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.

SED-MVS		97.92 1	98.27 2	97.52 1	98.88 11	99.60 1	98.80 5	95.08 8	98.57 2	95.63 2	96.98 10	99.73 1	97.67 1	97.26 10	95.86 22	99.04 14	99.89 5

MSP-MVS		97.74 2	98.32 1	97.06 7	98.66 14	99.35 6	98.66 7	94.75 13	98.22 4	93.60 5	97.99 1	98.58 7	97.41 3	98.24 2	95.95 18	99.27 4	99.91 1
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
DPE-MVS		97.69 3	98.16 3	97.14 5	99.01 5	99.52 4	99.12 2	95.38 3	98.00 7	93.31 9	97.71 2	99.61 3	96.94 4	96.99 15	95.45 26	99.09 12	99.81 8
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
DVP-MVS		97.61 4	97.87 6	97.30 2	98.94 10	99.60 1	98.21 12	95.11 5	98.39 3	95.83 1	94.40 28	99.70 2	96.79 5	97.16 12	95.95 18	98.92 26	99.90 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
CNVR-MVS		97.60 5	98.08 4	97.03 8	99.14 1	99.55 3	98.67 6	95.32 4	97.91 8	92.55 12	97.11 7	97.23 12	97.49 2	98.16 3	97.05 5	99.04 14	99.55 18

APDe-MVS		97.31 6	97.51 11	97.08 6	98.95 9	99.29 11	98.58 9	95.11 5	97.69 14	94.16 3	96.91 11	96.81 16	96.57 9	96.71 19	95.39 28	99.08 13	99.79 9

SF-MVS		97.17 7	97.18 14	97.17 3	99.11 2	99.20 13	99.05 3	95.55 1	97.39 17	93.56 6	97.48 4	96.71 18	96.75 6	95.73 31	94.40 43	98.98 19	99.33 24

NCCC		97.01 8	97.74 7	96.16 11	99.02 4	99.35 6	98.63 8	95.04 9	97.84 11	88.95 25	96.83 13	97.02 15	96.39 14	97.44 7	96.51 9	98.90 28	99.16 39

SMA-MVS		96.96 9	97.65 10	96.15 12	98.98 6	99.31 10	97.91 17	94.68 15	97.52 15	90.59 19	94.54 27	99.20 4	96.54 11	97.29 9	96.48 10	98.22 57	99.19 35
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
MCST-MVS		96.93 10	98.07 5	95.61 19	98.98 6	99.44 5	98.04 13	95.04 9	98.10 5	86.55 32	97.65 3	97.56 10	95.60 23	97.67 6	96.45 11	99.43 1	99.61 17

HPM-MVS++		96.91 11	97.70 8	96.00 14	98.97 8	99.16 16	97.82 20	94.81 12	98.04 6	89.61 22	96.56 15	98.60 6	96.39 14	97.09 13	95.22 30	98.39 51	99.22 33

SD-MVS		96.87 12	97.69 9	95.92 15	96.38 48	99.25 12	97.76 21	94.75 13	97.72 12	92.46 14	95.94 16	99.09 5	96.48 13	96.01 27	96.08 16	97.68 88	99.73 12
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
APD-MVS		96.79 13	96.99 17	96.56 9	98.76 13	98.87 25	98.42 10	94.93 11	97.70 13	91.83 15	95.52 19	95.94 23	96.63 8	95.94 28	95.47 25	98.80 34	99.47 21
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
TSAR-MVS + MP.		96.50 14	97.08 15	95.82 17	96.12 52	98.97 22	98.00 14	94.13 20	97.89 9	91.49 16	95.11 24	97.52 11	96.26 18	96.27 25	94.07 53	98.91 27	99.74 11
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
SteuartSystems-ACMMP		96.20 15	97.22 13	95.01 23	98.40 22	99.11 17	97.93 16	93.62 24	96.28 29	87.45 28	97.05 9	96.00 22	94.23 31	96.83 18	95.97 17	98.40 50	99.27 30
Skip Steuart: Steuart Systems R&D Blog.
HFP-MVS		96.09 16	96.41 22	95.72 18	98.58 17	98.84 26	97.95 15	93.08 28	96.96 22	90.24 20	96.60 14	94.40 31	96.52 12	95.13 41	94.33 45	97.93 78	98.59 64

zzz-MVS		95.87 17	95.63 29	96.15 12	98.60 16	98.83 27	97.89 18	93.65 23	96.24 30	93.08 10	91.13 35	95.46 28	95.72 22	95.64 33	93.67 61	97.97 75	98.46 71

ACMMP_NAP		95.81 18	96.50 21	95.01 23	98.79 12	99.17 15	97.52 26	94.20 19	96.19 31	85.71 36	93.80 31	96.20 21	95.89 19	96.62 21	94.98 36	97.93 78	98.52 67

train_agg		95.72 19	97.37 12	93.80 29	97.82 31	98.92 23	97.84 19	93.50 25	96.86 24	81.35 51	97.10 8	97.71 8	94.19 32	96.02 26	95.37 29	98.07 65	99.64 15

ACMMPR		95.59 20	95.89 24	95.25 21	98.41 21	98.74 29	97.69 24	92.73 32	96.88 23	88.95 25	95.33 21	92.91 38	95.79 20	94.73 51	94.33 45	97.92 80	98.32 77

DeepC-MVS_fast	91.53 1	95.57 21	95.67 27	95.45 20	98.57 18	99.00 21	97.76 21	94.41 17	97.06 20	86.84 31	86.39 47	92.27 43	96.38 16	97.89 5	98.06 3	98.73 40	99.01 47
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MSLP-MVS++		95.49 22	94.84 33	96.25 10	98.64 15	98.63 33	98.35 11	92.37 34	95.04 48	92.62 11	87.12 46	93.79 32	96.55 10	93.53 66	96.78 6	98.98 19	98.99 48

CP-MVS		95.43 23	95.67 27	95.14 22	98.24 27	98.60 34	97.45 27	92.80 30	95.98 34	89.21 24	95.22 22	93.60 33	95.43 24	94.37 57	93.22 68	97.68 88	98.72 55

DPM-MVS		95.36 24	95.84 25	94.82 25	96.70 44	98.49 44	99.27 1	95.09 7	96.71 25	83.87 44	86.34 49	96.44 20	95.06 26	98.35 1	98.82 1	98.89 29	95.69 130

MP-MVS		95.24 25	95.96 23	94.40 27	98.32 24	98.38 49	97.12 29	92.87 29	95.17 46	85.50 37	95.68 17	94.91 29	94.58 28	95.11 42	93.76 57	98.05 68	98.68 57
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
TSAR-MVS + ACMM		94.99 26	97.02 16	92.61 40	97.19 37	98.71 31	97.74 23	93.21 27	96.97 21	79.27 65	94.09 29	97.14 13	90.84 64	96.64 20	95.94 20	97.42 103	99.67 14

X-MVS		94.70 27	95.71 26	93.52 33	98.38 23	98.56 36	96.99 30	92.62 33	95.58 39	81.00 57	94.57 26	93.49 34	94.16 34	94.82 47	94.29 48	97.99 74	98.68 57

PGM-MVS		94.64 28	95.49 30	93.66 31	98.55 19	98.51 42	97.63 25	87.77 48	94.45 52	84.92 40	97.23 6	91.90 45	95.22 25	94.56 54	93.80 56	97.87 84	97.97 87

