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 13	94.08 20	95.58 54	97.48 15

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 6	96.93 3	98.99 1

SMA-MVS		94.70 7	95.35 7	93.93 11	97.57 3	97.57 9	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 21	93.59 25	93.08 22	97.39 4	96.82 23	95.38 24	90.71 23	90.82 36	88.07 27	92.83 21	90.29 30	91.32 27	94.03 30	93.19 41	95.61 52	97.16 21
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
NCCC		93.69 19	93.66 24	93.72 15	97.37 5	96.66 29	95.93 17	92.50 9	93.40 18	88.35 25	87.36 35	92.33 14	92.18 13	94.89 16	94.09 19	96.00 27	96.91 29

CNVR-MVS		94.37 12	94.65 12	94.04 10	97.29 6	97.11 12	96.00 11	92.43 10	93.45 15	89.85 18	90.92 26	93.04 9	92.59 10	95.77 5	94.82 7	96.11 25	97.42 17

HFP-MVS		94.02 15	94.22 19	93.78 13	97.25 7	96.85 21	95.81 19	90.94 22	94.12 11	90.29 15	94.09 14	89.98 32	92.52 11	93.94 33	93.49 33	95.87 33	97.10 24

APD-MVS		94.37 12	94.47 16	94.26 7	97.18 8	96.99 17	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 19
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
DeepC-MVS_fast	88.76 1	93.10 23	93.02 30	93.19 21	97.13 9	96.51 33	95.35 25	91.19 19	93.14 20	88.14 26	85.26 41	89.49 35	91.45 22	95.17 11	95.07 2	95.85 36	96.48 37
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 7	97.19 2	92.83 4	95.06 6	90.98 9	96.47 2	92.77 10	93.38 2	95.34 10	94.21 17	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 21	93.48 17	97.07 11	96.93 18	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 33

mPP-MVS					97.06 12							88.08 45

ACMMP_NAP		93.94 16	94.49 15	93.30 19	97.03 13	97.31 11	95.96 13	91.30 18	93.41 17	88.55 24	93.00 19	90.33 29	91.43 25	95.53 8	94.41 15	95.53 58	97.47 16

PGM-MVS		92.76 26	93.03 29	92.45 27	97.03 13	96.67 28	95.73 22	87.92 42	90.15 44	86.53 36	92.97 20	88.33 44	91.69 20	93.62 41	93.03 42	95.83 37	96.41 40

SteuartSystems-ACMMP		94.06 14	94.65 12	93.38 18	96.97 15	97.36 10	96.12 10	91.78 14	92.05 28	87.34 30	94.42 12	90.87 26	91.87 18	95.47 9	94.59 12	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 9	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 30	92.75 31	91.90 33	96.89 17	96.70 25	95.25 26	90.48 28	91.50 33	83.95 50	88.20 32	88.82 40	89.11 38	93.75 38	93.43 34	95.75 43	96.83 31

train_agg		92.87 25	93.53 26	92.09 30	96.88 18	95.38 52	95.94 15	90.59 27	90.65 38	83.65 53	94.31 13	91.87 20	90.30 32	93.38 43	92.42 52	95.17 78	96.73 33

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 4	96.90 4	98.45 3

CP-MVS		93.25 22	93.26 27	93.24 20	96.84 19	96.51 33	95.52 23	90.61 26	92.37 24	88.88 22	90.91 27	89.52 34	91.91 16	93.64 40	92.78 47	95.69 45	97.09 25

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 18	94.77 9	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 7	94.58 13	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 20	93.53 16	96.79 23	96.85 21	95.95 14	91.69 16	92.20 26	87.17 32	90.83 28	93.41 7	91.96 14	94.49 25	93.50 31	97.61 1	97.12 23

SF-MVS		94.61 8	94.96 10	94.20 9	96.75 24	97.07 13	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 18

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 83	92.95 47	88.03 39	85.74 61	83.36 54	87.29 36	83.05 64	80.98 104	92.22 60	91.85 57	93.69 145	95.58 55
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 31			86.52 37	87.18 37	92.17 16	86.76 65			95.56 55	93.85 87
Ray Leroy Khuboni and Hongjun Xu: Textureless Resilient Propagation Matching in Multiple View Stereosis (TPM-MVS). SATNAC 2025
CSCG		92.76 26	93.16 28	92.29 29	96.30 28	97.74 8	94.67 34	88.98 35	92.46 22	89.73 19	86.67 38	92.15 18	88.69 44	92.26 59	92.92 45	95.40 63	97.89 10

CDPH-MVS		91.14 39	92.01 33	90.11 41	96.18 29	96.18 37	94.89 31	88.80 37	88.76 49	77.88 90	89.18 31	87.71 47	87.29 61	93.13 46	93.31 38	95.62 50	95.84 48

DeepC-MVS	87.86 3	92.26 31	91.86 34	92.73 24	96.18 29	96.87 20	95.19 28	91.76 15	92.17 27	86.58 35	81.79 55	85.85 51	90.88 30	94.57 23	94.61 11	95.80 39	97.18 20
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 60	92.53 25	96.10 31	95.19 57	92.98 46	91.40 17	89.08 48	88.65 23	78.35 74	81.44 71	91.30 28	90.81 90	90.21 84	94.72 99	93.59 94

MSLP-MVS++		92.02 34	91.40 37	92.75 23	96.01 32	95.88 44	93.73 40	89.00 33	89.89 45	90.31 14	81.28 60	88.85 39	91.45 22	92.88 51	94.24 16	96.00 27	96.76 32

3Dnovator+	86.06 4	91.60 36	90.86 42	92.47 26	96.00 33	96.50 35	94.70 33	87.83 43	90.49 39	89.92 17	74.68 97	89.35 36	90.66 31	94.02 31	94.14 18	95.67 47	96.85 30

MVS_0304		93.46 20	94.44 17	92.32 28	95.88 34	97.84 6	95.25 26	87.99 40	92.23 25	89.16 21	91.23 25	91.51 22	88.98 39	95.64 6	95.04 3	96.67 11	97.57 14

TSAR-MVS + ACMM		92.97 24	94.51 14	91.16 37	95.88 34	96.59 30	95.09 29	90.45 29	93.42 16	83.01 56	94.68 10	90.74 27	88.74 43	94.75 20	93.78 24	93.82 140	97.63 12

ACMMP		92.03 33	92.16 32	91.87 34	95.88 34	96.55 31	94.47 35	89.49 32	91.71 31	85.26 43	91.52 24	84.48 57	90.21 34	92.82 52	91.63 59	95.92 30	96.42 39
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 37	97.03 15	95.77 21	91.95 12	94.41 8	91.35 7	94.97 8	93.34 8	91.80 19	94.72 21	93.99 21	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 35	91.48 35	92.00 31	95.53 38	95.75 47	95.94 15	91.07 20	91.20 34	85.58 41	81.63 58	90.74 27	88.40 47	93.40 42	93.75 25	95.45 62	93.85 87

TSAR-MVS + MP.		94.48 11	94.97 9	93.90 12	95.53 38	97.01 16	96.69 6	90.71 23	94.24 10	90.92 10	94.97 8	92.19 15	93.03 4	94.83 17	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 37	90.95 40	91.91 32	95.06 40	95.24 56	95.02 30	88.98 35	91.02 35	86.71 34	84.89 43	88.58 43	91.60 21	90.82 89	89.67 100	94.08 127	96.45 38

