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 …

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

Abstract We introduce Position Adaptive Convolution (PAConv), a generic convolution
operation for 3D point cloud processing. The key of PAConv is to construct the convolution …

The irregular domain and lack of ordering make it challenging to design deep neural
networks for point cloud processing. This paper presents a novel framework named Point …

Discrete point cloud objects lack sufficient shape descriptors of 3D geometries. In this paper,
we present a novel method for aggregating hypothetical curves in point clouds. Sequences …

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

Raw point clouds data inevitably contains outliers or noise through acquisition from 3D
sensors or reconstruction algorithms. In this paper, we present a novel end-to-end network …

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 …

Due to the sparsity and irregularity of the point cloud data, methods that directly consume
points have become popular. Among all point-based models, graph convolutional networks …