ABC: A Big CAD Model Dataset For Geometric Deep Learning

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)(2019)

引用 195|浏览233
暂无评分
摘要
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.
更多
查看译文
关键词
Datasets and Evaluation,Big Data,Large Scale Methods,Deep Learning,Recognition: Detection,Categorization,Retrieva,S
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要