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	97.70 112	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 86	93.76 90	98.07 81	96.69 104

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 61	96.26 18	98.10 10	95.75 31	96.25 47	95.45 57	98.01 88	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	82.98 108	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			97.79 102	95.95 134
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 80	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	81.99 106	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 99	95.95 51	94.12 35	94.94 33	94.09 12	85.90 72	90.77 63	95.58 33	94.52 85	93.32 104	97.55 122	95.00 156
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 97	96.08 47	92.14 46	91.65 67	89.16 46	94.07 31	90.17 70	87.78 134	95.24 64	94.97 67	97.09 141	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 97	96.81 163	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 86	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	89.14 108	86.50 71	75.80 145	90.38 69	92.69 74	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 70	96.35 16	97.85 13	93.99 53	97.19 21	96.37 31	97.12 139	99.13 7

MAR-MVS		92.71 62	92.63 63	92.79 67	97.70 41	97.15 81	93.75 96	87.98 105	90.71 73	85.76 84	86.28 70	86.38 79	94.35 47	94.95 68	95.49 56	97.22 132	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 91	95.95 134

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 78	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 79	97.69 114	96.15 126

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 66	97.79 102	97.49 74

CNLPA		93.69 53	92.50 65	95.06 37	97.11 50	97.36 70	93.88 93	93.30 38	95.64 23	93.44 18	80.32 119	90.73 64	94.99 40	93.58 108	93.33 102	97.67 116	96.57 109

LS3D		91.97 69	90.98 91	93.12 60	97.03 52	97.09 85	95.33 60	95.59 22	92.47 58	79.26 128	81.60 109	82.77 100	94.39 46	94.28 90	94.23 78	97.14 138	94.45 162

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 78	87.72 61	91.96 53	94.40 45	93.76 105	93.06 114	98.30 54	95.58 144
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	89.51 105	89.94 42	83.80 86	89.29 72	90.95 96	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		88.32 127	90.61 95	85.64 156	96.79 55	92.27 185	92.03 130	90.31 62	89.05 109	65.44 200	89.43 51	85.90 84	74.22 209	92.76 122	92.09 133	95.02 199	92.76 182
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MSDG		90.42 102	88.25 124	92.94 65	96.67 56	94.41 129	93.96 88	92.91 41	89.59 103	86.26 73	76.74 138	80.92 119	90.43 106	92.60 127	92.08 134	97.44 127	91.41 188

SPE-MVS-test		94.63 44	95.28 37	93.88 50	96.56 57	98.67 14	93.41 106	89.31 84	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	93.51 104	94.08 36	96.94 4	89.29 45	93.08 33	96.77 28	93.82 57	97.68 10	97.40 5	95.59 186	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	87.16 126	85.09 98	84.81 80	93.21 49	93.46 63	91.98 140	91.98 137	97.78 104	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 86	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 86	89.34 108	93.11 61	96.18 61	96.13 108	96.39 43	92.39 43	82.97 165	81.74 111	82.55 100	80.20 125	93.97 55	94.62 80	93.23 105	98.00 90	95.73 140
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 97	90.58 59	91.86 64	90.69 36	85.87 74	82.04 110	90.01 108	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 97	90.58 59	91.86 64	90.69 36	85.87 74	82.04 110	90.01 108	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 76	95.65 66	95.94 112	95.64 54	92.12 47	92.46 59	79.65 126	91.97 38	82.68 101	92.92 71	93.47 113	92.77 120	97.74 108	98.12 49

HyFIR lowres test		87.87 129	86.42 146	89.57 114	95.56 67	96.99 88	92.37 120	84.15 148	86.64 131	77.17 135	57.65 216	83.97 91	91.08 94	92.09 138	92.44 125	97.09 141	95.16 153

ACMM	88.76 10	91.70 78	90.43 96	93.19 57	95.56 67	95.14 120	93.35 108	91.48 53	92.26 60	87.12 66	84.02 84	79.34 129	93.99 53	94.07 96	92.68 121	97.62 120	95.50 145
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
COLMAP_ROB	84.39 15	87.61 131	86.03 151	89.46 115	95.54 69	94.48 126	91.77 134	90.14 66	87.16 126	75.50 140	73.41 160	76.86 150	87.33 141	90.05 173	89.76 185	96.48 167	90.46 197
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 88	95.46 70	96.18 107	95.97 50	89.85 68	90.45 81	77.76 131	91.92 39	80.07 126	92.34 79	94.27 91	93.47 98	98.11 76	97.90 61

CHOSEN 1792x2688		88.57 124	87.82 131	89.44 116	95.46 70	96.89 91	93.74 97	85.87 130	89.63 102	77.42 134	61.38 210	83.31 95	88.80 129	93.44 114	93.16 110	95.37 194	96.95 97

PVSNet_Blended_VisFu		91.92 71	92.39 69	91.36 96	95.45 72	97.85 54	92.25 123	89.54 79	88.53 117	87.47 63	79.82 121	90.53 66	85.47 160	96.31 46	95.16 63	97.99 91	98.56 25

PatchMatch-RL		90.30 103	88.93 115	91.89 81	95.41 73	95.68 114	90.94 138	88.67 94	89.80 100	86.95 68	85.90 72	72.51 159	92.46 76	93.56 110	92.18 130	96.93 155	92.89 181

TSAR-MVS + COLMAP		92.39 65	92.31 70	92.47 72	95.35 74	96.46 101	96.13 46	92.04 48	95.33 27	80.11 124	94.95 30	77.35 147	94.05 51	94.49 87	93.08 112	97.15 136	94.53 160

test2506		90.93 90	89.20 111	92.95 64	94.97 75	98.30 39	94.53 68	90.25 64	89.91 97	88.39 54	83.23 90	64.17 202	90.69 101	96.75 32	96.10 46	98.87 8	95.97 133

