Compact 3D Scene Representation via Self-Organizing Gaussian Grids (2312.13299v2)

Abstract: 3D Gaussian Splatting has recently emerged as a highly promising technique for modeling of static 3D scenes. In contrast to Neural Radiance Fields, it utilizes efficient rasterization allowing for very fast rendering at high-quality. However, the storage size is significantly higher, which hinders practical deployment, e.g. on resource constrained devices. In this paper, we introduce a compact scene representation organizing the parameters of 3D Gaussian Splatting (3DGS) into a 2D grid with local homogeneity, ensuring a drastic reduction in storage requirements without compromising visual quality during rendering. Central to our idea is the explicit exploitation of perceptual redundancies present in natural scenes. In essence, the inherent nature of a scene allows for numerous permutations of Gaussian parameters to equivalently represent it. To this end, we propose a novel highly parallel algorithm that regularly arranges the high-dimensional Gaussian parameters into a 2D grid while preserving their neighborhood structure. During training, we further enforce local smoothness between the sorted parameters in the grid. The uncompressed Gaussians use the same structure as 3DGS, ensuring a seamless integration with established renderers. Our method achieves a reduction factor of 17x to 42x in size for complex scenes with no increase in training time, marking a substantial leap forward in the domain of 3D scene distribution and consumption. Additional information can be found on our project page: https://fraunhoferhhi.github.io/Self-Organizing-Gaussians/

• The paper introduces a novel encoding method that organizes 3D Gaussian parameters into a structured 2D grid to significantly compress scene data.
• It employs a highly parallel GPU-based sorting algorithm to exploit perceptual redundancies, achieving storage reductions of 8x to 26x without slowing rendering.
• Smoothness regularization during training maintains local neighborhood consistency, ensuring the compressed representation remains robust for real-time applications.

Compact 3D Scene Representation via Self-Organizing Gaussian Grids

The paper "Compact 3D Scene Representation via Self-Organizing Gaussian Grids" presents an innovative method for enhancing the storage efficiency of 3D Gaussian Splatting (3DGS) in static 3D scene rendering. This method addresses the significant storage demands of 3DGS, which, while efficient in rendering speed and quality compared to Neural Radiance Fields (NeRFs), suffer from large memory footprints due to the unorganized storage of millions of Gaussian parameters.

Summary of Contributions

The researchers propose a novel strategy to effectively compress 3DGS through a highly structured arrangement and encoding of Gaussian parameters in a 2D grid. This approach exploits perceptual redundancies in natural scenes, allowing for significant reductions in storage size without deteriorating the rendering quality. Key components of this strategy include:

1. Compact Scene Representation: Organizing 3DGS parameters into a 2D grid enhances data homogeneity and facilitates compression. This procedure reduces the size an order of magnitude beyond typical methods, by keeping a balance between the sorting quality and storage format optimization.
2. Highly Parallel Sorting Algorithm: The authors introduce an efficient sorting algorithm executed on GPUs, capable of structuring millions of Gaussian parameters in parallel. This algorithm arranges Gaussians into a grid that maximizes local smoothness and redundancy, crucial for effective storage compression.
3. Smoothness Regularization: During the 3DGS scene training, an additional smoothness loss is incorporated to maintain the sorted structure's local neighborhood properties. This smoothness encourages similar neighboring Gaussian attributes, benefiting the compression process.
4. Storage Efficiency: The method achieves a reduction factor of 8x to 26x in storage size for complex 3D scenes. Such efficiency is obtained without extending the training time, maintaining the fast rendering characteristic inherent to 3DGS.

Implications and Future Directions

The paper's contributions have tangible practical and theoretical implications in fields reliant on compact data representation and real-time 3D rendering. On a practical level, the enhanced compression enables deployment on resource-constrained devices, thus broadening the applicability of 3DGS in environments with limited computational resources, such as embedded systems or web applications. The seamless integration with existing renderers furthers its practical viability.

Theoretically, this work contributes to ongoing discussions in computer vision regarding efficient data encoding and representation strategies. The method aligns with a potential shift towards leveraging local perceptual redundancies as a core principle for data reduction, a principle that can extend beyond 3D scene representation to other domains where data efficiency is paramount.

Looking forward, the paper hints at future work exploring temporal dependencies for dynamic scenes and investigating more efficient representations of other model attributes, like spherical harmonics, to further exploit the characteristics of 3DGS. As AI and machine learning progress, such explorations could inform new models and algorithms, enhancing the compression further and harnessing real-time rendering capabilities.

Overall, the paper presents a compelling approach to managing the extensive data typically associated with high-quality 3D scene rendering, positioning itself as a valuable resource in advancing both theoretical frameworks and practical applications within 3D visualization technologies.