The goal of this project is to learn a 3D shape representation that enables accurate surface
reconstruction, compact storage, efficient computation, consistency for similar shapes …

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 …

Template 3D shapes are useful for many tasks in graphics and vision, including fitting
observation data, analyzing shape collections, and transferring shape attributes. Because of …

Any solid object can be decomposed into a collection of convex polytopes (in short,
convexes). When a small number of convexes are used, such a decomposition can be …

Deep generative models of 3D shapes have received a great deal of research interest. Yet,
almost all of them generate discrete shape representations, such as voxels, point clouds …

Vector graphics are widely used to represent fonts, logos, digital artworks, and graphic
designs. But, while a vast body of work has focused on generative algorithms for raster …

We present a convolutional neural network based approach for indoor scene synthesis. By
representing 3D scenes with a semantically-enriched image-based representation based on …

Parametric computer-aided design (CAD) is a standard paradigm used to design
manufactured objects, where a 3D shape is represented as a program supported by the …

An e-graph efficiently represents a congruence relation over many expressions. Although
they were originally developed in the late 1970s for use in automated theorem provers, a …

We present an Adaptive Octree-based Convolutional Neural Network (Adaptive O-CNN) for
efficient 3D shape encoding and decoding. Different from volumetric-based or octree-based …