Few prior works study deep learning on point sets. PointNet is a pioneer in this direction.
However, by design PointNet does not capture local structures induced by the metric space …

Learning and analyzing 3D point clouds with deep networks is challenging due to the
sparseness and irregularity of the data. In this paper, we present a data-driven point cloud …

In this article, we present the Yale-Carnegie Mellon University (CMU)-Berkeley (YCB) object
and model set, intended to be used to facilitate benchmarking in robotic manipulation …

Point cloud upsampling algorithms can improve the resolution of point clouds and generate
dense and uniform point clouds, and are an important image processing technology …

In this paper we present the Yale-CMU-Berkeley (YCB) Object and Model set, intended to be
used to facilitate benchmarking in robotic manipulation, prosthetic design and rehabilitation …

Shape descriptor is a concise yet informative representation that provides a 3D object with
an identification as a member of some category. This paper developed a concise deep …

This paper presents a comprehensive review and analysis of recent spectral shape
descriptors for nonrigid 3D shape retrieval. More specifically, we compare the latest spectral …

While there has been a number of studies on Zero-Shot Learning (ZSL) for 2D images, its
application to 3D data is still recent and scarce, with just a few methods limited to …

Non-rigid 3D shape retrieval has become an important research topic in content-based 3D
object retrieval. The aim of this track is to measure and compare the performance of non …

Complex geometric variations of 3D models usually pose great challenges in 3D shape
matching and retrieval. In this paper, we propose a novel 3D shape feature learning method …