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arXiv · 2503.23337

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

Jingui Ma·Yang Hu·Luyang Tang·Jiayu Yang·Yongqi Zhai·Ronggang Wang
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Abstract

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!

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Jingui Ma, Yang Hu, Luyang Tang, Jiayu Yang, Yongqi Zhai, Ronggang Wang. 2025-03-30. Enhancing 3D Gaussian Splatting Compression via Spatial Condition-based Prediction. https://arxiv.org/abs/2503.23337

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