Most existing feature learning-based methods for RGB-D object recognition either combine
RGB and depth data in an undifferentiated manner from the outset, or learn features from …

This paper studies the problem of RGB-D object recognition. Inspired by the great success of
deep convolutional neural networks (DCNN) in AI, researchers have tried to apply it to …

We address the problem of object recognition from RGB-D images using deep convolutional
neural networks (CNNs). We advocate the use of 3D CNNs to fully exploit the 3D spatial …

Recognizing which part of an object is graspable or not is important for intelligent robot to
perform some complicated tasks. In order to obtain good grasping performance, learning …

Object recognition from RGB-D images has become a hot topic and gained a significant
popularity in recent years due to its numerous applications. In this paper, we propose a …

Deep learning based methods have achieved unprecedented success in solving several
computer vision problems involving RGB images. However, this level of success is yet to be …

Most existing approaches for RGB-D indoor scene labeling employ hand-crafted features for
each modality independently and combine them in a heuristic manner. There has been …

Understanding the semantics of objects and scenes using multi-modal RGB-D sensors
serves many robotics applications. Key challenges for accurate RGB-D image recognition …

This paper researches on the problem of object recognition using RGB-D data. Although
deep convolutional neural networks have so far made progress in this area, they are still …

Recognizing semantic category of objects and scenes captured using vision-based sensors
is a challenging yet essential capability for mobile robots and UAVs to perform high-level …