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++		94.60 9	94.91 11	94.24 8	97.86 1	96.53 32	96.14 9	92.51 8	93.87 14	90.76 11	93.45 18	93.84 5	92.62 9	95.11 12	94.08 19	95.58 54	97.48 14

DVP-MVS++		95.79 1	96.42 1	95.06 1	97.84 2	98.17 2	97.03 4	92.84 3	96.68 1	92.83 3	95.90 5	94.38 4	92.90 5	95.98 2	94.85 5	96.93 3	98.99 1

SMA-MVS		94.70 7	95.35 7	93.93 11	97.57 3	97.57 8	95.98 12	91.91 13	94.50 7	90.35 13	93.46 17	92.72 11	91.89 17	95.89 4	95.22 1	95.88 31	98.10 6
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
MP-MVS		93.35 20	93.59 24	93.08 22	97.39 4	96.82 22	95.38 24	90.71 23	90.82 35	88.07 26	92.83 21	90.29 29	91.32 27	94.03 30	93.19 40	95.61 52	97.16 20
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
NCCC		93.69 19	93.66 23	93.72 15	97.37 5	96.66 29	95.93 17	92.50 9	93.40 18	88.35 24	87.36 34	92.33 14	92.18 13	94.89 15	94.09 18	96.00 27	96.91 27

CNVR-MVS		94.37 12	94.65 12	94.04 10	97.29 6	97.11 11	96.00 11	92.43 10	93.45 15	89.85 18	90.92 25	93.04 9	92.59 10	95.77 5	94.82 6	96.11 25	97.42 16

HFP-MVS		94.02 15	94.22 18	93.78 13	97.25 7	96.85 20	95.81 19	90.94 22	94.12 11	90.29 15	94.09 14	89.98 31	92.52 11	93.94 33	93.49 33	95.87 33	97.10 23

APD-MVS		94.37 12	94.47 16	94.26 7	97.18 8	96.99 16	96.53 8	92.68 6	92.45 23	89.96 16	94.53 11	91.63 21	92.89 6	94.58 22	93.82 23	96.31 18	97.26 18
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
DeepC-MVS_fast	88.76 1	93.10 22	93.02 29	93.19 21	97.13 9	96.51 33	95.35 25	91.19 19	93.14 20	88.14 25	85.26 40	89.49 35	91.45 22	95.17 10	95.07 2	95.85 36	96.48 35
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
APDe-MVS		95.23 5	95.69 6	94.70 5	97.12 10	97.81 6	97.19 2	92.83 4	95.06 6	90.98 9	96.47 2	92.77 10	93.38 2	95.34 9	94.21 16	96.68 9	98.17 5
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
ACMMPR		93.72 18	93.94 20	93.48 17	97.07 11	96.93 17	95.78 20	90.66 25	93.88 13	89.24 20	93.53 16	89.08 38	92.24 12	93.89 35	93.50 31	95.88 31	96.73 31

mPP-MVS					97.06 12							88.08 45

ACMMP_NAP		93.94 16	94.49 15	93.30 19	97.03 13	97.31 10	95.96 13	91.30 18	93.41 17	88.55 23	93.00 19	90.33 28	91.43 25	95.53 7	94.41 14	95.53 58	97.47 15

PGM-MVS		92.76 25	93.03 28	92.45 27	97.03 13	96.67 28	95.73 22	87.92 41	90.15 42	86.53 35	92.97 20	88.33 44	91.69 20	93.62 41	93.03 41	95.83 37	96.41 38

SteuartSystems-ACMMP		94.06 14	94.65 12	93.38 18	96.97 15	97.36 9	96.12 10	91.78 14	92.05 27	87.34 29	94.42 12	90.87 25	91.87 18	95.47 8	94.59 11	96.21 23	97.77 11
Skip Steuart: Steuart Systems R&D Blog.
DVP-MVS		95.56 3	96.26 3	94.73 3	96.93 16	98.19 1	96.62 7	92.81 5	96.15 2	91.73 5	95.01 7	95.31 2	93.41 1	95.95 3	94.77 8	96.90 4	98.46 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
X-MVS		92.36 29	92.75 30	91.90 32	96.89 17	96.70 25	95.25 26	90.48 28	91.50 32	83.95 49	88.20 31	88.82 40	89.11 38	93.75 38	93.43 34	95.75 43	96.83 29

train_agg		92.87 24	93.53 25	92.09 29	96.88 18	95.38 51	95.94 15	90.59 27	90.65 37	83.65 52	94.31 13	91.87 20	90.30 32	93.38 43	92.42 51	95.17 76	96.73 31

SED-MVS		95.61 2	96.36 2	94.73 3	96.84 19	98.15 3	97.08 3	92.92 2	95.64 3	91.84 4	95.98 4	95.33 1	92.83 7	96.00 1	94.94 3	96.90 4	98.45 3

CP-MVS		93.25 21	93.26 26	93.24 20	96.84 19	96.51 33	95.52 23	90.61 26	92.37 24	88.88 21	90.91 26	89.52 34	91.91 16	93.64 40	92.78 46	95.69 45	97.09 24

MSP-MVS		95.12 6	95.83 5	94.30 6	96.82 21	97.94 5	96.98 5	92.37 11	95.40 4	90.59 12	96.16 3	93.71 6	92.70 8	94.80 17	94.77 8	96.37 14	97.99 8
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
DPE-MVS		95.53 4	96.13 4	94.82 2	96.81 22	98.05 4	97.42 1	93.09 1	94.31 9	91.49 6	97.12 1	95.03 3	93.27 3	95.55 6	94.58 12	96.86 6	98.25 4
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MCST-MVS		93.81 17	94.06 19	93.53 16	96.79 23	96.85 20	95.95 14	91.69 16	92.20 25	87.17 31	90.83 27	93.41 7	91.96 14	94.49 25	93.50 31	97.61 1	97.12 22

SF-MVS		94.61 8	94.96 10	94.20 9	96.75 24	97.07 12	95.82 18	92.60 7	93.98 12	91.09 8	95.89 6	92.54 12	91.93 15	94.40 27	93.56 30	97.04 2	97.27 17

SR-MVS					96.58 25			90.99 21				92.40 13

EPNet		89.60 49	89.91 48	89.24 54	96.45 26	93.61 81	92.95 47	88.03 39	85.74 60	83.36 53	87.29 35	83.05 64	80.98 100	92.22 60	91.85 56	93.69 142	95.58 54
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TPM-MVS					96.31 27	96.02 38	94.89 30			86.52 36	87.18 36	92.17 16	86.76 65			95.56 55	93.85 83
Ray Leroy Khuboni and Hongjun Xu: Textureless Resilient Propagation Matching in Multiple View Stereosis (TPM-MVS). SATNAC 2025
CSCG		92.76 25	93.16 27	92.29 28	96.30 28	97.74 7	94.67 33	88.98 35	92.46 22	89.73 19	86.67 37	92.15 18	88.69 43	92.26 59	92.92 44	95.40 63	97.89 10

CDPH-MVS		91.14 38	92.01 32	90.11 41	96.18 29	96.18 37	94.89 30	88.80 37	88.76 47	77.88 87	89.18 30	87.71 47	87.29 61	93.13 46	93.31 38	95.62 50	95.84 47

