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 87	98.07 80	96.69 101

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	82.98 103	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	95.95 129
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	81.99 105	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 96	95.95 51	94.12 35	94.94 33	94.09 12	85.90 72	90.77 63	95.58 33	94.52 84	93.32 101	97.55 119	95.00 151
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 94	96.08 47	92.14 46	91.65 67	89.16 46	94.07 31	90.17 70	87.78 129	95.24 64	94.97 66	97.09 138	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 94	96.81 160	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	89.14 106	86.50 70	75.80 140	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	97.12 136	99.13 7

MAR-MVS		92.71 62	92.63 63	92.79 67	97.70 41	97.15 79	93.75 94	87.98 100	90.71 73	85.76 81	86.28 70	86.38 79	94.35 47	94.95 68	95.49 56	97.22 129	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	95.95 129

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	96.15 121

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 91	93.30 38	95.64 23	93.44 18	80.32 115	90.73 64	94.99 40	93.58 105	93.33 99	97.67 113	96.57 106

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	79.26 123	81.60 108	82.77 100	94.39 46	94.28 88	94.23 75	97.14 135	94.45 157

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	93.76 103	93.06 111	98.30 54	95.58 139
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	89.51 103	89.94 42	83.80 86	89.29 72	90.95 93	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		88.32 122	90.61 93	85.64 151	96.79 55	92.27 180	92.03 127	90.31 62	89.05 107	65.44 195	89.43 51	85.90 84	74.22 204	92.76 119	92.09 130	95.02 193	92.76 177
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MSDG		90.42 99	88.25 119	92.94 65	96.67 56	94.41 124	93.96 86	92.91 41	89.59 101	86.26 72	76.74 133	80.92 118	90.43 101	92.60 124	92.08 131	97.44 124	91.41 183

SPE-MVS-test		94.63 44	95.28 37	93.88 50	96.56 57	98.67 14	93.41 103	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	93.51 101	94.08 36	96.94 4	89.29 45	93.08 33	96.77 28	93.82 57	97.68 10	97.40 5	95.59 183	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	87.16 121	85.09 94	84.81 80	93.21 49	93.46 63	91.98 137	91.98 134	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 84	89.34 105	93.11 61	96.18 61	96.13 105	96.39 43	92.39 43	82.97 160	81.74 106	82.55 100	80.20 122	93.97 55	94.62 80	93.23 102	98.00 88	95.73 135
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 95	90.58 59	91.86 64	90.69 36	85.87 74	82.04 110	90.01 103	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 95	90.58 59	91.86 64	90.69 36	85.87 74	82.04 110	90.01 103	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	95.94 109	95.64 54	92.12 47	92.46 59	79.65 121	91.97 38	82.68 101	92.92 71	93.47 110	92.77 117	97.74 105	98.12 49

HyFIR lowres test		87.87 124	86.42 141	89.57 109	95.56 67	96.99 85	92.37 117	84.15 143	86.64 126	77.17 130	57.65 211	83.97 91	91.08 91	92.09 135	92.44 122	97.09 138	95.16 148

ACMM	88.76 10	91.70 77	90.43 94	93.19 57	95.56 67	95.14 115	93.35 105	91.48 53	92.26 60	87.12 65	84.02 84	79.34 126	93.99 53	94.07 94	92.68 118	97.62 117	95.50 140
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
COLMAP_ROB	84.39 15	87.61 126	86.03 146	89.46 110	95.54 69	94.48 121	91.77 131	90.14 66	87.16 121	75.50 135	73.41 155	76.86 145	87.33 136	90.05 170	89.76 180	96.48 164	90.46 192
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 85	95.46 70	96.18 104	95.97 50	89.85 68	90.45 81	77.76 126	91.92 39	80.07 123	92.34 77	94.27 89	93.47 95	98.11 76	97.90 61

CHOSEN 1792x2688		88.57 119	87.82 126	89.44 111	95.46 70	96.89 88	93.74 95	85.87 125	89.63 100	77.42 129	61.38 205	83.31 95	88.80 124	93.44 111	93.16 107	95.37 188	96.95 95

PVSNet_Blended_VisFu		91.92 71	92.39 69	91.36 93	95.45 72	97.85 54	92.25 120	89.54 77	88.53 113	87.47 62	79.82 117	90.53 66	85.47 155	96.31 46	95.16 63	97.99 89	98.56 25

PatchMatch-RL		90.30 100	88.93 112	91.89 79	95.41 73	95.68 111	90.94 133	88.67 91	89.80 98	86.95 67	85.90 72	72.51 154	92.46 74	93.56 107	92.18 127	96.93 152	92.89 176

TSAR-MVS + COLMAP		92.39 65	92.31 70	92.47 71	95.35 74	96.46 98	96.13 46	92.04 48	95.33 27	80.11 119	94.95 30	77.35 142	94.05 51	94.49 86	93.08 109	97.15 133	94.53 155

test2506		90.93 88	89.20 108	92.95 64	94.97 75	98.30 39	94.53 68	90.25 64	89.91 95	88.39 54	83.23 90	64.17 197	90.69 96	96.75 32	96.10 46	98.87 8	95.97 128

ECVR-MVS		90.77 92	89.27 106	92.52 69	94.97 75	98.30 39	94.53 68	90.25 64	89.91 95	85.80 80	73.64 150	74.31 151	90.69 96	96.75 32	96.10 46	98.87 8	95.91 132

test1111		90.47 98	89.10 110	92.07 78	94.92 77	98.30 39	94.17 81	90.30 63	89.56 102	83.92 99	73.25 157	73.66 152	90.26 102	96.77 30	96.14 44	98.87 8	96.04 125

