30 169	67.85 80	53.29 126	71.12 58	70.37 84	61.81 121	81.59 78	80.96 77	86.09 127	84.73 100

CHOSEN 1792x2688		69.20 122	69.26 129	69.13 109	76.86 118	78.93 118	77.27 101	60.12 143	61.86 121	54.42 115	42.54 197	61.61 120	66.91 88	78.55 128	78.14 123	79.23 178	83.23 115

HyFIR lowres test		69.47 119	68.94 133	70.09 99	76.77 119	82.93 83	76.63 107	60.17 141	59.00 138	54.03 119	40.54 203	65.23 109	67.89 81	76.54 150	78.30 121	85.03 150	80.07 144

UniMVSNet (Re)		69.53 117	71.90 107	66.76 142	76.42 120	80.93 98	72.59 151	68.03 56	61.75 122	41.68 183	58.34 121	57.23 140	53.27 174	79.53 115	80.62 89	88.57 66	84.90 98

gm-plane-assit		57.00 195	57.62 202	56.28 191	76.10 121	62.43 208	47.62 216	46.57 207	33.84 216	23.24 210	37.52 204	40.19 213	59.61 133	79.81 110	77.55 132	84.55 155	72.03 187

DU-MVS		69.63 116	70.91 112	68.13 119	75.99 122	79.54 112	73.81 141	69.20 50	61.20 127	43.23 178	58.52 115	53.50 164	58.57 137	79.22 119	80.45 91	87.97 80	83.97 106

Baseline_NR-MVSNet		67.53 144	68.77 136	66.09 147	75.99 122	74.75 163	72.43 152	68.41 53	61.33 126	38.33 190	51.31 173	54.13 159	56.03 158	79.22 119	78.19 122	85.37 145	82.45 118

CostFormer		68.92 124	69.58 125	68.15 118	75.98 124	76.17 151	78.22 93	51.86 186	65.80 90	61.56 86	63.57 91	62.83 116	61.85 119	70.40 190	68.67 187	79.42 176	79.62 149

dmvs_re		67.22 148	67.92 146	66.40 145	75.94 125	70.55 179	74.97 122	63.87 89	57.07 150	44.75 174	54.29 143	56.72 144	54.65 168	79.53 115	77.51 133	84.20 157	79.78 147

tfpnnormal		64.27 163	63.64 179	65.02 151	75.84 126	75.61 155	71.24 157	62.52 116	47.79 197	42.97 180	42.65 196	44.49 206	52.66 176	78.77 125	76.86 143	84.88 153	79.29 150

baseline2		69.69 115	70.27 117	69.01 111	75.72 127	77.13 142	73.82 140	58.94 156	61.35 125	57.09 104	61.68 96	57.17 141	61.99 116	78.10 132	76.58 148	86.48 121	79.85 145

diffmvs		74.86 76	77.37 73	71.93 82	75.62 128	80.35 107	79.42 79	60.15 142	72.81 67	64.63 77	71.51 56	73.11 71	66.53 93	79.02 122	77.98 124	85.25 147	86.83 75
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
tpm cat1		65.41 155	63.81 178	67.28 133	75.61 129	72.88 169	75.32 112	52.85 180	62.97 112	63.66 81	53.24 156	53.29 172	61.83 120	65.54 201	64.14 203	74.43 198	74.60 179

CDS-MVSNet		67.65 141	69.83 122	65.09 150	75.39 130	76.55 147	74.42 129	63.75 90	53.55 176	49.37 148	59.41 111	62.45 117	44.44 189	79.71 111	79.82 99	83.17 164	77.36 162
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Fast-Effi-MVS+-dtu		68.34 129	69.47 126	67.01 138	75.15 131	77.97 134	77.12 102	55.40 171	57.87 141	46.68 163	56.17 131	60.39 123	62.36 110	76.32 151	76.25 152	85.35 146	81.34 130

