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.

SMA-MVS		97.53 7	97.93 7	97.07 10	99.21 1	99.02 9	98.08 19	96.25 11	96.36 12	93.57 15	96.56 14	99.27 5	96.78 16	97.91 4	97.43 4	98.51 26	98.94 12
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
APDe-MVS		97.79 5	97.96 6	97.60 2	99.20 2	99.10 6	98.88 2	96.68 2	96.81 7	94.64 6	97.84 3	98.02 11	97.24 3	97.74 8	97.02 15	98.97 5	99.16 6
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
DVP-MVS++		98.07 1	98.46 1	97.62 1	99.08 3	99.29 2	98.84 3	96.63 4	97.89 1	95.35 3	97.83 4	99.48 3	96.98 9	97.99 2	97.14 12	98.82 11	99.60 1

HPM-MVS++		97.22 11	97.40 12	97.01 11	99.08 3	98.55 25	98.19 14	96.48 7	96.02 19	93.28 20	96.26 18	98.71 8	96.76 17	97.30 17	96.25 39	98.30 54	98.68 18

ACMMP_NAP		96.93 16	97.27 16	96.53 23	99.06 5	98.95 10	98.24 13	96.06 15	95.66 22	90.96 33	95.63 25	97.71 16	96.53 20	97.66 11	96.68 21	98.30 54	98.61 23

DVP-MVS		97.93 3	98.23 3	97.58 3	99.05 6	99.31 1	98.64 6	96.62 5	97.56 2	95.08 5	96.61 13	99.64 1	97.32 1	97.91 4	97.31 7	98.77 15	99.26 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
PGM-MVS		96.16 25	96.33 29	95.95 26	99.04 7	98.63 20	98.32 12	92.76 42	93.42 50	90.49 38	96.30 17	95.31 42	96.71 18	96.46 40	96.02 48	98.38 45	98.19 44

APD-MVS		97.12 13	97.05 19	97.19 7	99.04 7	98.63 20	98.45 8	96.54 6	94.81 37	93.50 16	96.10 20	97.40 22	96.81 13	97.05 23	96.82 20	98.80 12	98.56 25
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
NCCC		96.75 19	96.67 25	96.85 16	99.03 9	98.44 34	98.15 16	96.28 10	96.32 13	92.39 26	92.16 36	97.55 20	96.68 19	97.32 15	96.65 23	98.55 25	98.26 41

CNVR-MVS		97.30 10	97.41 11	97.18 8	99.02 10	98.60 22	98.15 16	96.24 13	96.12 17	94.10 11	95.54 26	97.99 12	96.99 7	97.97 3	97.17 10	98.57 24	98.50 32

MSP-MVS		97.70 6	98.09 5	97.24 6	99.00 11	99.17 5	98.76 5	96.41 9	96.91 5	93.88 14	97.72 5	99.04 7	96.93 11	97.29 18	97.31 7	98.45 37	99.23 4
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
ACMMPR		96.92 17	96.96 20	96.87 15	98.99 12	98.78 12	98.38 10	95.52 24	96.57 10	92.81 24	96.06 21	95.90 37	97.07 5	96.60 37	96.34 35	98.46 34	98.42 36

HFP-MVS		97.11 14	97.19 17	97.00 12	98.97 13	98.73 13	98.37 11	95.69 21	96.60 9	93.28 20	96.87 8	96.64 29	97.27 2	96.64 35	96.33 36	98.44 38	98.56 25

SteuartSystems-ACMMP		97.10 15	97.49 10	96.65 18	98.97 13	98.95 10	98.43 9	95.96 17	95.12 29	91.46 29	96.85 9	97.60 18	96.37 24	97.76 6	97.16 11	98.68 19	98.97 11
Skip Steuart: Steuart Systems R&D Blog.
SF-MVS		97.20 12	97.29 15	97.10 9	98.95 15	98.51 30	97.51 30	96.48 7	96.17 16	94.64 6	97.32 6	97.57 19	96.23 26	96.78 29	96.15 43	98.79 14	98.55 30

SED-MVS		97.98 2	98.36 2	97.54 4	98.94 16	99.29 2	98.81 4	96.64 3	97.14 3	95.16 4	97.96 2	99.61 2	96.92 12	98.00 1	97.24 9	98.75 17	99.25 3

X-MVS		96.07 27	96.33 29	95.77 29	98.94 16	98.66 15	97.94 24	95.41 30	95.12 29	88.03 55	93.00 34	96.06 33	95.85 29	96.65 34	96.35 32	98.47 32	98.48 33

SR-MVS					98.93 18			96.00 16				97.75 15

MP-MVS		96.56 21	96.72 24	96.37 24	98.93 18	98.48 31	98.04 20	95.55 23	94.32 41	90.95 35	95.88 23	97.02 26	96.29 25	96.77 30	96.01 49	98.47 32	98.56 25
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MCST-MVS		96.83 18	97.06 18	96.57 19	98.88 20	98.47 32	98.02 21	96.16 14	95.58 24	90.96 33	95.78 24	97.84 14	96.46 22	97.00 26	96.17 41	98.94 7	98.55 30

CP-MVS		96.68 20	96.59 27	96.77 17	98.85 21	98.58 23	98.18 15	95.51 26	95.34 26	92.94 23	95.21 29	96.25 31	96.79 15	96.44 42	95.77 51	98.35 46	98.56 25

DPE-MVS		97.83 4	98.13 4	97.48 5	98.83 22	99.19 4	98.99 1	96.70 1	96.05 18	94.39 9	98.30 1	99.47 4	97.02 6	97.75 7	97.02 15	98.98 3	99.10 9
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
mPP-MVS					98.76 23							95.49 40

CSCG		95.68 31	95.46 36	95.93 27	98.71 24	99.07 7	97.13 35	93.55 37	95.48 25	93.35 19	90.61 46	93.82 47	95.16 37	94.60 82	95.57 55	97.70 109	99.08 10

DeepC-MVS_fast	93.32 1	96.48 23	96.42 28	96.56 20	98.70 25	98.31 38	97.97 23	95.76 20	96.31 14	92.01 28	91.43 41	95.42 41	96.46 22	97.65 12	97.69 1	98.49 31	98.12 49
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
AdaColmap		95.02 38	93.71 50	96.54 22	98.51 26	97.76 59	96.69 40	95.94 19	93.72 49	93.50 16	89.01 54	90.53 66	96.49 21	94.51 85	93.76 86	98.07 80	96.69 100

train_agg		96.15 26	96.64 26	95.58 34	98.44 27	98.03 48	98.14 18	95.40 31	93.90 47	87.72 60	96.26 18	98.10 10	95.75 31	96.25 47	95.45 57	98.01 87	98.47 34

CDPH-MVS		94.80 42	95.50 34	93.98 47	98.34 28	98.06 47	97.41 31	93.23 39	92.81 55	82.98 102	92.51 35	94.82 43	93.53 61	96.08 50	96.30 38	98.42 40	97.94 56

TPM-MVS					98.33 29	97.85 54	97.06 36			89.97 41	93.26 32	97.16 25	93.12 67			97.79 99	95.95 128
Ray Leroy Khuboni and Hongjun Xu: Textureless Resilient Propagation Matching in Multiple View Stereosis (TPM-MVS). SATNAC 2025
MSLP-MVS++		96.05 28	95.63 32	96.55 21	98.33 29	98.17 44	96.94 37	94.61 34	94.70 39	94.37 10	89.20 53	95.96 36	96.81 13	95.57 58	97.33 6	98.24 63	98.47 34

