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		95.53 1	95.79 1	95.23 1	97.60 9	98.92 1	95.99 5	92.05 8	97.14 1	94.19 1	94.71 7	93.25 1	95.08 1	94.32 11	92.59 15	96.49 18	99.58 3

DPE-MVS		95.10 2	95.53 2	94.60 5	97.77 7	98.64 3	96.60 4	92.45 6	96.34 5	91.41 5	96.70 2	92.26 5	93.56 4	93.68 17	91.73 29	95.79 37	99.37 7
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
DVP-MVS		95.06 3	95.37 4	94.70 2	97.59 10	98.89 2	95.37 11	92.04 9	96.85 3	94.00 2	92.81 14	93.02 2	92.93 5	94.22 14	92.15 20	96.30 24	99.61 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
MSP-MVS		95.00 4	95.47 3	94.45 6	96.78 18	98.11 9	95.72 7	90.91 14	96.68 4	91.57 4	96.98 1	89.47 13	94.76 2	95.24 3	92.15 20	96.98 7	99.64 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
CNVR-MVS		94.53 5	94.85 6	94.15 8	98.03 4	98.59 4	95.56 8	92.91 3	94.86 13	88.46 15	91.32 21	90.83 10	94.03 3	95.20 4	94.16 5	95.89 33	99.01 15

SF-MVS		94.40 6	94.15 11	94.70 2	98.25 2	98.24 6	96.86 2	93.46 1	94.87 11	90.26 9	95.96 3	88.42 16	92.76 8	92.29 30	90.84 41	96.62 13	98.44 27

APDe-MVS		94.31 7	94.30 9	94.33 7	97.57 11	98.06 11	95.79 6	91.98 10	95.50 8	92.19 3	95.25 5	87.97 19	92.93 5	93.01 24	91.02 39	95.52 39	99.29 8

MCST-MVS		94.10 8	94.77 7	93.31 10	98.31 1	98.34 5	95.43 9	92.54 5	94.41 17	83.05 31	91.38 19	90.97 9	92.24 13	95.05 6	94.02 6	98.31 1	99.20 10

HPM-MVS++		94.04 9	94.96 5	92.96 12	97.93 5	97.71 17	94.65 14	91.01 13	95.91 6	87.43 17	93.52 11	92.63 4	92.29 12	94.22 14	92.34 17	94.47 58	98.37 29

NCCC		93.59 10	94.00 13	93.10 11	97.90 6	97.93 13	95.40 10	92.39 7	94.47 16	84.94 22	91.21 22	89.32 14	92.53 10	93.90 16	92.98 12	95.44 41	98.22 32

SMA-MVS		93.47 11	94.29 10	92.52 14	97.72 8	97.77 16	94.46 17	90.19 17	94.96 10	87.15 18	90.15 25	90.99 8	91.49 16	94.31 12	93.33 10	94.10 64	98.53 25
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
APD-MVS		93.47 11	93.44 16	93.50 9	97.06 14	97.09 27	95.27 12	91.47 11	95.71 7	89.57 12	93.66 9	86.28 25	92.81 7	92.06 34	90.70 43	94.83 55	98.60 21
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
SD-MVS		93.36 13	94.33 8	92.22 16	94.68 44	97.89 15	94.56 15	90.89 15	94.80 14	90.04 11	93.53 10	90.14 11	89.78 24	92.74 26	92.17 18	93.35 104	99.07 13
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
TSAR-MVS + MP.		93.07 14	93.53 15	92.53 13	94.23 47	97.54 21	94.75 13	89.87 18	95.26 9	89.20 14	93.16 12	88.19 18	92.15 14	91.79 38	89.65 56	94.99 51	99.16 11
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
DPM-MVS		92.86 15	93.19 18	92.47 15	95.78 35	97.40 22	97.39 1	92.56 4	92.88 26	81.84 39	81.31 40	92.95 3	91.21 17	96.54 1	97.33 1	96.01 30	93.94 109

SteuartSystems-ACMMP		92.31 16	93.31 17	91.15 23	96.88 16	97.36 23	93.95 21	89.44 20	92.62 27	83.20 28	94.34 8	85.55 27	88.95 31	93.07 23	91.90 25	94.51 57	98.30 30
Skip Steuart: Steuart Systems R&D Blog.
ACMMP_NAP		92.16 17	92.91 21	91.28 22	96.95 15	97.36 23	93.66 22	89.23 22	93.33 21	83.71 26	90.53 23	86.84 22	90.39 20	93.30 22	91.56 31	93.74 75	97.43 46

HFP-MVS		92.02 18	92.13 23	91.89 20	97.16 13	96.46 40	93.57 23	87.60 26	93.79 19	88.17 16	93.15 13	83.94 38	91.19 18	90.81 49	89.83 51	93.66 79	96.94 62

train_agg		91.99 19	93.71 14	89.98 28	96.42 27	97.03 29	94.31 19	89.05 23	93.33 21	77.75 46	95.06 6	88.27 17	88.38 37	92.02 35	91.41 33	94.00 67	98.84 18

xxxxxxxxxxxxxcwj		91.86 20	89.43 37	94.70 2	98.25 2	98.24 6	96.86 2	93.46 1	94.87 11	90.26 9	95.96 3	55.37 145	92.76 8	92.29 30	90.84 41	96.62 13	98.44 27

DeepC-MVS_fast	86.59 2	91.69 21	91.39 26	92.05 19	97.43 12	96.92 32	94.05 20	90.23 16	93.31 24	83.19 29	77.91 46	84.23 34	92.42 11	94.62 9	94.83 3	95.00 50	97.88 36
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
zzz-MVS		91.59 22	91.12 27	92.13 17	96.76 19	96.68 35	93.39 24	88.00 25	93.63 20	90.76 8	83.97 36	85.33 29	89.89 23	91.60 40	89.65 56	94.00 67	96.97 60

TSAR-MVS + GP.		91.29 23	93.11 20	89.18 34	87.81 85	96.21 46	92.51 33	83.83 46	94.24 18	83.77 25	91.87 18	89.62 12	90.07 21	90.40 53	90.31 47	97.09 6	99.10 12

ACMMPR		91.15 24	91.44 25	90.81 24	96.61 21	96.25 44	93.09 25	87.08 29	93.32 23	84.78 23	92.08 17	82.10 44	89.71 25	90.24 54	89.82 52	93.61 84	96.30 74

DeepPCF-MVS	86.71 1	91.00 25	94.05 12	87.43 45	95.58 38	98.17 8	86.22 75	88.59 24	97.01 2	76.77 52	85.11 34	88.90 15	87.29 44	95.02 7	94.69 4	90.15 175	99.48 6

TSAR-MVS + ACMM		90.98 26	93.18 19	88.42 39	95.69 36	96.73 34	94.52 16	86.97 32	92.99 25	76.32 53	92.31 16	86.64 23	84.40 69	92.97 25	92.02 22	92.62 127	98.59 22

MP-MVS		90.81 27	91.45 24	90.06 27	96.59 22	96.33 43	92.46 34	87.19 28	90.27 41	82.54 35	91.38 19	84.88 31	88.27 38	90.58 51	89.30 62	93.30 106	97.44 44
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CP-MVS		90.57 28	90.68 29	90.44 25	96.13 29	95.90 51	92.77 30	86.86 33	92.12 31	84.19 24	89.18 28	82.37 42	89.43 29	89.65 63	88.43 70	93.27 107	97.13 54

