Reducing the Memory Footprint of 3D Gaussian Splatting (2406.17074v1)

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.

• The paper introduces methods to remove redundant 3D Gaussian primitives and adapt spherical harmonics based on resolution requirements.
• The paper employs codebook-based quantization and half-float representation, achieving a 27-fold memory reduction and a 1.7x speed boost.
• The paper demonstrates near-lossless visual quality improvements with practical benefits for real-time novel view synthesis and streaming applications.

Analyzing and Reducing the Memory Footprint of 3D Gaussian Splatting

The paper "Reducing the Memory Footprint of 3D Gaussian Splatting" authored by Panagiotis Papantonakis et al. addresses significant concerns about the memory requirements of 3D Gaussian Splatting (3DGS) for novel view synthesis (NVS). The research provides optimized methodologies to reduce memory consumption while maintaining visual quality and rendering speed. This essay will provide an in-depth overview of the key techniques proposed, the numerical results achieved, and potential future developments based on the research findings.

Key Techniques for Memory Reduction

The authors present a comprehensive analysis of the factors contributing to the large memory footprint in 3DGS and introduce three main strategies for memory optimization:

1. Scale- and Resolution-aware Redundant Primitive Removal: The authors identify that 3DGS often creates an overly dense set of 3D Gaussian primitives, leading to excessive memory usage. They introduce a redundancy scoring system that estimates spatial redundancy based on the highest resolution observed from the viewpoints. This method involves counting the number of overlapping primitives in a defined spherical region and applying a redundancy score.
2. Adaptive Adjustment of Spherical Harmonics (SH) Bands: SH coefficients account for a significant portion of the memory usage in 3DGS. The authors suggest an adaptive reduction in the number of SH bands, where only necessary bands are utilized based on the evaluated requirement of representing view-dependent material appearance. This is achieved by analyzing color variance and evaluating SH functions from all input views.
3. Codebook-based Quantization and Half-Float Representation: Lastly, the paper introduces clustering-based quantization for certain primitive attributes and the use of half-float precision for storage. These approaches significantly reduce the dynamic range and precision requirements without substantial quality degradation.

Numerical Results and Evaluations

The proposed methods were rigorously evaluated across various datasets, showcasing impressive reductions in memory footprint and improvements in rendering efficiency. The primary outcomes include:

• Memory Reduction: The authors achieve an approximate 27-fold decrease in memory usage, reducing overall representation sizes from the original range of 700 MB to 1.2 GB down to around 29 MB.
• Rendering Speed: The rendering speed is improved by 1.7 times, making the method more suitable for real-time applications.
• Visual Quality: The impact on visual quality is minimal with PSNR differences barely noticeable, ranging between a minor loss of -0.32 dB to a minimal gain of +0.16 dB. This demonstrates the effectiveness of the proposed memory reduction techniques without compromising the visual output.

Practical and Theoretical Implications

The practical implications of this research are profound. By reducing the memory footprint and increasing the rendering speed, 3DGS becomes more applicable to a wider range of devices, particularly mobile and embedded systems. The method's implementation in a WebGL framework highlighted a ∼24-fold improvement in download times and a significant increase in rendering speed, making it feasible for streaming applications.

The theoretical contributions provide new insights into the optimization of complex 3D representations for NVS. The introduction of redundancy-aware pruning, adaptive SH band utilization, and quantization techniques adds significant value to the ongoing research in efficient 3D representation and rendering.

Future Developments

The paper opens several avenues for future research. One potential direction is further reducing the number of primitives needed by refining initial densification strategies. Employing data-driven priors or supervision, such as depth information, may enhance initial point cloud sparsity handling. Future works could also explore more integrated quantization methods directly within the rendering pipeline, enhancing efficiency further.

Overall, the contributions by Papantonakis et al. are critical steps towards making 3DGS a more practical solution for real-time NVS applications across a variety of platforms. The proposed techniques for reducing memory footprint while sustaining high rendering quality and speed are promising advancements in the field of computer graphics and 3D representation.