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		95.61 1	96.36 1	94.73 2	96.84 19	98.15 2	97.08 3	92.92 2	95.64 2	91.84 4	95.98 4	95.33 1	92.83 6	96.00 1	94.94 3	96.90 4	98.45 2

DVP-MVS		95.56 2	96.26 2	94.73 2	96.93 16	98.19 1	96.62 6	92.81 4	96.15 1	91.73 5	95.01 7	95.31 2	93.41 1	95.95 2	94.77 7	96.90 4	98.46 1
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
DPE-MVS		95.53 3	96.13 3	94.82 1	96.81 22	98.05 3	97.42 1	93.09 1	94.31 8	91.49 6	97.12 1	95.03 3	93.27 3	95.55 5	94.58 11	96.86 6	98.25 3
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
HPM-MVS++		94.60 8	94.91 10	94.24 7	97.86 1	96.53 32	96.14 8	92.51 8	93.87 15	90.76 12	93.45 18	93.84 4	92.62 8	95.11 11	94.08 18	95.58 51	97.48 13

MSP-MVS		95.12 5	95.83 4	94.30 5	96.82 21	97.94 4	96.98 4	92.37 11	95.40 3	90.59 13	96.16 3	93.71 5	92.70 7	94.80 15	94.77 7	96.37 14	97.99 7
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
MCST-MVS		93.81 16	94.06 18	93.53 17	96.79 23	96.85 20	95.95 13	91.69 16	92.20 26	87.17 32	90.83 27	93.41 6	91.96 14	94.49 24	93.50 31	97.61 1	97.12 22

SD-MVS		94.53 9	95.22 7	93.73 15	95.69 36	97.03 14	95.77 22	91.95 12	94.41 7	91.35 7	94.97 8	93.34 7	91.80 20	94.72 18	93.99 19	95.82 35	98.07 6
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
CNVR-MVS		94.37 11	94.65 11	94.04 11	97.29 6	97.11 10	96.00 10	92.43 10	93.45 16	89.85 19	90.92 25	93.04 8	92.59 9	95.77 4	94.82 5	96.11 22	97.42 15

abl_6				90.66 40	94.65 47	96.27 37	92.21 50	86.94 48	90.23 41	86.38 37	85.50 40	92.96 9	88.37 47			95.40 58	95.46 54

APDe-MVS		95.23 4	95.69 5	94.70 4	97.12 10	97.81 5	97.19 2	92.83 3	95.06 5	90.98 10	96.47 2	92.77 10	93.38 2	95.34 8	94.21 15	96.68 8	98.17 4

SMA-MVS		94.70 6	95.35 6	93.93 12	97.57 2	97.57 7	95.98 11	91.91 13	94.50 6	90.35 14	93.46 17	92.72 11	91.89 18	95.89 3	95.22 1	95.88 28	98.10 5
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
SF-MVS		94.61 7	94.96 9	94.20 8	96.75 24	97.07 11	95.82 18	92.60 6	93.98 12	91.09 8	95.89 5	92.54 12	91.93 15	94.40 26	93.56 28	97.04 2	97.27 16

SR-MVS					96.58 26			90.99 22				92.40 13

NCCC		93.69 19	93.66 22	93.72 16	97.37 5	96.66 29	95.93 16	92.50 9	93.40 19	88.35 25	87.36 35	92.33 14	92.18 12	94.89 13	94.09 17	96.00 24	96.91 26

TSAR-MVS + MP.		94.48 10	94.97 8	93.90 13	95.53 37	97.01 15	96.69 5	90.71 24	94.24 9	90.92 11	94.97 8	92.19 15	93.03 4	94.83 14	93.60 26	96.51 13	97.97 8
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
9.14												92.16 16

CSCG		92.76 26	93.16 27	92.29 29	96.30 28	97.74 6	94.67 33	88.98 36	92.46 23	89.73 20	86.67 37	92.15 17	88.69 43	92.26 55	92.92 43	95.40 58	97.89 9

TSAR-MVS + GP.		92.71 28	93.91 20	91.30 35	91.96 72	96.00 41	93.43 41	87.94 41	92.53 22	86.27 40	93.57 15	91.94 18	91.44 25	93.29 41	92.89 44	96.78 7	97.15 21

train_agg		92.87 25	93.53 24	92.09 30	96.88 18	95.38 49	95.94 14	90.59 28	90.65 38	83.65 51	94.31 13	91.87 19	90.30 32	93.38 40	92.42 47	95.17 70	96.73 31

APD-MVS		94.37 11	94.47 15	94.26 6	97.18 8	96.99 16	96.53 7	92.68 5	92.45 24	89.96 17	94.53 11	91.63 20	92.89 5	94.58 21	93.82 22	96.31 17	97.26 18
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
DeepPCF-MVS	88.51 2	92.64 29	94.42 16	90.56 41	94.84 44	96.92 18	91.31 61	89.61 32	95.16 4	84.55 46	89.91 29	91.45 21	90.15 35	95.12 10	94.81 6	92.90 150	97.58 12

MTAPA										92.97 2		91.03 22

PHI-MVS		92.05 32	93.74 21	90.08 44	94.96 41	97.06 13	93.11 45	87.71 44	90.71 37	80.78 68	92.40 22	91.03 22	87.68 54	94.32 28	94.48 12	96.21 19	96.16 41

SteuartSystems-ACMMP		94.06 13	94.65 11	93.38 19	96.97 15	97.36 8	96.12 9	91.78 14	92.05 28	87.34 30	94.42 12	90.87 24	91.87 19	95.47 7	94.59 10	96.21 19	97.77 10
Skip Steuart: Steuart Systems R&D Blog.
zzz-MVS		93.80 17	93.45 25	94.20 8	97.53 3	96.43 36	95.88 17	91.12 20	94.09 11	92.74 3	87.68 33	90.77 25	92.04 13	94.74 17	93.56 28	95.91 27	96.85 27

DPM-MVS		91.72 35	91.48 35	92.00 31	95.53 37	95.75 45	95.94 14	91.07 21	91.20 34	85.58 41	81.63 56	90.74 26	88.40 46	93.40 39	93.75 24	95.45 57	93.85 83

TSAR-MVS + ACMM		92.97 23	94.51 13	91.16 37	95.88 34	96.59 30	95.09 29	90.45 30	93.42 17	83.01 53	94.68 10	90.74 26	88.74 42	94.75 16	93.78 23	93.82 131	97.63 11

ACMMP_NAP		93.94 15	94.49 14	93.30 20	97.03 13	97.31 9	95.96 12	91.30 18	93.41 18	88.55 24	93.00 19	90.33 28	91.43 26	95.53 6	94.41 13	95.53 53	97.47 14

MP-MVS		93.35 20	93.59 23	93.08 23	97.39 4	96.82 22	95.38 25	90.71 24	90.82 36	88.07 27	92.83 21	90.29 29	91.32 27	94.03 30	93.19 39	95.61 49	97.16 20
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MTMP										93.14 1		90.21 30

HFP-MVS		94.02 14	94.22 17	93.78 14	97.25 7	96.85 20	95.81 20	90.94 23	94.12 10	90.29 16	94.09 14	89.98 31	92.52 10	93.94 33	93.49 33	95.87 30	97.10 23

