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 11	95.19 5
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
NCCC		85.34 19	86.59 25	83.88 15	91.48 4	88.88 25	89.79 17	75.54 11	86.67 20	77.94 23	76.55 35	84.99 25	78.07 16	88.04 13	87.68 13	90.46 26	93.31 21

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

SF-MVS		87.47 8	89.70 8	84.86 8	91.26 6	91.10 8	90.90 6	75.65 8	89.21 9	81.25 7	91.12 8	88.93 7	78.82 10	87.42 20	86.23 30	91.28 3	93.90 13

APDe-MVS		88.00 6	90.50 6	85.08 5	90.95 7	91.58 7	92.03 1	75.53 12	91.15 5	80.10 14	92.27 5	88.34 11	80.80 5	88.00 15	86.99 19	91.09 5	95.16 6
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
DPE-MVS		88.63 4	91.29 4	85.53 3	90.87 8	92.20 4	91.98 2	76.00 6	90.55 8	82.09 6	93.85 1	90.75 2	81.25 1	88.62 8	87.59 15	90.96 9	95.48 4
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
HFP-MVS		86.15 15	87.95 18	84.06 13	90.80 9	89.20 24	89.62 19	74.26 16	87.52 14	80.63 11	86.82 16	84.19 29	78.22 14	87.58 18	87.19 17	90.81 13	93.13 25

SteuartSystems-ACMMP		85.99 16	88.31 16	83.27 20	90.73 10	89.84 15	90.27 14	74.31 15	84.56 29	75.88 31	87.32 14	85.04 24	77.31 23	89.01 7	88.46 3	91.14 4	93.96 12
Skip Steuart: Steuart Systems R&D Blog.
APD-MVS		86.84 12	88.91 14	84.41 10	90.66 11	90.10 13	90.78 7	75.64 9	87.38 16	78.72 18	90.68 10	86.82 17	80.15 7	87.13 25	86.45 29	90.51 21	93.83 14
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MP-MVS		85.50 18	87.40 21	83.28 19	90.65 12	89.51 20	89.16 23	74.11 18	83.70 34	78.06 22	85.54 20	84.89 28	77.31 23	87.40 22	87.14 18	90.41 28	93.65 19
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
train_agg		84.86 24	87.21 23	82.11 26	90.59 13	85.47 55	89.81 16	73.55 25	83.95 31	73.30 39	89.84 12	87.23 15	75.61 33	86.47 33	85.46 38	89.78 40	92.06 32

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

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

DVP-MVS		88.67 3	91.62 2	85.22 4	90.47 16	92.36 2	90.69 9	76.15 4	93.08 2	82.75 4	92.19 6	90.71 3	80.45 6	89.27 6	87.91 9	90.82 12	95.84 2
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
DeepC-MVS_fast	78.24 3	84.27 29	85.50 31	82.85 22	90.46 17	89.24 22	87.83 33	74.24 17	84.88 25	76.23 29	75.26 40	81.05 43	77.62 20	88.02 14	87.62 14	90.69 17	92.41 28
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ACMMP_NAP		86.52 13	89.01 11	83.62 16	90.28 18	90.09 14	90.32 13	74.05 19	88.32 13	79.74 15	87.04 15	85.59 23	76.97 28	89.35 4	88.44 4	90.35 30	94.27 11

SD-MVS		86.96 10	89.45 9	84.05 14	90.13 19	89.23 23	89.77 18	74.59 14	89.17 10	80.70 10	89.93 11	89.67 5	78.47 12	87.57 19	86.79 23	90.67 18	93.76 16
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
ACMMPR		85.52 17	87.53 20	83.17 21	90.13 19	89.27 21	89.30 20	73.97 20	86.89 19	77.14 25	86.09 18	83.18 32	77.74 19	87.42 20	87.20 16	90.77 14	92.63 26

TPM-MVS					90.07 21	88.36 35	88.45 29			77.10 26	75.60 38	83.98 30	71.33 63			89.75 43	89.62 53
Ray Leroy Khuboni and Hongjun Xu: Textureless Resilient Propagation Matching in Multiple View Stereosis (TPM-MVS). SATNAC 2025
PGM-MVS		84.42 28	86.29 28	82.23 25	90.04 22	88.82 26	89.23 22	71.74 35	82.82 39	74.61 34	84.41 23	82.09 35	77.03 27	87.13 25	86.73 25	90.73 16	92.06 32

MSP-MVS		88.09 5	90.84 5	84.88 7	90.00 23	91.80 6	91.63 5	75.80 7	91.99 4	81.23 8	92.54 2	89.18 6	80.89 4	87.99 16	87.91 9	89.70 45	94.51 7
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
CSCG		85.28 21	87.68 19	82.49 24	89.95 24	91.99 5	88.82 24	71.20 37	86.41 21	79.63 16	79.26 30	88.36 10	73.94 41	86.64 31	86.67 26	91.40 2	94.41 8

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 20	93.83 14
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
X-MVS		83.23 33	85.20 33	80.92 34	89.71 27	88.68 27	88.21 32	73.60 23	82.57 40	71.81 46	77.07 33	81.92 37	71.72 58	86.98 28	86.86 21	90.47 23	92.36 29

DPM-MVS		83.30 32	84.33 35	82.11 26	89.56 28	88.49 33	90.33 12	73.24 27	83.85 32	76.46 28	72.43 52	82.65 33	73.02 48	86.37 35	86.91 20	90.03 36	89.62 53

TSAR-MVS + ACMM		85.10 23	88.81 15	80.77 35	89.55 29	88.53 32	88.59 27	72.55 30	87.39 15	71.90 43	90.95 9	87.55 13	74.57 36	87.08 27	86.54 27	87.47 94	93.67 17

CP-MVS		84.74 26	86.43 27	82.77 23	89.48 30	88.13 39	88.64 25	73.93 21	84.92 24	76.77 27	81.94 27	83.50 31	77.29 25	86.92 30	86.49 28	90.49 22	93.14 24

CDPH-MVS		82.64 34	85.03 34	79.86 39	89.41 31	88.31 36	88.32 30	71.84 34	80.11 46	67.47 65	82.09 26	81.44 41	71.85 56	85.89 41	86.15 32	90.24 32	91.25 38

DeepC-MVS	78.47 2	84.81 25	86.03 29	83.37 18	89.29 32	90.38 12	88.61 26	76.50 1	86.25 22	77.22 24	75.12 41	80.28 45	77.59 21	88.39 10	88.17 6	91.02 6	93.66 18
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
AdaColmap		79.74 46	78.62 64	81.05 33	89.23 33	86.06 52	84.95 49	71.96 33	79.39 49	75.51 32	63.16 95	68.84 99	76.51 29	83.55 61	82.85 59	88.13 76	86.46 79

SR-MVS					88.99 34			73.57 24				87.54 14

EPNet		79.08 56	80.62 53	77.28 51	88.90 35	83.17 82	83.65 54	72.41 31	74.41 58	67.15 68	76.78 34	74.37 66	64.43 101	83.70 60	83.69 53	87.15 98	88.19 62
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 32	88.77 36	90.48 11	85.46 46	73.08 28	90.97 6	73.77 38	84.81 22	85.95 20	77.43 22	88.22 11	87.73 11	87.85 86	94.34 9

