Pillar-X: Integrating Self-Learned Image Features to Improve 3D Object Detection

Accurate 3D object detection is essential for robust perception systems in autonomous vehicles. This paper presents Pillar-X, a 3D object recognition framework designed to generate and use self-learned image features. Unlike approaches that rely on pre-trained networks, Pillar-X efficiently learns and fuses features from an image directly within an end-to-end pipeline. Comprehensive evaluations on the KITTI and CARLA-generated dataset demonstrate Pillar-X’s significant reduction in false positive detections, especially for the pedestrian and cyclist classes. The proposed model achieves this while maintaining a speed similar to the baseline algorithm (¿20 Hz). By comparing the network using both PASCAL and a specific criterion, it can be concluded that Pillar-X is able to improve accuracy and reliability in 3D object detection tasks.