CANet		91.33 38	91.46 36	91.18 36	95.01 40	96.71 24	93.77 38	87.39 46	87.72 51	87.26 31	81.77 53	89.73 32	87.32 60	94.43 25	93.86 21	96.31 17	96.02 44

MVS_0304		90.88 40	91.35 38	90.34 42	93.91 52	96.79 23	94.49 34	86.54 50	86.57 55	82.85 55	81.68 55	89.70 33	87.57 56	94.64 20	93.93 20	96.67 10	96.15 42

CP-MVS		93.25 21	93.26 26	93.24 21	96.84 19	96.51 33	95.52 24	90.61 27	92.37 25	88.88 22	90.91 26	89.52 34	91.91 17	93.64 37	92.78 45	95.69 42	97.09 24

DeepC-MVS_fast	88.76 1	93.10 22	93.02 29	93.19 22	97.13 9	96.51 33	95.35 26	91.19 19	93.14 21	88.14 26	85.26 41	89.49 35	91.45 23	95.17 9	95.07 2	95.85 33	96.48 35
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
3Dnovator+	86.06 4	91.60 36	90.86 42	92.47 27	96.00 33	96.50 35	94.70 32	87.83 43	90.49 39	89.92 18	74.68 91	89.35 36	90.66 30	94.02 31	94.14 16	95.67 44	96.85 27

UA-Net		86.07 76	87.78 66	84.06 100	92.85 65	95.11 53	87.73 106	84.38 63	73.22 149	73.18 103	79.99 63	89.22 37	71.47 170	93.22 42	93.03 40	94.76 87	90.69 139

ACMMPR		93.72 18	93.94 19	93.48 18	97.07 11	96.93 17	95.78 21	90.66 26	93.88 14	89.24 21	93.53 16	89.08 38	92.24 11	93.89 35	93.50 31	95.88 28	96.73 31

MSLP-MVS++		92.02 34	91.40 37	92.75 24	96.01 32	95.88 44	93.73 40	89.00 34	89.89 44	90.31 15	81.28 58	88.85 39	91.45 23	92.88 48	94.24 14	96.00 24	96.76 30

XVS					93.11 60	96.70 25	91.91 54			83.95 48		88.82 40				95.79 37

X-MVStestdata					93.11 60	96.70 25	91.91 54			83.95 48		88.82 40				95.79 37

X-MVS		92.36 30	92.75 30	91.90 33	96.89 17	96.70 25	95.25 27	90.48 29	91.50 33	83.95 48	88.20 31	88.82 40	89.11 38	93.75 36	93.43 35	95.75 40	96.83 29

CPTT-MVS		91.39 37	90.95 40	91.91 32	95.06 39	95.24 51	95.02 30	88.98 36	91.02 35	86.71 34	84.89 43	88.58 43	91.60 22	90.82 82	89.67 94	94.08 118	96.45 36

PGM-MVS		92.76 26	93.03 28	92.45 28	97.03 13	96.67 28	95.73 23	87.92 42	90.15 43	86.53 36	92.97 20	88.33 44	91.69 21	93.62 38	93.03 40	95.83 34	96.41 38

mPP-MVS					97.06 12							88.08 45

QAPM		89.49 49	89.58 51	89.38 52	94.73 45	95.94 42	92.35 49	85.00 60	85.69 60	80.03 72	76.97 79	87.81 46	87.87 51	92.18 59	92.10 49	96.33 15	96.40 39

CDPH-MVS		91.14 39	92.01 32	90.11 43	96.18 29	96.18 39	94.89 31	88.80 38	88.76 48	77.88 85	89.18 30	87.71 47	87.29 61	93.13 43	93.31 37	95.62 47	95.84 46

3Dnovator	85.17 5	90.48 42	89.90 48	91.16 37	94.88 43	95.74 46	93.82 37	85.36 57	89.28 45	87.81 28	74.34 93	87.40 48	88.56 44	93.07 44	93.74 25	96.53 12	95.71 48

CS-MVS		89.50 48	90.33 44	88.52 62	90.14 96	94.40 65	91.00 64	80.28 115	84.74 69	82.81 57	82.72 47	87.23 49	90.22 33	94.66 19	93.48 34	96.67 10	95.07 58

OMC-MVS		90.23 44	90.40 43	90.03 45	93.45 57	95.29 50	91.89 56	86.34 52	93.25 20	84.94 45	81.72 54	86.65 50	88.90 39	91.69 63	90.27 76	94.65 95	93.95 81

DeepC-MVS	87.86 3	92.26 31	91.86 34	92.73 25	96.18 29	96.87 19	95.19 28	91.76 15	92.17 27	86.58 35	81.79 52	85.85 51	90.88 29	94.57 22	94.61 9	95.80 36	97.18 19
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
OpenMVS	82.53 11	87.71 66	86.84 73	88.73 57	94.42 48	95.06 54	91.02 63	83.49 78	82.50 76	82.24 60	67.62 131	85.48 52	85.56 72	91.19 70	91.30 56	95.67 44	94.75 66

MVS_111021_HR		90.56 41	91.29 39	89.70 49	94.71 46	95.63 47	91.81 57	86.38 51	87.53 52	81.29 63	87.96 32	85.43 53	87.69 53	93.90 34	92.93 42	96.33 15	95.69 49

PCF-MVS	84.60 6	88.66 55	87.75 68	89.73 48	93.06 62	96.02 40	93.22 44	90.00 31	82.44 77	80.02 73	77.96 75	85.16 54	87.36 59	88.54 112	88.54 116	94.72 90	95.61 51
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
TAPA-MVS	84.37 7	88.91 53	88.93 54	88.89 55	93.00 63	94.85 58	92.00 53	84.84 61	91.68 32	80.05 71	79.77 64	84.56 55	88.17 49	90.11 93	89.00 111	95.30 65	92.57 112
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
ACMMP		92.03 33	92.16 31	91.87 34	95.88 34	96.55 31	94.47 35	89.49 33	91.71 31	85.26 42	91.52 24	84.48 56	90.21 34	92.82 49	91.63 53	95.92 26	96.42 37
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
DELS-MVS		89.71 46	89.68 50	89.74 47	93.75 54	96.22 38	93.76 39	85.84 53	82.53 74	85.05 44	78.96 69	84.24 57	84.25 77	94.91 12	94.91 4	95.78 39	96.02 44
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
CS-MVS-test		88.88 54	89.99 45	87.58 71	89.90 99	94.63 62	89.40 82	79.74 124	85.16 63	79.87 74	82.43 49	84.08 58	90.40 31	94.31 29	92.76 46	96.19 21	96.56 34

PVSNet_Blended_VisFu		87.40 70	87.80 65	86.92 74	92.86 64	95.40 48	88.56 98	83.45 82	79.55 104	82.26 59	74.49 92	84.03 59	79.24 126	92.97 47	91.53 55	95.15 72	96.65 33

MVS_111021_LR		90.14 45	90.89 41	89.26 53	93.23 59	94.05 71	90.43 67	84.65 62	90.16 42	84.52 47	90.14 28	83.80 60	87.99 50	92.50 53	90.92 62	94.74 88	94.70 68

GG-mvs-BLEND		57.56 206	82.61 105	28.34 214	0.22 222	90.10 122	79.37 184	0.14 220	79.56 103	0.40 223	71.25 108	83.40 61	0.30 220	86.27 144	83.87 170	89.59 183	83.83 183

