The ability to generate novel, diverse, and realistic 3D shapes along with associated part
semantics and structure is central to many applications requiring high-quality 3D assets or …

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 …

Abstract 3D mesh segmentation has become a crucial part of many applications in 3D shape
analysis. In this paper, a comprehensive survey on 3D mesh segmentation methods is …

This article presents a novel approach for 3D mesh labeling by using deep Convolutional
Neural Networks (CNNs). Many previous methods on 3D mesh labeling achieve impressive …

We propose a self-supervised capsule architecture for 3D point clouds. We compute capsule
decompositions of objects through permutation-equivariant attention, and self-supervise the …

Shape structure is about the arrangement and relations between shape parts. Structure-
aware shape processing goes beyond local geometry and low level processing to analyze …

Soft object manipulation has recently gained popularity within the robotics community due to
its potential applications in many economically important areas. Although great progress has …

Deep learning approaches to 3D shape segmentation are typically formulated as a multi-
class labeling problem. These models are trained for a fixed set of labels, which greatly …

We propose a shape editing method where the user creates geometric deformations using a
set of semantic attributes, thus avoiding the need for detailed geometric manipulations. In …

Object functionality is often expressed through part articulation--as when the two rigid parts
of a scissor pivot against each other to perform the cutting function. Such articulations are …