Hierarchical Prior Mining for Non-local Multi-View Stereo

Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang

科研成果: 书/报告/会议事项章节会议稿件同行评审

12 引用 (Scopus)

摘要

As a fundamental problem in computer vision, multi-view stereo (MVS) aims at recovering the 3D geometry of a target from a set of 2D images. Recent advances in MVS have shown that it is important to perceive non-local structured information for recovering geometry in low-textured areas. In this work, we propose a Hierarchical Prior Mining for Non-local Multi-View Stereo (HPM-MVS). The key characteristics are the following techniques that exploit non-local information to assist MVS: 1) A Non-local Extensible Sampling Pattern (NESP), which is able to adaptively change the size of sampled areas without becoming snared in locally optimal solutions. 2) A new approach to leverage non-local reliable points and construct a planar prior model based on K-Nearest Neighbor (KNN), to obtain potential hypotheses for the regions where prior construction is challenging. 3) A Hierarchical Prior Mining (HPM) framework, which is used to mine extensive non-local prior information at different scales to assist 3D model recovery, this strategy can achieve a considerable balance between the reconstruction of details and low-textured areas. Experimental results on the ETH3D and Tanks & Temples have verified the superior performance and strong generalization capability of our method. Our code will be available at https://github.com/CLinvx/HPM-MVS.

源语言英语
主期刊名Proceedings - 2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023
出版商Institute of Electrical and Electronics Engineers Inc.
3588-3597
页数10
ISBN(电子版)9798350307184
DOI
出版状态已出版 - 2023
活动2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023 - Paris, 法国
期限: 2 10月 20236 10月 2023

出版系列

姓名Proceedings of the IEEE International Conference on Computer Vision
ISSN(印刷版)1550-5499

会议

会议2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023
国家/地区法国
Paris
时期2/10/236/10/23

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