使用 CUDA 优化 GPU 上的稀疏卷积，以用于嵌入式系统中的 3D 点云处理
Optimizing Sparse Convolution on GPUs with CUDA for 3D Point Cloud Processing in Embedded Systems
AI总结：
该研究针对嵌入式系统中的 3D 点云处理，指出点云稀疏且无规则网格所带来的计算挑战，并聚焦于利用 CUDA 优化 GPU 上的稀疏卷积。
摘要：
近年来，深度学习方法，尤其是卷积神经网络（CNN）的使用显著增加，并已成为涉及结构化网格数据的各类领域（如图像分析与处理）中的主导方法。然而，LiDAR 和 3D 传感器在许多领域的使用呈指数级增长，导致对 3D 点云分析的需求不断增加。3D 点云的使用在物体识别和分割等多种应用中至关重要，因为它们提供了三维环境中物体的空间描述。与照片不同，点云具有稀疏性且缺乏规则网格，因此带来了独特的处理和计算问题。
英文摘要：
In recent years, there has been a significant increase in the utilization of deep learning methods, particularly convolutional neural networks (CNNs), which have emerged as the dominant approach in various domains that involve structured grid data, such as picture analysis and processing. Nevertheless, the exponential growth in the utilization of LiDAR and 3D sensors across many domains has resulted in an increased need for the analysis of 3D point clouds. The utilization of 3D point clouds is crucial in various applications, including object recognition and segmentation, as they offer a spatial depiction of things within a three-dimensional environment. In contrast to photos, point clouds exhibit sparsity and lack a regular grid, hence posing distinct processing and computational issues.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