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

ACMMP		83.42 31	85.27 32	81.26 31	88.47 38	88.49 33	88.31 31	72.09 32	83.42 35	72.77 41	82.65 24	78.22 50	75.18 34	86.24 38	85.76 35	90.74 15	92.13 31
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
3Dnovator+	75.73 4	82.40 35	82.76 39	81.97 28	88.02 39	89.67 18	86.60 38	71.48 36	81.28 44	78.18 21	64.78 89	77.96 52	77.13 26	87.32 23	86.83 22	90.41 28	91.48 36

OPM-MVS		79.68 47	79.28 62	80.15 38	87.99 40	86.77 46	88.52 28	72.72 29	64.55 102	67.65 64	67.87 77	74.33 67	74.31 39	86.37 35	85.25 40	89.73 44	89.81 51
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
MAR-MVS		79.21 52	80.32 57	77.92 49	87.46 41	88.15 38	83.95 53	67.48 64	74.28 59	68.25 59	64.70 90	77.04 53	72.17 52	85.42 43	85.00 42	88.22 72	87.62 67
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
HQP-MVS		81.19 40	83.27 37	78.76 44	87.40 42	85.45 56	86.95 36	70.47 40	81.31 43	66.91 69	79.24 31	76.63 54	71.67 59	84.43 54	83.78 52	89.19 56	92.05 34

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

MSLP-MVS++		82.09 37	82.66 40	81.42 30	87.03 44	87.22 43	85.82 42	70.04 42	80.30 45	78.66 19	68.67 73	81.04 44	77.81 18	85.19 46	84.88 43	89.19 56	91.31 37

ACMM	72.26 8	78.86 57	78.13 66	79.71 40	86.89 45	83.40 77	86.02 40	70.50 39	75.28 56	71.49 50	63.01 96	69.26 93	73.57 43	84.11 56	83.98 48	89.76 42	87.84 65
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
XVS					86.63 46	88.68 27	85.00 47			71.81 46		81.92 37				90.47 23

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

PHI-MVS		82.36 36	85.89 30	78.24 47	86.40 48	89.52 19	85.52 44	69.52 48	82.38 42	65.67 72	81.35 28	82.36 34	73.07 47	87.31 24	86.76 24	89.24 52	91.56 35

LGP-MVS_train		79.83 43	81.22 49	78.22 48	86.28 49	85.36 58	86.76 37	69.59 46	77.34 51	65.14 75	75.68 37	70.79 83	71.37 62	84.60 50	84.01 47	90.18 33	90.74 42

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

MVS_111021_HR		80.13 42	81.46 46	78.58 45	85.77 51	85.17 59	83.45 55	69.28 49	74.08 62	70.31 54	74.31 44	75.26 63	73.13 46	86.46 34	85.15 41	89.53 47	89.81 51

ACMP	73.23 7	79.79 44	80.53 54	78.94 42	85.61 52	85.68 53	85.61 43	69.59 46	77.33 52	71.00 52	74.45 43	69.16 94	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 79	77.80 69	70.59 94	85.33 53	85.40 57	73.54 147	65.98 73	60.65 133	56.00 112	72.11 53	79.15 46	54.63 172	83.13 68	82.25 64	88.04 80	81.92 129

TSAR-MVS + GP.		83.69 30	86.58 26	80.32 36	85.14 54	86.96 44	84.91 50	70.25 41	84.71 28	73.91 37	85.16 21	85.63 22	77.92 17	85.44 42	85.71 36	89.77 41	92.45 27

LS3D		74.08 81	73.39 97	74.88 68	85.05 55	82.62 87	79.71 76	68.66 53	72.82 67	58.80 95	57.61 127	61.31 124	71.07 65	80.32 104	78.87 117	86.00 136	80.18 146

QAPM		78.47 59	80.22 58	76.43 58	85.03 56	86.75 47	80.62 67	66.00 72	73.77 64	65.35 74	65.54 85	78.02 51	72.69 50	83.71 59	83.36 56	88.87 62	90.41 47

OpenMVS	70.44 10	76.15 72	76.82 80	75.37 65	85.01 57	84.79 61	78.99 85	62.07 122	71.27 71	67.88 63	57.91 126	72.36 75	70.15 67	82.23 76	81.41 72	88.12 77	87.78 66

CLD-MVS		79.35 50	81.23 48	77.16 53	85.01 57	86.92 45	85.87 41	60.89 134	80.07 48	75.35 33	72.96 48	73.21 71	68.43 80	85.41 44	84.63 44	87.41 95	85.44 91
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 55	78.84 43	84.94 59	87.35 41	84.43 52	65.54 75	78.29 50	73.97 36	63.00 97	75.62 62	74.07 40	85.00 47	85.34 39	90.11 35	89.04 56

PCF-MVS	73.28 6	79.42 49	80.41 56	78.26 46	84.88 60	88.17 37	86.08 39	69.85 43	75.23 57	68.43 58	68.03 76	78.38 48	71.76 57	81.26 90	80.65 89	88.56 68	91.18 39
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
DELS-MVS		79.15 55	81.07 51	76.91 55	83.54 61	87.31 42	84.45 51	64.92 80	69.98 72	69.34 56	71.62 56	76.26 55	69.84 68	86.57 32	85.90 34	89.39 49	89.88 50
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
OMC-MVS		80.26 41	82.59 41	77.54 50	83.04 62	85.54 54	83.25 56	65.05 79	87.32 18	72.42 42	72.04 54	78.97 47	73.30 45	83.86 57	81.60 71	88.15 75	88.83 58

EC-MVSNet		79.44 48	81.35 47	77.22 52	82.95 63	84.67 63	81.31 61	63.65 92	72.47 69	68.75 57	73.15 47	78.33 49	75.99 32	86.06 40	83.96 49	90.67 18	90.79 41

PLC	68.99 11	75.68 73	75.31 85	76.12 60	82.94 64	81.26 97	79.94 72	66.10 70	77.15 53	66.86 70	59.13 116	68.53 101	73.73 42	80.38 103	79.04 113	87.13 102	81.68 131
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CNLPA		77.20 65	77.54 71	76.80 56	82.63 65	84.31 66	79.77 74	64.64 81	85.17 23	73.18 40	56.37 133	69.81 90	74.53 38	81.12 93	78.69 118	86.04 134	87.29 70

ACMH+	66.54 13	71.36 103	70.09 121	72.85 80	82.59 66	81.13 99	78.56 89	68.04 56	61.55 126	52.52 135	51.50 175	54.14 160	68.56 79	78.85 126	79.50 108	86.82 110	83.94 111

ACMH	65.37 14	70.71 107	70.00 122	71.54 86	82.51 67	82.47 88	77.78 97	68.13 55	56.19 161	46.06 171	54.30 145	51.20 187	68.68 78	80.66 99	80.72 82	86.07 130	84.45 108
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EIA-MVS		75.64 74	76.60 81	74.53 73	82.43 68	83.84 71	78.32 93	62.28 120	65.96 92	63.28 85	68.95 69	67.54 104	71.61 60	82.55 73	81.63 70	89.24 52	85.72 85

sasdasda		79.16 53	82.37 42	75.41 63	82.33 69	86.38 50	80.80 64	63.18 98	82.90 37	67.34 66	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 64	63.18 98	82.90 37	67.34 66	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 63	75.58 62	82.24 71	83.64 75	79.98 70	64.02 88	69.64 77	63.90 81	70.89 60	69.94 89	73.41 44	85.39 45	83.91 51	89.92 37	88.31 61

