HackerPapers

arXiv:2406.17074cs.CV

Reducing the Memory Footprint of 3D Gaussian Splatting

Panagiotis Papantonakis, Georgios Kopanas, Bernhard Kerbl, Alexandre Lanvin, George Drettakis

HN submission. Found in a submission link or body. Submitters do not count as commenters in the main ranking; a submission is not an endorsement.

Abstract

3D Gaussian splatting provides excellent visual quality for novel view synthesis, with fast training and real-time rendering; unfortunately, the memory requirements of this method for storing and transmission are unreasonably high. We first analyze the reasons for this, identifying three main areas where storage can be reduced: the number of 3D Gaussian primitives used to represent a scene, the number of coefficients for the spherical harmonics used to represent directional radiance, and the precision required to store Gaussian primitive attributes. We present a solution to each of these issues. First, we propose an efficient, resolution-aware primitive pruning approach, reducing the primitive count by half. Second, we introduce an adaptive adjustment method to choose the number of coefficients used to represent directional radiance for each Gaussian primitive, and finally a codebook-based quantization method, together with a half-float representation for further memory reduction. Taken together, these three components result in a 27 reduction in overall size on disk on the standard datasets we tested, along with a 1.7 speedup in rendering speed. We demonstrate our method on standard datasets and show how our solution results in significantly reduced download times when using the method on a mobile device.

arXiv comments
Project website: https://repo-sam.inria.fr/fungraph/reduced_3dgs/
Journal reference
Proceedings of the ACM on Computer Graphics and Interactive Techniques, Volume 7, Issue 1 Article No.: 16, Pages 1 - 17, 2024
DOI
doi:10.1145/3651282
Subjects
cs.CVComputer Vision and Pattern Recognition

On Hacker News

No comments linking this paper were found in the collected sources. Found in 1 HN submission from Jul 2024 to Jul 2024. Open submission discussions.

Comments linking this paper, per year.

Discussed in comments0 threads

No comments linking this paper were found in the collected sources. Open submission discussions.

Submitted to Hacker News

Connected through shared HN threads, not inferred similarity or a recommendation.