Current 3D object detection methods are heavily influenced by 2D detectors. In order to
leverage architectures in 2D detectors, they often convert 3D point clouds to regular grids …

Abstract 3D object detection has seen quick progress thanks to advances in deep learning
on point clouds. A few recent works have even shown state-of-the-art performance with just …

This paper presents a local 3D descriptor for surface matching dubbed SHOT. Our proposal
stems from a taxonomy of existing methods which highlights two major approaches, referred …

3D object recognition in cluttered scenes is a rapidly growing research area. Based on the
used types of features, 3D object recognition methods can broadly be divided into two …

In this paper, we present a systematic literature review concerning 3D object recognition and
classification. We cover articles published between 2006 and 2016 available in three …

Despite advances in feature representation, leveraging geometric relations is crucial for
establishing reliable visual correspondences under large variations of images. In this work …

Segmentation and classification of urban range data into different object classes have
several challenges due to certain properties of the data, such as density variation …

In this paper, we present a framework for detecting interest points in 3-D meshes and
computing their corresponding descriptors. For that, we propose an intrinsic scale detection …

The histogram of oriented gradients (HOG) is widely used for image description and proves
to be very effective. In many vision problems, rotation-invariant analysis is necessary or …

In this paper, we propose a novel unsupervised algorithm for automatically segmenting a
single 3D shape or co-segmenting a family of 3D shapes using deep learning. The algorithm …