# ABC: A Big CAD Model Dataset For Geometric Deep Learning

## TL;DR

The paper introduces ABC-Dataset, a large collection of CAD models designed to advance geometric deep learning research by providing diverse, parametrized data for various shape analysis tasks and benchmarking existing methods.

## Contribution

It provides a comprehensive, parametrized CAD model dataset for geometric deep learning, enabling fair evaluation and comparison of algorithms across multiple formats and resolutions.

## Key findings

- Benchmark results for surface normal estimation methods.
- Comparison of data-driven and traditional normal estimation techniques.
- Demonstration of dataset's utility for geometric learning tasks.

## 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.