UGNet		85.90 79	88.23 59	83.18 110	88.96 107	94.10 69	87.52 108	83.60 74	81.66 84	77.90 84	80.76 60	83.19 62	66.70 187	91.13 76	90.71 68	94.39 111	96.06 43
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
EPNet		89.60 47	89.91 47	89.24 54	96.45 27	93.61 76	92.95 47	88.03 40	85.74 59	83.36 52	87.29 36	83.05 63	80.98 96	92.22 56	91.85 51	93.69 136	95.58 52
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MAR-MVS		88.39 60	88.44 57	88.33 65	94.90 42	95.06 54	90.51 66	83.59 75	85.27 61	79.07 77	77.13 77	82.89 64	87.70 52	92.19 58	92.32 48	94.23 115	94.20 79
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
ETV-MVS		89.22 51	89.76 49	88.60 60	91.60 73	94.61 63	89.48 81	83.46 81	85.20 62	81.58 61	82.75 46	82.59 65	88.80 40	94.57 22	93.28 38	96.68 8	95.31 56

EPP-MVSNet		86.55 72	87.76 67	85.15 84	90.52 86	94.41 64	87.24 115	82.32 96	81.79 83	73.60 101	78.57 71	82.41 66	82.07 90	91.23 67	90.39 74	95.14 73	95.48 53

Vis-MVSNet (Re-imp)		83.65 99	86.81 75	79.96 144	90.46 89	92.71 90	84.84 145	82.00 98	80.93 93	62.44 160	76.29 81	82.32 67	65.54 190	92.29 54	91.66 52	94.49 105	91.47 134

IS_MVSNet		86.18 75	88.18 60	83.85 103	91.02 79	94.72 61	87.48 109	82.46 95	81.05 91	70.28 115	76.98 78	82.20 68	76.65 142	93.97 32	93.38 36	95.18 69	94.97 61

CNLPA		88.40 58	87.00 72	90.03 45	93.73 55	94.28 66	89.56 79	85.81 54	91.87 29	87.55 29	69.53 119	81.49 69	89.23 37	89.45 103	88.59 115	94.31 114	93.82 84

AdaColmap		90.29 43	88.38 58	92.53 26	96.10 31	95.19 52	92.98 46	91.40 17	89.08 47	88.65 23	78.35 72	81.44 70	91.30 28	90.81 83	90.21 77	94.72 90	93.59 89

baseline		84.89 86	86.06 81	83.52 108	87.25 125	89.67 135	87.76 105	75.68 164	84.92 65	78.40 79	80.10 61	80.98 71	80.20 110	86.69 137	87.05 131	91.86 162	92.99 95

PLC	83.76 9	88.61 57	86.83 74	90.70 39	94.22 49	92.63 93	91.50 59	87.19 47	89.16 46	86.87 33	75.51 86	80.87 72	89.98 36	90.01 94	89.20 105	94.41 110	90.45 144
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
Vis-MVSNet		84.38 94	86.68 77	81.70 124	87.65 121	94.89 57	88.14 101	80.90 106	74.48 135	68.23 127	77.53 76	80.72 73	69.98 174	92.68 50	91.90 50	95.33 64	94.58 70
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
DI_MVS_plusplus_trai		86.41 74	85.54 86	87.42 72	89.24 103	93.13 82	92.16 52	82.65 92	82.30 78	80.75 69	68.30 127	80.41 74	85.01 74	90.56 89	90.07 80	94.70 92	94.01 80

HQP-MVS		89.13 52	89.58 51	88.60 60	93.53 56	93.67 74	93.29 43	87.58 45	88.53 49	75.50 90	87.60 34	80.32 75	87.07 62	90.66 88	89.95 85	94.62 97	96.35 40

EIA-MVS		87.94 65	88.05 62	87.81 67	91.46 74	95.00 56	88.67 94	82.81 86	82.53 74	80.81 67	80.04 62	80.20 76	87.48 57	92.58 52	91.61 54	95.63 46	94.36 73

CANet_DTU		85.43 81	87.72 69	82.76 114	90.95 82	93.01 86	89.99 71	75.46 165	82.67 73	64.91 145	83.14 45	80.09 77	80.68 100	92.03 61	91.03 59	94.57 100	92.08 120

CHOSEN 280x420		80.28 128	81.66 109	78.67 155	82.92 178	79.24 205	85.36 140	66.79 197	78.11 113	70.32 113	75.03 90	79.87 78	81.09 95	89.07 106	83.16 175	85.54 202	87.17 166

RPSCF		83.46 100	83.36 98	83.59 106	87.75 117	87.35 162	84.82 146	79.46 128	83.84 70	78.12 81	82.69 48	79.87 78	82.60 86	82.47 182	81.13 185	88.78 187	86.13 174

MVS_Test		86.93 71	87.24 70	86.56 75	90.10 97	93.47 78	90.31 68	80.12 117	83.55 71	78.12 81	79.58 65	79.80 80	85.45 73	90.17 92	90.59 71	95.29 66	93.53 90

PMMVS		81.65 118	84.05 94	78.86 151	78.56 199	82.63 193	83.10 157	67.22 195	81.39 85	70.11 117	84.91 42	79.74 81	82.12 89	87.31 124	85.70 155	92.03 160	86.67 172

GBi-Net		84.51 90	84.80 89	84.17 97	84.20 160	89.95 123	89.70 75	80.37 110	81.17 87	75.50 90	69.63 115	79.69 82	79.75 118	90.73 84	90.72 65	95.52 54	91.71 127

test1		84.51 90	84.80 89	84.17 97	84.20 160	89.95 123	89.70 75	80.37 110	81.17 87	75.50 90	69.63 115	79.69 82	79.75 118	90.73 84	90.72 65	95.52 54	91.71 127

FMVSNet3		84.44 92	84.64 91	84.21 96	84.32 159	90.13 121	89.85 74	80.37 110	81.17 87	75.50 90	69.63 115	79.69 82	79.62 121	89.72 98	90.52 73	95.59 50	91.58 133

canonicalmvs		89.36 50	89.92 46	88.70 58	91.38 75	95.92 43	91.81 57	82.61 94	90.37 40	82.73 58	82.09 50	79.28 85	88.30 48	91.17 71	93.59 27	95.36 61	97.04 25

DCV-MVSNet		85.88 80	86.17 78	85.54 82	89.10 106	89.85 128	89.34 83	80.70 107	83.04 72	78.08 83	76.19 82	79.00 86	82.42 87	89.67 99	90.30 75	93.63 139	95.12 57

Anonymous20231211		84.42 93	83.02 99	86.05 78	88.85 108	92.70 91	88.92 93	83.40 83	79.99 99	78.31 80	55.83 187	78.92 87	83.33 81	89.06 107	89.76 92	93.50 141	94.90 62

MS-PatchMatch		81.79 117	81.44 112	82.19 121	90.35 91	89.29 142	88.08 104	75.36 166	77.60 117	69.00 124	64.37 151	78.87 88	77.14 141	88.03 118	85.70 155	93.19 147	86.24 173

CLD-MVS		88.66 55	88.52 56	88.82 56	91.37 76	94.22 67	92.82 48	82.08 97	88.27 50	85.14 43	81.86 51	78.53 89	85.93 71	91.17 71	90.61 70	95.55 52	95.00 60
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
PVSNet_BlendedMVS		88.19 63	88.00 63	88.42 63	92.71 68	94.82 59	89.08 87	83.81 70	84.91 66	86.38 37	79.14 66	78.11 90	82.66 84	93.05 45	91.10 57	95.86 31	94.86 64