ACMMP		95.54 32	95.49 35	95.61 32	98.27 31	98.53 27	97.16 34	94.86 32	94.88 35	89.34 44	95.36 28	91.74 55	95.50 35	95.51 59	94.16 77	98.50 29	98.22 42
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+	90.56 5	95.06 37	94.56 45	95.65 31	98.11 32	98.15 45	97.19 33	91.59 52	95.11 31	93.23 22	81.99 104	94.71 44	95.43 36	96.48 39	96.88 19	98.35 46	98.63 20

3Dnovator	90.28 7	94.70 43	94.34 48	95.11 36	98.06 33	98.21 42	96.89 38	91.03 57	94.72 38	91.45 30	82.87 94	93.10 50	94.61 42	96.24 48	97.08 14	98.63 22	98.16 45

MVS_0304		96.54 22	97.36 14	95.60 33	98.03 34	99.07 7	98.02 21	92.24 45	95.87 20	92.54 25	96.41 15	96.08 32	94.03 52	97.69 9	97.47 3	98.73 18	98.90 13

PLC	90.69 4	94.32 47	92.99 58	95.87 28	97.91 35	96.49 95	95.95 51	94.12 35	94.94 33	94.09 12	85.90 72	90.77 63	95.58 33	94.52 84	93.32 100	97.55 118	95.00 150
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
EPNet		93.92 50	94.40 46	93.36 54	97.89 36	96.55 93	96.08 47	92.14 46	91.65 66	89.16 46	94.07 31	90.17 70	87.78 128	95.24 64	94.97 66	97.09 137	98.15 46
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CPTT-MVS		95.54 32	95.07 38	96.10 25	97.88 37	97.98 50	97.92 25	94.86 32	94.56 40	92.16 27	91.01 42	95.71 38	96.97 10	94.56 83	93.50 93	96.81 159	98.14 47

QAPM		94.13 49	94.33 49	93.90 48	97.82 38	98.37 37	96.47 42	90.89 58	92.73 57	85.63 83	85.35 76	93.87 46	94.17 49	95.71 57	95.90 50	98.40 42	98.42 36

DeepC-MVS	92.10 3	95.22 35	94.77 42	95.75 30	97.77 39	98.54 26	97.63 29	95.96 17	95.07 32	88.85 49	85.35 76	91.85 54	95.82 30	96.88 28	97.10 13	98.44 38	98.63 20
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
OpenMVS	88.18 11	92.51 63	91.61 80	93.55 53	97.74 40	98.02 49	95.66 53	90.46 61	89.14 105	86.50 70	75.80 139	90.38 69	92.69 72	94.99 67	95.30 59	98.27 58	97.63 67

TSAR-MVS + ACMM		96.19 24	97.39 13	94.78 38	97.70 41	98.41 35	97.72 28	95.49 27	96.47 11	86.66 69	96.35 16	97.85 13	93.99 53	97.19 21	96.37 31	97.12 135	99.13 7

MAR-MVS		92.71 62	92.63 63	92.79 67	97.70 41	97.15 79	93.75 93	87.98 99	90.71 72	85.76 81	86.28 70	86.38 79	94.35 47	94.95 68	95.49 56	97.22 128	97.44 75
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
PHI-MVS		95.86 29	96.93 23	94.61 41	97.60 43	98.65 19	96.49 41	93.13 40	94.07 44	87.91 59	97.12 7	97.17 24	93.90 56	96.46 40	96.93 18	98.64 21	98.10 51

DPM-MVS		95.07 36	94.84 41	95.34 35	97.44 44	97.49 68	97.76 27	95.52 24	94.88 35	88.92 48	87.25 62	96.44 30	94.41 44	95.78 55	96.11 45	97.99 89	95.95 128

SD-MVS		97.35 8	97.73 8	96.90 14	97.35 45	98.66 15	97.85 26	96.25 11	96.86 6	94.54 8	96.75 11	99.13 6	96.99 7	96.94 27	96.58 24	98.39 44	99.20 5
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
MVS_111021_HR		94.84 40	95.91 31	93.60 52	97.35 45	98.46 33	95.08 62	91.19 54	94.18 43	85.97 75	95.38 27	92.56 52	93.61 60	96.61 36	96.25 39	98.40 42	97.92 58

TSAR-MVS + MP.		97.31 9	97.64 9	96.92 13	97.28 47	98.56 24	98.61 7	95.48 28	96.72 8	94.03 13	96.73 12	98.29 9	97.15 4	97.61 13	96.42 26	98.96 6	99.13 7
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
CANet		94.85 39	94.92 40	94.78 38	97.25 48	98.52 29	97.20 32	91.81 49	93.25 52	91.06 32	86.29 69	94.46 45	92.99 68	97.02 25	96.68 21	98.34 48	98.20 43

OMC-MVS		94.49 46	94.36 47	94.64 40	97.17 49	97.73 61	95.49 55	92.25 44	96.18 15	90.34 39	88.51 56	92.88 51	94.90 41	94.92 70	94.17 76	97.69 111	96.15 120

MVS_111021_LR		94.84 40	95.57 33	94.00 45	97.11 50	97.72 63	94.88 66	91.16 55	95.24 28	88.74 50	96.03 22	91.52 59	94.33 48	95.96 52	95.01 65	97.79 99	97.49 74

CNLPA		93.69 53	92.50 65	95.06 37	97.11 50	97.36 70	93.88 90	93.30 38	95.64 23	93.44 18	80.32 114	90.73 64	94.99 40	93.58 104	93.33 98	97.67 113	96.57 105

LS3D		91.97 69	90.98 90	93.12 60	97.03 52	97.09 82	95.33 60	95.59 22	92.47 58	79.26 122	81.60 107	82.77 100	94.39 46	94.28 88	94.23 75	97.14 134	94.45 156

TAPA-MVS	90.35 6	93.69 53	93.52 51	93.90 48	96.89 53	97.62 65	96.15 45	91.67 51	94.94 33	85.97 75	87.72 61	91.96 53	94.40 45	93.76 102	93.06 110	98.30 54	95.58 138
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
DELS-MVS		93.71 52	93.47 52	94.00 45	96.82 54	98.39 36	96.80 39	91.07 56	89.51 102	89.94 42	83.80 86	89.29 72	90.95 92	97.32 15	97.65 2	98.42 40	98.32 39
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
EPNet_dtu		88.32 121	90.61 92	85.64 150	96.79 55	92.27 179	92.03 126	90.31 62	89.05 106	65.44 194	89.43 51	85.90 84	74.22 203	92.76 118	92.09 129	95.02 192	92.76 176
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MSDG		90.42 98	88.25 118	92.94 65	96.67 56	94.41 123	93.96 85	92.91 41	89.59 100	86.26 72	76.74 132	80.92 118	90.43 100	92.60 123	92.08 130	97.44 123	91.41 182

SPE-MVS-test		94.63 44	95.28 37	93.88 50	96.56 57	98.67 14	93.41 102	89.31 81	94.27 42	89.64 43	90.84 44	91.64 57	95.58 33	97.04 24	96.17 41	98.77 15	98.32 39

DeepPCF-MVS	92.65 2	95.50 34	96.96 20	93.79 51	96.44 58	98.21 42	93.51 100	94.08 36	96.94 4	89.29 45	93.08 33	96.77 28	93.82 57	97.68 10	97.40 5	95.59 182	98.65 19

PCF-MVS	90.19 8	92.98 57	92.07 73	94.04 44	96.39 59	97.87 51	96.03 48	95.47 29	87.16 120	85.09 94	84.81 80	93.21 49	93.46 63	91.98 136	91.98 133	97.78 101	97.51 73
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
CS-MVS		94.53 45	94.73 43	94.31 43	96.30 60	98.53 27	94.98 63	89.24 83	93.37 51	90.24 40	88.96 55	89.76 71	96.09 28	97.48 14	96.42 26	98.99 2	98.59 24

