Adapting deep learning networks for point cloud data recognition in self-driving vehicles faces challenges due to the variability in datasets and sensor technologies, emphasizing the need for adaptive techniques to maintain accuracy across different conditions. In this paper, we introduce the 3D Adaptive Structural Convolution Network (3D-ASCN), a cutting-edge framework for 3D point cloud recognition. It combines 3D convolution kernels, a structural tree structure, and adaptive neighborhood sampling for effective geometric feature extraction. This method obtains domain-invariant features and demonstrates robust, adaptable performance on a variety of point cloud datasets, ensuring compatibility across diverse sensor configurations without the need for parameter adjustments. This highlights its potential to significantly enhance the reliability and efficiency of self-driving vehicle technology.

翻译：在自动驾驶车辆中，针对点云数据识别的深度学习网络适配面临数据集和传感器技术多样性的挑战，这凸显了在不同条件下保持准确性的自适应技术的必要性。本文提出三维自适应结构卷积网络（3D-ASCN），一种用于三维点云识别的先进框架。它结合了三维卷积核、结构树形架构和自适应邻域采样，以实现有效的几何特征提取。该方法获取领域不变特征，并在多种点云数据集上表现出鲁棒且自适应的性能，确保在不同传感器配置下的兼容性而无需参数调整。这突显了其显著提升自动驾驶技术可靠性与效率的潜力。