优化立体视觉道路重建，实现实时高效高精度建模。
Study on Real-Time Road Surface Reconstruction Using Stereo Vision
- 采用同构全局结构化剪枝压缩主干网络，兼顾速度与性能。
- 重构头部网络，提升推理速度并降低重建误差。
- 适合边缘设备部署，适用于自动驾驶实时道路感知。
道路表面重建在自动驾驶中至关重要，为安全平稳导航提供关键信息。本文针对边缘设备上的实时推理，优化了RoadBEV框架的效率与精度。通过在立体特征提取主干网络中应用同构全局结构化剪枝，降低网络复杂度同时保持性能；并重新设计头部网络，包含优化的hourglass结构、动态注意力头、减少特征通道数、混合精度推理及高效的概率体计算。该方法在提升推理速度的同时实现更低的重建误差，适用于自动驾驶中的实时道路表面重建。
原文摘要 · Abstract (English)
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.
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