Lift-PCAC: Lifting Based Point Cloud Attribute Compression

Point cloud (PC) compression is crucial for efficient transmission and storage in applications like virtual and augmented reality, where point counts can reach millions. While learning-based methods have shown promise in compressing PC geometry, attribute compression remains relatively unexplored. Existing methods often rely on variational autoencoders (VAEs). However, VAEs, with their low-dimensional bottlenecks, inherently limit the achievable reconstruction quality, especially at high bitrates. In this paper we introduce a novel approach to compressing PC attributes using a lifting framework. The invertibility of our method enables better reconstruction quality at high bitrates. Our Lifting Based Point Cloud Attribute Compression (Lift-PCAC) outperforms existing learning-based attribute compression methods on higher bitrates and shows comparable performance to G-PCC v.21 in some cases, highlighting the potential of this approach for point cloud compression.