CANet		91.33 38	91.46 36	91.18 36	95.01 41	96.71 24	93.77 38	87.39 46	87.72 53	87.26 31	81.77 56	89.73 33	87.32 60	94.43 26	93.86 22	96.31 18	96.02 46

PHI-MVS		92.05 32	93.74 23	90.08 42	94.96 42	97.06 14	93.11 45	87.71 44	90.71 37	80.78 71	92.40 22	91.03 24	87.68 55	94.32 28	94.48 14	96.21 23	96.16 44

MAR-MVS		88.39 61	88.44 59	88.33 67	94.90 43	95.06 61	90.51 68	83.59 79	85.27 63	79.07 82	77.13 80	82.89 65	87.70 53	92.19 62	92.32 53	94.23 123	94.20 81
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 37	94.88 44	95.74 48	93.82 37	85.36 55	89.28 46	87.81 28	74.34 100	87.40 48	88.56 45	93.07 47	93.74 26	96.53 12	95.71 50

DeepPCF-MVS	88.51 2	92.64 29	94.42 18	90.56 40	94.84 45	96.92 19	91.31 63	89.61 31	95.16 5	84.55 48	89.91 30	91.45 23	90.15 35	95.12 12	94.81 8	92.90 160	97.58 13

QAPM		89.49 50	89.58 53	89.38 52	94.73 46	95.94 41	92.35 49	85.00 58	85.69 62	80.03 78	76.97 82	87.81 46	87.87 52	92.18 63	92.10 55	96.33 16	96.40 42

MVS_111021_HR		90.56 40	91.29 38	89.70 48	94.71 47	95.63 49	91.81 57	86.38 49	87.53 54	81.29 66	87.96 33	85.43 53	87.69 54	93.90 34	92.93 44	96.33 16	95.69 51

OpenMVS	82.53 11	87.71 69	86.84 77	88.73 58	94.42 48	95.06 61	91.02 66	83.49 82	82.50 83	82.24 62	67.62 138	85.48 52	85.56 73	91.19 74	91.30 62	95.67 47	94.75 66

PLC	83.76 9	88.61 58	86.83 78	90.70 39	94.22 49	92.63 102	91.50 60	87.19 47	89.16 47	86.87 33	75.51 92	80.87 73	89.98 36	90.01 101	89.20 113	94.41 118	90.45 153
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
LS3D		85.96 83	84.37 101	87.81 69	94.13 50	93.27 89	90.26 73	89.00 33	84.91 69	72.84 115	71.74 113	72.47 128	87.45 58	89.53 110	89.09 115	93.20 156	89.60 156

EPNet_dtu		81.98 121	83.82 106	79.83 154	94.10 51	85.97 180	87.29 122	84.08 70	80.61 105	59.96 187	81.62 59	77.19 103	62.91 203	87.21 133	86.38 153	90.66 185	87.77 173
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
OPM-MVS		87.56 71	85.80 89	89.62 49	93.90 52	94.09 77	94.12 36	88.18 38	75.40 137	77.30 93	76.41 85	77.93 97	88.79 42	92.20 61	90.82 71	95.40 63	93.72 92
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
DELS-MVS		89.71 48	89.68 52	89.74 46	93.75 53	96.22 36	93.76 39	85.84 51	82.53 81	85.05 45	78.96 71	84.24 58	84.25 80	94.91 15	94.91 5	95.78 42	96.02 46
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 75	90.03 44	93.73 54	94.28 72	89.56 83	85.81 52	91.87 29	87.55 29	69.53 127	81.49 70	89.23 37	89.45 111	88.59 123	94.31 122	93.82 89

HQP-MVS		89.13 54	89.58 53	88.60 62	93.53 55	93.67 81	93.29 43	87.58 45	88.53 50	75.50 95	87.60 34	80.32 76	87.07 62	90.66 96	89.95 92	94.62 105	96.35 43

OMC-MVS		90.23 45	90.40 45	90.03 44	93.45 56	95.29 53	91.89 55	86.34 50	93.25 19	84.94 46	81.72 57	86.65 50	88.90 40	91.69 67	90.27 83	94.65 103	93.95 85

ACMM	83.27 10	87.68 70	86.09 85	89.54 50	93.26 57	92.19 108	91.43 61	86.74 48	86.02 59	82.85 57	75.63 90	75.14 110	88.41 46	90.68 95	89.99 89	94.59 106	92.97 102
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 78	90.43 69	84.65 61	90.16 43	84.52 49	90.14 29	83.80 60	87.99 51	92.50 56	90.92 68	94.74 97	94.70 68

SPE-MVS-test		90.29 43	90.96 39	89.51 51	93.18 59	95.87 45	89.18 89	83.72 75	88.32 51	84.82 47	84.89 43	85.23 54	90.25 33	94.04 29	92.66 51	95.94 29	95.69 51

CS-MVS		90.34 42	90.58 44	90.07 43	93.11 60	95.82 46	90.57 67	83.62 76	87.07 56	85.35 42	82.98 47	83.47 61	91.37 26	94.94 14	93.37 37	96.37 14	96.41 40

XVS					93.11 60	96.70 25	91.91 53			83.95 50		88.82 40				95.79 40

X-MVStestdata					93.11 60	96.70 25	91.91 53			83.95 50		88.82 40				95.79 40

PCF-MVS	84.60 6	88.66 56	87.75 70	89.73 47	93.06 63	96.02 38	93.22 44	90.00 30	82.44 84	80.02 79	77.96 77	85.16 55	87.36 59	88.54 121	88.54 124	94.72 99	95.61 54
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 56	88.89 56	93.00 64	94.85 65	92.00 52	84.84 59	91.68 32	80.05 76	79.77 66	84.56 56	88.17 50	90.11 100	89.00 119	95.30 70	92.57 117
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PVSNet_Blended_VisFu		87.40 73	87.80 67	86.92 76	92.86 65	95.40 51	88.56 107	83.45 86	79.55 113	82.26 60	74.49 99	84.03 59	79.24 134	92.97 50	91.53 61	95.15 80	96.65 36

UA-Net		86.07 81	87.78 68	84.06 107	92.85 66	95.11 60	87.73 115	84.38 65	73.22 157	73.18 111	79.99 65	89.22 37	71.47 179	93.22 45	93.03 42	94.76 96	90.69 148

LGP-MVS_train		88.25 64	88.55 57	87.89 68	92.84 67	93.66 82	93.35 42	85.22 57	85.77 60	74.03 106	86.60 39	76.29 107	86.62 67	91.20 73	90.58 79	95.29 71	95.75 49

TSAR-MVS + COLMAP		88.40 59	89.09 55	87.60 72	92.72 68	93.92 80	92.21 50	85.57 54	91.73 30	73.72 107	91.75 23	73.22 126	87.64 56	91.49 69	89.71 99	93.73 143	91.82 131

PVSNet_BlendedMVS		88.19 65	88.00 65	88.42 64	92.71 69	94.82 66	89.08 94	83.81 72	84.91 69	86.38 38	79.14 68	78.11 95	82.66 90	93.05 48	91.10 63	95.86 34	94.86 64