MSLP-MVS++		90.33 29	88.82 41	92.10 18	96.52 25	95.93 47	94.35 18	86.26 34	88.37 54	89.24 13	75.94 52	82.60 41	89.71 25	89.45 66	92.17 18	96.51 17	97.24 51

CANet		89.98 30	90.42 33	89.47 33	94.13 48	98.05 12	91.76 39	83.27 49	90.87 37	81.90 38	72.32 58	84.82 32	88.42 35	94.52 10	93.78 8	97.34 4	98.58 23

PGM-MVS		89.97 31	90.64 31	89.18 34	96.53 24	95.90 51	93.06 26	82.48 57	90.04 43	80.37 41	92.75 15	80.96 49	88.93 32	89.88 59	89.08 64	93.69 78	95.86 78

PHI-MVS		89.88 32	92.75 22	86.52 55	94.97 41	97.57 20	89.99 50	84.56 42	92.52 29	69.72 86	90.35 24	87.11 21	84.89 61	91.82 37	92.37 16	95.02 49	97.51 42

CSCG		89.81 33	89.69 34	89.96 29	96.55 23	97.90 14	92.89 28	87.06 30	88.74 52	86.17 19	78.24 45	86.53 24	84.75 64	87.82 86	90.59 44	92.32 132	98.01 34

X-MVS		89.73 34	90.65 30	88.66 37	96.44 26	95.93 47	92.26 36	86.98 31	90.73 38	76.32 53	89.56 27	82.05 45	86.51 50	89.98 57	89.60 58	93.43 99	96.72 69

EPNet		89.30 35	90.89 28	87.44 44	95.67 37	96.81 33	91.13 42	83.12 51	91.14 34	76.31 57	87.60 30	80.40 53	84.45 67	92.13 33	91.12 38	93.96 69	97.01 58
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DeepC-MVS	84.14 3	88.80 36	88.03 45	89.71 31	94.83 42	96.56 36	92.57 32	89.38 21	89.25 49	79.59 43	70.02 68	77.05 65	88.24 39	92.44 28	92.79 13	93.65 82	98.10 33
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CDPH-MVS		88.76 37	90.43 32	86.81 51	96.04 31	96.53 39	92.95 27	85.95 36	90.36 40	67.93 91	85.80 33	80.69 50	83.82 72	90.81 49	91.85 28	94.18 62	96.99 59

3Dnovator+	81.14 5	88.59 38	87.49 47	89.88 30	95.83 34	96.45 42	91.94 38	82.41 58	87.09 59	85.94 21	62.80 96	85.37 28	89.46 27	91.51 41	91.89 27	93.72 76	97.30 49

ACMMP		88.48 39	88.71 42	88.22 41	94.61 45	95.53 56	90.64 46	85.60 38	90.97 35	78.62 45	89.88 26	74.20 78	86.29 51	88.16 83	86.37 89	93.57 85	95.86 78
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
AdaColmap		88.46 40	85.75 61	91.62 21	96.25 28	95.35 60	90.71 44	91.08 12	90.22 42	86.17 19	74.33 54	73.67 82	92.00 15	86.31 103	85.82 98	93.52 88	94.53 96

MVS_0304		88.43 41	89.46 36	87.21 46	91.85 60	97.60 18	92.62 31	81.10 64	87.16 58	73.80 64	72.19 60	83.36 40	87.03 45	94.64 8	93.67 9	96.88 9	97.64 41

3Dnovator	80.58 8	88.20 42	86.53 53	90.15 26	96.86 17	96.46 40	91.97 37	83.06 52	85.16 64	83.66 27	62.28 99	82.15 43	88.98 30	90.99 47	92.65 14	96.38 23	96.03 75

CPTT-MVS		88.17 43	87.84 46	88.55 38	93.33 50	93.75 76	92.33 35	84.75 41	89.87 45	81.72 40	83.93 37	81.12 48	88.45 34	85.42 112	84.07 117	90.72 167	96.72 69

MVS_111021_HR		87.82 44	88.84 40	86.62 53	94.42 46	97.36 23	88.21 59	83.26 50	83.42 67	72.52 74	82.63 38	76.93 66	84.95 60	91.93 36	91.15 37	96.39 22	98.49 26

DELS-MVS		87.75 45	86.92 51	88.71 36	94.69 43	97.34 26	92.78 29	84.50 43	77.87 91	81.94 37	67.17 76	75.49 73	82.84 79	95.38 2	95.93 2	95.55 38	99.27 9
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
MVSTER		87.68 46	89.12 39	86.01 57	88.11 83	90.05 114	89.28 53	77.05 84	91.37 32	79.97 42	76.70 50	85.25 30	84.89 61	93.53 18	91.41 33	96.73 11	95.55 85

MVS_111021_LR		87.58 47	88.67 43	86.31 56	92.58 54	95.89 53	86.20 76	82.49 56	89.08 51	77.47 49	86.20 32	74.22 77	85.49 56	90.03 56	88.52 68	93.66 79	96.74 68

CS-MVS		87.41 48	89.43 37	85.04 61	86.76 94	94.49 69	86.38 74	74.86 97	90.66 39	75.65 59	77.20 49	80.41 52	90.66 19	93.47 19	91.22 35	95.96 31	98.58 23

QAPM		87.06 49	86.46 54	87.75 42	96.63 20	97.09 27	91.71 40	82.62 55	80.58 81	71.28 79	66.04 83	84.24 33	87.01 46	89.93 58	89.91 50	97.26 5	97.44 44

PVSNet_BlendedMVS		86.98 50	87.05 49	86.90 48	93.03 51	96.98 30	86.57 71	81.82 60	89.78 46	82.78 33	71.54 61	66.07 110	80.73 90	93.46 20	91.97 23	96.45 20	99.53 4

PVSNet_Blended		86.98 50	87.05 49	86.90 48	93.03 51	96.98 30	86.57 71	81.82 60	89.78 46	82.78 33	71.54 61	66.07 110	80.73 90	93.46 20	91.97 23	96.45 20	99.53 4

ETV-MVS		86.94 52	89.49 35	83.95 68	87.28 90	95.61 55	83.58 100	76.37 89	92.59 28	73.20 66	80.35 41	76.42 69	87.38 43	92.20 32	90.45 46	95.90 32	98.83 19

OMC-MVS		86.38 53	86.21 58	86.57 54	92.30 56	94.35 70	87.60 63	83.51 48	92.32 30	77.37 50	72.27 59	77.83 58	86.59 49	87.62 88	85.95 95	92.08 136	93.11 122

HQP-MVS		86.17 54	87.35 48	84.80 64	91.41 63	92.37 94	91.05 43	84.35 45	88.52 53	64.21 98	87.05 31	68.91 100	84.80 63	89.12 69	88.16 74	92.96 118	97.31 48

canonicalmvs		85.93 55	86.26 57	85.54 58	88.94 74	95.44 57	89.56 51	76.01 91	87.83 55	77.70 47	76.43 51	68.66 102	87.80 42	87.02 91	91.51 32	93.25 108	96.95 61

