Recent advancements in point cloud compression have primarily emphasized geometry compression while comparatively fewer efforts have been dedicated to attribute compression. This study introduces an end-to-end learned dynamic lossy attribute coding approach, utilizing an efficient high-dimensional convolution to capture extensive inter-point dependencies. This enables the efficient projection of attribute features into latent variables. Subsequently, we employ a context model that leverage previous latent space in conjunction with an auto-regressive context model for encoding the latent tensor into a bitstream. Evaluation of our method on widely utilized point cloud datasets from the MPEG and Microsoft demonstrates its superior performance compared to the core attribute compression module Region-Adaptive Hierarchical Transform method from MPEG Geometry Point Cloud Compression with 38.1% Bjontegaard Delta-rate saving in average while ensuring a low-complexity encoding/decoding.

翻译：近期点云压缩领域的进展主要集中于几何压缩，相比之下属性压缩方面的研究投入相对较少。本研究提出了一种端到端学习的动态有损属性编码方法，通过高效的高维卷积捕获广泛的点间依赖关系，从而将属性特征高效投影至隐变量空间。随后，我们采用一种结合历史隐空间的上下文模型与自回归上下文模型，将隐张量编码为比特流。在MPEG和微软广泛使用的点云数据集上的评估表明，相较于MPEG几何点云压缩标准中的核心属性压缩模块——区域自适应分层变换方法，本方法在保证低复杂度编解码的同时，平均实现了38.1%的Bjontegaard Delta-rate节省。