PVSNet_Blended		88.19 65	88.00 65	88.42 64	92.71 69	94.82 66	89.08 94	83.81 72	84.91 69	86.38 38	79.14 68	78.11 95	82.66 90	93.05 48	91.10 63	95.86 34	94.86 64

ACMP	83.90 8	88.32 63	88.06 63	88.62 61	92.18 71	93.98 79	91.28 64	85.24 56	86.69 57	81.23 67	85.62 40	75.13 111	87.01 64	89.83 103	89.77 97	94.79 93	95.43 57
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MSDG		83.87 105	81.02 127	87.19 75	92.17 72	89.80 138	89.15 92	85.72 53	80.61 105	79.24 81	66.66 141	68.75 144	82.69 89	87.95 128	87.44 134	94.19 124	85.92 185

TSAR-MVS + GP.		92.71 28	93.91 22	91.30 35	91.96 73	96.00 40	93.43 41	87.94 41	92.53 21	86.27 40	93.57 15	91.94 19	91.44 24	93.29 44	92.89 46	96.78 7	97.15 22

test2506		85.20 91	84.11 103	86.47 79	91.84 74	95.28 54	89.18 89	84.49 63	82.59 79	75.34 99	74.66 98	58.07 193	81.68 97	93.76 36	92.71 48	96.28 21	91.71 133

ECVR-MVS		85.25 90	84.47 99	86.16 82	91.84 74	95.28 54	89.18 89	84.49 63	82.59 79	73.49 109	66.12 143	69.28 141	81.68 97	93.76 36	92.71 48	96.28 21	91.58 140

test1111		84.86 96	84.21 102	85.61 88	91.75 76	95.14 59	88.63 104	84.57 62	81.88 90	71.21 118	65.66 150	68.51 145	81.19 101	93.74 39	92.68 50	96.31 18	91.86 130

ETV-MVS		89.22 53	89.76 50	88.60 62	91.60 77	94.61 69	89.48 85	83.46 85	85.20 65	81.58 64	82.75 49	82.59 66	88.80 41	94.57 23	93.28 39	96.68 9	95.31 58

EIA-MVS		87.94 68	88.05 64	87.81 69	91.46 78	95.00 63	88.67 101	82.81 92	82.53 81	80.81 70	80.04 64	80.20 77	87.48 57	92.58 55	91.61 60	95.63 49	94.36 75

sasdasda		89.36 51	89.92 46	88.70 59	91.38 79	95.92 42	91.81 57	82.61 100	90.37 40	82.73 58	82.09 51	79.28 86	88.30 48	91.17 75	93.59 28	95.36 65	97.04 26

canonicalmvs		89.36 51	89.92 46	88.70 59	91.38 79	95.92 42	91.81 57	82.61 100	90.37 40	82.73 58	82.09 51	79.28 86	88.30 48	91.17 75	93.59 28	95.36 65	97.04 26

CLD-MVS		88.66 56	88.52 58	88.82 57	91.37 81	94.22 73	92.82 48	82.08 105	88.27 52	85.14 44	81.86 54	78.53 93	85.93 71	91.17 75	90.61 77	95.55 56	95.00 60
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MGCFI-Net		88.38 62	89.72 51	86.83 77	91.21 82	95.59 50	91.14 65	82.37 103	90.25 42	75.33 100	81.89 53	79.13 88	85.69 72	90.98 86	93.23 40	95.23 75	96.94 28

CHOSEN 1792x2688		82.16 119	80.91 130	83.61 112	91.14 83	92.01 109	89.55 84	79.15 140	79.87 109	70.29 122	52.51 206	72.56 127	81.39 99	88.87 119	88.17 127	90.15 189	92.37 124

IB-MVS	79.09 12	82.60 116	82.19 115	83.07 118	91.08 84	93.55 84	80.90 185	81.35 113	76.56 129	80.87 69	64.81 158	69.97 137	68.87 186	85.64 160	90.06 88	95.36 65	94.74 67
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 80	88.18 62	83.85 110	91.02 85	94.72 68	87.48 118	82.46 102	81.05 100	70.28 123	76.98 81	82.20 69	76.65 151	93.97 32	93.38 35	95.18 77	94.97 61

HyFIR lowres test		81.62 129	79.45 150	84.14 106	91.00 86	93.38 88	88.27 109	78.19 149	76.28 131	70.18 124	48.78 210	73.69 121	83.52 84	87.05 136	87.83 131	93.68 146	89.15 159

COLMAP_ROB	76.78 15	80.50 136	78.49 155	82.85 119	90.96 87	89.65 144	86.20 139	83.40 87	77.15 127	66.54 141	62.27 166	65.62 154	77.89 142	85.23 167	84.70 174	92.11 167	84.83 189
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 88	87.72 71	82.76 121	90.95 88	93.01 94	89.99 74	75.46 173	82.67 78	64.91 154	83.14 46	80.09 78	80.68 108	92.03 65	91.03 65	94.57 108	92.08 125

FC-MVSNet-train		85.18 92	85.31 93	85.03 93	90.67 89	91.62 113	87.66 116	83.61 77	79.75 111	74.37 104	78.69 72	71.21 132	78.91 135	91.23 71	89.96 91	94.96 86	94.69 70

baseline1		84.54 99	84.43 100	84.67 95	90.62 90	91.16 116	88.63 104	83.75 74	79.78 110	71.16 119	75.14 94	74.10 116	77.84 143	91.56 68	90.67 76	96.04 26	88.58 162

thres600view7		82.53 118	81.02 127	84.28 102	90.61 91	93.05 92	88.57 106	82.67 96	74.12 148	68.56 134	65.09 155	62.13 173	80.40 114	91.15 78	89.02 118	94.88 89	92.59 115

casdiffmvs_mvg		87.97 67	87.63 72	88.37 66	90.55 92	94.42 70	91.82 56	84.69 60	84.05 73	82.08 63	76.57 84	79.00 89	85.49 74	92.35 57	92.29 54	95.55 56	94.70 68
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 93	95.15 58	91.34 62	81.43 111	85.27 63	83.08 55	82.83 48	87.22 49	90.97 29	94.79 19	93.38 35	96.73 8	96.71 35

thres400		82.68 115	81.15 125	84.47 98	90.52 94	92.89 96	88.95 99	82.71 94	74.33 145	69.22 131	65.31 152	62.61 168	80.63 110	90.96 87	89.50 104	94.79 93	92.45 123

EPP-MVSNet		86.55 76	87.76 69	85.15 92	90.52 94	94.41 71	87.24 124	82.32 104	81.79 92	73.60 108	78.57 73	82.41 67	82.07 95	91.23 71	90.39 81	95.14 81	95.48 56

ACMH	78.52 14	81.86 123	80.45 134	83.51 116	90.51 96	91.22 115	85.62 146	84.23 67	70.29 174	62.21 170	69.04 131	64.05 159	84.48 79	87.57 131	88.45 126	94.01 131	92.54 119
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Vis-MVSNet (Re-imp)		83.65 108	86.81 79	79.96 152	90.46 97	92.71 99	84.84 154	82.00 106	80.93 102	62.44 169	76.29 86	82.32 68	65.54 199	92.29 58	91.66 58	94.49 113	91.47 142

