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.

DVP-MVS		97.93 2	98.23 2	97.58 2	99.05 6	99.31 1	98.64 5	96.62 4	97.56 1	95.08 5	96.61 13	99.64 1	97.32 1	97.91 3	97.31 6	98.77 11	99.26 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
SED-MVS		97.98 1	98.36 1	97.54 3	98.94 17	99.29 2	98.81 3	96.64 3	97.14 2	95.16 4	97.96 2	99.61 2	96.92 11	98.00 1	97.24 8	98.75 12	99.25 2

DeepPCF-MVS	92.65 2	95.50 34	96.96 18	93.79 51	96.44 57	98.21 38	93.51 92	94.08 37	96.94 3	89.29 43	93.08 31	96.77 27	93.82 54	97.68 8	97.40 4	95.59 171	98.65 14

MSP-MVS		97.70 5	98.09 4	97.24 6	99.00 11	99.17 4	98.76 4	96.41 9	96.91 4	93.88 15	97.72 4	99.04 6	96.93 10	97.29 15	97.31 6	98.45 31	99.23 3
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
SD-MVS		97.35 7	97.73 7	96.90 15	97.35 45	98.66 13	97.85 25	96.25 11	96.86 5	94.54 9	96.75 11	99.13 5	96.99 7	96.94 23	96.58 22	98.39 39	99.20 4
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
APDe-MVS		97.79 4	97.96 5	97.60 1	99.20 2	99.10 5	98.88 2	96.68 2	96.81 6	94.64 6	97.84 3	98.02 10	97.24 3	97.74 7	97.02 13	98.97 3	99.16 5

TSAR-MVS + MP.		97.31 8	97.64 8	96.92 14	97.28 47	98.56 22	98.61 6	95.48 29	96.72 7	94.03 14	96.73 12	98.29 8	97.15 4	97.61 11	96.42 25	98.96 4	99.13 6
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
HFP-MVS		97.11 13	97.19 15	97.00 13	98.97 13	98.73 11	98.37 11	95.69 22	96.60 8	93.28 21	96.87 8	96.64 28	97.27 2	96.64 31	96.33 32	98.44 32	98.56 19

ACMMPR		96.92 17	96.96 18	96.87 16	98.99 12	98.78 10	98.38 10	95.52 25	96.57 9	92.81 25	96.06 20	95.90 36	97.07 5	96.60 33	96.34 31	98.46 28	98.42 32

TSAR-MVS + ACMM		96.19 23	97.39 12	94.78 38	97.70 40	98.41 34	97.72 27	95.49 28	96.47 10	86.66 66	96.35 15	97.85 12	93.99 50	97.19 18	96.37 27	97.12 125	99.13 6

SMA-MVS		97.53 6	97.93 6	97.07 11	99.21 1	99.02 7	98.08 19	96.25 11	96.36 11	93.57 16	96.56 14	99.27 4	96.78 16	97.91 3	97.43 3	98.51 21	98.94 11
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
NCCC		96.75 19	96.67 24	96.85 17	99.03 9	98.44 33	98.15 16	96.28 10	96.32 12	92.39 26	92.16 35	97.55 19	96.68 19	97.32 12	96.65 21	98.55 20	98.26 36

DeepC-MVS_fast	93.32 1	96.48 22	96.42 27	96.56 21	98.70 26	98.31 37	97.97 22	95.76 21	96.31 13	92.01 28	91.43 40	95.42 40	96.46 22	97.65 10	97.69 1	98.49 25	98.12 45
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
OMC-MVS		94.49 44	94.36 44	94.64 41	97.17 49	97.73 56	95.49 56	92.25 45	96.18 14	90.34 40	88.51 53	92.88 50	94.90 38	94.92 64	94.17 68	97.69 101	96.15 115

xxxxxxxxxxxxxcwj		95.62 31	94.35 45	97.10 9	98.95 15	98.51 27	97.51 29	96.48 6	96.17 15	94.64 6	97.32 5	76.98 136	96.23 26	96.78 26	96.15 37	98.79 9	98.55 24

SF-MVS		97.20 11	97.29 13	97.10 9	98.95 15	98.51 27	97.51 29	96.48 6	96.17 15	94.64 6	97.32 5	97.57 18	96.23 26	96.78 26	96.15 37	98.79 9	98.55 24

CNVR-MVS		97.30 9	97.41 10	97.18 8	99.02 10	98.60 20	98.15 16	96.24 13	96.12 17	94.10 12	95.54 25	97.99 11	96.99 7	97.97 2	97.17 9	98.57 19	98.50 27

zzz-MVS		96.98 15	96.68 23	97.33 5	99.09 3	98.71 12	98.43 8	96.01 16	96.11 18	95.19 3	92.89 33	97.32 22	96.84 12	97.20 16	96.09 40	98.44 32	98.46 31

DPE-MVS		97.83 3	98.13 3	97.48 4	98.83 23	99.19 3	98.99 1	96.70 1	96.05 19	94.39 10	98.30 1	99.47 3	97.02 6	97.75 6	97.02 13	98.98 2	99.10 8
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
HPM-MVS++		97.22 10	97.40 11	97.01 12	99.08 4	98.55 23	98.19 14	96.48 6	96.02 20	93.28 21	96.26 17	98.71 7	96.76 17	97.30 14	96.25 34	98.30 49	98.68 13

TSAR-MVS + GP.		95.86 28	96.95 20	94.60 43	94.07 83	98.11 42	96.30 44	91.76 51	95.67 21	91.07 32	96.82 10	97.69 16	95.71 31	95.96 48	95.75 46	98.68 13	98.63 15

ACMMP_NAP		96.93 16	97.27 14	96.53 24	99.06 5	98.95 8	98.24 13	96.06 15	95.66 22	90.96 34	95.63 24	97.71 15	96.53 20	97.66 9	96.68 19	98.30 49	98.61 18

CNLPA		93.69 51	92.50 62	95.06 37	97.11 50	97.36 64	93.88 82	93.30 39	95.64 23	93.44 19	80.32 107	90.73 63	94.99 37	93.58 95	93.33 89	97.67 103	96.57 101

MCST-MVS		96.83 18	97.06 16	96.57 20	98.88 21	98.47 31	98.02 21	96.16 14	95.58 24	90.96 34	95.78 23	97.84 13	96.46 22	97.00 22	96.17 36	98.94 5	98.55 24

CSCG		95.68 30	95.46 35	95.93 28	98.71 25	99.07 6	97.13 36	93.55 38	95.48 25	93.35 20	90.61 45	93.82 46	95.16 35	94.60 76	95.57 48	97.70 100	99.08 9

CP-MVS		96.68 20	96.59 26	96.77 18	98.85 22	98.58 21	98.18 15	95.51 27	95.34 26	92.94 24	95.21 28	96.25 31	96.79 15	96.44 38	95.77 45	98.35 41	98.56 19

TSAR-MVS + COLMAP		92.39 62	92.31 67	92.47 66	95.35 73	96.46 88	96.13 46	92.04 48	95.33 27	80.11 109	94.95 29	77.35 134	94.05 49	94.49 80	93.08 98	97.15 122	94.53 144