DeepC-MVS	87.86 3	92.26 30	91.86 33	92.73 24	96.18 29	96.87 19	95.19 27	91.76 15	92.17 26	86.58 34	81.79 53	85.85 51	90.88 30	94.57 23	94.61 10	95.80 39	97.18 19
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
AdaColmap		90.29 43	88.38 59	92.53 25	96.10 31	95.19 56	92.98 46	91.40 17	89.08 46	88.65 22	78.35 73	81.44 71	91.30 28	90.81 89	90.21 82	94.72 97	93.59 90

MSLP-MVS++		92.02 33	91.40 36	92.75 23	96.01 32	95.88 44	93.73 40	89.00 33	89.89 43	90.31 14	81.28 59	88.85 39	91.45 22	92.88 51	94.24 15	96.00 27	96.76 30

3Dnovator+	86.06 4	91.60 35	90.86 42	92.47 26	96.00 33	96.50 35	94.70 32	87.83 42	90.49 38	89.92 17	74.68 93	89.35 36	90.66 31	94.02 31	94.14 17	95.67 47	96.85 28

TSAR-MVS + ACMM		92.97 23	94.51 14	91.16 36	95.88 34	96.59 30	95.09 28	90.45 29	93.42 16	83.01 55	94.68 10	90.74 26	88.74 42	94.75 19	93.78 24	93.82 137	97.63 12

ACMMP		92.03 32	92.16 31	91.87 33	95.88 34	96.55 31	94.47 35	89.49 32	91.71 30	85.26 42	91.52 24	84.48 57	90.21 34	92.82 52	91.63 58	95.92 30	96.42 37
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
SD-MVS		94.53 10	95.22 8	93.73 14	95.69 36	97.03 14	95.77 21	91.95 12	94.41 8	91.35 7	94.97 8	93.34 8	91.80 19	94.72 20	93.99 20	95.82 38	98.07 7
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
DPM-MVS		91.72 34	91.48 34	92.00 30	95.53 37	95.75 47	95.94 15	91.07 20	91.20 33	85.58 40	81.63 57	90.74 26	88.40 46	93.40 42	93.75 25	95.45 62	93.85 83

TSAR-MVS + MP.		94.48 11	94.97 9	93.90 12	95.53 37	97.01 15	96.69 6	90.71 23	94.24 10	90.92 10	94.97 8	92.19 15	93.03 4	94.83 16	93.60 27	96.51 13	97.97 9
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
CPTT-MVS		91.39 36	90.95 40	91.91 31	95.06 39	95.24 55	95.02 29	88.98 35	91.02 34	86.71 33	84.89 42	88.58 43	91.60 21	90.82 88	89.67 98	94.08 124	96.45 36

CANet		91.33 37	91.46 35	91.18 35	95.01 40	96.71 24	93.77 38	87.39 45	87.72 51	87.26 30	81.77 54	89.73 32	87.32 60	94.43 26	93.86 22	96.31 18	96.02 45

PHI-MVS		92.05 31	93.74 22	90.08 42	94.96 41	97.06 13	93.11 45	87.71 43	90.71 36	80.78 71	92.40 22	91.03 23	87.68 54	94.32 28	94.48 13	96.21 23	96.16 42

MAR-MVS		88.39 61	88.44 58	88.33 67	94.90 42	95.06 60	90.51 67	83.59 79	85.27 62	79.07 79	77.13 78	82.89 65	87.70 52	92.19 62	92.32 52	94.23 121	94.20 79
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
3Dnovator	85.17 5	90.48 41	89.90 49	91.16 36	94.88 43	95.74 48	93.82 37	85.36 55	89.28 44	87.81 27	74.34 96	87.40 48	88.56 44	93.07 47	93.74 26	96.53 12	95.71 49

DeepPCF-MVS	88.51 2	92.64 28	94.42 17	90.56 39	94.84 44	96.92 18	91.31 63	89.61 31	95.16 5	84.55 47	89.91 29	91.45 22	90.15 35	95.12 11	94.81 7	92.90 156	97.58 13

QAPM		89.49 50	89.58 52	89.38 52	94.73 45	95.94 41	92.35 49	85.00 58	85.69 61	80.03 75	76.97 80	87.81 46	87.87 51	92.18 63	92.10 54	96.33 16	96.40 40

MVS_111021_HR		90.56 40	91.29 38	89.70 48	94.71 46	95.63 49	91.81 57	86.38 49	87.53 52	81.29 66	87.96 32	85.43 53	87.69 53	93.90 34	92.93 43	96.33 16	95.69 50

OpenMVS	82.53 11	87.71 68	86.84 75	88.73 58	94.42 47	95.06 60	91.02 65	83.49 82	82.50 80	82.24 62	67.62 134	85.48 52	85.56 72	91.19 74	91.30 61	95.67 47	94.75 65

PLC	83.76 9	88.61 58	86.83 76	90.70 38	94.22 48	92.63 99	91.50 60	87.19 46	89.16 45	86.87 32	75.51 88	80.87 73	89.98 36	90.01 99	89.20 110	94.41 116	90.45 149
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
LS3D		85.96 80	84.37 97	87.81 69	94.13 49	93.27 87	90.26 71	89.00 33	84.91 67	72.84 111	71.74 109	72.47 124	87.45 58	89.53 107	89.09 112	93.20 152	89.60 152

EPNet_dtu		81.98 117	83.82 102	79.83 150	94.10 50	85.97 176	87.29 118	84.08 70	80.61 101	59.96 183	81.62 58	77.19 100	62.91 199	87.21 130	86.38 149	90.66 181	87.77 169
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MVS_0304		90.88 39	91.35 37	90.34 40	93.91 51	96.79 23	94.49 34	86.54 48	86.57 56	82.85 56	81.68 56	89.70 33	87.57 56	94.64 21	93.93 21	96.67 11	96.15 43

OPM-MVS		87.56 70	85.80 86	89.62 49	93.90 52	94.09 75	94.12 36	88.18 38	75.40 133	77.30 90	76.41 82	77.93 96	88.79 41	92.20 61	90.82 69	95.40 63	93.72 88
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
DELS-MVS		89.71 48	89.68 51	89.74 46	93.75 53	96.22 36	93.76 39	85.84 51	82.53 78	85.05 44	78.96 70	84.24 58	84.25 78	94.91 14	94.91 4	95.78 42	96.02 45
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
CNLPA		88.40 59	87.00 74	90.03 44	93.73 54	94.28 71	89.56 80	85.81 52	91.87 28	87.55 28	69.53 123	81.49 70	89.23 37	89.45 108	88.59 120	94.31 120	93.82 85

HQP-MVS		89.13 54	89.58 52	88.60 62	93.53 55	93.67 79	93.29 43	87.58 44	88.53 48	75.50 92	87.60 33	80.32 76	87.07 62	90.66 94	89.95 90	94.62 103	96.35 41

OMC-MVS		90.23 45	90.40 45	90.03 44	93.45 56	95.29 52	91.89 55	86.34 50	93.25 19	84.94 45	81.72 55	86.65 50	88.90 39	91.69 67	90.27 81	94.65 101	93.95 81

ACMM	83.27 10	87.68 69	86.09 82	89.54 50	93.26 57	92.19 105	91.43 61	86.74 47	86.02 58	82.85 56	75.63 87	75.14 106	88.41 45	90.68 93	89.99 87	94.59 104	92.97 98
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MVS_111021_LR		90.14 46	90.89 41	89.26 53	93.23 58	94.05 76	90.43 68	84.65 61	90.16 41	84.52 48	90.14 28	83.80 60	87.99 50	92.50 56	90.92 67	94.74 95	94.70 67

