ExtrudeNet: Unsupervised Inverse Sketch-and-Extrude for Shape Parsing
Daxuan Ren, Jianmin Zheng, Jianfei Cai, Jiatong Li, Junzhe Zhang
Abstract
"Sketch-and-extrude is a common and intuitive modeling process in computer aided design. This paper studies the problem of learning the shape given in the form of point clouds by “inverse” sketch-and-extrude. We present ExtrudeNet, an unsupervised end-to-end network for discovering sketch and extrude from point clouds. Behind ExtrudeNet are two new technical components: 1) the use of a specially-designed rational Bézier representation for sketch and extrude, which can model extrusion with freeform sketches and conventional cylinder and box primitives as well; and 2) a numerical method for computing the signed distance field which is used in the network learning. This is the first attempt that uses machine learning to reverse engineer the sketch-and-extrude modeling process of a shape in an unsupervised fashion. ExtrudeNet not only outputs a compact, editable and interpretable representation of the shape that can be seamlessly integrated into modern CAD software, but also aligns with the standard CAD modeling process facilitating various editing applications, which distinguishes our work from existing shape parsing research. Code will be open-sourced upon acceptance."
BibTeX
@inproceedings{eccv2022_extrudenetunsupe,
title = {ExtrudeNet: Unsupervised Inverse Sketch-and-Extrude for Shape Parsing},
author = {Daxuan Ren and Jianmin Zheng and Jianfei Cai and Jiatong Li and Junzhe Zhang},
booktitle = {ECCV 2022},
year = {2022}
}