MVS_111021_LR		94.84 40	95.57 32	94.00 45	97.11 50	97.72 58	94.88 62	91.16 57	95.24 28	88.74 48	96.03 21	91.52 58	94.33 46	95.96 48	95.01 57	97.79 91	97.49 72

X-MVS		96.07 26	96.33 28	95.77 30	98.94 17	98.66 13	97.94 23	95.41 31	95.12 29	88.03 51	93.00 32	96.06 32	95.85 28	96.65 30	96.35 28	98.47 26	98.48 28

SteuartSystems-ACMMP		97.10 14	97.49 9	96.65 19	98.97 13	98.95 8	98.43 8	95.96 18	95.12 29	91.46 29	96.85 9	97.60 17	96.37 24	97.76 5	97.16 10	98.68 13	98.97 10
Skip Steuart: Steuart Systems R&D Blog.
3Dnovator+	90.56 5	95.06 37	94.56 42	95.65 32	98.11 32	98.15 41	97.19 34	91.59 53	95.11 31	93.23 23	81.99 99	94.71 43	95.43 34	96.48 35	96.88 17	98.35 41	98.63 15

DeepC-MVS	92.10 3	95.22 35	94.77 39	95.75 31	97.77 38	98.54 24	97.63 28	95.96 18	95.07 32	88.85 47	85.35 73	91.85 54	95.82 29	96.88 25	97.10 11	98.44 32	98.63 15
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PLC	90.69 4	94.32 45	92.99 56	95.87 29	97.91 34	96.49 86	95.95 51	94.12 36	94.94 33	94.09 13	85.90 69	90.77 62	95.58 32	94.52 78	93.32 91	97.55 108	95.00 140
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
TAPA-MVS	90.35 6	93.69 51	93.52 50	93.90 48	96.89 53	97.62 59	96.15 45	91.67 52	94.94 33	85.97 70	87.72 57	91.96 53	94.40 43	93.76 93	93.06 100	98.30 49	95.58 128
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
DPM-MVS		95.07 36	94.84 38	95.34 35	97.44 44	97.49 62	97.76 26	95.52 25	94.88 35	88.92 46	87.25 58	96.44 30	94.41 42	95.78 51	96.11 39	97.99 81	95.95 120

ACMMP		95.54 32	95.49 34	95.61 33	98.27 31	98.53 25	97.16 35	94.86 33	94.88 35	89.34 42	95.36 27	91.74 55	95.50 33	95.51 55	94.16 69	98.50 23	98.22 38
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
APD-MVS		97.12 12	97.05 17	97.19 7	99.04 7	98.63 18	98.45 7	96.54 5	94.81 37	93.50 17	96.10 19	97.40 21	96.81 13	97.05 20	96.82 18	98.80 7	98.56 19
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
3Dnovator	90.28 7	94.70 43	94.34 46	95.11 36	98.06 33	98.21 38	96.89 38	91.03 59	94.72 38	91.45 30	82.87 90	93.10 49	94.61 39	96.24 44	97.08 12	98.63 16	98.16 41

MSLP-MVS++		96.05 27	95.63 31	96.55 22	98.33 30	98.17 40	96.94 37	94.61 35	94.70 39	94.37 11	89.20 51	95.96 35	96.81 13	95.57 54	97.33 5	98.24 57	98.47 29

CPTT-MVS		95.54 32	95.07 36	96.10 26	97.88 36	97.98 48	97.92 24	94.86 33	94.56 40	92.16 27	91.01 42	95.71 37	96.97 9	94.56 77	93.50 85	96.81 148	98.14 43

MP-MVS		96.56 21	96.72 22	96.37 25	98.93 19	98.48 29	98.04 20	95.55 24	94.32 41	90.95 36	95.88 22	97.02 25	96.29 25	96.77 28	96.01 42	98.47 26	98.56 19
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MVS_111021_HR		94.84 40	95.91 30	93.60 52	97.35 45	98.46 32	95.08 59	91.19 56	94.18 42	85.97 70	95.38 26	92.56 51	93.61 57	96.61 32	96.25 34	98.40 37	97.92 54

PHI-MVS		95.86 28	96.93 21	94.61 42	97.60 42	98.65 17	96.49 41	93.13 41	94.07 43	87.91 54	97.12 7	97.17 24	93.90 53	96.46 36	96.93 16	98.64 15	98.10 47

canonicalmvs		93.08 54	93.09 54	93.07 61	94.24 78	97.86 50	95.45 57	87.86 99	94.00 44	87.47 58	88.32 54	82.37 102	95.13 36	93.96 92	96.41 26	98.27 53	98.73 12

train_agg		96.15 25	96.64 25	95.58 34	98.44 28	98.03 45	98.14 18	95.40 32	93.90 45	87.72 55	96.26 17	98.10 9	95.75 30	96.25 43	95.45 50	98.01 79	98.47 29

abl_6				94.78 38	97.46 43	97.99 47	95.76 52	91.80 50	93.72 46	91.25 31	91.33 41	96.47 29	94.28 47			98.14 66	97.39 75

AdaColmap		95.02 38	93.71 49	96.54 23	98.51 27	97.76 54	96.69 40	95.94 20	93.72 46	93.50 17	89.01 52	90.53 65	96.49 21	94.51 79	93.76 78	98.07 73	96.69 96

PGM-MVS		96.16 24	96.33 28	95.95 27	99.04 7	98.63 18	98.32 12	92.76 43	93.42 48	90.49 39	96.30 16	95.31 41	96.71 18	96.46 36	96.02 41	98.38 40	98.19 40

CANet		94.85 39	94.92 37	94.78 38	97.25 48	98.52 26	97.20 33	91.81 49	93.25 49	91.06 33	86.29 65	94.46 44	92.99 64	97.02 21	96.68 19	98.34 43	98.20 39

baseline		91.19 77	91.89 73	90.38 89	92.76 109	95.04 105	93.55 91	84.54 130	92.92 50	85.71 76	86.68 63	86.96 73	89.28 102	92.00 125	92.62 108	96.46 153	96.99 88

CLD-MVS		92.50 61	91.96 72	93.13 58	93.93 89	96.24 92	95.69 53	88.77 81	92.92 50	89.01 45	88.19 56	81.74 107	93.13 63	93.63 94	93.08 98	98.23 58	97.91 56
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
CDPH-MVS		94.80 42	95.50 33	93.98 47	98.34 29	98.06 43	97.41 31	93.23 40	92.81 52	82.98 93	92.51 34	94.82 42	93.53 58	96.08 46	96.30 33	98.42 35	97.94 52

MVS_0304		94.30 46	94.68 40	93.86 50	96.33 59	98.48 29	97.41 31	91.20 55	92.75 53	86.96 63	86.03 68	93.81 47	92.64 68	96.89 24	96.54 24	98.61 17	98.24 37

diffmvs		91.37 75	91.09 83	91.70 75	92.71 112	96.47 87	94.03 76	88.78 80	92.74 54	85.43 84	83.63 85	80.37 112	91.76 77	93.39 102	93.78 77	97.50 110	97.23 81

QAPM		94.13 47	94.33 47	93.90 48	97.82 37	98.37 36	96.47 42	90.89 60	92.73 55	85.63 77	85.35 73	93.87 45	94.17 48	95.71 53	95.90 43	98.40 37	98.42 32