ECVR-MVS		90.77 95	89.27 109	92.52 70	94.97 75	98.30 39	94.53 68	90.25 64	89.91 97	85.80 83	73.64 155	74.31 156	90.69 101	96.75 32	96.10 46	98.87 8	95.91 137

test1111		90.47 101	89.10 113	92.07 79	94.92 77	98.30 39	94.17 81	90.30 63	89.56 104	83.92 103	73.25 162	73.66 157	90.26 107	96.77 30	96.14 44	98.87 8	96.04 130

ACMP	89.13 9	92.03 68	91.70 79	92.41 74	94.92 77	96.44 103	93.95 89	89.96 67	91.81 66	85.48 91	90.97 43	79.12 130	92.42 77	93.28 119	92.55 124	97.76 106	97.74 66
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
UA-Net		90.81 91	92.58 64	88.74 123	94.87 79	97.44 69	92.61 117	88.22 101	82.35 169	78.93 129	85.20 78	95.61 39	79.56 195	96.52 38	96.57 25	98.23 64	94.37 163

IB-MVS	85.10 14	87.98 128	87.97 129	87.99 132	94.55 80	96.86 92	84.52 204	88.21 102	86.48 136	88.54 53	74.41 153	77.74 144	74.10 211	89.65 181	92.85 118	98.06 83	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 97	92.93 61	88.19 129	94.36 81	96.61 94	94.34 74	84.66 141	90.66 74	68.75 179	90.41 47	86.89 77	89.78 110	95.46 60	94.87 68	97.25 131	95.62 142

sasdasda		93.08 55	93.09 55	93.07 62	94.24 82	97.86 52	95.45 57	87.86 111	94.00 45	87.47 63	88.32 57	82.37 105	95.13 38	93.96 101	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	87.86 111	94.00 45	87.47 63	88.32 57	82.37 105	95.13 38	93.96 101	96.41 29	98.27 58	98.73 14

MGCFI-Net		92.75 60	92.98 59	92.48 71	94.18 84	97.77 58	95.28 61	87.77 113	93.88 48	85.28 95	88.19 59	82.17 109	94.14 50	93.86 103	96.32 37	98.20 67	98.69 17

UGNet		91.52 80	93.41 53	89.32 117	94.13 85	97.15 81	91.83 133	89.01 87	90.62 76	85.86 82	86.83 63	91.73 56	77.40 200	94.68 79	94.43 75	97.71 110	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		89.28 121	87.47 140	91.39 93	94.12 86	97.25 75	93.94 91	89.74 71	85.62 143	80.63 122	75.24 149	69.33 174	91.66 89	94.92 70	93.23 105	98.27 58	96.72 103

IS_MVSNet		91.87 72	93.35 54	90.14 111	94.09 87	97.73 61	93.09 111	88.12 103	88.71 114	79.98 125	84.49 81	90.63 65	87.49 139	97.07 22	96.96 17	98.07 81	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		89.40 117	87.58 137	91.53 90	94.06 89	97.21 79	94.19 80	89.83 69	85.69 140	81.08 118	75.50 147	69.76 172	91.80 85	94.79 77	93.51 94	98.20 67	96.60 107

ETV-MVS		93.80 51	94.57 44	92.91 66	93.98 90	97.50 67	93.62 100	88.70 92	91.95 62	87.57 62	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	87.31 134	86.59 144	88.14 130	93.96 91	94.51 125	89.00 174	87.99 104	81.58 172	70.15 169	78.41 129	71.78 164	90.60 104	91.30 149	91.99 136	97.17 135	96.58 108
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MS-PatchMatch		87.63 130	87.61 135	87.65 137	93.95 92	94.09 135	92.60 118	81.52 181	86.64 131	76.41 138	73.46 159	85.94 83	85.01 164	92.23 136	90.00 179	96.43 170	90.93 194

thres200		89.49 116	87.72 132	91.55 89	93.95 92	97.25 75	94.34 74	89.74 71	85.66 141	81.18 115	76.12 144	70.19 171	91.80 85	94.92 70	93.51 94	98.27 58	96.40 116

CLD-MVS		92.50 64	91.96 75	93.13 59	93.93 94	96.24 105	95.69 52	88.77 91	92.92 53	89.01 47	88.19 59	81.74 113	93.13 66	93.63 107	93.08 112	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		89.36 118	87.61 135	91.39 93	93.90 95	96.86 92	94.35 73	89.66 75	85.87 138	81.15 116	76.46 140	70.38 168	91.17 92	94.09 95	93.43 100	98.13 73	96.16 125

tfpn200view9		89.55 115	87.86 130	91.53 90	93.90 95	97.26 72	94.31 76	89.74 71	85.87 138	81.15 116	76.46 140	70.38 168	91.76 87	94.92 70	93.51 94	98.28 57	96.61 106

EIA-MVS		92.72 61	92.96 60	92.44 73	93.86 97	97.76 59	93.13 110	88.65 95	89.78 101	86.68 69	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 95	92.14 72	89.17 119	93.86 97	92.81 173	93.16 109	80.22 189	90.21 87	84.67 102	89.89 50	91.38 60	90.57 105	94.94 69	92.11 132	92.52 210	93.65 173

FC-MVSNet-train		90.55 99	90.19 99	90.97 99	93.78 99	95.16 119	92.11 128	88.85 89	87.64 123	83.38 107	84.36 83	78.41 136	89.53 112	94.69 78	93.15 111	98.15 71	97.92 58

FA-MVS(training)		90.79 94	91.33 83	90.17 109	93.76 100	97.22 78	92.74 115	77.79 200	90.60 78	88.03 55	78.80 126	87.41 74	91.00 95	95.40 62	93.43 100	97.70 112	96.46 113

Vis-MVSNet (Re-imp)		90.54 100	92.76 62	87.94 133	93.73 101	96.94 90	92.17 126	87.91 106	88.77 113	76.12 139	83.68 87	90.80 61	79.49 196	96.34 45	96.35 32	98.21 66	96.46 113

baseline1		90.81 91	90.29 97	91.42 92	93.67 102	95.86 113	93.94 91	89.69 74	89.29 107	82.85 109	82.91 93	80.30 123	89.60 111	95.05 66	94.79 70	98.80 12	93.82 171