OPM-MVS		91.08 83	89.34 104	93.11 61	96.18 61	96.13 104	96.39 43	92.39 43	82.97 159	81.74 105	82.55 100	80.20 122	93.97 55	94.62 80	93.23 101	98.00 88	95.73 134
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
PVSNet_BlendedMVS		92.80 58	92.44 67	93.23 55	96.02 62	97.83 56	93.74 94	90.58 59	91.86 63	90.69 36	85.87 74	82.04 110	90.01 102	96.39 43	95.26 60	98.34 48	97.81 63

PVSNet_Blended		92.80 58	92.44 67	93.23 55	96.02 62	97.83 56	93.74 94	90.58 59	91.86 63	90.69 36	85.87 74	82.04 110	90.01 102	96.39 43	95.26 60	98.34 48	97.81 63

XVS					95.68 64	98.66 15	94.96 64			88.03 55		96.06 33				98.46 34

X-MVStestdata					95.68 64	98.66 15	94.96 64			88.03 55		96.06 33				98.46 34

HQP-MVS		92.39 65	92.49 66	92.29 75	95.65 66	95.94 108	95.64 54	92.12 47	92.46 59	79.65 120	91.97 38	82.68 101	92.92 71	93.47 109	92.77 116	97.74 105	98.12 49

HyFIR lowres test		87.87 123	86.42 140	89.57 108	95.56 67	96.99 85	92.37 116	84.15 142	86.64 125	77.17 129	57.65 210	83.97 91	91.08 90	92.09 134	92.44 121	97.09 137	95.16 147

ACMM	88.76 10	91.70 77	90.43 93	93.19 57	95.56 67	95.14 114	93.35 104	91.48 53	92.26 60	87.12 65	84.02 84	79.34 125	93.99 53	94.07 94	92.68 117	97.62 117	95.50 139
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
COLMAP_ROB	84.39 15	87.61 125	86.03 145	89.46 109	95.54 69	94.48 120	91.77 130	90.14 66	87.16 120	75.50 134	73.41 154	76.86 144	87.33 135	90.05 169	89.76 179	96.48 163	90.46 191
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
LGP-MVS_train		91.83 73	92.04 74	91.58 84	95.46 70	96.18 103	95.97 50	89.85 68	90.45 80	77.76 125	91.92 39	80.07 123	92.34 77	94.27 89	93.47 94	98.11 76	97.90 61

CHOSEN 1792x2688		88.57 118	87.82 125	89.44 110	95.46 70	96.89 88	93.74 94	85.87 124	89.63 99	77.42 128	61.38 204	83.31 95	88.80 123	93.44 110	93.16 106	95.37 187	96.95 94

PVSNet_Blended_VisFu		91.92 71	92.39 69	91.36 92	95.45 72	97.85 54	92.25 119	89.54 77	88.53 112	87.47 62	79.82 116	90.53 66	85.47 154	96.31 46	95.16 63	97.99 89	98.56 25

PatchMatch-RL		90.30 99	88.93 111	91.89 79	95.41 73	95.68 110	90.94 132	88.67 90	89.80 97	86.95 67	85.90 72	72.51 153	92.46 74	93.56 106	92.18 126	96.93 151	92.89 175

TSAR-MVS + COLMAP		92.39 65	92.31 70	92.47 71	95.35 74	96.46 97	96.13 46	92.04 48	95.33 27	80.11 118	94.95 30	77.35 141	94.05 51	94.49 86	93.08 108	97.15 132	94.53 154

test2506		90.93 87	89.20 107	92.95 64	94.97 75	98.30 39	94.53 68	90.25 64	89.91 94	88.39 54	83.23 90	64.17 196	90.69 95	96.75 32	96.10 46	98.87 8	95.97 127

ECVR-MVS		90.77 91	89.27 105	92.52 69	94.97 75	98.30 39	94.53 68	90.25 64	89.91 94	85.80 80	73.64 149	74.31 150	90.69 95	96.75 32	96.10 46	98.87 8	95.91 131

test1111		90.47 97	89.10 109	92.07 78	94.92 77	98.30 39	94.17 81	90.30 63	89.56 101	83.92 98	73.25 156	73.66 151	90.26 101	96.77 30	96.14 44	98.87 8	96.04 124

ACMP	89.13 9	92.03 68	91.70 79	92.41 73	94.92 77	96.44 99	93.95 86	89.96 67	91.81 65	85.48 88	90.97 43	79.12 126	92.42 75	93.28 115	92.55 120	97.76 103	97.74 66
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
UA-Net		90.81 88	92.58 64	88.74 117	94.87 79	97.44 69	92.61 113	88.22 95	82.35 163	78.93 123	85.20 78	95.61 39	79.56 189	96.52 38	96.57 25	98.23 64	94.37 157

IB-MVS	85.10 14	87.98 122	87.97 123	87.99 126	94.55 80	96.86 89	84.52 198	88.21 96	86.48 130	88.54 53	74.41 147	77.74 138	74.10 205	89.65 175	92.85 114	98.06 82	97.80 65
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
CANet_DTU		90.74 93	92.93 61	88.19 123	94.36 81	96.61 91	94.34 74	84.66 135	90.66 73	68.75 173	90.41 47	86.89 77	89.78 104	95.46 60	94.87 67	97.25 127	95.62 136

sasdasda		93.08 55	93.09 55	93.07 62	94.24 82	97.86 52	95.45 57	87.86 105	94.00 45	87.47 62	88.32 57	82.37 105	95.13 38	93.96 99	96.41 29	98.27 58	98.73 14

canonicalmvs		93.08 55	93.09 55	93.07 62	94.24 82	97.86 52	95.45 57	87.86 105	94.00 45	87.47 62	88.32 57	82.37 105	95.13 38	93.96 99	96.41 29	98.27 58	98.73 14

MGCFI-Net		92.75 60	92.98 59	92.48 70	94.18 84	97.77 58	95.28 61	87.77 107	93.88 48	85.28 92	88.19 59	82.17 109	94.14 50	93.86 101	96.32 37	98.20 67	98.69 17

UGNet		91.52 79	93.41 53	89.32 111	94.13 85	97.15 79	91.83 129	89.01 84	90.62 75	85.86 79	86.83 63	91.73 56	77.40 194	94.68 79	94.43 72	97.71 107	98.40 38
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
thres600view7		89.28 115	87.47 134	91.39 89	94.12 86	97.25 74	93.94 88	89.74 71	85.62 137	80.63 116	75.24 143	69.33 168	91.66 85	94.92 70	93.23 101	98.27 58	96.72 99

IS_MVSNet		91.87 72	93.35 54	90.14 105	94.09 87	97.73 61	93.09 107	88.12 97	88.71 109	79.98 119	84.49 81	90.63 65	87.49 133	97.07 22	96.96 17	98.07 80	97.88 62

TSAR-MVS + GP.		95.86 29	96.95 22	94.60 42	94.07 88	98.11 46	96.30 44	91.76 50	95.67 21	91.07 31	96.82 10	97.69 17	95.71 32	95.96 52	95.75 52	98.68 19	98.63 20

thres400		89.40 111	87.58 131	91.53 86	94.06 89	97.21 77	94.19 80	89.83 69	85.69 134	81.08 112	75.50 141	69.76 166	91.80 81	94.79 77	93.51 90	98.20 67	96.60 103

