Study on Real-Time Road Surface Reconstruction Using Stereo Vision
This work addresses real-time road surface reconstruction for autonomous driving, but it is incremental as it builds on an existing framework.
This paper enhances the RoadBEV framework for real-time road surface reconstruction in autonomous driving by optimizing efficiency and accuracy, achieving improved inference speed and lower reconstruction error.
Road surface reconstruction plays a crucial role in autonomous driving, providing essential information for safe and smooth navigation. This paper enhances the RoadBEV [1] framework for real-time inference on edge devices by optimizing both efficiency and accuracy. To achieve this, we proposed to apply Isomorphic Global Structured Pruning to the stereo feature extraction backbone, reducing network complexity while maintaining performance. Additionally, the head network is redesigned with an optimized hourglass structure, dynamic attention heads, reduced feature channels, mixed precision inference, and efficient probability volume computation. Our approach improves inference speed while achieving lower reconstruction error, making it well-suited for real-time road surface reconstruction in autonomous driving.