CS-MVS-test		90.29 43	90.96 39	89.51 51	93.18 59	95.87 45	89.18 85	83.72 75	88.32 49	84.82 46	84.89 42	85.23 54	90.25 33	94.04 29	92.66 50	95.94 29	95.69 50

CS-MVS		90.34 42	90.58 44	90.07 43	93.11 60	95.82 46	90.57 66	83.62 76	87.07 54	85.35 41	82.98 46	83.47 61	91.37 26	94.94 13	93.37 37	96.37 14	96.41 38

XVS					93.11 60	96.70 25	91.91 53			83.95 49		88.82 40				95.79 40

X-MVStestdata					93.11 60	96.70 25	91.91 53			83.95 49		88.82 40				95.79 40

PCF-MVS	84.60 6	88.66 56	87.75 69	89.73 47	93.06 63	96.02 38	93.22 44	90.00 30	82.44 81	80.02 76	77.96 76	85.16 55	87.36 59	88.54 118	88.54 121	94.72 97	95.61 53
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
TAPA-MVS	84.37 7	88.91 55	88.93 55	88.89 56	93.00 64	94.85 64	92.00 52	84.84 59	91.68 31	80.05 74	79.77 65	84.56 56	88.17 49	90.11 98	89.00 116	95.30 70	92.57 113
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PVSNet_Blended_VisFu		87.40 72	87.80 66	86.92 75	92.86 65	95.40 50	88.56 103	83.45 86	79.55 109	82.26 60	74.49 95	84.03 59	79.24 130	92.97 50	91.53 60	95.15 78	96.65 34

UA-Net		86.07 78	87.78 67	84.06 103	92.85 66	95.11 59	87.73 111	84.38 65	73.22 153	73.18 107	79.99 64	89.22 37	71.47 175	93.22 45	93.03 41	94.76 94	90.69 144

LGP-MVS_train		88.25 63	88.55 56	87.89 68	92.84 67	93.66 80	93.35 42	85.22 57	85.77 59	74.03 102	86.60 38	76.29 103	86.62 67	91.20 73	90.58 77	95.29 71	95.75 48

TSAR-MVS + COLMAP		88.40 59	89.09 54	87.60 72	92.72 68	93.92 78	92.21 50	85.57 54	91.73 29	73.72 103	91.75 23	73.22 122	87.64 55	91.49 69	89.71 97	93.73 140	91.82 127

PVSNet_BlendedMVS		88.19 64	88.00 64	88.42 64	92.71 69	94.82 65	89.08 90	83.81 72	84.91 67	86.38 37	79.14 67	78.11 94	82.66 86	93.05 48	91.10 62	95.86 34	94.86 63

PVSNet_Blended		88.19 64	88.00 64	88.42 64	92.71 69	94.82 65	89.08 90	83.81 72	84.91 67	86.38 37	79.14 67	78.11 94	82.66 86	93.05 48	91.10 62	95.86 34	94.86 63

ACMP	83.90 8	88.32 62	88.06 62	88.62 61	92.18 71	93.98 77	91.28 64	85.24 56	86.69 55	81.23 67	85.62 39	75.13 107	87.01 64	89.83 101	89.77 95	94.79 91	95.43 56
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MSDG		83.87 101	81.02 123	87.19 74	92.17 72	89.80 134	89.15 88	85.72 53	80.61 101	79.24 78	66.66 137	68.75 140	82.69 85	87.95 125	87.44 130	94.19 122	85.92 181

TSAR-MVS + GP.		92.71 27	93.91 21	91.30 34	91.96 73	96.00 40	93.43 41	87.94 40	92.53 21	86.27 39	93.57 15	91.94 19	91.44 24	93.29 44	92.89 45	96.78 7	97.15 21

test2506		85.20 87	84.11 99	86.47 77	91.84 74	95.28 53	89.18 85	84.49 63	82.59 76	75.34 96	74.66 94	58.07 189	81.68 93	93.76 36	92.71 47	96.28 21	91.71 129

ECVR-MVS		85.25 86	84.47 95	86.16 79	91.84 74	95.28 53	89.18 85	84.49 63	82.59 76	73.49 105	66.12 139	69.28 137	81.68 93	93.76 36	92.71 47	96.28 21	91.58 136

test1111		84.86 92	84.21 98	85.61 84	91.75 76	95.14 58	88.63 100	84.57 62	81.88 86	71.21 114	65.66 146	68.51 141	81.19 97	93.74 39	92.68 49	96.31 18	91.86 126

ETV-MVS		89.22 53	89.76 50	88.60 62	91.60 77	94.61 68	89.48 82	83.46 85	85.20 64	81.58 64	82.75 48	82.59 66	88.80 40	94.57 23	93.28 39	96.68 9	95.31 57

EIA-MVS		87.94 67	88.05 63	87.81 69	91.46 78	95.00 62	88.67 97	82.81 91	82.53 78	80.81 70	80.04 63	80.20 77	87.48 57	92.58 55	91.61 59	95.63 49	94.36 73

sasdasda		89.36 51	89.92 46	88.70 59	91.38 79	95.92 42	91.81 57	82.61 99	90.37 39	82.73 58	82.09 50	79.28 86	88.30 47	91.17 75	93.59 28	95.36 65	97.04 25

canonicalmvs		89.36 51	89.92 46	88.70 59	91.38 79	95.92 42	91.81 57	82.61 99	90.37 39	82.73 58	82.09 50	79.28 86	88.30 47	91.17 75	93.59 28	95.36 65	97.04 25

CLD-MVS		88.66 56	88.52 57	88.82 57	91.37 81	94.22 72	92.82 48	82.08 103	88.27 50	85.14 43	81.86 52	78.53 92	85.93 71	91.17 75	90.61 75	95.55 56	95.00 59
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
CHOSEN 1792x2688		82.16 115	80.91 126	83.61 108	91.14 82	92.01 106	89.55 81	79.15 136	79.87 105	70.29 118	52.51 202	72.56 123	81.39 95	88.87 116	88.17 124	90.15 185	92.37 120

IB-MVS	79.09 12	82.60 112	82.19 111	83.07 114	91.08 83	93.55 82	80.90 181	81.35 109	76.56 125	80.87 69	64.81 154	69.97 133	68.87 182	85.64 156	90.06 86	95.36 65	94.74 66
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
IS_MVSNet		86.18 77	88.18 61	83.85 106	91.02 84	94.72 67	87.48 114	82.46 101	81.05 96	70.28 119	76.98 79	82.20 69	76.65 147	93.97 32	93.38 35	95.18 75	94.97 60

HyFIR lowres test		81.62 125	79.45 146	84.14 102	91.00 85	93.38 86	88.27 105	78.19 145	76.28 127	70.18 120	48.78 206	73.69 117	83.52 80	87.05 133	87.83 128	93.68 143	89.15 155

COLMAP_ROB	76.78 15	80.50 132	78.49 151	82.85 115	90.96 86	89.65 140	86.20 135	83.40 87	77.15 123	66.54 137	62.27 162	65.62 150	77.89 138	85.23 163	84.70 170	92.11 163	84.83 185
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
CANet_DTU		85.43 84	87.72 70	82.76 117	90.95 87	93.01 92	89.99 72	75.46 169	82.67 75	64.91 150	83.14 45	80.09 78	80.68 104	92.03 65	91.03 64	94.57 106	92.08 121