ACMP	89.13 9	92.03 68	91.70 79	92.41 73	94.92 77	96.44 100	93.95 87	89.96 67	91.81 66	85.48 88	90.97 43	79.12 127	92.42 75	93.28 116	92.55 121	97.76 103	97.74 66
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
UA-Net		90.81 89	92.58 64	88.74 118	94.87 79	97.44 69	92.61 114	88.22 96	82.35 164	78.93 124	85.20 78	95.61 39	79.56 190	96.52 38	96.57 25	98.23 64	94.37 158

IB-MVS	85.10 14	87.98 123	87.97 124	87.99 127	94.55 80	96.86 89	84.52 199	88.21 97	86.48 131	88.54 53	74.41 148	77.74 139	74.10 206	89.65 176	92.85 115	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 94	92.93 61	88.19 124	94.36 81	96.61 91	94.34 74	84.66 136	90.66 74	68.75 174	90.41 47	86.89 77	89.78 105	95.46 60	94.87 67	97.25 128	95.62 137

sasdasda		93.08 55	93.09 55	93.07 62	94.24 82	97.86 52	95.45 57	87.86 106	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	87.86 106	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	87.77 108	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	89.32 112	94.13 85	97.15 79	91.83 130	89.01 84	90.62 76	85.86 79	86.83 63	91.73 56	77.40 195	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		89.28 116	87.47 135	91.39 90	94.12 86	97.25 74	93.94 89	89.74 71	85.62 138	80.63 117	75.24 144	69.33 169	91.66 86	94.92 70	93.23 102	98.27 58	96.72 100

IS_MVSNet		91.87 72	93.35 54	90.14 106	94.09 87	97.73 61	93.09 108	88.12 98	88.71 110	79.98 120	84.49 81	90.63 65	87.49 134	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		89.40 112	87.58 132	91.53 87	94.06 89	97.21 77	94.19 80	89.83 69	85.69 135	81.08 113	75.50 142	69.76 167	91.80 82	94.79 77	93.51 91	98.20 67	96.60 104

ETV-MVS		93.80 51	94.57 44	92.91 66	93.98 90	97.50 67	93.62 98	88.70 89	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	87.31 129	86.59 139	88.14 125	93.96 91	94.51 120	89.00 169	87.99 99	81.58 167	70.15 164	78.41 124	71.78 159	90.60 99	91.30 146	91.99 133	97.17 132	96.58 105
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MS-PatchMatch		87.63 125	87.61 130	87.65 132	93.95 92	94.09 130	92.60 115	81.52 176	86.64 126	76.41 133	73.46 154	85.94 83	85.01 159	92.23 133	90.00 174	96.43 167	90.93 189

thres200		89.49 111	87.72 127	91.55 86	93.95 92	97.25 74	94.34 74	89.74 71	85.66 136	81.18 110	76.12 139	70.19 166	91.80 82	94.92 70	93.51 91	98.27 58	96.40 111

CLD-MVS		92.50 64	91.96 75	93.13 59	93.93 94	96.24 102	95.69 52	88.77 88	92.92 53	89.01 47	88.19 59	81.74 113	93.13 66	93.63 104	93.08 109	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		89.36 113	87.61 130	91.39 90	93.90 95	96.86 89	94.35 73	89.66 75	85.87 133	81.15 111	76.46 135	70.38 163	91.17 89	94.09 93	93.43 97	98.13 73	96.16 120

tfpn200view9		89.55 110	87.86 125	91.53 87	93.90 95	97.26 72	94.31 76	89.74 71	85.87 133	81.15 111	76.46 135	70.38 163	91.76 84	94.92 70	93.51 91	98.28 57	96.61 103

EIA-MVS		92.72 61	92.96 60	92.44 72	93.86 97	97.76 59	93.13 107	88.65 92	89.78 99	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 92	92.14 72	89.17 114	93.86 97	92.81 168	93.16 106	80.22 184	90.21 86	84.67 98	89.89 50	91.38 60	90.57 100	94.94 69	92.11 129	92.52 204	93.65 168

FC-MVSNet-train		90.55 96	90.19 97	90.97 96	93.78 99	95.16 114	92.11 125	88.85 86	87.64 118	83.38 102	84.36 83	78.41 133	89.53 107	94.69 78	93.15 108	98.15 71	97.92 58

FA-MVS(training)		90.79 91	91.33 83	90.17 104	93.76 100	97.22 76	92.74 112	77.79 194	90.60 78	88.03 55	78.80 121	87.41 74	91.00 92	95.40 62	93.43 97	97.70 109	96.46 108

Vis-MVSNet (Re-imp)		90.54 97	92.76 62	87.94 128	93.73 101	96.94 87	92.17 123	87.91 101	88.77 109	76.12 134	83.68 87	90.80 61	79.49 191	96.34 45	96.35 32	98.21 66	96.46 108

baseline1		90.81 89	90.29 95	91.42 89	93.67 102	95.86 110	93.94 89	89.69 74	89.29 105	82.85 104	82.91 93	80.30 121	89.60 106	95.05 66	94.79 69	98.80 12	93.82 166

EPP-MVSNet		92.13 67	93.06 57	91.05 95	93.66 103	97.30 71	92.18 121	87.90 102	90.24 85	83.63 100	86.14 71	90.52 68	90.76 95	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	86.37 121	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	87.19 131	86.02 147	88.56 120	93.42 105	94.41 124	89.91 153	87.66 112	83.45 157	72.25 151	76.42 137	71.99 158	90.78 94	89.86 171	90.94 148	97.32 125	95.11 150

