Abstract: Self-supervised learning has achieved significant success in various fields such as point cloud detection and segmentation. However, self-supervised learning for point cloud registration is ...
Abstract: This paper addresses the critical need for accurate and reliable point cloud quality assessment (PCQA) in various applications, such as autonomous driving, robotics, virtual reality, and 3D ...
Point clouds are widely applied in 3D visual sensing and perception. However, manually annotating point clouds is much more tedious and time-consuming than that for 2D images. Fortunately, ...
Abstract: As one of the most crucial topics in the recommendation system field, point-of-interest (POI) recommendation aims to recommending potential interesting POIs to users. Recently, graph neural ...
With NetBeans 27, Gradle 9.1.0 and Java 25, when trying to load a Gradle project, I keep getting this message in a dialog named "Resolve ProjectProblems": java version "25" 2025-09-16 LTS Java(TM) SE ...
Abstract: Since point clouds acquired by scanners inevitably contain noise, recovering a clean version from a noisy point cloud is essential for further 3D geometry processing applications. Several ...
RLGrid: Reinforcement Learning Controlled Grid Deformation for Coarse-to-Fine Point Cloud Completion
Abstract: Many point cloud completion methods typically rely on two steps: coarse generation and 2D grid deformed fine output. However, in the fine generation, the expansion range (2D grid scale) ...
Abstract: In the past decade, deep neural networks have achieved significant progress in point cloud learning. However, collecting large-scale precisely-annotated point clouds is extremely laborious ...
The Java ecosystem brings you unmatched speed and stability. Here’s our review of seven top-shelf Java microframeworks built for modern, lightweight application development. Java microframeworks are ...
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