FC-MVSNet-train		85.18 88	85.31 90	85.03 89	90.67 88	91.62 109	87.66 112	83.61 77	79.75 107	74.37 100	78.69 71	71.21 128	78.91 131	91.23 71	89.96 89	94.96 84	94.69 69

baseline1		84.54 95	84.43 96	84.67 91	90.62 89	91.16 112	88.63 100	83.75 74	79.78 106	71.16 115	75.14 90	74.10 112	77.84 139	91.56 68	90.67 74	96.04 26	88.58 158

thres600view7		82.53 114	81.02 123	84.28 98	90.61 90	93.05 90	88.57 102	82.67 95	74.12 144	68.56 130	65.09 151	62.13 169	80.40 110	91.15 78	89.02 115	94.88 87	92.59 111

casdiffmvs_mvg		87.97 66	87.63 71	88.37 66	90.55 91	94.42 69	91.82 56	84.69 60	84.05 70	82.08 63	76.57 81	79.00 88	85.49 73	92.35 57	92.29 53	95.55 56	94.70 67
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
EC-MVSNet		89.96 47	90.77 43	89.01 55	90.54 92	95.15 57	91.34 62	81.43 108	85.27 62	83.08 54	82.83 47	87.22 49	90.97 29	94.79 18	93.38 35	96.73 8	96.71 33

thres400		82.68 111	81.15 121	84.47 94	90.52 93	92.89 94	88.95 95	82.71 93	74.33 141	69.22 127	65.31 148	62.61 164	80.63 106	90.96 86	89.50 102	94.79 91	92.45 119

EPP-MVSNet		86.55 74	87.76 68	85.15 88	90.52 93	94.41 70	87.24 120	82.32 102	81.79 88	73.60 104	78.57 72	82.41 67	82.07 91	91.23 71	90.39 79	95.14 79	95.48 55

ACMH	78.52 14	81.86 119	80.45 130	83.51 112	90.51 95	91.22 111	85.62 142	84.23 67	70.29 170	62.21 166	69.04 127	64.05 155	84.48 77	87.57 128	88.45 123	94.01 128	92.54 115
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Vis-MVSNet (Re-imp)		83.65 104	86.81 77	79.96 148	90.46 96	92.71 96	84.84 150	82.00 104	80.93 98	62.44 165	76.29 83	82.32 68	65.54 195	92.29 58	91.66 57	94.49 111	91.47 138

FA-MVS(training)		85.65 83	85.79 87	85.48 86	90.44 97	93.47 83	88.66 99	73.11 177	83.34 73	82.26 60	71.79 108	78.39 93	83.14 83	91.00 83	89.47 104	95.28 73	93.06 96

thres200		82.77 110	81.25 120	84.54 92	90.38 98	93.05 90	89.13 89	82.67 95	74.40 140	69.53 124	65.69 145	63.03 161	80.63 106	91.15 78	89.42 105	94.88 87	92.04 123

MS-PatchMatch		81.79 121	81.44 117	82.19 125	90.35 99	89.29 146	88.08 108	75.36 170	77.60 121	69.00 128	64.37 157	78.87 91	77.14 145	88.03 124	85.70 160	93.19 153	86.24 178

PatchMatch-RL		83.34 106	81.36 118	85.65 82	90.33 100	89.52 142	84.36 154	81.82 105	80.87 100	79.29 77	74.04 97	62.85 163	86.05 70	88.40 121	87.04 137	92.04 164	86.77 174

thres100view900		82.55 113	81.01 125	84.34 95	90.30 101	92.27 103	89.04 93	82.77 92	75.14 134	69.56 122	65.72 143	63.13 158	79.62 125	89.97 100	89.26 108	94.73 96	91.61 135

tfpn200view9		82.86 108	81.46 116	84.48 93	90.30 101	93.09 89	89.05 92	82.71 93	75.14 134	69.56 122	65.72 143	63.13 158	80.38 111	91.15 78	89.51 101	94.91 86	92.50 117

casdiffmvs		87.45 71	87.15 73	87.79 71	90.15 103	94.22 72	89.96 73	83.93 71	85.08 65	80.91 68	75.81 86	77.88 97	86.08 69	91.86 66	90.86 68	95.74 44	94.37 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
MVS_Test		86.93 73	87.24 72	86.56 76	90.10 104	93.47 83	90.31 69	80.12 122	83.55 72	78.12 83	79.58 66	79.80 81	85.45 74	90.17 97	90.59 76	95.29 71	93.53 91

ACMH+	79.08 13	81.84 120	80.06 135	83.91 105	89.92 105	90.62 116	86.21 134	83.48 84	73.88 146	65.75 142	66.38 138	65.30 151	84.63 76	85.90 153	87.25 133	93.45 148	91.13 142

Effi-MVS+		85.33 85	85.08 91	85.63 83	89.69 106	93.42 85	89.90 74	80.31 120	79.32 110	72.48 113	73.52 102	74.03 113	86.55 68	90.99 84	89.98 88	94.83 89	94.27 78

Anonymous202405211			82.75 109		89.58 107	92.97 93	89.04 93	84.13 69	78.72 115		57.18 189	76.64 102	83.13 84	89.55 106	89.92 91	93.38 150	94.28 77

GeoE		84.62 94	83.98 101	85.35 87	89.34 108	92.83 95	88.34 104	78.95 137	79.29 111	77.16 91	68.10 131	74.56 109	83.40 81	89.31 110	89.23 109	94.92 85	94.57 71

tttt0517		85.11 90	85.81 85	84.30 97	89.24 109	92.68 98	87.12 125	80.11 123	81.98 85	74.31 101	78.08 75	73.57 118	79.90 118	91.01 82	89.58 99	95.11 82	93.77 86

DI_MVS_plusplus_trai		86.41 76	85.54 89	87.42 73	89.24 109	93.13 88	92.16 51	82.65 97	82.30 82	80.75 72	68.30 130	80.41 75	85.01 75	90.56 95	90.07 85	94.70 99	94.01 80

thisisatest0530		85.15 89	85.86 84	84.33 96	89.19 111	92.57 102	87.22 121	80.11 123	82.15 84	74.41 99	78.15 74	73.80 116	79.90 118	90.99 84	89.58 99	95.13 80	93.75 87

DCV-MVSNet		85.88 82	86.17 80	85.54 85	89.10 112	89.85 132	89.34 83	80.70 113	83.04 74	78.08 85	76.19 84	79.00 88	82.42 89	89.67 104	90.30 80	93.63 145	95.12 58

UGNet		85.90 81	88.23 60	83.18 113	88.96 113	94.10 74	87.52 113	83.60 78	81.66 89	77.90 86	80.76 61	83.19 63	66.70 192	91.13 81	90.71 73	94.39 117	96.06 44
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
Anonymous20231211		84.42 99	83.02 105	86.05 80	88.85 114	92.70 97	88.92 96	83.40 87	79.99 104	78.31 82	55.83 193	78.92 90	83.33 82	89.06 112	89.76 96	93.50 147	94.90 61

MVSTER		86.03 79	86.12 81	85.93 81	88.62 115	89.93 130	89.33 84	79.91 127	81.87 87	81.35 65	81.07 60	74.91 108	80.66 105	92.13 64	90.10 84	95.68 46	92.80 103

TDRefinement		79.05 150	77.05 169	81.39 132	88.45 116	89.00 153	86.92 126	82.65 97	74.21 143	64.41 151	59.17 181	59.16 185	74.52 161	85.23 163	85.09 165	91.37 173	87.51 170