WR-MVS		63.03 167	67.40 152	57.92 185	75.14 132	77.60 139	60.56 199	66.10 70	54.11 175	23.88 208	53.94 149	53.58 162	34.50 204	73.93 163	77.71 128	87.35 94	80.94 133

test-LLR		64.42 161	64.36 174	64.49 155	75.02 133	63.93 199	66.61 178	61.96 122	54.41 171	47.77 156	57.46 125	60.25 124	55.20 166	70.80 184	69.33 182	80.40 174	74.38 181

test0.0.03 1		58.80 191	61.58 192	55.56 193	75.02 133	68.45 187	59.58 203	61.96 122	52.74 179	29.57 201	49.75 181	54.56 155	31.46 207	71.19 179	69.77 180	75.75 191	64.57 202

v1144		69.93 114	69.36 128	70.61 91	74.89 135	80.93 98	79.11 82	60.64 134	55.97 160	55.31 113	53.85 150	54.14 157	66.54 92	78.10 132	77.44 135	87.14 99	85.09 93

v10		70.22 110	69.76 123	70.74 87	74.79 136	80.30 109	79.22 81	59.81 146	57.71 146	56.58 108	54.22 148	55.31 150	66.95 86	78.28 130	77.47 134	87.12 102	85.07 94

v8		70.23 109	69.86 121	70.67 90	74.69 137	79.82 111	78.79 86	59.18 151	58.80 139	58.20 99	55.00 139	57.33 139	66.31 95	77.51 137	76.71 146	86.82 108	83.88 109

v2v482		70.05 113	69.46 127	70.74 87	74.62 138	80.32 108	79.00 83	60.62 135	57.41 148	56.89 105	55.43 137	55.14 152	66.39 94	77.25 140	77.14 140	86.90 105	83.57 113

v1192		69.50 118	68.83 134	70.29 95	74.49 139	80.92 100	78.55 88	60.54 136	55.04 166	54.21 116	52.79 164	52.33 177	66.92 87	77.88 134	77.35 138	87.04 103	85.51 85

UniMVSNet_ETH3D		67.18 149	67.03 154	67.36 130	74.44 140	78.12 127	74.07 136	66.38 67	52.22 183	46.87 160	48.64 183	51.84 181	56.96 151	77.29 139	78.53 117	85.42 144	82.59 117

DTE-MVSNet		61.85 180	64.96 171	58.22 184	74.32 141	74.39 165	61.01 198	67.85 58	51.76 188	21.91 215	53.28 154	48.17 195	37.74 201	72.22 172	76.44 149	86.52 120	78.49 155

Vis-MVSNet		72.77 87	77.20 75	67.59 127	74.19 142	84.01 67	76.61 108	61.69 126	60.62 131	50.61 141	70.25 63	71.31 78	55.57 164	83.85 58	82.28 62	86.90 105	88.08 62
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
v144192		69.34 120	68.68 138	70.12 98	74.06 143	80.54 103	78.08 94	60.54 136	54.99 168	54.13 118	52.92 162	52.80 175	66.73 90	77.13 142	76.72 145	87.15 96	85.63 83

v1921920		69.03 123	68.32 142	69.86 101	74.03 144	80.37 106	77.55 96	60.25 140	54.62 170	53.59 124	52.36 168	51.50 183	66.75 89	77.17 141	76.69 147	86.96 104	85.56 84

PEN-MVS		62.96 168	65.77 162	59.70 178	73.98 145	75.45 156	63.39 192	67.61 60	52.49 181	25.49 207	53.39 152	49.12 194	40.85 197	71.94 175	77.26 139	86.86 107	80.72 136

v1240		68.64 128	67.89 148	69.51 106	73.89 146	80.26 110	76.73 106	59.97 145	53.43 178	53.08 127	51.82 171	50.84 186	66.62 91	76.79 146	76.77 144	86.78 110	85.34 89

thisisatest0530		71.48 98	73.01 97	69.70 104	73.83 147	78.62 124	74.53 125	59.12 152	64.13 102	58.63 95	64.60 88	58.63 133	64.27 100	80.28 104	80.17 96	87.82 86	84.64 102

GA-MVS		68.14 130	69.17 131	66.93 140	73.77 148	78.50 126	74.45 126	58.28 160	55.11 165	48.44 152	60.08 105	53.99 160	61.50 123	78.43 129	77.57 131	85.13 148	80.54 138

tttt0517		71.41 99	72.95 98	69.60 105	73.70 149	78.70 123	74.42 129	59.12 152	63.89 106	58.35 98	64.56 89	58.39 135	64.27 100	80.29 103	80.17 96	87.74 88	84.69 101

pm-mvs1		65.62 154	67.42 151	63.53 162	73.66 150	76.39 148	69.66 160	60.87 133	49.73 193	43.97 177	51.24 174	57.00 143	48.16 183	79.89 109	77.84 126	84.85 154	79.82 146

dps		64.00 165	62.99 181	65.18 149	73.29 151	72.07 172	68.98 165	53.07 179	57.74 145	58.41 97	55.55 135	47.74 198	60.89 129	69.53 193	67.14 196	76.44 190	71.19 189

v148		67.85 136	67.53 149	68.23 117	73.25 152	77.57 140	74.26 133	57.36 166	55.70 161	57.45 103	53.53 151	55.42 149	61.96 117	75.23 155	73.92 164	85.08 149	81.32 131