MAR-MVS		85.65 56	86.30 56	84.88 63	95.51 40	95.89 53	86.50 73	76.71 85	89.23 50	68.59 88	70.93 65	74.49 75	88.55 33	89.40 67	90.30 48	93.42 100	93.88 113
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	82.38 4	85.52 57	84.41 67	86.81 51	91.51 62	96.23 45	90.27 47	89.81 19	77.87 91	70.67 82	69.20 70	77.86 56	85.55 55	85.92 108	86.38 88	93.03 115	97.43 46
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
CLD-MVS		85.43 58	84.24 70	86.83 50	87.69 87	93.16 84	90.01 49	82.72 54	87.17 57	79.28 44	71.43 64	65.81 112	86.02 52	87.33 90	86.96 82	95.25 45	97.83 38
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
OpenMVS	77.91 11	85.09 59	83.42 74	87.03 47	96.12 30	96.55 38	89.36 52	81.59 62	79.19 87	75.20 60	55.84 125	79.04 55	84.45 67	88.47 77	89.35 61	95.48 40	95.48 86

TSAR-MVS + COLMAP		84.93 60	85.79 60	83.92 69	90.90 65	93.57 80	89.25 54	82.00 59	91.29 33	61.66 106	88.25 29	59.46 133	86.71 48	89.79 60	87.09 80	93.01 116	91.09 142

TAPA-MVS	80.99 7	84.83 61	84.42 66	85.31 59	91.89 59	93.73 78	88.53 58	82.80 53	89.99 44	69.78 85	71.53 63	75.03 74	85.47 57	86.26 104	84.54 112	93.39 102	89.90 152
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PLC	81.02 6	84.81 62	81.81 91	88.31 40	93.77 49	90.35 109	88.80 56	84.47 44	86.76 60	82.17 36	66.56 79	71.01 93	88.41 36	85.48 110	84.28 115	92.26 134	88.21 165
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
EIA-MVS		84.75 63	86.43 55	82.79 74	86.88 93	95.36 59	82.84 106	76.39 88	87.61 56	71.03 80	74.33 54	71.12 92	85.16 58	89.69 62	88.70 67	94.40 59	98.23 31

CNLPA		84.72 64	82.14 86	87.73 43	92.85 53	93.83 75	84.70 91	85.07 39	90.90 36	83.16 30	56.28 121	71.53 89	88.14 40	84.19 117	84.00 121	92.48 129	94.26 103

MVS_Test		84.60 65	85.13 65	83.99 67	88.17 81	95.27 61	88.21 59	73.15 112	84.31 66	70.55 83	68.67 74	68.78 101	86.99 47	91.71 39	91.90 25	96.84 10	95.27 90

casdiffmvs		83.84 66	82.65 82	85.22 60	87.25 91	94.62 67	86.01 79	79.62 67	79.48 84	77.59 48	61.92 102	64.34 116	85.57 54	90.55 52	90.51 45	95.26 43	97.14 53

baseline		83.83 67	84.38 68	83.18 73	86.65 96	94.59 68	85.79 82	73.78 109	85.83 62	72.94 67	69.28 69	70.80 95	83.45 75	86.80 94	87.59 76	96.47 19	95.77 82

diffmvs		83.69 68	83.17 78	84.31 65	85.45 109	93.92 71	86.89 66	78.62 70	82.71 73	75.95 58	66.78 78	63.90 119	83.84 71	87.90 85	89.16 63	95.10 48	97.82 39

CS-MVS-test		83.47 69	85.25 64	81.40 83	84.78 115	93.07 85	83.19 101	71.71 124	80.79 80	66.54 92	70.55 66	74.16 79	88.00 41	91.42 43	88.74 66	94.20 61	97.98 35

CANet_DTU		83.33 70	86.59 52	79.53 95	88.88 75	94.87 64	86.63 70	68.85 145	85.45 63	50.54 152	77.86 47	69.94 98	85.62 53	92.63 27	90.88 40	96.63 12	94.46 97

DI_MVS_plusplus_trai		83.32 71	82.53 84	84.25 66	86.26 103	93.66 79	90.23 48	77.16 83	77.05 98	74.06 63	53.74 135	74.33 76	83.61 74	91.40 44	89.82 52	94.17 63	97.73 40

baseline1		82.63 72	82.02 87	83.34 72	88.30 80	91.89 98	88.03 62	80.86 65	75.05 105	65.96 94	64.27 90	72.20 87	80.01 94	91.32 45	89.56 59	96.90 8	89.85 153

PVSNet_Blended_VisFu		82.55 73	83.70 73	81.21 84	89.66 69	95.15 63	82.41 107	77.36 82	72.53 121	73.64 65	61.15 105	77.19 64	70.35 149	91.31 46	89.72 55	93.84 71	98.85 17

ET-MVSNet_ETH3D		82.37 74	85.68 62	78.51 105	62.90 208	94.66 65	87.06 65	73.57 110	83.13 69	61.52 108	78.37 44	76.01 71	89.99 22	84.14 118	89.03 65	96.03 29	94.42 98

PMMVS		82.26 75	85.48 63	78.51 105	85.92 106	91.92 97	78.30 138	70.77 130	86.30 61	61.11 110	82.46 39	70.88 94	84.70 65	88.05 84	84.78 108	90.24 174	93.98 107

ACMP	79.58 9	82.23 76	81.82 90	82.71 75	88.15 82	90.95 106	85.23 87	78.52 72	81.70 75	72.52 74	78.41 43	60.63 127	80.48 92	82.88 128	83.44 125	91.37 153	94.70 94
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
CHOSEN 280x420		82.15 77	85.87 59	77.80 111	86.54 99	93.42 82	81.74 109	59.96 187	78.99 89	63.99 99	74.50 53	83.95 37	80.99 85	89.53 65	85.01 103	93.56 87	95.71 84

LGP-MVS_train		82.12 78	82.57 83	81.59 80	89.26 73	90.23 112	88.76 57	78.05 73	81.26 77	61.64 107	79.52 42	62.11 122	79.59 96	85.20 113	84.68 110	92.27 133	95.02 92

FMVSNet3		81.93 79	81.98 88	81.88 79	79.49 148	87.02 130	88.15 61	72.57 115	83.02 70	72.63 71	56.55 117	73.48 83	82.34 82	91.49 42	91.20 36	96.07 25	91.13 141

thisisatest0530		81.67 80	84.27 69	78.63 101	85.53 107	93.88 74	81.77 108	73.84 106	81.35 76	63.85 101	68.79 72	77.64 60	73.02 130	88.73 75	85.73 99	93.76 74	93.80 117

tttt0517		81.51 81	84.12 72	78.47 107	85.33 111	93.74 77	81.42 113	73.84 106	81.21 78	63.59 102	68.73 73	77.46 63	73.02 130	88.47 77	85.73 99	93.63 83	93.49 121

OPM-MVS		81.34 82	78.18 107	85.02 62	91.27 64	91.78 99	90.66 45	83.62 47	62.39 150	65.91 95	63.35 94	64.33 117	85.03 59	87.77 87	85.88 97	93.66 79	91.75 138

baseline2		81.21 83	83.36 77	78.70 99	83.22 126	92.71 87	80.32 119	74.25 105	80.39 82	63.94 100	68.89 71	68.44 103	74.67 116	89.61 64	86.68 86	95.83 36	96.81 67