PVSNet_Blended		88.19 63	88.00 63	88.42 63	92.71 68	94.82 59	89.08 87	83.81 70	84.91 66	86.38 37	79.14 66	78.11 90	82.66 84	93.05 45	91.10 57	95.86 31	94.86 64

OPM-MVS		87.56 68	85.80 84	89.62 50	93.90 53	94.09 70	94.12 36	88.18 39	75.40 129	77.30 88	76.41 80	77.93 92	88.79 41	92.20 57	90.82 64	95.40 58	93.72 87

casdiffmvs		87.45 69	87.15 71	87.79 69	90.15 95	94.22 67	89.96 72	83.93 69	85.08 64	80.91 65	75.81 84	77.88 93	86.08 69	91.86 62	90.86 63	95.74 41	94.37 72

FMVSNet2		83.87 95	83.73 97	84.05 101	84.20 160	89.95 123	89.70 75	80.21 116	79.17 108	74.89 94	65.91 136	77.49 94	79.75 118	90.87 81	91.00 61	95.52 54	91.71 127

diffmvs		86.52 73	86.76 76	86.23 77	88.31 113	92.63 93	89.58 78	81.61 101	86.14 56	80.26 70	79.00 68	77.27 95	83.58 78	88.94 108	89.06 108	94.05 120	94.29 74

EPNet_dtu		81.98 113	83.82 96	79.83 146	94.10 51	85.97 171	87.29 113	84.08 68	80.61 96	59.96 178	81.62 57	77.19 96	62.91 194	87.21 125	86.38 144	90.66 176	87.77 164
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
gg-mvs-nofinetune		75.64 182	77.26 161	73.76 187	87.92 116	92.20 98	87.32 112	64.67 205	51.92 211	35.35 215	46.44 203	77.05 97	71.97 167	92.64 51	91.02 60	95.34 63	89.53 148

Anonymous202405211			82.75 104		89.58 101	92.97 87	89.04 90	84.13 67	78.72 110		57.18 183	76.64 98	83.13 82	89.55 101	89.92 86	93.38 144	94.28 77

LGP-MVS_train		88.25 62	88.55 55	87.89 66	92.84 66	93.66 75	93.35 42	85.22 59	85.77 58	74.03 99	86.60 38	76.29 99	86.62 67	91.20 69	90.58 72	95.29 66	95.75 47

SCA		79.51 139	80.15 130	78.75 153	86.58 131	87.70 159	83.07 158	68.53 190	81.31 86	66.40 134	73.83 95	75.38 100	79.30 125	80.49 189	79.39 190	88.63 189	82.96 188

IterMVS-LS		83.28 102	82.95 102	83.65 104	88.39 112	88.63 153	86.80 124	78.64 138	76.56 121	73.43 102	72.52 104	75.35 101	80.81 98	86.43 143	88.51 117	93.84 130	92.66 107
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ACMM	83.27 10	87.68 67	86.09 80	89.54 51	93.26 58	92.19 99	91.43 60	86.74 49	86.02 57	82.85 55	75.63 85	75.14 102	88.41 45	90.68 87	89.99 82	94.59 98	92.97 96
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMP	83.90 8	88.32 61	88.06 61	88.62 59	92.18 70	93.98 72	91.28 62	85.24 58	86.69 54	81.23 64	85.62 39	75.13 103	87.01 64	89.83 96	89.77 91	94.79 84	95.43 55
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MVSTER		86.03 77	86.12 79	85.93 79	88.62 109	89.93 126	89.33 84	79.91 122	81.87 82	81.35 62	81.07 59	74.91 104	80.66 101	92.13 60	90.10 79	95.68 43	92.80 102

test_part1		83.23 103	80.55 125	86.35 76	88.60 110	90.61 112	90.78 65	81.13 105	70.89 160	83.01 53	55.72 188	74.60 105	82.19 88	87.79 120	89.26 102	92.39 155	95.01 59

GeoE		84.62 88	83.98 95	85.35 83	89.34 102	92.83 89	88.34 99	78.95 133	79.29 106	77.16 89	68.10 128	74.56 106	83.40 80	89.31 105	89.23 104	94.92 79	94.57 71

test-LLR		79.47 140	79.84 135	79.03 150	87.47 122	82.40 196	81.24 173	78.05 143	73.72 144	62.69 157	73.76 96	74.42 107	73.49 161	84.61 167	82.99 177	91.25 170	87.01 167

TESTMET0.1,1		77.78 158	79.84 135	75.38 179	80.86 194	82.40 196	81.24 173	62.72 208	73.72 144	62.69 157	73.76 96	74.42 107	73.49 161	84.61 167	82.99 177	91.25 170	87.01 167

baseline1		84.54 89	84.43 92	84.67 88	90.62 84	91.16 107	88.63 96	83.75 72	79.78 101	71.16 111	75.14 88	74.10 109	77.84 135	91.56 64	90.67 69	96.04 23	88.58 153

Effi-MVS+		85.33 82	85.08 88	85.63 81	89.69 100	93.42 79	89.90 73	80.31 114	79.32 105	72.48 110	73.52 99	74.03 110	86.55 68	90.99 78	89.98 83	94.83 83	94.27 78

CDS-MVSNet		81.63 120	82.09 107	81.09 133	87.21 126	90.28 117	87.46 111	80.33 113	69.06 170	70.66 112	71.30 106	73.87 111	67.99 180	89.58 100	89.87 87	92.87 151	90.69 139
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
test-mter		77.79 157	80.02 132	75.18 180	81.18 193	82.85 191	80.52 179	62.03 209	73.62 146	62.16 162	73.55 98	73.83 112	73.81 159	84.67 166	83.34 174	91.37 168	88.31 156

thisisatest0530		85.15 84	85.86 82	84.33 93	89.19 105	92.57 96	87.22 116	80.11 118	82.15 80	74.41 96	78.15 73	73.80 113	79.90 114	90.99 78	89.58 95	95.13 74	93.75 86

HyFIR lowres test		81.62 121	79.45 141	84.14 99	91.00 80	93.38 80	88.27 100	78.19 141	76.28 123	70.18 116	48.78 200	73.69 114	83.52 79	87.05 128	87.83 123	93.68 137	89.15 150

tttt0517		85.11 85	85.81 83	84.30 94	89.24 103	92.68 92	87.12 120	80.11 118	81.98 81	74.31 98	78.08 74	73.57 115	79.90 114	91.01 77	89.58 95	95.11 76	93.77 85

FC-MVSNet-test		76.53 170	81.62 110	70.58 195	84.99 152	85.73 174	74.81 197	78.85 136	77.00 120	39.13 213	75.90 83	73.50 116	54.08 202	86.54 140	85.99 152	91.65 164	86.68 170

PatchmatchNet		78.67 150	78.85 144	78.46 158	86.85 130	86.03 170	83.77 154	68.11 193	80.88 94	66.19 135	72.90 102	73.40 117	78.06 132	79.25 195	77.71 195	87.75 192	81.75 191
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MDTV_nov1_ep13		79.14 144	79.49 140	78.74 154	85.40 144	86.89 166	84.32 151	70.29 183	78.85 109	69.42 121	75.37 87	73.29 118	75.64 147	80.61 188	79.48 189	87.36 193	81.91 190