ETV-MVS		93.80 51	94.57 44	92.91 66	93.98 90	97.50 67	93.62 97	88.70 88	91.95 62	87.57 61	90.21 48	90.79 62	94.56 43	97.20 20	96.35 32	99.02 1	97.98 53

ACMH	85.51 13	87.31 128	86.59 138	88.14 124	93.96 91	94.51 119	89.00 168	87.99 98	81.58 166	70.15 163	78.41 123	71.78 158	90.60 98	91.30 145	91.99 132	97.17 131	96.58 104
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MS-PatchMatch		87.63 124	87.61 129	87.65 131	93.95 92	94.09 129	92.60 114	81.52 175	86.64 125	76.41 132	73.46 153	85.94 83	85.01 158	92.23 132	90.00 173	96.43 166	90.93 188

thres200		89.49 110	87.72 126	91.55 85	93.95 92	97.25 74	94.34 74	89.74 71	85.66 135	81.18 109	76.12 138	70.19 165	91.80 81	94.92 70	93.51 90	98.27 58	96.40 110

CLD-MVS		92.50 64	91.96 75	93.13 59	93.93 94	96.24 101	95.69 52	88.77 87	92.92 53	89.01 47	88.19 59	81.74 113	93.13 66	93.63 103	93.08 108	98.23 64	97.91 60
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
thres100view900		89.36 112	87.61 129	91.39 89	93.90 95	96.86 89	94.35 73	89.66 75	85.87 132	81.15 110	76.46 134	70.38 162	91.17 88	94.09 93	93.43 96	98.13 73	96.16 119

tfpn200view9		89.55 109	87.86 124	91.53 86	93.90 95	97.26 72	94.31 76	89.74 71	85.87 132	81.15 110	76.46 134	70.38 162	91.76 83	94.92 70	93.51 90	98.28 57	96.61 102

EIA-MVS		92.72 61	92.96 60	92.44 72	93.86 97	97.76 59	93.13 106	88.65 91	89.78 98	86.68 68	86.69 66	87.57 73	93.74 58	96.07 51	95.32 58	98.58 23	97.53 72

CHOSEN 280x420		90.77 91	92.14 72	89.17 113	93.86 97	92.81 167	93.16 105	80.22 183	90.21 85	84.67 97	89.89 50	91.38 60	90.57 99	94.94 69	92.11 128	92.52 203	93.65 167

FC-MVSNet-train		90.55 95	90.19 96	90.97 95	93.78 99	95.16 113	92.11 124	88.85 85	87.64 117	83.38 101	84.36 83	78.41 132	89.53 106	94.69 78	93.15 107	98.15 71	97.92 58

FA-MVS(training)		90.79 90	91.33 83	90.17 103	93.76 100	97.22 76	92.74 111	77.79 193	90.60 77	88.03 55	78.80 120	87.41 74	91.00 91	95.40 62	93.43 96	97.70 109	96.46 107

Vis-MVSNet (Re-imp)		90.54 96	92.76 62	87.94 127	93.73 101	96.94 87	92.17 122	87.91 100	88.77 108	76.12 133	83.68 87	90.80 61	79.49 190	96.34 45	96.35 32	98.21 66	96.46 107

baseline1		90.81 88	90.29 94	91.42 88	93.67 102	95.86 109	93.94 88	89.69 74	89.29 104	82.85 103	82.91 93	80.30 121	89.60 105	95.05 66	94.79 69	98.80 12	93.82 165

EPP-MVSNet		92.13 67	93.06 57	91.05 94	93.66 103	97.30 71	92.18 120	87.90 101	90.24 84	83.63 99	86.14 71	90.52 68	90.76 94	94.82 75	94.38 73	98.18 70	97.98 53

EC-MVSNet		94.19 48	95.05 39	93.18 58	93.56 104	97.65 64	95.34 59	86.37 120	92.05 61	88.71 51	89.91 49	93.32 48	96.14 27	97.29 18	96.42 26	98.98 3	98.70 16

ACMH+	85.75 12	87.19 130	86.02 146	88.56 119	93.42 105	94.41 123	89.91 152	87.66 111	83.45 156	72.25 150	76.42 136	71.99 157	90.78 93	89.86 170	90.94 147	97.32 124	95.11 149

casdiffmvs_mvg		91.94 70	91.25 85	92.75 68	93.41 106	97.19 78	95.48 56	89.77 70	89.86 96	86.41 71	81.02 111	82.23 108	92.93 69	95.44 61	95.61 54	98.51 26	97.40 77
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
viewmanbaseed2359cas		91.57 78	91.09 88	92.12 76	93.36 107	97.26 72	94.02 83	89.62 76	90.50 79	84.95 96	82.00 103	81.36 114	92.69 72	94.47 87	95.04 64	98.09 78	97.00 91

MVS_Test		91.81 74	92.19 71	91.37 91	93.24 108	96.95 86	94.43 70	86.25 121	91.45 69	83.45 100	86.31 68	85.15 87	92.93 69	93.99 95	94.71 70	97.92 93	96.77 98

MVSTER		91.73 75	91.61 80	91.86 80	93.18 109	94.56 117	94.37 72	87.90 101	90.16 88	88.69 52	89.23 52	81.28 116	88.92 121	95.75 56	93.95 83	98.12 74	96.37 111

Anonymous202405211			88.00 121		93.16 110	96.38 100	93.58 98	89.34 80	87.92 116		65.04 193	83.03 97	92.07 78	92.67 120	93.33 98	96.96 146	97.63 67

casdiffmvs		91.72 76	91.16 87	92.38 74	93.16 110	97.15 79	93.95 86	89.49 78	91.58 68	86.03 74	80.75 113	80.95 117	93.16 65	95.25 63	95.22 62	98.50 29	97.23 83
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
tttt0517		91.01 86	91.71 78	90.19 102	92.98 112	97.07 83	91.96 128	87.63 112	90.61 76	81.42 107	86.76 65	82.26 107	89.23 113	94.86 74	93.03 112	97.90 94	97.36 78

Effi-MVS+		89.79 106	89.83 101	89.74 107	92.98 112	96.45 98	93.48 101	84.24 140	87.62 118	76.45 131	81.76 105	77.56 140	93.48 62	94.61 81	93.59 89	97.82 98	97.22 85

RPSCF		89.68 107	89.24 106	90.20 101	92.97 114	92.93 163	92.30 117	87.69 109	90.44 81	85.12 93	91.68 40	85.84 85	90.69 95	87.34 192	86.07 194	92.46 204	90.37 192

TDRefinement		84.97 156	83.39 171	86.81 139	92.97 114	94.12 128	92.18 120	87.77 107	82.78 160	71.31 155	68.43 174	68.07 174	81.10 185	89.70 174	89.03 186	95.55 184	91.62 180

thisisatest0530		91.04 85	91.74 77	90.21 100	92.93 116	97.00 84	92.06 125	87.63 112	90.74 71	81.51 106	86.81 64	82.48 102	89.23 113	94.81 76	93.03 112	97.90 94	97.33 80

DCV-MVSNet		91.24 81	91.26 84	91.22 93	92.84 117	93.44 145	93.82 91	86.75 117	91.33 70	85.61 84	84.00 85	85.46 86	91.27 86	92.91 117	93.62 88	97.02 142	98.05 52

baseline		91.19 82	91.89 76	90.38 96	92.76 118	95.04 115	93.55 99	84.54 138	92.92 53	85.71 82	86.68 67	86.96 76	89.28 112	92.00 135	92.62 119	96.46 164	96.99 92

EPMVS		85.77 144	86.24 142	85.23 155	92.76 118	93.78 135	89.91 152	73.60 206	90.19 86	74.22 138	82.18 102	78.06 134	87.55 132	85.61 201	85.38 199	93.32 197	88.48 204

