Point completion by a Stack‐Style Folding Network with multi‐scaled graphical features

Autor: Yunbo Rao, Ping Xu, Shaoning Zeng, Jianping Gou
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: IET Computer Vision, Vol 17, Iss 5, Pp 576-585 (2023)
Druh dokumentu: article
ISSN: 1751-9640
1751-9632
DOI: 10.1049/cvi2.12196
Popis: Abstract Point cloud completion is prevalent due to the insufficient results from current point cloud acquisition equipments, where a large number of point data failed to represent a relatively complete shape. Existing point cloud completion algorithms, mostly encoder‐decoder structures with grids transform (also presented as folding operation), can hardly obtain a persuasive representation of input clouds due to the issue that their bottleneck‐shape result cannot tell a precise relationship between the global and local structures. For this reason, this article proposes a novel point cloud completion model based on a Stack‐Style Folding Network (SSFN). Firstly, to enhance the deep latent feature extraction, SSFN enhances the exploitation of shape feature extractor by integrating both low‐level point feature and high‐level graphical feature. Next, a precise presentation is obtained from a high dimensional semantic space to improve the reconstruction ability. Finally, a refining module is designed to make a more evenly distributed result. Experimental results shows that our SSFN produces the most promising results of multiple representative metrics with a smaller scale parameters than current models.
Databáze: Directory of Open Access Journals
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