Presentation
Hardware-Aware Neural Architecture Search for Real-Time BEV-Based 3D Object Detection
DescriptionBird's-eye-view (BEV) fusion of multi-modal sensor data is a leading approach for high-performance 3D object detection. However, state-of-the-art models rarely exceed 10 FPS even on powerful GPUs, limiting their use in real-time applications. In this paper, we present a neural architecture search (NAS) framework that identifies models achieving both high accuracy and real-time inference. The search space is explicitly designed with hardware constraints to balance accuracy and computational efficiency on resource-limited platforms. To better align LiDAR and camera features in the BEV conversion module, we introduce a lightweight depth estimation network and a LiDAR–camera cross-attention mechanism that enhances detection accuracy with minimal overhead. On the challenging nuScenes benchmark, our model achieves 70.1% mAP, which represents a 2.3% point improvement over the baseline model, while running at 35.6 FPS on the Nvidia Jetson AGX Orin, demonstrating its suitability for real-time autonomous driving.
Event Type
Research Manuscript
TimeTuesday, July 285:06pm - 5:18pm PDT
LocationMtg Room 101A