Enhancing 3D Gaussian Splatting Compression via Spatial Condition-based Prediction

Recently, 3D Gaussian Spatting (3DGS) has gained widespread attention in Novel View Synthesis (NVS) due to the remarkable real-time rendering performance. However, the substantial cost of storage and transmission of vanilla 3DGS hinders its further application (hundreds of megabytes or even gigabytes for a single scene). Motivated by the achievements of prediction in video compression, we introduce the prediction technique into the anchor-based Gaussian representation to effectively reduce the bit rate. Specifically, we propose a spatial condition-based prediction module to utilize the grid-captured scene information for prediction, with a residual compensation strategy designed to learn the missing fine-grained information. Besides, to further compress the residual, we propose an instance-aware hyper prior, developing a structure-aware and instance-aware entropy model. Extensive experiments demonstrate the effectiveness of our prediction-based compression framework and each technical component. Even compared with SOTA compression method, our framework still achieves a bit rate savings of 24.42 percent. Code is to be released!

@article{ma2025_2503.23337,
  title={ Enhancing 3D Gaussian Splatting Compression via Spatial Condition-based Prediction },
  author={ Jingui Ma and Yang Hu and Luyang Tang and Jiayu Yang and Yongqi Zhai and Ronggang Wang },
  journal={arXiv preprint arXiv:2503.23337},
  year={ 2025 }
}