用少量截面重建三维形状，自动学习形状结构。
Curvy: A Parametric Cross-section based Surface Reconstruction
- 用自适应分割的参数化折线表示截面，简化形状表达。
- 仅需少量截面即可重建高质量表面，对输入数量不敏感。
- 适合需要少样本重建的工业设计与逆向工程场景。
本文提出一种基于平面稀疏截面的新型形状点云重建方法，借助生成建模实现。传统方法在类别泛化能力差且依赖复杂数学工具。本工作采用紧凑的参数化折线表示，结合自适应分割策略刻画截面，并利用图神经网络进行学习，实现自适应的形状重建，显著降低对输入截面数量的依赖。该方法在大规模数据集上可从少量输入截面生成大量点云，有效提升重建效率与通用性。
原文摘要 · Abstract (English)
In this work, we present a novel approach for reconstructing shape point clouds using planar sparse cross-sections with the help of generative modeling. We present unique challenges pertaining to the representation and reconstruction in this problem setting. Most methods in the classical literature lack the ability to generalize based on object class and employ complex mathematical machinery to reconstruct reliable surfaces. We present a simple learnable approach to generate a large number of points from a small number of input cross-sections over a large dataset. We use a compact parametric polyline representation using adaptive splitting to represent the cross-sections and perform learning using a Graph Neural Network to reconstruct the underlying shape in an adaptive manner reducing the dependence on the number of cross-sections provided.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。