# Adaptive Voxelization for Transform coding of 3D Gaussian splatting data

arXiv: https://arxiv.org/abs/2506.00271 · HTML: https://mlsanity.com/paper/2506.00271/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2025.

## Built on by (most cited first, 7 of 7)

- [SUCCESS-GS: Survey of Compactness and Compression for Efficient Static and Dynamic Gaussian Splatting](https://mlsanity.com/paper/2512.07197/) (2025)
- [Lightweight 3D Gaussian Splatting Compression via Video Codec](https://mlsanity.com/paper/2512.11186/) (2025)
- [HPC: Hierarchical Point-based Latent Representation for Streaming Dynamic Gaussian Splatting Compression](https://mlsanity.com/paper/2602.00671/) (2026)
- [GS-NFS: Bandwidth-adaptive Streaming of Dynamic Gaussian Splats and Point Clouds](https://mlsanity.com/paper/2606.05650/) (2026)
- [Non-Uniform Quantisation for 3DGS Compression](https://mlsanity.com/paper/2608.28272/) (2026)
- [Robust, Estimator-Agnostic Dynamic 3DGS Compression](https://mlsanity.com/paper/2609.25633/) (2026)
- [Efficient Gaussian Splatting Sequence Compression with Standard Video Codecs](https://mlsanity.com/paper/2610.07795/) (2026)

## Abstract (arXiv)

We present a novel compression framework for 3D Gaussian splatting (3DGS) data that leverages transform coding tools originally developed for point clouds. Contrary to existing 3DGS compression methods, our approach can produce compressed 3DGS models at multiple bitrates in a computationally efficient way. Point cloud voxelization is a discretization technique that point cloud codecs use to improve coding efficiency while enabling the use of fast transform coding algorithms. We propose an adaptive voxelization algorithm tailored to 3DGS data, to avoid the inefficiencies introduced by uniform voxelization used in point cloud codecs. We ensure the positions of larger volume Gaussians are represented at high resolution, as these significantly impact rendering quality. Meanwhile, a low-resolution representation is used for dense regions with smaller Gaussians, which have a relatively lower impact on rendering quality. This adaptive voxelization approach significantly reduces the number of Gaussians and the bitrate required to encode the 3DGS data. After voxelization, many Gaussians are moved or eliminated. Thus, we propose to fine-tune/recolor the remaining 3DGS attributes with an initialization that can reduce the amount of retraining required. Experimental results on pre-trained datasets show that our proposed compression framework outperforms existing methods.