QuantBEVFusion: A Fully Quantized Framework for LiDAR-camera 3D Object Detection

3D object detection is essential for robust environmental perception in autonomous driving and robotics. While LiDAR-camera fusion methods offer high accuracy, their computational complexity hinders deployment on resource-constrained edge devices. To address this, we introduce QuantBEVFusion, a fully quantized 3D object detection framework that prioritizes both quantization and operator optimization. Our approach tackles the inherent asymmetry between LiDAR and camera data in pillar-based models by incorporating a novel pillar bird’s-eye view (BEV) encoder, significantly boosting performance. Furthermore, we introduce 1) an optimized LiDAR input processing method that filters noise and enables per-tensor quantization; 2) an improved sparse feature quantization process with log-histogram balancing, adaptive bin widths, and distillation loss for enhanced accuracy; and 3) a deployment-friendly 3D-to-2D transformation operator facilitating fixed-point implementation. Extensive experiments demonstrate that QuantBEVFusion achieves state-of-the-art quantization performance while maintaining accuracy suitable for real-time applications on edge devices.