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 97	86.41 71	81.02 112	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 84	89.62 76	90.50 80	84.95 97	82.00 104	81.36 114	92.69 72	94.47 87	95.04 64	98.09 78	97.00 92

MVS_Test		91.81 74	92.19 71	91.37 92	93.24 108	96.95 86	94.43 70	86.25 122	91.45 70	83.45 101	86.31 68	85.15 87	92.93 69	93.99 95	94.71 70	97.92 93	96.77 99

MVSTER		91.73 75	91.61 80	91.86 80	93.18 109	94.56 118	94.37 72	87.90 102	90.16 89	88.69 52	89.23 52	81.28 116	88.92 122	95.75 56	93.95 83	98.12 74	96.37 112

Anonymous202405211			88.00 122		93.16 110	96.38 101	93.58 99	89.34 80	87.92 117		65.04 194	83.03 97	92.07 78	92.67 121	93.33 99	96.96 147	97.63 67

casdiffmvs		91.72 76	91.16 87	92.38 74	93.16 110	97.15 79	93.95 87	89.49 78	91.58 69	86.03 74	80.75 114	80.95 117	93.16 65	95.25 63	95.22 62	98.50 29	97.23 84
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 87	91.71 78	90.19 103	92.98 112	97.07 83	91.96 129	87.63 113	90.61 77	81.42 108	86.76 65	82.26 107	89.23 114	94.86 74	93.03 113	97.90 94	97.36 78

Effi-MVS+		89.79 107	89.83 102	89.74 108	92.98 112	96.45 99	93.48 102	84.24 141	87.62 119	76.45 132	81.76 106	77.56 141	93.48 62	94.61 81	93.59 90	97.82 98	97.22 86

RPSCF		89.68 108	89.24 107	90.20 102	92.97 114	92.93 164	92.30 118	87.69 110	90.44 82	85.12 93	91.68 40	85.84 85	90.69 96	87.34 193	86.07 195	92.46 205	90.37 193

TDRefinement		84.97 157	83.39 172	86.81 140	92.97 114	94.12 129	92.18 121	87.77 108	82.78 161	71.31 156	68.43 175	68.07 175	81.10 186	89.70 175	89.03 187	95.55 185	91.62 181

thisisatest0530		91.04 86	91.74 77	90.21 101	92.93 116	97.00 84	92.06 126	87.63 113	90.74 72	81.51 107	86.81 64	82.48 102	89.23 114	94.81 76	93.03 113	97.90 94	97.33 80

DCV-MVSNet		91.24 81	91.26 84	91.22 94	92.84 117	93.44 146	93.82 92	86.75 118	91.33 71	85.61 84	84.00 85	85.46 86	91.27 87	92.91 118	93.62 89	97.02 143	98.05 52

baseline		91.19 83	91.89 76	90.38 97	92.76 118	95.04 116	93.55 100	84.54 139	92.92 53	85.71 82	86.68 67	86.96 76	89.28 113	92.00 136	92.62 120	96.46 165	96.99 93

EPMVS		85.77 145	86.24 143	85.23 156	92.76 118	93.78 136	89.91 153	73.60 207	90.19 87	74.22 139	82.18 103	78.06 135	87.55 133	85.61 202	85.38 200	93.32 198	88.48 205

GeoE		89.29 115	88.68 114	89.99 107	92.75 120	96.03 108	93.07 110	83.79 148	86.98 123	81.34 109	74.72 145	78.92 128	91.22 88	93.31 114	93.21 105	97.78 101	97.60 71

diffmvs		91.37 80	91.09 88	91.70 83	92.71 121	96.47 97	94.03 83	88.78 87	92.74 56	85.43 90	83.63 88	80.37 120	91.76 84	93.39 112	93.78 86	97.50 121	97.23 84
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
diffmvs_AUTHOR		91.22 82	90.82 92	91.68 84	92.69 122	96.56 93	94.05 82	88.87 85	91.87 63	85.08 95	82.26 102	80.04 124	91.84 81	93.80 102	93.93 84	97.56 118	97.26 82

DI_MVS_pp		91.05 85	90.15 98	92.11 77	92.67 123	96.61 91	96.03 48	88.44 94	90.25 84	85.92 77	73.73 149	84.89 89	91.92 79	94.17 92	94.07 81	97.68 112	97.31 81

viewmambaseed2359dif		90.70 95	89.81 103	91.73 82	92.66 124	96.10 106	93.97 85	88.69 90	89.92 94	86.12 73	80.79 113	80.73 119	91.92 79	91.13 151	92.81 116	97.06 140	97.20 87

Anonymous20231211		89.82 106	88.18 120	91.74 81	92.52 125	96.09 107	93.38 104	89.30 82	88.95 108	85.90 78	64.55 199	84.39 90	92.41 76	92.24 132	93.06 111	96.93 152	97.95 55

tpmrst		83.72 175	83.45 169	84.03 172	92.21 126	91.66 192	88.74 172	73.58 208	88.14 115	72.67 148	77.37 129	72.11 157	86.34 145	82.94 210	82.05 209	90.63 214	89.86 197

CostFormer		86.78 134	86.05 145	87.62 134	92.15 127	93.20 155	91.55 132	75.83 199	88.11 116	85.29 91	81.76 106	76.22 147	87.80 128	84.45 205	85.21 201	93.12 199	93.42 171

