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

• URL: http://arxiv.org/abs/2503.23337v1
• Date: Sun, 30 Mar 2025 06:41:43 GMT
• Title: Enhancing 3D Gaussian Splatting Compression via Spatial Condition-based Prediction
• Authors: Jingui Ma, Yang Hu, Luyang Tang, Jiayu Yang, Yongqi Zhai, Ronggang Wang,
• Abstract summary: We introduce the prediction technique into the anchor-based Gaussian representation to effectively reduce the bit rate.<n>Our framework still achieves a bit rate savings of 24.42 percent.
• Score: 24.061525432639943
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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