GeoE		89.29 114	88.68 113	89.99 106	92.75 120	96.03 107	93.07 109	83.79 147	86.98 122	81.34 108	74.72 144	78.92 127	91.22 87	93.31 113	93.21 104	97.78 101	97.60 71

diffmvs		91.37 80	91.09 88	91.70 83	92.71 121	96.47 96	94.03 82	88.78 86	92.74 56	85.43 90	83.63 88	80.37 120	91.76 83	93.39 111	93.78 85	97.50 120	97.23 83
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
DI_MVS_pp		91.05 84	90.15 97	92.11 77	92.67 122	96.61 91	96.03 48	88.44 93	90.25 83	85.92 77	73.73 148	84.89 89	91.92 79	94.17 92	94.07 81	97.68 112	97.31 81

viewmambaseed2359dif		90.70 94	89.81 102	91.73 82	92.66 123	96.10 105	93.97 84	88.69 89	89.92 93	86.12 73	80.79 112	80.73 119	91.92 79	91.13 150	92.81 115	97.06 139	97.20 86

Anonymous20231211		89.82 105	88.18 119	91.74 81	92.52 124	96.09 106	93.38 103	89.30 82	88.95 107	85.90 78	64.55 198	84.39 90	92.41 76	92.24 131	93.06 110	96.93 151	97.95 55

tpmrst		83.72 174	83.45 168	84.03 171	92.21 125	91.66 191	88.74 171	73.58 207	88.14 114	72.67 147	77.37 128	72.11 156	86.34 144	82.94 209	82.05 208	90.63 213	89.86 196

CostFormer		86.78 133	86.05 144	87.62 133	92.15 126	93.20 154	91.55 131	75.83 198	88.11 115	85.29 91	81.76 105	76.22 146	87.80 127	84.45 204	85.21 200	93.12 198	93.42 170

Vis-MVSNet		89.36 112	91.49 82	86.88 138	92.10 127	97.60 66	92.16 123	85.89 123	84.21 148	75.20 135	82.58 98	87.13 75	77.40 194	95.90 54	95.63 53	98.51 26	97.36 78
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
IterMVS-LS		88.60 117	88.45 114	88.78 116	92.02 128	92.44 177	92.00 127	83.57 151	86.52 128	78.90 124	78.61 122	81.34 115	89.12 116	90.68 158	93.18 105	97.10 136	96.35 112
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PatchmatchNet		85.70 145	86.65 137	84.60 162	91.79 129	93.40 146	89.27 161	73.62 205	90.19 86	72.63 148	82.74 97	81.93 112	87.64 130	84.99 202	84.29 204	92.64 202	89.00 199
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpm cat1		84.13 167	81.99 187	86.63 142	91.74 130	91.50 194	90.68 134	75.69 199	86.12 131	85.44 89	72.39 159	70.72 160	85.16 156	80.89 213	81.56 209	91.07 211	90.71 189

USDC		86.73 134	85.96 148	87.63 132	91.64 131	93.97 131	92.76 110	84.58 137	88.19 113	70.67 160	80.10 115	67.86 175	89.43 107	91.81 137	89.77 178	96.69 161	90.05 195

SCA		86.25 136	87.52 132	84.77 159	91.59 132	93.90 132	89.11 165	73.25 210	90.38 82	72.84 146	83.26 89	83.79 93	88.49 125	86.07 199	85.56 197	93.33 196	89.67 197

gg-mvs-nofinetune		81.83 193	83.58 166	79.80 200	91.57 133	96.54 94	93.79 92	68.80 217	62.71 221	43.01 226	55.28 213	85.06 88	83.65 168	96.13 49	94.86 68	97.98 92	94.46 155

Fast-Effi-MVS+		88.56 119	87.99 122	89.22 112	91.56 134	95.21 112	92.29 118	82.69 158	86.82 123	77.73 126	76.24 137	73.39 152	93.36 64	94.22 91	93.64 87	97.65 114	96.43 109

CMPMVS	61.19 17	79.86 200	77.46 208	82.66 189	91.54 135	91.82 189	83.25 201	81.57 174	70.51 213	68.64 174	59.89 209	66.77 181	79.63 188	84.00 207	84.30 203	91.34 209	84.89 212
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
ADS-MVSNet		84.08 168	84.95 156	83.05 184	91.53 136	91.75 190	88.16 175	70.70 214	89.96 92	69.51 168	78.83 119	76.97 143	86.29 145	84.08 206	84.60 202	92.13 207	88.48 204

test-LLR		86.88 131	88.28 116	85.24 154	91.22 137	92.07 183	87.41 181	83.62 149	84.58 141	69.33 169	83.00 91	82.79 98	84.24 162	92.26 129	89.81 176	95.64 180	93.44 168

test0.0.03 1		85.58 147	87.69 128	83.11 181	91.22 137	92.54 174	85.60 197	83.62 149	85.66 135	67.84 180	82.79 96	79.70 124	73.51 207	91.15 149	90.79 149	96.88 155	91.23 185

baseline2		88.97 116	89.50 103	88.36 120	91.14 139	95.30 111	90.13 146	85.17 132	87.24 119	80.80 114	84.46 82	78.44 131	85.60 151	93.54 107	91.87 134	97.31 125	95.66 135

Effi-MVS+-dtu		87.51 126	88.13 120	86.77 140	91.10 140	94.90 116	90.91 133	82.67 159	83.47 155	71.55 152	81.11 110	77.04 142	89.41 108	92.65 122	91.68 140	95.00 193	96.09 122

RPMNet		84.82 158	85.90 149	83.56 176	91.10 140	92.10 181	88.73 172	71.11 213	84.75 139	68.79 172	73.56 150	77.62 139	85.33 155	90.08 168	89.43 182	96.32 167	93.77 166

CR-MVSNet		85.48 149	86.29 141	84.53 164	91.08 142	92.10 181	89.18 163	73.30 208	84.75 139	71.08 157	73.12 158	77.91 136	86.27 146	91.48 141	90.75 152	96.27 168	93.94 162

TinyColmap		84.04 169	82.01 186	86.42 144	90.87 143	91.84 188	88.89 170	84.07 144	82.11 165	69.89 165	71.08 163	60.81 209	89.04 117	90.52 160	89.19 184	95.76 174	88.50 203

tpm		83.16 180	83.64 165	82.60 190	90.75 144	91.05 197	88.49 173	73.99 203	82.36 162	67.08 186	78.10 124	68.79 169	84.17 164	85.95 200	85.96 195	91.09 210	93.23 172

dps		85.00 155	83.21 175	87.08 136	90.73 145	92.55 173	89.34 160	75.29 200	84.94 138	87.01 66	79.27 118	67.69 176	87.27 136	84.22 205	83.56 205	92.83 201	90.25 193

MDTV_nov1_ep13		86.64 135	87.50 133	85.65 149	90.73 145	93.69 139	89.96 150	78.03 192	89.48 103	76.85 130	84.92 79	82.42 104	86.14 148	86.85 196	86.15 193	92.17 205	88.97 200

CDS-MVSNet		88.34 120	88.71 112	87.90 128	90.70 147	94.54 118	92.38 115	86.02 122	80.37 172	79.42 121	79.30 117	83.43 94	82.04 177	93.39 111	94.01 82	96.86 157	95.93 130
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IterMVS-SCA-FT		85.44 151	86.71 136	83.97 172	90.59 148	90.84 200	89.73 156	78.34 189	84.07 152	66.40 189	77.27 130	78.66 129	83.06 170	91.20 146	90.10 171	95.72 177	94.78 151

