The paper explores the optimization of sparse convolution for 3D point cloud analysis on Graphics Processing Units (GPUs) using CUDA technology. With the increasing use of LiDAR and 3D sensors, the analysis of 3D point clouds has become crucial for various applications like object detection and segmentation. However, due to the sparse nature of point clouds, traditional Convolutional Neural Networks (CNNs) are not suitable. The paper presents a novel approach that combines the theoretical benefits of sparse neural networks with efficient GPU-based implementations, providing insights and techniques for effectively using 3D point cloud analysis.
Publication date: 13 Feb 2024
Project Page: Not provided
Paper: https://arxiv.org/pdf/2402.07710