Vis-MVSNet		89.36 113	91.49 82	86.88 139	92.10 128	97.60 66	92.16 124	85.89 124	84.21 149	75.20 136	82.58 98	87.13 75	77.40 195	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		88.60 118	88.45 115	88.78 117	92.02 129	92.44 178	92.00 128	83.57 152	86.52 129	78.90 125	78.61 123	81.34 115	89.12 117	90.68 159	93.18 106	97.10 137	96.35 113
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PatchmatchNet		85.70 146	86.65 138	84.60 163	91.79 130	93.40 147	89.27 162	73.62 206	90.19 87	72.63 149	82.74 97	81.93 112	87.64 131	84.99 203	84.29 205	92.64 203	89.00 200
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpm cat1		84.13 168	81.99 188	86.63 143	91.74 131	91.50 195	90.68 135	75.69 200	86.12 132	85.44 89	72.39 160	70.72 161	85.16 157	80.89 214	81.56 210	91.07 212	90.71 190

USDC		86.73 135	85.96 149	87.63 133	91.64 132	93.97 132	92.76 111	84.58 138	88.19 114	70.67 161	80.10 116	67.86 176	89.43 108	91.81 138	89.77 179	96.69 162	90.05 196

SCA		86.25 137	87.52 133	84.77 160	91.59 133	93.90 133	89.11 166	73.25 211	90.38 83	72.84 147	83.26 89	83.79 93	88.49 126	86.07 200	85.56 198	93.33 197	89.67 198

gg-mvs-nofinetune		81.83 194	83.58 167	79.80 201	91.57 134	96.54 95	93.79 93	68.80 218	62.71 222	43.01 227	55.28 214	85.06 88	83.65 169	96.13 49	94.86 68	97.98 92	94.46 156

Fast-Effi-MVS+		88.56 120	87.99 123	89.22 113	91.56 135	95.21 113	92.29 119	82.69 159	86.82 124	77.73 127	76.24 138	73.39 153	93.36 64	94.22 91	93.64 88	97.65 114	96.43 110

CMPMVS	61.19 17	79.86 201	77.46 209	82.66 190	91.54 136	91.82 190	83.25 202	81.57 175	70.51 214	68.64 175	59.89 210	66.77 182	79.63 189	84.00 208	84.30 204	91.34 210	84.89 213
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
ADS-MVSNet		84.08 169	84.95 157	83.05 185	91.53 137	91.75 191	88.16 176	70.70 215	89.96 93	69.51 169	78.83 120	76.97 144	86.29 146	84.08 207	84.60 203	92.13 208	88.48 205

test-LLR		86.88 132	88.28 117	85.24 155	91.22 138	92.07 184	87.41 182	83.62 150	84.58 142	69.33 170	83.00 91	82.79 98	84.24 163	92.26 130	89.81 177	95.64 181	93.44 169

test0.0.03 1		85.58 148	87.69 129	83.11 182	91.22 138	92.54 175	85.60 198	83.62 150	85.66 136	67.84 181	82.79 96	79.70 125	73.51 208	91.15 150	90.79 150	96.88 156	91.23 186

baseline2		88.97 117	89.50 104	88.36 121	91.14 140	95.30 112	90.13 147	85.17 133	87.24 120	80.80 115	84.46 82	78.44 132	85.60 152	93.54 108	91.87 135	97.31 126	95.66 136

Effi-MVS+-dtu		87.51 127	88.13 121	86.77 141	91.10 141	94.90 117	90.91 134	82.67 160	83.47 156	71.55 153	81.11 111	77.04 143	89.41 109	92.65 123	91.68 141	95.00 194	96.09 123

RPMNet		84.82 159	85.90 150	83.56 177	91.10 141	92.10 182	88.73 173	71.11 214	84.75 140	68.79 173	73.56 151	77.62 140	85.33 156	90.08 169	89.43 183	96.32 168	93.77 167

CR-MVSNet		85.48 150	86.29 142	84.53 165	91.08 143	92.10 182	89.18 164	73.30 209	84.75 140	71.08 158	73.12 159	77.91 137	86.27 147	91.48 142	90.75 153	96.27 169	93.94 163

TinyColmap		84.04 170	82.01 187	86.42 145	90.87 144	91.84 189	88.89 171	84.07 145	82.11 166	69.89 166	71.08 164	60.81 210	89.04 118	90.52 161	89.19 185	95.76 175	88.50 204

tpm		83.16 181	83.64 166	82.60 191	90.75 145	91.05 198	88.49 174	73.99 204	82.36 163	67.08 187	78.10 125	68.79 170	84.17 165	85.95 201	85.96 196	91.09 211	93.23 173

dps		85.00 156	83.21 176	87.08 137	90.73 146	92.55 174	89.34 161	75.29 201	84.94 139	87.01 66	79.27 119	67.69 177	87.27 137	84.22 206	83.56 206	92.83 202	90.25 194

MDTV_nov1_ep13		86.64 136	87.50 134	85.65 150	90.73 146	93.69 140	89.96 151	78.03 193	89.48 104	76.85 131	84.92 79	82.42 104	86.14 149	86.85 197	86.15 194	92.17 206	88.97 201

CDS-MVSNet		88.34 121	88.71 113	87.90 129	90.70 148	94.54 119	92.38 116	86.02 123	80.37 173	79.42 122	79.30 118	83.43 94	82.04 178	93.39 112	94.01 82	96.86 158	95.93 131
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IterMVS-SCA-FT		85.44 152	86.71 137	83.97 173	90.59 149	90.84 201	89.73 157	78.34 190	84.07 153	66.40 190	77.27 131	78.66 130	83.06 171	91.20 147	90.10 172	95.72 178	94.78 152