IterMVS-LS		83.28 107	82.95 107	83.65 107	88.39 117	88.63 157	86.80 129	78.64 142	76.56 125	73.43 106	72.52 107	75.35 105	80.81 102	86.43 148	88.51 122	93.84 136	92.66 108
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
diffmvs		86.52 75	86.76 78	86.23 78	88.31 118	92.63 99	89.58 79	81.61 107	86.14 57	80.26 73	79.00 69	77.27 99	83.58 79	88.94 113	89.06 113	94.05 126	94.29 74
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
Fast-Effi-MVS+		83.77 103	82.98 106	84.69 90	87.98 119	91.87 107	88.10 107	77.70 151	78.10 119	73.04 109	69.13 125	68.51 141	86.66 66	90.49 96	89.85 93	94.67 100	92.88 100

gg-mvs-nofinetune		75.64 187	77.26 166	73.76 191	87.92 120	92.20 104	87.32 117	64.67 210	51.92 215	35.35 220	46.44 209	77.05 101	71.97 172	92.64 54	91.02 65	95.34 68	89.53 153

RPSCF		83.46 105	83.36 104	83.59 109	87.75 121	87.35 167	84.82 151	79.46 132	83.84 71	78.12 83	82.69 49	79.87 79	82.60 88	82.47 187	81.13 190	88.78 192	86.13 179

Effi-MVS+-dtu		82.05 116	81.76 113	82.38 122	87.72 122	90.56 117	86.90 128	78.05 147	73.85 147	66.85 136	71.29 111	71.90 126	82.00 92	86.64 143	85.48 162	92.76 158	92.58 112

CostFormer		80.94 129	80.21 132	81.79 127	87.69 123	88.58 158	87.47 115	70.66 186	80.02 103	77.88 87	73.03 103	71.40 127	78.24 135	79.96 196	79.63 192	88.82 191	88.84 156

baseline2		82.80 109	82.86 108	82.73 118	87.68 124	90.50 118	84.92 149	78.93 138	78.07 120	73.06 108	75.08 91	69.77 134	77.31 142	88.90 115	86.94 138	94.50 109	90.74 143

Vis-MVSNet		84.38 100	86.68 79	81.70 128	87.65 125	94.89 63	88.14 106	80.90 112	74.48 139	68.23 131	77.53 77	80.72 74	69.98 179	92.68 53	91.90 55	95.33 69	94.58 70
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test-LLR		79.47 145	79.84 140	79.03 154	87.47 126	82.40 201	81.24 178	78.05 147	73.72 148	62.69 162	73.76 99	74.42 110	73.49 166	84.61 172	82.99 182	91.25 175	87.01 172

test0.0.03 1		76.03 181	78.51 150	73.12 195	87.47 126	85.13 187	76.32 199	78.05 147	73.19 155	50.98 205	70.64 113	69.28 137	55.53 203	85.33 161	84.38 174	90.39 183	81.63 197

tpmrst		76.55 174	75.99 181	77.20 168	87.32 128	83.05 194	82.86 164	65.62 205	78.61 117	67.22 135	69.19 124	65.71 149	75.87 151	76.75 206	75.33 205	84.31 210	83.28 191

baseline		84.89 91	86.06 83	83.52 111	87.25 129	89.67 139	87.76 110	75.68 168	84.92 66	78.40 81	80.10 62	80.98 72	80.20 114	86.69 142	87.05 136	91.86 167	92.99 97

CDS-MVSNet		81.63 124	82.09 112	81.09 137	87.21 130	90.28 121	87.46 116	80.33 119	69.06 174	70.66 116	71.30 110	73.87 114	67.99 185	89.58 105	89.87 92	92.87 157	90.69 144
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
tpm cat1		77.78 163	75.28 189	80.70 140	87.14 131	85.84 178	85.81 138	70.40 187	77.44 122	78.80 80	63.72 158	64.01 156	76.55 148	75.60 208	75.21 206	85.51 208	85.12 183

tpm		76.30 180	76.05 180	76.59 174	86.97 132	83.01 195	83.83 158	67.06 201	71.83 159	63.87 156	69.56 122	62.88 162	73.41 168	79.79 197	78.59 196	84.41 209	86.68 175

EPMVS		77.53 165	78.07 158	76.90 172	86.89 133	84.91 188	82.18 173	66.64 203	81.00 97	64.11 154	72.75 106	69.68 135	74.42 163	79.36 199	78.13 198	87.14 200	80.68 202

PatchmatchNet		78.67 155	78.85 149	78.46 162	86.85 134	86.03 175	83.77 159	68.11 198	80.88 99	66.19 139	72.90 105	73.40 120	78.06 136	79.25 200	77.71 200	87.75 197	81.75 196
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
dmvs_re		81.08 128	79.92 138	82.44 121	86.66 135	87.70 163	87.91 109	83.30 89	72.86 156	65.29 148	65.76 142	63.43 157	76.69 146	88.93 114	89.50 102	94.80 90	91.23 141

SCA		79.51 144	80.15 134	78.75 157	86.58 136	87.70 163	83.07 163	68.53 195	81.31 91	66.40 138	73.83 98	75.38 104	79.30 129	80.49 194	79.39 195	88.63 194	82.96 193

USDC		80.69 130	79.89 139	81.62 130	86.48 137	89.11 151	86.53 131	78.86 139	81.15 95	63.48 158	72.98 104	59.12 187	81.16 98	87.10 131	85.01 166	93.23 151	84.77 186

Fast-Effi-MVS+-dtu		79.95 136	80.69 127	79.08 153	86.36 138	89.14 150	85.85 137	72.28 180	72.85 157	59.32 186	70.43 117	68.42 143	77.57 140	86.14 150	86.44 148	93.11 154	91.39 139

tfpnnormal		77.46 166	74.86 191	80.49 144	86.34 139	88.92 154	84.33 155	81.26 110	61.39 202	61.70 173	51.99 203	53.66 208	74.84 158	88.63 117	87.38 132	94.50 109	92.08 121

dps		78.02 160	75.94 182	80.44 145	86.06 140	86.62 173	82.58 165	69.98 190	75.14 134	77.76 89	69.08 126	59.93 178	78.47 133	79.47 198	77.96 199	87.78 196	83.40 190

IterMVS-SCA-FT		79.41 146	80.20 133	78.49 161	85.88 141	86.26 174	83.95 157	71.94 181	73.55 151	61.94 169	70.48 116	70.50 130	75.23 153	85.81 155	84.61 172	91.99 166	90.18 150

LTVRE_ROB	74.41 16	75.78 186	74.72 192	77.02 171	85.88 141	89.22 147	82.44 168	77.17 154	50.57 216	45.45 211	65.44 147	52.29 210	81.25 96	85.50 159	87.42 131	89.94 187	92.62 109
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
EG-PatchMatch MVS		76.40 178	75.47 187	77.48 167	85.86 143	90.22 123	82.45 167	73.96 175	59.64 207	59.60 185	52.75 201	62.20 168	68.44 184	88.23 122	87.50 129	94.55 107	87.78 168

CR-MVSNet		78.71 154	78.86 148	78.55 160	85.85 144	85.15 185	82.30 170	68.23 196	74.71 137	65.37 145	64.39 156	69.59 136	77.18 143	85.10 168	84.87 167	92.34 162	88.21 162

GA-MVS		79.52 143	79.71 143	79.30 152	85.68 145	90.36 120	84.55 152	78.44 143	70.47 169	57.87 191	68.52 129	61.38 170	76.21 149	89.40 109	87.89 125	93.04 155	89.96 151

UniMVSNet_ETH3D		79.24 148	76.47 174	82.48 120	85.66 146	90.97 113	86.08 136	81.63 106	64.48 194	68.94 129	54.47 195	57.65 191	78.83 132	85.20 166	88.91 117	93.72 141	93.60 89