IS_MVSNet		80.92 84	84.14 71	77.16 114	87.43 88	93.90 73	80.44 115	74.64 99	75.05 105	61.10 111	65.59 85	76.89 67	67.39 157	90.88 48	90.05 49	91.95 140	96.62 72

ACMM	78.09 10	80.91 85	78.39 105	83.86 70	89.61 72	87.71 127	85.16 88	80.67 66	79.04 88	74.18 62	63.82 93	60.84 126	82.59 80	84.33 115	83.59 124	90.96 161	89.39 158
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EPP-MVSNet		80.82 86	82.79 80	78.52 103	86.31 102	92.37 94	79.83 122	74.51 100	73.79 114	64.46 97	67.01 77	80.63 51	74.33 119	85.63 109	84.35 114	91.68 146	95.79 81

CostFormer		80.72 87	81.81 91	79.44 97	86.50 100	91.65 100	84.31 93	59.84 188	80.86 79	72.69 69	62.46 98	73.74 80	79.93 95	82.58 132	84.50 113	93.37 103	96.90 65

GBi-Net		80.72 87	80.49 94	81.00 87	78.18 152	86.19 144	86.73 67	72.57 115	83.02 70	72.63 71	56.55 117	73.48 83	80.99 85	86.57 96	86.83 83	94.89 52	90.77 145

test1		80.72 87	80.49 94	81.00 87	78.18 152	86.19 144	86.73 67	72.57 115	83.02 70	72.63 71	56.55 117	73.48 83	80.99 85	86.57 96	86.83 83	94.89 52	90.77 145

UGNet		80.71 90	83.09 79	77.93 110	87.02 92	92.71 87	80.28 120	76.53 86	73.83 113	71.35 78	70.07 67	73.71 81	58.93 177	87.39 89	86.97 81	93.48 95	96.94 62
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
CHOSEN 1792x2688		80.23 91	79.16 102	81.48 81	91.97 57	96.56 36	86.18 77	75.40 95	76.17 101	61.32 109	37.43 195	61.08 125	76.52 109	92.35 29	91.64 30	97.46 3	98.86 16

thres100view900		79.83 92	77.79 111	82.21 76	88.42 77	93.54 81	87.07 64	81.11 63	70.15 128	61.01 112	56.65 115	51.22 150	81.78 83	89.77 61	85.95 95	93.84 71	97.26 50

Effi-MVS+		79.80 93	80.04 96	79.52 96	85.53 107	93.31 83	85.28 85	70.68 132	74.15 109	58.79 122	62.03 101	60.51 128	83.37 76	88.41 79	86.09 94	93.49 94	95.80 80

DCV-MVSNet		79.76 94	79.17 101	80.44 92	84.65 116	84.51 168	84.20 95	72.36 120	75.17 104	70.81 81	66.21 82	66.56 107	80.99 85	82.89 127	84.56 111	89.65 180	94.30 102

FC-MVSNet-train		79.54 95	78.20 106	81.09 86	86.55 98	88.63 123	79.96 121	78.53 71	70.90 126	68.24 89	65.87 84	56.45 143	80.29 93	86.20 106	84.08 116	92.97 117	95.31 89

test-LLR		79.52 96	83.42 74	74.97 123	81.79 132	91.26 101	76.17 159	70.57 133	77.71 93	52.14 139	66.26 80	77.47 61	73.10 126	87.02 91	87.16 78	96.05 27	97.02 56

FMVSNet2		79.24 97	78.14 108	80.53 91	78.18 152	86.19 144	86.73 67	71.91 121	72.97 117	70.48 84	50.63 145	66.56 107	80.99 85	90.10 55	89.77 54	94.89 52	90.77 145

TESTMET0.1,1		79.15 98	83.42 74	74.18 129	79.81 146	91.26 101	76.17 159	67.83 159	77.71 93	52.14 139	66.26 80	77.47 61	73.10 126	87.02 91	87.16 78	96.05 27	97.02 56

tfpn200view9		79.05 99	77.21 114	81.18 85	88.42 77	92.55 92	85.12 89	77.94 75	70.15 128	61.01 112	56.65 115	51.22 150	81.11 84	88.23 80	84.80 107	93.50 93	96.90 65

PatchMatch-RL		78.75 100	76.47 122	81.41 82	88.53 76	91.10 103	78.09 139	77.51 81	77.33 95	71.98 76	64.38 89	48.10 162	82.55 81	84.06 119	82.35 134	89.78 177	87.97 167

LS3D		78.72 101	75.79 126	82.15 77	91.91 58	89.39 120	83.66 98	85.88 37	76.81 99	59.22 121	57.67 112	58.53 137	83.72 73	82.07 137	81.63 145	88.50 188	84.39 178

thres200		78.69 102	76.71 117	80.99 89	88.35 79	92.56 90	86.03 78	77.94 75	66.27 135	60.66 114	56.08 122	51.11 152	79.45 97	88.23 80	85.54 102	93.52 88	97.20 52

Anonymous20231211		78.61 103	75.57 129	82.15 77	84.43 120	90.26 110	84.08 96	77.68 78	71.09 124	72.90 68	39.24 188	66.21 109	84.23 70	82.15 135	84.04 118	89.61 181	96.03 75

IB-MVS	74.10 12	78.52 104	78.51 104	78.52 103	90.15 67	95.39 58	71.95 179	77.53 80	74.95 107	77.25 51	58.93 109	55.92 144	58.37 179	79.01 162	87.89 75	95.88 34	97.47 43
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
EPNet_dtu		78.49 105	81.96 89	74.45 128	92.57 55	88.74 122	82.98 102	78.83 69	83.28 68	44.64 183	77.40 48	67.73 104	53.98 188	85.44 111	84.91 104	93.71 77	86.22 173
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
thres400		78.39 106	76.39 123	80.73 90	88.02 84	92.94 86	84.77 90	78.88 68	65.20 143	59.70 119	55.20 128	50.85 153	79.45 97	88.81 72	84.81 106	93.57 85	96.91 64

UA-Net		78.30 107	80.92 93	75.25 122	87.42 89	92.48 93	79.54 125	75.49 94	60.47 154	60.52 115	68.44 75	84.08 36	57.54 181	88.54 76	88.45 69	90.96 161	83.97 180

Vis-MVSNet (Re-imp)		78.28 108	82.68 81	73.16 140	86.64 97	92.68 89	78.07 140	74.48 101	74.05 110	53.47 132	64.22 91	76.52 68	54.28 184	88.96 71	88.29 72	92.03 138	94.00 106

MSDG		78.11 109	73.17 142	83.86 70	91.78 61	86.83 132	85.25 86	86.02 35	72.84 119	69.69 87	51.43 142	54.00 147	77.61 101	81.95 140	82.27 136	92.83 123	82.91 185

HyFIR lowres test		78.08 110	76.81 115	79.56 94	90.77 66	94.64 66	82.97 103	69.85 138	69.81 130	59.53 120	33.52 200	64.66 113	78.97 99	88.77 74	88.38 71	95.27 42	97.86 37

