Overfitted Point Cloud Attribute Codec Using Sparse Hierarchical Implicit Neural Representations

Compressing attributes of 3D point clouds remains challenging due to their inherent sparsity and irregular distribution. To address this, we propose an efficient framework based on sparse hierarchical Implicit Neural Representations (INRs). Specifically, we introduce a novel vertex-based INR framework, which integrates interpolation to enable accurate and compact implicit representations of point cloud attributes. To effectively capture the varying importance of latent features, we design an adaptive quantization scheme. Furthermore, we develop efficient level-wise entropy models to exploit dependencies within and across hierarchical levels. Finally, point cloud attributes are reconstructed from concatenated multi-resolution latent representations via a sparse convolution-based reconstruction module. Experimental results demonstrate that our approach significantly outperforms previous INR-based methods, achieving superior performance compared to the latest G-PCC (TMC13v28) standard and state-of-the-art learning-based methods.