TransMVSNet (Re)		76.57 173	75.16 190	78.22 164	85.60 147	87.24 168	82.46 166	81.23 111	59.80 206	59.05 189	57.07 190	59.14 186	66.60 193	88.09 123	86.82 139	94.37 118	87.95 167

RPMNet		77.07 168	77.63 164	76.42 175	85.56 148	85.15 185	81.37 175	65.27 207	74.71 137	60.29 182	63.71 159	66.59 147	73.64 165	82.71 185	82.12 187	92.38 161	88.39 160

MDTV_nov1_ep13		79.14 149	79.49 145	78.74 158	85.40 149	86.89 171	84.32 156	70.29 188	78.85 114	69.42 125	75.37 89	73.29 121	75.64 152	80.61 193	79.48 194	87.36 198	81.91 195

UniMVSNet (Re)		81.22 126	81.08 122	81.39 132	85.35 150	91.76 108	84.93 148	82.88 90	76.13 128	65.02 149	64.94 152	63.09 160	75.17 155	87.71 127	89.04 114	94.97 83	94.88 62

UniMVSNet_NR-MVSNet		81.87 118	81.33 119	82.50 119	85.31 151	91.30 110	85.70 139	84.25 66	75.89 129	64.21 152	66.95 136	64.65 153	80.22 112	87.07 132	89.18 111	95.27 74	94.29 74

IterMVS		78.79 153	79.71 143	77.71 165	85.26 152	85.91 177	84.54 153	69.84 192	73.38 152	61.25 177	70.53 115	70.35 131	74.43 162	85.21 165	83.80 177	90.95 179	88.77 157
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
NR-MVSNet		80.25 134	79.98 137	80.56 143	85.20 153	90.94 114	85.65 141	83.58 80	75.74 130	61.36 176	65.30 149	56.75 196	72.38 171	88.46 120	88.80 118	95.16 77	93.87 82

CMPMVS	56.49 17	73.84 195	71.73 201	76.31 178	85.20 153	85.67 180	75.80 200	73.23 176	62.26 199	65.40 144	53.40 200	59.70 180	71.77 174	80.25 195	79.56 193	86.45 204	81.28 198
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
TinyColmap		76.73 170	73.95 194	79.96 148	85.16 155	85.64 181	82.34 169	78.19 145	70.63 167	62.06 168	60.69 173	49.61 213	80.81 102	85.12 167	83.69 178	91.22 177	82.27 194

gm-plane-assit		70.29 200	70.65 202	69.88 200	85.03 156	78.50 211	58.41 218	65.47 206	50.39 217	40.88 216	49.60 205	50.11 212	75.14 156	91.43 70	89.78 94	94.32 119	84.73 187

FC-MVSNet-test		76.53 175	81.62 115	70.58 199	84.99 157	85.73 179	74.81 202	78.85 140	77.00 124	39.13 218	75.90 85	73.50 119	54.08 207	86.54 145	85.99 157	91.65 169	86.68 175

DU-MVS		81.20 127	80.30 131	82.25 123	84.98 158	90.94 114	85.70 139	83.58 80	75.74 130	64.21 152	65.30 149	59.60 182	80.22 112	86.89 135	89.31 106	94.77 93	94.29 74

Baseline_NR-MVSNet		79.84 138	78.37 155	81.55 131	84.98 158	86.66 172	85.06 146	83.49 82	75.57 132	63.31 159	58.22 188	60.97 172	78.00 137	86.89 135	87.13 134	94.47 112	93.15 94

TranMVSNet+NR-MVSNet		80.52 131	79.84 140	81.33 134	84.92 160	90.39 119	85.53 144	84.22 68	74.27 142	60.68 181	64.93 153	59.96 177	77.48 141	86.75 140	89.28 107	95.12 81	93.29 92

pm-mvs1		78.51 158	77.75 163	79.40 151	84.83 161	89.30 145	83.55 161	79.38 133	62.64 198	63.68 157	58.73 186	64.68 152	70.78 178	89.79 102	87.84 126	94.17 123	91.28 140

testgi		71.92 198	74.20 193	69.27 201	84.58 162	83.06 193	73.40 205	74.39 172	64.04 196	46.17 210	68.90 128	57.15 194	48.89 211	84.07 177	83.08 181	88.18 195	79.09 206

thisisatest0515		79.76 140	80.59 129	78.80 156	84.40 163	88.91 155	79.48 187	76.94 157	72.29 158	67.33 134	67.82 133	65.99 148	70.80 177	88.50 119	87.84 126	93.86 135	92.75 106

FMVSNet3		84.44 98	84.64 94	84.21 99	84.32 164	90.13 125	89.85 75	80.37 116	81.17 92	75.50 92	69.63 119	79.69 83	79.62 125	89.72 103	90.52 78	95.59 53	91.58 136

GBi-Net		84.51 96	84.80 92	84.17 100	84.20 165	89.95 127	89.70 76	80.37 116	81.17 92	75.50 92	69.63 119	79.69 83	79.75 122	90.73 90	90.72 70	95.52 59	91.71 129

test1		84.51 96	84.80 92	84.17 100	84.20 165	89.95 127	89.70 76	80.37 116	81.17 92	75.50 92	69.63 119	79.69 83	79.75 122	90.73 90	90.72 70	95.52 59	91.71 129

FMVSNet2		83.87 101	83.73 103	84.05 104	84.20 165	89.95 127	89.70 76	80.21 121	79.17 113	74.89 97	65.91 140	77.49 98	79.75 122	90.87 87	91.00 66	95.52 59	91.71 129

WR-MVS		76.63 172	78.02 160	75.02 185	84.14 168	89.76 136	78.34 194	80.64 114	69.56 171	52.32 200	61.26 166	61.24 171	60.66 200	84.45 174	87.07 135	93.99 129	92.77 104

v8		79.90 137	78.39 154	81.66 129	83.97 169	89.81 133	87.16 123	77.40 153	71.49 160	67.71 132	61.24 167	62.49 165	79.83 121	85.48 160	86.17 152	93.89 133	92.02 125

v2v482		79.84 138	78.07 158	81.90 126	83.75 170	90.21 124	87.17 122	79.85 128	70.65 166	65.93 141	61.93 164	60.07 176	80.82 101	85.25 162	86.71 141	93.88 134	91.70 133

v10		79.62 141	78.19 156	81.28 135	83.73 171	89.69 138	87.27 119	76.86 158	70.50 168	65.46 143	60.58 174	60.47 174	80.44 109	86.91 134	86.63 144	93.93 130	92.55 114

v1144		79.38 147	77.83 161	81.18 136	83.62 172	90.23 122	87.15 124	78.35 144	69.13 173	64.02 155	60.20 176	59.41 183	80.14 116	86.78 138	86.57 145	93.81 138	92.53 116

v148		78.59 156	76.84 172	80.62 142	83.61 173	89.16 149	83.65 160	79.24 135	69.38 172	69.34 126	59.88 178	60.41 175	75.19 154	83.81 178	84.63 171	92.70 159	90.63 146

SixPastTwentyTwo		76.02 182	75.72 184	76.36 176	83.38 174	87.54 165	75.50 201	76.22 162	65.50 191	57.05 192	70.64 113	53.97 207	74.54 160	80.96 192	82.12 187	91.44 171	89.35 154