GeoE		78.04 111	77.52 113	78.65 100	84.51 118	90.84 107	80.94 114	69.24 143	72.86 118	66.06 93	53.45 136	60.46 129	77.37 102	84.20 116	84.85 105	93.78 73	96.00 77

test-mter		77.90 112	82.44 85	72.60 145	78.52 150	90.24 111	73.85 172	65.31 173	76.37 100	51.29 143	65.58 86	75.94 72	71.36 140	85.98 107	86.26 90	95.26 43	96.71 71

thres600view7		77.66 113	75.67 127	79.98 93	87.71 86	92.56 90	83.79 97	77.94 75	64.41 145	58.69 123	54.32 134	50.54 154	78.23 100	88.23 80	83.06 128	93.52 88	96.55 73

MS-PatchMatch		77.47 114	76.48 121	78.63 101	89.89 68	90.42 108	85.42 84	69.53 140	70.79 127	60.43 116	50.05 147	70.62 97	70.66 146	86.71 95	82.54 131	95.86 35	84.23 179

Fast-Effi-MVS+		77.37 115	76.68 118	78.17 108	82.84 128	89.94 115	81.47 111	68.01 154	72.99 115	60.26 117	55.07 129	53.20 148	82.99 77	86.47 101	86.12 92	93.46 96	92.98 125

DROMVSNet		77.37 115	76.68 118	78.17 108	82.84 128	89.94 115	81.47 111	68.01 154	72.99 115	60.26 117	55.07 129	53.20 148	82.99 77	86.47 101	86.12 92	93.46 96	92.98 125

Vis-MVSNet		77.24 117	79.99 99	74.02 130	84.62 117	93.92 71	80.33 118	72.55 118	62.58 149	55.25 130	64.45 88	69.49 99	57.00 182	88.78 73	88.21 73	94.36 60	92.54 129
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MDTV_nov1_ep13		77.20 118	80.04 96	73.90 132	82.22 130	90.14 113	79.25 129	61.52 183	78.63 90	56.98 124	65.52 87	72.80 86	73.05 128	80.93 148	83.20 126	90.36 171	89.05 161

EPMVS		77.16 119	79.08 103	74.92 124	86.73 95	91.98 96	78.62 134	55.44 196	79.43 85	56.59 126	61.24 104	70.73 96	76.97 106	80.59 151	81.43 152	95.15 47	88.17 166

tpm cat1		76.93 120	76.19 125	77.79 112	85.08 114	88.58 124	82.96 104	59.33 189	75.72 103	72.64 70	51.25 143	64.41 115	75.74 113	77.90 171	80.10 168	90.97 160	95.35 87

PatchmatchNet		76.85 121	80.03 98	73.15 141	84.08 122	91.04 105	77.76 144	55.85 195	79.43 85	52.74 137	62.08 100	76.02 70	74.56 117	79.92 156	81.41 153	93.92 70	90.29 150
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
IterMVS-LS		76.80 122	76.33 124	77.35 113	84.07 123	84.11 170	81.54 110	68.52 147	66.17 136	61.74 105	57.84 111	64.31 118	74.88 115	83.48 124	86.21 91	93.34 105	92.16 133
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CDS-MVSNet		76.57 123	76.78 116	76.32 117	80.94 139	89.75 117	82.94 105	72.64 114	59.01 160	62.95 104	58.60 110	62.67 121	66.91 159	86.26 104	87.20 77	91.57 148	93.97 108
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
SCA		76.41 124	79.90 100	72.35 149	84.26 121	85.24 159	75.57 166	54.56 198	79.95 83	52.72 138	64.22 91	77.84 57	73.73 123	80.48 152	81.37 154	93.25 108	90.20 151

tpmrst		76.27 125	77.65 112	74.66 126	86.13 105	89.53 119	79.31 128	54.91 197	77.19 97	56.27 127	55.87 124	64.58 114	77.25 103	80.85 149	80.21 165	94.07 65	95.32 88

dps		75.76 126	75.02 131	76.63 116	84.51 118	88.12 125	77.51 145	58.33 191	75.91 102	71.98 76	57.37 113	57.85 138	76.81 108	77.89 172	78.40 177	90.63 168	89.63 155

CR-MVSNet		74.84 127	77.91 109	71.26 162	81.77 134	85.52 155	78.32 136	54.14 200	74.05 110	51.09 146	50.00 148	71.38 91	70.77 144	86.48 99	84.03 119	91.46 152	93.92 110

Effi-MVS+-dtu		74.57 128	74.60 135	74.53 127	81.38 136	86.74 134	80.39 117	67.70 160	67.36 134	53.06 133	59.86 107	57.50 139	75.84 112	80.19 154	78.62 175	88.79 187	91.95 137

test_part1		74.38 129	70.16 155	79.31 98	83.30 125	84.45 169	84.31 93	71.43 127	55.24 173	74.88 61	38.77 190	59.61 132	75.29 114	78.96 163	81.53 147	86.63 196	92.55 128

RPSCF		74.27 130	73.24 141	75.48 121	81.01 138	80.18 192	76.24 158	72.37 119	74.84 108	68.24 89	72.47 57	67.39 105	73.89 120	71.05 196	69.38 203	81.14 208	77.37 197

FMVSNet1		74.26 131	71.95 147	76.95 115	74.28 184	83.94 172	83.61 99	69.99 136	57.08 166	65.08 96	42.39 177	57.41 140	76.98 105	86.57 96	86.83 83	91.77 145	89.42 156

GA-MVS		73.62 132	74.52 136	72.58 146	79.93 144	89.29 121	78.02 141	71.67 125	60.79 153	42.68 187	54.41 133	49.07 158	70.07 150	89.39 68	86.55 87	93.13 113	92.12 134

Fast-Effi-MVS+-dtu		73.56 133	75.32 130	71.50 158	80.35 141	86.83 132	79.72 123	58.07 192	67.64 133	44.83 180	60.28 106	54.07 146	73.59 125	81.90 142	82.30 135	92.46 130	94.18 104

tpm		73.50 134	74.85 132	71.93 152	83.19 127	86.84 131	78.61 135	55.91 194	65.64 138	48.90 159	56.30 120	61.09 124	72.31 132	79.10 161	80.61 164	92.68 125	94.35 101

RPMNet		73.46 135	77.85 110	68.34 172	81.71 135	85.52 155	73.83 173	50.54 207	74.05 110	46.10 174	53.03 139	71.91 88	66.31 161	83.55 122	82.18 138	91.55 150	94.71 93

USDC		73.43 136	72.31 145	74.73 125	80.86 140	86.21 142	80.42 116	71.83 123	71.69 123	46.94 167	59.60 108	42.58 183	76.47 110	82.66 131	81.22 157	91.88 142	82.24 191

pmmvs4		73.38 137	71.53 150	75.55 120	75.95 170	85.24 159	77.25 149	71.59 126	71.03 125	63.10 103	49.09 153	44.22 173	73.73 123	82.04 138	80.18 166	91.68 146	88.89 163

UniMVSNet_NR-MVSNet		73.11 138	72.59 143	73.71 135	76.90 161	86.58 138	77.01 150	75.82 92	65.59 139	48.82 160	50.97 144	48.42 160	71.61 136	79.19 160	83.03 129	92.11 135	94.37 99

