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About this paper
Lei Liu, Zhenghao Chen, Wei Jiang, Wei Wang, Dong Xu
HEMGS introduces a hybrid entropy model for 3D Gaussian Splatting data that enables single-model variable-rate lossy compression. This framework integrates scene-agnostic and scene-specific priors with an adaptive context selection mechanism, reducing 3DGS data storage by approximately 40% on average compared to leading methods while maintaining rendering fidelity.
Key insights
- A Variable-rate Predictor enables a single neural compression model to efficiently support diverse rate-distortion trade-offs, eliminating the need for training and storing multiple separate models.
- Combining generalizable scene-agnostic priors with instance-specific scene-specific priors provides a more robust and comprehensive contextual foundation for entropy coding complex 3D data.
- An adaptive context selection mechanism in autoregressive networks, which dynamically adjusts its receptive field based on data density, is crucial for effectively modeling the sparse and irregular structure of 3D Gaussian data.
Summary by alphaXiv.
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