Abstract: It is possible to use GPU (Graphic Processing Unit) to increase deep learning performance. This requires us to invest in separate GPUs, which can be relatively expensive. However, if we ...
Abstract: The point cloud is a 3D geometric data that lacks a specific structure and is permutation-invariant. The applications of point clouds have gained significant attention recently in the field ...
Abstract: In partial-to-complete point cloud completion, it is imperative that enabling every patch in the output point cloud faithfully represents the corresponding patch in partial input, ensuring ...
Abstract: This paper introduces a novel self-supervised learning framework for enhancing 3D perception in autonomous driving scenes. Specifically, our approach, namely NCLR, focuses on 2D-3D neural ...
Abstract: Java has found numerous applications in diverse systems such as servers, smartphones, and embedded systems due to the reliability and portability in object-oriented programming. To ...
Abstract: Deep learning has shown promising results for multiple 3D point cloud registration datasets. However, in the underwater domain, most registration of multibeam echo-sounder (MBES) point cloud ...
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: Generalizable 3D object reconstructionfrom single-view RGB-D images remains a challenging task, particularly with real-world data. Current state-of-the-art methods develop Transformer-based ...
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