FMVSNet5		72.83 139	73.89 139	71.59 156	67.42 202	76.28 200	75.88 163	63.74 177	77.27 96	54.59 131	53.32 137	71.48 90	73.85 121	81.95 140	81.69 143	94.06 66	75.20 201

PatchT		72.66 140	76.58 120	68.09 174	79.02 149	86.09 148	59.81 201	51.78 205	72.00 122	51.09 146	46.84 157	66.70 106	70.77 144	86.48 99	84.03 119	96.07 25	93.92 110

ACMH	71.22 14	72.65 141	70.13 156	75.59 119	86.19 104	86.14 147	75.76 164	77.63 79	54.79 175	46.16 173	53.28 138	47.28 164	77.24 104	78.91 164	81.18 158	90.57 169	89.33 159
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
IterMVS		72.43 142	74.05 137	70.55 166	80.34 142	81.17 186	77.44 146	61.00 186	63.57 148	46.82 169	55.88 123	59.09 136	65.03 163	83.15 125	83.83 122	92.67 126	91.65 139
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ACMH+	72.14 13	72.38 143	69.34 163	75.93 118	85.21 112	84.89 163	76.96 153	76.04 90	59.76 155	51.63 142	50.37 146	48.69 159	76.90 107	76.06 180	78.69 173	88.85 186	86.90 171

DU-MVS		72.19 144	71.35 151	73.17 139	75.95 170	86.02 149	77.01 150	74.42 102	65.39 141	48.82 160	49.10 151	42.81 181	71.61 136	78.67 165	83.10 127	91.22 156	94.37 99

IterMVS-SCA-FT		72.18 145	73.96 138	70.11 168	80.15 143	81.11 187	77.42 147	61.09 185	63.67 147	46.73 170	55.77 126	59.15 135	63.95 166	82.83 129	83.70 123	91.31 154	91.49 140

UniMVSNet (Re)		72.12 146	72.28 146	71.93 152	76.77 162	87.38 129	75.73 165	73.51 111	65.76 137	50.24 154	48.65 154	46.49 165	63.85 167	80.10 155	82.47 132	91.49 151	95.13 91

ADS-MVSNet		72.11 147	73.72 140	70.24 167	81.24 137	86.59 137	74.75 169	50.56 206	72.58 120	49.17 157	55.40 127	61.46 123	73.80 122	76.01 181	78.14 178	91.93 141	85.86 174

gg-mvs-nofinetune		72.10 148	74.79 133	68.97 171	83.31 124	95.22 62	85.66 83	48.77 208	35.68 211	22.17 217	30.49 203	77.73 59	76.37 111	94.30 13	93.03 11	97.55 2	97.05 55

TAMVS		72.06 149	71.76 149	72.41 148	76.68 163	88.12 125	74.82 168	68.09 152	53.52 180	56.91 125	52.94 140	56.93 142	66.91 159	81.37 145	82.44 133	91.07 158	86.99 170

v2v482		71.73 150	69.80 158	73.99 131	75.88 174	86.66 136	79.58 124	71.90 122	57.58 164	50.41 153	45.35 161	43.24 179	73.05 128	79.69 157	82.18 138	93.08 114	93.87 114

test0.0.03 1		71.70 151	74.68 134	68.23 173	81.79 132	83.81 173	68.64 183	70.57 133	68.81 132	43.47 184	62.77 97	60.09 131	51.77 195	82.48 133	81.67 144	93.16 111	83.13 183

V42		71.58 152	70.11 157	73.30 138	75.66 177	86.68 135	79.17 131	69.92 137	59.29 159	52.80 136	44.36 165	45.66 167	68.83 151	79.48 159	81.49 149	93.44 98	93.82 116

NR-MVSNet		71.47 153	71.11 152	71.90 154	77.73 157	86.02 149	76.88 154	74.42 102	65.39 141	46.09 175	49.10 151	39.87 196	64.27 165	81.40 144	82.24 137	91.99 139	93.75 118

v8		71.42 154	69.69 159	73.43 137	76.45 166	85.12 162	79.53 126	67.47 163	59.34 158	52.90 135	44.60 163	45.82 166	71.05 142	79.56 158	81.45 151	93.17 110	91.96 136

TranMVSNet+NR-MVSNet		71.12 155	70.24 154	72.15 150	76.01 169	84.80 165	76.55 156	75.65 93	61.99 151	45.29 178	48.42 155	43.07 180	67.55 155	78.28 168	82.83 130	91.85 143	92.29 130

v10		70.97 156	69.44 160	72.75 142	75.90 173	84.58 167	79.43 127	66.45 168	58.07 162	49.93 155	43.87 171	43.68 174	71.91 134	82.04 138	81.70 142	92.89 121	92.11 135

v1144		70.93 157	69.42 162	72.70 143	75.48 178	86.26 140	79.22 130	69.39 142	55.61 171	48.05 165	43.47 172	42.55 184	71.51 138	82.11 136	81.74 141	92.56 128	94.17 105

thisisatest0515		70.62 158	71.94 148	69.07 170	76.48 165	85.59 154	68.03 184	68.02 153	59.70 156	52.94 134	52.19 141	50.36 155	58.10 180	83.15 125	81.63 145	90.87 164	90.99 143

Baseline_NR-MVSNet		70.61 159	68.87 166	72.65 144	75.95 170	80.49 190	75.92 162	74.75 98	65.10 144	48.78 162	41.28 183	44.28 172	68.45 152	78.67 165	79.64 169	92.04 137	92.62 127

v148		70.34 160	68.46 169	72.54 147	76.04 168	86.38 139	74.83 167	72.73 113	55.88 170	55.26 129	43.32 174	43.49 175	64.52 164	76.93 178	80.11 167	91.85 143	93.11 122

v1192		70.32 161	68.77 167	72.12 151	74.76 180	85.62 153	78.73 132	68.53 146	55.08 174	46.34 172	42.39 177	40.67 191	71.90 135	82.27 134	81.53 147	92.43 131	93.86 115

v144192		70.10 162	68.55 168	71.90 154	74.55 181	85.67 152	77.81 142	68.22 151	54.65 176	46.91 168	42.76 175	41.27 188	70.95 143	80.48 152	81.11 162	92.96 118	93.90 112

pmmvs5		70.01 163	69.31 164	70.82 165	75.80 176	86.26 140	72.94 174	67.91 156	53.84 179	47.22 166	47.31 156	41.47 187	67.61 154	83.93 121	81.93 140	93.42 100	90.42 149

COLMAP_ROB	66.31 15	69.91 164	66.61 174	73.76 133	86.44 101	82.76 177	76.59 155	76.46 87	63.82 146	50.92 150	45.60 160	49.13 157	65.87 162	74.96 186	74.45 193	86.30 198	75.57 200
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
v1921920		69.85 165	68.38 170	71.58 157	74.35 182	85.39 157	77.78 143	67.88 158	54.64 177	45.39 177	42.11 180	39.97 195	71.10 141	81.68 143	81.17 160	92.96 118	93.69 120

pm-mvs1		69.62 166	68.07 172	71.44 159	77.21 159	85.32 158	76.11 161	71.05 128	46.55 200	51.17 145	41.83 181	48.20 161	61.81 173	84.00 120	81.14 161	91.28 155	89.42 156