IterMVS		85.25 154	86.49 140	83.80 174	90.42 150	90.77 204	90.02 149	78.04 192	84.10 151	66.27 191	77.28 130	78.41 133	83.01 172	90.88 153	89.72 181	95.04 192	94.24 159
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Fast-Effi-MVS+-dtu		86.25 137	87.70 128	84.56 164	90.37 151	93.70 139	90.54 137	78.14 191	83.50 155	65.37 196	81.59 109	75.83 149	86.09 151	91.70 140	91.70 139	96.88 156	95.84 133

dmvs_re		87.31 129	86.10 144	88.74 118	89.84 152	94.28 127	92.66 113	89.41 79	82.61 162	74.69 137	74.69 146	69.47 168	87.78 129	92.38 128	93.23 102	98.03 84	96.02 127

FC-MVSNet-test		86.15 140	89.10 110	82.71 189	89.83 153	93.18 156	87.88 179	84.69 135	86.54 128	62.18 205	82.39 101	83.31 95	74.18 205	92.52 126	91.86 136	97.50 121	93.88 165

GA-MVS		85.08 155	85.65 153	84.42 166	89.77 154	94.25 128	89.26 163	84.62 137	81.19 170	62.25 204	75.72 141	68.44 173	84.14 166	93.57 106	91.68 141	96.49 163	94.71 154

PMMVS		89.88 105	91.19 86	88.35 122	89.73 155	91.97 188	90.62 136	81.92 171	90.57 79	80.58 118	92.16 36	86.85 78	91.17 89	92.31 129	91.35 145	96.11 171	93.11 175

tfpnnormal		83.80 174	81.26 196	86.77 141	89.60 156	93.26 154	89.72 158	87.60 115	72.78 207	70.44 162	60.53 208	61.15 209	85.55 153	92.72 120	91.44 143	97.71 107	96.92 96

CVMVSNet		83.83 173	85.53 154	81.85 196	89.60 156	90.92 199	87.81 180	83.21 156	80.11 176	60.16 209	76.47 134	78.57 131	76.79 197	89.76 172	90.13 167	93.51 196	92.75 178

testgi		81.94 193	84.09 164	79.43 202	89.53 158	90.83 202	82.49 205	81.75 174	80.59 171	59.46 211	82.82 95	65.75 186	67.97 210	90.10 168	89.52 182	95.39 187	89.03 199

UniMVSNet_ETH3D		84.57 160	81.40 194	88.28 123	89.34 159	94.38 126	90.33 139	86.50 120	74.74 205	77.52 128	59.90 209	62.04 205	88.78 125	88.82 186	92.65 119	97.22 129	97.24 83

LTVRE_ROB	81.71 16	82.44 191	81.84 189	83.13 181	89.01 160	92.99 161	88.90 170	82.32 166	66.26 218	54.02 219	74.68 147	59.62 216	88.87 123	90.71 158	92.02 132	95.68 180	96.62 102
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		84.94 158	84.95 157	84.93 159	88.82 161	93.18 156	88.44 175	81.28 178	77.16 192	73.76 143	75.43 143	76.57 146	82.04 178	90.59 160	90.79 150	95.22 190	90.94 188

EG-PatchMatch MVS		81.70 196	81.31 195	82.15 194	88.75 162	93.81 135	87.14 185	78.89 189	71.57 210	64.12 201	61.20 207	68.46 172	76.73 199	91.48 142	90.77 152	97.28 127	91.90 180

TransMVSNet (Re)		82.67 188	80.93 199	84.69 162	88.71 163	91.50 195	87.90 178	87.15 116	71.54 212	68.24 178	63.69 201	64.67 196	78.51 194	91.65 141	90.73 155	97.64 115	92.73 179

FMVSNet3		90.19 103	90.06 101	90.34 98	88.69 164	93.85 134	94.58 67	85.78 126	90.03 90	85.56 85	77.38 126	86.13 80	89.22 116	93.29 115	94.36 74	98.20 67	95.40 145

GBi-Net		90.21 101	90.11 99	90.32 99	88.66 165	93.65 142	94.25 77	85.78 126	90.03 90	85.56 85	77.38 126	86.13 80	89.38 110	93.97 96	94.16 77	98.31 51	95.47 141

test1		90.21 101	90.11 99	90.32 99	88.66 165	93.65 142	94.25 77	85.78 126	90.03 90	85.56 85	77.38 126	86.13 80	89.38 110	93.97 96	94.16 77	98.31 51	95.47 141

FMVSNet2		89.61 109	89.14 109	90.16 105	88.66 165	93.65 142	94.25 77	85.44 130	88.57 112	84.96 96	73.53 152	83.82 92	89.38 110	94.23 90	94.68 71	98.31 51	95.47 141

PatchT		83.86 172	85.51 155	81.94 195	88.41 168	91.56 194	78.79 213	71.57 213	84.08 152	71.08 158	70.62 165	76.13 148	86.27 147	91.48 142	90.75 153	95.52 186	93.94 163

UniMVSNet (Re)		86.22 139	85.46 156	87.11 136	88.34 169	94.42 123	89.65 159	87.10 117	84.39 146	74.61 138	70.41 169	68.10 174	85.10 158	91.17 149	91.79 137	97.84 97	97.94 56

NR-MVSNet		85.46 151	84.54 161	86.52 144	88.33 170	93.78 136	90.45 138	87.87 104	84.40 144	71.61 152	70.59 166	62.09 204	82.79 174	91.75 139	91.75 138	98.10 77	97.44 75