CVMVSNet		76.70 171	78.46 152	74.64 189	83.34 175	84.48 189	81.83 174	74.58 171	68.88 175	51.23 204	69.77 118	70.05 132	67.49 188	84.27 175	83.81 176	89.38 189	87.96 166

v1192		78.94 151	77.33 165	80.82 139	83.25 176	89.90 131	86.91 127	77.72 150	68.63 177	62.61 164	59.17 181	57.53 192	80.62 108	86.89 135	86.47 147	93.79 139	92.75 106

DTE-MVSNet		75.14 189	75.44 188	74.80 187	83.18 177	87.19 169	78.25 196	80.11 123	66.05 186	48.31 207	60.88 171	54.67 203	64.54 196	82.57 186	86.17 152	94.43 115	90.53 148

PEN-MVS		76.02 182	76.07 178	75.95 180	83.17 178	87.97 161	79.65 185	80.07 126	66.57 184	51.45 202	60.94 170	55.47 201	66.81 191	82.72 184	86.80 140	94.59 104	92.03 124

TAMVS		76.42 176	77.16 168	75.56 181	83.05 179	85.55 182	80.58 183	71.43 183	65.40 193	61.04 180	67.27 135	69.22 139	67.99 185	84.88 170	84.78 169	89.28 190	83.01 192

pmmvs4		79.99 135	78.08 157	82.22 124	83.04 180	87.16 170	84.95 147	78.80 141	78.64 116	74.53 98	64.61 155	59.41 183	79.45 127	84.13 176	84.54 173	92.53 160	88.08 164

v144192		78.81 152	77.22 167	80.67 141	82.95 181	89.79 135	86.40 132	77.42 152	68.26 179	63.13 160	59.50 179	58.13 188	80.08 117	85.93 152	86.08 154	94.06 125	92.83 102

v1921920		78.57 157	76.99 170	80.41 146	82.93 182	89.63 141	86.38 133	77.14 155	68.31 178	61.80 172	58.89 185	56.79 195	80.19 115	86.50 147	86.05 156	94.02 127	92.76 105

CHOSEN 280x420		80.28 133	81.66 114	78.67 159	82.92 183	79.24 210	85.36 145	66.79 202	78.11 118	70.32 117	75.03 92	79.87 79	81.09 99	89.07 111	83.16 180	85.54 207	87.17 171

WR-MVS_H		75.84 185	76.93 171	74.57 190	82.86 184	89.50 143	78.34 194	79.36 134	66.90 182	52.51 199	60.20 176	59.71 179	59.73 201	83.61 179	85.77 159	94.65 101	92.84 101

v1240		78.15 159	76.53 173	80.04 147	82.85 185	89.48 144	85.61 143	76.77 159	67.05 181	61.18 179	58.37 187	56.16 199	79.89 120	86.11 151	86.08 154	93.92 131	92.47 118

V42		79.59 142	78.43 153	80.94 138	82.79 186	89.71 137	86.66 130	76.73 160	71.38 161	67.42 133	61.01 169	62.30 167	78.39 134	85.56 158	86.48 146	93.65 144	92.60 110

CP-MVSNet		76.36 179	76.41 175	76.32 177	82.73 187	88.64 156	79.39 188	79.62 129	67.21 180	53.70 196	60.72 172	55.22 202	67.91 187	83.52 180	86.34 150	94.55 107	93.19 93

PS-CasMVS		75.90 184	75.86 183	75.96 179	82.59 188	88.46 159	79.23 191	79.56 131	66.00 187	52.77 198	59.48 180	54.35 206	67.14 190	83.37 181	86.23 151	94.47 112	93.10 95

test20.03		68.31 203	70.05 204	66.28 206	82.41 189	80.84 205	67.35 212	76.11 164	58.44 209	40.80 217	53.77 199	54.54 204	42.28 214	83.07 182	81.96 189	88.73 193	77.76 208

FMVSNet1		81.64 123	80.61 128	82.84 116	82.36 190	89.20 148	88.67 97	79.58 130	70.79 165	72.63 112	58.95 184	72.26 125	79.34 128	90.73 90	90.72 70	94.47 112	91.62 134

pmmvs6		74.83 190	72.89 197	77.09 169	82.11 191	87.50 166	80.88 182	76.97 156	52.79 214	61.91 171	46.66 208	60.49 173	69.28 181	86.74 141	85.46 163	91.39 172	90.56 147

pmmvs5		76.93 169	76.33 176	77.62 166	81.97 192	88.40 160	81.32 177	74.35 173	65.42 192	61.42 175	63.07 160	57.95 190	73.23 169	85.60 157	85.35 164	93.41 149	88.55 159

v7n		77.22 167	76.23 177	78.38 163	81.89 193	89.10 152	82.24 172	76.36 161	65.96 188	61.21 178	56.56 191	55.79 200	75.07 157	86.55 144	86.68 142	93.52 146	92.95 99

our_test_3					81.81 194	83.96 192	76.61 198

Anonymous20231206		70.80 199	70.59 203	71.04 198	81.60 195	82.49 200	74.64 203	75.87 166	64.17 195	49.27 206	44.85 212	53.59 209	54.68 206	83.07 182	82.34 186	90.17 184	83.65 189

ADS-MVSNet		74.53 192	75.69 185	73.17 194	81.57 196	80.71 206	79.27 190	63.03 212	79.27 112	59.94 184	67.86 132	68.32 145	71.08 176	77.33 204	76.83 202	84.12 212	79.53 203

pmnet_mix02		71.95 197	71.83 200	72.10 196	81.40 197	80.63 207	73.78 204	72.85 179	70.90 164	54.89 194	62.17 163	57.42 193	62.92 198	76.80 205	73.98 209	86.74 203	80.87 201

test-mter		77.79 162	80.02 136	75.18 184	81.18 198	82.85 196	80.52 184	62.03 214	73.62 150	62.16 167	73.55 101	73.83 115	73.81 164	84.67 171	83.34 179	91.37 173	88.31 161

TESTMET0.1,1		77.78 163	79.84 140	75.38 183	80.86 199	82.40 201	81.24 178	62.72 213	73.72 148	62.69 162	73.76 99	74.42 110	73.49 166	84.61 172	82.99 182	91.25 175	87.01 172

MDTV_nov1_ep13_2view		73.21 196	72.91 196	73.56 193	80.01 200	84.28 191	78.62 192	66.43 204	68.64 176	59.12 187	60.39 175	59.69 181	69.81 180	78.82 202	77.43 201	87.36 198	81.11 200

FPMVS		63.63 208	60.08 213	67.78 203	80.01 200	71.50 216	72.88 207	69.41 194	61.82 201	53.11 197	45.12 211	42.11 220	50.86 209	66.69 214	63.84 215	80.41 214	69.46 214

anonymousdsp		77.94 161	79.00 147	76.71 173	79.03 202	87.83 162	79.58 186	72.87 178	65.80 189	58.86 190	65.82 141	62.48 166	75.99 150	86.77 139	88.66 119	93.92 131	95.68 52

N_pmnet		66.85 204	66.63 205	67.11 205	78.73 203	74.66 214	70.53 209	71.07 184	66.46 185	46.54 209	51.68 204	51.91 211	55.48 204	74.68 209	72.38 210	80.29 215	74.65 211

PMMVS		81.65 122	84.05 100	78.86 155	78.56 204	82.63 198	83.10 162	67.22 200	81.39 90	70.11 121	84.91 41	79.74 82	82.12 90	87.31 129	85.70 160	92.03 165	86.67 177

PatchT		76.42 176	77.81 162	74.80 187	78.46 205	84.30 190	71.82 208	65.03 209	73.89 145	65.37 145	61.58 165	66.70 146	77.18 143	85.10 168	84.87 167	90.94 180	88.21 162

