Trimming the Fat: Efficient Compression of 3D Gaussian Splats through Pruning


Muhammad Salman Ali (Kyung Hee University), Maryam Qamar (Kyung Hee University), Sung-Ho Bae (Kyung Hee University), Enzo Tartaglione (Institut Polytechnique de Paris)
The 35th British Machine Vision Conference
PDFPosterVideo (Right click to download)Supplementary

Abstract

In recent times, the utilization of 3D models has gained traction, owing to the capacity for end-to-end training initially offered by Neural Radiance Fields and more recently by 3D Gaussian Splatting (3DGS) models. The latter holds a significant advantage by inherently easing rapid convergence during training and offering extensive editability. However, despite rapid advancements, the literature still lives in its infancy regarding the scalability of these models. In this study, we take some initial steps in addressing this gap, showing an approach that enables both the memory and computational scalability of such models. Specifically, we propose ``Trimming the fat'', a post-hoc gradient-informed iterative pruning technique to eliminate redundant information encoded in the model. Our experimental findings on widely acknowledged benchmarks attest to the effectiveness of our approach, revealing that up to 75\% of the Gaussians can be removed while maintaining or even improving upon baseline performance. Our approach achieves around 50$\times$ compression while preserving performance similar to the baseline model, and is able to speed-up computation up to 600~FPS. The code can be found at https://github.com/salmanali96/Trimming-the-Fat.

Citation

@inproceedings{Ali_2024_BMVC,
author    = {Muhammad Salman Ali and Maryam Qamar and Sung-Ho Bae and Enzo Tartaglione},
title     = {Trimming the Fat: Efficient Compression of 3D Gaussian Splats through Pruning},
booktitle = {35th British Machine Vision Conference 2024, {BMVC} 2024, Glasgow, UK, November 25-28, 2024},
publisher = {BMVA},
year      = {2024},
url       = {https://papers.bmvc2024.org/0358.pdf}
}


Copyright © 2024 The British Machine Vision Association and Society for Pattern Recognition
The British Machine Vision Conference is organised by The British Machine Vision Association and Society for Pattern Recognition. The Association is a Company limited by guarantee, No.2543446, and a non-profit-making body, registered in England and Wales as Charity No.1002307 (Registered Office: Dept. of Computer Science, Durham University, South Road, Durham, DH1 3LE, UK).

Imprint | Data Protection