系统梳理碎片重组成完整物体的计算方法与应用
A Survey on Computational Solutions for Reconstructing Complete Objects by Reassembling Their Fractured Parts
- 从形状分割、匹配到先验学习，整合多种重建思路
- 对比传统方法与深度学习方法的优劣与演进
- 覆盖数据集、开源工具与实际应用场景
从碎片重构完整物体是计算机图形学等多个领域的基础问题。本文系统综述该问题的计算解决方案，涵盖个体碎片属性理解与片间匹配机制，许多方法还引入了完整物体的先验模型。现有方法紧密关联于形状分割、形状匹配和形状先验学习。文章梳理了相关算法，强调其与通用方法的异同，并追溯从早期非深度学习方法到近期深度学习方法的发展趋势。此外，还介绍现有数据集、开源软件包及典型应用。据我们所知，这是该领域首篇全面综述。
原文摘要 · Abstract (English)
Reconstructing a complete object from its parts is a fundamental problem in many scientific domains. The purpose of this article is to provide a systematic survey on this topic. The reassembly problem requires understanding the attributes of individual pieces and establishing matches between different pieces. Many approaches also model priors of the underlying complete object. Existing approaches are tightly connected problems of shape segmentation, shape matching, and learning shape priors. We provide existing algorithms in this context and emphasize their similarities and differences to general-purpose approaches. We also survey the trends from early non-deep learning approaches to more recent deep learning approaches. In addition to algorithms, this survey will also describe existing datasets, open-source software packages, and applications. To the best of our knowledge, this is the first comprehensive survey on this topic in computer graphics.
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