EPP-MVSNet		92.13 67	93.06 57	91.05 98	93.66 103	97.30 71	92.18 124	87.90 107	90.24 86	83.63 105	86.14 71	90.52 68	90.76 100	94.82 75	94.38 76	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	86.37 126	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	87.19 136	86.02 152	88.56 125	93.42 105	94.41 129	89.91 158	87.66 117	83.45 162	72.25 156	76.42 142	71.99 163	90.78 99	89.86 176	90.94 153	97.32 128	95.11 155

casdiffmvs_mvg		91.94 70	91.25 85	92.75 68	93.41 106	97.19 80	95.48 56	89.77 70	89.86 99	86.41 72	81.02 114	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 79	91.09 89	92.12 77	93.36 107	97.26 72	94.02 85	89.62 77	90.50 80	84.95 101	82.00 105	81.36 115	92.69 74	94.47 88	95.04 65	98.09 79	97.00 92

viewcassd2359sk11		91.81 74	91.13 88	92.61 69	93.28 108	97.26 72	94.16 82	89.64 76	90.27 84	87.79 60	82.51 101	81.72 114	92.78 72	94.43 89	94.69 72	98.01 88	96.99 93

MVS_Test		91.81 74	92.19 71	91.37 95	93.24 109	96.95 89	94.43 70	86.25 127	91.45 70	83.45 106	86.31 68	85.15 87	92.93 69	93.99 97	94.71 71	97.92 96	96.77 102

viewdifsd2359ckpt13		91.32 82	90.71 94	92.04 80	93.21 110	97.23 77	93.57 102	89.54 79	89.94 95	85.21 96	81.31 112	80.56 121	92.78 72	94.56 83	94.57 74	97.95 95	96.80 100

MVSTER		91.73 76	91.61 80	91.86 82	93.18 111	94.56 123	94.37 72	87.90 107	90.16 90	88.69 52	89.23 52	81.28 117	88.92 127	95.75 56	93.95 86	98.12 74	96.37 117

viewmacassd2359aftdt		90.80 93	89.95 104	91.78 83	93.17 112	97.14 84	93.99 86	89.56 78	87.66 122	83.65 104	78.82 125	80.23 124	92.23 80	93.74 106	95.11 64	98.10 77	96.97 95

Anonymous202405211			88.00 127		93.16 113	96.38 104	93.58 101	89.34 83	87.92 121		65.04 199	83.03 97	92.07 81	92.67 124	93.33 102	96.96 150	97.63 67

casdiffmvs		91.72 77	91.16 87	92.38 75	93.16 113	97.15 81	93.95 89	89.49 81	91.58 69	86.03 77	80.75 116	80.95 118	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 89	91.71 78	90.19 108	92.98 115	97.07 86	91.96 132	87.63 118	90.61 77	81.42 113	86.76 65	82.26 107	89.23 119	94.86 74	93.03 116	97.90 97	97.36 78

Effi-MVS+		89.79 110	89.83 105	89.74 113	92.98 115	96.45 102	93.48 105	84.24 146	87.62 124	76.45 137	81.76 107	77.56 146	93.48 62	94.61 81	93.59 93	97.82 101	97.22 86

RPSCF		89.68 111	89.24 110	90.20 107	92.97 117	92.93 169	92.30 121	87.69 115	90.44 82	85.12 97	91.68 40	85.84 85	90.69 101	87.34 198	86.07 201	92.46 211	90.37 198

TDRefinement		84.97 162	83.39 177	86.81 145	92.97 117	94.12 134	92.18 124	87.77 113	82.78 166	71.31 161	68.43 180	68.07 180	81.10 191	89.70 180	89.03 192	95.55 190	91.62 186

thisisatest0530		91.04 88	91.74 77	90.21 106	92.93 119	97.00 87	92.06 129	87.63 118	90.74 72	81.51 112	86.81 64	82.48 102	89.23 119	94.81 76	93.03 116	97.90 97	97.33 80

DCV-MVSNet		91.24 83	91.26 84	91.22 97	92.84 120	93.44 151	93.82 94	86.75 123	91.33 71	85.61 87	84.00 85	85.46 86	91.27 90	92.91 121	93.62 92	97.02 146	98.05 52

baseline		91.19 85	91.89 76	90.38 102	92.76 121	95.04 121	93.55 103	84.54 144	92.92 53	85.71 85	86.68 67	86.96 76	89.28 118	92.00 139	92.62 123	96.46 168	96.99 93

EPMVS		85.77 150	86.24 148	85.23 161	92.76 121	93.78 141	89.91 158	73.60 213	90.19 88	74.22 144	82.18 104	78.06 140	87.55 138	85.61 208	85.38 206	93.32 204	88.48 210

GeoE		89.29 120	88.68 117	89.99 112	92.75 123	96.03 111	93.07 113	83.79 153	86.98 128	81.34 114	74.72 150	78.92 131	91.22 91	93.31 117	93.21 108	97.78 104	97.60 71

diffmvs		91.37 81	91.09 89	91.70 86	92.71 124	96.47 100	94.03 84	88.78 90	92.74 56	85.43 93	83.63 88	80.37 122	91.76 87	93.39 115	93.78 89	97.50 124	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 84	90.82 93	91.68 87	92.69 125	96.56 96	94.05 83	88.87 88	91.87 63	85.08 99	82.26 103	80.04 127	91.84 84	93.80 104	93.93 87	97.56 121	97.26 82

DI_MVS_pp		91.05 87	90.15 100	92.11 78	92.67 126	96.61 94	96.03 48	88.44 97	90.25 85	85.92 80	73.73 154	84.89 89	91.92 82	94.17 94	94.07 84	97.68 115	97.31 81

viewmambaseed2359dif		90.70 98	89.81 106	91.73 85	92.66 127	96.10 109	93.97 87	88.69 93	89.92 96	86.12 75	80.79 115	80.73 120	91.92 82	91.13 154	92.81 119	97.06 143	97.20 87