TSAR-MVS + COLMAP		88.40 58	89.09 53	87.60 70	92.72 67	93.92 73	92.21 50	85.57 56	91.73 30	73.72 100	91.75 23	73.22 119	87.64 55	91.49 65	89.71 93	93.73 134	91.82 125

CHOSEN 1792x2688		82.16 111	80.91 121	83.61 105	91.14 77	92.01 100	89.55 80	79.15 132	79.87 100	70.29 114	52.51 196	72.56 120	81.39 92	88.87 110	88.17 119	90.15 180	92.37 119

LS3D		85.96 78	84.37 93	87.81 67	94.13 50	93.27 81	90.26 70	89.00 34	84.91 66	72.84 108	71.74 105	72.47 121	87.45 58	89.53 102	89.09 107	93.20 146	89.60 147

FMVSNet1		81.64 119	80.61 123	82.84 113	82.36 185	89.20 144	88.67 94	79.58 126	70.79 161	72.63 109	58.95 178	72.26 122	79.34 124	90.73 84	90.72 65	94.47 106	91.62 131

Effi-MVS+-dtu		82.05 112	81.76 108	82.38 118	87.72 118	90.56 113	86.90 123	78.05 143	73.85 143	66.85 132	71.29 107	71.90 123	82.00 91	86.64 138	85.48 157	92.76 152	92.58 111

CostFormer		80.94 124	80.21 128	81.79 123	87.69 119	88.58 154	87.47 110	70.66 181	80.02 98	77.88 85	73.03 100	71.40 124	78.24 131	79.96 191	79.63 187	88.82 186	88.84 151

FC-MVSNet-train		85.18 83	85.31 87	85.03 85	90.67 83	91.62 104	87.66 107	83.61 73	79.75 102	74.37 97	78.69 70	71.21 125	78.91 127	91.23 67	89.96 84	94.96 78	94.69 69

ET-MVSNet_ETH3D		84.65 87	85.58 85	83.56 107	74.99 208	92.62 95	90.29 69	80.38 109	82.16 79	73.01 107	83.41 44	71.10 126	87.05 63	87.77 121	90.17 78	95.62 47	91.82 125

xxxxxxxxxxxxxcwj		92.95 24	91.88 33	94.20 8	96.75 24	97.07 11	95.82 18	92.60 6	93.98 12	91.09 8	95.89 5	71.01 127	91.93 15	94.40 26	93.56 28	97.04 2	97.27 16

IterMVS-SCA-FT		79.41 141	80.20 129	78.49 157	85.88 136	86.26 169	83.95 152	71.94 176	73.55 147	61.94 164	70.48 112	70.50 128	75.23 148	85.81 150	84.61 167	91.99 161	90.18 145

IterMVS		78.79 148	79.71 138	77.71 161	85.26 147	85.91 172	84.54 148	69.84 187	73.38 148	61.25 172	70.53 111	70.35 129	74.43 157	85.21 160	83.80 172	90.95 174	88.77 152
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CVMVSNet		76.70 166	78.46 147	74.64 185	83.34 170	84.48 184	81.83 169	74.58 167	68.88 171	51.23 199	69.77 114	70.05 130	67.49 183	84.27 170	83.81 171	89.38 184	87.96 161

IB-MVS	79.09 12	82.60 108	82.19 106	83.07 111	91.08 78	93.55 77	80.90 176	81.35 102	76.56 121	80.87 66	64.81 148	69.97 131	68.87 177	85.64 151	90.06 81	95.36 61	94.74 67
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
baseline2		82.80 105	82.86 103	82.73 115	87.68 120	90.50 114	84.92 144	78.93 134	78.07 116	73.06 104	75.08 89	69.77 132	77.31 138	88.90 109	86.94 133	94.50 103	90.74 138

EPMVS		77.53 160	78.07 153	76.90 168	86.89 129	84.91 183	82.18 168	66.64 198	81.00 92	64.11 149	72.75 103	69.68 133	74.42 158	79.36 194	78.13 193	87.14 195	80.68 197

CR-MVSNet		78.71 149	78.86 143	78.55 156	85.85 139	85.15 180	82.30 165	68.23 191	74.71 133	65.37 141	64.39 150	69.59 134	77.18 139	85.10 163	84.87 162	92.34 157	88.21 157

test0.0.03 1		76.03 176	78.51 145	73.12 191	87.47 122	85.13 182	76.32 194	78.05 143	73.19 151	50.98 200	70.64 109	69.28 135	55.53 198	85.33 156	84.38 169	90.39 178	81.63 192

TAMVS		76.42 171	77.16 163	75.56 177	83.05 174	85.55 177	80.58 178	71.43 178	65.40 189	61.04 175	67.27 132	69.22 136	67.99 180	84.88 165	84.78 164	89.28 185	83.01 187

MSDG		83.87 95	81.02 118	87.19 73	92.17 71	89.80 130	89.15 85	85.72 55	80.61 96	79.24 76	66.66 134	68.75 137	82.69 83	87.95 119	87.44 125	94.19 116	85.92 176

Fast-Effi-MVS+		83.77 97	82.98 100	84.69 86	87.98 114	91.87 101	88.10 102	77.70 147	78.10 114	73.04 105	69.13 121	68.51 138	86.66 65	90.49 90	89.85 88	94.67 93	92.88 98

DROMVSNet		83.77 97	82.98 100	84.69 86	87.98 114	91.87 101	88.10 102	77.70 147	78.10 114	73.04 105	69.13 121	68.51 138	86.66 65	90.49 90	89.85 88	94.67 93	92.88 98

Fast-Effi-MVS+-dtu		79.95 131	80.69 122	79.08 149	86.36 133	89.14 146	85.85 132	72.28 175	72.85 152	59.32 181	70.43 113	68.42 140	77.57 136	86.14 145	86.44 143	93.11 148	91.39 135

FMVSNet5		75.50 183	76.07 173	74.83 182	76.16 204	81.19 199	81.34 171	70.21 184	73.20 150	61.59 169	58.97 177	68.33 141	68.50 178	85.87 149	85.85 153	91.18 173	79.11 200

ADS-MVSNet		74.53 187	75.69 180	73.17 190	81.57 191	80.71 201	79.27 185	63.03 207	79.27 107	59.94 179	67.86 129	68.32 142	71.08 171	77.33 199	76.83 197	84.12 207	79.53 198

PatchT		76.42 171	77.81 157	74.80 183	78.46 200	84.30 185	71.82 203	65.03 204	73.89 141	65.37 141	61.58 159	66.70 143	77.18 139	85.10 163	84.87 162	90.94 175	88.21 157

RPMNet		77.07 163	77.63 159	76.42 171	85.56 143	85.15 180	81.37 170	65.27 202	74.71 133	60.29 177	63.71 153	66.59 144	73.64 160	82.71 180	82.12 182	92.38 156	88.39 155

thisisatest0515		79.76 135	80.59 124	78.80 152	84.40 158	88.91 151	79.48 182	76.94 154	72.29 153	67.33 130	67.82 130	65.99 145	70.80 172	88.50 113	87.84 121	93.86 129	92.75 105

tpmrst		76.55 169	75.99 176	77.20 164	87.32 124	83.05 189	82.86 159	65.62 200	78.61 112	67.22 131	69.19 120	65.71 146	75.87 146	76.75 201	75.33 200	84.31 205	83.28 186

