Deep-learning-based point cloud completion methods: A review

IF 2.5 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Graphical Models Pub Date : 2024-10-03 DOI:10.1016/j.gmod.2024.101233

Kun Zhang , Ao Zhang , Xiaohong Wang , Weisong Li

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引用次数: 0

Abstract

Point cloud completion aims to utilize algorithms to repair missing parts in 3D data for high-quality point clouds. This technology is crucial for applications such as autonomous driving and urban planning. With deep learning’s progress, the robustness and accuracy of point cloud completion have improved significantly. However, the quality of completed point clouds requires further enhancement to satisfy practical requirements. In this study, we conducted an extensive survey of point cloud completion methods, with the following main objectives: (i) We classified point cloud completion methods into categories based on their principles, such as point-based, convolution-based, GAN-based, and geometry-based methods, and thoroughly investigated the advantages and limitations of each category. (ii) We collected publicly available datasets for point cloud completion algorithms and conducted experimental comparisons using various typical deep-learning networks to draw conclusions. (iii) With our research in this paper, we discuss future research trends in this rapidly evolving field.

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基于深度学习的点云补全方法：综述

点云补全旨在利用算法修复三维数据中的缺失部分，以获得高质量的点云。这项技术对于自动驾驶和城市规划等应用至关重要。随着深度学习的发展，点云补全的鲁棒性和准确性都有了显著提高。然而，完成点云的质量还需要进一步提高才能满足实际需求。在本研究中，我们对点云补全方法进行了广泛调查，主要目的如下：(i) 我们根据点云补全方法的原理将其分为几类，如基于点的方法、基于卷积的方法、基于 GAN 的方法和基于几何的方法，并深入研究了每一类方法的优势和局限性。(ii) 我们收集了公开的点云补全算法数据集，并使用各种典型的深度学习网络进行了实验比较，从而得出结论。(iii) 通过本文的研究，我们探讨了这一快速发展领域的未来研究趋势。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Graphical Models 工程技术-计算机：软件工程

CiteScore

3.60

自引率

5.90%

发文量

审稿时长

47 days

期刊介绍： Graphical Models is recognized internationally as a highly rated, top tier journal and is focused on the creation, geometric processing, animation, and visualization of graphical models and on their applications in engineering, science, culture, and entertainment. GMOD provides its readers with thoroughly reviewed and carefully selected papers that disseminate exciting innovations, that teach rigorous theoretical foundations, that propose robust and efficient solutions, or that describe ambitious systems or applications in a variety of topics. We invite papers in five categories: research (contributions of novel theoretical or practical approaches or solutions), survey (opinionated views of the state-of-the-art and challenges in a specific topic), system (the architecture and implementation details of an innovative architecture for a complete system that supports model/animation design, acquisition, analysis, visualization?), application (description of a novel application of know techniques and evaluation of its impact), or lecture (an elegant and inspiring perspective on previously published results that clarifies them and teaches them in a new way). GMOD offers its authors an accelerated review, feedback from experts in the field, immediate online publication of accepted papers, no restriction on color and length (when justified by the content) in the online version, and a broad promotion of published papers. A prestigious group of editors selected from among the premier international researchers in their fields oversees the review process.