论文标题
RAW 3D点云的可变速率压缩
Variable Rate Compression for Raw 3D Point Clouds
论文作者
论文摘要
在本文中，我们提出了一种新型的可变速率深度压缩体系结构，该结构可在RAW 3D点云数据上运行。大多数基于学习的点云压缩方法在数据的下采样表示方面工作。此外，许多现有技术需要培训多个网络以不同的压缩率，以产生质量不同的合并点云。相比之下，我们的网络能够明确处理点云，并在全面的比特率范围内生成压缩描述。此外，我们的方法可确保由于体素化过程而不会丢失信息，并且点云的密度不会影响编码器/解码器的性能。广泛的实验评估表明，我们的模型获得了最先进的结果，它在计算上是有效的，并且可以直接与点云数据一起使用，从而避免了昂贵的素化表示。
In this paper, we propose a novel variable rate deep compression architecture that operates on raw 3D point cloud data. The majority of learning-based point cloud compression methods work on a downsampled representation of the data. Moreover, many existing techniques require training multiple networks for different compression rates to generate consolidated point clouds of varying quality. In contrast, our network is capable of explicitly processing point clouds and generating a compressed description at a comprehensive range of bitrates. Furthermore, our approach ensures that there is no loss of information as a result of the voxelization process and the density of the point cloud does not affect the encoder/decoder performance. An extensive experimental evaluation shows that our model obtains state-of-the-art results, it is computationally efficient, and it can work directly with point cloud data thus avoiding an expensive voxelized representation.