TSAR-MVS + GP.		94.59 29	96.60 20	92.25 41	90.25 88	98.17 55	96.22 36	86.53 54	97.49 16	87.26 29	95.21 23	97.06 14	94.07 36	94.34 59	94.20 50	99.18 5	99.71 13

xxxxxxxxxxxxxcwj		94.57 30	92.34 48	97.17 3	99.11 2	99.20 13	99.05 3	95.55 1	97.39 17	93.56 6	97.48 4	62.85 147	96.75 6	95.73 31	94.40 43	98.98 19	99.33 24

PHI-MVS		94.49 31	96.72 19	91.88 43	97.06 39	98.88 24	94.99 47	89.13 42	96.15 32	79.70 61	96.91 11	95.78 25	91.87 54	94.65 52	95.68 23	98.53 44	98.98 50

AdaColmap		94.28 32	92.94 44	95.84 16	98.32 24	98.33 51	96.06 38	94.62 16	96.29 28	91.22 17	89.89 39	85.50 74	96.38 16	91.85 95	90.89 83	98.44 46	97.81 90

DeepPCF-MVS	91.00 2	94.15 33	96.87 18	90.97 51	96.82 42	99.33 9	89.40 96	92.76 31	98.76 1	82.36 48	88.74 40	95.49 27	90.58 71	98.13 4	97.80 4	93.88 183	99.88 6

CPTT-MVS		94.11 34	93.99 39	94.25 28	96.58 45	97.66 59	97.31 28	91.94 35	94.84 49	88.72 27	92.51 32	93.04 37	95.78 21	91.51 98	89.97 99	95.15 172	98.37 74

EPNet		93.69 35	95.34 31	91.76 44	96.98 41	98.47 46	95.40 44	86.79 51	95.47 40	82.84 46	95.66 18	89.17 50	90.47 72	95.25 40	94.69 39	98.10 62	98.68 57
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
ACMMP		93.32 36	93.59 42	93.00 38	97.03 40	98.24 52	95.27 45	91.66 38	95.20 44	83.25 45	95.39 20	85.52 72	92.80 44	92.60 85	90.21 95	98.01 71	97.99 85
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
CANet		93.23 37	93.72 41	92.65 39	95.48 55	99.09 19	96.55 34	86.74 52	95.28 43	85.22 38	77.30 72	91.25 47	92.60 47	97.06 14	96.63 7	99.31 2	99.45 22

CDPH-MVS		93.22 38	95.08 32	91.04 50	97.57 34	98.49 44	96.74 32	89.35 41	95.19 45	73.57 94	90.26 37	91.59 46	90.68 68	95.09 44	96.15 14	98.31 56	98.81 53

CSCG		93.16 39	92.65 46	93.76 30	98.32 24	99.09 19	96.12 37	89.91 40	93.15 61	89.64 21	83.62 55	88.91 53	92.40 49	91.09 103	93.70 58	96.14 155	98.99 48

MVS_111021_LR		93.05 40	94.53 35	91.32 48	96.43 47	98.38 49	92.81 61	87.20 50	95.94 36	81.45 50	94.75 25	86.08 68	92.12 52	94.83 46	93.34 64	97.89 83	98.42 73

3Dnovator+	86.26 7	92.90 41	92.45 47	93.42 34	97.25 36	98.45 48	95.82 39	85.71 60	93.83 56	89.55 23	72.31 101	92.28 42	94.01 37	95.10 43	95.92 21	98.17 58	99.23 32

MVS_111021_HR		92.73 42	94.83 34	90.28 56	96.27 49	99.10 18	92.77 62	86.15 57	93.41 59	77.11 84	93.82 30	87.39 60	90.61 69	95.60 34	95.15 32	98.79 35	99.32 26

PLC	89.12 3	92.67 43	90.84 58	94.81 26	97.69 32	96.10 86	95.42 43	91.70 36	95.82 38	92.52 13	81.24 58	86.01 69	94.36 29	92.44 89	90.27 92	97.19 112	93.99 153
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
3Dnovator	85.78 8	92.53 44	91.96 50	93.20 36	97.99 28	98.47 46	95.78 40	85.94 58	93.07 63	86.40 33	73.43 93	89.00 52	94.08 35	94.74 50	96.44 12	99.01 18	98.57 65

DeepC-MVS	88.77 4	92.39 45	91.74 52	93.14 37	96.21 50	98.55 39	96.30 35	93.84 21	93.06 64	81.09 55	74.69 87	85.20 77	93.48 40	95.41 37	96.13 15	97.92 80	99.18 36
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
OMC-MVS		92.05 46	91.88 51	92.25 41	96.51 46	97.94 57	93.18 58	88.97 44	96.53 26	84.47 42	80.79 60	87.85 56	93.25 42	92.48 88	91.81 76	97.12 113	95.73 129

MVSTER		91.91 47	93.43 43	90.14 57	89.81 94	92.32 125	94.53 50	81.32 84	96.00 33	84.77 41	85.41 53	92.39 41	91.32 56	96.41 22	94.01 54	99.11 8	97.45 99

MVS_0304		91.90 48	92.93 45	90.69 55	93.66 63	98.78 28	96.73 33	85.43 64	93.13 62	78.11 78	77.02 75	89.09 51	91.10 60	96.98 16	96.54 8	99.11 8	98.96 51

QAPM		91.68 49	91.97 49	91.34 47	97.86 30	98.72 30	95.60 42	85.72 59	90.86 77	77.14 83	76.06 76	90.35 48	92.69 46	94.10 60	94.60 40	99.04 14	99.09 40

CS-MVS		91.55 50	94.12 37	88.55 66	89.68 96	97.47 63	90.90 85	79.76 95	95.94 36	79.26 66	88.32 41	88.42 55	92.80 44	95.93 30	93.70 58	98.93 25	99.07 42

CNLPA		91.53 51	89.74 70	93.63 32	96.75 43	97.63 61	91.16 80	91.70 36	96.38 27	90.82 18	69.66 112	85.52 72	93.76 38	90.44 109	91.14 82	97.55 97	97.40 100

ETV-MVS		91.51 52	94.06 38	88.54 67	89.39 100	97.52 62	89.48 93	80.88 87	97.09 19	79.41 63	87.87 42	86.18 67	92.95 43	95.94 28	94.33 45	99.13 7	99.52 20

DELS-MVS		91.09 53	90.56 66	91.71 45	95.82 53	98.59 35	95.74 41	86.68 53	85.86 104	85.12 39	72.71 96	81.36 84	88.06 91	97.31 8	98.27 2	98.86 32	99.82 7
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
TAPA-MVS	87.40 6	90.98 54	90.71 60	91.30 49	96.14 51	97.66 59	94.80 48	89.00 43	94.74 51	77.42 82	80.22 61	86.70 63	92.27 50	91.65 97	90.17 97	98.15 61	93.83 157
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PVSNet_BlendedMVS		90.74 55	90.66 62	90.82 53	94.75 58	98.54 40	91.30 77	86.53 54	95.43 41	85.75 34	78.66 67	70.67 120	87.60 92	96.37 23	95.08 34	98.98 19	99.90 2

