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. The situation is different for three-dimensional geometric models. Acquiring or creating high-quality models of this type is still difficult, despite growing availability of 3D sensors, and improvements in 3D design tools. Inherent ir-Figure 1: Random CAD models from the ABC-Dataset: https://deep-geometry.github.io/abc-dataset 9593
In SPARE3D, we take advantage of 3D solid models from the ABC dataset REF , which is proposed for digital geometry processing tasks.
ABC: A Big CAD Model Dataset for Geometric Deep Learning
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Computer Science