论文标题
稀疏张量的点云属性压缩
Sparse Tensor-based Point Cloud Attribute Compression
论文作者
论文摘要
最近，已经开发了许多基于学习的压缩方法，以表现出出色的性能，用于编码点云的几何信息。相反，有限的探索已专门用于点云属性压缩（PCAC）。因此，这项研究通过应用稀疏卷积来重点放在PCAC上，因为它具有较高的效率来表示无组织点的几何形状。提出的方法只是堆叠稀疏的卷积，以构建变异自动编码器（VAE）框架以压缩给定点云的颜色属性。为了更好地编码瓶颈上的潜在元素，我们将适用于超级先验和自回归邻居的联合利用来准确估计比特率的自适应熵模型。所提出方法的定性测量已经以类似的比特速率与最新的G-PCC（或TMC13）版本14相媲美。而且，我们的方法显示了对G-PCC 6版本的明显定量改进，并且在很大程度上优于现有的基于学习的方法，这有望鼓励学习PCAC的潜力。
Recently, numerous learning-based compression methods have been developed with outstanding performance for the coding of the geometry information of point clouds. On the contrary, limited explorations have been devoted to point cloud attribute compression (PCAC). Thus, this study focuses on the PCAC by applying sparse convolution because of its superior efficiency for representing the geometry of unorganized points. The proposed method simply stacks sparse convolutions to construct the variational autoencoder (VAE) framework to compress the color attributes of a given point cloud. To better encode latent elements at the bottleneck, we apply the adaptive entropy model with the joint utilization of hyper prior and autoregressive neighbors to accurately estimate the bit rate. The qualitative measurement of the proposed method already rivals the latest G-PCC (or TMC13) version 14 at a similar bit rate. And, our method shows clear quantitative improvements to G-PCC version 6, and largely outperforms existing learning-based methods, which promises encouraging potentials for learnt PCAC.