IterMVS		85.25 153	86.49 139	83.80 173	90.42 149	90.77 203	90.02 148	78.04 191	84.10 150	66.27 190	77.28 129	78.41 132	83.01 171	90.88 152	89.72 180	95.04 191	94.24 158
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Fast-Effi-MVS+-dtu		86.25 136	87.70 127	84.56 163	90.37 150	93.70 138	90.54 136	78.14 190	83.50 154	65.37 195	81.59 108	75.83 148	86.09 150	91.70 139	91.70 138	96.88 155	95.84 132

dmvs_re		87.31 128	86.10 143	88.74 117	89.84 151	94.28 126	92.66 112	89.41 79	82.61 161	74.69 136	74.69 145	69.47 167	87.78 128	92.38 127	93.23 101	98.03 84	96.02 126

FC-MVSNet-test		86.15 139	89.10 109	82.71 188	89.83 152	93.18 155	87.88 178	84.69 134	86.54 127	62.18 204	82.39 101	83.31 95	74.18 204	92.52 125	91.86 135	97.50 120	93.88 164

GA-MVS		85.08 154	85.65 152	84.42 165	89.77 153	94.25 127	89.26 162	84.62 136	81.19 169	62.25 203	75.72 140	68.44 172	84.14 165	93.57 105	91.68 140	96.49 162	94.71 153

PMMVS		89.88 104	91.19 86	88.35 121	89.73 154	91.97 187	90.62 135	81.92 170	90.57 78	80.58 117	92.16 36	86.85 78	91.17 88	92.31 128	91.35 144	96.11 170	93.11 174

tfpnnormal		83.80 173	81.26 195	86.77 140	89.60 155	93.26 153	89.72 157	87.60 114	72.78 206	70.44 161	60.53 207	61.15 208	85.55 152	92.72 119	91.44 142	97.71 107	96.92 95

CVMVSNet		83.83 172	85.53 153	81.85 195	89.60 155	90.92 198	87.81 179	83.21 155	80.11 175	60.16 208	76.47 133	78.57 130	76.79 196	89.76 171	90.13 166	93.51 195	92.75 177

testgi		81.94 192	84.09 163	79.43 201	89.53 157	90.83 201	82.49 204	81.75 173	80.59 170	59.46 210	82.82 95	65.75 185	67.97 209	90.10 167	89.52 181	95.39 186	89.03 198

UniMVSNet_ETH3D		84.57 159	81.40 193	88.28 122	89.34 158	94.38 125	90.33 138	86.50 119	74.74 204	77.52 127	59.90 208	62.04 204	88.78 124	88.82 185	92.65 118	97.22 128	97.24 82

LTVRE_ROB	81.71 16	82.44 190	81.84 188	83.13 180	89.01 159	92.99 160	88.90 169	82.32 165	66.26 217	54.02 218	74.68 146	59.62 215	88.87 122	90.71 157	92.02 131	95.68 179	96.62 101
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
TAMVS		84.94 157	84.95 156	84.93 158	88.82 160	93.18 155	88.44 174	81.28 177	77.16 191	73.76 142	75.43 142	76.57 145	82.04 177	90.59 159	90.79 149	95.22 189	90.94 187

EG-PatchMatch MVS		81.70 195	81.31 194	82.15 193	88.75 161	93.81 134	87.14 184	78.89 188	71.57 209	64.12 200	61.20 206	68.46 171	76.73 198	91.48 141	90.77 151	97.28 126	91.90 179

TransMVSNet (Re)		82.67 187	80.93 198	84.69 161	88.71 162	91.50 194	87.90 177	87.15 115	71.54 211	68.24 177	63.69 200	64.67 195	78.51 193	91.65 140	90.73 154	97.64 115	92.73 178

FMVSNet3		90.19 102	90.06 100	90.34 97	88.69 163	93.85 133	94.58 67	85.78 125	90.03 89	85.56 85	77.38 125	86.13 80	89.22 115	93.29 114	94.36 74	98.20 67	95.40 144

GBi-Net		90.21 100	90.11 98	90.32 98	88.66 164	93.65 141	94.25 77	85.78 125	90.03 89	85.56 85	77.38 125	86.13 80	89.38 109	93.97 96	94.16 77	98.31 51	95.47 140

test1		90.21 100	90.11 98	90.32 98	88.66 164	93.65 141	94.25 77	85.78 125	90.03 89	85.56 85	77.38 125	86.13 80	89.38 109	93.97 96	94.16 77	98.31 51	95.47 140

FMVSNet2		89.61 108	89.14 108	90.16 104	88.66 164	93.65 141	94.25 77	85.44 129	88.57 111	84.96 95	73.53 151	83.82 92	89.38 109	94.23 90	94.68 71	98.31 51	95.47 140

PatchT		83.86 171	85.51 154	81.94 194	88.41 167	91.56 193	78.79 212	71.57 212	84.08 151	71.08 157	70.62 164	76.13 147	86.27 146	91.48 141	90.75 152	95.52 185	93.94 162

UniMVSNet (Re)		86.22 138	85.46 155	87.11 135	88.34 168	94.42 122	89.65 158	87.10 116	84.39 145	74.61 137	70.41 168	68.10 173	85.10 157	91.17 148	91.79 136	97.84 97	97.94 56

NR-MVSNet		85.46 150	84.54 160	86.52 143	88.33 169	93.78 135	90.45 137	87.87 103	84.40 143	71.61 151	70.59 165	62.09 203	82.79 173	91.75 138	91.75 137	98.10 77	97.44 75

UniMVSNet_NR-MVSNet		86.80 132	85.86 150	87.89 129	88.17 170	94.07 130	90.15 144	88.51 92	84.20 149	73.45 143	72.38 160	70.30 164	88.95 119	90.25 163	92.21 125	98.12 74	97.62 69

thisisatest0515		85.70 145	87.00 135	84.19 168	88.16 171	93.67 140	84.20 200	84.14 143	83.39 157	72.91 145	76.79 131	74.75 149	78.82 192	92.57 124	91.26 145	96.94 148	96.56 106

pm-mvs1		84.55 160	83.46 167	85.82 146	88.16 171	93.39 147	89.05 167	85.36 131	74.03 205	72.43 149	65.08 192	71.11 159	82.30 176	93.48 108	91.70 138	97.64 115	95.43 143

gm-plane-assit		77.65 205	78.50 203	76.66 205	87.96 173	85.43 216	64.70 222	74.50 201	64.15 219	51.26 221	61.32 205	58.17 217	84.11 166	95.16 65	93.83 84	97.45 122	91.41 182

test-mter		86.09 142	88.38 115	83.43 178	87.89 174	92.61 171	86.89 186	77.11 196	84.30 146	68.62 175	82.57 99	82.45 103	84.34 161	92.40 126	90.11 170	95.74 175	94.21 160

pmmvs4		86.00 143	84.28 162	88.00 125	87.80 175	92.01 186	89.94 151	84.91 133	86.79 124	80.98 113	73.41 154	66.34 184	88.12 126	89.31 178	88.90 187	96.24 169	93.20 173

TESTMET0.1,1		86.11 141	88.28 116	83.59 175	87.80 175	92.07 183	87.41 181	77.12 195	84.58 141	69.33 169	83.00 91	82.79 98	84.24 162	92.26 129	89.81 176	95.64 180	93.44 168

DU-MVS		86.12 140	84.81 158	87.66 130	87.77 177	93.78 135	90.15 144	87.87 103	84.40 143	73.45 143	70.59 165	64.82 193	88.95 119	90.14 164	92.33 122	97.76 103	97.62 69