PVSNet_Blended		90.74 55	90.66 62	90.82 53	94.75 58	98.54 40	91.30 77	86.53 54	95.43 41	85.75 34	78.66 67	70.67 120	87.60 92	96.37 23	95.08 34	98.98 19	99.90 2

CHOSEN 280x420		90.61 57	94.27 36	86.35 85	93.12 67	98.16 56	89.99 89	69.62 173	92.48 68	76.89 87	87.28 45	96.72 17	90.31 74	94.81 48	92.33 73	98.17 58	98.08 83

MAR-MVS		90.44 58	91.17 56	89.59 60	97.48 35	97.92 58	90.96 83	79.80 92	95.07 47	77.03 85	80.83 59	79.10 94	94.68 27	93.16 71	94.46 42	97.59 96	97.63 92
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
PCF-MVS	88.14 5	90.42 59	89.56 75	91.41 46	94.44 60	98.18 54	94.35 52	94.33 18	84.55 116	76.61 88	75.84 79	88.47 54	91.29 57	90.37 111	90.66 89	97.46 99	98.88 52
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
OpenMVS	83.41 11	89.84 60	88.89 81	90.95 52	97.63 33	98.51 42	94.64 49	85.47 63	88.14 90	78.39 76	65.06 124	85.42 75	91.04 62	93.06 74	93.70 58	98.53 44	98.37 74

EIA-MVS		89.82 61	91.48 54	87.89 75	89.16 102	97.31 65	88.99 97	80.92 86	94.29 53	77.65 80	82.16 57	79.77 92	91.90 53	94.61 53	93.03 70	98.70 41	99.21 34

canonicalmvs		89.62 62	89.87 69	89.33 62	90.47 83	97.02 71	93.46 57	79.67 96	92.45 69	81.05 56	82.84 56	73.00 109	93.71 39	90.38 110	94.85 37	97.65 92	98.54 66

TSAR-MVS + COLMAP		89.59 63	89.64 72	89.53 61	93.32 66	96.51 78	95.03 46	88.53 45	95.98 34	69.10 110	91.81 33	64.53 143	93.40 41	93.53 66	91.35 81	97.77 85	93.75 160

HQP-MVS		89.57 64	90.57 65	88.41 69	92.77 68	94.71 101	94.24 53	87.97 46	93.44 58	68.18 113	91.75 34	71.54 119	89.90 77	92.31 92	91.43 79	97.39 104	98.80 54

MVS_Test		89.02 65	90.20 67	87.64 77	89.83 93	97.05 70	92.30 64	77.59 115	92.89 65	75.01 92	77.36 71	76.10 103	92.27 50	95.30 39	95.42 27	98.83 33	97.30 103

CLD-MVS		88.99 66	88.07 84	90.07 58	89.61 97	94.94 98	93.82 56	85.70 61	92.73 67	82.73 47	79.97 62	69.59 123	90.44 73	90.32 112	89.93 101	98.10 62	99.04 44
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
baseline		88.91 67	89.94 68	87.70 76	89.44 99	96.74 76	91.62 71	77.92 112	93.79 57	78.76 71	77.55 70	78.46 97	89.38 83	92.26 93	92.52 72	99.10 10	98.23 78

PMMVS		88.56 68	91.22 55	85.47 93	90.04 90	95.60 94	86.62 119	78.49 107	93.86 55	70.62 105	90.00 38	80.08 90	91.64 55	92.36 90	89.80 105	95.40 167	96.84 112

baseline1		88.16 69	88.15 83	88.17 73	90.02 91	94.79 100	91.85 69	83.89 67	87.37 96	75.67 91	73.75 91	79.89 91	88.44 90	94.41 55	93.33 66	99.18 5	93.55 162

thisisatest0530		87.99 70	90.76 59	84.75 96	88.36 110	96.82 73	87.65 109	79.67 96	91.77 71	70.93 101	79.94 63	87.65 58	84.21 109	92.98 77	89.07 116	97.66 91	97.13 106

tttt0517		87.93 71	90.71 60	84.68 97	88.33 111	96.76 75	87.42 112	79.67 96	91.74 72	70.83 102	79.91 64	87.61 59	84.21 109	92.88 82	89.07 116	97.62 94	97.03 108

CANet_DTU		87.91 72	91.57 53	83.64 105	90.96 77	97.12 68	91.90 68	75.97 126	92.83 66	53.16 167	86.02 50	79.02 95	90.80 65	95.40 38	94.15 51	99.03 17	96.47 123

diffmvs		87.86 73	87.40 90	88.39 70	88.57 108	96.10 86	91.24 79	83.15 71	90.62 78	79.13 68	72.45 99	67.71 129	90.07 76	92.58 86	93.31 67	98.17 58	99.03 45

IS_MVSNet		87.83 74	90.66 62	84.53 98	90.08 89	96.79 74	88.16 103	79.89 91	85.44 106	72.20 96	75.50 83	87.14 61	80.21 137	95.53 35	95.22 30	96.65 129	99.02 46

EPP-MVSNet		87.72 75	89.74 70	85.37 94	89.11 103	95.57 95	86.31 120	79.44 99	85.83 105	75.73 90	77.23 73	90.05 49	84.78 106	91.22 101	90.25 93	96.83 120	98.04 84

ET-MVSNet_ETH3D		87.63 76	91.08 57	83.59 106	67.96 207	96.30 85	92.06 66	78.47 108	91.95 70	69.87 107	87.57 44	84.14 81	94.34 30	88.58 125	92.10 74	98.88 30	96.93 109

DI_MVS_plusplus_trai		87.63 76	87.13 92	88.22 72	88.61 107	95.92 90	94.09 55	81.41 83	87.00 99	78.38 77	59.70 143	80.52 88	89.08 85	94.37 57	93.34 64	97.73 86	99.05 43

casdiffmvs		87.59 78	86.69 96	88.64 65	89.06 104	96.32 84	90.18 87	83.21 70	87.74 94	80.20 60	67.99 116	68.34 127	90.79 66	93.83 62	94.08 52	98.41 49	98.50 69

PVSNet_Blended_VisFu		87.44 79	88.72 82	85.95 89	92.02 72	97.26 66	86.88 117	82.66 78	83.86 121	79.16 67	66.96 119	84.91 78	77.26 154	94.97 45	93.48 62	97.73 86	99.64 15

FMVSNet3		87.19 80	87.32 91	87.04 83	82.82 146	90.21 140	92.88 60	76.53 121	91.69 73	81.31 52	64.81 127	80.64 85	89.79 81	94.80 49	94.76 38	98.88 30	94.32 149

LS3D		87.19 80	85.48 103	89.18 63	94.96 57	95.47 96	92.02 67	93.36 26	88.69 88	67.01 114	70.56 108	72.10 114	92.47 48	89.96 115	89.93 101	95.25 169	91.68 171

ACMP	85.16 9	87.15 82	87.04 93	87.27 81	90.80 79	94.45 104	89.41 95	83.09 75	89.15 85	76.98 86	86.35 48	65.80 136	86.94 95	88.45 126	87.52 135	96.42 144	97.56 97
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
UGNet		87.04 83	89.59 74	84.07 100	90.94 78	95.95 89	86.02 122	81.65 82	85.94 103	78.54 75	78.00 69	85.40 76	69.62 174	91.83 96	91.53 78	97.63 93	98.51 68
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
LGP-MVS_train		86.95 84	87.65 87	86.12 88	91.77 75	93.84 110	93.04 59	82.77 77	88.04 91	65.33 119	87.69 43	67.09 133	86.79 96	90.20 113	88.99 119	97.05 115	97.71 91

