MesonGS: Post-training Compression of 3D Gaussians via Efficient Attribute Transformation

TL;DR

MesonGS is a post-training compression method for 3D Gaussians used in view synthesis, significantly reducing file size by removing insignificant points and transforming attributes, while maintaining high rendering quality.

Contribution

We introduce MesonGS, a novel codec that compresses 3D Gaussians post-training through attribute transformation and point removal, avoiding extensive retraining.

Findings

Reduces 3D Gaussian file size significantly

Maintains competitive rendering quality after compression

Employs attribute transformations to enhance entropy coding

Abstract

3D Gaussian Splatting demonstrates excellent quality and speed in novel view synthesis. Nevertheless, the huge file size of the 3D Gaussians presents challenges for transmission and storage. Current works design compact models to replace the substantial volume and attributes of 3D Gaussians, along with intensive training to distill information. These endeavors demand considerable training time, presenting formidable hurdles for practical deployment. To this end, we propose MesonGS, a codec for post-training compression of 3D Gaussians. Initially, we introduce a measurement criterion that considers both view-dependent and view-independent factors to assess the impact of each Gaussian point on the rendering output, enabling the removal of insignificant points. Subsequently, we decrease the entropy of attributes through two transformations that complement subsequent entropy coding techniques to enhance the file compression rate. More specifically, we first replace rotation quaternions with Euler angles; then, we apply region adaptive hierarchical transform to key attributes to reduce entropy. Lastly, we adopt finer-grained quantization to avoid excessive information loss. Moreover, a well-crafted finetune scheme is devised to restore quality. Extensive experiments demonstrate that MesonGS significantly reduces the size of 3D Gaussians while preserving competitive quality.