UniMVSNet_ETH3D		69.49 167	65.86 176	73.72 134	76.51 164	85.88 151	78.65 133	70.52 135	48.08 197	55.71 128	37.64 192	40.56 192	71.38 139	75.05 185	81.49 149	89.57 183	92.29 130

tfpnnormal		69.29 168	65.58 177	73.62 136	79.87 145	84.82 164	76.97 152	75.12 96	45.29 201	49.03 158	35.57 198	37.20 204	68.02 153	82.70 130	81.24 156	92.69 124	92.20 132

v1240		69.28 169	67.82 173	71.00 164	74.09 186	85.13 161	76.54 157	67.28 165	53.17 181	44.70 181	41.55 182	39.38 197	70.51 148	81.29 146	81.18 158	92.88 122	93.02 124

CVMVSNet		68.95 170	70.79 153	66.79 180	79.69 147	83.75 174	72.05 178	70.90 129	56.20 168	36.30 199	54.94 132	59.22 134	54.03 187	78.33 167	78.65 174	87.77 193	84.44 177

MIMVSNet		68.66 171	69.43 161	67.76 175	64.92 205	84.68 166	74.16 170	54.10 202	60.85 152	51.27 144	39.47 187	49.48 156	67.48 156	84.86 114	85.57 101	94.63 56	81.10 192

TDRefinement		67.82 172	64.91 183	71.22 163	82.08 131	81.45 182	77.42 147	73.79 108	59.62 157	48.35 164	42.35 179	42.40 185	60.87 175	74.69 187	74.64 192	84.83 202	79.20 195

anonymousdsp		67.61 173	68.94 165	66.04 181	71.44 198	83.97 171	66.45 188	63.53 179	50.54 190	42.42 188	49.39 149	45.63 168	62.84 170	77.99 170	81.34 155	89.59 182	93.75 118

TinyColmap		67.16 174	63.51 190	71.42 160	77.94 155	79.54 196	72.80 175	69.78 139	56.58 167	45.52 176	44.53 164	33.53 209	74.45 118	76.91 179	77.06 184	88.03 192	76.41 198

FC-MVSNet-test		67.04 175	72.47 144	60.70 198	76.92 160	81.41 183	61.52 198	69.45 141	65.58 140	26.74 213	61.79 103	60.40 130	41.17 204	77.60 174	77.78 180	88.41 189	82.70 187

TransMVSNet (Re)		66.87 176	64.30 185	69.88 169	78.32 151	81.35 185	73.88 171	74.34 104	43.19 205	45.20 179	40.12 185	42.37 186	55.97 183	80.85 149	79.15 170	91.56 149	83.06 184

CMPMVS	50.59 17	66.74 177	62.72 194	71.42 160	85.40 110	89.72 118	72.69 176	70.72 131	51.24 186	51.75 141	38.91 189	44.40 170	63.74 168	70.84 197	71.52 197	84.19 203	72.45 205
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
v7n		66.43 178	65.51 178	67.51 176	71.63 197	83.10 175	70.89 182	65.02 174	50.13 193	44.68 182	39.59 186	38.77 198	62.57 171	77.59 175	78.91 171	90.29 173	90.44 148

EG-PatchMatch MVS		66.23 179	65.20 180	67.43 177	77.74 156	86.20 143	72.51 177	63.68 178	43.95 203	43.44 185	36.22 197	45.43 169	54.04 186	81.00 147	80.95 163	93.15 112	82.67 188

WR-MVS		64.98 180	66.59 175	63.09 191	74.34 183	82.68 178	64.98 194	69.17 144	54.42 178	36.18 200	44.32 166	44.35 171	44.65 198	73.60 188	77.83 179	89.21 185	88.96 162

gm-plane-assit		64.86 181	68.15 171	61.02 197	76.44 167	68.29 209	41.60 214	53.37 203	34.68 213	26.19 215	33.22 201	57.09 141	71.97 133	95.12 5	93.97 7	96.54 16	94.66 95

CP-MVSNet		64.84 182	64.97 181	64.69 186	72.09 193	81.04 188	66.66 187	67.53 162	52.45 183	37.40 195	44.00 170	38.37 200	53.54 190	72.26 192	76.93 185	90.94 163	89.75 154

MDTV_nov1_ep13_2view		64.72 183	64.94 182	64.46 187	71.14 199	81.94 181	67.53 185	54.54 199	55.92 169	43.29 186	44.02 169	43.27 178	59.87 176	71.85 194	74.77 191	90.36 171	82.82 186

MVS-HIRNet		64.63 184	64.03 189	65.33 183	75.01 179	82.84 176	58.54 205	52.10 204	55.42 172	49.29 156	29.83 206	43.48 176	66.97 158	78.28 168	78.81 172	90.07 176	79.52 194

pmnet_mix02		64.58 185	64.11 188	65.12 184	74.16 185	80.17 193	63.24 196	67.91 156	57.87 163	41.69 189	45.86 159	40.99 190	53.97 189	69.92 200	71.67 196	89.77 178	82.29 190

LTVRE_ROB	63.07 16	64.49 186	63.16 193	66.04 181	77.47 158	82.64 179	70.98 181	65.02 174	34.01 214	29.61 209	49.12 150	35.58 208	70.57 147	75.10 184	78.45 176	82.60 206	87.24 169
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
PEN-MVS		64.35 187	64.29 186	64.42 188	72.67 189	79.83 194	66.97 186	68.24 150	51.21 187	35.29 202	44.09 167	38.51 199	52.36 193	71.06 195	77.65 181	90.99 159	87.68 168

pmmvs6		64.24 188	61.77 198	67.12 178	72.39 192	81.39 184	71.33 180	65.95 172	36.05 210	48.48 163	30.55 202	43.45 177	58.75 178	77.88 173	76.36 188	85.83 199	86.70 172

pmmvs-eth3d		64.24 188	61.96 196	66.90 179	66.35 203	76.04 202	66.09 190	66.31 169	52.59 182	50.94 149	37.61 193	32.79 211	62.43 172	75.78 182	75.48 190	89.27 184	83.39 182

PS-CasMVS		64.22 190	64.19 187	64.25 189	71.86 195	80.67 189	66.42 189	67.43 164	50.64 189	36.48 197	42.60 176	37.46 203	52.56 192	71.98 193	76.69 187	90.76 165	89.29 160

WR-MVS_H		64.14 191	65.36 179	62.71 193	72.47 191	82.33 180	65.13 191	66.99 166	51.81 185	36.47 198	43.33 173	42.77 182	43.99 200	72.41 191	75.99 189	91.20 157	88.86 164

SixPastTwentyTwo		63.75 192	63.42 191	64.13 190	72.91 188	80.34 191	61.29 199	63.90 176	49.58 194	40.42 191	54.99 131	37.13 205	60.90 174	68.46 201	70.80 198	85.37 201	82.65 189

PM-MVS		63.52 193	62.51 195	64.70 185	64.79 207	76.08 201	65.07 192	62.08 181	58.13 161	46.56 171	44.98 162	31.31 212	62.89 169	72.58 190	69.93 202	86.81 195	84.55 176