FA-MVS(training)		85.65 86	85.79 90	85.48 90	90.44 98	93.47 85	88.66 103	73.11 181	83.34 76	82.26 60	71.79 112	78.39 94	83.14 87	91.00 83	89.47 106	95.28 73	93.06 100

thres200		82.77 114	81.25 124	84.54 96	90.38 99	93.05 92	89.13 93	82.67 96	74.40 144	69.53 128	65.69 149	63.03 165	80.63 110	91.15 78	89.42 107	94.88 89	92.04 127

MS-PatchMatch		81.79 125	81.44 121	82.19 129	90.35 100	89.29 150	88.08 112	75.36 174	77.60 125	69.00 132	64.37 161	78.87 92	77.14 149	88.03 127	85.70 164	93.19 157	86.24 182

PatchMatch-RL		83.34 110	81.36 122	85.65 86	90.33 101	89.52 146	84.36 158	81.82 107	80.87 104	79.29 80	74.04 101	62.85 167	86.05 70	88.40 124	87.04 141	92.04 168	86.77 178

thres100view900		82.55 117	81.01 129	84.34 99	90.30 102	92.27 106	89.04 97	82.77 93	75.14 138	69.56 126	65.72 147	63.13 162	79.62 129	89.97 102	89.26 111	94.73 98	91.61 139

tfpn200view9		82.86 112	81.46 120	84.48 97	90.30 102	93.09 91	89.05 96	82.71 94	75.14 138	69.56 126	65.72 147	63.13 162	80.38 115	91.15 78	89.51 103	94.91 88	92.50 121

casdiffmvs		87.45 72	87.15 74	87.79 71	90.15 104	94.22 73	89.96 75	83.93 71	85.08 67	80.91 68	75.81 89	77.88 98	86.08 69	91.86 66	90.86 70	95.74 44	94.37 73
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 75	87.24 73	86.56 78	90.10 105	93.47 85	90.31 70	80.12 126	83.55 75	78.12 86	79.58 67	79.80 81	85.45 75	90.17 99	90.59 78	95.29 71	93.53 95

viewmanbaseed2359cas		87.17 74	86.90 76	87.48 73	90.08 106	94.14 75	90.30 71	83.19 90	84.17 72	80.68 73	76.78 83	77.43 100	85.43 76	90.78 91	90.92 68	95.21 76	94.10 82

ACMH+	79.08 13	81.84 124	80.06 139	83.91 109	89.92 107	90.62 120	86.21 138	83.48 84	73.88 150	65.75 146	66.38 142	65.30 155	84.63 78	85.90 157	87.25 137	93.45 151	91.13 146

Effi-MVS+		85.33 89	85.08 94	85.63 87	89.69 108	93.42 87	89.90 76	80.31 124	79.32 114	72.48 117	73.52 106	74.03 117	86.55 68	90.99 84	89.98 90	94.83 91	94.27 80

Anonymous202405211			82.75 113		89.58 109	92.97 95	89.04 97	84.13 69	78.72 119		57.18 193	76.64 106	83.13 88	89.55 109	89.92 93	93.38 153	94.28 79

GeoE		84.62 98	83.98 105	85.35 91	89.34 110	92.83 97	88.34 108	78.95 141	79.29 115	77.16 94	68.10 135	74.56 113	83.40 85	89.31 113	89.23 112	94.92 87	94.57 72

tttt0517		85.11 94	85.81 88	84.30 101	89.24 111	92.68 101	87.12 129	80.11 127	81.98 89	74.31 105	78.08 76	73.57 122	79.90 122	91.01 82	89.58 101	95.11 84	93.77 90

DI_MVS_pp		86.41 79	85.54 92	87.42 74	89.24 111	93.13 90	92.16 51	82.65 98	82.30 85	80.75 72	68.30 134	80.41 75	85.01 77	90.56 97	90.07 87	94.70 101	94.01 83

thisisatest0530		85.15 93	85.86 87	84.33 100	89.19 113	92.57 105	87.22 125	80.11 127	82.15 88	74.41 103	78.15 75	73.80 120	79.90 122	90.99 84	89.58 101	95.13 82	93.75 91

DCV-MVSNet		85.88 85	86.17 83	85.54 89	89.10 114	89.85 136	89.34 87	80.70 117	83.04 77	78.08 88	76.19 87	79.00 89	82.42 93	89.67 106	90.30 82	93.63 148	95.12 59

UGNet		85.90 84	88.23 61	83.18 117	88.96 115	94.10 76	87.52 117	83.60 78	81.66 93	77.90 89	80.76 62	83.19 63	66.70 196	91.13 81	90.71 75	94.39 119	96.06 45
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		84.42 103	83.02 109	86.05 84	88.85 116	92.70 100	88.92 100	83.40 87	79.99 108	78.31 85	55.83 197	78.92 91	83.33 86	89.06 115	89.76 98	93.50 150	94.90 62

MVSTER		86.03 82	86.12 84	85.93 85	88.62 117	89.93 134	89.33 88	79.91 131	81.87 91	81.35 65	81.07 61	74.91 112	80.66 109	92.13 64	90.10 86	95.68 46	92.80 107

TDRefinement		79.05 154	77.05 173	81.39 136	88.45 118	89.00 157	86.92 130	82.65 98	74.21 147	64.41 155	59.17 185	59.16 189	74.52 165	85.23 167	85.09 169	91.37 177	87.51 174

viewmambaseed2359dif		85.52 87	85.01 95	86.12 83	88.39 119	91.96 110	89.39 86	81.43 111	82.16 86	80.47 74	75.52 91	76.85 105	83.66 82	87.03 137	87.60 132	93.37 154	93.98 84

IterMVS-LS		83.28 111	82.95 111	83.65 111	88.39 119	88.63 161	86.80 133	78.64 146	76.56 129	73.43 110	72.52 111	75.35 109	80.81 106	86.43 152	88.51 125	93.84 139	92.66 112
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
diffmvs_AUTHOR		86.44 78	86.59 82	86.26 80	88.33 121	92.74 98	89.66 81	81.74 108	85.17 66	80.04 77	77.70 78	77.20 102	83.68 81	89.66 107	89.28 109	94.14 126	94.37 73

diffmvs		86.52 77	86.76 80	86.23 81	88.31 122	92.63 102	89.58 82	81.61 110	86.14 58	80.26 75	79.00 70	77.27 101	83.58 83	88.94 116	89.06 116	94.05 129	94.29 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
Fast-Effi-MVS+		83.77 107	82.98 110	84.69 94	87.98 123	91.87 111	88.10 111	77.70 155	78.10 123	73.04 113	69.13 129	68.51 145	86.66 66	90.49 98	89.85 95	94.67 102	92.88 104

gg-mvs-nofinetune		75.64 191	77.26 170	73.76 195	87.92 124	92.20 107	87.32 121	64.67 214	51.92 219	35.35 224	46.44 213	77.05 104	71.97 176	92.64 54	91.02 66	95.34 68	89.53 157

RPSCF		83.46 109	83.36 108	83.59 113	87.75 125	87.35 171	84.82 155	79.46 136	83.84 74	78.12 86	82.69 50	79.87 79	82.60 92	82.47 191	81.13 194	88.78 196	86.13 183

Effi-MVS+-dtu		82.05 120	81.76 117	82.38 126	87.72 126	90.56 121	86.90 132	78.05 151	73.85 151	66.85 140	71.29 115	71.90 130	82.00 96	86.64 147	85.48 166	92.76 162	92.58 116

