With the invention of the low-cost Microsoft Kinect sensor, high-resolution depth and visual
(RGB) sensing has become available for widespread use. The complementary nature of the …

Unsupervised learning methods play an essential role in many deep learning approaches
because the training of complex models with several parameters is an extremely datahungry …

We present an accurate, real-time approach to robotic grasp detection based on
convolutional neural networks. Our network performs single-stage regression to graspable …

Robust object recognition is a crucial ingredient of many, if not all, real-world robotics
applications. This paper leverages recent progress on Convolutional Neural Networks …

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

The depth information of RGB-D sensors has greatly simplified some common challenges in
computer vision and enabled breakthroughs for several tasks. In this paper, we propose to …

Recent advances in 3D sensing technologies make it possible to easily record color and
depth images which together can improve object recognition. Most current methods rely on …

This paper presents an approach for labeling objects in 3D scenes. We introduce HMP3D, a
hierarchical sparse coding technique for learning features from 3D point cloud data. HMP3D …

Recently introduced RGB-D cameras are capable of providing high quality synchronized
videos of both color and depth. With its advanced sensing capabilities, this technology …

This paper presents an efficient framework to perform recognition and grasp detection of
objects from RGB-D images of real scenes. The framework uses a novel architecture of …