PatchMatch-RL		86.75 85	85.43 104	88.29 71	94.06 61	96.37 83	86.82 118	82.94 76	88.94 86	79.59 62	79.83 65	59.17 155	89.46 82	91.12 102	88.81 123	96.88 119	93.78 158

baseline2		86.51 86	89.35 78	83.19 108	85.70 132	94.88 99	85.75 127	77.13 117	89.87 82	70.65 104	79.03 66	79.14 93	81.51 130	93.70 63	90.22 94	98.38 52	98.60 63

thres100view900		86.48 87	85.08 106	88.12 74	90.54 80	96.90 72	92.39 63	84.82 65	84.16 119	71.65 97	70.86 105	60.49 150	91.23 59	93.65 64	90.19 96	98.10 62	99.32 26

ACMM	84.23 10	86.40 88	84.64 109	88.46 68	91.90 73	91.93 131	88.11 104	85.59 62	88.61 89	79.13 68	75.31 84	66.25 134	89.86 80	89.88 116	87.64 132	96.16 154	92.86 167
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GBi-Net		86.16 89	86.00 99	86.35 85	81.81 152	89.52 149	91.40 73	76.53 121	91.69 73	81.31 52	64.81 127	80.64 85	88.72 86	90.54 106	90.72 85	98.34 53	94.08 150

test1		86.16 89	86.00 99	86.35 85	81.81 152	89.52 149	91.40 73	76.53 121	91.69 73	81.31 52	64.81 127	80.64 85	88.72 86	90.54 106	90.72 85	98.34 53	94.08 150

tfpn200view9		86.07 91	84.76 108	87.61 78	90.54 80	96.39 80	91.35 76	83.15 71	84.16 119	71.65 97	70.86 105	60.49 150	90.91 63	92.89 79	89.34 108	98.05 68	99.17 37

DCV-MVSNet		85.90 92	85.88 101	85.93 90	87.86 116	88.37 166	89.45 94	77.46 116	87.33 97	77.51 81	76.06 76	75.76 105	88.48 89	87.40 134	88.89 122	94.80 178	97.37 101

Vis-MVSNet (Re-imp)		85.89 93	89.62 73	81.55 118	89.85 92	96.08 88	87.55 110	79.80 92	84.80 113	66.55 116	73.70 92	86.71 62	68.25 181	94.40 56	94.53 41	97.32 107	97.09 107

MSDG		85.81 94	82.29 131	89.93 59	95.52 54	92.61 120	91.51 72	91.46 39	85.12 110	78.56 73	63.25 133	69.01 125	85.31 103	88.45 126	88.23 126	97.21 111	89.33 182

thres200		85.80 95	84.38 110	87.46 79	90.51 82	96.39 80	91.64 70	83.15 71	81.59 128	71.54 99	70.24 109	60.41 152	89.88 78	92.89 79	89.85 104	98.06 66	99.26 31

OPM-MVS		85.69 96	82.79 124	89.06 64	93.42 64	94.21 108	94.21 54	87.61 49	72.68 153	70.79 103	71.09 103	67.27 132	90.74 67	91.29 100	89.05 118	97.61 95	93.94 155

thres400		85.59 97	84.08 113	87.36 80	90.45 84	96.60 77	90.95 84	83.67 69	80.99 131	71.17 100	69.08 114	60.25 153	89.88 78	93.14 72	89.34 108	98.02 70	99.17 37

CostFormer		85.47 98	86.98 94	83.71 103	88.70 106	94.02 109	88.07 105	62.72 190	89.78 83	78.68 72	72.69 97	78.37 98	87.35 94	85.96 147	89.32 112	96.73 126	98.72 55

thres600view7		85.14 99	83.58 119	86.96 84	90.37 87	96.39 80	90.33 86	83.15 71	80.46 132	70.60 106	67.96 117	60.04 154	89.22 84	92.89 79	88.28 125	98.06 66	99.08 41

test-LLR		85.11 100	89.49 76	80.00 127	85.32 136	94.49 102	82.27 157	74.18 136	87.83 92	56.70 145	75.55 81	86.26 64	82.75 122	93.06 74	90.60 90	98.77 37	98.65 61

FMVSNet2		84.89 101	84.02 115	85.91 91	81.81 152	89.52 149	91.40 73	75.79 127	84.45 117	79.39 64	58.75 146	74.35 107	88.72 86	93.51 68	93.46 63	98.34 53	94.08 150

FC-MVSNet-train		84.88 102	84.08 113	85.82 92	89.21 101	91.74 132	85.87 123	81.20 85	81.71 127	74.66 93	73.38 94	64.99 140	86.60 97	90.75 104	88.08 127	97.36 105	97.90 88

EPNet_dtu		84.87 103	89.01 79	80.05 126	95.25 56	92.88 118	88.84 99	84.11 66	91.69 73	49.28 183	85.69 51	78.95 96	65.39 186	92.22 94	91.66 77	97.43 102	89.95 178
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Effi-MVS+		84.80 104	85.71 102	83.73 102	87.94 115	95.76 91	90.08 88	73.45 143	85.12 110	62.66 128	72.39 100	64.97 141	90.59 70	92.95 78	90.69 88	97.67 90	98.12 80

UA-Net		84.69 105	87.64 88	81.25 120	90.38 86	95.67 92	87.33 113	79.41 100	72.07 157	66.48 117	75.09 85	92.48 40	66.88 182	94.03 61	94.25 49	97.01 118	89.88 179

TESTMET0.1,1		84.62 106	89.49 76	78.94 136	82.18 149	94.49 102	82.27 157	70.94 163	87.83 92	56.70 145	75.55 81	86.26 64	82.75 122	93.06 74	90.60 90	98.77 37	98.65 61

CHOSEN 1792x2688		84.59 107	84.30 112	84.93 95	93.71 62	98.23 53	89.91 90	77.96 111	84.81 112	65.93 118	45.19 191	71.76 118	83.13 120	95.46 36	95.13 33	98.94 24	99.53 19

Anonymous20231211		84.23 108	81.71 136	87.17 82	87.38 123	93.59 113	88.95 98	82.14 80	83.82 122	78.56 73	48.09 184	73.89 108	91.25 58	86.38 141	88.06 129	94.74 179	98.14 79

MDTV_nov1_ep13		84.17 109	88.03 85	79.66 129	86.00 130	94.41 105	85.05 129	66.01 185	90.36 79	64.34 124	77.13 74	84.56 79	82.71 124	87.12 138	88.92 120	93.84 185	93.69 161

test-mter		84.06 110	89.00 80	78.29 141	81.92 150	94.23 107	81.07 167	70.38 167	87.12 98	56.10 154	74.75 86	85.80 70	81.81 129	92.52 87	90.10 98	98.43 47	98.49 70

