Adaptive Downsampling and Spatial Upconversion for Point Cloud Compression

Although ultra-high resolution point clouds have been acquired more easily, the huge amount of the data makes it more challenging to store and transmit, and also difficult to be applied in lightweight terminal. To address this challenge, we propose an Adaptive Downsampling and Spatial Upconversion framework for point cloud compression (ADSU). In the proposed adaptive downsampling, we introduce two key components, feature-aware augmented graph convolution (FAGC) and adaptive-sampling-based global graph aggregation module (ASGGA), to capture correlations between local and global features. For upsampling, we propose a high-frequency feature generation module (HFFG) to generate detailed information, which plays a crucial role in achieving precise reconstruction. Experimental results demonstrate that the combination of our proposed ADSU with popular point cloud compression methods can significantly improve compression performance.