Baseline_NR-MVSNet		85.28 152	83.42 170	87.46 134	87.77 177	90.80 202	89.90 154	87.69 109	83.93 153	74.16 139	64.72 196	66.43 183	87.48 134	90.14 164	90.83 148	97.73 106	97.11 89

SixPastTwentyTwo		83.12 182	83.44 169	82.74 187	87.71 179	93.11 159	82.30 205	82.33 164	79.24 180	64.33 198	78.77 121	62.75 199	84.11 166	88.11 187	87.89 189	95.70 178	94.21 160

TranMVSNet+NR-MVSNet		85.57 148	84.41 161	86.92 137	87.67 180	93.34 148	90.31 140	88.43 94	83.07 158	70.11 164	69.99 171	65.28 188	86.96 138	89.73 172	92.27 123	98.06 82	97.17 88

WR-MVS		83.14 181	83.38 172	82.87 186	87.55 181	93.29 150	86.36 191	84.21 141	80.05 176	66.41 188	66.91 180	66.92 180	75.66 201	88.96 183	90.56 157	97.05 140	96.96 93

v8		84.45 165	83.30 174	85.80 147	87.53 182	92.95 161	90.31 140	82.46 163	80.46 171	71.43 153	66.99 179	67.16 178	86.14 148	89.26 179	90.22 165	96.94 148	96.06 123

WR-MVS_H		82.86 186	82.66 180	83.10 182	87.44 183	93.33 149	85.71 196	83.20 156	77.36 190	68.20 178	66.37 183	65.23 189	76.05 200	89.35 176	90.13 166	97.99 89	96.89 96

v148		83.61 175	82.10 184	85.37 151	87.34 184	92.94 162	87.48 180	85.72 128	78.92 181	73.87 141	65.71 189	64.69 194	81.78 181	87.82 188	89.35 183	96.01 171	95.26 146

v10		84.18 166	83.17 176	85.37 151	87.34 184	92.68 169	90.32 139	81.33 176	79.93 179	69.23 171	66.33 184	65.74 186	87.03 137	90.84 153	90.38 160	96.97 144	96.29 116

v2v482		84.51 161	83.05 177	86.20 145	87.25 186	93.28 151	90.22 142	85.40 130	79.94 178	69.78 166	67.74 176	65.15 190	87.57 131	89.12 181	90.55 158	96.97 144	95.60 137

CP-MVSNet		83.11 183	82.15 183	84.23 167	87.20 187	92.70 168	86.42 190	83.53 152	77.83 188	67.67 181	66.89 182	60.53 211	82.47 174	89.23 180	90.65 156	98.08 79	97.20 86

v1144		84.03 170	82.88 178	85.37 151	87.17 188	93.15 158	90.18 143	83.31 154	78.83 182	67.85 179	65.99 186	64.99 191	86.79 140	90.75 155	90.33 162	96.90 153	96.15 120

V42		84.48 163	83.36 173	85.79 148	87.14 189	93.28 151	90.03 147	83.98 145	80.30 173	71.20 156	66.90 181	67.17 177	85.55 152	89.35 176	90.27 163	96.82 158	96.27 117

pmmvs5		83.37 178	82.68 179	84.18 169	87.13 190	93.18 155	86.74 187	82.08 168	76.48 195	67.28 184	71.26 162	62.70 200	84.71 159	90.77 154	90.12 169	97.15 132	94.24 158

FMVSNet1		87.33 127	86.00 147	88.89 114	87.13 190	92.83 166	93.08 108	84.46 139	81.35 168	82.20 104	66.33 184	77.96 135	88.96 118	93.97 96	94.16 77	97.54 119	95.38 145

PS-CasMVS		82.53 188	81.54 191	83.68 174	87.08 192	92.54 174	86.20 192	83.46 153	76.46 196	65.73 193	65.71 189	59.41 216	81.61 182	89.06 182	90.55 158	98.03 84	97.07 90

our_test_3					86.93 193	89.77 204	81.61 206

PEN-MVS		82.49 189	81.58 190	83.56 176	86.93 193	92.05 185	86.71 188	83.84 146	76.94 193	64.68 197	67.24 177	60.11 212	81.17 184	87.78 189	90.70 155	98.02 86	96.21 118

v1192		83.56 176	82.35 181	84.98 156	86.84 195	92.84 164	90.01 149	82.70 157	78.54 183	66.48 187	64.88 194	62.91 198	86.91 139	90.72 156	90.25 164	96.94 148	96.32 114

v144192		83.48 177	82.23 182	84.94 157	86.65 196	92.84 164	89.63 159	82.48 162	77.87 187	67.36 183	65.33 191	63.50 197	86.51 142	89.72 173	89.99 174	97.03 141	96.35 112

DTE-MVSNet		81.76 194	81.04 196	82.60 190	86.63 197	91.48 196	85.97 194	83.70 148	76.45 197	62.44 202	67.16 178	59.98 213	78.98 191	87.15 193	89.93 175	97.88 96	95.12 148

pmnet_mix02		80.14 199	80.21 200	80.06 198	86.61 198	89.66 205	80.40 209	82.20 167	82.29 164	61.35 205	71.52 161	66.67 182	76.75 197	82.55 210	80.18 213	93.05 199	88.62 201

v1921920		83.30 179	82.09 185	84.70 160	86.59 199	92.67 170	89.82 155	82.23 166	78.32 184	65.76 192	64.64 197	62.35 201	86.78 141	90.34 162	90.02 172	97.02 142	96.31 115

v1240		82.88 185	81.66 189	84.29 166	86.46 200	92.52 176	89.06 166	81.82 172	77.16 191	65.09 196	64.17 199	61.50 206	86.36 143	90.12 166	90.13 166	96.95 147	96.04 124

anonymousdsp		84.51 161	85.85 151	82.95 185	86.30 201	93.51 144	85.77 195	80.38 182	78.25 186	63.42 201	73.51 152	72.20 155	84.64 160	93.21 116	92.16 127	97.19 130	98.14 47

pmmvs6		80.90 196	78.77 202	83.38 179	85.84 202	91.61 192	86.01 193	82.54 161	64.17 218	70.43 162	54.14 217	67.06 179	80.73 186	90.50 161	89.17 185	94.74 194	94.75 152

MVS-HIRNet		78.16 203	77.57 207	78.83 202	85.83 203	87.76 210	76.67 213	70.22 215	75.82 201	67.39 182	55.61 212	70.52 161	81.96 179	86.67 197	85.06 201	90.93 212	81.58 215

test20.03		76.41 207	78.49 204	73.98 208	85.64 204	87.50 211	75.89 214	80.71 181	70.84 212	51.07 222	68.06 175	61.40 207	54.99 218	88.28 186	87.20 191	95.58 183	86.15 208

v7n		82.25 191	81.54 191	83.07 183	85.55 205	92.58 172	86.68 189	81.10 180	76.54 194	65.97 191	62.91 201	60.56 210	82.36 175	91.07 151	90.35 161	96.77 160	96.80 97

N_pmnet		77.55 206	76.68 209	78.56 203	85.43 206	87.30 213	78.84 211	81.88 171	78.30 185	60.61 206	61.46 203	62.15 202	74.03 206	82.04 211	80.69 212	90.59 214	84.81 213

Anonymous20231206		78.09 204	78.11 205	78.07 204	85.19 207	89.17 206	80.99 207	81.24 179	75.46 202	58.25 212	54.78 216	59.90 214	66.73 212	88.94 184	88.26 188	96.01 171	90.25 193

