I3DGS: Improve 3D Gaussian Splatting from Multiple Dimensions

TL;DR

This paper introduces I3DS, a comprehensive evaluation and optimization framework for 3D Gaussian Splatting, significantly enhancing its training efficiency and performance through extensive experiments and novel compression techniques.

Contribution

The paper presents a systematic evaluation method and novel compression techniques to improve 3D Gaussian Splatting's efficiency and performance.

Findings

I3DS achieves notable performance improvements over previous methods.

Extensive experiments reveal key factors affecting training efficiency.

Novel compression methods reduce data size and improve processing speed.

Abstract

3D Gaussian Splatting is a novel method for 3D view synthesis, which can gain an implicit neural learning rendering result than the traditional neural rendering technology but keep the more high-definition fast rendering speed. But it is still difficult to achieve a fast enough efficiency on 3D Gaussian Splatting for the practical applications. To Address this issue, we propose the I3DS, a synthetic model performance improvement evaluation solution and experiments test. From multiple and important levels or dimensions of the original 3D Gaussian Splatting, we made more than two thousand various kinds of experiments to test how the selected different items and components can make an impact on the training efficiency of the 3D Gaussian Splatting model. In this paper, we will share abundant and meaningful experiences and methods about how to improve the training, performance and the impacts caused by different items of the model. A special but normal Integer compression in base 95 and a floating-point compression in base 94 with ASCII encoding and decoding mechanism is presented. Many real and effective experiments and test results or phenomena will be recorded. After a series of reasonable fine-tuning, I3DS can gain excellent performance improvements than the previous one. The project code is available as open source.