Anonymous20231211		89.82 109	88.18 125	91.74 84	92.52 128	96.09 110	93.38 107	89.30 85	88.95 110	85.90 81	64.55 204	84.39 90	92.41 78	92.24 135	93.06 114	96.93 155	97.95 55

viewdifsd2359ckpt11		89.68 111	88.67 118	90.86 100	92.35 129	95.23 116	91.72 135	88.40 99	88.84 111	86.14 74	80.75 116	78.17 139	90.95 96	90.02 174	91.15 151	95.59 186	96.50 111

viewmsd2359difaftdt		89.67 113	88.66 119	90.85 101	92.35 129	95.23 116	91.72 135	88.40 99	88.80 112	86.12 75	80.75 116	78.20 138	90.94 98	90.02 174	91.15 151	95.59 186	96.50 111

tpmrst		83.72 180	83.45 174	84.03 177	92.21 131	91.66 197	88.74 177	73.58 214	88.14 119	72.67 153	77.37 134	72.11 162	86.34 150	82.94 216	82.05 215	90.63 220	89.86 202

CostFormer		86.78 139	86.05 150	87.62 139	92.15 132	93.20 160	91.55 137	75.83 205	88.11 120	85.29 94	81.76 107	76.22 152	87.80 133	84.45 211	85.21 207	93.12 205	93.42 176

Vis-MVSNet		89.36 118	91.49 82	86.88 144	92.10 133	97.60 66	92.16 127	85.89 129	84.21 154	75.20 141	82.58 98	87.13 75	77.40 200	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		88.60 123	88.45 120	88.78 122	92.02 134	92.44 183	92.00 131	83.57 157	86.52 134	78.90 130	78.61 128	81.34 116	89.12 122	90.68 162	93.18 109	97.10 140	96.35 118
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PatchmatchNet		85.70 151	86.65 143	84.60 168	91.79 135	93.40 152	89.27 167	73.62 212	90.19 88	72.63 154	82.74 97	81.93 112	87.64 136	84.99 209	84.29 211	92.64 209	89.00 205
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpm cat1		84.13 173	81.99 193	86.63 148	91.74 136	91.50 200	90.68 140	75.69 206	86.12 137	85.44 92	72.39 165	70.72 166	85.16 162	80.89 220	81.56 216	91.07 218	90.71 195

USDC		86.73 140	85.96 154	87.63 138	91.64 137	93.97 137	92.76 114	84.58 143	88.19 118	70.67 166	80.10 120	67.86 181	89.43 113	91.81 141	89.77 184	96.69 165	90.05 201

SCA		86.25 142	87.52 138	84.77 165	91.59 138	93.90 138	89.11 171	73.25 217	90.38 83	72.84 152	83.26 89	83.79 93	88.49 131	86.07 205	85.56 204	93.33 203	89.67 203

gg-mvs-nofinetune		81.83 199	83.58 172	79.80 206	91.57 139	96.54 98	93.79 95	68.80 224	62.71 228	43.01 233	55.28 219	85.06 88	83.65 174	96.13 49	94.86 69	97.98 94	94.46 161

Fast-Effi-MVS+		88.56 125	87.99 128	89.22 118	91.56 140	95.21 118	92.29 122	82.69 164	86.82 129	77.73 132	76.24 143	73.39 158	93.36 64	94.22 93	93.64 91	97.65 117	96.43 115

CMPMVS	61.19 17	79.86 206	77.46 214	82.66 195	91.54 141	91.82 195	83.25 207	81.57 180	70.51 219	68.64 180	59.89 215	66.77 187	79.63 194	84.00 214	84.30 210	91.34 216	84.89 219
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
ADS-MVSNet		84.08 174	84.95 162	83.05 190	91.53 142	91.75 196	88.16 181	70.70 221	89.96 94	69.51 174	78.83 124	76.97 149	86.29 151	84.08 213	84.60 209	92.13 214	88.48 210

test-LLR		86.88 137	88.28 122	85.24 160	91.22 143	92.07 189	87.41 187	83.62 155	84.58 147	69.33 175	83.00 91	82.79 98	84.24 168	92.26 133	89.81 182	95.64 184	93.44 174

test0.0.03 1		85.58 153	87.69 134	83.11 187	91.22 143	92.54 180	85.60 203	83.62 155	85.66 141	67.84 186	82.79 96	79.70 128	73.51 213	91.15 153	90.79 155	96.88 159	91.23 191

baseline2		88.97 122	89.50 107	88.36 126	91.14 145	95.30 115	90.13 152	85.17 138	87.24 125	80.80 120	84.46 82	78.44 135	85.60 157	93.54 111	91.87 138	97.31 129	95.66 141

Effi-MVS+-dtu		87.51 132	88.13 126	86.77 146	91.10 146	94.90 122	90.91 139	82.67 165	83.47 161	71.55 158	81.11 113	77.04 148	89.41 114	92.65 126	91.68 144	95.00 200	96.09 128

RPMNet		84.82 164	85.90 155	83.56 182	91.10 146	92.10 187	88.73 178	71.11 220	84.75 145	68.79 178	73.56 156	77.62 145	85.33 161	90.08 172	89.43 188	96.32 171	93.77 172

CR-MVSNet		85.48 155	86.29 147	84.53 170	91.08 148	92.10 187	89.18 169	73.30 215	84.75 145	71.08 163	73.12 164	77.91 142	86.27 152	91.48 145	90.75 158	96.27 172	93.94 168

TinyColmap		84.04 175	82.01 192	86.42 150	90.87 149	91.84 194	88.89 176	84.07 150	82.11 171	69.89 171	71.08 169	60.81 215	89.04 123	90.52 164	89.19 190	95.76 178	88.50 209

tpm		83.16 186	83.64 171	82.60 196	90.75 150	91.05 203	88.49 179	73.99 210	82.36 168	67.08 192	78.10 130	68.79 175	84.17 170	85.95 207	85.96 202	91.09 217	93.23 178

dps		85.00 161	83.21 181	87.08 142	90.73 151	92.55 179	89.34 166	75.29 207	84.94 144	87.01 67	79.27 123	67.69 182	87.27 142	84.22 212	83.56 212	92.83 208	90.25 199

