Mamba-PCGC: Mamba-Based Point Cloud Geometry Compression

Point cloud compression plays a critical role in efficiently managing extensive 3D datasets, thereby facilitating their practical utilization. Robust feature extraction is essential for enabling learned codecs to attain optimal compression performance. Recent advancements in state space models (SSMs), coupled with efficient hardware-aware designs such as Mamba, have exhibited significant potential for effectively modeling long sequences. In light of these developments, we propose a novel approach: Mamba-based point cloud geometry compression (Mamba-PCGC), which not only achieves linear computational complexity but also preserves expansive receptive fields to better extract latent features. The comprehensive results from our experiments strongly demonstrate the effectiveness of our proposed approach. When compared against established point cloud coding standards, specifically G-PCC and V-PCC, Mamba-PCGC demonstrates remarkable bitrate savings of $50.0 \%$ and $34.3 \%$, respectively, in terms of D1-PSNR. Furthermore, in a comparison with PCGCv2, our approach consistently outperforms, showcasing an average bitrate reduction of $13.1 \%$ in terms of D1-PSNR.