IB-MVS	79.58 12	83.83 111	84.81 107	82.68 110	91.85 74	97.35 64	75.75 186	82.57 79	86.55 101	84.01 43	70.90 104	65.43 138	63.18 192	84.19 161	89.92 103	98.74 39	99.31 28
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
EPMVS		83.71 112	86.76 95	80.16 125	89.72 95	95.64 93	84.68 130	59.73 195	89.61 84	62.67 127	72.65 98	81.80 83	86.22 99	86.23 143	88.03 130	97.96 76	93.35 163

HyFIR lowres test		83.43 113	82.94 122	84.01 101	93.41 65	97.10 69	87.21 114	74.04 138	80.15 134	64.98 120	41.09 199	76.61 102	86.51 98	93.31 69	93.01 71	97.91 82	99.30 29

PatchmatchNet		83.28 114	87.57 89	78.29 141	87.46 121	94.95 97	83.36 139	59.43 198	90.20 81	58.10 140	74.29 89	86.20 66	84.13 111	85.27 153	87.39 136	97.25 110	94.67 147
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
SCA		83.26 115	87.76 86	78.00 146	87.45 122	92.20 126	82.63 153	58.42 200	90.30 80	58.23 138	75.74 80	87.75 57	83.97 114	86.10 146	87.64 132	97.30 108	94.62 148

GeoE		83.17 116	82.86 123	83.53 107	87.24 124	93.78 111	87.94 106	72.75 148	82.19 125	69.76 108	60.54 140	65.95 135	86.01 100	89.41 120	89.72 106	97.47 98	98.43 72

CDS-MVSNet		83.13 117	83.73 118	82.43 116	84.52 141	92.92 117	88.26 102	77.67 114	72.08 156	69.08 111	66.96 119	74.66 106	78.61 143	90.70 105	91.96 75	96.46 143	96.86 111
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
RPSCF		82.91 118	81.86 133	84.13 99	88.25 112	88.32 167	87.67 108	80.86 88	84.78 114	76.57 89	85.56 52	76.00 104	84.61 107	78.20 197	76.52 200	86.81 206	83.63 199

Vis-MVSNet		82.88 119	86.04 98	79.20 134	87.77 119	96.42 79	86.10 121	76.70 119	74.82 147	61.38 130	70.70 107	77.91 99	64.83 188	93.22 70	93.19 69	98.43 47	96.01 126
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
dps		82.63 120	82.64 127	82.62 112	87.81 118	92.81 119	84.39 131	61.96 191	86.43 102	81.63 49	69.72 111	67.60 131	84.42 108	82.51 175	83.90 174	95.52 163	95.50 137

IterMVS-LS		82.62 121	82.75 126	82.48 113	87.09 125	87.48 181	87.19 115	72.85 146	79.09 135	66.63 115	65.22 122	72.14 113	84.06 113	88.33 129	91.39 80	97.03 117	95.60 136
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Fast-Effi-MVS+		82.61 122	82.51 129	82.72 109	85.49 135	93.06 116	87.17 116	71.39 160	84.18 118	64.59 122	63.03 134	58.89 156	90.22 75	91.39 99	90.83 84	97.44 100	96.21 125

tpm cat1		82.39 123	82.32 130	82.47 114	88.13 113	92.42 124	87.43 111	62.79 189	85.30 107	78.05 79	60.14 141	72.10 114	83.20 119	82.26 178	85.67 153	95.23 170	98.35 76

MS-PatchMatch		82.16 124	82.18 132	82.12 117	91.65 76	93.50 114	89.51 92	71.95 154	81.48 129	64.45 123	59.58 145	77.54 100	77.23 155	89.88 116	85.62 154	97.94 77	87.68 186

tpmrst		81.71 125	83.87 117	79.20 134	89.01 105	93.67 112	84.22 132	60.14 193	87.45 95	59.49 134	64.97 125	71.86 117	85.30 104	84.72 157	86.30 144	97.04 116	98.09 82

RPMNet		81.47 126	86.24 97	75.90 164	86.72 126	92.12 128	82.82 151	55.76 206	85.21 108	53.73 165	63.45 131	83.16 82	80.13 138	92.34 91	89.52 107	96.23 152	97.90 88

CR-MVSNet		81.44 127	85.29 105	76.94 155	86.53 127	92.12 128	83.86 133	58.37 201	85.21 108	56.28 149	59.60 144	80.39 89	80.50 135	92.77 83	89.32 112	96.12 156	97.59 95

Effi-MVS+-dtu		81.18 128	82.77 125	79.33 132	84.70 140	92.54 122	85.81 124	71.55 158	78.84 136	57.06 144	71.98 102	63.77 145	85.09 105	88.94 122	87.62 134	91.79 198	95.68 131

test0.0.03 1		80.99 129	84.37 111	77.05 153	85.32 136	89.79 145	78.43 177	74.18 136	84.78 114	57.98 143	76.06 76	72.88 110	69.14 178	88.02 131	87.70 131	97.27 109	91.37 172

Fast-Effi-MVS+-dtu		80.57 130	83.44 120	77.22 151	83.98 144	91.52 134	85.78 126	64.54 188	80.38 133	50.28 179	74.06 90	62.89 146	82.00 128	89.10 121	88.91 121	96.75 124	97.21 105

FMVSNet5		80.56 131	82.53 128	78.26 143	73.80 202	81.52 200	82.26 159	68.36 178	88.85 87	64.21 125	69.09 113	84.38 80	83.49 118	87.13 137	86.76 141	97.44 100	79.95 202

ADS-MVSNet		80.25 132	82.96 121	77.08 152	87.86 116	92.60 121	81.82 164	56.19 205	86.95 100	56.16 152	68.19 115	72.42 112	83.70 117	82.05 179	85.45 159	96.75 124	93.08 166

FMVSNet1		80.18 133	78.07 147	82.65 111	78.55 176	87.57 180	88.41 101	73.93 139	70.16 162	73.57 94	49.80 174	64.45 144	85.35 102	90.54 106	90.72 85	96.10 157	93.21 164

USDC		80.10 134	79.33 143	81.00 122	86.36 128	91.71 133	88.74 100	75.77 128	81.90 126	54.90 159	67.67 118	52.05 168	83.94 115	88.44 128	86.25 145	96.31 147	87.28 190

COLMAP_ROB	75.69 15	79.47 135	76.90 154	82.46 115	92.20 69	90.53 136	85.30 128	83.69 68	78.27 139	61.47 129	58.26 148	62.75 148	78.28 146	82.41 176	82.13 187	93.83 187	83.98 198
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
test_part1		79.37 136	75.64 159	83.71 103	86.18 129	87.74 174	87.84 107	75.69 130	66.33 179	78.93 70	45.92 189	64.85 142	82.44 125	83.08 173	85.69 152	91.17 199	95.90 128

pmmvs4		79.32 137	77.78 149	81.11 121	80.18 161	88.96 161	83.39 137	76.07 124	81.27 130	69.35 109	58.66 147	51.19 171	82.01 127	87.16 136	84.39 171	95.66 161	92.82 168

PatchT		79.28 138	83.88 116	73.93 173	85.54 134	90.95 135	66.14 203	56.53 204	83.21 123	56.28 149	56.50 151	76.80 101	80.50 135	92.77 83	89.32 112	98.57 43	97.59 95