LS3D		91.97 66	90.98 84	93.12 59	97.03 52	97.09 73	95.33 58	95.59 23	92.47 56	79.26 113	81.60 102	82.77 97	94.39 44	94.28 81	94.23 67	97.14 124	94.45 146

HQP-MVS		92.39 62	92.49 63	92.29 70	95.65 65	95.94 98	95.64 55	92.12 47	92.46 57	79.65 111	91.97 37	82.68 98	92.92 66	93.47 100	92.77 105	97.74 96	98.12 45

ACMM	88.76 10	91.70 73	90.43 87	93.19 57	95.56 66	95.14 104	93.35 95	91.48 54	92.26 58	87.12 61	84.02 81	79.34 117	93.99 50	94.07 87	92.68 106	97.62 107	95.50 129
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ETV-MVS		93.80 49	94.57 41	92.91 64	93.98 85	97.50 61	93.62 89	88.70 82	91.95 59	87.57 56	90.21 47	90.79 61	94.56 40	97.20 16	96.35 28	99.02 1	97.98 49

PVSNet_BlendedMVS		92.80 56	92.44 64	93.23 55	96.02 61	97.83 52	93.74 86	90.58 61	91.86 60	90.69 37	85.87 71	82.04 104	90.01 92	96.39 39	95.26 53	98.34 43	97.81 59

PVSNet_Blended		92.80 56	92.44 64	93.23 55	96.02 61	97.83 52	93.74 86	90.58 61	91.86 60	90.69 37	85.87 71	82.04 104	90.01 92	96.39 39	95.26 53	98.34 43	97.81 59

ACMP	89.13 9	92.03 65	91.70 76	92.41 68	94.92 74	96.44 90	93.95 78	89.96 66	91.81 62	85.48 82	90.97 43	79.12 118	92.42 70	93.28 106	92.55 109	97.76 94	97.74 62
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
EPNet		93.92 48	94.40 43	93.36 54	97.89 35	96.55 84	96.08 47	92.14 46	91.65 63	89.16 44	94.07 30	90.17 69	87.78 119	95.24 58	94.97 58	97.09 127	98.15 42
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CS-MVS		93.68 53	94.33 47	92.93 63	94.15 79	98.04 44	94.43 64	87.99 91	91.64 64	87.54 57	88.22 55	92.09 52	94.56 40	96.77 28	95.85 44	98.88 6	97.71 63

NP-MVS									91.63 65

casdiffmvs		91.72 72	91.16 82	92.38 69	93.16 101	97.15 70	93.95 78	89.49 75	91.58 66	86.03 69	80.75 106	80.95 110	93.16 62	95.25 57	95.22 55	98.50 23	97.23 81

MVS_Test		91.81 70	92.19 68	91.37 83	93.24 99	96.95 77	94.43 64	86.25 112	91.45 67	83.45 91	86.31 64	85.15 84	92.93 65	93.99 88	94.71 62	97.92 85	96.77 94

DCV-MVSNet		91.24 76	91.26 80	91.22 85	92.84 108	93.44 134	93.82 83	86.75 109	91.33 68	85.61 78	84.00 82	85.46 83	91.27 80	92.91 108	93.62 80	97.02 131	98.05 48

thisisatest0530		91.04 80	91.74 74	90.21 93	92.93 107	97.00 75	92.06 115	87.63 104	90.74 69	81.51 97	86.81 60	82.48 99	89.23 104	94.81 70	93.03 102	97.90 86	97.33 78

MAR-MVS		92.71 59	92.63 60	92.79 65	97.70 40	97.15 70	93.75 85	87.98 93	90.71 70	85.76 75	86.28 66	86.38 76	94.35 45	94.95 62	95.49 49	97.22 118	97.44 73
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
CANet_DTU		90.74 85	92.93 58	88.19 114	94.36 77	96.61 82	94.34 69	84.66 127	90.66 71	68.75 163	90.41 46	86.89 74	89.78 94	95.46 56	94.87 59	97.25 117	95.62 126

ET-MVSNet_ETH3D		89.93 93	90.84 85	88.87 107	79.60 206	96.19 93	94.43 64	86.56 110	90.63 72	80.75 106	90.71 44	77.78 130	93.73 56	91.36 134	93.45 87	98.15 64	95.77 123

UGNet		91.52 74	93.41 52	89.32 103	94.13 80	97.15 70	91.83 119	89.01 78	90.62 73	85.86 74	86.83 59	91.73 56	77.40 184	94.68 73	94.43 64	97.71 98	98.40 34
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
tttt0517		91.01 81	91.71 75	90.19 95	92.98 103	97.07 74	91.96 118	87.63 104	90.61 74	81.42 98	86.76 61	82.26 103	89.23 104	94.86 68	93.03 102	97.90 86	97.36 76

PMMVS		89.88 94	91.19 81	88.35 112	89.73 144	91.97 177	90.62 125	81.92 161	90.57 75	80.58 108	92.16 35	86.85 75	91.17 82	92.31 118	91.35 134	96.11 159	93.11 164

LGP-MVS_train		91.83 69	92.04 71	91.58 76	95.46 69	96.18 94	95.97 50	89.85 67	90.45 76	77.76 116	91.92 38	80.07 115	92.34 72	94.27 82	93.47 86	98.11 70	97.90 57

RPSCF		89.68 97	89.24 98	90.20 94	92.97 105	92.93 153	92.30 107	87.69 101	90.44 77	85.12 86	91.68 39	85.84 82	90.69 88	87.34 182	86.07 184	92.46 194	90.37 182

SCA		86.25 126	87.52 122	84.77 150	91.59 123	93.90 121	89.11 155	73.25 200	90.38 78	72.84 136	83.26 86	83.79 90	88.49 116	86.07 189	85.56 187	93.33 186	89.67 187

DI_MVS_plusplus_trai		91.05 79	90.15 91	92.11 71	92.67 113	96.61 82	96.03 48	88.44 86	90.25 79	85.92 72	73.73 139	84.89 86	91.92 74	94.17 85	94.07 73	97.68 102	97.31 79

EPP-MVSNet		92.13 64	93.06 55	91.05 86	93.66 97	97.30 65	92.18 110	87.90 95	90.24 80	83.63 90	86.14 67	90.52 67	90.76 87	94.82 69	94.38 65	98.18 63	97.98 49

CHOSEN 280x420		90.77 84	92.14 69	89.17 105	93.86 92	92.81 157	93.16 97	80.22 174	90.21 81	84.67 89	89.89 48	91.38 59	90.57 90	94.94 63	92.11 118	92.52 193	93.65 157

EPMVS		85.77 134	86.24 132	85.23 146	92.76 109	93.78 124	89.91 142	73.60 196	90.19 82	74.22 128	82.18 98	78.06 127	87.55 122	85.61 191	85.38 189	93.32 187	88.48 194

