ABC: A Big CAD Model Dataset for Geometric Deep Learning

Abstract

We introduce ABC-Dataset, a collection of one million Computer-Aided Design (CAD) models for research of geometric deep learning methods and applications. Each model is a collection of explicitly parametrized curves and surfaces, providing ground truth for differential quantities, patch segmentation, geometric feature detection, and shape reconstruction. Sampling the parametric descriptions of surfaces and curves allows generating data in different formats and resolutions, enabling fair comparisons for a wide range of geometric learning algorithms. As a use case for our dataset, we perform a large-scale benchmark for estimation of surface normals, comparing existing data driven methods and evaluating their performance against both the ground truth and traditional normal estimation methods.

Resources

Fulltext PDF: ABC: A Big CAD Model Dataset for Geometric Deep Learning
Supplementary PDF: ABC: A Big CAD Model Dataset for Geometric Deep Learning Supplementary Materials
Official project page: https://deep-geometry.github.io/abc-dataset/

Citing:

@inproceedings{2019-abc-dataset,
 author = {Koch, Sebastian and Matveev, Albert and Jiang, Zhongshi and Williams, Francis and Artemov, Alexey and Burnaev, Evgeny and Alexa, Marc and Zorin, Denis and Panozzo, Daniele},
 booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
 pages = {9601--9611},
 title = {Abc: A big cad model dataset for geometric deep learning},
 year = {2019}
}

Sebastian Koch	Albert Matveev	Zhongshi Jiang	Francis Williams
Alexey Artemov	Evgeny Burnaev	Marc Alexa	Denis Zorin
Daniele Panozzo