UniMVSNet_NR-MVSNet		86.80 133	85.86 151	87.89 130	88.17 171	94.07 131	90.15 145	88.51 93	84.20 150	73.45 144	72.38 161	70.30 165	88.95 120	90.25 164	92.21 126	98.12 74	97.62 69

thisisatest0515		85.70 146	87.00 136	84.19 169	88.16 172	93.67 141	84.20 201	84.14 144	83.39 158	72.91 146	76.79 132	74.75 150	78.82 193	92.57 125	91.26 146	96.94 149	96.56 107

pm-mvs1		84.55 161	83.46 168	85.82 147	88.16 172	93.39 148	89.05 168	85.36 132	74.03 206	72.43 150	65.08 193	71.11 160	82.30 177	93.48 109	91.70 139	97.64 115	95.43 144

gm-plane-assit		77.65 206	78.50 204	76.66 206	87.96 174	85.43 217	64.70 223	74.50 202	64.15 220	51.26 222	61.32 206	58.17 218	84.11 167	95.16 65	93.83 85	97.45 123	91.41 183

test-mter		86.09 143	88.38 116	83.43 179	87.89 175	92.61 172	86.89 187	77.11 197	84.30 147	68.62 176	82.57 99	82.45 103	84.34 162	92.40 127	90.11 171	95.74 176	94.21 161

pmmvs4		86.00 144	84.28 163	88.00 126	87.80 176	92.01 187	89.94 152	84.91 134	86.79 125	80.98 114	73.41 155	66.34 185	88.12 127	89.31 179	88.90 188	96.24 170	93.20 174

TESTMET0.1,1		86.11 142	88.28 117	83.59 176	87.80 176	92.07 184	87.41 182	77.12 196	84.58 142	69.33 170	83.00 91	82.79 98	84.24 163	92.26 130	89.81 177	95.64 181	93.44 169

DU-MVS		86.12 141	84.81 159	87.66 131	87.77 178	93.78 136	90.15 145	87.87 104	84.40 144	73.45 144	70.59 166	64.82 194	88.95 120	90.14 165	92.33 123	97.76 103	97.62 69

Baseline_NR-MVSNet		85.28 153	83.42 171	87.46 135	87.77 178	90.80 203	89.90 155	87.69 110	83.93 154	74.16 140	64.72 197	66.43 184	87.48 135	90.14 165	90.83 149	97.73 106	97.11 90

SixPastTwentyTwo		83.12 183	83.44 170	82.74 188	87.71 180	93.11 160	82.30 206	82.33 165	79.24 181	64.33 199	78.77 122	62.75 200	84.11 167	88.11 188	87.89 190	95.70 179	94.21 161

TranMVSNet+NR-MVSNet		85.57 149	84.41 162	86.92 138	87.67 181	93.34 149	90.31 141	88.43 95	83.07 159	70.11 165	69.99 172	65.28 189	86.96 139	89.73 173	92.27 124	98.06 82	97.17 89

WR-MVS		83.14 182	83.38 173	82.87 187	87.55 182	93.29 151	86.36 192	84.21 142	80.05 177	66.41 189	66.91 181	66.92 181	75.66 202	88.96 184	90.56 158	97.05 141	96.96 94

v8		84.45 166	83.30 175	85.80 148	87.53 183	92.95 162	90.31 141	82.46 164	80.46 172	71.43 154	66.99 180	67.16 179	86.14 149	89.26 180	90.22 166	96.94 149	96.06 124

WR-MVS_H		82.86 187	82.66 181	83.10 183	87.44 184	93.33 150	85.71 197	83.20 157	77.36 191	68.20 179	66.37 184	65.23 190	76.05 201	89.35 177	90.13 167	97.99 89	96.89 97

v148		83.61 176	82.10 185	85.37 152	87.34 185	92.94 163	87.48 181	85.72 129	78.92 182	73.87 142	65.71 190	64.69 195	81.78 182	87.82 189	89.35 184	96.01 172	95.26 147

v10		84.18 167	83.17 177	85.37 152	87.34 185	92.68 170	90.32 140	81.33 177	79.93 180	69.23 172	66.33 185	65.74 187	87.03 138	90.84 154	90.38 161	96.97 145	96.29 117

v2v482		84.51 162	83.05 178	86.20 146	87.25 187	93.28 152	90.22 143	85.40 131	79.94 179	69.78 167	67.74 177	65.15 191	87.57 132	89.12 182	90.55 159	96.97 145	95.60 138

CP-MVSNet		83.11 184	82.15 184	84.23 168	87.20 188	92.70 169	86.42 191	83.53 153	77.83 189	67.67 182	66.89 183	60.53 212	82.47 175	89.23 181	90.65 157	98.08 79	97.20 87

v1144		84.03 171	82.88 179	85.37 152	87.17 189	93.15 159	90.18 144	83.31 155	78.83 183	67.85 180	65.99 187	64.99 192	86.79 141	90.75 156	90.33 163	96.90 154	96.15 121

V42		84.48 164	83.36 174	85.79 149	87.14 190	93.28 152	90.03 148	83.98 146	80.30 174	71.20 157	66.90 182	67.17 178	85.55 153	89.35 177	90.27 164	96.82 159	96.27 118

pmmvs5		83.37 179	82.68 180	84.18 170	87.13 191	93.18 156	86.74 188	82.08 169	76.48 196	67.28 185	71.26 163	62.70 201	84.71 160	90.77 155	90.12 170	97.15 133	94.24 159

FMVSNet1		87.33 128	86.00 148	88.89 115	87.13 191	92.83 167	93.08 109	84.46 140	81.35 169	82.20 105	66.33 185	77.96 136	88.96 119	93.97 96	94.16 77	97.54 120	95.38 146