CostFormer		80.94 133	80.21 136	81.79 131	87.69 127	88.58 162	87.47 119	70.66 190	80.02 107	77.88 90	73.03 107	71.40 131	78.24 139	79.96 200	79.63 196	88.82 195	88.84 160

baseline2		82.80 113	82.86 112	82.73 122	87.68 128	90.50 122	84.92 153	78.93 142	78.07 124	73.06 112	75.08 95	69.77 138	77.31 146	88.90 118	86.94 142	94.50 111	90.74 147

Vis-MVSNet		84.38 104	86.68 81	81.70 132	87.65 129	94.89 64	88.14 110	80.90 116	74.48 143	68.23 135	77.53 79	80.72 74	69.98 183	92.68 53	91.90 56	95.33 69	94.58 71
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test-LLR		79.47 149	79.84 144	79.03 158	87.47 130	82.40 205	81.24 182	78.05 151	73.72 152	62.69 166	73.76 103	74.42 114	73.49 170	84.61 176	82.99 186	91.25 179	87.01 176

test0.0.03 1		76.03 185	78.51 154	73.12 199	87.47 130	85.13 191	76.32 203	78.05 151	73.19 159	50.98 209	70.64 117	69.28 141	55.53 207	85.33 165	84.38 178	90.39 187	81.63 201

tpmrst		76.55 178	75.99 185	77.20 172	87.32 132	83.05 198	82.86 168	65.62 209	78.61 121	67.22 139	69.19 128	65.71 153	75.87 155	76.75 210	75.33 209	84.31 214	83.28 195

baseline		84.89 95	86.06 86	83.52 115	87.25 133	89.67 143	87.76 114	75.68 172	84.92 68	78.40 84	80.10 63	80.98 72	80.20 118	86.69 146	87.05 140	91.86 171	92.99 101

CDS-MVSNet		81.63 128	82.09 116	81.09 141	87.21 134	90.28 125	87.46 120	80.33 123	69.06 178	70.66 120	71.30 114	73.87 118	67.99 189	89.58 108	89.87 94	92.87 161	90.69 148
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
tpm cat1		77.78 167	75.28 193	80.70 144	87.14 135	85.84 182	85.81 142	70.40 191	77.44 126	78.80 83	63.72 162	64.01 160	76.55 152	75.60 212	75.21 210	85.51 212	85.12 187

tpm		76.30 184	76.05 184	76.59 178	86.97 136	83.01 199	83.83 162	67.06 205	71.83 163	63.87 160	69.56 126	62.88 166	73.41 172	79.79 201	78.59 200	84.41 213	86.68 179

EPMVS		77.53 169	78.07 162	76.90 176	86.89 137	84.91 192	82.18 177	66.64 207	81.00 101	64.11 158	72.75 110	69.68 139	74.42 167	79.36 203	78.13 202	87.14 204	80.68 206

PatchmatchNet		78.67 159	78.85 153	78.46 166	86.85 138	86.03 179	83.77 163	68.11 202	80.88 103	66.19 143	72.90 109	73.40 124	78.06 140	79.25 204	77.71 204	87.75 201	81.75 200
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
dmvs_re		81.08 132	79.92 142	82.44 125	86.66 139	87.70 167	87.91 113	83.30 89	72.86 160	65.29 152	65.76 146	63.43 161	76.69 150	88.93 117	89.50 104	94.80 92	91.23 145

SCA		79.51 148	80.15 138	78.75 161	86.58 140	87.70 167	83.07 167	68.53 199	81.31 95	66.40 142	73.83 102	75.38 108	79.30 133	80.49 198	79.39 199	88.63 198	82.96 197

USDC		80.69 134	79.89 143	81.62 134	86.48 141	89.11 155	86.53 135	78.86 143	81.15 99	63.48 162	72.98 108	59.12 191	81.16 102	87.10 134	85.01 170	93.23 155	84.77 190

Fast-Effi-MVS+-dtu		79.95 140	80.69 131	79.08 157	86.36 142	89.14 154	85.85 141	72.28 184	72.85 161	59.32 190	70.43 121	68.42 147	77.57 144	86.14 154	86.44 152	93.11 158	91.39 143

tfpnnormal		77.46 170	74.86 195	80.49 148	86.34 143	88.92 158	84.33 159	81.26 114	61.39 206	61.70 177	51.99 207	53.66 212	74.84 162	88.63 120	87.38 136	94.50 111	92.08 125

dps		78.02 164	75.94 186	80.44 149	86.06 144	86.62 177	82.58 169	69.98 194	75.14 138	77.76 92	69.08 130	59.93 182	78.47 137	79.47 202	77.96 203	87.78 200	83.40 194

IterMVS-SCA-FT		79.41 150	80.20 137	78.49 165	85.88 145	86.26 178	83.95 161	71.94 185	73.55 155	61.94 173	70.48 120	70.50 134	75.23 157	85.81 159	84.61 176	91.99 170	90.18 154

LTVRE_ROB	74.41 16	75.78 190	74.72 196	77.02 175	85.88 145	89.22 151	82.44 172	77.17 158	50.57 220	45.45 215	65.44 151	52.29 214	81.25 100	85.50 163	87.42 135	89.94 191	92.62 113
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		76.40 182	75.47 191	77.48 171	85.86 147	90.22 127	82.45 171	73.96 179	59.64 211	59.60 189	52.75 205	62.20 172	68.44 188	88.23 125	87.50 133	94.55 109	87.78 172

CR-MVSNet		78.71 158	78.86 152	78.55 164	85.85 148	85.15 189	82.30 174	68.23 200	74.71 141	65.37 149	64.39 160	69.59 140	77.18 147	85.10 172	84.87 171	92.34 166	88.21 166

GA-MVS		79.52 147	79.71 147	79.30 156	85.68 149	90.36 124	84.55 156	78.44 147	70.47 173	57.87 195	68.52 133	61.38 174	76.21 153	89.40 112	87.89 128	93.04 159	89.96 155

UniMVSNet_ETH3D		79.24 152	76.47 178	82.48 124	85.66 150	90.97 117	86.08 140	81.63 109	64.48 198	68.94 133	54.47 199	57.65 195	78.83 136	85.20 170	88.91 120	93.72 144	93.60 93

TransMVSNet (Re)		76.57 177	75.16 194	78.22 168	85.60 151	87.24 172	82.46 170	81.23 115	59.80 210	59.05 193	57.07 194	59.14 190	66.60 197	88.09 126	86.82 143	94.37 120	87.95 171

RPMNet		77.07 172	77.63 168	76.42 179	85.56 152	85.15 189	81.37 179	65.27 211	74.71 141	60.29 186	63.71 163	66.59 151	73.64 169	82.71 189	82.12 191	92.38 165	88.39 164

MDTV_nov1_ep13		79.14 153	79.49 149	78.74 162	85.40 153	86.89 175	84.32 160	70.29 192	78.85 118	69.42 129	75.37 93	73.29 125	75.64 156	80.61 197	79.48 198	87.36 202	81.91 199

UniMVSNet (Re)		81.22 130	81.08 126	81.39 136	85.35 154	91.76 112	84.93 152	82.88 91	76.13 132	65.02 153	64.94 156	63.09 164	75.17 159	87.71 130	89.04 117	94.97 85	94.88 63

