Due to the large amount of data involved in the three-dimensional (3D) LiDAR point clouds, point cloud compression (PCC) becomes indispensable to many real-time applications. In autonomous driving of connected vehicles for example, point clouds are constantly acquired along the time and subjected to be compressed. Among the existing PCC methods, very few of them have effectively removed the temporal redundancy inherited in the point clouds. To address this issue, a novel lossy LiDAR PCC system is proposed in this paper, which consists of the inter -frame coding and the intra -frame coding. For the former, a deep-learning approach is proposed to conduct bi-directional frame prediction using an asymmetric residual module and 3D space-time convolutions; the proposed network is called the bi-directional prediction network (BPNet). For the latter, a novel range-adaptive floating-point coding (RAFC) algorithm is proposed for encoding the reference frames and the B-frame prediction residuals in the 32-bit floating-point precision. Since the pixel-value distribution of these two types of data are quite different, various encoding modes are designed for providing adaptive selection. Extensive simulation experiments have been conducted using multiple point cloud datasets, and the results clearly show that our proposed PCC system consistently outperforms the state-of-the-art MPEG G-PCC in terms of data fidelity and localization, while delivering real-time performance.

中文翻译：

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使用双向预测和范围自适应浮点编码的实时 LiDAR 点云压缩

由于三维（3D）激光雷达点云中涉及大量数据，点云压缩（PCC）对于许多实时应用来说变得不可或缺。例如，在联网车辆的自动驾驶中，点云会随着时间不断获取并进行压缩。在现有的 PCC 方法中，很少有能有效去除在点云中继承的时间冗余。为了解决这个问题，本文提出了一种新颖的有损 LiDAR PCC 系统，该系统由帧间编码和帧内编码。对于前者，提出了一种深度学习方法，使用非对称残差模块和 3D 时空卷积进行双向帧预测；提议的网络称为双向预测网络（BPNet）。对于后者，小说提出了范围自适应浮点编码（RAFC）算法，以32位浮点精度对参考帧和B帧预测残差进行编码。由于这两种数据的像素值分布差异很大，因此设计了各种编码模式来提供自适应选择。已经使用多个点云数据集进行了广泛的模拟实验，结果清楚地表明，我们提出的 PCC 系统在数据保真度和定位方面始终优于最先进的 MPEG G-PCC，同时提供实时性能.