PatchMatch-RL		67.78 138	66.65 158	69.10 110	73.01 153	72.69 170	68.49 166	61.85 124	62.93 113	60.20 90	56.83 129	50.42 188	69.52 71	75.62 153	74.46 163	81.51 168	73.62 185

GBi-Net		70.78 102	73.37 95	67.76 120	72.95 154	78.00 129	75.15 115	62.72 108	64.13 102	51.44 134	58.37 118	69.02 92	57.59 145	81.33 84	80.72 81	86.70 112	82.02 120

test1		70.78 102	73.37 95	67.76 120	72.95 154	78.00 129	75.15 115	62.72 108	64.13 102	51.44 134	58.37 118	69.02 92	57.59 145	81.33 84	80.72 81	86.70 112	82.02 120

FMVSNet2		70.39 108	72.67 102	67.72 123	72.95 154	78.00 129	75.15 115	62.69 112	63.29 110	51.25 138	55.64 133	68.49 99	57.59 145	80.91 94	80.35 93	86.70 112	82.02 120

FMVSNet3		70.49 106	72.90 100	67.67 125	72.88 157	77.98 132	74.96 123	62.72 108	64.13 102	51.44 134	58.37 118	69.02 92	57.43 148	79.43 117	79.57 104	86.59 118	81.81 127

LTVRE_ROB	59.44 16	61.82 183	62.64 185	60.87 172	72.83 158	77.19 141	64.37 188	58.97 154	33.56 217	28.00 204	52.59 167	42.21 209	63.93 103	74.52 159	76.28 150	77.15 185	82.13 119
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
v7n		67.05 150	66.94 155	67.17 134	72.35 159	78.97 117	73.26 149	58.88 157	51.16 189	50.90 139	48.21 185	50.11 190	60.96 126	77.70 135	77.38 136	86.68 115	85.05 95

tpm		62.41 174	63.15 180	61.55 169	72.24 160	63.79 201	71.31 156	46.12 209	57.82 142	55.33 112	59.90 108	54.74 154	53.63 172	67.24 200	64.29 202	70.65 208	74.25 183

test20.03		53.93 203	56.28 204	51.19 202	72.19 161	65.83 194	53.20 210	61.08 129	42.74 206	22.08 213	37.07 206	45.76 204	24.29 215	70.44 188	69.04 184	74.31 199	63.05 206

CP-MVSNet		62.68 170	65.49 165	59.40 181	71.84 162	75.34 157	62.87 194	67.04 65	52.64 180	27.19 205	53.38 153	48.15 196	41.40 195	71.26 178	75.68 154	86.07 128	82.00 123

PS-CasMVS		62.38 176	65.06 168	59.25 182	71.73 163	75.21 161	62.77 195	66.99 66	51.94 187	26.96 206	52.00 170	47.52 199	41.06 196	71.16 181	75.60 155	85.97 135	81.97 125

WR-MVS_H		61.83 182	65.87 161	57.12 188	71.72 164	76.87 143	61.45 197	66.19 68	51.97 186	22.92 212	53.13 159	52.30 179	33.80 205	71.03 182	75.00 159	86.65 116	80.78 135

USDC		67.36 146	67.90 147	66.74 143	71.72 164	75.23 160	71.58 154	60.28 139	67.45 81	50.54 142	60.93 99	45.20 205	62.08 113	76.56 149	74.50 162	84.25 156	75.38 176

