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: With the exponential growth of data, many technologies have also been developed to cope with the need to process such big dataset and generate meaningful information out of those dataset. To ...

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: Point cloud completion is to restore complete 3D scenes and objects from incomplete observations or limited sensor data. Existing fully-supervised methods rely on paired datasets of ...

Abstract: To address the drawbacks that current multibeam bathymetric outlier removal methods lack repeatability, often require parameter adjustment for different regions, still require a lot of ...

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 ...

Abstract: Analysing data on a large scale is becoming important and engages in convincing many researchers to use new platforms and tools that can handle large amounts of data. In this article, we ...

Abstract: Point cloud semantic segmentation has achieved considerable progress in the past decade. To alleviate expensive data annotation efforts, weakly supervised learning methods are preferable, ...