Kavli Affiliate: Wei Gao
| First 5 Authors: Kangli Wang, Shihao Li, Qianxi Yi, Wei Gao,
| Summary:
Recently, immersive media and autonomous driving applications have
significantly advanced through 3D Gaussian Splatting (3DGS), which offers
high-fidelity rendering and computational efficiency. Despite these advantages,
3DGS as a display-oriented representation requires substantial storage due to
its numerous Gaussian attributes. Current compression methods have shown
promising results but typically neglect the compression of Gaussian spatial
positions, creating unnecessary bitstream overhead. We conceptualize Gaussian
primitives as point clouds and propose leveraging point cloud compression
techniques for more effective storage. AI-based point cloud compression
demonstrates superior performance and faster inference compared to MPEG
Geometry-based Point Cloud Compression (G-PCC). However, direct application of
existing models to Gaussian compression may yield suboptimal results, as
Gaussian point clouds tend to exhibit globally sparse yet locally dense
geometric distributions that differ from conventional point cloud
characteristics. To address these challenges, we introduce GausPcgc for
Gaussian point cloud geometry compression along with a specialized training
dataset GausPcc-1K. Our work pioneers the integration of AI-based point cloud
compression into Gaussian compression pipelines, achieving superior compression
ratios. The framework complements existing Gaussian compression methods while
delivering significant performance improvements. All code, data, and
pre-trained models will be publicly released to facilitate further research
advances in this field.
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