PS-CasMVS		82.53 189	81.54 192	83.68 175	87.08 193	92.54 175	86.20 193	83.46 154	76.46 197	65.73 194	65.71 190	59.41 217	81.61 183	89.06 183	90.55 159	98.03 84	97.07 91

our_test_3					86.93 194	89.77 205	81.61 207

PEN-MVS		82.49 190	81.58 191	83.56 177	86.93 194	92.05 186	86.71 189	83.84 147	76.94 194	64.68 198	67.24 178	60.11 213	81.17 185	87.78 190	90.70 156	98.02 86	96.21 119

v1192		83.56 177	82.35 182	84.98 157	86.84 196	92.84 165	90.01 150	82.70 158	78.54 184	66.48 188	64.88 195	62.91 199	86.91 140	90.72 157	90.25 165	96.94 149	96.32 115

v144192		83.48 178	82.23 183	84.94 158	86.65 197	92.84 165	89.63 160	82.48 163	77.87 188	67.36 184	65.33 192	63.50 198	86.51 143	89.72 174	89.99 175	97.03 142	96.35 113

DTE-MVSNet		81.76 195	81.04 197	82.60 191	86.63 198	91.48 197	85.97 195	83.70 149	76.45 198	62.44 203	67.16 179	59.98 214	78.98 192	87.15 194	89.93 176	97.88 96	95.12 149

pmnet_mix02		80.14 200	80.21 201	80.06 199	86.61 199	89.66 206	80.40 210	82.20 168	82.29 165	61.35 206	71.52 162	66.67 183	76.75 198	82.55 211	80.18 214	93.05 200	88.62 202

v1921920		83.30 180	82.09 186	84.70 161	86.59 200	92.67 171	89.82 156	82.23 167	78.32 185	65.76 193	64.64 198	62.35 202	86.78 142	90.34 163	90.02 173	97.02 143	96.31 116

v1240		82.88 186	81.66 190	84.29 167	86.46 201	92.52 177	89.06 167	81.82 173	77.16 192	65.09 197	64.17 200	61.50 207	86.36 144	90.12 167	90.13 167	96.95 148	96.04 125

anonymousdsp		84.51 162	85.85 152	82.95 186	86.30 202	93.51 145	85.77 196	80.38 183	78.25 187	63.42 202	73.51 153	72.20 156	84.64 161	93.21 117	92.16 128	97.19 131	98.14 47

pmmvs6		80.90 197	78.77 203	83.38 180	85.84 203	91.61 193	86.01 194	82.54 162	64.17 219	70.43 163	54.14 218	67.06 180	80.73 187	90.50 162	89.17 186	94.74 195	94.75 153

MVS-HIRNet		78.16 204	77.57 208	78.83 203	85.83 204	87.76 211	76.67 214	70.22 216	75.82 202	67.39 183	55.61 213	70.52 162	81.96 180	86.67 198	85.06 202	90.93 213	81.58 216

test20.03		76.41 208	78.49 205	73.98 209	85.64 205	87.50 212	75.89 215	80.71 182	70.84 213	51.07 223	68.06 176	61.40 208	54.99 219	88.28 187	87.20 192	95.58 184	86.15 209

v7n		82.25 192	81.54 192	83.07 184	85.55 206	92.58 173	86.68 190	81.10 181	76.54 195	65.97 192	62.91 202	60.56 211	82.36 176	91.07 152	90.35 162	96.77 161	96.80 98

N_pmnet		77.55 207	76.68 210	78.56 204	85.43 207	87.30 214	78.84 212	81.88 172	78.30 186	60.61 207	61.46 204	62.15 203	74.03 207	82.04 212	80.69 213	90.59 215	84.81 214

Anonymous20231206		78.09 205	78.11 206	78.07 205	85.19 208	89.17 207	80.99 208	81.24 180	75.46 203	58.25 213	54.78 217	59.90 215	66.73 213	88.94 185	88.26 189	96.01 172	90.25 194

MDTV_nov1_ep13_2view		80.43 198	80.94 198	79.84 200	84.82 209	90.87 200	84.23 200	73.80 205	80.28 175	64.33 199	70.05 171	68.77 171	79.67 188	84.83 204	83.50 207	92.17 206	88.25 207

FPMVS		69.87 214	67.10 217	73.10 211	84.09 210	78.35 222	79.40 211	76.41 198	71.92 208	57.71 214	54.06 219	50.04 223	56.72 217	71.19 220	68.70 220	84.25 220	75.43 220

EU-MVSNet		78.43 203	80.25 200	76.30 207	83.81 211	87.27 215	80.99 208	79.52 186	76.01 199	54.12 218	70.44 168	64.87 193	67.40 212	86.23 199	85.54 199	91.95 209	91.41 183

FMVSNet5		84.47 165	84.72 160	84.18 170	83.30 212	88.43 209	88.09 177	79.42 187	84.25 148	74.14 141	73.15 158	78.74 129	83.65 169	91.19 148	91.19 147	96.46 165	86.07 210

WB-MVS		60.76 217	66.86 218	53.64 217	82.24 213	72.70 223	48.70 229	82.04 170	63.91 221	12.91 232	64.77 196	49.00 226	22.74 227	75.95 218	75.36 218	73.22 226	66.33 224

MIMVSNet		82.97 185	84.00 165	81.77 197	82.23 214	92.25 181	87.40 184	72.73 212	81.48 168	69.55 168	68.79 174	72.42 155	81.82 181	92.23 133	92.25 125	96.89 155	88.61 203