MDTV_nov1_ep13		86.64 141	87.50 139	85.65 155	90.73 151	93.69 145	89.96 156	78.03 199	89.48 106	76.85 136	84.92 79	82.42 104	86.14 154	86.85 202	86.15 200	92.17 212	88.97 206

CDS-MVSNet		88.34 126	88.71 116	87.90 134	90.70 153	94.54 124	92.38 119	86.02 128	80.37 178	79.42 127	79.30 122	83.43 94	82.04 183	93.39 115	94.01 85	96.86 161	95.93 136
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IterMVS-SCA-FT		85.44 157	86.71 142	83.97 178	90.59 154	90.84 206	89.73 162	78.34 196	84.07 158	66.40 195	77.27 136	78.66 133	83.06 176	91.20 150	90.10 177	95.72 181	94.78 157

IterMVS		85.25 159	86.49 145	83.80 179	90.42 155	90.77 209	90.02 154	78.04 198	84.10 156	66.27 196	77.28 135	78.41 136	83.01 177	90.88 156	89.72 186	95.04 198	94.24 164
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Fast-Effi-MVS+-dtu		86.25 142	87.70 133	84.56 169	90.37 156	93.70 144	90.54 142	78.14 197	83.50 160	65.37 201	81.59 110	75.83 154	86.09 156	91.70 143	91.70 142	96.88 159	95.84 138

dmvs_re		87.31 134	86.10 149	88.74 123	89.84 157	94.28 132	92.66 116	89.41 82	82.61 167	74.69 142	74.69 151	69.47 173	87.78 134	92.38 131	93.23 105	98.03 85	96.02 132

FC-MVSNet-test		86.15 145	89.10 113	82.71 194	89.83 158	93.18 161	87.88 184	84.69 140	86.54 133	62.18 210	82.39 102	83.31 95	74.18 210	92.52 129	91.86 139	97.50 124	93.88 170

GA-MVS		85.08 160	85.65 158	84.42 171	89.77 159	94.25 133	89.26 168	84.62 142	81.19 175	62.25 209	75.72 146	68.44 178	84.14 171	93.57 109	91.68 144	96.49 166	94.71 159

PMMVS		89.88 108	91.19 86	88.35 127	89.73 160	91.97 193	90.62 141	81.92 176	90.57 79	80.58 123	92.16 36	86.85 78	91.17 92	92.31 132	91.35 148	96.11 174	93.11 180

tfpnnormal		83.80 179	81.26 201	86.77 146	89.60 161	93.26 159	89.72 163	87.60 120	72.78 212	70.44 167	60.53 213	61.15 214	85.55 158	92.72 123	91.44 146	97.71 110	96.92 98

CVMVSNet		83.83 178	85.53 159	81.85 201	89.60 161	90.92 204	87.81 185	83.21 161	80.11 181	60.16 214	76.47 139	78.57 134	76.79 202	89.76 177	90.13 172	93.51 202	92.75 183

testgi		81.94 198	84.09 169	79.43 207	89.53 163	90.83 207	82.49 210	81.75 179	80.59 176	59.46 216	82.82 95	65.75 191	67.97 215	90.10 171	89.52 187	95.39 193	89.03 204

UniMVSNet_ETH3D		84.57 165	81.40 199	88.28 128	89.34 164	94.38 131	90.33 144	86.50 125	74.74 210	77.52 133	59.90 214	62.04 210	88.78 130	88.82 191	92.65 122	97.22 132	97.24 83

LTVRE_ROB	81.71 16	82.44 196	81.84 194	83.13 186	89.01 165	92.99 166	88.90 175	82.32 171	66.26 224	54.02 224	74.68 152	59.62 221	88.87 128	90.71 161	92.02 135	95.68 183	96.62 105
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		84.94 163	84.95 162	84.93 164	88.82 166	93.18 161	88.44 180	81.28 183	77.16 197	73.76 148	75.43 148	76.57 151	82.04 183	90.59 163	90.79 155	95.22 196	90.94 193

EG-PatchMatch MVS		81.70 201	81.31 200	82.15 199	88.75 167	93.81 140	87.14 190	78.89 195	71.57 215	64.12 206	61.20 212	68.46 177	76.73 204	91.48 145	90.77 157	97.28 130	91.90 185

TransMVSNet (Re)		82.67 193	80.93 204	84.69 167	88.71 168	91.50 200	87.90 183	87.15 121	71.54 217	68.24 183	63.69 206	64.67 201	78.51 199	91.65 144	90.73 160	97.64 118	92.73 184

FMVSNet3		90.19 106	90.06 103	90.34 103	88.69 169	93.85 139	94.58 67	85.78 131	90.03 91	85.56 88	77.38 131	86.13 80	89.22 121	93.29 118	94.36 77	98.20 67	95.40 150

GBi-Net		90.21 104	90.11 101	90.32 104	88.66 170	93.65 147	94.25 77	85.78 131	90.03 91	85.56 88	77.38 131	86.13 80	89.38 115	93.97 98	94.16 80	98.31 51	95.47 146

test1		90.21 104	90.11 101	90.32 104	88.66 170	93.65 147	94.25 77	85.78 131	90.03 91	85.56 88	77.38 131	86.13 80	89.38 115	93.97 98	94.16 80	98.31 51	95.47 146

FMVSNet2		89.61 114	89.14 112	90.16 110	88.66 170	93.65 147	94.25 77	85.44 135	88.57 116	84.96 100	73.53 157	83.82 92	89.38 115	94.23 92	94.68 73	98.31 51	95.47 146

PatchT		83.86 177	85.51 160	81.94 200	88.41 173	91.56 199	78.79 218	71.57 219	84.08 157	71.08 163	70.62 170	76.13 153	86.27 152	91.48 145	90.75 158	95.52 192	93.94 168

UniMVSNet (Re)		86.22 144	85.46 161	87.11 141	88.34 174	94.42 128	89.65 164	87.10 122	84.39 151	74.61 143	70.41 174	68.10 179	85.10 163	91.17 152	91.79 140	97.84 100	97.94 56

