This paper surveys mesh segmentation techniques and algorithms, with a focus on part‐
based segmentation, that is, segmentation that divides a mesh (featuring a 3D object) into …

Large repositories of 3D shapes provide valuable input for data-driven analysis and
modeling tools. They are especially powerful once annotated with semantic information such …

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 …

Efficient representation of articulated objects such as human bodies is an important problem
in computer vision and graphics. To efficiently simulate deformation, existing approaches …

This paper introduces a deep architecture for segmenting 3D objects into their labeled
semantic parts. Our architecture combines image-based Fully Convolutional Networks …

We present a learning framework for abstracting complex shapes by learning to assemble
objects using 3D volumetric primitives. In addition to generating simple and geometrically …

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 …

We present a novel representation of maps between pairs of shapes that allows for efficient
inference and manipulation. Key to our approach is a generalization of the notion of map …

We propose to represent shapes as the deformation and combination of learnt elementary
3D structures. We demonstrate this decomposition in learnt elementary 3D structures is …

Deep implicit functions (DIFs), as a kind of 3D shape representation, are becoming more
and more popular in the 3D vision community due to their compactness and strong …