Abstract

In recent years, 3D point cloud processing has gained attention in robotics and autonomous vehicles for its potential in enhancing perception and decisionmaking. Deep neural networks have excelled in tasks like segmentation and object reconstruction using 3D point cloud data. However, challenges arise due to varying point density and diverse environments, limiting their realworld applicability. To tackle this, we introduce Adaptive-PointNet, a novel framework. Adaptive-PointNet employs adaptive sampling to handle nonuniform point densities and dynamic feature extraction for better contextual understanding. Integrated into this architecture, these modules significantly enhance 3D point cloud processing. We rigorously test Adaptive-PointNet across tasks like semantic segmentation and object classification, demonstrating its superiority in accuracy, robustness, and generalization. Moreover, its practical applications in robotics and autonomous vehicles, including SLAM and obstacle detection, highlight its real-time potential. We also address ethical concerns, ensuring Adaptive-PointNet adheres to ethical standards and incorporates fail-safe mechanisms, guaranteeing safe deployment in autonomous systems.

Keywords: 3D point cloud processing, Deep neural networks, Robotics, Autonomous vehicles, Adaptive sampling, Dynamic feature extraction, Semantic segmentation, Object classification, 3D object reconstruction, Real-time applications, Robustness, Generalization, Ethical considerations