PatchmatchNet		85.70 135	86.65 127	84.60 153	91.79 120	93.40 135	89.27 151	73.62 195	90.19 82	72.63 138	82.74 93	81.93 106	87.64 120	84.99 192	84.29 194	92.64 192	89.00 189
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MVSTER		91.73 71	91.61 77	91.86 73	93.18 100	94.56 107	94.37 67	87.90 95	90.16 84	88.69 49	89.23 50	81.28 109	88.92 112	95.75 52	93.95 75	98.12 68	96.37 106

GBi-Net		90.21 90	90.11 92	90.32 91	88.66 154	93.65 130	94.25 72	85.78 117	90.03 85	85.56 79	77.38 117	86.13 77	89.38 99	93.97 89	94.16 69	98.31 46	95.47 130

test1		90.21 90	90.11 92	90.32 91	88.66 154	93.65 130	94.25 72	85.78 117	90.03 85	85.56 79	77.38 117	86.13 77	89.38 99	93.97 89	94.16 69	98.31 46	95.47 130

FMVSNet3		90.19 92	90.06 94	90.34 90	88.69 153	93.85 122	94.58 63	85.78 117	90.03 85	85.56 79	77.38 117	86.13 77	89.22 106	93.29 105	94.36 66	98.20 61	95.40 134

ADS-MVSNet		84.08 158	84.95 146	83.05 175	91.53 127	91.75 180	88.16 165	70.70 204	89.96 88	69.51 158	78.83 112	76.97 137	86.29 135	84.08 196	84.60 192	92.13 197	88.48 194

PatchMatch-RL		90.30 89	88.93 101	91.89 72	95.41 72	95.68 100	90.94 122	88.67 83	89.80 89	86.95 64	85.90 69	72.51 145	92.46 69	93.56 97	92.18 115	96.93 140	92.89 165

EIA-MVS		92.72 58	92.96 57	92.44 67	93.86 92	97.76 54	93.13 98	88.65 84	89.78 90	86.68 65	86.69 62	87.57 71	93.74 55	96.07 47	95.32 51	98.58 18	97.53 70

CHOSEN 1792x2688		88.57 108	87.82 115	89.44 102	95.46 69	96.89 79	93.74 86	85.87 115	89.63 91	77.42 119	61.38 193	83.31 92	88.80 114	93.44 101	93.16 96	95.37 176	96.95 90

MSDG		90.42 88	88.25 108	92.94 62	96.67 56	94.41 113	93.96 77	92.91 42	89.59 92	86.26 68	76.74 124	80.92 111	90.43 91	92.60 114	92.08 120	97.44 113	91.41 172

DELS-MVS		93.71 50	93.47 51	94.00 45	96.82 54	98.39 35	96.80 39	91.07 58	89.51 93	89.94 41	83.80 83	89.29 70	90.95 85	97.32 12	97.65 2	98.42 35	98.32 35
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
MDTV_nov1_ep13		86.64 125	87.50 123	85.65 140	90.73 136	93.69 128	89.96 140	78.03 183	89.48 94	76.85 121	84.92 76	82.42 101	86.14 138	86.85 186	86.15 183	92.17 195	88.97 190

baseline1		90.81 82	90.29 88	91.42 80	93.67 96	95.86 99	93.94 80	89.69 72	89.29 95	82.85 94	82.91 89	80.30 113	89.60 95	95.05 60	94.79 61	98.80 7	93.82 155

OpenMVS	88.18 11	92.51 60	91.61 77	93.55 53	97.74 39	98.02 46	95.66 54	90.46 63	89.14 96	86.50 67	75.80 131	90.38 68	92.69 67	94.99 61	95.30 52	98.27 53	97.63 64

EPNet_dtu		88.32 111	90.61 86	85.64 141	96.79 55	92.27 169	92.03 116	90.31 64	89.05 97	65.44 184	89.43 49	85.90 81	74.22 193	92.76 109	92.09 119	95.02 182	92.76 166
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Anonymous20231211		89.82 95	88.18 109	91.74 74	92.52 114	96.09 96	93.38 94	89.30 77	88.95 98	85.90 73	64.55 186	84.39 87	92.41 71	92.24 121	93.06 100	96.93 140	97.95 51

Vis-MVSNet (Re-imp)		90.54 87	92.76 59	87.94 118	93.73 95	96.94 78	92.17 112	87.91 94	88.77 99	76.12 124	83.68 84	90.80 60	79.49 180	96.34 41	96.35 28	98.21 60	96.46 103

GG-mvs-BLEND		62.84 204	90.21 89	30.91 213	0.57 221	94.45 111	86.99 175	0.34 219	88.71 100	0.98 221	81.55 104	91.58 57	0.86 218	92.66 112	91.43 133	95.73 165	91.11 176

IS_MVSNet		91.87 68	93.35 53	90.14 97	94.09 82	97.73 56	93.09 99	88.12 90	88.71 100	79.98 110	84.49 78	90.63 64	87.49 123	97.07 19	96.96 15	98.07 73	97.88 58

FMVSNet2		89.61 98	89.14 99	90.16 96	88.66 154	93.65 130	94.25 72	85.44 121	88.57 102	84.96 88	73.53 141	83.82 89	89.38 99	94.23 83	94.68 63	98.31 46	95.47 130

PVSNet_Blended_VisFu		91.92 67	92.39 66	91.36 84	95.45 71	97.85 51	92.25 109	89.54 74	88.53 103	87.47 58	79.82 109	90.53 65	85.47 144	96.31 42	95.16 56	97.99 81	98.56 19

USDC		86.73 124	85.96 137	87.63 123	91.64 122	93.97 120	92.76 102	84.58 129	88.19 104	70.67 150	80.10 108	67.86 166	89.43 97	91.81 127	89.77 168	96.69 150	90.05 185

tpmrst		83.72 164	83.45 158	84.03 162	92.21 115	91.66 181	88.74 161	73.58 197	88.14 105	72.67 137	77.37 120	72.11 148	86.34 134	82.94 199	82.05 198	90.63 203	89.86 186

CostFormer		86.78 123	86.05 133	87.62 124	92.15 116	93.20 144	91.55 121	75.83 188	88.11 106	85.29 85	81.76 100	76.22 140	87.80 118	84.45 194	85.21 190	93.12 188	93.42 160

Anonymous202405211			88.00 111		93.16 101	96.38 91	93.58 90	89.34 76	87.92 107		65.04 182	83.03 94	92.07 73	92.67 111	93.33 89	96.96 135	97.63 64

FC-MVSNet-train		90.55 86	90.19 90	90.97 87	93.78 94	95.16 103	92.11 114	88.85 79	87.64 108	83.38 92	84.36 80	78.41 125	89.53 96	94.69 72	93.15 97	98.15 64	97.92 54

Effi-MVS+		89.79 96	89.83 95	89.74 99	92.98 103	96.45 89	93.48 93	84.24 132	87.62 109	76.45 122	81.76 100	77.56 133	93.48 59	94.61 75	93.59 81	97.82 90	97.22 83

baseline2		88.97 106	89.50 96	88.36 111	91.14 130	95.30 101	90.13 136	85.17 124	87.24 110	80.80 105	84.46 79	78.44 124	85.60 141	93.54 98	91.87 124	97.31 115	95.66 125