ACMH	78.51 14	79.27 139	78.08 146	80.65 123	89.52 98	90.40 137	80.45 169	79.77 94	69.54 167	54.85 160	64.83 126	56.16 162	83.94 115	84.58 159	86.01 149	95.41 166	95.03 144
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
TAMVS		79.23 140	78.95 145	79.56 130	81.89 151	92.52 123	82.97 146	73.70 140	67.27 173	64.97 121	61.66 139	65.06 139	78.61 143	87.12 138	88.07 128	95.23 170	90.95 174

ACMH+	79.09 13	79.12 141	77.22 153	81.35 119	88.50 109	90.36 138	82.14 161	79.38 102	72.78 152	58.59 135	62.31 138	56.44 161	84.10 112	82.03 180	84.05 172	95.40 167	92.55 169

UniMVSNet_NR-MVSNet		78.89 142	78.04 148	79.88 128	79.40 167	89.70 146	82.92 148	80.17 89	76.37 145	58.56 136	57.10 150	54.92 164	81.44 131	83.51 166	87.12 138	96.76 123	97.60 93

tpm		78.87 143	81.33 139	76.00 162	85.57 133	90.19 141	82.81 152	59.66 196	78.35 138	51.40 174	66.30 121	67.92 128	80.94 133	83.28 169	85.73 150	95.65 162	97.56 97

GA-MVS		78.86 144	80.42 140	77.05 153	83.27 145	92.17 127	83.24 141	75.73 129	73.75 149	46.27 193	62.43 136	57.12 158	76.94 157	93.14 72	89.34 108	96.83 120	95.00 145

IterMVS		78.85 145	81.36 137	75.93 163	84.27 143	85.74 187	83.83 135	66.35 183	76.82 140	50.48 177	63.48 130	68.82 126	73.99 162	89.68 118	89.34 108	96.63 132	95.67 132
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS-SCA-FT		78.71 146	81.34 138	75.64 168	84.31 142	85.67 188	83.51 136	66.14 184	76.67 141	50.38 178	63.45 131	69.02 124	73.23 164	89.66 119	89.22 115	96.24 151	95.67 132

UniMVSNet (Re)		78.00 147	77.52 150	78.57 139	79.66 166	90.36 138	82.09 162	77.86 113	76.38 144	60.26 131	54.63 157	52.07 167	75.31 160	84.97 156	86.10 147	96.22 153	98.11 81

DU-MVS		77.98 148	76.71 155	79.46 131	78.68 173	89.26 155	82.92 148	79.06 104	76.52 142	58.56 136	54.89 155	48.35 185	81.44 131	83.16 171	87.21 137	96.08 158	97.60 93

FC-MVSNet-test		77.95 149	81.85 134	73.39 178	82.31 147	88.99 160	79.33 173	74.24 135	78.75 137	47.40 191	70.22 110	72.09 116	60.78 198	86.66 140	85.62 154	96.30 148	90.61 175

NR-MVSNet		77.21 150	76.41 156	78.14 145	80.18 161	89.26 155	83.38 138	79.06 104	76.52 142	56.59 147	54.89 155	45.32 195	72.89 166	85.39 152	86.12 146	96.71 127	97.36 102

thisisatest0515		77.13 151	79.36 142	74.52 170	79.79 165	89.65 147	73.54 191	73.69 141	74.10 148	58.14 139	62.79 135	60.57 149	66.49 184	88.08 130	85.16 164	95.49 165	95.15 142

gg-mvs-nofinetune		77.08 152	79.79 141	73.92 174	85.95 131	97.23 67	92.18 65	52.65 209	46.19 212	27.79 216	38.27 203	85.63 71	85.67 101	96.95 17	95.62 24	99.30 3	98.67 60

TranMVSNet+NR-MVSNet		77.02 153	75.76 158	78.49 140	78.46 179	88.24 168	83.03 145	79.97 90	73.49 151	54.73 161	54.00 160	48.74 180	78.15 148	82.36 177	86.90 140	96.59 134	96.55 117

CVMVSNet		76.86 154	79.09 144	74.26 171	85.29 138	89.44 152	79.91 172	78.47 108	68.94 170	44.45 198	62.35 137	69.70 122	64.50 189	85.82 148	87.03 139	92.94 193	90.33 176

Baseline_NR-MVSNet		76.71 155	74.56 166	79.23 133	78.68 173	84.15 196	82.45 155	78.87 106	75.83 146	60.05 132	47.92 185	50.18 177	79.06 142	83.16 171	83.86 175	96.26 149	96.80 113

v2v482		76.25 156	74.78 163	77.96 147	78.50 178	89.14 158	83.05 144	76.02 125	68.78 171	54.11 162	51.36 166	48.59 182	79.49 140	83.53 165	85.60 157	96.59 134	96.49 122

V42		76.21 157	75.04 162	77.58 148	78.68 173	89.33 154	82.93 147	74.64 133	69.84 164	56.13 153	50.42 171	50.93 172	76.30 159	83.32 167	84.89 168	96.83 120	96.54 118

v8		75.89 158	74.74 164	77.23 150	79.09 169	88.00 171	83.19 142	71.08 162	70.03 163	56.29 148	50.50 169	50.88 173	77.06 156	83.32 167	84.99 166	96.68 128	95.49 138

TinyColmap		75.75 159	73.19 177	78.74 138	84.82 139	87.69 176	81.59 165	74.62 134	71.81 158	54.01 163	55.79 154	44.42 200	82.89 121	84.61 158	83.76 176	94.50 180	84.22 197

MIMVSNet		75.71 160	77.26 151	73.90 175	70.93 203	88.71 164	79.98 171	57.67 203	73.58 150	58.08 142	53.93 161	58.56 157	79.41 141	90.04 114	89.97 99	97.34 106	86.04 191

UniMVSNet_ETH3D		75.63 161	71.59 186	80.35 124	81.03 156	89.90 144	83.25 140	76.58 120	60.08 195	64.19 126	42.89 198	45.01 196	82.14 126	80.20 190	86.75 142	94.90 175	96.29 124

pm-mvs1		75.61 162	74.19 168	77.26 149	80.16 163	88.79 162	81.49 166	75.49 132	59.49 197	58.09 141	48.32 182	55.53 163	72.35 167	88.61 124	85.48 158	95.99 159	93.12 165

v10		75.57 163	74.67 165	76.62 158	78.73 172	87.46 182	83.14 143	69.41 174	69.27 168	53.44 166	49.73 175	49.21 179	78.44 145	86.17 145	85.18 163	96.53 139	95.65 135

v1144		75.54 164	74.55 167	76.69 156	78.33 182	88.77 163	82.89 150	72.76 147	67.18 175	51.73 171	49.34 177	48.37 183	78.10 149	86.22 144	85.24 161	96.35 146	96.74 114

