  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.
