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 …

Autonomous vehicles were experiencing rapid development in the past few years. However,
achieving full autonomy is not a trivial task, due to the nature of the complex and dynamic …

Abstract 3D point cloud segmentation has made tremendous progress in recent years. Most
current methods focus on aggregating local features, but fail to directly model long-range …

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 …

Recently, directly detecting 3D objects from 3D point clouds has received increasing
attention. To extract object representation from an irregular point cloud, existing methods …

Point clouds can be represented in many forms (views), typically, point-based sets, voxel-
based cells or range-based images (ie, panoramic view). The point-based view is …

Invariance and equivariance to the rotation group have been widely discussed in the 3D
deep learning community for pointclouds. Yet most proposed methods either use complex …

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 …

Pretraining on large labeled datasets is a prerequisite to achieve good performance in many
computer vision tasks like image recognition, video understanding etc. However, pretraining …