MSDG		71.52 100	69.87 123	73.44 78	82.21 72	79.35 118	79.52 78	64.59 82	66.15 90	61.87 86	53.21 160	56.09 150	65.85 99	78.94 125	78.50 120	86.60 119	76.85 169

MGCFI-Net		76.55 68	81.71 44	70.52 95	81.71 73	84.62 64	75.02 122	62.17 121	82.91 36	53.58 127	72.78 51	75.87 61	61.75 124	82.96 69	82.61 62	88.86 63	90.26 48

casdiffmvs_mvg		77.79 62	79.55 61	75.73 61	81.56 74	84.70 62	82.12 57	64.26 87	74.27 60	67.93 62	70.83 61	74.66 65	69.19 75	83.33 66	81.94 66	89.29 51	87.14 72
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 97	72.95 101	70.29 98	81.49 75	83.27 78	75.74 111	67.59 62	68.19 80	49.81 148	61.15 101	49.73 195	58.82 138	84.76 48	82.94 57	88.27 70	80.63 140

ECVR-MVS		72.20 93	73.91 93	70.20 100	81.49 75	83.27 78	75.74 111	67.59 62	68.19 80	49.31 152	55.77 135	62.00 122	58.82 138	84.76 48	82.94 57	88.27 70	80.41 144

CS-MVS		79.22 51	81.11 50	77.01 54	81.36 77	84.03 67	80.35 68	63.25 96	73.43 66	70.37 53	74.10 46	76.03 59	76.40 30	86.32 37	83.95 50	90.34 31	89.93 49

IS_MVSNet		73.33 86	77.34 76	68.65 118	81.29 78	83.47 76	74.45 129	63.58 94	65.75 94	48.49 154	67.11 82	70.61 84	54.63 172	84.51 52	83.58 54	89.48 48	86.34 80

test1111		71.56 99	73.44 96	69.38 111	81.16 79	82.95 83	74.99 123	67.68 60	66.89 86	46.33 168	55.19 141	60.91 125	57.99 146	84.59 51	82.70 61	88.12 77	80.85 137

Effi-MVS+		75.28 76	76.20 82	74.20 75	81.15 80	83.24 80	81.11 62	63.13 101	66.37 88	60.27 91	64.30 93	68.88 98	70.93 66	81.56 80	81.69 69	88.61 66	87.35 68

MVS_111021_LR		78.13 61	79.85 60	76.13 59	81.12 81	81.50 93	80.28 69	65.25 77	76.09 55	71.32 51	76.49 36	72.87 73	72.21 51	82.79 72	81.29 73	86.59 120	87.91 64

SPE-MVS-test		78.79 58	80.72 52	76.53 57	81.11 82	83.88 70	79.69 77	63.72 91	73.80 63	69.95 55	75.40 39	76.17 56	74.85 35	84.50 53	82.78 60	89.87 39	88.54 60

FC-MVSNet-train		72.60 91	75.07 87	69.71 106	81.10 83	78.79 125	73.74 146	65.23 78	66.10 91	53.34 128	70.36 63	63.40 118	56.92 156	81.44 83	80.96 78	87.93 82	84.46 107

MS-PatchMatch		70.17 114	70.49 118	69.79 105	80.98 84	77.97 137	77.51 99	58.95 158	62.33 120	55.22 116	53.14 161	65.90 110	62.03 117	79.08 123	77.11 143	84.08 161	77.91 161

Anonymous202405211			72.16 109		80.85 85	81.85 90	76.88 107	65.40 76	62.89 117		46.35 192	67.99 103	62.05 116	81.15 92	80.38 93	85.97 137	84.50 106

TAPA-MVS	71.42 9	77.69 63	80.05 59	74.94 67	80.68 86	84.52 65	81.36 60	63.14 100	84.77 26	64.82 77	68.72 71	75.91 60	71.86 55	81.62 78	79.55 107	87.80 88	85.24 94
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PVSNet_Blended_VisFu		76.57 67	77.90 67	75.02 66	80.56 87	86.58 48	79.24 81	66.18 69	64.81 99	68.18 60	65.61 83	71.45 77	67.05 84	84.16 55	81.80 68	88.90 60	90.92 40

EPP-MVSNet		74.00 82	77.41 74	70.02 103	80.53 88	83.91 69	74.99 123	62.68 113	65.06 97	49.77 149	68.68 72	72.09 76	63.06 109	82.49 75	80.73 81	89.12 58	88.91 57

COLMAP_ROB	62.73 15	67.66 143	66.76 160	68.70 117	80.49 89	77.98 135	75.29 115	62.95 103	63.62 111	49.96 146	47.32 191	50.72 190	58.57 140	76.87 148	75.50 160	84.94 155	75.33 180
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
GeoE		74.23 80	74.84 89	73.52 77	80.42 90	81.46 94	79.77 74	61.06 131	67.23 85	63.67 82	59.56 113	68.74 100	67.90 81	80.25 108	79.37 111	88.31 69	87.26 71

casdiffmvs		76.76 66	78.46 65	74.77 69	80.32 91	83.73 74	80.65 66	63.24 97	73.58 65	66.11 71	69.39 68	74.09 68	69.49 73	82.52 74	79.35 112	88.84 64	86.52 78
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 84	75.78 84	71.16 88	80.19 92	79.27 119	77.45 102	61.68 128	66.73 87	58.72 96	65.31 86	69.96 88	62.19 114	81.29 89	80.97 77	86.74 113	86.91 73

Anonymous20231211		71.90 95	72.48 106	71.21 87	80.14 93	81.53 92	76.92 105	62.89 104	64.46 104	58.94 93	43.80 196	70.98 82	62.22 113	80.70 98	80.19 96	86.18 127	85.73 84

TSAR-MVS + COLMAP		78.34 60	81.64 45	74.48 74	80.13 94	85.01 60	81.73 58	65.93 74	84.75 27	61.68 87	85.79 19	66.27 109	71.39 61	82.91 70	80.78 80	86.01 135	85.98 81

viewmanbaseed2359cas		76.36 69	77.87 68	74.60 72	79.81 95	82.88 85	81.69 59	61.02 132	72.14 70	67.97 61	69.61 66	72.45 74	69.53 71	81.53 81	79.83 100	87.57 92	86.65 77

baseline1		70.10 115	72.17 108	67.69 127	79.74 96	76.80 147	73.91 140	64.38 84	62.74 118	48.30 156	64.94 87	64.08 115	54.17 174	81.46 82	78.92 115	85.66 142	76.22 171

EPNet_dtu		68.08 135	71.00 114	64.67 157	79.64 97	68.62 189	75.05 121	63.30 95	66.36 89	45.27 175	67.40 80	66.84 108	43.64 194	75.37 157	74.98 163	81.15 173	77.44 164
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet_BlendedMVS		76.21 70	77.52 72	74.69 70	79.46 98	83.79 72	77.50 100	64.34 85	69.88 73	71.88 44	68.54 74	70.42 85	67.05 84	83.48 62	79.63 103	87.89 84	86.87 74

