@inproceedings{452feca2440f4263929beb0ebed2f9ab,
title = "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.",
keywords = "Big Data, Categorization, Datasets and Evaluation, Deep Learning, Large Scale Methods, Recognition: Detection, Retrieval, S",
author = "Sebastian Koch and Albert Matveev and Zhongshi Jiang and Francis Williams and Alexey Artemov and Evgeny Burnaev and Marc Alexa and Denis Zorin and Daniele Panozzo",
year = "2019",
month = jun
doi = "10.1109/CVPR.2019.00983",
language = "English (US)",
series = "Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition",
publisher = "IEEE Computer Society",
pages = "9593--9603",
booktitle = "Proceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019",
note = "32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019 ; Conference date: 16-06-2019 Through 20-06-2019",
}