Deep learning

Solid modeling

Pattern recognition (psychology)

Feature (linguistics)

Mathematics

Parametric statistics

Data mining

Statistics

Filter (signal processing)

Geodesy

Engineering

Philosophy

Sampling (signal processing)

Ground truth

Benchmark (surveying)

Artificial intelligence

Composite material

Geography

Parametric model

Materials science

Engineering drawing

Range (aeronautics)

Data point

Computer science

Linguistics

Computer vision

Scalability

Database

Segmentation

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.

ABC: A Big CAD Model Dataset for Geometric Deep Learning