PVSNet_Blended		76.21 70	77.52 72	74.69 70	79.46 98	83.79 72	77.50 100	64.34 85	69.88 73	71.88 44	68.54 74	70.42 85	67.05 84	83.48 62	79.63 103	87.89 84	86.87 74

IB-MVS	66.94 12	71.21 104	71.66 112	70.68 91	79.18 100	82.83 86	72.61 153	61.77 126	59.66 138	63.44 84	53.26 158	59.65 132	59.16 137	76.78 150	82.11 65	87.90 83	87.33 69
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
MVS_Test		75.37 75	77.13 78	73.31 79	79.07 101	81.32 96	79.98 70	60.12 146	69.72 75	64.11 80	70.53 62	73.22 70	68.90 76	80.14 110	79.48 109	87.67 90	85.50 89

Effi-MVS+-dtu		71.82 96	71.86 111	71.78 85	78.77 102	80.47 106	78.55 90	61.67 129	60.68 132	55.49 113	58.48 120	65.48 111	68.85 77	76.92 147	75.55 159	87.35 96	85.46 90

EG-PatchMatch MVS		67.24 150	66.94 158	67.60 129	78.73 103	81.35 95	73.28 151	59.49 151	46.89 203	51.42 140	43.65 197	53.49 168	55.50 168	81.38 85	80.66 88	87.15 98	81.17 135

gg-mvs-nofinetune		62.55 174	65.05 172	59.62 182	78.72 104	77.61 141	70.83 161	53.63 176	39.71 215	22.04 217	36.36 210	64.32 114	47.53 187	81.16 91	79.03 114	85.00 154	77.17 166

FA-MVS(training)		73.66 83	74.95 88	72.15 82	78.63 105	80.46 107	78.92 87	54.79 175	69.71 76	65.37 73	62.04 98	66.89 107	67.10 83	80.72 97	79.87 99	88.10 79	84.97 99

Vis-MVSNet (Re-imp)		67.83 140	73.52 95	61.19 173	78.37 106	76.72 149	66.80 179	62.96 102	65.50 95	34.17 200	67.19 81	69.68 91	39.20 203	79.39 120	79.44 110	85.68 141	76.73 170

DI_MVS_pp		75.13 77	76.12 83	73.96 76	78.18 107	81.55 91	80.97 63	62.54 115	68.59 78	65.13 76	61.43 100	74.81 64	69.32 74	81.01 95	79.59 105	87.64 91	85.89 82

thres600view7		67.68 142	68.43 144	66.80 144	77.90 108	78.86 123	73.84 142	62.75 106	56.07 162	44.70 179	52.85 166	52.81 177	55.58 166	80.41 100	77.77 129	86.05 132	80.28 145

thres400		67.95 137	68.62 142	67.17 137	77.90 108	78.59 128	74.27 135	62.72 108	56.34 160	45.77 173	53.00 163	53.35 173	56.46 158	80.21 109	78.43 121	85.91 139	80.43 143

thres200		67.98 136	68.55 143	67.30 135	77.89 110	78.86 123	74.18 138	62.75 106	56.35 159	46.48 167	52.98 164	53.54 166	56.46 158	80.41 100	77.97 127	86.05 132	79.78 150

thres100view900		67.60 146	68.02 147	67.12 139	77.83 111	77.75 139	73.90 141	62.52 116	56.64 156	46.82 164	52.65 168	53.47 170	55.92 162	78.77 127	77.62 132	85.72 140	79.23 154

tfpn200view9		68.11 134	68.72 140	67.40 132	77.83 111	78.93 121	74.28 134	62.81 105	56.64 156	46.82 164	52.65 168	53.47 170	56.59 157	80.41 100	78.43 121	86.11 128	80.52 142

Fast-Effi-MVS+		73.11 88	73.66 94	72.48 81	77.72 113	80.88 103	78.55 90	58.83 161	65.19 96	60.36 90	59.98 110	62.42 121	71.22 64	81.66 77	80.61 91	88.20 73	84.88 102

UniMVSNet_NR-MVSNet		70.59 108	72.19 107	68.72 116	77.72 113	80.72 104	73.81 144	69.65 45	61.99 122	43.23 181	60.54 106	57.50 141	58.57 140	79.56 116	81.07 76	89.34 50	83.97 109

IterMVS-LS		71.69 98	72.82 104	70.37 97	77.54 115	76.34 152	75.13 120	60.46 140	61.53 127	57.57 103	64.89 88	67.33 105	66.04 98	77.09 146	77.37 139	85.48 145	85.18 95
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
NR-MVSNet		68.79 129	70.56 117	66.71 147	77.48 116	79.54 115	73.52 148	69.20 50	61.20 130	39.76 188	58.52 118	50.11 193	51.37 181	80.26 107	80.71 86	88.97 59	83.59 115

TransMVSNet (Re)		64.74 163	65.66 166	63.66 164	77.40 117	75.33 161	69.86 162	62.67 114	47.63 201	41.21 187	50.01 181	52.33 180	45.31 191	79.57 115	77.69 131	85.49 144	77.07 168

TranMVSNet+NR-MVSNet		69.25 124	70.81 116	67.43 131	77.23 118	79.46 117	73.48 149	69.66 44	60.43 135	39.56 189	58.82 117	53.48 169	55.74 165	79.59 114	81.21 74	88.89 61	82.70 119

CANet_DTU		73.29 87	76.96 79	69.00 115	77.04 119	82.06 89	79.49 79	56.30 172	67.85 83	53.29 129	71.12 59	70.37 87	61.81 123	81.59 79	80.96 78	86.09 129	84.73 103

CHOSEN 1792x2688		69.20 125	69.26 132	69.13 112	76.86 120	78.93 121	77.27 103	60.12 146	61.86 124	54.42 117	42.54 200	61.61 123	66.91 89	78.55 130	78.14 125	79.23 181	83.23 118

HyFIR lowres test		69.47 122	68.94 136	70.09 102	76.77 121	82.93 84	76.63 109	60.17 144	59.00 141	54.03 121	40.54 206	65.23 112	67.89 82	76.54 153	78.30 123	85.03 153	80.07 147

UniMVSNet (Re)		69.53 120	71.90 110	66.76 145	76.42 122	80.93 100	72.59 154	68.03 57	61.75 125	41.68 186	58.34 124	57.23 143	53.27 177	79.53 117	80.62 90	88.57 67	84.90 101

gm-plane-assit		57.00 198	57.62 205	56.28 194	76.10 123	62.43 211	47.62 219	46.57 210	33.84 219	23.24 213	37.52 207	40.19 216	59.61 136	79.81 112	77.55 134	84.55 158	72.03 190

DU-MVS		69.63 119	70.91 115	68.13 122	75.99 124	79.54 115	73.81 144	69.20 50	61.20 130	43.23 181	58.52 118	53.50 167	58.57 140	79.22 121	80.45 92	87.97 81	83.97 109

Baseline_NR-MVSNet		67.53 147	68.77 139	66.09 150	75.99 124	74.75 166	72.43 155	68.41 54	61.33 129	38.33 193	51.31 176	54.13 162	56.03 161	79.22 121	78.19 124	85.37 147	82.45 121

