Few-shot learning (FSL) has emerged as an effective learning method and shows great
potential. Despite the recent creative works in tackling FSL tasks, learning valid information …

Point cloud learning has lately attracted increasing attention due to its wide applications in
many areas, such as computer vision, autonomous driving, and robotics. As a dominating …

Point cloud segmentation is fundamental in understanding 3D environments. However,
current 3D point cloud segmentation methods usually perform poorly on scene boundaries …

The requirement of fine-grained perception by autonomous driving systems has resulted in
recently increased research in the online semantic segmentation of single-scan LiDAR …

Abstract We present Kernel Point Convolution (KPConv), a new design of point convolution,
ie that operates on point clouds without any intermediate representation. The convolution …

Semantic scene understanding is important for various applications. In particular, self-driving
cars need a fine-grained understanding of the surfaces and objects in their vicinity. Light …

Ripe with possibilities offered by deep-learning techniques and useful in applications
related to remote sensing, computer vision, and robotics, 3D point cloud semantic …

A point cloud is a set of points defined in a 3D metric space. Point clouds have become one
of the most significant data formats for 3D representation and are gaining increased …

We study the problem of efficient semantic segmentation of large-scale 3D point clouds. By
relying on expensive sampling techniques or computationally heavy pre/post-processing …

An essential prerequisite for unleashing the potential of supervised deep learning
algorithms in the area of 3D scene understanding is the availability of large-scale and richly …