PCF-MVS	90.19 8	92.98 55	92.07 70	94.04 44	96.39 58	97.87 49	96.03 48	95.47 30	87.16 111	85.09 87	84.81 77	93.21 48	93.46 60	91.98 126	91.98 123	97.78 92	97.51 71
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
COLMAP_ROB	84.39 15	87.61 115	86.03 134	89.46 101	95.54 68	94.48 110	91.77 120	90.14 65	87.16 111	75.50 125	73.41 144	76.86 138	87.33 125	90.05 159	89.76 169	96.48 152	90.46 181
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
GeoE		89.29 104	88.68 103	89.99 98	92.75 111	96.03 97	93.07 101	83.79 139	86.98 113	81.34 99	74.72 136	78.92 119	91.22 81	93.31 104	93.21 94	97.78 92	97.60 69

Fast-Effi-MVS+		88.56 109	87.99 112	89.22 104	91.56 125	95.21 102	92.29 108	82.69 150	86.82 114	77.73 117	76.24 129	73.39 144	93.36 61	94.22 84	93.64 79	97.65 104	96.43 104

pmmvs4		86.00 133	84.28 152	88.00 116	87.80 165	92.01 176	89.94 141	84.91 125	86.79 115	80.98 104	73.41 144	66.34 175	88.12 117	89.31 168	88.90 177	96.24 158	93.20 163

MS-PatchMatch		87.63 114	87.61 119	87.65 122	93.95 87	94.09 118	92.60 104	81.52 166	86.64 116	76.41 123	73.46 143	85.94 80	85.01 148	92.23 122	90.00 163	96.43 155	90.93 178

HyFIR lowres test		87.87 113	86.42 130	89.57 100	95.56 66	96.99 76	92.37 106	84.15 134	86.64 116	77.17 120	57.65 199	83.97 88	91.08 84	92.09 124	92.44 110	97.09 127	95.16 137

FC-MVSNet-test		86.15 129	89.10 100	82.71 179	89.83 142	93.18 145	87.88 168	84.69 126	86.54 118	62.18 194	82.39 97	83.31 92	74.18 194	92.52 116	91.86 125	97.50 110	93.88 154

IterMVS-LS		88.60 107	88.45 104	88.78 108	92.02 119	92.44 167	92.00 117	83.57 143	86.52 119	78.90 115	78.61 114	81.34 108	89.12 107	90.68 147	93.18 95	97.10 126	96.35 107
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tmp_tt				50.24 209	68.55 212	46.86 217	48.90 217	18.28 216	86.51 120	68.32 166	70.19 158	65.33 178	26.69 215	74.37 207	66.80 209	70.72 215

IB-MVS	85.10 14	87.98 112	87.97 113	87.99 117	94.55 76	96.86 80	84.52 188	88.21 89	86.48 121	88.54 50	74.41 138	77.74 131	74.10 195	89.65 165	92.85 104	98.06 75	97.80 61
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
tpm cat1		84.13 157	81.99 177	86.63 133	91.74 121	91.50 184	90.68 124	75.69 189	86.12 122	85.44 83	72.39 148	70.72 152	85.16 146	80.89 203	81.56 199	91.07 201	90.71 179

thres100view900		89.36 102	87.61 119	91.39 81	93.90 90	96.86 80	94.35 68	89.66 73	85.87 123	81.15 101	76.46 126	70.38 154	91.17 82	94.09 86	93.43 88	98.13 67	96.16 114

tfpn200view9		89.55 99	87.86 114	91.53 78	93.90 90	97.26 66	94.31 71	89.74 69	85.87 123	81.15 101	76.46 126	70.38 154	91.76 77	94.92 64	93.51 82	98.28 52	96.61 98

thres400		89.40 101	87.58 121	91.53 78	94.06 84	97.21 69	94.19 75	89.83 68	85.69 125	81.08 103	75.50 133	69.76 158	91.80 75	94.79 71	93.51 82	98.20 61	96.60 99

thres200		89.49 100	87.72 116	91.55 77	93.95 87	97.25 67	94.34 69	89.74 69	85.66 126	81.18 100	76.12 130	70.19 157	91.80 75	94.92 64	93.51 82	98.27 53	96.40 105

test0.0.03 1		85.58 137	87.69 118	83.11 172	91.22 128	92.54 164	85.60 187	83.62 141	85.66 126	67.84 170	82.79 92	79.70 116	73.51 197	91.15 139	90.79 139	96.88 144	91.23 175

thres600view7		89.28 105	87.47 124	91.39 81	94.12 81	97.25 67	93.94 80	89.74 69	85.62 128	80.63 107	75.24 135	69.33 159	91.66 79	94.92 64	93.23 92	98.27 53	96.72 95

dps		85.00 145	83.21 165	87.08 127	90.73 136	92.55 163	89.34 150	75.29 190	84.94 129	87.01 62	79.27 111	67.69 167	87.27 126	84.22 195	83.56 195	92.83 191	90.25 183

CR-MVSNet		85.48 139	86.29 131	84.53 155	91.08 133	92.10 171	89.18 153	73.30 198	84.75 130	71.08 147	73.12 147	77.91 129	86.27 136	91.48 131	90.75 142	96.27 157	93.94 152

RPMNet		84.82 148	85.90 138	83.56 167	91.10 131	92.10 171	88.73 162	71.11 203	84.75 130	68.79 162	73.56 140	77.62 132	85.33 145	90.08 158	89.43 172	96.32 156	93.77 156

test-LLR		86.88 121	88.28 106	85.24 145	91.22 128	92.07 173	87.41 171	83.62 141	84.58 132	69.33 159	83.00 87	82.79 95	84.24 152	92.26 119	89.81 166	95.64 169	93.44 158

TESTMET0.1,1		86.11 131	88.28 106	83.59 166	87.80 165	92.07 173	87.41 171	77.12 185	84.58 132	69.33 159	83.00 87	82.79 95	84.24 152	92.26 119	89.81 166	95.64 169	93.44 158

DU-MVS		86.12 130	84.81 148	87.66 121	87.77 167	93.78 124	90.15 134	87.87 97	84.40 134	73.45 133	70.59 154	64.82 184	88.95 110	90.14 154	92.33 111	97.76 94	97.62 66

NR-MVSNet		85.46 140	84.54 150	86.52 134	88.33 159	93.78 124	90.45 127	87.87 97	84.40 134	71.61 141	70.59 154	62.09 193	82.79 163	91.75 128	91.75 127	98.10 71	97.44 73

UniMVSNet (Re)		86.22 128	85.46 144	87.11 126	88.34 158	94.42 112	89.65 148	87.10 108	84.39 136	74.61 127	70.41 157	68.10 164	85.10 147	91.17 138	91.79 126	97.84 89	97.94 52

test-mter		86.09 132	88.38 105	83.43 169	87.89 164	92.61 161	86.89 176	77.11 186	84.30 137	68.62 165	82.57 95	82.45 100	84.34 151	92.40 117	90.11 160	95.74 164	94.21 150

FMVSNet5		84.47 154	84.72 149	84.18 160	83.30 201	88.43 198	88.09 166	79.42 177	84.25 138	74.14 130	73.15 146	78.74 120	83.65 158	91.19 137	91.19 136	96.46 153	86.07 199

