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 …

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 …

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 …

This article aims to present a review of the segmentation techniques of the 3D data
representing human body in the form of point clouds. The techniques discussed are divided …

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

In this paper, we present a novel approach for 3D dental model segmentation via deep
Convolutional Neural Networks (CNNs). Traditional geometry-based methods tend to …

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 treat shape co-segmentation as a representation learning problem and introduce BAE-
NET, a branched autoencoder network, for the task. The unsupervised BAE-NET is trained …

Unsupervised co-analysis of a set of shapes is a difficult problem since the geometry of the
shapes alone cannot always fully describe the semantics of the shape parts. In this paper …