CostFormer		68.92 127	69.58 128	68.15 121	75.98 126	76.17 154	78.22 95	51.86 189	65.80 93	61.56 88	63.57 94	62.83 119	61.85 121	70.40 193	68.67 190	79.42 179	79.62 152

dmvs_re		67.22 151	67.92 149	66.40 148	75.94 127	70.55 182	74.97 125	63.87 89	57.07 153	44.75 177	54.29 146	56.72 147	54.65 171	79.53 117	77.51 135	84.20 160	79.78 150

viewmambaseed2359dif		73.61 85	75.14 86	71.84 84	75.87 128	79.69 114	78.99 85	60.42 141	68.19 80	64.15 79	67.85 78	71.20 81	66.55 93	77.41 141	75.78 156	85.04 152	85.85 83

tfpnnormal		64.27 166	63.64 182	65.02 154	75.84 129	75.61 158	71.24 160	62.52 116	47.79 200	42.97 183	42.65 199	44.49 209	52.66 179	78.77 127	76.86 145	84.88 156	79.29 153

baseline2		69.69 118	70.27 120	69.01 114	75.72 130	77.13 145	73.82 143	58.94 159	61.35 128	57.09 106	61.68 99	57.17 144	61.99 118	78.10 134	76.58 150	86.48 123	79.85 148

diffmvs		74.86 78	77.37 75	71.93 83	75.62 131	80.35 109	79.42 80	60.15 145	72.81 68	64.63 78	71.51 57	73.11 72	66.53 95	79.02 124	77.98 126	85.25 149	86.83 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
tpm cat1		65.41 158	63.81 181	67.28 136	75.61 132	72.88 172	75.32 114	52.85 183	62.97 115	63.66 83	53.24 159	53.29 175	61.83 122	65.54 204	64.14 206	74.43 201	74.60 182

CDS-MVSNet		67.65 144	69.83 125	65.09 153	75.39 133	76.55 150	74.42 132	63.75 90	53.55 179	49.37 151	59.41 114	62.45 120	44.44 192	79.71 113	79.82 101	83.17 167	77.36 165
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Fast-Effi-MVS+-dtu		68.34 132	69.47 129	67.01 141	75.15 134	77.97 137	77.12 104	55.40 174	57.87 144	46.68 166	56.17 134	60.39 126	62.36 112	76.32 154	76.25 154	85.35 148	81.34 133

WR-MVS		63.03 170	67.40 155	57.92 188	75.14 135	77.60 142	60.56 202	66.10 70	54.11 178	23.88 211	53.94 152	53.58 165	34.50 207	73.93 166	77.71 130	87.35 96	80.94 136

test-LLR		64.42 164	64.36 177	64.49 158	75.02 136	63.93 202	66.61 181	61.96 123	54.41 174	47.77 159	57.46 128	60.25 127	55.20 169	70.80 187	69.33 185	80.40 177	74.38 184

test0.0.03 1		58.80 194	61.58 195	55.56 196	75.02 136	68.45 190	59.58 206	61.96 123	52.74 182	29.57 204	49.75 184	54.56 158	31.46 210	71.19 182	69.77 183	75.75 194	64.57 205

v1144		69.93 117	69.36 131	70.61 93	74.89 138	80.93 100	79.11 83	60.64 136	55.97 163	55.31 115	53.85 153	54.14 160	66.54 94	78.10 134	77.44 137	87.14 101	85.09 96

v10		70.22 113	69.76 126	70.74 89	74.79 139	80.30 111	79.22 82	59.81 149	57.71 149	56.58 110	54.22 151	55.31 153	66.95 87	78.28 132	77.47 136	87.12 104	85.07 97

v8		70.23 112	69.86 124	70.67 92	74.69 140	79.82 113	78.79 88	59.18 154	58.80 142	58.20 101	55.00 142	57.33 142	66.31 97	77.51 139	76.71 148	86.82 110	83.88 112

v2v482		70.05 116	69.46 130	70.74 89	74.62 141	80.32 110	79.00 84	60.62 137	57.41 151	56.89 107	55.43 140	55.14 155	66.39 96	77.25 143	77.14 142	86.90 107	83.57 116

v1192		69.50 121	68.83 137	70.29 98	74.49 142	80.92 102	78.55 90	60.54 138	55.04 169	54.21 118	52.79 167	52.33 180	66.92 88	77.88 136	77.35 140	87.04 105	85.51 88

UniMVSNet_ETH3D		67.18 152	67.03 157	67.36 133	74.44 143	78.12 130	74.07 139	66.38 67	52.22 186	46.87 163	48.64 186	51.84 184	56.96 154	77.29 142	78.53 119	85.42 146	82.59 120

DTE-MVSNet		61.85 183	64.96 174	58.22 187	74.32 144	74.39 168	61.01 201	67.85 59	51.76 191	21.91 218	53.28 157	48.17 198	37.74 204	72.22 175	76.44 151	86.52 122	78.49 158

Vis-MVSNet		72.77 90	77.20 77	67.59 130	74.19 145	84.01 68	76.61 110	61.69 127	60.62 134	50.61 144	70.25 64	71.31 80	55.57 167	83.85 58	82.28 63	86.90 107	88.08 63
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
v144192		69.34 123	68.68 141	70.12 101	74.06 146	80.54 105	78.08 96	60.54 138	54.99 171	54.13 120	52.92 165	52.80 178	66.73 91	77.13 145	76.72 147	87.15 98	85.63 86

v1921920		69.03 126	68.32 145	69.86 104	74.03 147	80.37 108	77.55 98	60.25 143	54.62 173	53.59 126	52.36 171	51.50 186	66.75 90	77.17 144	76.69 149	86.96 106	85.56 87

PEN-MVS		62.96 171	65.77 165	59.70 181	73.98 148	75.45 159	63.39 195	67.61 61	52.49 184	25.49 210	53.39 155	49.12 197	40.85 200	71.94 178	77.26 141	86.86 109	80.72 139

v1240		68.64 131	67.89 151	69.51 109	73.89 149	80.26 112	76.73 108	59.97 148	53.43 181	53.08 130	51.82 174	50.84 189	66.62 92	76.79 149	76.77 146	86.78 112	85.34 92

thisisatest0530		71.48 101	73.01 100	69.70 107	73.83 150	78.62 127	74.53 128	59.12 155	64.13 105	58.63 97	64.60 91	58.63 136	64.27 102	80.28 106	80.17 97	87.82 87	84.64 105

GA-MVS		68.14 133	69.17 134	66.93 143	73.77 151	78.50 129	74.45 129	58.28 163	55.11 168	48.44 155	60.08 108	53.99 163	61.50 126	78.43 131	77.57 133	85.13 150	80.54 141

tttt0517		71.41 102	72.95 101	69.60 108	73.70 152	78.70 126	74.42 132	59.12 155	63.89 109	58.35 100	64.56 92	58.39 138	64.27 102	80.29 105	80.17 97	87.74 89	84.69 104

pm-mvs1		65.62 157	67.42 154	63.53 165	73.66 153	76.39 151	69.66 163	60.87 135	49.73 196	43.97 180	51.24 177	57.00 146	48.16 186	79.89 111	77.84 128	84.85 157	79.82 149

dps		64.00 168	62.99 184	65.18 152	73.29 154	72.07 175	68.98 168	53.07 182	57.74 148	58.41 99	55.55 138	47.74 201	60.89 132	69.53 196	67.14 199	76.44 193	71.19 192

v148		67.85 139	67.53 152	68.23 120	73.25 155	77.57 143	74.26 136	57.36 169	55.70 164	57.45 105	53.53 154	55.42 152	61.96 119	75.23 158	73.92 167	85.08 151	81.32 134