UniMVSNet_NR-MVSNet		81.87 122	81.33 123	82.50 123	85.31 155	91.30 114	85.70 143	84.25 66	75.89 133	64.21 156	66.95 140	64.65 157	80.22 116	87.07 135	89.18 114	95.27 74	94.29 76

IterMVS		78.79 157	79.71 147	77.71 169	85.26 156	85.91 181	84.54 157	69.84 196	73.38 156	61.25 181	70.53 119	70.35 135	74.43 166	85.21 169	83.80 181	90.95 183	88.77 161
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
NR-MVSNet		80.25 138	79.98 141	80.56 147	85.20 157	90.94 118	85.65 145	83.58 80	75.74 134	61.36 180	65.30 153	56.75 200	72.38 175	88.46 123	88.80 121	95.16 79	93.87 86

CMPMVS	56.49 17	73.84 199	71.73 205	76.31 182	85.20 157	85.67 184	75.80 204	73.23 180	62.26 203	65.40 148	53.40 204	59.70 184	71.77 178	80.25 199	79.56 197	86.45 208	81.28 202
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
TinyColmap		76.73 174	73.95 198	79.96 152	85.16 159	85.64 185	82.34 173	78.19 149	70.63 171	62.06 172	60.69 177	49.61 217	80.81 106	85.12 171	83.69 182	91.22 181	82.27 198

gm-plane-assit		70.29 204	70.65 206	69.88 204	85.03 160	78.50 215	58.41 222	65.47 210	50.39 221	40.88 220	49.60 209	50.11 216	75.14 160	91.43 70	89.78 96	94.32 121	84.73 191

FC-MVSNet-test		76.53 179	81.62 119	70.58 203	84.99 161	85.73 183	74.81 206	78.85 144	77.00 128	39.13 222	75.90 88	73.50 123	54.08 211	86.54 149	85.99 161	91.65 173	86.68 179

DU-MVS		81.20 131	80.30 135	82.25 127	84.98 162	90.94 118	85.70 143	83.58 80	75.74 134	64.21 156	65.30 153	59.60 186	80.22 116	86.89 139	89.31 108	94.77 95	94.29 76

Baseline_NR-MVSNet		79.84 142	78.37 159	81.55 135	84.98 162	86.66 176	85.06 150	83.49 82	75.57 136	63.31 163	58.22 192	60.97 176	78.00 141	86.89 139	87.13 138	94.47 114	93.15 98

TranMVSNet+NR-MVSNet		80.52 135	79.84 144	81.33 138	84.92 164	90.39 123	85.53 148	84.22 68	74.27 146	60.68 185	64.93 157	59.96 181	77.48 145	86.75 144	89.28 109	95.12 83	93.29 96

pm-mvs1		78.51 162	77.75 167	79.40 155	84.83 165	89.30 149	83.55 165	79.38 137	62.64 202	63.68 161	58.73 190	64.68 156	70.78 182	89.79 104	87.84 129	94.17 125	91.28 144

testgi		71.92 202	74.20 197	69.27 205	84.58 166	83.06 197	73.40 209	74.39 176	64.04 200	46.17 214	68.90 132	57.15 198	48.89 215	84.07 181	83.08 185	88.18 199	79.09 210

thisisatest0515		79.76 144	80.59 133	78.80 160	84.40 167	88.91 159	79.48 191	76.94 161	72.29 162	67.33 138	67.82 137	65.99 152	70.80 181	88.50 122	87.84 129	93.86 138	92.75 110

FMVSNet3		84.44 102	84.64 98	84.21 103	84.32 168	90.13 129	89.85 77	80.37 120	81.17 96	75.50 95	69.63 123	79.69 83	79.62 129	89.72 105	90.52 80	95.59 53	91.58 140

GBi-Net		84.51 100	84.80 96	84.17 104	84.20 169	89.95 131	89.70 78	80.37 120	81.17 96	75.50 95	69.63 123	79.69 83	79.75 126	90.73 92	90.72 72	95.52 59	91.71 133

test1		84.51 100	84.80 96	84.17 104	84.20 169	89.95 131	89.70 78	80.37 120	81.17 96	75.50 95	69.63 123	79.69 83	79.75 126	90.73 92	90.72 72	95.52 59	91.71 133

FMVSNet2		83.87 105	83.73 107	84.05 108	84.20 169	89.95 131	89.70 78	80.21 125	79.17 117	74.89 101	65.91 144	77.49 99	79.75 126	90.87 88	91.00 67	95.52 59	91.71 133

WR-MVS		76.63 176	78.02 164	75.02 189	84.14 172	89.76 140	78.34 198	80.64 118	69.56 175	52.32 204	61.26 170	61.24 175	60.66 204	84.45 178	87.07 139	93.99 132	92.77 108

v8		79.90 141	78.39 158	81.66 133	83.97 173	89.81 137	87.16 127	77.40 157	71.49 164	67.71 136	61.24 171	62.49 169	79.83 125	85.48 164	86.17 156	93.89 136	92.02 129

v2v482		79.84 142	78.07 162	81.90 130	83.75 174	90.21 128	87.17 126	79.85 132	70.65 170	65.93 145	61.93 168	60.07 180	80.82 105	85.25 166	86.71 145	93.88 137	91.70 137

v10		79.62 145	78.19 160	81.28 139	83.73 175	89.69 142	87.27 123	76.86 162	70.50 172	65.46 147	60.58 178	60.47 178	80.44 113	86.91 138	86.63 148	93.93 133	92.55 118

v1144		79.38 151	77.83 165	81.18 140	83.62 176	90.23 126	87.15 128	78.35 148	69.13 177	64.02 159	60.20 180	59.41 187	80.14 120	86.78 142	86.57 149	93.81 141	92.53 120

v148		78.59 160	76.84 176	80.62 146	83.61 177	89.16 153	83.65 164	79.24 139	69.38 176	69.34 130	59.88 182	60.41 179	75.19 158	83.81 182	84.63 175	92.70 163	90.63 150

SixPastTwentyTwo		76.02 186	75.72 188	76.36 180	83.38 178	87.54 169	75.50 205	76.22 166	65.50 195	57.05 196	70.64 117	53.97 211	74.54 164	80.96 196	82.12 191	91.44 175	89.35 158

CVMVSNet		76.70 175	78.46 156	74.64 193	83.34 179	84.48 193	81.83 178	74.58 175	68.88 179	51.23 208	69.77 122	70.05 136	67.49 192	84.27 179	83.81 180	89.38 193	87.96 170

v1192		78.94 155	77.33 169	80.82 143	83.25 180	89.90 135	86.91 131	77.72 154	68.63 181	62.61 168	59.17 185	57.53 196	80.62 112	86.89 139	86.47 151	93.79 142	92.75 110

DTE-MVSNet		75.14 193	75.44 192	74.80 191	83.18 181	87.19 173	78.25 200	80.11 127	66.05 190	48.31 211	60.88 175	54.67 207	64.54 200	82.57 190	86.17 156	94.43 117	90.53 152

PEN-MVS		76.02 186	76.07 182	75.95 184	83.17 182	87.97 165	79.65 189	80.07 130	66.57 188	51.45 206	60.94 174	55.47 205	66.81 195	82.72 188	86.80 144	94.59 106	92.03 128