Vis-MVSNet		89.36 102	91.49 79	86.88 129	92.10 118	97.60 60	92.16 113	85.89 114	84.21 139	75.20 126	82.58 94	87.13 72	77.40 184	95.90 50	95.63 47	98.51 21	97.36 76
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
UniMVSNet_NR-MVSNet		86.80 122	85.86 139	87.89 120	88.17 160	94.07 119	90.15 134	88.51 85	84.20 140	73.45 133	72.38 149	70.30 156	88.95 110	90.25 153	92.21 114	98.12 68	97.62 66

IterMVS		85.25 143	86.49 129	83.80 164	90.42 140	90.77 193	90.02 138	78.04 182	84.10 141	66.27 180	77.28 121	78.41 125	83.01 161	90.88 141	89.72 170	95.04 181	94.24 148
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PatchT		83.86 161	85.51 143	81.94 185	88.41 157	91.56 183	78.79 202	71.57 202	84.08 142	71.08 147	70.62 153	76.13 141	86.27 136	91.48 131	90.75 142	95.52 174	93.94 152

IterMVS-SCA-FT		85.44 141	86.71 126	83.97 163	90.59 139	90.84 190	89.73 146	78.34 180	84.07 143	66.40 179	77.27 122	78.66 121	83.06 160	91.20 136	90.10 161	95.72 166	94.78 141

Baseline_NR-MVSNet		85.28 142	83.42 160	87.46 125	87.77 167	90.80 192	89.90 144	87.69 101	83.93 144	74.16 129	64.72 184	66.43 174	87.48 124	90.14 154	90.83 138	97.73 97	97.11 86

Fast-Effi-MVS+-dtu		86.25 126	87.70 117	84.56 154	90.37 141	93.70 127	90.54 126	78.14 181	83.50 145	65.37 185	81.59 103	75.83 142	86.09 140	91.70 129	91.70 128	96.88 144	95.84 122

Effi-MVS+-dtu		87.51 117	88.13 110	86.77 131	91.10 131	94.90 106	90.91 123	82.67 151	83.47 146	71.55 142	81.11 105	77.04 135	89.41 98	92.65 113	91.68 130	95.00 183	96.09 117

ACMH+	85.75 12	87.19 120	86.02 135	88.56 110	93.42 98	94.41 113	89.91 142	87.66 103	83.45 147	72.25 140	76.42 128	71.99 149	90.78 86	89.86 160	90.94 137	97.32 114	95.11 139

thisisatest0515		85.70 135	87.00 125	84.19 159	88.16 161	93.67 129	84.20 190	84.14 135	83.39 148	72.91 135	76.79 123	74.75 143	78.82 182	92.57 115	91.26 135	96.94 137	96.56 102

TranMVSNet+NR-MVSNet		85.57 138	84.41 151	86.92 128	87.67 170	93.34 137	90.31 130	88.43 87	83.07 149	70.11 154	69.99 160	65.28 179	86.96 128	89.73 162	92.27 112	98.06 75	97.17 85

OPM-MVS		91.08 78	89.34 97	93.11 60	96.18 60	96.13 95	96.39 43	92.39 44	82.97 150	81.74 96	82.55 96	80.20 114	93.97 52	94.62 74	93.23 92	98.00 80	95.73 124

TDRefinement		84.97 146	83.39 161	86.81 130	92.97 105	94.12 117	92.18 110	87.77 100	82.78 151	71.31 145	68.43 163	68.07 165	81.10 175	89.70 164	89.03 176	95.55 173	91.62 170

tpm		83.16 170	83.64 155	82.60 181	90.75 135	91.05 187	88.49 163	73.99 193	82.36 152	67.08 176	78.10 116	68.79 160	84.17 154	85.95 190	85.96 185	91.09 200	93.23 162

UA-Net		90.81 82	92.58 61	88.74 109	94.87 75	97.44 63	92.61 103	88.22 88	82.35 153	78.93 114	85.20 75	95.61 38	79.56 179	96.52 34	96.57 23	98.23 58	94.37 147

pmnet_mix02		80.14 189	80.21 190	80.06 189	86.61 188	89.66 195	80.40 199	82.20 159	82.29 154	61.35 195	71.52 150	66.67 173	76.75 187	82.55 200	80.18 203	93.05 189	88.62 191

TinyColmap		84.04 159	82.01 176	86.42 135	90.87 134	91.84 178	88.89 160	84.07 136	82.11 155	69.89 155	71.08 152	60.81 199	89.04 108	90.52 150	89.19 174	95.76 163	88.50 193

ACMH	85.51 13	87.31 119	86.59 128	88.14 115	93.96 86	94.51 109	89.00 158	87.99 91	81.58 156	70.15 153	78.41 115	71.78 150	90.60 89	91.30 135	91.99 122	97.17 121	96.58 100
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MIMVSNet		82.97 174	84.00 154	81.77 187	82.23 202	92.25 170	87.40 173	72.73 201	81.48 157	69.55 157	68.79 162	72.42 146	81.82 170	92.23 122	92.25 113	96.89 143	88.61 192

FMVSNet1		87.33 118	86.00 136	88.89 106	87.13 180	92.83 156	93.08 100	84.46 131	81.35 158	82.20 95	66.33 173	77.96 128	88.96 109	93.97 89	94.16 69	97.54 109	95.38 135

GA-MVS		85.08 144	85.65 141	84.42 156	89.77 143	94.25 116	89.26 152	84.62 128	81.19 159	62.25 193	75.72 132	68.44 163	84.14 155	93.57 96	91.68 130	96.49 151	94.71 143

testgi		81.94 182	84.09 153	79.43 192	89.53 147	90.83 191	82.49 194	81.75 164	80.59 160	59.46 200	82.82 91	65.75 176	67.97 199	90.10 157	89.52 171	95.39 175	89.03 188

v8		84.45 155	83.30 164	85.80 138	87.53 172	92.95 151	90.31 130	82.46 155	80.46 161	71.43 143	66.99 168	67.16 169	86.14 138	89.26 169	90.22 155	96.94 137	96.06 118

CDS-MVSNet		88.34 110	88.71 102	87.90 119	90.70 138	94.54 108	92.38 105	86.02 113	80.37 162	79.42 112	79.30 110	83.43 91	82.04 167	93.39 102	94.01 74	96.86 146	95.93 121
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
V42		84.48 153	83.36 163	85.79 139	87.14 179	93.28 141	90.03 137	83.98 137	80.30 163	71.20 146	66.90 170	67.17 168	85.55 142	89.35 166	90.27 153	96.82 147	96.27 112

MDTV_nov1_ep13_2view		80.43 187	80.94 187	79.84 190	84.82 198	90.87 189	84.23 189	73.80 194	80.28 164	64.33 188	70.05 159	68.77 161	79.67 177	84.83 193	83.50 196	92.17 195	88.25 196

CVMVSNet		83.83 162	85.53 142	81.85 186	89.60 145	90.92 188	87.81 169	83.21 147	80.11 165	60.16 198	76.47 125	78.57 123	76.79 186	89.76 161	90.13 156	93.51 185	92.75 167

WR-MVS		83.14 171	83.38 162	82.87 177	87.55 171	93.29 140	86.36 181	84.21 133	80.05 166	66.41 178	66.91 169	66.92 171	75.66 191	88.96 173	90.56 147	97.05 129	96.96 89

