The large amount of data collected by LiDAR sensors brings the issue of LiDAR point cloud compression (PCC). Previous works on LiDAR PCC have used range image representations and followed the predictive coding paradigm to create a basic prototype of a coding framework. However, their prediction methods give an inaccurate result due to the negligence of invalid pixels in range images and the omission of future frames in the time step. Moreover, their handcrafted design of residual coding methods could not fully exploit spatial redundancy. To remedy this, we propose a coding framework BIRD-PCC. Our prediction module is aware of the coordinates of invalid pixels in range images and takes a bidirectional scheme. Also, we introduce a deep-learned residual coding module that can further exploit spatial redundancy within a residual frame. Experiments conducted on SemanticKITTI and KITTI-360 datasets show that BIRD-PCC outperforms other methods in most bitrate conditions and generalizes well to unseen environments.

翻译：激光雷达传感器采集的大量数据带来了点云压缩问题。以往关于激光雷达点云压缩的研究采用距离图像表示，并遵循预测编码范式构建了编码框架的基本原型。然而，由于忽略了距离图像中的无效像素且未考虑未来帧在时间步上的信息，其预测方法结果不准确。此外，其手工设计的残差编码方法无法充分挖掘空间冗余。为此，我们提出编码框架BIRD-PCC。其预测模块能感知距离图像中无效像素的坐标，并采用双向方案。同时，我们引入深度学习的残差编码模块，进一步利用残差帧内的空间冗余。在SemanticKITTI和KITTI-360数据集上的实验表明，BIRD-PCC在大多数比特率条件下优于其他方法，并对未见过场景具有良好的泛化能力。