UGNet		72.78 86	77.67 68	67.07 137	71.65 166	83.24 79	75.20 114	63.62 93	64.93 95	56.72 106	71.82 54	73.30 68	49.02 182	81.02 92	80.70 86	86.22 124	88.67 58
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
tpmrst		62.00 178	62.35 189	61.58 168	71.62 167	64.14 198	69.07 164	48.22 205	62.21 118	53.93 120	58.26 122	55.30 151	55.81 161	63.22 206	62.62 205	70.85 207	70.70 190

pmmvs4		67.89 135	67.39 153	68.48 116	71.60 168	73.57 167	74.45 126	60.98 131	64.65 97	57.97 100	54.95 140	51.73 182	61.88 118	73.78 164	75.11 158	83.99 160	77.91 158

testgi		54.39 202	57.86 200	50.35 203	71.59 169	67.24 190	54.95 208	53.25 177	43.36 205	23.78 209	44.64 192	47.87 197	24.96 212	70.45 187	68.66 188	73.60 201	62.78 207

pmmvs6		62.41 174	62.88 182	61.87 167	71.38 170	75.18 162	67.76 169	59.45 150	41.64 208	42.52 182	37.33 205	52.91 173	46.87 185	77.67 136	76.26 151	83.23 163	79.18 152

FMVSNet1		68.84 125	70.47 116	66.94 139	71.35 171	77.68 137	74.71 124	62.35 119	56.93 151	49.94 144	50.01 178	64.59 110	57.07 150	81.33 84	80.72 81	86.25 123	82.00 123

IterMVS-SCA-FT		66.89 151	69.22 130	64.17 156	71.30 172	75.64 154	71.33 155	53.17 178	57.63 147	49.08 150	60.72 101	60.05 127	63.09 106	74.99 157	73.92 164	77.07 186	81.57 129

PatchmatchNet		64.21 164	64.65 172	63.69 160	71.29 173	68.66 185	69.63 161	51.70 188	63.04 111	53.77 122	59.83 109	58.34 136	60.23 132	68.54 197	66.06 199	75.56 193	68.08 197
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
baseline		70.45 107	74.09 89	66.20 146	70.95 174	75.67 153	74.26 133	53.57 174	68.33 77	58.42 96	69.87 64	71.45 75	61.55 122	74.84 158	74.76 161	78.42 180	83.72 111

SCA		65.40 156	66.58 159	64.02 158	70.65 175	73.37 168	67.35 170	53.46 176	63.66 107	54.14 117	60.84 100	60.20 126	61.50 123	69.96 191	68.14 192	77.01 187	69.91 191

CR-MVSNet		64.83 159	65.54 164	64.01 159	70.64 176	69.41 181	65.97 181	52.74 181	57.81 143	52.65 129	54.27 144	56.31 146	60.92 127	72.20 173	73.09 169	81.12 171	75.69 173

MVSTER		72.06 91	74.24 87	69.51 106	70.39 177	75.97 152	76.91 104	57.36 166	64.64 98	61.39 87	68.86 68	63.76 113	63.46 104	81.44 81	79.70 100	87.56 91	85.31 90

Anonymous20231206		56.36 197	57.80 201	54.67 196	70.08 178	66.39 193	60.46 200	57.54 163	49.50 195	29.30 202	33.86 210	46.64 200	35.18 203	70.44 188	68.88 186	75.47 194	68.88 196

thisisatest0515		67.40 145	68.78 135	65.80 148	70.02 179	75.24 159	69.36 163	57.37 165	54.94 169	53.67 123	55.53 136	54.85 153	58.00 142	78.19 131	78.91 114	86.39 122	83.78 110

CMPMVS	47.78 17	62.49 173	62.52 186	62.46 165	70.01 180	70.66 178	62.97 193	51.84 187	51.98 185	56.71 107	42.87 195	53.62 161	57.80 144	72.23 171	70.37 179	75.45 195	75.91 170
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
TDRefinement		66.09 153	65.03 170	67.31 131	69.73 181	76.75 145	75.33 111	64.55 83	60.28 133	49.72 147	45.63 191	42.83 208	60.46 131	75.75 152	75.95 153	84.08 158	78.04 157

TinyColmap		62.84 169	61.03 194	64.96 152	69.61 182	71.69 173	68.48 167	59.76 147	55.41 162	47.69 158	47.33 187	34.20 217	62.76 109	74.52 159	72.59 172	81.44 169	71.47 188

RPMNet		61.71 184	62.88 182	60.34 174	69.51 183	69.41 181	63.48 191	49.23 197	57.81 143	45.64 171	50.51 176	50.12 189	53.13 175	68.17 199	68.49 190	81.07 172	75.62 175

