3D Adaptive Structural Convolution Network for Domain-Invariant Point Cloud Recognition

TL;DR

The paper introduces 3D-ASCN, a novel neural network framework that extracts domain-invariant features from 3D point clouds, improving recognition robustness across different datasets and sensor configurations in autonomous driving.

Contribution

It proposes a new adaptive 3D convolution network with structural and neighborhood sampling techniques for domain-invariant point cloud recognition.

Findings

Achieves robust performance across multiple datasets

Maintains accuracy without parameter tuning across sensors

Enhances reliability of autonomous vehicle perception systems

Abstract

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.