DepthNet：自动驾驶车辆的实时LiDAR点云深度完成
DepthNet: Real-Time LiDAR Point Cloud Depth Completion for Autonomous Vehicles
摘要：
自动驾驶汽车在很大程度上依赖于摄像头和激光雷达等传感器，这些传感器为感知、规划和控制任务提供有关周围环境的实时信息。通常，由于扫描线的数量有限，LiDAR只能提供稀疏的点云。通过采用深度完成，可以通过为每个相机像素分配对应的深度值来生成密集深度图。然而，现有的深度完成卷积神经网络非常复杂，需要高端GPU进行处理，因此它们不适用于实时自动驾驶。针对LiDAR点云深度补全任务，提出了一种轻量级网络。尽管参数数量减少了96.2%，但它仍然达到了与最先进网络相当的性能（MAE提高了9.3%，但RMSE降低了3.9%）。对于实时嵌入式平台，深度可分离技术应用于卷积和反卷积操作，参数的数量进一步减少了7.3倍，RMSE和MAE性能只有很小的百分比增加。此外，在基于PYNQ的FPGA平台上开发了用于深度完成的片上系统架构，该架构以每秒11.1帧的速度实现了对HDL-64 E LiDAR的实时处理。
英文摘要：
Autonomous vehicles rely heavily on sensors such as camera and LiDAR, which provide real-time information about their surroundings for the tasks of perception, planning and control. Typically a LiDAR can only provide sparse point cloud owing to a limited number of scanning lines. By employing depth completion, a dense depth map can be generated by assigning each camera pixel a corresponding depth value. However, the existing depth completion convolutional neural networks are very complex that requires high-end GPUs for processing, and thus they are not applicable to real-time autonomous driving. In this paper, a light-weight network is proposed for the task of LiDAR point cloud depth completion. With an astonishing 96.2% reduction in the number of parameters, it still achieves comparable performance (9.3% better in MAE but 3.9% worse in RMSE) to the state-of-the-art network. For real-time embedded platforms, depthwise separable technique is applied to both convolution and deconvolution operations and the number of parameters decreases further by a factor of 7.3, with only a small percentage increase in RMSE and MAE performance. Moreover, a system-on-chip architecture for depth completion is developed on a PYNQ-based FPGA platform that achieves real-time processing for HDL-64E LiDAR at the speed 11.1 frame per second.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
