Mikaela Angelina Uy*, Yen-yu Chang*, Minhyuk Sung, Purvi Goel, Joseph Lambourne, Tolga Birdal and Leonidas J. Guibas, Point2Cyl: Reverse Engineering 3D Objects from Point Clouds to Extrusion Cylinders, Conference on Computer Vision and Pattern Recognition (CVPR), 2022

Abstract:

We propose Point2Cyl, a supervised network transforming a raw 3D point cloud to a set of extrusion cylinders. Reverse engineering from a raw geometry to a CAD model is an essential task to enable manipulation of the 3D data in shape editing software and thus expand their usages in many downstream applications. Particularly, the form of CAD models having a sequence of extrusion cylinders -- a 2D sketch plus an extrusion axis and range -- and their boolean combinations is not only widely used in the CAD community/software but also has great expressivity of shapes, compared to having limited types of primitives (e.g., planes, spheres, and cylinders). In this work, we introduce a neural network that solves the extrusion cylinder decomposition problem in a geometry-grounded way by first learning underlying geometric proxies. Precisely, our approach first predicts per-point segmentation, base/barrel labels and normals, then estimates for the underlying extrusion parameters in differentiable and closed-form formulations. Our experiments show that our approach demonstrates the best performance on two recent CAD datasets, Fusion Gallery and DeepCAD, and we further showcase our approach on reverse engineering and editing.

Bibtex: 

@inproceedings{uy-point2cyl-cvpr22,
      title = {Point2Cyl: Reverse Engineering 3D Objects from Point Clouds to Extrusion Cylinders},
      author = {Mikaela Angelina Uy and Yen-yu Chang and Minhyuk Sung and Purvi Goel and Joseph Lambourne and Tolga Birdal and Leonidas Guibas},
      booktitle = {Conference on Computer Vision and Pattern Recognition (CVPR)},
      year = {2022}
  }