DTE-MVSNet		63.26 194	63.41 192	63.08 192	72.59 190	78.56 197	65.03 193	68.28 149	50.53 191	32.38 206	44.03 168	37.79 202	49.48 196	70.83 198	76.73 186	90.73 166	85.42 175

testgi		63.11 195	64.88 184	61.05 196	75.83 175	78.51 198	60.42 200	66.20 170	48.77 195	34.56 203	56.96 114	40.35 193	40.95 205	77.46 176	77.22 183	88.37 191	74.86 203

GG-mvs-BLEND		62.08 196	88.31 44	31.46 211	0.16 222	98.10 10	91.57 41	0.09 219	85.07 65	0.21 223	73.90 56	83.74 39	0.19 220	88.98 70	89.39 60	96.58 15	99.02 14

Anonymous20231206		62.05 197	61.83 197	62.30 195	72.09 193	77.84 199	63.10 197	67.62 161	50.20 192	36.68 196	29.59 207	37.05 206	43.90 201	77.33 177	77.31 182	90.41 170	83.49 181

N_pmnet		60.52 198	58.83 201	62.50 194	68.97 201	75.61 203	59.72 203	66.47 167	51.90 184	41.26 190	35.42 199	35.63 207	52.25 194	67.07 204	70.08 201	86.35 197	76.10 199

EU-MVSNet		58.73 199	60.92 199	56.17 201	66.17 204	72.39 206	58.85 204	61.24 184	48.47 196	27.91 211	46.70 158	40.06 194	39.07 206	68.27 202	70.34 200	83.77 204	80.23 193

test20.03		57.93 200	59.22 200	56.44 200	71.84 196	73.78 205	53.55 208	65.96 171	43.02 206	28.46 210	37.50 194	38.17 201	30.41 210	75.25 183	74.42 194	88.41 189	72.37 206

MDA-MVSNet-bldmvs		54.99 201	52.66 205	57.71 199	52.74 213	74.87 204	55.61 206	68.41 148	43.65 204	32.54 204	37.93 191	22.11 218	54.11 185	48.85 211	67.34 204	82.85 205	73.88 204

new-patchmatchnet		53.91 202	52.69 204	55.33 203	64.83 206	70.90 207	52.24 209	61.75 182	41.09 207	30.82 207	29.90 205	28.22 214	36.69 207	61.52 206	65.08 205	85.64 200	72.14 207

MIMVSNet1		52.76 203	53.95 203	51.38 205	41.96 216	70.79 208	53.56 207	63.03 180	39.36 208	27.83 212	22.73 212	33.07 210	34.47 209	70.49 199	72.69 195	87.41 194	68.51 208

pmmvs3		52.59 204	52.43 206	52.78 204	54.53 212	64.49 211	50.07 210	46.89 211	35.31 212	30.19 208	27.27 209	26.96 216	53.02 191	67.28 203	70.54 199	81.96 207	75.20 201

new_pmnet		50.32 205	51.36 207	49.11 206	49.19 214	64.89 210	48.66 212	47.99 210	47.55 198	26.27 214	29.51 208	28.66 213	44.89 197	61.12 207	62.74 207	77.66 209	65.03 209

FPMVS		50.25 206	45.67 209	55.58 202	70.48 200	60.12 212	59.78 202	59.33 189	46.66 199	37.94 193	30.22 204	27.51 215	35.94 208	50.98 210	47.90 210	70.02 211	56.31 210

test_method		47.92 207	55.39 202	39.21 209	19.90 220	49.24 214	39.29 215	34.65 216	57.37 165	32.54 204	25.11 210	41.02 189	44.31 199	66.58 205	57.57 209	64.59 214	90.82 144

PMVS	36.83 18	40.62 208	36.39 210	45.56 207	58.40 209	33.20 217	32.62 217	56.02 193	28.25 215	37.92 194	22.29 213	26.15 217	25.29 212	48.49 212	43.82 213	63.13 215	52.53 213
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gipuma		35.20 209	33.96 211	36.65 210	43.30 215	32.51 218	26.96 219	48.31 209	38.87 209	20.08 218	8.08 215	7.41 222	26.44 211	53.60 208	58.43 208	54.81 216	38.79 215
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMMVS2		32.52 210	33.92 212	30.88 212	34.15 219	44.70 216	27.79 218	39.69 215	22.21 216	4.31 222	15.73 214	14.13 220	12.45 217	40.11 213	47.00 211	66.88 212	53.54 211

E-PMN		21.42 211	17.56 214	25.94 213	36.25 218	19.02 221	11.56 220	43.72 213	15.25 218	6.99 220	8.04 216	4.53 224	21.77 214	16.13 216	26.16 215	35.34 218	33.77 216

MVE	25.07 19	21.25 212	23.51 213	18.62 215	15.07 221	29.77 220	10.67 222	34.60 217	12.51 219	9.46 219	7.84 217	3.82 225	14.38 216	27.45 215	42.42 214	27.56 220	40.74 214
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		20.61 213	16.32 215	25.62 214	36.41 217	18.93 222	11.51 221	43.75 212	15.65 217	6.53 221	7.56 218	4.68 223	22.03 213	14.56 217	23.10 216	33.51 219	29.77 217

testmvs		0.76 214	1.23 216	0.21 216	0.05 223	0.21 223	0.38 224	0.09 219	0.94 220	0.05 224	2.13 220	0.08 226	0.60 219	0.82 218	0.77 217	0.11 221	3.62 219

test123		0.67 215	1.11 217	0.16 217	0.01 224	0.14 224	0.20 225	0.04 221	0.77 221	0.02 225	2.15 219	0.02 227	0.61 218	0.23 219	0.72 218	0.07 222	3.76 218

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										39.41 192

9.14												91.16 7

SR-MVS					96.04 31			87.51 27				87.60 20

Anonymous202405211			75.59 128		85.13 113	91.06 104	84.62 92	77.96 74	69.47 131		40.79 184	63.84 120	84.57 66	83.55 122	84.69 109	89.69 179	95.75 83

our_test_3					73.80 187	79.57 195	64.47 195

ambc			50.35 208		55.61 211	59.93 213	48.73 211		44.08 202	35.81 201	24.01 211	10.64 221	41.57 203	72.83 189	63.35 206	74.99 210	77.61 196

MTAPA										91.14 6		85.84 26

MTMP										90.95 7		84.13 35

Patchmatch-RL test							8.17 223

tmp_tt				39.78 208	56.31 210	31.71 219	35.84 216	15.08 218	82.57 74	50.83 151	63.07 95	47.51 163	15.28 215	52.23 209	44.24 212	65.35 213

XVS					89.65 70	95.93 47	85.97 80			76.32 53		82.05 45				93.51 91

X-MVStestdata					89.65 70	95.93 47	85.97 80			76.32 53		82.05 45				93.51 91

abl_6				89.54 32	95.55 39	97.59 19	89.01 55	85.00 40	94.67 15	83.04 32	84.70 35	91.47 6	89.46 27			95.20 46	98.63 20

mPP-MVS					95.90 33							80.22 54

NP-MVS									89.55 48

Patchmtry						87.41 128	78.32 136	54.14 200		51.09 146

DeepMVS_CX						48.96 215	43.77 213	40.58 214	50.93 188	24.67 216	36.95 196	20.18 219	41.60 202	38.92 214		52.37 217	53.31 212

High-resolution multi-view benchmark