Local density of point clouds is crucial for representing local details, but has been overlooked by existing point cloud compression methods. To address this, we propose a novel deep point cloud compression method that preserves local density information. Our method works in an auto-encoder fashion: the encoder downsamples the points and learns point-wise features, while the decoder upsamples the points using these features. Specifically, we propose to encode local geometry and density with three embeddings: density embedding, local position embedding and ancestor embedding. During the decoding, we explicitly predict the upsampling factor for each point, and the directions and scales of the upsampled points. To mitigate the clustered points issue in existing methods, we design a novel sub-point convolution layer, and an upsampling block with adaptive scale. Furthermore, our method can also compress point-wise attributes, such as normal. Extensive qualitative and quantitative results on SemanticKITTI and ShapeNet demonstrate that our method achieves the state-of-the-art rate-distortion trade-off.

本研究提出了一种新型深度点云压缩方法，可以有效保留本地密度信息，采用自动编码器的方式进行降采样和上采样操作，通过密度嵌入、本地位置嵌入和先祖嵌入等方式编码点云局部几何和密度，并在解码时预测每个点的上采样因子和方向尺度，同时也可以压缩点云属性。实验结果表明，该算法在SemanticKITTI和ShapeNet数据集上实现了最先进的码率失真平衡。

深度点云压缩保密度