COLMAP_ROB	76.78 15	80.50 127	78.49 146	82.85 112	90.96 81	89.65 136	86.20 130	83.40 83	77.15 119	66.54 133	62.27 156	65.62 147	77.89 134	85.23 158	84.70 165	92.11 158	84.83 180
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
ACMH+	79.08 13	81.84 116	80.06 131	83.91 102	89.92 98	90.62 111	86.21 129	83.48 80	73.88 142	65.75 138	66.38 135	65.30 148	84.63 75	85.90 148	87.25 128	93.45 142	91.13 137

pm-mvs1		78.51 153	77.75 158	79.40 147	84.83 156	89.30 141	83.55 156	79.38 129	62.64 194	63.68 152	58.73 180	64.68 149	70.78 173	89.79 97	87.84 121	94.17 117	91.28 136

UniMVSNet_NR-MVSNet		81.87 114	81.33 114	82.50 116	85.31 146	91.30 105	85.70 134	84.25 64	75.89 125	64.21 147	66.95 133	64.65 150	80.22 108	87.07 127	89.18 106	95.27 68	94.29 74

MIMVSNet		74.69 186	75.60 181	73.62 188	76.02 206	85.31 179	81.21 175	67.43 194	71.02 158	59.07 183	54.48 189	64.07 151	66.14 189	86.52 141	86.64 138	91.83 163	81.17 194

ACMH	78.52 14	81.86 115	80.45 126	83.51 109	90.51 88	91.22 106	85.62 137	84.23 65	70.29 166	62.21 161	69.04 124	64.05 152	84.48 76	87.57 123	88.45 118	94.01 122	92.54 114
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tpm cat1		77.78 158	75.28 184	80.70 136	87.14 127	85.84 173	85.81 133	70.40 182	77.44 118	78.80 78	63.72 152	64.01 153	76.55 143	75.60 203	75.21 201	85.51 203	85.12 178

thres100view900		82.55 109	81.01 120	84.34 92	90.30 93	92.27 97	89.04 90	82.77 87	75.14 130	69.56 118	65.72 138	63.13 154	79.62 121	89.97 95	89.26 102	94.73 89	91.61 132

tfpn200view9		82.86 104	81.46 111	84.48 90	90.30 93	93.09 83	89.05 89	82.71 88	75.14 130	69.56 118	65.72 138	63.13 154	80.38 107	91.15 73	89.51 97	94.91 80	92.50 116

UniMVSNet (Re)		81.22 122	81.08 117	81.39 128	85.35 145	91.76 103	84.93 143	82.88 85	76.13 124	65.02 144	64.94 146	63.09 156	75.17 150	87.71 122	89.04 109	94.97 77	94.88 63

thres200		82.77 106	81.25 115	84.54 89	90.38 90	93.05 84	89.13 86	82.67 90	74.40 136	69.53 120	65.69 140	63.03 157	80.63 102	91.15 73	89.42 99	94.88 81	92.04 122

tpm		76.30 175	76.05 175	76.59 170	86.97 128	83.01 190	83.83 153	67.06 196	71.83 154	63.87 151	69.56 118	62.88 158	73.41 163	79.79 192	78.59 191	84.41 204	86.68 170

PatchMatch-RL		83.34 101	81.36 113	85.65 80	90.33 92	89.52 138	84.36 149	81.82 99	80.87 95	79.29 75	74.04 94	62.85 159	86.05 70	88.40 115	87.04 132	92.04 159	86.77 169

thres400		82.68 107	81.15 116	84.47 91	90.52 86	92.89 88	88.95 92	82.71 88	74.33 137	69.22 123	65.31 142	62.61 160	80.63 102	90.96 80	89.50 98	94.79 84	92.45 118

v8		79.90 132	78.39 149	81.66 125	83.97 164	89.81 129	87.16 118	77.40 150	71.49 155	67.71 128	61.24 161	62.49 161	79.83 117	85.48 155	86.17 147	93.89 127	92.02 124

anonymousdsp		77.94 156	79.00 142	76.71 169	79.03 197	87.83 158	79.58 181	72.87 173	65.80 185	58.86 185	65.82 137	62.48 162	75.99 145	86.77 134	88.66 114	93.92 125	95.68 50

V42		79.59 137	78.43 148	80.94 134	82.79 181	89.71 133	86.66 125	76.73 157	71.38 156	67.42 129	61.01 163	62.30 163	78.39 130	85.56 153	86.48 141	93.65 138	92.60 109

EG-PatchMatch MVS		76.40 173	75.47 182	77.48 163	85.86 138	90.22 119	82.45 162	73.96 171	59.64 203	59.60 180	52.75 195	62.20 164	68.44 179	88.23 116	87.50 124	94.55 101	87.78 163

thres600view7		82.53 110	81.02 118	84.28 95	90.61 85	93.05 84	88.57 97	82.67 90	74.12 140	68.56 126	65.09 145	62.13 165	80.40 106	91.15 73	89.02 110	94.88 81	92.59 110

GA-MVS		79.52 138	79.71 138	79.30 148	85.68 140	90.36 116	84.55 147	78.44 139	70.47 165	57.87 186	68.52 126	61.38 166	76.21 144	89.40 104	87.89 120	93.04 149	89.96 146

WR-MVS		76.63 167	78.02 155	75.02 181	84.14 163	89.76 132	78.34 189	80.64 108	69.56 167	52.32 195	61.26 160	61.24 167	60.66 195	84.45 169	87.07 130	93.99 123	92.77 103

Baseline_NR-MVSNet		79.84 133	78.37 150	81.55 127	84.98 153	86.66 167	85.06 141	83.49 78	75.57 128	63.31 154	58.22 182	60.97 168	78.00 133	86.89 130	87.13 129	94.47 106	93.15 93

pmmvs6		74.83 185	72.89 192	77.09 165	82.11 186	87.50 161	80.88 177	76.97 153	52.79 210	61.91 166	46.66 202	60.49 169	69.28 176	86.74 136	85.46 158	91.39 167	90.56 142

v10		79.62 136	78.19 151	81.28 131	83.73 166	89.69 134	87.27 114	76.86 155	70.50 164	65.46 139	60.58 168	60.47 170	80.44 105	86.91 129	86.63 139	93.93 124	92.55 113

v148		78.59 151	76.84 167	80.62 138	83.61 168	89.16 145	83.65 155	79.24 131	69.38 168	69.34 122	59.88 172	60.41 171	75.19 149	83.81 173	84.63 166	92.70 153	90.63 141

v2v482		79.84 133	78.07 153	81.90 122	83.75 165	90.21 120	87.17 117	79.85 123	70.65 162	65.93 137	61.93 158	60.07 172	80.82 97	85.25 157	86.71 136	93.88 128	91.70 130

TranMVSNet+NR-MVSNet		80.52 126	79.84 135	81.33 130	84.92 155	90.39 115	85.53 139	84.22 66	74.27 138	60.68 176	64.93 147	59.96 173	77.48 137	86.75 135	89.28 101	95.12 75	93.29 91

dps		78.02 155	75.94 177	80.44 141	86.06 135	86.62 168	82.58 160	69.98 185	75.14 130	77.76 87	69.08 123	59.93 174	78.47 129	79.47 193	77.96 194	87.78 191	83.40 185