PatchMatch-RL		67.78 141	66.65 161	69.10 113	73.01 156	72.69 173	68.49 169	61.85 125	62.93 116	60.20 92	56.83 132	50.42 191	69.52 72	75.62 156	74.46 166	81.51 171	73.62 188

GBi-Net		70.78 105	73.37 98	67.76 123	72.95 157	78.00 132	75.15 117	62.72 108	64.13 105	51.44 137	58.37 121	69.02 95	57.59 148	81.33 86	80.72 82	86.70 114	82.02 123

test1		70.78 105	73.37 98	67.76 123	72.95 157	78.00 132	75.15 117	62.72 108	64.13 105	51.44 137	58.37 121	69.02 95	57.59 148	81.33 86	80.72 82	86.70 114	82.02 123

FMVSNet2		70.39 111	72.67 105	67.72 126	72.95 157	78.00 132	75.15 117	62.69 112	63.29 113	51.25 141	55.64 136	68.49 102	57.59 148	80.91 96	80.35 94	86.70 114	82.02 123

FMVSNet3		70.49 109	72.90 103	67.67 128	72.88 160	77.98 135	74.96 126	62.72 108	64.13 105	51.44 137	58.37 121	69.02 95	57.43 151	79.43 119	79.57 106	86.59 120	81.81 130

LTVRE_ROB	59.44 16	61.82 186	62.64 188	60.87 175	72.83 161	77.19 144	64.37 191	58.97 157	33.56 220	28.00 207	52.59 170	42.21 212	63.93 105	74.52 162	76.28 152	77.15 188	82.13 122
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		67.05 153	66.94 158	67.17 137	72.35 162	78.97 120	73.26 152	58.88 160	51.16 192	50.90 142	48.21 188	50.11 193	60.96 129	77.70 137	77.38 138	86.68 117	85.05 98

tpm		62.41 177	63.15 183	61.55 172	72.24 163	63.79 204	71.31 159	46.12 212	57.82 145	55.33 114	59.90 111	54.74 157	53.63 175	67.24 203	64.29 205	70.65 211	74.25 186

test20.03		53.93 206	56.28 207	51.19 205	72.19 164	65.83 197	53.20 213	61.08 130	42.74 209	22.08 216	37.07 209	45.76 207	24.29 218	70.44 191	69.04 187	74.31 202	63.05 209

CP-MVSNet		62.68 173	65.49 168	59.40 184	71.84 165	75.34 160	62.87 197	67.04 65	52.64 183	27.19 208	53.38 156	48.15 199	41.40 198	71.26 181	75.68 157	86.07 130	82.00 126

PS-CasMVS		62.38 179	65.06 171	59.25 185	71.73 166	75.21 164	62.77 198	66.99 66	51.94 190	26.96 209	52.00 173	47.52 202	41.06 199	71.16 184	75.60 158	85.97 137	81.97 128

WR-MVS_H		61.83 185	65.87 164	57.12 191	71.72 167	76.87 146	61.45 200	66.19 68	51.97 189	22.92 215	53.13 162	52.30 182	33.80 208	71.03 185	75.00 162	86.65 118	80.78 138

USDC		67.36 149	67.90 150	66.74 146	71.72 167	75.23 163	71.58 157	60.28 142	67.45 84	50.54 145	60.93 102	45.20 208	62.08 115	76.56 152	74.50 165	84.25 159	75.38 179

UGNet		72.78 89	77.67 70	67.07 140	71.65 169	83.24 80	75.20 116	63.62 93	64.93 98	56.72 108	71.82 55	73.30 69	49.02 185	81.02 94	80.70 87	86.22 126	88.67 59
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
tpmrst		62.00 181	62.35 192	61.58 171	71.62 170	64.14 201	69.07 167	48.22 208	62.21 121	53.93 122	58.26 125	55.30 154	55.81 164	63.22 209	62.62 208	70.85 210	70.70 193

pmmvs4		67.89 138	67.39 156	68.48 119	71.60 171	73.57 170	74.45 129	60.98 133	64.65 100	57.97 102	54.95 143	51.73 185	61.88 120	73.78 167	75.11 161	83.99 163	77.91 161

testgi		54.39 205	57.86 203	50.35 206	71.59 172	67.24 193	54.95 211	53.25 180	43.36 208	23.78 212	44.64 195	47.87 200	24.96 215	70.45 190	68.66 191	73.60 204	62.78 210

pmmvs6		62.41 177	62.88 185	61.87 170	71.38 173	75.18 165	67.76 172	59.45 153	41.64 211	42.52 185	37.33 208	52.91 176	46.87 188	77.67 138	76.26 153	83.23 166	79.18 155

FMVSNet1		68.84 128	70.47 119	66.94 142	71.35 174	77.68 140	74.71 127	62.35 119	56.93 154	49.94 147	50.01 181	64.59 113	57.07 153	81.33 86	80.72 82	86.25 125	82.00 126

IterMVS-SCA-FT		66.89 154	69.22 133	64.17 159	71.30 175	75.64 157	71.33 158	53.17 181	57.63 150	49.08 153	60.72 104	60.05 130	63.09 108	74.99 160	73.92 167	77.07 189	81.57 132

PatchmatchNet		64.21 167	64.65 175	63.69 163	71.29 176	68.66 188	69.63 164	51.70 191	63.04 114	53.77 124	59.83 112	58.34 139	60.23 135	68.54 200	66.06 202	75.56 196	68.08 200
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
baseline		70.45 110	74.09 92	66.20 149	70.95 177	75.67 156	74.26 136	53.57 177	68.33 79	58.42 98	69.87 65	71.45 77	61.55 125	74.84 161	74.76 164	78.42 183	83.72 114

SCA		65.40 159	66.58 162	64.02 161	70.65 178	73.37 171	67.35 173	53.46 179	63.66 110	54.14 119	60.84 103	60.20 129	61.50 126	69.96 194	68.14 195	77.01 190	69.91 194

CR-MVSNet		64.83 162	65.54 167	64.01 162	70.64 179	69.41 184	65.97 184	52.74 184	57.81 146	52.65 132	54.27 147	56.31 149	60.92 130	72.20 176	73.09 172	81.12 174	75.69 176

MVSTER		72.06 94	74.24 90	69.51 109	70.39 180	75.97 155	76.91 106	57.36 169	64.64 101	61.39 89	68.86 70	63.76 116	63.46 106	81.44 83	79.70 102	87.56 93	85.31 93

Anonymous20231206		56.36 200	57.80 204	54.67 199	70.08 181	66.39 196	60.46 203	57.54 166	49.50 198	29.30 205	33.86 213	46.64 203	35.18 206	70.44 191	68.88 189	75.47 197	68.88 199

thisisatest0515		67.40 148	68.78 138	65.80 151	70.02 182	75.24 162	69.36 166	57.37 168	54.94 172	53.67 125	55.53 139	54.85 156	58.00 145	78.19 133	78.91 116	86.39 124	83.78 113

