Q-TempFusion: Quantization-Aware Temporal Multi-Sensor Fusion on Bird's-Eye View Representation
Pinrui Yu, Zhenglun Kong, Pu Zhao, Peiyan Dong, Hao Tang, Fei Sun, Xue Lin, Yanzhi Wang · 2025
Recent advancements in bird's-eye view (BEV) perception models have highlighted the superior performance of LiDAR-camera fusion systems over single-modality approaches, garnering considerable interest in the field. Despite the progress, the integration of temporal information, a technique that has considerably benefitted camera-only BEV models, remains underexplored for LiDAR-camera fusion. This paper presents Q-TempFusion, a novel approach for temporal multi-sensor fusion designed to enhance the BEV model's inference speed while keeping high predictive performance compared with the current state-of-the-art. Moreover, we are the first to make the multi-modality BEV model profiling on hardware devices. To address the challenges of substantial memory demands and non-trivial latency that hinder deployment in on-vehicle systems, particularly when temporal dynamics are incorporated into complex multi-sensor models, we introduce an activation-aware quantization framework to generate the fully 8-bit quantized Q-TempFusion model based on the profiling result, which can be directly deployed to target devices with negligible detection performance degradation. Our experiments show that our Q-TempFusion (8-bit) achieves 70.3% mAP and 72.7% NDS with 3×~18× FPS improvement over leading multi-modality baselines and the Q-TempFusion (32-bit) achieves 72.1% mAP and 74.8% NDS, comparable to SOTA multi-modality approaches. The results suggest that Q-TempFusion is a promising step toward real-time multi-sensor BEV applications, setting a new benchmark for efficient and reliable perception.