Real-Time Streaming Point Cloud Compression for 3D LiDAR Sensor Using U-Net
Real-Time Streaming Point Cloud Compression for 3D LiDAR Sensor Using U-Net
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DOI：
10.1109/access.2019.2935253
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发表时间：
2019-08
期刊：
影响因子：
3.9
通讯作者：
Chenxi Tu;E. Takeuchi;Alexander Carballo;K. Takeda
中科院分区：
文献类型：
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作者：
Chenxi Tu;E. Takeuchi;Alexander Carballo;K. Takeda
来自激光雷达传感器的点云数据是目前大多数L4自动驾驶系统的基础。共享和存储点云对于未来的应用也将非常重要，例如事故调查或V2V/V2X网络。由于涉及的数据量巨大，存储长时间收集的点云和实时传输点云是一项困难的任务，使得压缩成为存储或传输之前不可或缺的步骤。以前的流点云压缩方法，例如八叉树压缩或基于视频压缩的方法，难以将这些数据实时压缩成具有低信息损失的非常小的数据量。为了高效、快速地减少时间冗余，本文提出了一种基于U网的实时流点云数据压缩方法。通过利用来自LiDAR传感器的原始数据包，我们可以将3D点云信息无损地存储在2D矩阵中，并将流传输的点云数据转换为类似视频的格式。通过将部分帧指定为参考帧，然后使用U-Net对剩余的LiDAR帧进行内插，可以大大减少时间冗余。我们使用U-Net的灵感来自于另一项研究中使用的视频内插方法。激光雷达数据中的噪声是一个很大的问题，它显著影响网络训练和压缩效果。在本文中，我们提出了一种填充策略来缓解这种噪声的负面影响。由于这些改进的结果，我们提出的方法比八叉树压缩、基于MPEG的压缩和我们以前提出的基于SLAM的压缩方法都要好。
Point cloud data from LiDAR sensors is the currently the basis of most L4 autonomous driving systems. Sharing and storing point clouds will also be important for future applications, such as accident investigation or V2V/V2X networks. Due to the huge volume of data involved, storing point clouds collected over long periods of time and transmitting point clouds in real-time are difficult tasks, making compression an indispensable step before storing or transmitting. Previous streaming point cloud compression methods, such as octree compression or video compression-based approaches, have difficulty compressing this data in real-time into very small volumes with low information loss. To reduce temporal redundancy efficiently and rapidly, in this paper we propose a real-time streaming point cloud data compression method using U-net. By utilizing raw packet data from LiDAR sensors, we can store 3D point cloud information losslessly in a 2D matrix, and convert streaming point cloud data into a video-like format. By designating some frames as reference frames and then using U-net to interpolate the remaining LiDAR frames, we can greatly reduce temporal redundancy. Our use of U-net was inspired by a video interpolation approach employed in another study. Noise in LiDAR data is a big issue which significantly affects network training and compression results. In this paper, we propose a padding strategy to alleviate the negative impact of this noise. As a result of these improvements, our proposed method can outperform octree compression, MPEG-based compression and our previously proposed SLAM-based compression method.