ABC: A Big CAD Model Dataset For Geometric Deep Learning

Sebastian Koch1Albert Matveev2, 3Zhongshi Jiang4Francis Williams4Alexey Artemov2Evgeny Burnaev2Marc Alexa1Denis Zorin4, 2Daniele Panozzo4

1TU Berlin2Skolkovo Institute of Science and Technology3IITP4New York University

Conference on Computer Vision and Pattern Recognition 2019


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.




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