TDRefinement		75.54 164	73.22 175	78.25 144	87.65 120	89.65 147	85.81 124	79.28 103	71.14 160	56.06 155	52.17 164	51.96 169	68.74 180	81.60 181	80.58 189	91.94 196	85.45 192

pmmvs5		75.46 166	75.12 161	75.87 165	79.39 168	89.44 152	78.12 179	72.27 152	65.98 181	51.54 172	55.83 153	46.23 190	76.80 158	88.77 123	85.73 150	97.07 114	93.84 156

tfpnnormal		75.27 167	72.12 183	78.94 136	82.30 148	88.52 165	82.41 156	79.41 100	58.03 198	55.59 157	43.83 197	44.71 197	77.35 152	87.70 133	85.45 159	96.60 133	96.61 116

anonymousdsp		75.14 168	77.25 152	72.69 181	76.68 192	89.26 155	75.26 188	68.44 177	65.53 184	46.65 192	58.16 149	56.67 160	73.96 163	87.84 132	86.05 148	95.13 173	97.22 104

v148		74.98 169	73.52 173	76.69 156	78.84 171	89.02 159	78.78 175	76.82 118	67.22 174	59.61 133	49.18 178	47.94 187	70.57 173	80.76 185	83.99 173	95.52 163	96.52 120

v1192		74.96 170	73.92 169	76.17 159	77.76 185	88.19 170	82.54 154	71.94 155	66.84 176	50.07 181	48.10 183	46.14 191	78.28 146	86.30 142	85.23 162	96.41 145	96.67 115

v144192		74.76 171	73.64 170	76.06 161	77.58 186	88.23 169	81.87 163	71.63 157	66.03 180	51.08 175	48.63 181	46.77 189	77.59 151	84.53 160	84.76 169	96.64 131	96.54 118

v1921920		74.60 172	73.56 172	75.81 166	77.43 188	87.94 172	82.18 160	71.33 161	66.48 178	49.23 185	47.84 186	45.56 193	78.03 150	85.70 150	84.92 167	96.65 129	96.50 121

v1240		74.04 173	73.04 179	75.20 169	77.19 190	87.69 176	80.93 168	70.72 166	65.08 185	48.47 186	47.31 187	44.71 197	77.33 153	85.50 151	85.07 165	96.59 134	95.94 127

testgi		73.22 174	75.84 157	70.16 192	81.67 155	85.50 191	71.45 193	70.81 164	69.56 166	44.74 197	74.52 88	49.25 178	58.45 199	84.10 163	83.37 180	93.86 184	84.56 196

CP-MVSNet		73.19 175	72.37 181	74.15 172	77.54 187	86.77 185	76.34 182	72.05 153	65.66 183	51.47 173	50.49 170	43.66 201	70.90 169	80.93 184	83.40 179	96.59 134	95.66 134

WR-MVS		72.93 176	73.57 171	72.19 184	78.14 183	87.71 175	76.21 184	73.02 145	67.78 172	50.09 180	50.35 172	50.53 175	61.27 197	80.42 188	83.10 183	94.43 181	95.11 143

TransMVSNet (Re)		72.90 177	70.51 190	75.69 167	80.88 157	85.26 193	79.25 174	78.43 110	56.13 204	52.81 168	46.81 188	48.20 186	66.77 183	85.18 155	83.70 177	95.98 160	88.28 185

WR-MVS_H		72.69 178	72.80 180	72.56 183	77.94 184	87.83 173	75.26 188	71.53 159	64.75 186	52.19 170	49.83 173	48.62 181	61.96 195	81.12 183	82.44 185	96.50 140	95.00 145

SixPastTwentyTwo		72.65 179	73.22 175	71.98 187	78.40 180	87.64 178	70.09 196	70.37 168	66.49 177	47.60 189	65.09 123	45.94 192	73.09 165	78.94 192	78.66 195	92.33 194	89.82 180

LTVRE_ROB	71.82 16	72.62 180	71.77 184	73.62 176	80.74 158	87.59 179	80.42 170	70.37 168	49.73 208	37.12 210	59.76 142	42.52 206	80.92 134	83.20 170	85.61 156	92.13 195	93.95 154
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
PS-CasMVS		72.37 181	71.47 188	73.43 177	77.32 189	86.43 186	75.99 185	71.94 155	63.37 189	49.24 184	49.07 179	42.42 207	69.60 175	80.59 187	83.18 182	96.48 142	95.23 140

MVS-HIRNet		72.32 182	73.45 174	71.00 190	80.58 159	89.97 142	68.51 200	55.28 207	70.89 161	52.27 169	39.09 201	57.11 159	75.02 161	85.76 149	86.33 143	94.36 182	85.00 194

PEN-MVS		72.24 183	71.30 189	73.33 179	77.08 191	85.57 189	76.75 180	72.52 150	63.89 188	48.12 187	50.79 167	43.09 204	69.03 179	78.54 194	83.46 178	96.50 140	93.76 159

v7n		72.11 184	71.66 185	72.63 182	75.26 197	86.85 183	76.74 181	68.77 176	62.70 192	49.40 182	45.92 189	43.51 202	70.63 172	84.16 162	83.21 181	94.99 174	95.25 139

EG-PatchMatch MVS		71.81 185	71.54 187	72.12 185	80.53 160	89.94 143	78.51 176	66.56 182	57.38 200	47.46 190	44.28 196	52.22 166	63.10 193	85.22 154	84.42 170	96.56 138	87.35 189

CMPMVS	54.54 17	71.74 186	67.94 195	76.16 160	90.41 85	93.25 115	78.32 178	75.60 131	59.81 196	53.95 164	44.64 194	51.22 170	70.70 170	74.59 203	75.88 201	88.01 203	76.23 205
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MDTV_nov1_ep13_2view		71.65 187	73.08 178	69.97 193	75.22 198	86.81 184	73.98 190	59.61 197	69.75 165	48.01 188	54.21 159	53.06 165	69.19 177	78.50 195	80.43 190	93.84 185	88.79 183

pmnet_mix02		71.64 188	72.36 182	70.81 191	78.39 181	85.57 189	68.64 198	73.65 142	72.13 154	45.07 196	56.01 152	50.61 174	65.34 187	76.21 200	76.60 199	93.75 188	89.35 181

gm-plane-assit		71.33 189	75.18 160	66.83 196	79.06 170	75.57 207	48.05 214	60.33 192	48.28 209	34.67 214	44.34 195	67.70 130	79.78 139	97.25 11	96.21 13	99.10 10	96.92 110

DTE-MVSNet		71.19 190	70.45 191	72.06 186	76.61 193	84.59 195	75.61 187	72.32 151	63.12 191	45.70 195	50.72 168	43.02 205	65.89 185	77.53 199	82.23 186	96.26 149	91.93 170

pmmvs6		70.29 191	67.90 196	73.07 180	76.17 194	85.31 192	76.29 183	70.75 165	47.39 211	55.33 158	37.15 207	50.49 176	69.55 176	82.96 174	80.85 188	90.34 202	91.18 173

PM-MVS		70.17 192	69.42 193	71.04 189	70.82 204	81.26 202	71.25 194	67.80 179	69.16 169	51.04 176	53.15 163	34.93 211	72.19 168	80.30 189	76.95 198	93.16 192	90.21 177

pmmvs-eth3d		69.59 193	67.57 198	71.95 188	70.04 205	80.05 203	71.48 192	70.00 172	62.57 193	55.99 156	44.92 192	35.73 210	70.64 171	81.56 182	79.69 191	93.55 189	88.43 184

N_pmnet		68.54 194	67.83 197	69.38 194	75.77 195	81.90 199	66.21 202	72.53 149	65.91 182	46.09 194	44.67 193	45.48 194	63.82 191	74.66 202	77.39 197	91.87 197	84.77 195

