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EfficientGS: Streamlining Gaussian Splatting for Large-Scale High-Resolution Scene Representation

Published 19 Apr 2024 in cs.CV | (2404.12777v1)

Abstract: In the domain of 3D scene representation, 3D Gaussian Splatting (3DGS) has emerged as a pivotal technology. However, its application to large-scale, high-resolution scenes (exceeding 4k$\times$4k pixels) is hindered by the excessive computational requirements for managing a large number of Gaussians. Addressing this, we introduce 'EfficientGS', an advanced approach that optimizes 3DGS for high-resolution, large-scale scenes. We analyze the densification process in 3DGS and identify areas of Gaussian over-proliferation. We propose a selective strategy, limiting Gaussian increase to key primitives, thereby enhancing the representational efficiency. Additionally, we develop a pruning mechanism to remove redundant Gaussians, those that are merely auxiliary to adjacent ones. For further enhancement, we integrate a sparse order increment for Spherical Harmonics (SH), designed to alleviate storage constraints and reduce training overhead. Our empirical evaluations, conducted on a range of datasets including extensive 4K+ aerial images, demonstrate that 'EfficientGS' not only expedites training and rendering times but also achieves this with a model size approximately tenfold smaller than conventional 3DGS while maintaining high rendering fidelity.

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Citations (6)
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Summary

  • The paper introduces selective densification and pruning to streamline high-resolution 3D scene Gaussian splatting, reducing computational overhead.
  • It employs an adaptive sparse order increment for spherical harmonics to dynamically manage color disparities and optimize rendering.
  • Empirical tests show a tenfold reduction in model size while preserving visual fidelity in large-scale scene representations.

Efficient Gaussian Splatting for Large-Scale High-Resolution 3D Scene Representation

Introduction

In the specialized domain of large-scale 3D scene representation, improving computational efficiency while maintaining high fidelity is a critical challenge. This paper introduces 'EfficientGS', a refined adaptation of 3D Gaussian Splatting (3DGS) tailored to address the inefficiencies associated with traditional 3DGS methods when applied to high-resolution scenes.

Optimization of 3D Gaussian Splatting

3DGS has been proven effective for rendering, leveraging the properties of 3D Gaussians initialized from sparse Structure-From-Motion (SFM) points. However, its scalability to larger and higher-resolution datasets, particularly those exceeding 4k resolutions, has been limited due to increased computational demands.

To tackle these limitations, the paper proposes:

  1. Selective Gaussian Densification: This approach focuses on enhancing only key Gaussians that have not reached a steady state, thus avoiding unnecessary proliferation of Gaussians and reducing computational overhead.
  2. Gaussian Pruning: After densification, this mechanism removes Gaussians that contribute minimally to the visual output, focusing computational resources on Gaussians that have a significant impact on the scene representation.
  3. Sparse Order Increment for Spherical Harmonics (SH): To further optimize the model, the paper introduces a method to manage the SH order dynamically based on the observed color disparity from different angles, reducing the memory and computational costs.

Methodological Advancements

Selective Gaussian Densification

The introduced method enhances only those Gaussians that do not exhibit steady-state behavior. By focusing on these particular Gaussians and limiting unnecessary Gaussian replication, the process aims to maintain rendering quality without incurring additional computational costs typically associated with higher resolution datasets.

Pruning Redundant Gaussians

The pruning strategy is aimed at eliminating redundant Gaussians after densification. By identifying and retaining only those Gaussians that significantly affect the scene's appearance, this method reduces the model's complexity and ensures efficient rendering and storage.

Sparse Order Implementation for SH

EfficientGS introduces a novel approach by applying a incremental strategy for SH during training. This reduces the overhead by only incrementing the SH order where it is needed, based on assessing visual discrepancies from multiple viewing angles.

Empirical Evaluations

Empirical evaluations demonstrate a substantial reduction in the number of Gaussians required for scene representation when using EfficientGS, resulting in markedly faster training and rendering times for high-resolution, large-scale datasets. This optimization translates into a tenfold decrease in model size compared to conventional 3DGS methods, without compromising rendering fidelity.

Conclusion and Future Directions

Overall, EfficientGS presents a significantly optimized method for 3D Gaussian Splatting, particularly suited to large-scale, high-resolution scenes. The selective focus on key primitives, efficient pruning of Gaussians, and adaptive handling of Spherical Harmonics collectively enhance the computational and storage efficiency of 3D scene representation technologies.

Future developments could explore deeper integration with neural rendering techniques and further refinement of the spherical harmonics management to adaptively optimize based on scene complexity and lighting variations, potentially extending these methods to real-time applications in virtual reality and digital twinning.

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