MVS-HIRNet		68.83 202	66.39 206	71.68 197	77.58 206	75.52 213	66.45 213	65.05 208	62.16 200	62.84 161	44.76 213	56.60 198	71.96 173	78.04 203	75.06 207	86.18 206	72.56 212

pmmvs-eth3d		74.32 193	71.96 199	77.08 170	77.33 207	82.71 197	78.41 193	76.02 165	66.65 183	65.98 140	54.23 197	49.02 215	73.14 170	82.37 188	82.69 184	91.61 170	86.05 180

new-patchmatchnet		63.80 207	63.31 209	64.37 207	76.49 208	75.99 212	63.73 215	70.99 185	57.27 210	43.08 213	45.86 210	43.80 217	45.13 213	73.20 210	70.68 213	86.80 202	76.34 210

FMVSNet5		75.50 188	76.07 178	74.83 186	76.16 209	81.19 204	81.34 176	70.21 189	73.20 154	61.59 174	58.97 183	68.33 144	68.50 183	85.87 154	85.85 158	91.18 178	79.11 205

PM-MVS		74.17 194	73.10 195	75.41 182	76.07 210	82.53 199	77.56 197	71.69 182	71.04 162	61.92 170	61.23 168	47.30 216	74.82 159	81.78 190	79.80 191	90.42 182	88.05 165

MIMVSNet		74.69 191	75.60 186	73.62 192	76.02 211	85.31 184	81.21 180	67.43 199	71.02 163	59.07 188	54.48 194	64.07 154	66.14 194	86.52 146	86.64 143	91.83 168	81.17 199

EU-MVSNet		69.98 201	72.30 198	67.28 204	75.67 212	79.39 209	73.12 206	69.94 191	63.59 197	42.80 214	62.93 161	56.71 197	55.07 205	79.13 201	78.55 197	87.06 201	85.82 182

WB-MVS		52.27 213	57.26 214	46.45 213	75.64 213	65.62 219	40.45 224	75.80 167	47.10 219	9.11 227	53.83 198	38.98 223	14.47 222	69.44 212	68.29 214	63.24 220	57.56 219

ET-MVSNet_ETH3D		84.65 93	85.58 88	83.56 110	74.99 214	92.62 101	90.29 70	80.38 115	82.16 83	73.01 110	83.41 44	71.10 129	87.05 63	87.77 126	90.17 83	95.62 50	91.82 127

PMVS	50.48 18	55.81 212	51.93 215	60.33 210	72.90 215	49.34 221	48.78 219	69.51 193	43.49 220	54.25 195	36.26 218	41.04 222	39.71 216	65.07 215	60.70 216	76.85 217	67.58 215
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
ambc			61.92 210		70.98 216	73.54 215	63.64 216		60.06 204	52.23 201	38.44 216	19.17 227	57.12 202	82.33 189	75.03 208	83.21 213	84.89 184

pmmvs3		61.89 209	61.74 211	62.06 209	64.30 217	70.83 217	64.22 214	52.14 218	48.78 218	44.47 212	41.67 215	41.70 221	63.03 197	76.06 207	76.02 203	84.18 211	77.14 209

MDA-MVSNet-bldmvs		66.22 205	64.49 208	68.24 202	61.67 218	82.11 203	70.07 210	76.16 163	59.14 208	47.94 208	54.35 196	35.82 224	67.33 189	64.94 216	75.68 204	86.30 205	79.36 204

new_pmnet		59.28 210	61.47 212	56.73 211	61.66 219	68.29 218	59.57 217	54.91 215	60.83 203	34.38 221	44.66 214	43.65 218	49.90 210	71.66 211	71.56 212	79.94 216	69.67 213

Gipuma		49.17 214	47.05 217	51.65 212	59.67 220	48.39 222	41.98 222	63.47 211	55.64 213	33.33 222	14.90 221	13.78 228	41.34 215	69.31 213	72.30 211	70.11 218	55.00 220
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MIMVSNet1		65.00 206	66.24 207	63.55 208	58.41 221	80.01 208	69.00 211	74.03 174	55.81 212	41.88 215	36.81 217	49.48 214	47.89 212	81.32 191	82.40 185	90.08 186	77.88 207

EMVS		30.49 219	25.44 221	36.39 216	51.47 222	29.89 226	20.17 227	54.00 217	26.49 222	12.02 226	13.94 224	8.84 229	34.37 218	25.04 223	34.37 222	46.29 225	39.53 223

E-PMN		31.40 217	26.80 220	36.78 215	51.39 223	29.96 225	20.20 226	54.17 216	25.93 223	12.75 225	14.73 222	8.58 230	34.10 219	27.36 222	37.83 221	48.07 224	43.18 222

PMMVS2		41.68 216	44.74 218	38.10 214	46.97 224	52.32 220	40.63 223	48.08 219	35.51 221	7.36 228	26.86 220	24.64 226	16.72 221	55.24 219	59.03 217	68.85 219	59.59 218

tmp_tt				32.73 218	43.96 225	21.15 227	26.71 225	8.99 223	65.67 190	51.39 203	56.01 192	42.64 219	11.76 223	56.60 218	50.81 219	53.55 223

MVE	30.17 19	30.88 218	33.52 219	27.80 220	23.78 226	39.16 224	18.69 228	46.90 220	21.88 224	15.39 224	14.37 223	7.31 231	24.41 220	41.63 221	56.22 218	37.64 226	54.07 221
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		41.78 215	48.10 216	34.42 217	10.74 227	19.78 228	44.64 221	17.73 222	59.83 205	38.67 219	35.82 219	54.41 205	34.94 217	62.87 217	43.13 220	59.81 221	60.82 217

GG-mvs-BLEND		57.56 211	82.61 110	28.34 219	0.22 228	90.10 126	79.37 189	0.14 225	79.56 108	0.40 229	71.25 112	83.40 62	0.30 226	86.27 149	83.87 175	89.59 188	83.83 188

testmvs		1.03 220	1.63 222	0.34 221	0.09 229	0.35 229	0.61 230	0.16 224	1.49 225	0.10 230	3.15 225	0.15 232	0.86 225	1.32 224	1.18 223	0.20 227	3.76 225

test123		0.87 221	1.40 223	0.25 222	0.03 230	0.25 230	0.35 231	0.08 226	1.21 226	0.05 231	2.84 226	0.03 233	0.89 224	0.43 225	1.16 224	0.13 228	3.87 224

uanet_test		0.00 222	0.00 224	0.00 223	0.00 231	0.00 231	0.00 232	0.00 227	0.00 227	0.00 232	0.00 227	0.00 234	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	0.00 231	0.00 231	0.00 232	0.00 227	0.00 227	0.00 232	0.00 227	0.00 234	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	0.00 231	0.00 231	0.00 232	0.00 227	0.00 227	0.00 232	0.00 227	0.00 234	0.00 227	0.00 226	0.00 225	0.00 229	0.00 226

RE-MVS-def										56.08 193

9.14												92.16 17

MTAPA										92.97 2		91.03 23

MTMP										93.14 1		90.21 30

Patchmatch-RL test							8.55 229

NP-MVS									87.47 53

Patchmtry						85.54 183	82.30 170	68.23 196		65.37 145

DeepMVS_CX						48.31 223	48.03 220	26.08 221	56.42 211	25.77 223	47.51 207	31.31 225	51.30 208	48.49 220		53.61 222	61.52 216

High-resolution multi-view benchmark