IterMVS		66.36 152	68.30 143	64.10 157	69.48 184	74.61 164	73.41 147	50.79 192	57.30 149	48.28 154	60.64 102	59.92 128	60.85 130	74.14 162	72.66 171	81.80 167	78.82 154
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
SixPastTwentyTwo		61.84 181	62.45 187	61.12 171	69.20 185	72.20 171	62.03 196	57.40 164	46.54 201	38.03 192	57.14 128	41.72 210	58.12 141	69.67 192	71.58 175	81.94 166	78.30 156

MDTV_nov1_ep13		64.37 162	65.24 166	63.37 164	68.94 186	70.81 176	72.40 153	50.29 195	60.10 134	53.91 121	60.07 106	59.15 131	57.21 149	69.43 194	67.30 194	77.47 183	69.78 193

EPMVS		60.00 189	61.97 190	57.71 186	68.46 187	63.17 205	64.54 187	48.23 204	63.30 109	44.72 175	60.19 104	56.05 148	50.85 179	65.27 204	62.02 206	69.44 210	63.81 204

our_test_3					67.93 188	70.99 175	66.89 174

FC-MVSNet-test		56.90 196	65.20 167	47.21 206	66.98 189	63.20 204	49.11 215	58.60 159	59.38 137	11.50 222	65.60 81	56.68 145	24.66 214	71.17 180	71.36 177	72.38 204	69.02 195

CVMVSNet		62.55 171	65.89 160	58.64 183	66.95 190	69.15 183	66.49 180	56.29 170	52.46 182	32.70 198	59.27 112	58.21 137	50.09 180	71.77 176	71.39 176	79.31 177	78.99 153

FPMVS		51.87 205	50.00 210	54.07 197	66.83 191	57.25 212	60.25 201	50.91 190	50.25 191	34.36 196	36.04 208	32.02 219	41.49 194	58.98 212	56.07 212	70.56 209	59.36 212

pmmvs-eth3d		63.52 166	62.44 188	64.77 153	66.82 192	70.12 180	69.41 162	59.48 149	54.34 174	52.71 128	46.24 190	44.35 207	56.93 152	72.37 168	73.77 166	83.30 162	75.91 170

TAMVS		59.58 190	62.81 184	55.81 192	66.03 193	65.64 196	63.86 190	48.74 200	49.95 192	37.07 194	54.77 141	58.54 134	44.44 189	72.29 170	71.79 173	74.70 197	66.66 199

MDTV_nov1_ep13_2view		60.16 188	60.51 196	59.75 177	65.39 194	69.05 184	68.00 168	48.29 203	51.99 184	45.95 169	48.01 186	49.64 193	53.39 173	68.83 196	66.52 198	77.47 183	69.55 194

pmmvs5		62.37 177	64.04 176	60.42 173	65.03 195	71.67 174	67.17 172	52.70 183	50.30 190	44.80 173	54.23 147	51.19 185	49.37 181	72.88 167	73.48 168	83.45 161	74.55 180

ambc			53.42 205		64.99 196	63.36 203	49.96 213		47.07 199	37.12 193	28.97 214	16.36 226	41.82 193	75.10 156	67.34 193	71.55 206	75.72 172

V42		68.76 127	69.63 124	67.74 122	64.93 197	78.01 128	78.30 92	56.48 168	58.65 140	56.30 109	54.26 146	57.03 142	64.85 98	77.47 138	77.01 142	85.60 141	84.96 97

pmnet_mix02		55.30 199	57.01 203	53.30 201	64.14 198	59.09 210	58.39 205	50.24 196	53.47 177	38.68 189	49.75 181	45.86 203	40.14 199	65.38 203	60.22 208	68.19 212	65.33 201

PMVS	39.38 18	46.06 211	43.30 214	49.28 205	62.93 199	38.75 220	41.88 218	53.50 175	33.33 218	35.46 195	28.90 215	31.01 220	33.04 206	58.61 214	54.63 215	68.86 211	57.88 213
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
new-patchmatchnet		46.97 209	49.47 211	44.05 210	62.82 200	56.55 213	45.35 217	52.01 185	42.47 207	17.04 220	35.73 209	35.21 216	21.84 218	61.27 209	54.83 214	65.26 214	60.26 209

ET-MVSNet_ETH3D		72.46 89	74.19 88	70.44 93	62.50 201	81.17 96	79.90 72	62.46 118	64.52 100	57.52 102	71.49 57	59.15 131	72.08 53	78.61 127	81.11 74	88.16 73	83.29 114