CMPMVS	47.78 17	62.49 176	62.52 189	62.46 168	70.01 183	70.66 181	62.97 196	51.84 190	51.98 188	56.71 109	42.87 198	53.62 164	57.80 147	72.23 174	70.37 182	75.45 198	75.91 173
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
TDRefinement		66.09 156	65.03 173	67.31 134	69.73 184	76.75 148	75.33 113	64.55 83	60.28 136	49.72 150	45.63 194	42.83 211	60.46 134	75.75 155	75.95 155	84.08 161	78.04 160

TinyColmap		62.84 172	61.03 197	64.96 155	69.61 185	71.69 176	68.48 170	59.76 150	55.41 165	47.69 161	47.33 190	34.20 220	62.76 111	74.52 162	72.59 175	81.44 172	71.47 191

RPMNet		61.71 187	62.88 185	60.34 177	69.51 186	69.41 184	63.48 194	49.23 200	57.81 146	45.64 174	50.51 179	50.12 192	53.13 178	68.17 202	68.49 193	81.07 175	75.62 178

IterMVS		66.36 155	68.30 146	64.10 160	69.48 187	74.61 167	73.41 150	50.79 195	57.30 152	48.28 157	60.64 105	59.92 131	60.85 133	74.14 165	72.66 174	81.80 170	78.82 157
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
SixPastTwentyTwo		61.84 184	62.45 190	61.12 174	69.20 188	72.20 174	62.03 199	57.40 167	46.54 204	38.03 195	57.14 131	41.72 213	58.12 144	69.67 195	71.58 178	81.94 169	78.30 159

MDTV_nov1_ep13		64.37 165	65.24 169	63.37 167	68.94 189	70.81 179	72.40 156	50.29 198	60.10 137	53.91 123	60.07 109	59.15 134	57.21 152	69.43 197	67.30 197	77.47 186	69.78 196

EPMVS		60.00 192	61.97 193	57.71 189	68.46 190	63.17 208	64.54 190	48.23 207	63.30 112	44.72 178	60.19 107	56.05 151	50.85 182	65.27 207	62.02 209	69.44 213	63.81 207

our_test_3					67.93 191	70.99 178	66.89 177

FC-MVSNet-test		56.90 199	65.20 170	47.21 209	66.98 192	63.20 207	49.11 218	58.60 162	59.38 140	11.50 225	65.60 84	56.68 148	24.66 217	71.17 183	71.36 180	72.38 207	69.02 198

CVMVSNet		62.55 174	65.89 163	58.64 186	66.95 193	69.15 186	66.49 183	56.29 173	52.46 185	32.70 201	59.27 115	58.21 140	50.09 183	71.77 179	71.39 179	79.31 180	78.99 156

FPMVS		51.87 208	50.00 213	54.07 200	66.83 194	57.25 215	60.25 204	50.91 193	50.25 194	34.36 199	36.04 211	32.02 222	41.49 197	58.98 215	56.07 215	70.56 212	59.36 215

pmmvs-eth3d		63.52 169	62.44 191	64.77 156	66.82 195	70.12 183	69.41 165	59.48 152	54.34 177	52.71 131	46.24 193	44.35 210	56.93 155	72.37 171	73.77 169	83.30 165	75.91 173

TAMVS		59.58 193	62.81 187	55.81 195	66.03 196	65.64 199	63.86 193	48.74 203	49.95 195	37.07 197	54.77 144	58.54 137	44.44 192	72.29 173	71.79 176	74.70 200	66.66 202

MDTV_nov1_ep13_2view		60.16 191	60.51 199	59.75 180	65.39 197	69.05 187	68.00 171	48.29 206	51.99 187	45.95 172	48.01 189	49.64 196	53.39 176	68.83 199	66.52 201	77.47 186	69.55 197

pmmvs5		62.37 180	64.04 179	60.42 176	65.03 198	71.67 177	67.17 175	52.70 186	50.30 193	44.80 176	54.23 150	51.19 188	49.37 184	72.88 170	73.48 171	83.45 164	74.55 183

ambc			53.42 208		64.99 199	63.36 206	49.96 216		47.07 202	37.12 196	28.97 217	16.36 229	41.82 196	75.10 159	67.34 196	71.55 209	75.72 175

V42		68.76 130	69.63 127	67.74 125	64.93 200	78.01 131	78.30 94	56.48 171	58.65 143	56.30 111	54.26 149	57.03 145	64.85 100	77.47 140	77.01 144	85.60 143	84.96 100

pmnet_mix02		55.30 202	57.01 206	53.30 204	64.14 201	59.09 213	58.39 208	50.24 199	53.47 180	38.68 192	49.75 184	45.86 206	40.14 202	65.38 206	60.22 211	68.19 215	65.33 204

PMVS	39.38 18	46.06 214	43.30 217	49.28 208	62.93 202	38.75 223	41.88 221	53.50 178	33.33 221	35.46 198	28.90 218	31.01 223	33.04 209	58.61 217	54.63 218	68.86 214	57.88 216
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
new-patchmatchnet		46.97 212	49.47 214	44.05 213	62.82 203	56.55 216	45.35 220	52.01 188	42.47 210	17.04 223	35.73 212	35.21 219	21.84 221	61.27 212	54.83 217	65.26 217	60.26 212

ET-MVSNet_ETH3D		72.46 92	74.19 91	70.44 96	62.50 204	81.17 98	79.90 73	62.46 118	64.52 103	57.52 104	71.49 58	59.15 134	72.08 53	78.61 129	81.11 75	88.16 74	83.29 117

ADS-MVSNet		55.94 201	58.01 202	53.54 203	62.48 205	58.48 214	59.12 207	46.20 211	59.65 139	42.88 184	52.34 172	53.31 174	46.31 189	62.00 211	60.02 212	64.23 218	60.24 214

RPSCF		67.64 145	71.25 113	63.43 166	61.86 206	70.73 180	67.26 174	50.86 194	74.20 61	58.91 94	67.49 79	69.33 92	64.10 104	71.41 180	68.45 194	77.61 185	77.17 166

MIMVSNet		58.52 196	61.34 196	55.22 197	60.76 207	67.01 194	66.81 178	49.02 202	56.43 158	38.90 191	40.59 205	54.54 159	40.57 201	73.16 169	71.65 177	75.30 199	66.00 203

PatchT		61.97 182	64.04 179	59.55 183	60.49 208	67.40 192	56.54 209	48.65 204	56.69 155	52.65 132	51.10 178	52.14 183	60.92 130	72.20 176	73.09 172	78.03 184	75.69 176

N_pmnet		47.35 211	50.13 212	44.11 212	59.98 209	51.64 220	51.86 214	44.80 213	49.58 197	20.76 219	40.65 204	40.05 217	29.64 211	59.84 213	55.15 216	57.63 219	54.00 217

MVS-HIRNet		54.41 204	52.10 211	57.11 192	58.99 210	56.10 217	49.68 217	49.10 201	46.18 205	52.15 136	33.18 214	46.11 205	56.10 160	63.19 210	59.70 213	76.64 192	60.25 213