Anonymous20231206		68.09 195	68.68 194	67.39 195	75.16 199	82.55 197	69.33 197	70.06 171	63.34 190	42.28 201	37.91 205	43.12 203	52.67 202	83.56 164	82.71 184	94.84 177	87.59 187

EU-MVSNet		68.07 196	70.25 192	65.52 197	74.68 201	81.30 201	68.53 199	70.31 170	62.40 194	37.43 209	54.62 158	48.36 184	51.34 203	78.32 196	79.27 192	90.84 200	87.47 188

GG-mvs-BLEND		65.67 197	93.78 40	32.89 210	0.47 220	99.35 6	96.92 31	0.22 219	93.28 60	0.51 221	84.07 54	92.50 39	0.62 218	93.59 65	93.86 55	98.59 42	99.79 9

test20.03		65.17 198	67.41 199	62.55 199	75.35 196	79.31 204	62.22 204	68.83 175	56.50 203	35.35 213	51.97 165	44.70 199	40.01 208	80.69 186	79.25 193	93.55 189	79.47 204

MDA-MVSNet-bldmvs		62.23 199	61.13 203	63.52 198	58.94 211	82.44 198	60.71 207	73.28 144	57.22 201	38.42 207	49.63 176	27.64 217	62.83 194	54.98 209	74.16 202	86.96 205	81.83 201

new_pmnet		61.60 200	62.68 201	60.35 202	63.02 208	74.93 208	60.97 206	58.86 199	64.21 187	35.38 212	39.51 200	39.89 208	57.37 200	72.78 204	72.56 204	86.49 207	74.85 207

new-patchmatchnet		60.74 201	59.78 205	61.87 200	69.52 206	76.67 206	57.99 210	65.78 186	52.63 206	38.47 206	38.08 204	32.92 214	48.88 205	68.50 205	69.87 205	90.56 201	79.75 203

pmmvs3		60.52 202	60.87 204	60.12 203	61.38 209	71.62 209	57.42 211	53.94 208	48.09 210	35.95 211	38.62 202	32.19 216	64.12 190	75.33 201	77.99 196	87.89 204	82.28 200

MIMVSNet1		60.51 203	61.43 202	59.44 204	48.75 214	77.21 205	60.98 205	66.84 181	52.09 207	38.74 205	29.29 210	39.40 209	48.08 206	77.60 198	78.87 194	93.22 191	75.56 206

test_method		60.40 204	66.30 200	53.52 206	37.48 218	64.10 213	55.56 212	42.45 214	71.79 159	41.87 202	33.74 208	46.80 188	61.71 196	79.18 191	73.33 203	82.01 209	95.17 141

FPMVS		56.54 205	52.82 207	60.87 201	74.90 200	67.58 212	67.69 201	65.38 187	57.86 199	41.51 203	37.83 206	34.19 212	41.21 207	55.88 208	53.09 210	74.55 212	63.31 210

PMVS	42.57 18	45.71 206	42.61 209	49.32 207	61.35 210	37.82 217	36.96 216	60.10 194	37.20 213	41.50 204	28.53 211	33.11 213	28.82 213	53.45 210	48.70 212	67.22 214	59.42 211
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gipuma		43.95 207	42.62 208	45.50 208	50.79 213	41.20 216	35.55 217	52.51 210	52.95 205	29.09 215	12.92 213	11.48 220	38.15 209	62.01 207	66.62 207	66.89 215	51.17 212
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMMVS2		41.25 208	42.55 210	39.74 209	43.25 215	55.05 215	38.15 215	47.11 213	31.78 214	11.83 218	21.16 212	19.12 218	20.98 215	49.95 212	56.09 209	77.09 210	64.68 209

E-PMN		27.87 209	24.36 212	31.97 211	41.27 217	25.56 220	16.62 219	49.16 211	22.00 216	9.90 219	11.75 215	7.86 222	29.57 212	22.22 214	34.70 213	45.27 216	46.41 214

MVE	32.98 19	27.61 210	29.89 211	24.94 213	21.97 219	37.22 218	15.56 221	38.83 215	17.49 217	14.72 217	11.64 217	5.62 223	21.26 214	35.20 213	50.95 211	37.29 218	51.13 213
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		26.96 211	22.96 213	31.63 212	41.91 216	25.73 219	16.30 220	49.10 212	22.38 215	9.03 220	11.22 218	8.12 221	29.93 211	20.16 215	31.04 214	43.49 217	42.04 215

testmvs		5.16 212	8.14 214	1.69 214	0.36 221	1.65 221	3.02 222	0.66 217	7.17 218	0.50 222	12.58 214	0.69 224	4.67 216	5.42 216	5.65 215	0.92 219	23.86 217

test123		4.39 213	7.11 215	1.21 215	0.11 222	1.16 222	1.67 223	0.35 218	5.91 219	0.16 223	11.65 216	0.16 225	4.45 217	1.72 217	4.92 216	0.51 220	24.28 216

uanet_test		0.00 214	0.00 216	0.00 216	0.00 223	0.00 223	0.00 224	0.00 220	0.00 220	0.00 224	0.00 219	0.00 226	0.00 219	0.00 218	0.00 217	0.00 221	0.00 218

sosnet-low-res		0.00 214	0.00 216	0.00 216	0.00 223	0.00 223	0.00 224	0.00 220	0.00 220	0.00 224	0.00 219	0.00 226	0.00 219	0.00 218	0.00 217	0.00 221	0.00 218

sosnet		0.00 214	0.00 216	0.00 216	0.00 223	0.00 223	0.00 224	0.00 220	0.00 220	0.00 224	0.00 219	0.00 226	0.00 219	0.00 218	0.00 217	0.00 221	0.00 218

RE-MVS-def										43.17 199

9.14												97.59 9

SR-MVS					98.52 20			93.70 22				96.63 19

Anonymous202405211			81.72 135		88.09 114	94.27 106	89.62 91	82.14 80	82.27 124		48.83 180	72.58 111	91.08 61	87.40 134	88.70 124	94.90 175	97.99 85

our_test_3					78.55 176	84.98 194	70.12 195

ambc			57.08 206		58.68 212	67.71 211	60.07 208		57.13 202	42.79 200	30.00 209	11.64 219	50.18 204	78.89 193	69.14 206	82.64 208	85.02 193

MTAPA										93.37 8		95.71 26

MTMP										93.84 4		94.86 30

Patchmatch-RL test							19.65 218

tmp_tt				57.89 205	79.94 164	59.29 214	52.84 213	36.65 216	94.77 50	68.22 112	72.96 95	65.62 137	33.65 210	66.20 206	58.02 208	76.06 211

XVS					92.16 70	98.56 36	91.04 81			81.00 57		93.49 34				98.00 72

X-MVStestdata					92.16 70	98.56 36	91.04 81			81.00 57		93.49 34				98.00 72

abl_6				93.25 35	97.12 38	98.71 31	94.40 51	87.81 47	97.86 10	87.19 30	91.07 36	95.80 24	94.18 33			98.78 36	99.36 23

mPP-MVS					97.95 29							92.24 44

NP-MVS									94.12 54

Patchmtry						92.08 130	83.86 133	58.37 201		56.28 149

DeepMVS_CX						70.68 210	59.61 209	67.36 180	72.12 155	38.41 208	53.88 162	32.44 215	55.15 201	50.88 211		74.35 213	68.42 208

High-resolution multi-view benchmark