TAMVS		76.42 180	77.16 172	75.56 185	83.05 183	85.55 186	80.58 187	71.43 187	65.40 197	61.04 184	67.27 139	69.22 143	67.99 189	84.88 174	84.78 173	89.28 194	83.01 196

pmmvs4		79.99 139	78.08 161	82.22 128	83.04 184	87.16 174	84.95 151	78.80 145	78.64 120	74.53 102	64.61 159	59.41 187	79.45 131	84.13 180	84.54 177	92.53 164	88.08 168

v144192		78.81 156	77.22 171	80.67 145	82.95 185	89.79 139	86.40 136	77.42 156	68.26 183	63.13 164	59.50 183	58.13 192	80.08 121	85.93 156	86.08 158	94.06 128	92.83 106

v1921920		78.57 161	76.99 174	80.41 150	82.93 186	89.63 145	86.38 137	77.14 159	68.31 182	61.80 176	58.89 189	56.79 199	80.19 119	86.50 151	86.05 160	94.02 130	92.76 109

CHOSEN 280x420		80.28 137	81.66 118	78.67 163	82.92 187	79.24 214	85.36 149	66.79 206	78.11 122	70.32 121	75.03 96	79.87 79	81.09 103	89.07 114	83.16 184	85.54 211	87.17 175

WR-MVS_H		75.84 189	76.93 175	74.57 194	82.86 188	89.50 147	78.34 198	79.36 138	66.90 186	52.51 203	60.20 180	59.71 183	59.73 205	83.61 183	85.77 163	94.65 103	92.84 105

v1240		78.15 163	76.53 177	80.04 151	82.85 189	89.48 148	85.61 147	76.77 163	67.05 185	61.18 183	58.37 191	56.16 203	79.89 124	86.11 155	86.08 158	93.92 134	92.47 122

V42		79.59 146	78.43 157	80.94 142	82.79 190	89.71 141	86.66 134	76.73 164	71.38 165	67.42 137	61.01 173	62.30 171	78.39 138	85.56 162	86.48 150	93.65 147	92.60 114

CP-MVSNet		76.36 183	76.41 179	76.32 181	82.73 191	88.64 160	79.39 192	79.62 133	67.21 184	53.70 200	60.72 176	55.22 206	67.91 191	83.52 184	86.34 154	94.55 109	93.19 97

PS-CasMVS		75.90 188	75.86 187	75.96 183	82.59 192	88.46 163	79.23 195	79.56 135	66.00 191	52.77 202	59.48 184	54.35 210	67.14 194	83.37 185	86.23 155	94.47 114	93.10 99

test20.03		68.31 207	70.05 208	66.28 210	82.41 193	80.84 209	67.35 216	76.11 168	58.44 213	40.80 221	53.77 203	54.54 208	42.28 218	83.07 186	81.96 193	88.73 197	77.76 212

FMVSNet1		81.64 127	80.61 132	82.84 120	82.36 194	89.20 152	88.67 101	79.58 134	70.79 169	72.63 116	58.95 188	72.26 129	79.34 132	90.73 92	90.72 72	94.47 114	91.62 138

pmmvs6		74.83 194	72.89 201	77.09 173	82.11 195	87.50 170	80.88 186	76.97 160	52.79 218	61.91 175	46.66 212	60.49 177	69.28 185	86.74 145	85.46 167	91.39 176	90.56 151

pmmvs5		76.93 173	76.33 180	77.62 170	81.97 196	88.40 164	81.32 181	74.35 177	65.42 196	61.42 179	63.07 164	57.95 194	73.23 173	85.60 161	85.35 168	93.41 152	88.55 163

v7n		77.22 171	76.23 181	78.38 167	81.89 197	89.10 156	82.24 176	76.36 165	65.96 192	61.21 182	56.56 195	55.79 204	75.07 161	86.55 148	86.68 146	93.52 149	92.95 103

our_test_3					81.81 198	83.96 196	76.61 202

Anonymous20231206		70.80 203	70.59 207	71.04 202	81.60 199	82.49 204	74.64 207	75.87 170	64.17 199	49.27 210	44.85 216	53.59 213	54.68 210	83.07 186	82.34 190	90.17 188	83.65 193

ADS-MVSNet		74.53 196	75.69 189	73.17 198	81.57 200	80.71 210	79.27 194	63.03 216	79.27 116	59.94 188	67.86 136	68.32 149	71.08 180	77.33 208	76.83 206	84.12 216	79.53 207

pmnet_mix02		71.95 201	71.83 204	72.10 200	81.40 201	80.63 211	73.78 208	72.85 183	70.90 168	54.89 198	62.17 167	57.42 197	62.92 202	76.80 209	73.98 213	86.74 207	80.87 205

test-mter		77.79 166	80.02 140	75.18 188	81.18 202	82.85 200	80.52 188	62.03 218	73.62 154	62.16 171	73.55 105	73.83 119	73.81 168	84.67 175	83.34 183	91.37 177	88.31 165

TESTMET0.1,1		77.78 167	79.84 144	75.38 187	80.86 203	82.40 205	81.24 182	62.72 217	73.72 152	62.69 166	73.76 103	74.42 114	73.49 170	84.61 176	82.99 186	91.25 179	87.01 176

MDTV_nov1_ep13_2view		73.21 200	72.91 200	73.56 197	80.01 204	84.28 195	78.62 196	66.43 208	68.64 180	59.12 191	60.39 179	59.69 185	69.81 184	78.82 206	77.43 205	87.36 202	81.11 204

FPMVS		63.63 212	60.08 217	67.78 207	80.01 204	71.50 220	72.88 211	69.41 198	61.82 205	53.11 201	45.12 215	42.11 224	50.86 213	66.69 218	63.84 219	80.41 218	69.46 218

anonymousdsp		77.94 165	79.00 151	76.71 177	79.03 206	87.83 166	79.58 190	72.87 182	65.80 193	58.86 194	65.82 145	62.48 170	75.99 154	86.77 143	88.66 122	93.92 134	95.68 53

N_pmnet		66.85 208	66.63 209	67.11 209	78.73 207	74.66 218	70.53 213	71.07 188	66.46 189	46.54 213	51.68 208	51.91 215	55.48 208	74.68 213	72.38 214	80.29 219	74.65 215

PMMVS		81.65 126	84.05 104	78.86 159	78.56 208	82.63 202	83.10 166	67.22 204	81.39 94	70.11 125	84.91 42	79.74 82	82.12 94	87.31 132	85.70 164	92.03 169	86.67 181

PatchT		76.42 180	77.81 166	74.80 191	78.46 209	84.30 194	71.82 212	65.03 213	73.89 149	65.37 149	61.58 169	66.70 150	77.18 147	85.10 172	84.87 171	90.94 184	88.21 166

MVS-HIRNet		68.83 206	66.39 210	71.68 201	77.58 210	75.52 217	66.45 217	65.05 212	62.16 204	62.84 165	44.76 217	56.60 202	71.96 177	78.04 207	75.06 211	86.18 210	72.56 216

pmmvs-eth3d		74.32 197	71.96 203	77.08 174	77.33 211	82.71 201	78.41 197	76.02 169	66.65 187	65.98 144	54.23 201	49.02 219	73.14 174	82.37 192	82.69 188	91.61 174	86.05 184

new-patchmatchnet		63.80 211	63.31 213	64.37 211	76.49 212	75.99 216	63.73 219	70.99 189	57.27 214	43.08 217	45.86 214	43.80 221	45.13 217	73.20 214	70.68 217	86.80 206	76.34 214