ADS-MVSNet		55.94 198	58.01 199	53.54 200	62.48 202	58.48 211	59.12 204	46.20 208	59.65 136	42.88 181	52.34 169	53.31 171	46.31 186	62.00 208	60.02 209	64.23 215	60.24 211

RPSCF		67.64 142	71.25 110	63.43 163	61.86 203	70.73 177	67.26 171	50.86 191	74.20 60	58.91 92	67.49 76	69.33 89	64.10 102	71.41 177	68.45 191	77.61 182	77.17 163

MIMVSNet		58.52 193	61.34 193	55.22 194	60.76 204	67.01 191	66.81 175	49.02 199	56.43 155	38.90 188	40.59 202	54.54 156	40.57 198	73.16 166	71.65 174	75.30 196	66.00 200

PatchT		61.97 179	64.04 176	59.55 180	60.49 205	67.40 189	56.54 206	48.65 201	56.69 152	52.65 129	51.10 175	52.14 180	60.92 127	72.20 173	73.09 169	78.03 181	75.69 173

N_pmnet		47.35 208	50.13 209	44.11 209	59.98 206	51.64 217	51.86 211	44.80 210	49.58 194	20.76 216	40.65 201	40.05 214	29.64 208	59.84 210	55.15 213	57.63 216	54.00 214

MVS-HIRNet		54.41 201	52.10 208	57.11 189	58.99 207	56.10 214	49.68 214	49.10 198	46.18 202	52.15 133	33.18 211	46.11 202	56.10 157	63.19 207	59.70 210	76.64 189	60.25 210

PM-MVS		60.48 187	60.94 195	59.94 176	58.85 208	66.83 192	64.27 189	51.39 189	55.03 167	48.03 155	50.00 180	40.79 212	58.26 140	69.20 195	67.13 197	78.84 179	77.60 160

WB-MVS		40.01 212	45.06 213	34.13 212	58.84 209	53.28 216	28.60 221	58.10 161	32.93 219	4.65 227	40.92 199	28.33 222	7.26 221	58.86 213	56.09 211	47.36 219	44.98 216

anonymousdsp		65.28 157	67.98 145	62.13 166	58.73 210	73.98 166	67.10 173	50.69 193	48.41 196	47.66 159	54.27 144	52.75 176	61.45 125	76.71 148	80.20 94	87.13 100	89.53 54

TESTMET0.1,1		61.10 185	64.36 174	57.29 187	57.53 211	63.93 199	66.61 178	36.22 215	54.41 171	47.77 156	57.46 125	60.25 124	55.20 166	70.80 184	69.33 182	80.40 174	74.38 181

EU-MVSNet		54.63 200	58.69 198	49.90 204	56.99 212	62.70 207	56.41 207	50.64 194	45.95 203	23.14 211	50.42 177	46.51 201	36.63 202	65.51 202	64.85 201	75.57 192	74.91 178

FMVSNet5		57.24 194	60.02 197	53.99 198	56.45 213	62.74 206	65.27 184	47.03 206	55.14 164	39.55 187	40.88 200	53.42 169	41.83 192	72.35 169	71.10 178	73.79 200	64.50 203

test-mter		60.84 186	64.62 173	56.42 190	55.99 214	64.18 197	65.39 183	34.23 216	54.39 173	46.21 167	57.40 127	59.49 130	55.86 160	71.02 183	69.65 181	80.87 173	76.20 169

CHOSEN 280x420		58.70 192	61.88 191	54.98 195	55.45 215	50.55 218	64.92 185	40.36 212	55.21 163	38.13 191	48.31 184	63.76 113	63.03 108	73.73 165	68.58 189	68.00 213	73.04 186

PMMVS		65.06 158	69.17 131	60.26 175	55.25 216	63.43 202	66.71 177	43.01 211	62.41 116	50.64 140	69.44 65	67.04 103	63.29 105	74.36 161	73.54 167	82.68 165	73.99 184

Gipuma		36.38 214	35.80 216	37.07 211	45.76 217	33.90 221	29.81 220	48.47 202	39.91 211	18.02 219	8.00 224	8.14 228	25.14 211	59.29 211	61.02 207	55.19 218	40.31 217
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
pmmvs3		47.65 207	49.08 212	45.99 207	44.61 218	54.79 215	50.04 212	31.95 219	33.91 215	29.90 200	30.37 212	33.53 218	46.31 186	63.50 205	63.67 204	73.14 203	63.77 205

