
Point2CAD: Reverse Engineering CAD Models from 3D Point Clouds
Point2CAD reconstructs complex CAD models from 3D point clouds.
A point cloud is segmented into clusters corresponding to CAD faces.
Each face is fitted with a geometric primitive or a parametric surface using a novel neural representation.
Due to the analytic representation, the surfaces can be extended and intersected such that
topology emerges, which is then used to clip the surface primitives.
Abstract
Computer-Aided Design (CAD) model reconstruction from point clouds is an important problem at the
intersection of computer vision, graphics, and machine learning; it saves the designer a significant amount
of time when iterating on in-the-wild objects. Recent advancements in this direction achieve rather reliable
semantic segmentation but still struggle to produce an adequate topology of the CAD model. In this work, we
analyze the current state of the art for that ill-posed task and identify shortcomings of existing methods.
We propose a hybrid analytic-neural reconstruction scheme that bridges the gap between segmented point
clouds and structured CAD models and can be readily combined with different segmentation backbones.
Moreover, to power the surface fitting stage, we propose a novel implicit neural representation of freeform
surfaces, driving up the performance of our overall CAD reconstruction scheme. We extensively evaluate our
method on the popular ABC benchmark of CAD models and set a new state-of-the-art for that dataset.
3D CAD Models Comparison
The window below provides a split view of CAD model input point clouds and their reconstructions with various
methods, including flavors of Point2CAD.
Use the mouse or gestures to zoom, spin the model, and slide the
splitter for a better view of reconstruction quality.
Featured in
Poster
View the workshop posterSource code
Point2CAD
: The official repository of this project
Video (90 sec)
Citation
@inproceedings{liu2024point2cad, title={Point2CAD: Reverse Engineering CAD Models from 3D Point Clouds}, author={Liu, Yujia and Obukhov, Anton and Wegner, Jan Dirk and Schindler, Konrad}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, pages={3763--3772}, year={2024} }