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I3DGS: Improve 3D Gaussian Splatting from Multiple Dimensions

Jinwei Lin

TL;DR

The paper tackles the practical bottleneck of training speed in 3D Gaussian Splatting for high-resolution view synthesis. It introduces I3DS, a synthetic performance evaluation framework that conducts thousands of ablations across color handling, spherical harmonics, background, data sampling, learning-rate schedules, and encoding/decoding strategies. Key findings show that omitting color components during learning can yield up to 5–8x speedups, reducing SH degree from 3 to 0 accelerates training by about 16%, background color tuning can cut CPU/GPU time by roughly 20%, and ASCII-based compression offers additional speed gains around 6%, while adding back color remains challenging. Collectively, these strategies offer practical pathways to real-time, high-fidelity rendering, and the authors provide open-source code to facilitate adoption and further optimization.

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.

I3DGS: Improve 3D Gaussian Splatting from Multiple Dimensions

TL;DR

The paper tackles the practical bottleneck of training speed in 3D Gaussian Splatting for high-resolution view synthesis. It introduces I3DS, a synthetic performance evaluation framework that conducts thousands of ablations across color handling, spherical harmonics, background, data sampling, learning-rate schedules, and encoding/decoding strategies. Key findings show that omitting color components during learning can yield up to 5–8x speedups, reducing SH degree from 3 to 0 accelerates training by about 16%, background color tuning can cut CPU/GPU time by roughly 20%, and ASCII-based compression offers additional speed gains around 6%, while adding back color remains challenging. Collectively, these strategies offer practical pathways to real-time, high-fidelity rendering, and the authors provide open-source code to facilitate adoption and further optimization.

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.
Paper Structure (11 sections, 10 equations, 1 figure, 7 tables)

This paper contains 11 sections, 10 equations, 1 figure, 7 tables.

Table of Contents

  1. Introduction
  2. Background and Related Work
  3. Neural Radiance Field
  4. Explicit and Implicit Research
  5. 3D Gaussian Splatting
  6. Spherical Harmonics Algorithm
  7. Methodology and Evaluation
  8. Color
  9. Learning and Structure
  10. Encoding and Decoding
  11. Conclusion

Figures (1)

  • Figure 1: Running and design architecture of Anywhere.