WR-MVS_H		75.84 180	76.93 166	74.57 186	82.86 179	89.50 139	78.34 189	79.36 130	66.90 178	52.51 194	60.20 170	59.71 175	59.73 196	83.61 174	85.77 154	94.65 95	92.84 100

CMPMVS	56.49 17	73.84 190	71.73 196	76.31 174	85.20 148	85.67 175	75.80 195	73.23 172	62.26 195	65.40 140	53.40 194	59.70 176	71.77 169	80.25 190	79.56 188	86.45 199	81.28 193
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MDTV_nov1_ep13_2view		73.21 191	72.91 191	73.56 189	80.01 195	84.28 186	78.62 187	66.43 199	68.64 172	59.12 182	60.39 169	59.69 177	69.81 175	78.82 197	77.43 196	87.36 193	81.11 195

DU-MVS		81.20 123	80.30 127	82.25 119	84.98 153	90.94 109	85.70 134	83.58 76	75.74 126	64.21 147	65.30 143	59.60 178	80.22 108	86.89 130	89.31 100	94.77 86	94.29 74

v1144		79.38 142	77.83 156	81.18 132	83.62 167	90.23 118	87.15 119	78.35 140	69.13 169	64.02 150	60.20 170	59.41 179	80.14 112	86.78 133	86.57 140	93.81 132	92.53 115

pmmvs4		79.99 130	78.08 152	82.22 120	83.04 175	87.16 165	84.95 142	78.80 137	78.64 111	74.53 95	64.61 149	59.41 179	79.45 123	84.13 171	84.54 168	92.53 154	88.08 159

TDRefinement		79.05 145	77.05 164	81.39 128	88.45 111	89.00 149	86.92 121	82.65 92	74.21 139	64.41 146	59.17 175	59.16 181	74.52 156	85.23 158	85.09 160	91.37 168	87.51 165

TransMVSNet (Re)		76.57 168	75.16 185	78.22 160	85.60 142	87.24 163	82.46 161	81.23 104	59.80 202	59.05 184	57.07 184	59.14 182	66.60 188	88.09 117	86.82 134	94.37 112	87.95 162

USDC		80.69 125	79.89 134	81.62 126	86.48 132	89.11 147	86.53 126	78.86 135	81.15 90	63.48 153	72.98 101	59.12 183	81.16 94	87.10 126	85.01 161	93.23 145	84.77 181

v144192		78.81 147	77.22 162	80.67 137	82.95 176	89.79 131	86.40 127	77.42 149	68.26 175	63.13 155	59.50 173	58.13 184	80.08 113	85.93 147	86.08 149	94.06 119	92.83 101

pmmvs5		76.93 164	76.33 171	77.62 162	81.97 187	88.40 156	81.32 172	74.35 169	65.42 188	61.42 170	63.07 154	57.95 185	73.23 164	85.60 152	85.35 159	93.41 143	88.55 154

UniMVSNet_ETH3D		79.24 143	76.47 169	82.48 117	85.66 141	90.97 108	86.08 131	81.63 100	64.48 190	68.94 125	54.47 190	57.65 186	78.83 128	85.20 161	88.91 112	93.72 135	93.60 88

v1192		78.94 146	77.33 160	80.82 135	83.25 171	89.90 127	86.91 122	77.72 146	68.63 173	62.61 159	59.17 175	57.53 187	80.62 104	86.89 130	86.47 142	93.79 133	92.75 105

pmnet_mix02		71.95 192	71.83 195	72.10 192	81.40 192	80.63 202	73.78 199	72.85 174	70.90 159	54.89 189	62.17 157	57.42 188	62.92 193	76.80 200	73.98 204	86.74 198	80.87 196

testgi		71.92 193	74.20 188	69.27 197	84.58 157	83.06 188	73.40 200	74.39 168	64.04 192	46.17 205	68.90 125	57.15 189	48.89 206	84.07 172	83.08 176	88.18 190	79.09 201

v1921920		78.57 152	76.99 165	80.41 142	82.93 177	89.63 137	86.38 128	77.14 152	68.31 174	61.80 167	58.89 179	56.79 190	80.19 111	86.50 142	86.05 151	94.02 121	92.76 104

NR-MVSNet		80.25 129	79.98 133	80.56 139	85.20 148	90.94 109	85.65 136	83.58 76	75.74 126	61.36 171	65.30 143	56.75 191	72.38 166	88.46 114	88.80 113	95.16 71	93.87 82

EU-MVSNet		69.98 196	72.30 193	67.28 200	75.67 207	79.39 204	73.12 201	69.94 186	63.59 193	42.80 209	62.93 155	56.71 192	55.07 200	79.13 196	78.55 192	87.06 196	85.82 177

MVS-HIRNet		68.83 197	66.39 201	71.68 193	77.58 201	75.52 208	66.45 208	65.05 203	62.16 196	62.84 156	44.76 207	56.60 193	71.96 168	78.04 198	75.06 202	86.18 201	72.56 207

v1240		78.15 154	76.53 168	80.04 143	82.85 180	89.48 140	85.61 138	76.77 156	67.05 177	61.18 174	58.37 181	56.16 194	79.89 116	86.11 146	86.08 149	93.92 125	92.47 117

v7n		77.22 162	76.23 172	78.38 159	81.89 188	89.10 148	82.24 167	76.36 158	65.96 184	61.21 173	56.56 185	55.79 195	75.07 152	86.55 139	86.68 137	93.52 140	92.95 97

PEN-MVS		76.02 177	76.07 173	75.95 176	83.17 173	87.97 157	79.65 180	80.07 121	66.57 180	51.45 197	60.94 164	55.47 196	66.81 186	82.72 179	86.80 135	94.59 98	92.03 123

CP-MVSNet		76.36 174	76.41 170	76.32 173	82.73 182	88.64 152	79.39 183	79.62 125	67.21 176	53.70 191	60.72 166	55.22 197	67.91 182	83.52 175	86.34 145	94.55 101	93.19 92

DTE-MVSNet		75.14 184	75.44 183	74.80 183	83.18 172	87.19 164	78.25 191	80.11 118	66.05 182	48.31 202	60.88 165	54.67 198	64.54 191	82.57 181	86.17 147	94.43 109	90.53 143

test20.03		68.31 198	70.05 199	66.28 202	82.41 184	80.84 200	67.35 207	76.11 161	58.44 205	40.80 212	53.77 193	54.54 199	42.28 209	83.07 177	81.96 184	88.73 188	77.76 203

test_method		41.78 209	48.10 210	34.42 212	10.74 221	19.78 222	44.64 216	17.73 217	59.83 201	38.67 214	35.82 213	54.41 200	34.94 212	62.87 211	43.13 214	59.81 215	60.82 212

PS-CasMVS		75.90 179	75.86 178	75.96 175	82.59 183	88.46 155	79.23 186	79.56 127	66.00 183	52.77 193	59.48 174	54.35 201	67.14 185	83.37 176	86.23 146	94.47 106	93.10 94

SixPastTwentyTwo		76.02 177	75.72 179	76.36 172	83.38 169	87.54 160	75.50 196	76.22 159	65.50 187	57.05 187	70.64 109	53.97 202	74.54 155	80.96 187	82.12 182	91.44 166	89.35 149

tfpnnormal		77.46 161	74.86 186	80.49 140	86.34 134	88.92 150	84.33 150	81.26 103	61.39 198	61.70 168	51.99 197	53.66 203	74.84 153	88.63 111	87.38 127	94.50 103	92.08 120

