Abstract: Grid-based feature learning network plays a key role in recent point-cloud based 3D perception. However, high point sparsity and special operators lead to large memory footprint and long ...
Abstract: Self-supervised learning of point cloud aims to leverage unlabeled 3D data to learn meaningful representations without reliance on manual annotations. However, current approaches face ...
Abstract: As radar can directly provide the velocity of the targets in autonomous driving and is known for the robustness against adverse weather conditions, it plays an important role in contrast to ...
Abstract: Weakly supervised point cloud semantic segmentation methods that require 1% or fewer labels with the aim of realizing almost the same performance as fully supervised approaches have recently ...
Abstract: Autonomous driving requires perceiving surrounding scenes and predicting the future in order to navigate safely. 3D point clouds from LiDAR sensor accurately characterize the environment and ...
DA-Net: Density-Adaptive Downsampling Network for Point Cloud Classification via End-to-End Learning
Abstract: Since a point cloud might contain large quantities of points in practical scenarios, it is desirable to perform downsampling before point cloud analysis. Classic downsampling strategies, ...
Abstract: In this paper, we question whether we have a reliable self-supervised point cloud model that can be used for diverse 3D tasks via simple linear probing, even with limited data and minimal ...
Abstract: In this study, deep learning techniques and algorithms used in point cloud processing have been analysed. Methods, technical properties and algorithms developed for 3D Object Classification ...
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