NR-MVSNet		85.46 156	84.54 166	86.52 149	88.33 175	93.78 141	90.45 143	87.87 109	84.40 149	71.61 157	70.59 171	62.09 209	82.79 179	91.75 142	91.75 141	98.10 77	97.44 75

UniMVSNet_NR-MVSNet		86.80 138	85.86 156	87.89 135	88.17 176	94.07 136	90.15 150	88.51 96	84.20 155	73.45 149	72.38 166	70.30 170	88.95 125	90.25 167	92.21 129	98.12 74	97.62 69

thisisatest0515		85.70 151	87.00 141	84.19 174	88.16 177	93.67 146	84.20 206	84.14 149	83.39 163	72.91 151	76.79 137	74.75 155	78.82 198	92.57 128	91.26 149	96.94 152	96.56 110

pm-mvs1		84.55 166	83.46 173	85.82 152	88.16 177	93.39 153	89.05 173	85.36 137	74.03 211	72.43 155	65.08 198	71.11 165	82.30 182	93.48 112	91.70 142	97.64 118	95.43 149

gm-plane-assit		77.65 211	78.50 209	76.66 211	87.96 179	85.43 222	64.70 229	74.50 208	64.15 226	51.26 227	61.32 211	58.17 223	84.11 172	95.16 65	93.83 88	97.45 126	91.41 188

test-mter		86.09 148	88.38 121	83.43 184	87.89 180	92.61 177	86.89 192	77.11 203	84.30 152	68.62 181	82.57 99	82.45 103	84.34 167	92.40 130	90.11 176	95.74 179	94.21 166

pmmvs4		86.00 149	84.28 168	88.00 131	87.80 181	92.01 192	89.94 157	84.91 139	86.79 130	80.98 119	73.41 160	66.34 190	88.12 132	89.31 184	88.90 193	96.24 173	93.20 179

TESTMET0.1,1		86.11 147	88.28 122	83.59 181	87.80 181	92.07 189	87.41 187	77.12 202	84.58 147	69.33 175	83.00 91	82.79 98	84.24 168	92.26 133	89.81 182	95.64 184	93.44 174

DU-MVS		86.12 146	84.81 164	87.66 136	87.77 183	93.78 141	90.15 150	87.87 109	84.40 149	73.45 149	70.59 171	64.82 199	88.95 125	90.14 168	92.33 126	97.76 106	97.62 69

Baseline_NR-MVSNet		85.28 158	83.42 176	87.46 140	87.77 183	90.80 208	89.90 160	87.69 115	83.93 159	74.16 145	64.72 202	66.43 189	87.48 140	90.14 168	90.83 154	97.73 109	97.11 90

SixPastTwentyTwo		83.12 188	83.44 175	82.74 193	87.71 185	93.11 165	82.30 211	82.33 170	79.24 186	64.33 204	78.77 127	62.75 205	84.11 172	88.11 193	87.89 195	95.70 182	94.21 166

TranMVSNet+NR-MVSNet		85.57 154	84.41 167	86.92 143	87.67 186	93.34 154	90.31 146	88.43 98	83.07 164	70.11 170	69.99 177	65.28 194	86.96 144	89.73 178	92.27 127	98.06 83	97.17 89

WR-MVS		83.14 187	83.38 178	82.87 192	87.55 187	93.29 156	86.36 197	84.21 147	80.05 182	66.41 194	66.91 186	66.92 186	75.66 207	88.96 189	90.56 163	97.05 144	96.96 96

v8		84.45 171	83.30 180	85.80 153	87.53 188	92.95 167	90.31 146	82.46 169	80.46 177	71.43 159	66.99 185	67.16 184	86.14 154	89.26 185	90.22 171	96.94 152	96.06 129

WR-MVS_H		82.86 192	82.66 186	83.10 188	87.44 189	93.33 155	85.71 202	83.20 162	77.36 196	68.20 184	66.37 189	65.23 195	76.05 206	89.35 182	90.13 172	97.99 91	96.89 99

v148		83.61 181	82.10 190	85.37 157	87.34 190	92.94 168	87.48 186	85.72 134	78.92 187	73.87 147	65.71 195	64.69 200	81.78 187	87.82 194	89.35 189	96.01 175	95.26 152

v10		84.18 172	83.17 182	85.37 157	87.34 190	92.68 175	90.32 145	81.33 182	79.93 185	69.23 177	66.33 190	65.74 192	87.03 143	90.84 157	90.38 166	96.97 148	96.29 122

v2v482		84.51 167	83.05 183	86.20 151	87.25 192	93.28 157	90.22 148	85.40 136	79.94 184	69.78 172	67.74 182	65.15 196	87.57 137	89.12 187	90.55 164	96.97 148	95.60 143

CP-MVSNet		83.11 189	82.15 189	84.23 173	87.20 193	92.70 174	86.42 196	83.53 158	77.83 194	67.67 187	66.89 188	60.53 217	82.47 180	89.23 186	90.65 162	98.08 80	97.20 87

v1144		84.03 176	82.88 184	85.37 157	87.17 194	93.15 164	90.18 149	83.31 160	78.83 188	67.85 185	65.99 192	64.99 197	86.79 146	90.75 159	90.33 168	96.90 157	96.15 126

V42		84.48 169	83.36 179	85.79 154	87.14 195	93.28 157	90.03 153	83.98 151	80.30 179	71.20 162	66.90 187	67.17 183	85.55 158	89.35 182	90.27 169	96.82 162	96.27 123

pmmvs5		83.37 184	82.68 185	84.18 175	87.13 196	93.18 161	86.74 193	82.08 174	76.48 201	67.28 190	71.26 168	62.70 206	84.71 165	90.77 158	90.12 175	97.15 136	94.24 164

FMVSNet1		87.33 133	86.00 153	88.89 120	87.13 196	92.83 172	93.08 112	84.46 145	81.35 174	82.20 110	66.33 190	77.96 141	88.96 124	93.97 98	94.16 80	97.54 123	95.38 151

