基于立体视觉的实时道路表面重建研究
计算机视觉与模式识别
2025-04-28 v1
摘要
道路表面重建在自动驾驶中起着关键作用，为安全顺畅的导航提供必要信息。本文通过优化效率和准确性，使RoadBEV框架能够在边缘设备上实现实时推理。为实现此目标，我们提出应用等构全结构剪枝于立体特征提取主干网络，以降低网络复杂度而保持性能。此外，头部网络 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.
引用
@article{arxiv.2504.18112,
title = {Study on Real-Time Road Surface Reconstruction Using Stereo Vision},
author = {Deepak Ghimire and Byoungjun Kim and Donghoon Kim and SungHwan Jeong},
journal= {arXiv preprint arXiv:2504.18112},
year = {2025}
}
备注
Stereo Vision, Efficient CNN, Pruning, Optimization. 2025 Intelligent Information and Control Conference (IICC 2025), Jeonju, Korea