Interactive 3D simulations have enabled break-throughs in robotics and computer vision, but
simulating the broad diversity of environments needed for deep learning requires large …

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 …

Empirically validating new 3D-printing related algorithms and implementations requires
testing data representative of inputs encountered\emph {in the wild}. An ideal benchmarking …

INTRODUCTION Compositionality, generalization, and learning from a few examples are
among the hallmarks of human intelligence. CAPTCHAs (Completely Automated Public …

We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by
3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic …

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 …

Our goal is to recover a complete 3D model from a depth image of an object. Existing
approaches rely on user interaction or apply to a limited class of objects, such as chairs. We …

Large-scale 3D shape retrieval has become an important research direction in content-
based 3D shape retrieval. To promote this research area, two Shape Retrieval Contest …

Data-driven methods serve an increasingly important role in discovering geometric,
structural, and semantic relationships between shapes. In contrast to traditional approaches …

Large scale sketch-based 3D shape retrieval has received more and more attentions in the
community of contentbased 3D object retrieval. The objective of this track is to evaluate the …