Feature Prediction for 3D Gaussian Splatting Compression

Recently, 3D Gaussian Splatting (3DGS) has emerged as a promising scene representation technique for novel view synthesis. However, the large number of Gaussians used to represent the 3D scene poses challenges for storage and transmission. Existing advanced compression methods focus on designing various context models for entropy modeling, but lack the exploration of prediction techniques. In this paper, we dig into feature correlations in the anchor-based Gaussian representation and propose two types of feature prediction techniques to further reduce the scene redundancy. Firstly, we observe that context features contain rich scene priors, which are also helpful for reconstruction but often ignored by previous methods. To this end, we design a Context-based Weighted Prediction module to adaptively aggregate the anchor feature and the context feature for rendering, which can reduce the storage costs in anchors. Secondly, a high degree of similarity is discovered between different feature channels. To utilize the cross-channel correlations, we propose a Cross-channel Residual Prediction module, which further reduces the bit cost for coding anchor features. Extensive experiments show that our method can further enhance compression performance while maintaining rendering quality compared to existing 3DGS compression methods. Our code is available at https://github.com/Pomelomm/FP-GS.