PM-MVS		80.29 188	79.30 191	81.45 188	81.91 203	88.23 199	82.61 193	79.01 178	79.99 167	67.15 175	69.07 161	51.39 211	82.92 162	87.55 181	85.59 186	95.08 180	93.28 161

v2v482		84.51 151	83.05 167	86.20 136	87.25 176	93.28 141	90.22 132	85.40 122	79.94 168	69.78 156	67.74 165	65.15 181	87.57 121	89.12 171	90.55 148	96.97 133	95.60 127

v10		84.18 156	83.17 166	85.37 142	87.34 174	92.68 159	90.32 129	81.33 167	79.93 169	69.23 161	66.33 173	65.74 177	87.03 127	90.84 142	90.38 150	96.97 133	96.29 111

test_part1		87.53 116	84.97 145	90.52 88	92.11 117	93.31 139	93.32 96	85.79 116	79.56 170	87.38 60	62.89 190	78.60 122	89.25 103	90.65 148	92.17 116	95.24 178	97.62 66

SixPastTwentyTwo		83.12 172	83.44 159	82.74 178	87.71 169	93.11 149	82.30 195	82.33 156	79.24 171	64.33 188	78.77 113	62.75 189	84.11 156	88.11 177	87.89 179	95.70 167	94.21 150

v148		83.61 165	82.10 174	85.37 142	87.34 174	92.94 152	87.48 170	85.72 120	78.92 172	73.87 131	65.71 178	64.69 185	81.78 171	87.82 178	89.35 173	96.01 160	95.26 136

v1144		84.03 160	82.88 168	85.37 142	87.17 178	93.15 148	90.18 133	83.31 146	78.83 173	67.85 169	65.99 175	64.99 182	86.79 130	90.75 144	90.33 152	96.90 142	96.15 115

v1192		83.56 166	82.35 171	84.98 147	86.84 185	92.84 154	90.01 139	82.70 149	78.54 174	66.48 177	64.88 183	62.91 188	86.91 129	90.72 145	90.25 154	96.94 137	96.32 109

v1921920		83.30 169	82.09 175	84.70 151	86.59 189	92.67 160	89.82 145	82.23 158	78.32 175	65.76 182	64.64 185	62.35 191	86.78 131	90.34 152	90.02 162	97.02 131	96.31 110

N_pmnet		77.55 196	76.68 199	78.56 194	85.43 196	87.30 203	78.84 201	81.88 162	78.30 176	60.61 196	61.46 192	62.15 192	74.03 196	82.04 201	80.69 202	90.59 204	84.81 203

anonymousdsp		84.51 151	85.85 140	82.95 176	86.30 191	93.51 133	85.77 185	80.38 173	78.25 177	63.42 191	73.51 142	72.20 147	84.64 150	93.21 107	92.16 117	97.19 120	98.14 43

v144192		83.48 167	82.23 172	84.94 148	86.65 186	92.84 154	89.63 149	82.48 154	77.87 178	67.36 173	65.33 180	63.50 187	86.51 132	89.72 163	89.99 164	97.03 130	96.35 107

CP-MVSNet		83.11 173	82.15 173	84.23 158	87.20 177	92.70 158	86.42 180	83.53 144	77.83 179	67.67 171	66.89 171	60.53 201	82.47 164	89.23 170	90.65 146	98.08 72	97.20 84

DeepMVS_CX						71.82 212	68.37 211	48.05 214	77.38 180	46.88 214	65.77 177	47.03 216	67.48 200	64.27 212		76.89 214	76.72 208

WR-MVS_H		82.86 176	82.66 170	83.10 173	87.44 173	93.33 138	85.71 186	83.20 148	77.36 181	68.20 168	66.37 172	65.23 180	76.05 190	89.35 166	90.13 156	97.99 81	96.89 92

v1240		82.88 175	81.66 179	84.29 157	86.46 190	92.52 166	89.06 156	81.82 163	77.16 182	65.09 186	64.17 187	61.50 196	86.36 133	90.12 156	90.13 156	96.95 136	96.04 119

TAMVS		84.94 147	84.95 146	84.93 149	88.82 150	93.18 145	88.44 164	81.28 168	77.16 182	73.76 132	75.43 134	76.57 139	82.04 167	90.59 149	90.79 139	95.22 179	90.94 177

PEN-MVS		82.49 179	81.58 180	83.56 167	86.93 183	92.05 175	86.71 178	83.84 138	76.94 184	64.68 187	67.24 166	60.11 202	81.17 174	87.78 179	90.70 145	98.02 78	96.21 113

v7n		82.25 181	81.54 181	83.07 174	85.55 195	92.58 162	86.68 179	81.10 171	76.54 185	65.97 181	62.91 189	60.56 200	82.36 165	91.07 140	90.35 151	96.77 149	96.80 93

pmmvs5		83.37 168	82.68 169	84.18 160	87.13 180	93.18 145	86.74 177	82.08 160	76.48 186	67.28 174	71.26 151	62.70 190	84.71 149	90.77 143	90.12 159	97.15 122	94.24 148

PS-CasMVS		82.53 178	81.54 181	83.68 165	87.08 182	92.54 164	86.20 182	83.46 145	76.46 187	65.73 183	65.71 178	59.41 206	81.61 172	89.06 172	90.55 148	98.03 77	97.07 87

DTE-MVSNet		81.76 184	81.04 186	82.60 181	86.63 187	91.48 186	85.97 184	83.70 140	76.45 188	62.44 192	67.16 167	59.98 203	78.98 181	87.15 183	89.93 165	97.88 88	95.12 138

EU-MVSNet		78.43 192	80.25 189	76.30 197	83.81 200	87.27 204	80.99 197	79.52 176	76.01 189	54.12 207	70.44 156	64.87 183	67.40 201	86.23 188	85.54 188	91.95 198	91.41 172

new_pmnet		72.29 201	73.25 201	71.16 204	75.35 208	81.38 208	73.72 208	69.27 206	75.97 190	49.84 213	56.27 200	56.12 209	69.08 198	81.73 202	80.86 201	89.72 207	80.44 207

test_method		58.10 207	64.61 207	50.51 208	28.26 219	41.71 218	61.28 213	32.07 215	75.92 191	52.04 210	47.94 208	61.83 195	51.80 209	79.83 204	63.95 212	77.60 213	81.05 206

MVS-HIRNet		78.16 193	77.57 197	78.83 193	85.83 193	87.76 200	76.67 203	70.22 205	75.82 192	67.39 172	55.61 201	70.52 153	81.96 169	86.67 187	85.06 191	90.93 202	81.58 205

Anonymous20231206		78.09 194	78.11 195	78.07 195	85.19 197	89.17 196	80.99 197	81.24 170	75.46 193	58.25 202	54.78 205	59.90 204	66.73 202	88.94 174	88.26 178	96.01 160	90.25 183

pmmvs-eth3d		79.78 191	77.58 196	82.34 183	81.57 204	87.46 202	82.92 192	81.28 168	75.33 194	71.34 144	61.88 191	52.41 210	81.59 173	87.56 180	86.90 182	95.36 177	91.48 171