Anonymous20231206		70.80 194	70.59 198	71.04 194	81.60 190	82.49 195	74.64 198	75.87 163	64.17 191	49.27 201	44.85 206	53.59 204	54.68 201	83.07 177	82.34 181	90.17 179	83.65 184

LTVRE_ROB	74.41 16	75.78 181	74.72 187	77.02 167	85.88 136	89.22 143	82.44 163	77.17 151	50.57 212	45.45 206	65.44 141	52.29 205	81.25 93	85.50 154	87.42 126	89.94 182	92.62 108
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
N_pmnet		66.85 199	66.63 200	67.11 201	78.73 198	74.66 209	70.53 204	71.07 179	66.46 181	46.54 204	51.68 198	51.91 206	55.48 199	74.68 204	72.38 205	80.29 210	74.65 206

gm-plane-assit		70.29 195	70.65 197	69.88 196	85.03 151	78.50 206	58.41 213	65.47 201	50.39 213	40.88 211	49.60 199	50.11 207	75.14 151	91.43 66	89.78 90	94.32 113	84.73 182

TinyColmap		76.73 165	73.95 189	79.96 144	85.16 150	85.64 176	82.34 164	78.19 141	70.63 163	62.06 163	60.69 167	49.61 208	80.81 98	85.12 162	83.69 173	91.22 172	82.27 189

MIMVSNet1		65.00 201	66.24 202	63.55 204	58.41 215	80.01 203	69.00 206	74.03 170	55.81 208	41.88 210	36.81 211	49.48 209	47.89 207	81.32 186	82.40 180	90.08 181	77.88 202

pmmvs-eth3d		74.32 188	71.96 194	77.08 166	77.33 202	82.71 192	78.41 188	76.02 162	66.65 179	65.98 136	54.23 192	49.02 210	73.14 165	82.37 183	82.69 179	91.61 165	86.05 175

PM-MVS		74.17 189	73.10 190	75.41 178	76.07 205	82.53 194	77.56 192	71.69 177	71.04 157	61.92 165	61.23 162	47.30 211	74.82 154	81.78 185	79.80 186	90.42 177	88.05 160

new-patchmatchnet		63.80 202	63.31 204	64.37 203	76.49 203	75.99 207	63.73 210	70.99 180	57.27 206	43.08 208	45.86 204	43.80 212	45.13 208	73.20 205	70.68 208	86.80 197	76.34 205

new_pmnet		59.28 205	61.47 207	56.73 207	61.66 213	68.29 213	59.57 212	54.91 210	60.83 199	34.38 216	44.66 208	43.65 213	49.90 205	71.66 206	71.56 207	79.94 211	69.67 208

tmp_tt				32.73 213	43.96 219	21.15 221	26.71 219	8.99 218	65.67 186	51.39 198	56.01 186	42.64 214	11.76 217	56.60 212	50.81 213	53.55 217

FPMVS		63.63 203	60.08 208	67.78 199	80.01 195	71.50 211	72.88 202	69.41 189	61.82 197	53.11 192	45.12 205	42.11 215	50.86 204	66.69 208	63.84 209	80.41 209	69.46 209

pmmvs3		61.89 204	61.74 206	62.06 205	64.30 211	70.83 212	64.22 209	52.14 213	48.78 214	44.47 207	41.67 209	41.70 216	63.03 192	76.06 202	76.02 198	84.18 206	77.14 204

PMVS	50.48 18	55.81 207	51.93 209	60.33 206	72.90 209	49.34 215	48.78 214	69.51 188	43.49 215	54.25 190	36.26 212	41.04 217	39.71 211	65.07 209	60.70 210	76.85 212	67.58 210
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MDA-MVSNet-bldmvs		66.22 200	64.49 203	68.24 198	61.67 212	82.11 198	70.07 205	76.16 160	59.14 204	47.94 203	54.35 191	35.82 218	67.33 184	64.94 210	75.68 199	86.30 200	79.36 199

DeepMVS_CX						48.31 217	48.03 215	26.08 216	56.42 207	25.77 218	47.51 201	31.31 219	51.30 203	48.49 214		53.61 216	61.52 211

PMMVS2		41.68 210	44.74 212	38.10 209	46.97 218	52.32 214	40.63 218	48.08 214	35.51 216	7.36 222	26.86 214	24.64 220	16.72 216	55.24 213	59.03 211	68.85 214	59.59 213

ambc			61.92 205		70.98 210	73.54 210	63.64 211		60.06 200	52.23 196	38.44 210	19.17 221	57.12 197	82.33 184	75.03 203	83.21 208	84.89 179

Gipuma		49.17 208	47.05 211	51.65 208	59.67 214	48.39 216	41.98 217	63.47 206	55.64 209	33.33 217	14.90 215	13.78 222	41.34 210	69.31 207	72.30 206	70.11 213	55.00 214
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
EMVS		30.49 213	25.44 215	36.39 211	51.47 216	29.89 220	20.17 221	54.00 212	26.49 217	12.02 221	13.94 218	8.84 223	34.37 213	25.04 217	34.37 216	46.29 219	39.53 217

E-PMN		31.40 211	26.80 214	36.78 210	51.39 217	29.96 219	20.20 220	54.17 211	25.93 218	12.75 220	14.73 216	8.58 224	34.10 214	27.36 216	37.83 215	48.07 218	43.18 216

MVE	30.17 19	30.88 212	33.52 213	27.80 215	23.78 220	39.16 218	18.69 222	46.90 215	21.88 219	15.39 219	14.37 217	7.31 225	24.41 215	41.63 215	56.22 212	37.64 220	54.07 215
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		1.03 214	1.63 216	0.34 216	0.09 223	0.35 223	0.61 224	0.16 219	1.49 220	0.10 224	3.15 219	0.15 226	0.86 219	1.32 218	1.18 217	0.20 221	3.76 219

test123		0.87 215	1.40 217	0.25 217	0.03 224	0.25 224	0.35 225	0.08 221	1.21 221	0.05 225	2.84 220	0.03 227	0.89 218	0.43 219	1.16 218	0.13 222	3.87 218

uanet_test		0.00 216	0.00 218	0.00 218	0.00 225	0.00 225	0.00 226	0.00 222	0.00 222	0.00 226	0.00 221	0.00 228	0.00 221	0.00 220	0.00 219	0.00 223	0.00 220

sosnet-low-res		0.00 216	0.00 218	0.00 218	0.00 225	0.00 225	0.00 226	0.00 222	0.00 222	0.00 226	0.00 221	0.00 228	0.00 221	0.00 220	0.00 219	0.00 223	0.00 220

sosnet		0.00 216	0.00 218	0.00 218	0.00 225	0.00 225	0.00 226	0.00 222	0.00 222	0.00 226	0.00 221	0.00 228	0.00 221	0.00 220	0.00 219	0.00 223	0.00 220

RE-MVS-def										56.08 188

our_test_3					81.81 189	83.96 187	76.61 193

Patchmatch-RL test							8.55 223

NP-MVS									87.47 53

Patchmtry						85.54 178	82.30 165	68.23 191		65.37 141

High-resolution multi-view benchmark