PS-CasMVS		82.53 194	81.54 197	83.68 180	87.08 198	92.54 180	86.20 198	83.46 159	76.46 202	65.73 199	65.71 195	59.41 222	81.61 188	89.06 188	90.55 164	98.03 85	97.07 91

our_test_3					86.93 199	89.77 210	81.61 212

PEN-MVS		82.49 195	81.58 196	83.56 182	86.93 199	92.05 191	86.71 194	83.84 152	76.94 199	64.68 203	67.24 183	60.11 218	81.17 190	87.78 195	90.70 161	98.02 87	96.21 124

v1192		83.56 182	82.35 187	84.98 162	86.84 201	92.84 170	90.01 155	82.70 163	78.54 189	66.48 193	64.88 200	62.91 204	86.91 145	90.72 160	90.25 170	96.94 152	96.32 120

v144192		83.48 183	82.23 188	84.94 163	86.65 202	92.84 170	89.63 165	82.48 168	77.87 193	67.36 189	65.33 197	63.50 203	86.51 148	89.72 179	89.99 180	97.03 145	96.35 118

DTE-MVSNet		81.76 200	81.04 202	82.60 196	86.63 203	91.48 202	85.97 200	83.70 154	76.45 203	62.44 208	67.16 184	59.98 219	78.98 197	87.15 199	89.93 181	97.88 99	95.12 154

pmnet_mix02		80.14 205	80.21 206	80.06 204	86.61 204	89.66 211	80.40 215	82.20 173	82.29 170	61.35 211	71.52 167	66.67 188	76.75 203	82.55 217	80.18 220	93.05 206	88.62 207

v1921920		83.30 185	82.09 191	84.70 166	86.59 205	92.67 176	89.82 161	82.23 172	78.32 190	65.76 198	64.64 203	62.35 207	86.78 147	90.34 166	90.02 178	97.02 146	96.31 121

v1240		82.88 191	81.66 195	84.29 172	86.46 206	92.52 182	89.06 172	81.82 178	77.16 197	65.09 202	64.17 205	61.50 212	86.36 149	90.12 170	90.13 172	96.95 151	96.04 130

anonymousdsp		84.51 167	85.85 157	82.95 191	86.30 207	93.51 150	85.77 201	80.38 188	78.25 192	63.42 207	73.51 158	72.20 161	84.64 166	93.21 120	92.16 131	97.19 134	98.14 47

pmmvs6		80.90 202	78.77 208	83.38 185	85.84 208	91.61 198	86.01 199	82.54 167	64.17 225	70.43 168	54.14 223	67.06 185	80.73 192	90.50 165	89.17 191	94.74 201	94.75 158

MVS-HIRNet		78.16 209	77.57 213	78.83 208	85.83 209	87.76 216	76.67 219	70.22 222	75.82 207	67.39 188	55.61 218	70.52 167	81.96 185	86.67 203	85.06 208	90.93 219	81.58 222

test20.03		76.41 213	78.49 210	73.98 214	85.64 210	87.50 217	75.89 221	80.71 187	70.84 218	51.07 228	68.06 181	61.40 213	54.99 225	88.28 192	87.20 198	95.58 189	86.15 215

v7n		82.25 197	81.54 197	83.07 189	85.55 211	92.58 178	86.68 195	81.10 186	76.54 200	65.97 197	62.91 207	60.56 216	82.36 181	91.07 155	90.35 167	96.77 164	96.80 100

N_pmnet		77.55 212	76.68 215	78.56 209	85.43 212	87.30 219	78.84 217	81.88 177	78.30 191	60.61 212	61.46 209	62.15 208	74.03 212	82.04 218	80.69 219	90.59 221	84.81 220

Anonymous20231206		78.09 210	78.11 211	78.07 210	85.19 213	89.17 212	80.99 213	81.24 185	75.46 208	58.25 218	54.78 222	59.90 220	66.73 219	88.94 190	88.26 194	96.01 175	90.25 199

MDTV_nov1_ep13_2view		80.43 203	80.94 203	79.84 205	84.82 214	90.87 205	84.23 205	73.80 211	80.28 180	64.33 204	70.05 176	68.77 176	79.67 193	84.83 210	83.50 213	92.17 212	88.25 212

FPMVS		69.87 220	67.10 223	73.10 216	84.09 215	78.35 228	79.40 216	76.41 204	71.92 213	57.71 219	54.06 224	50.04 229	56.72 223	71.19 226	68.70 226	84.25 226	75.43 226

EU-MVSNet		78.43 208	80.25 205	76.30 212	83.81 216	87.27 220	80.99 213	79.52 192	76.01 204	54.12 223	70.44 173	64.87 198	67.40 217	86.23 204	85.54 205	91.95 215	91.41 188

FMVSNet5		84.47 170	84.72 165	84.18 175	83.30 217	88.43 214	88.09 182	79.42 193	84.25 153	74.14 146	73.15 163	78.74 132	83.65 174	91.19 151	91.19 150	96.46 168	86.07 216

WB-MVS		60.76 223	66.86 224	53.64 223	82.24 218	72.70 229	48.70 235	82.04 175	63.91 227	12.91 238	64.77 201	49.00 232	22.74 233	75.95 224	75.36 224	73.22 232	66.33 230

MIMVSNet		82.97 190	84.00 170	81.77 202	82.23 219	92.25 186	87.40 189	72.73 218	81.48 173	69.55 173	68.79 179	72.42 160	81.82 186	92.23 136	92.25 128	96.89 158	88.61 208

PM-MVS		80.29 204	79.30 207	81.45 203	81.91 220	88.23 215	82.61 209	79.01 194	79.99 183	67.15 191	69.07 178	51.39 228	82.92 178	87.55 197	85.59 203	95.08 197	93.28 177

pmmvs-eth3d		79.78 207	77.58 212	82.34 198	81.57 221	87.46 218	82.92 208	81.28 183	75.33 209	71.34 160	61.88 208	52.41 227	81.59 189	87.56 196	86.90 199	95.36 195	91.48 187

new-patchmatchnet		72.32 217	71.09 220	73.74 215	81.17 222	84.86 224	72.21 226	77.48 201	68.32 221	54.89 222	55.10 220	49.31 231	63.68 222	79.30 222	76.46 223	93.03 207	84.32 221

