PCNet3D++: A pillar-based cascaded 3D object detection model with an enhanced 2D backbone

Abstract

Autonomous Vehicles (AVs) depend on sophisticated perception systems to serve as the vital component of intelligent transportation to ensure secure and smooth navigation. Perception is an essential component of AVs and enables real-time analysis and understanding of the environment for effective decision-making. 3D object detection (3D-OD) is crucial among perception tasks as it accurately determines the 3D geometry and spatial positioning of surrounding objects. The commonly used modalities for 3D-OD are camera, LiDAR, and sensor fusion. In this work, we propose a LiDAR-based 3D-OD approach using point cloud data. The proposed model achieves superior performance while maintaining computational efficiency. This approach utilizes Pillar-based LiDAR processing and uses only 2D convolutions. The model pipeline becomes simple and more efficient by employing only 2D convolutions. We propose a Cascaded Convolutional Backbone (CCB) integrated with 1 × 1 convolutions to improve detection accuracy. We combined the fast Pillar-based encoding with our lightweight backbone. The proposed model reduces complexity to make it well-suited for real-time navigation of an AV. We evaluated our model on the official KITTI test server. The model results are decent in 3D and Bird’s Eye View (BEV) detection benchmarks for the car and cyclist classes. The results of our proposed model are featured on the official KITTI leaderboard.