FMVSNet5		75.50 192	76.07 182	74.83 190	76.16 213	81.19 208	81.34 180	70.21 193	73.20 158	61.59 178	58.97 187	68.33 148	68.50 187	85.87 158	85.85 162	91.18 182	79.11 209

PM-MVS		74.17 198	73.10 199	75.41 186	76.07 214	82.53 203	77.56 201	71.69 186	71.04 166	61.92 174	61.23 172	47.30 220	74.82 163	81.78 194	79.80 195	90.42 186	88.05 169

MIMVSNet		74.69 195	75.60 190	73.62 196	76.02 215	85.31 188	81.21 184	67.43 203	71.02 167	59.07 192	54.48 198	64.07 158	66.14 198	86.52 150	86.64 147	91.83 172	81.17 203

EU-MVSNet		69.98 205	72.30 202	67.28 208	75.67 216	79.39 213	73.12 210	69.94 195	63.59 201	42.80 218	62.93 165	56.71 201	55.07 209	79.13 205	78.55 201	87.06 205	85.82 186

WB-MVS		52.27 217	57.26 218	46.45 217	75.64 217	65.62 223	40.45 228	75.80 171	47.10 223	9.11 231	53.83 202	38.98 227	14.47 226	69.44 216	68.29 218	63.24 224	57.56 223

ET-MVSNet_ETH3D		84.65 97	85.58 91	83.56 114	74.99 218	92.62 104	90.29 72	80.38 119	82.16 86	73.01 114	83.41 45	71.10 133	87.05 63	87.77 129	90.17 85	95.62 50	91.82 131

PMVS	50.48 18	55.81 216	51.93 219	60.33 214	72.90 219	49.34 225	48.78 223	69.51 197	43.49 224	54.25 199	36.26 222	41.04 226	39.71 220	65.07 219	60.70 220	76.85 221	67.58 219
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
ambc			61.92 214		70.98 220	73.54 219	63.64 220		60.06 208	52.23 205	38.44 220	19.17 231	57.12 206	82.33 193	75.03 212	83.21 217	84.89 188

pmmvs3		61.89 213	61.74 215	62.06 213	64.30 221	70.83 221	64.22 218	52.14 222	48.78 222	44.47 216	41.67 219	41.70 225	63.03 201	76.06 211	76.02 207	84.18 215	77.14 213

MDA-MVSNet-bldmvs		66.22 209	64.49 212	68.24 206	61.67 222	82.11 207	70.07 214	76.16 167	59.14 212	47.94 212	54.35 200	35.82 228	67.33 193	64.94 220	75.68 208	86.30 209	79.36 208

new_pmnet		59.28 214	61.47 216	56.73 215	61.66 223	68.29 222	59.57 221	54.91 219	60.83 207	34.38 225	44.66 218	43.65 222	49.90 214	71.66 215	71.56 216	79.94 220	69.67 217

Gipuma		49.17 218	47.05 221	51.65 216	59.67 224	48.39 226	41.98 226	63.47 215	55.64 217	33.33 226	14.90 225	13.78 232	41.34 219	69.31 217	72.30 215	70.11 222	55.00 224
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MIMVSNet1		65.00 210	66.24 211	63.55 212	58.41 225	80.01 212	69.00 215	74.03 178	55.81 216	41.88 219	36.81 221	49.48 218	47.89 216	81.32 195	82.40 189	90.08 190	77.88 211

EMVS		30.49 223	25.44 225	36.39 220	51.47 226	29.89 230	20.17 231	54.00 221	26.49 226	12.02 230	13.94 228	8.84 233	34.37 222	25.04 227	34.37 226	46.29 229	39.53 227

E-PMN		31.40 221	26.80 224	36.78 219	51.39 227	29.96 229	20.20 230	54.17 220	25.93 227	12.75 229	14.73 226	8.58 234	34.10 223	27.36 226	37.83 225	48.07 228	43.18 226

PMMVS2		41.68 220	44.74 222	38.10 218	46.97 228	52.32 224	40.63 227	48.08 223	35.51 225	7.36 232	26.86 224	24.64 230	16.72 225	55.24 223	59.03 221	68.85 223	59.59 222

tmp_tt				32.73 222	43.96 229	21.15 231	26.71 229	8.99 227	65.67 194	51.39 207	56.01 196	42.64 223	11.76 227	56.60 222	50.81 223	53.55 227

MVE	30.17 19	30.88 222	33.52 223	27.80 224	23.78 230	39.16 228	18.69 232	46.90 224	21.88 228	15.39 228	14.37 227	7.31 235	24.41 224	41.63 225	56.22 222	37.64 230	54.07 225
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		41.78 219	48.10 220	34.42 221	10.74 231	19.78 232	44.64 225	17.73 226	59.83 209	38.67 223	35.82 223	54.41 209	34.94 221	62.87 221	43.13 224	59.81 225	60.82 221

GG-mvs-BLEND		57.56 215	82.61 114	28.34 223	0.22 232	90.10 130	79.37 193	0.14 229	79.56 112	0.40 233	71.25 116	83.40 62	0.30 230	86.27 153	83.87 179	89.59 192	83.83 192

testmvs		1.03 224	1.63 226	0.34 225	0.09 233	0.35 233	0.61 234	0.16 228	1.49 229	0.10 234	3.15 229	0.15 236	0.86 229	1.32 228	1.18 227	0.20 231	3.76 229

test123		0.87 225	1.40 227	0.25 226	0.03 234	0.25 234	0.35 235	0.08 230	1.21 230	0.05 235	2.84 230	0.03 237	0.89 228	0.43 229	1.16 228	0.13 232	3.87 228

uanet_test		0.00 226	0.00 228	0.00 227	0.00 235	0.00 235	0.00 236	0.00 231	0.00 231	0.00 236	0.00 231	0.00 238	0.00 231	0.00 230	0.00 229	0.00 233	0.00 230

sosnet-low-res		0.00 226	0.00 228	0.00 227	0.00 235	0.00 235	0.00 236	0.00 231	0.00 231	0.00 236	0.00 231	0.00 238	0.00 231	0.00 230	0.00 229	0.00 233	0.00 230

sosnet		0.00 226	0.00 228	0.00 227	0.00 235	0.00 235	0.00 236	0.00 231	0.00 231	0.00 236	0.00 231	0.00 238	0.00 231	0.00 230	0.00 229	0.00 233	0.00 230

RE-MVS-def										56.08 197

9.14												92.16 17

MTAPA										92.97 2		91.03 24

MTMP										93.14 1		90.21 31

Patchmatch-RL test							8.55 233

NP-MVS									87.47 55

Patchmtry						85.54 187	82.30 174	68.23 200		65.37 149

DeepMVS_CX						48.31 227	48.03 224	26.08 225	56.42 215	25.77 227	47.51 211	31.31 229	51.30 212	48.49 224		53.61 226	61.52 220

High-resolution multi-view benchmark