MDTV_nov1_ep13_2view		80.43 197	80.94 197	79.84 199	84.82 208	90.87 199	84.23 199	73.80 204	80.28 174	64.33 198	70.05 170	68.77 170	79.67 187	84.83 203	83.50 206	92.17 205	88.25 206

FPMVS		69.87 213	67.10 216	73.10 210	84.09 209	78.35 221	79.40 210	76.41 197	71.92 207	57.71 213	54.06 218	50.04 222	56.72 216	71.19 219	68.70 219	84.25 219	75.43 219

EU-MVSNet		78.43 202	80.25 199	76.30 206	83.81 210	87.27 214	80.99 207	79.52 185	76.01 198	54.12 217	70.44 167	64.87 192	67.40 211	86.23 198	85.54 198	91.95 208	91.41 182

FMVSNet5		84.47 164	84.72 159	84.18 169	83.30 211	88.43 208	88.09 176	79.42 186	84.25 147	74.14 140	73.15 157	78.74 128	83.65 168	91.19 147	91.19 146	96.46 164	86.07 209

WB-MVS		60.76 216	66.86 217	53.64 216	82.24 212	72.70 222	48.70 228	82.04 169	63.91 220	12.91 231	64.77 195	49.00 225	22.74 226	75.95 217	75.36 217	73.22 225	66.33 223

MIMVSNet		82.97 184	84.00 164	81.77 196	82.23 213	92.25 180	87.40 183	72.73 211	81.48 167	69.55 167	68.79 173	72.42 154	81.82 180	92.23 132	92.25 124	96.89 154	88.61 202

PM-MVS		80.29 198	79.30 201	81.45 197	81.91 214	88.23 209	82.61 203	79.01 187	79.99 177	67.15 185	69.07 172	51.39 221	82.92 172	87.55 191	85.59 196	95.08 190	93.28 171

pmmvs-eth3d		79.78 201	77.58 206	82.34 192	81.57 215	87.46 212	82.92 202	81.28 177	75.33 203	71.34 154	61.88 202	52.41 220	81.59 183	87.56 190	86.90 192	95.36 188	91.48 181

new-patchmatchnet		72.32 210	71.09 213	73.74 209	81.17 216	84.86 217	72.21 219	77.48 194	68.32 215	54.89 216	55.10 214	49.31 224	63.68 215	79.30 215	76.46 216	93.03 200	84.32 214

ET-MVSNet_ETH3D		89.93 103	90.84 91	88.87 115	79.60 217	96.19 102	94.43 70	86.56 118	90.63 74	80.75 115	90.71 45	77.78 137	93.73 59	91.36 144	93.45 95	98.15 71	95.77 133

PMVS	56.77 18	61.27 215	58.64 219	64.35 214	75.66 218	54.60 226	53.62 225	74.23 202	53.69 223	58.37 211	44.27 222	49.38 223	44.16 222	69.51 221	65.35 221	80.07 221	73.66 220
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
new_pmnet		72.29 211	73.25 211	71.16 213	75.35 219	81.38 218	73.72 218	69.27 216	75.97 199	49.84 223	56.27 211	56.12 219	69.08 208	81.73 212	80.86 211	89.72 217	80.44 217

ambc			67.96 215		73.69 220	79.79 220	73.82 217		71.61 208	59.80 209	46.00 220	20.79 230	66.15 213	86.92 195	80.11 214	89.13 218	90.50 190

pmmvs3		71.13 212	71.06 214	71.21 212	73.54 221	80.19 219	71.69 220	64.86 219	62.04 222	52.10 219	54.92 215	48.00 226	75.03 202	83.75 208	83.24 207	90.04 216	85.27 210

MDA-MVSNet-bldmvs		73.81 208	72.56 212	75.28 207	72.52 222	88.87 207	74.95 216	82.67 159	71.57 209	55.02 215	65.96 187	42.84 228	76.11 199	70.61 220	81.47 210	90.38 215	86.59 207

tmp_tt				50.24 219	68.55 223	46.86 228	48.90 227	18.28 226	86.51 129	68.32 176	70.19 169	65.33 187	26.69 225	74.37 218	66.80 220	70.72 226

Gipuma		58.52 217	56.17 220	61.27 215	67.14 224	58.06 225	52.16 226	68.40 218	69.00 214	45.02 225	22.79 224	20.57 231	55.11 217	76.27 216	79.33 215	79.80 222	67.16 222
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MIMVSNet1		73.19 209	73.70 210	72.60 211	65.42 225	86.69 215	75.56 215	79.65 184	67.87 216	55.30 214	45.24 221	56.41 218	63.79 214	86.98 194	87.66 190	95.85 173	85.04 211

PMMVS2		53.68 219	55.72 221	51.30 217	58.84 226	67.02 224	54.23 224	60.97 222	47.50 224	19.42 228	34.81 223	31.97 229	30.88 224	65.84 222	69.99 218	83.47 220	72.92 221

EMVS		39.04 222	34.32 224	44.54 221	58.25 227	39.35 230	27.61 230	62.55 221	35.99 225	16.40 230	20.04 227	14.77 232	44.80 220	33.12 226	44.10 225	57.61 228	52.89 226

E-PMN		40.00 220	35.74 223	44.98 220	57.69 228	39.15 231	28.05 229	62.70 220	35.52 226	17.78 229	20.90 225	14.36 233	44.47 221	35.89 225	47.86 224	59.15 227	56.47 225

MVE	39.81 19	39.52 221	41.58 222	37.11 222	33.93 229	49.06 227	26.45 231	54.22 223	29.46 227	24.15 227	20.77 226	10.60 234	34.42 223	51.12 224	65.27 222	49.49 229	64.81 224
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		58.10 218	64.61 218	50.51 218	28.26 230	41.71 229	61.28 223	32.07 225	75.92 200	52.04 220	47.94 219	61.83 205	51.80 219	79.83 214	63.95 223	77.60 223	81.05 216

testmvs		4.35 223	6.54 225	1.79 224	0.60 231	1.82 232	3.06 233	0.95 227	7.22 228	0.88 233	12.38 228	1.25 235	3.87 228	6.09 227	5.58 226	1.40 230	11.42 228

GG-mvs-BLEND		62.84 214	90.21 95	30.91 223	0.57 232	94.45 121	86.99 185	0.34 229	88.71 109	0.98 232	81.55 109	91.58 58	0.86 229	92.66 121	91.43 143	95.73 176	91.11 186

test123		3.48 224	5.31 226	1.34 225	0.20 233	1.52 233	2.17 234	0.58 228	6.13 229	0.31 234	9.85 229	0.31 236	3.90 227	2.65 228	5.28 227	0.87 231	11.46 227

uanet_test		0.00 225	0.00 227	0.00 226	0.00 234	0.00 234	0.00 235	0.00 230	0.00 230	0.00 235	0.00 230	0.00 237	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	0.00 234	0.00 234	0.00 235	0.00 230	0.00 230	0.00 235	0.00 230	0.00 237	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	0.00 234	0.00 234	0.00 235	0.00 230	0.00 230	0.00 235	0.00 230	0.00 237	0.00 230	0.00 229	0.00 228	0.00 232	0.00 229

RE-MVS-def										60.19 207

9.14												97.28 23

MTAPA										95.36 2		97.46 21

MTMP										95.70 1		96.90 27

Patchmatch-RL test							18.47 232

NP-MVS									91.63 67

Patchmtry						92.39 178	89.18 163	73.30 208		71.08 157

DeepMVS_CX						71.82 223	68.37 221	48.05 224	77.38 189	46.88 224	65.77 188	47.03 227	67.48 210	64.27 223		76.89 224	76.72 218

High-resolution multi-view benchmark