ELMGS: Enhancing memory and computation scaLability through coMpression for 3D Gaussian Splatting

TL;DR

This paper introduces ELMGS, a method that improves the scalability of 3D Gaussian Splatting models by using pruning and compression techniques, enabling efficient deployment on limited hardware.

Contribution

The paper presents a novel approach combining iterative pruning and differentiable quantization to enhance memory and computation scalability of 3D Gaussian Splatting models.

Findings

Effective reduction in model size and computation

Maintains high-quality 3D model representations

Enables deployment on resource-constrained devices

Abstract

3D models have recently been popularized by the potentiality of end-to-end training offered first by Neural Radiance Fields and most recently by 3D Gaussian Splatting models. The latter has the big advantage of naturally providing fast training convergence and high editability. However, as the research around these is still in its infancy, there is still a gap in the literature regarding the model's scalability. In this work, we propose an approach enabling both memory and computation scalability of such models. More specifically, we propose an iterative pruning strategy that removes redundant information encoded in the model. We also enhance compressibility for the model by including in the optimization strategy a differentiable quantization and entropy coding estimator. Our results on popular benchmarks showcase the effectiveness of the proposed approach and open the road to the broad deployability of such a solution even on resource-constrained devices.