PM-MVS		80.29 199	79.30 202	81.45 198	81.91 215	88.23 210	82.61 204	79.01 188	79.99 178	67.15 186	69.07 173	51.39 222	82.92 173	87.55 192	85.59 197	95.08 191	93.28 172

pmmvs-eth3d		79.78 202	77.58 207	82.34 193	81.57 216	87.46 213	82.92 203	81.28 178	75.33 204	71.34 155	61.88 203	52.41 221	81.59 184	87.56 191	86.90 193	95.36 189	91.48 182

new-patchmatchnet		72.32 211	71.09 214	73.74 210	81.17 217	84.86 218	72.21 220	77.48 195	68.32 216	54.89 217	55.10 215	49.31 225	63.68 216	79.30 216	76.46 217	93.03 201	84.32 215

ET-MVSNet_ETH3D		89.93 104	90.84 91	88.87 116	79.60 218	96.19 103	94.43 70	86.56 119	90.63 75	80.75 116	90.71 45	77.78 138	93.73 59	91.36 145	93.45 96	98.15 71	95.77 134

PMVS	56.77 18	61.27 216	58.64 220	64.35 215	75.66 219	54.60 227	53.62 226	74.23 203	53.69 224	58.37 212	44.27 223	49.38 224	44.16 223	69.51 222	65.35 222	80.07 222	73.66 221
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
new_pmnet		72.29 212	73.25 212	71.16 214	75.35 220	81.38 219	73.72 219	69.27 217	75.97 200	49.84 224	56.27 212	56.12 220	69.08 209	81.73 213	80.86 212	89.72 218	80.44 218

ambc			67.96 216		73.69 221	79.79 221	73.82 218		71.61 209	59.80 210	46.00 221	20.79 231	66.15 214	86.92 196	80.11 215	89.13 219	90.50 191

pmmvs3		71.13 213	71.06 215	71.21 213	73.54 222	80.19 220	71.69 221	64.86 220	62.04 223	52.10 220	54.92 216	48.00 227	75.03 203	83.75 209	83.24 208	90.04 217	85.27 211

MDA-MVSNet-bldmvs		73.81 209	72.56 213	75.28 208	72.52 223	88.87 208	74.95 217	82.67 160	71.57 210	55.02 216	65.96 188	42.84 229	76.11 200	70.61 221	81.47 211	90.38 216	86.59 208

tmp_tt				50.24 220	68.55 224	46.86 229	48.90 228	18.28 227	86.51 130	68.32 177	70.19 170	65.33 188	26.69 226	74.37 219	66.80 221	70.72 227

Gipuma		58.52 218	56.17 221	61.27 216	67.14 225	58.06 226	52.16 227	68.40 219	69.00 215	45.02 226	22.79 225	20.57 232	55.11 218	76.27 217	79.33 216	79.80 223	67.16 223
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MIMVSNet1		73.19 210	73.70 211	72.60 212	65.42 226	86.69 216	75.56 216	79.65 185	67.87 217	55.30 215	45.24 222	56.41 219	63.79 215	86.98 195	87.66 191	95.85 174	85.04 212

PMMVS2		53.68 220	55.72 222	51.30 218	58.84 227	67.02 225	54.23 225	60.97 223	47.50 225	19.42 229	34.81 224	31.97 230	30.88 225	65.84 223	69.99 219	83.47 221	72.92 222

EMVS		39.04 223	34.32 225	44.54 222	58.25 228	39.35 231	27.61 231	62.55 222	35.99 226	16.40 231	20.04 228	14.77 233	44.80 221	33.12 227	44.10 226	57.61 229	52.89 227

E-PMN		40.00 221	35.74 224	44.98 221	57.69 229	39.15 232	28.05 230	62.70 221	35.52 227	17.78 230	20.90 226	14.36 234	44.47 222	35.89 226	47.86 225	59.15 228	56.47 226

MVE	39.81 19	39.52 222	41.58 223	37.11 223	33.93 230	49.06 228	26.45 232	54.22 224	29.46 228	24.15 228	20.77 227	10.60 235	34.42 224	51.12 225	65.27 223	49.49 230	64.81 225
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		58.10 219	64.61 219	50.51 219	28.26 231	41.71 230	61.28 224	32.07 226	75.92 201	52.04 221	47.94 220	61.83 206	51.80 220	79.83 215	63.95 224	77.60 224	81.05 217

testmvs		4.35 224	6.54 226	1.79 225	0.60 232	1.82 233	3.06 234	0.95 228	7.22 229	0.88 234	12.38 229	1.25 236	3.87 229	6.09 228	5.58 227	1.40 231	11.42 229

GG-mvs-BLEND		62.84 215	90.21 96	30.91 224	0.57 233	94.45 122	86.99 186	0.34 230	88.71 110	0.98 233	81.55 110	91.58 58	0.86 230	92.66 122	91.43 144	95.73 177	91.11 187

test123		3.48 225	5.31 227	1.34 226	0.20 234	1.52 234	2.17 235	0.58 229	6.13 230	0.31 235	9.85 230	0.31 237	3.90 228	2.65 229	5.28 228	0.87 232	11.46 228

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										60.19 208

9.14												97.28 23

MTAPA										95.36 2		97.46 21

MTMP										95.70 1		96.90 27

Patchmatch-RL test							18.47 233

NP-MVS									91.63 68

Patchmtry						92.39 179	89.18 164	73.30 209		71.08 158

DeepMVS_CX						71.82 224	68.37 222	48.05 225	77.38 190	46.88 225	65.77 189	47.03 228	67.48 211	64.27 224		76.89 225	76.72 219

High-resolution multi-view benchmark