The concept of symmetry has received significant attention in computer graphics and
computer vision research in recent years. Numerous methods have been proposed to find …

We present PartNet: a consistent, large-scale dataset of 3D objects annotated with fine-
grained, instance-level, and hierarchical 3D part information. Our dataset consists of …

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 …

While advanced text generation algorithms (eg, GPT-3) have enabled writers to co-create
stories with an AI, guiding the narrative remains a challenge. Existing systems often …

We introduce a novel neural network architecture for encoding and synthesis of 3D shapes,
particularly their structures. Our key insight is that 3D shapes are effectively characterized by …

We introduce SDM-NET, a deep generative neural network which produces structured
deformable meshes. Specifically, the network is trained to generate a spatial arrangement of …

We introduce PQ-NET, a deep neural network which represents and generates 3D shapes
via sequential part assembly. The input to our network is a 3D shape segmented into parts …

We present an approach to synthesizing shapes from complex domains, by identifying new
plausible combinations of components from existing shapes. Our primary contribution is a …

We present a new local descriptor for 3D shapes, directly applicable to a wide range of
shape analysis problems such as point correspondences, semantic segmentation …

As large repositories of 3D shape collections continue to grow, understanding the data,
especially encoding the inter-model similarity and their variations, is of central importance …