ET-MVSNet_ETH3D		89.93 107	90.84 92	88.87 121	79.60 223	96.19 106	94.43 70	86.56 124	90.63 75	80.75 121	90.71 45	77.78 143	93.73 59	91.36 148	93.45 99	98.15 71	95.77 139

PMVS	56.77 18	61.27 222	58.64 226	64.35 221	75.66 224	54.60 233	53.62 232	74.23 209	53.69 230	58.37 217	44.27 229	49.38 230	44.16 229	69.51 228	65.35 228	80.07 228	73.66 227
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
new_pmnet		72.29 218	73.25 218	71.16 220	75.35 225	81.38 225	73.72 225	69.27 223	75.97 205	49.84 230	56.27 217	56.12 225	69.08 214	81.73 219	80.86 218	89.72 224	80.44 224

ambc			67.96 222		73.69 226	79.79 227	73.82 224		71.61 214	59.80 215	46.00 227	20.79 237	66.15 220	86.92 201	80.11 221	89.13 225	90.50 196

pmmvs3		71.13 219	71.06 221	71.21 219	73.54 227	80.19 226	71.69 227	64.86 226	62.04 229	52.10 225	54.92 221	48.00 233	75.03 208	83.75 215	83.24 214	90.04 223	85.27 217

MDA-MVSNet-bldmvs		73.81 214	72.56 219	75.28 213	72.52 228	88.87 213	74.95 223	82.67 165	71.57 215	55.02 221	65.96 193	42.84 235	76.11 205	70.61 227	81.47 217	90.38 222	86.59 214

tmp_tt				50.24 226	68.55 229	46.86 235	48.90 234	18.28 233	86.51 135	68.32 182	70.19 175	65.33 193	26.69 232	74.37 225	66.80 227	70.72 233

Gipuma		58.52 224	56.17 227	61.27 222	67.14 230	58.06 232	52.16 233	68.40 225	69.00 220	45.02 232	22.79 231	20.57 238	55.11 224	76.27 223	79.33 222	79.80 229	67.16 229
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MIMVSNet1		73.19 216	73.70 217	72.60 217	65.42 231	86.69 221	75.56 222	79.65 191	67.87 222	55.30 220	45.24 228	56.41 224	63.79 221	86.98 200	87.66 196	95.85 177	85.04 218

FE-MVSNET		73.24 215	74.06 216	72.28 218	64.92 232	85.32 223	76.06 220	79.75 190	67.71 223	50.14 229	49.61 225	54.40 226	67.26 218	85.97 206	87.33 197	95.53 191	88.10 213

PMMVS2		53.68 226	55.72 228	51.30 224	58.84 233	67.02 231	54.23 231	60.97 229	47.50 231	19.42 235	34.81 230	31.97 236	30.88 231	65.84 229	69.99 225	83.47 227	72.92 228

EMVS		39.04 229	34.32 231	44.54 228	58.25 234	39.35 237	27.61 237	62.55 228	35.99 232	16.40 237	20.04 234	14.77 239	44.80 227	33.12 233	44.10 232	57.61 235	52.89 233

E-PMN		40.00 227	35.74 230	44.98 227	57.69 235	39.15 238	28.05 236	62.70 227	35.52 233	17.78 236	20.90 232	14.36 240	44.47 228	35.89 232	47.86 231	59.15 234	56.47 232

MVE	39.81 19	39.52 228	41.58 229	37.11 229	33.93 236	49.06 234	26.45 238	54.22 230	29.46 234	24.15 234	20.77 233	10.60 241	34.42 230	51.12 231	65.27 229	49.49 236	64.81 231
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		58.10 225	64.61 225	50.51 225	28.26 237	41.71 236	61.28 230	32.07 232	75.92 206	52.04 226	47.94 226	61.83 211	51.80 226	79.83 221	63.95 230	77.60 230	81.05 223

testmvs		4.35 230	6.54 232	1.79 231	0.60 238	1.82 239	3.06 240	0.95 234	7.22 235	0.88 240	12.38 235	1.25 242	3.87 235	6.09 234	5.58 233	1.40 237	11.42 235

GG-mvs-BLEND		62.84 221	90.21 98	30.91 230	0.57 239	94.45 127	86.99 191	0.34 236	88.71 114	0.98 239	81.55 111	91.58 58	0.86 236	92.66 125	91.43 147	95.73 180	91.11 192

test123		3.48 231	5.31 233	1.34 232	0.20 240	1.52 240	2.17 241	0.58 235	6.13 236	0.31 241	9.85 236	0.31 243	3.90 234	2.65 235	5.28 234	0.87 238	11.46 234

uanet_test		0.00 232	0.00 234	0.00 233	0.00 241	0.00 241	0.00 242	0.00 237	0.00 237	0.00 242	0.00 237	0.00 244	0.00 237	0.00 236	0.00 235	0.00 239	0.00 236

sosnet-low-res		0.00 232	0.00 234	0.00 233	0.00 241	0.00 241	0.00 242	0.00 237	0.00 237	0.00 242	0.00 237	0.00 244	0.00 237	0.00 236	0.00 235	0.00 239	0.00 236

sosnet		0.00 232	0.00 234	0.00 233	0.00 241	0.00 241	0.00 242	0.00 237	0.00 237	0.00 242	0.00 237	0.00 244	0.00 237	0.00 236	0.00 235	0.00 239	0.00 236

RE-MVS-def										60.19 213

9.14												97.28 23

MTAPA										95.36 2		97.46 21

MTMP										95.70 1		96.90 27

Patchmatch-RL test							18.47 239

NP-MVS									91.63 68

Patchmtry						92.39 184	89.18 169	73.30 215		71.08 163

DeepMVS_CX						71.82 230	68.37 228	48.05 231	77.38 195	46.88 231	65.77 194	47.03 234	67.48 216	64.27 230		76.89 231	76.72 225

High-resolution multi-view benchmark