BIRD-PCC：基于双向距离像的深度LiDAR点云压缩
BIRD-PCC: Bi-directional Range Image-based Deep LiDAR Point Cloud Compression
摘要：
LiDAR传感器采集的大量数据带来了LiDAR点云压缩（PCC）问题。LiDAR PCC上的先前工作使用了距离图像表示，并遵循预测编码范式来创建编码框架的基本原型。然而，他们的预测方法给出了一个不准确的结果，由于忽略了无效像素的距离图像和遗漏的未来帧的时间步长。此外，他们手工设计的残差编码方法不能充分利用空间冗余。为了解决这个问题，我们提出了一个编码框架BIRD-PCC。我们的预测模块是知道的距离图像中的无效像素的坐标，并采取双向方案。此外，我们还引入了一个深度学习的残差编码模块，可以进一步利用残差帧内的空间冗余。在SemanticKITTI和KITTI-360数据集上进行的实验表明，BIRD-PCC在大多数比特率条件下都优于其他方法，并且可以很好地推广到未知环境。
英文摘要：
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.
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。