UniMVSNet_ETH3D		84.57 149	81.40 183	88.28 113	89.34 148	94.38 115	90.33 128	86.50 111	74.74 195	77.52 118	59.90 197	62.04 194	88.78 115	88.82 175	92.65 107	97.22 118	97.24 80

pm-mvs1		84.55 150	83.46 157	85.82 137	88.16 161	93.39 136	89.05 157	85.36 123	74.03 196	72.43 139	65.08 181	71.11 151	82.30 166	93.48 99	91.70 128	97.64 105	95.43 133

tfpnnormal		83.80 163	81.26 185	86.77 131	89.60 145	93.26 143	89.72 147	87.60 106	72.78 197	70.44 151	60.53 196	61.15 198	85.55 142	92.72 110	91.44 132	97.71 98	96.92 91

FPMVS		69.87 203	67.10 206	73.10 201	84.09 199	78.35 211	79.40 200	76.41 187	71.92 198	57.71 203	54.06 207	50.04 212	56.72 206	71.19 208	68.70 208	84.25 209	75.43 209

ambc			67.96 205		73.69 209	79.79 210	73.82 207		71.61 199	59.80 199	46.00 209	20.79 219	66.15 203	86.92 185	80.11 204	89.13 208	90.50 180

MDA-MVSNet-bldmvs		73.81 198	72.56 202	75.28 198	72.52 211	88.87 197	74.95 206	82.67 151	71.57 200	55.02 205	65.96 176	42.84 217	76.11 189	70.61 209	81.47 200	90.38 205	86.59 197

EG-PatchMatch MVS		81.70 185	81.31 184	82.15 184	88.75 151	93.81 123	87.14 174	78.89 179	71.57 200	64.12 190	61.20 195	68.46 162	76.73 188	91.48 131	90.77 141	97.28 116	91.90 169

TransMVSNet (Re)		82.67 177	80.93 188	84.69 152	88.71 152	91.50 184	87.90 167	87.15 107	71.54 202	68.24 167	63.69 188	64.67 186	78.51 183	91.65 130	90.73 144	97.64 105	92.73 168

test20.03		76.41 197	78.49 194	73.98 199	85.64 194	87.50 201	75.89 204	80.71 172	70.84 203	51.07 212	68.06 164	61.40 197	54.99 208	88.28 176	87.20 181	95.58 172	86.15 198

CMPMVS	61.19 17	79.86 190	77.46 198	82.66 180	91.54 126	91.82 179	83.25 191	81.57 165	70.51 204	68.64 164	59.89 198	66.77 172	79.63 178	84.00 197	84.30 193	91.34 199	84.89 202
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Gipuma		58.52 206	56.17 209	61.27 206	67.14 213	58.06 214	52.16 216	68.40 208	69.00 205	45.02 215	22.79 213	20.57 220	55.11 207	76.27 206	79.33 205	79.80 212	67.16 212
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
new-patchmatchnet		72.32 200	71.09 203	73.74 200	81.17 205	84.86 207	72.21 209	77.48 184	68.32 206	54.89 206	55.10 203	49.31 214	63.68 205	79.30 205	76.46 206	93.03 190	84.32 204

MIMVSNet1		73.19 199	73.70 200	72.60 202	65.42 214	86.69 205	75.56 205	79.65 175	67.87 207	55.30 204	45.24 210	56.41 208	63.79 204	86.98 184	87.66 180	95.85 162	85.04 201

LTVRE_ROB	81.71 16	82.44 180	81.84 178	83.13 171	89.01 149	92.99 150	88.90 159	82.32 157	66.26 208	54.02 208	74.68 137	59.62 205	88.87 113	90.71 146	92.02 121	95.68 168	96.62 97
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
pmmvs6		80.90 186	78.77 192	83.38 170	85.84 192	91.61 182	86.01 183	82.54 153	64.17 209	70.43 152	54.14 206	67.06 170	80.73 176	90.50 151	89.17 175	94.74 184	94.75 142

gm-plane-assit		77.65 195	78.50 193	76.66 196	87.96 163	85.43 206	64.70 212	74.50 191	64.15 210	51.26 211	61.32 194	58.17 207	84.11 156	95.16 59	93.83 76	97.45 112	91.41 172

gg-mvs-nofinetune		81.83 183	83.58 156	79.80 191	91.57 124	96.54 85	93.79 84	68.80 207	62.71 211	43.01 216	55.28 202	85.06 85	83.65 158	96.13 45	94.86 60	97.98 84	94.46 145

pmmvs3		71.13 202	71.06 204	71.21 203	73.54 210	80.19 209	71.69 210	64.86 209	62.04 212	52.10 209	54.92 204	48.00 215	75.03 192	83.75 198	83.24 197	90.04 206	85.27 200

PMVS	56.77 18	61.27 205	58.64 208	64.35 205	75.66 207	54.60 215	53.62 215	74.23 192	53.69 213	58.37 201	44.27 211	49.38 213	44.16 212	69.51 210	65.35 210	80.07 211	73.66 210
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PMMVS2		53.68 208	55.72 210	51.30 207	58.84 215	67.02 213	54.23 214	60.97 212	47.50 214	19.42 218	34.81 212	31.97 218	30.88 214	65.84 211	69.99 207	83.47 210	72.92 211

EMVS		39.04 211	34.32 213	44.54 211	58.25 216	39.35 219	27.61 219	62.55 211	35.99 215	16.40 220	20.04 216	14.77 221	44.80 210	33.12 215	44.10 214	57.61 217	52.89 215

E-PMN		40.00 209	35.74 212	44.98 210	57.69 217	39.15 220	28.05 218	62.70 210	35.52 216	17.78 219	20.90 214	14.36 222	44.47 211	35.89 214	47.86 213	59.15 216	56.47 214

MVE	39.81 19	39.52 210	41.58 211	37.11 212	33.93 218	49.06 216	26.45 220	54.22 213	29.46 217	24.15 217	20.77 215	10.60 223	34.42 213	51.12 213	65.27 211	49.49 218	64.81 213
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		4.35 212	6.54 214	1.79 214	0.60 220	1.82 221	3.06 222	0.95 217	7.22 218	0.88 222	12.38 217	1.25 224	3.87 217	6.09 216	5.58 215	1.40 219	11.42 217

test123		3.48 213	5.31 215	1.34 215	0.20 222	1.52 222	2.17 223	0.58 218	6.13 219	0.31 223	9.85 218	0.31 225	3.90 216	2.65 217	5.28 216	0.87 220	11.46 216

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										60.19 197

9.14												97.28 23

SR-MVS					98.93 19			96.00 17				97.75 14

our_test_3					86.93 183	89.77 194	81.61 196

MTAPA										95.36 2		97.46 20

MTMP										95.70 1		96.90 26

Patchmatch-RL test							18.47 221

XVS					95.68 63	98.66 13	94.96 60			88.03 51		96.06 32				98.46 28

X-MVStestdata					95.68 63	98.66 13	94.96 60			88.03 51		96.06 32				98.46 28

mPP-MVS					98.76 24							95.49 39

Patchmtry						92.39 168	89.18 153	73.30 198		71.08 147

High-resolution multi-view benchmark