PM-MVS		60.48 190	60.94 198	59.94 179	58.85 211	66.83 195	64.27 192	51.39 192	55.03 170	48.03 158	50.00 183	40.79 215	58.26 143	69.20 198	67.13 200	78.84 182	77.60 163

WB-MVS		40.01 215	45.06 216	34.13 215	58.84 212	53.28 219	28.60 224	58.10 164	32.93 222	4.65 230	40.92 202	28.33 225	7.26 224	58.86 216	56.09 214	47.36 222	44.98 219

anonymousdsp		65.28 160	67.98 148	62.13 169	58.73 213	73.98 169	67.10 176	50.69 196	48.41 199	47.66 162	54.27 147	52.75 179	61.45 128	76.71 151	80.20 95	87.13 102	89.53 55

TESTMET0.1,1		61.10 188	64.36 177	57.29 190	57.53 214	63.93 202	66.61 181	36.22 218	54.41 174	47.77 159	57.46 128	60.25 127	55.20 169	70.80 187	69.33 185	80.40 177	74.38 184

EU-MVSNet		54.63 203	58.69 201	49.90 207	56.99 215	62.70 210	56.41 210	50.64 197	45.95 206	23.14 214	50.42 180	46.51 204	36.63 205	65.51 205	64.85 204	75.57 195	74.91 181

FMVSNet5		57.24 197	60.02 200	53.99 201	56.45 216	62.74 209	65.27 187	47.03 209	55.14 167	39.55 190	40.88 203	53.42 172	41.83 195	72.35 172	71.10 181	73.79 203	64.50 206

test-mter		60.84 189	64.62 176	56.42 193	55.99 217	64.18 200	65.39 186	34.23 219	54.39 176	46.21 170	57.40 130	59.49 133	55.86 163	71.02 186	69.65 184	80.87 176	76.20 172

CHOSEN 280x420		58.70 195	61.88 194	54.98 198	55.45 218	50.55 221	64.92 188	40.36 215	55.21 166	38.13 194	48.31 187	63.76 116	63.03 110	73.73 168	68.58 192	68.00 216	73.04 189

PMMVS		65.06 161	69.17 134	60.26 178	55.25 219	63.43 205	66.71 180	43.01 214	62.41 119	50.64 143	69.44 67	67.04 106	63.29 107	74.36 164	73.54 170	82.68 168	73.99 187

Gipuma		36.38 217	35.80 219	37.07 214	45.76 220	33.90 224	29.81 223	48.47 205	39.91 214	18.02 222	8.00 227	8.14 231	25.14 214	59.29 214	61.02 210	55.19 221	40.31 220
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
pmmvs3		47.65 210	49.08 215	45.99 210	44.61 221	54.79 218	50.04 215	31.95 222	33.91 218	29.90 203	30.37 215	33.53 221	46.31 189	63.50 208	63.67 207	73.14 206	63.77 208

MIMVSNet1		49.27 209	53.25 209	44.62 211	44.61 221	61.52 212	53.61 212	52.18 187	41.62 212	18.68 221	28.14 219	41.58 214	25.50 213	68.46 201	69.04 187	73.15 205	62.37 211

MDA-MVSNet-bldmvs		53.37 207	53.01 210	53.79 202	43.67 223	67.95 191	59.69 205	57.92 165	43.69 207	32.41 202	41.47 201	27.89 226	52.38 180	56.97 218	65.99 203	76.68 191	67.13 201

E-PMN		21.77 220	18.24 223	25.89 217	40.22 224	19.58 227	12.46 229	39.87 216	18.68 226	6.71 227	9.57 224	4.31 234	22.36 220	19.89 225	27.28 223	33.73 225	28.34 224

EMVS		20.98 221	17.15 224	25.44 218	39.51 225	19.37 228	12.66 228	39.59 217	19.10 225	6.62 228	9.27 225	4.40 233	22.43 219	17.99 226	24.40 224	31.81 226	25.53 225

new_pmnet		38.40 216	42.64 218	33.44 216	37.54 226	45.00 222	36.60 222	32.72 221	40.27 213	12.72 224	29.89 216	28.90 224	24.78 216	53.17 219	52.90 219	56.31 220	48.34 218

PMMVS2		25.60 218	29.75 220	20.76 220	28.00 227	30.93 225	23.10 226	29.18 223	23.14 224	1.46 231	18.23 223	16.54 228	5.08 225	40.22 220	41.40 221	37.76 223	37.79 222

tmp_tt				14.50 223	14.68 228	7.17 230	10.46 231	2.21 226	37.73 216	28.71 206	25.26 220	16.98 227	4.37 226	31.49 222	29.77 222	26.56 227

MVE	19.12 19	20.47 222	23.27 222	17.20 222	12.66 229	25.41 226	10.52 230	34.14 220	14.79 227	6.53 229	8.79 226	4.68 232	16.64 223	29.49 223	41.63 220	22.73 228	38.11 221
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		22.26 219	25.94 221	17.95 221	3.24 230	7.17 230	23.83 225	7.27 225	37.35 217	20.44 220	21.87 222	39.16 218	18.67 222	34.56 221	20.84 225	34.28 224	20.64 226

GG-mvs-BLEND		46.86 213	67.51 153	22.75 219	0.05 231	76.21 153	64.69 189	0.04 227	61.90 123	0.09 232	55.57 137	71.32 79	0.08 227	70.54 189	67.19 198	71.58 208	69.86 195

testmvs		0.09 223	0.15 225	0.02 224	0.01 232	0.02 232	0.05 233	0.01 228	0.11 228	0.01 233	0.26 229	0.01 235	0.06 229	0.10 227	0.10 226	0.01 230	0.43 228

uanet_test		0.00 225	0.00 227	0.00 226	0.00 233	0.00 234	0.00 235	0.00 230	0.00 230	0.00 234	0.00 230	0.00 236	0.00 230	0.00 229	0.00 228	0.00 232	0.00 229

sosnet-low-res		0.00 225	0.00 227	0.00 226	0.00 233	0.00 234	0.00 235	0.00 230	0.00 230	0.00 234	0.00 230	0.00 236	0.00 230	0.00 229	0.00 228	0.00 232	0.00 229

sosnet		0.00 225	0.00 227	0.00 226	0.00 233	0.00 234	0.00 235	0.00 230	0.00 230	0.00 234	0.00 230	0.00 236	0.00 230	0.00 229	0.00 228	0.00 232	0.00 229

test123		0.09 223	0.14 226	0.02 224	0.00 233	0.02 232	0.02 234	0.01 228	0.09 229	0.00 234	0.30 228	0.00 236	0.08 227	0.03 228	0.09 227	0.01 230	0.45 227

RE-MVS-def										46.24 169

9.14												86.88 16

MTAPA										83.48 1		86.45 19

MTMP										82.66 5		84.91 27

Patchmatch-RL test							2.85 232

NP-MVS									80.10 47

Patchmtry						65.80 198	65.97 184	52.74 184		52.65 132

DeepMVS_CX						18.74 229	18.55 227	8.02 224	26.96 223	7.33 226	23.81 221	13.05 230	25.99 212	25.17 224		22.45 229	36.25 223

High-resolution multi-view benchmark