MIMVSNet1		49.27 206	53.25 206	44.62 208	44.61 218	61.52 209	53.61 209	52.18 184	41.62 209	18.68 218	28.14 216	41.58 211	25.50 210	68.46 198	69.04 184	73.15 202	62.37 208

MDA-MVSNet-bldmvs		53.37 204	53.01 207	53.79 199	43.67 220	67.95 188	59.69 202	57.92 162	43.69 204	32.41 199	41.47 198	27.89 223	52.38 177	56.97 215	65.99 200	76.68 188	67.13 198

E-PMN		21.77 217	18.24 220	25.89 214	40.22 221	19.58 224	12.46 226	39.87 213	18.68 223	6.71 224	9.57 221	4.31 231	22.36 217	19.89 222	27.28 220	33.73 222	28.34 221

EMVS		20.98 218	17.15 221	25.44 215	39.51 222	19.37 225	12.66 225	39.59 214	19.10 222	6.62 225	9.27 222	4.40 230	22.43 216	17.99 223	24.40 221	31.81 223	25.53 222

new_pmnet		38.40 213	42.64 215	33.44 213	37.54 223	45.00 219	36.60 219	32.72 218	40.27 210	12.72 221	29.89 213	28.90 221	24.78 213	53.17 216	52.90 216	56.31 217	48.34 215

PMMVS2		25.60 215	29.75 217	20.76 217	28.00 224	30.93 222	23.10 223	29.18 220	23.14 221	1.46 228	18.23 220	16.54 225	5.08 222	40.22 217	41.40 218	37.76 220	37.79 219

tmp_tt				14.50 220	14.68 225	7.17 227	10.46 228	2.21 223	37.73 213	28.71 203	25.26 217	16.98 224	4.37 223	31.49 219	29.77 219	26.56 224

MVE	19.12 19	20.47 219	23.27 219	17.20 219	12.66 226	25.41 223	10.52 227	34.14 217	14.79 224	6.53 226	8.79 223	4.68 229	16.64 220	29.49 220	41.63 217	22.73 225	38.11 218
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		22.26 216	25.94 218	17.95 218	3.24 227	7.17 227	23.83 222	7.27 222	37.35 214	20.44 217	21.87 219	39.16 215	18.67 219	34.56 218	20.84 222	34.28 221	20.64 223

GG-mvs-BLEND		46.86 210	67.51 150	22.75 216	0.05 228	76.21 150	64.69 186	0.04 224	61.90 120	0.09 229	55.57 134	71.32 77	0.08 224	70.54 186	67.19 195	71.58 205	69.86 192

testmvs		0.09 220	0.15 222	0.02 221	0.01 229	0.02 229	0.05 230	0.01 225	0.11 225	0.01 230	0.26 226	0.01 232	0.06 226	0.10 224	0.10 223	0.01 227	0.43 225

uanet_test		0.00 222	0.00 224	0.00 223	0.00 230	0.00 231	0.00 232	0.00 227	0.00 227	0.00 231	0.00 227	0.00 233	0.00 227	0.00 226	0.00 225	0.00 229	0.00 226

sosnet-low-res		0.00 222	0.00 224	0.00 223	0.00 230	0.00 231	0.00 232	0.00 227	0.00 227	0.00 231	0.00 227	0.00 233	0.00 227	0.00 226	0.00 225	0.00 229	0.00 226

sosnet		0.00 222	0.00 224	0.00 223	0.00 230	0.00 231	0.00 232	0.00 227	0.00 227	0.00 231	0.00 227	0.00 233	0.00 227	0.00 226	0.00 225	0.00 229	0.00 226

test123		0.09 220	0.14 223	0.02 221	0.00 230	0.02 229	0.02 231	0.01 225	0.09 226	0.00 231	0.30 225	0.00 233	0.08 224	0.03 225	0.09 224	0.01 227	0.45 224

RE-MVS-def										46.24 166

9.14												86.88 16

MTAPA										83.48 1		86.45 19

MTMP										82.66 5		84.91 26

Patchmatch-RL test							2.85 229

NP-MVS									80.10 45

Patchmtry						65.80 195	65.97 181	52.74 181		52.65 129

DeepMVS_CX						18.74 226	18.55 224	8.02 221	26.96 220	7.33 223	23.81 218	13.05 227	25.99 209	25.17 221		22.45 226	36.25 220

High-resolution multi-view benchmark