ELMGS: Enhancing memory and computation scaLability through coMpression for 3D Gaussian Splatting (2410.23213v1)

Abstract: 3D models have recently been popularized by the potentiality of end-to-end training offered first by Neural Radiance Fields and most recently by 3D Gaussian Splatting models. The latter has the big advantage of naturally providing fast training convergence and high editability. However, as the research around these is still in its infancy, there is still a gap in the literature regarding the model's scalability. In this work, we propose an approach enabling both memory and computation scalability of such models. More specifically, we propose an iterative pruning strategy that removes redundant information encoded in the model. We also enhance compressibility for the model by including in the optimization strategy a differentiable quantization and entropy coding estimator. Our results on popular benchmarks showcase the effectiveness of the proposed approach and open the road to the broad deployability of such a solution even on resource-constrained devices.

• The paper introduces ELMGS with novel pruning and quantization strategies that significantly reduce memory usage and computation time.
• It leverages gradient and opacity aware pruning combined with quantization-aware training to achieve up to 38× compression with minimal performance degradation.
• Entropy coding with Morton ordering further optimizes storage and nearly triples rendering speeds, enabling efficient deployment on low-power devices.

Enhancing Memory and Computation Scalability in 3D Gaussian Splatting: An Overview of ELMGS

The paper "ELMGS: Enhancing Memory and Computation Scalability through Compression for 3D Gaussian Splatting" introduces a novel methodology aimed at improving the scalability of 3D Gaussian Splatting models through compression techniques. This work addresses the challenges associated with the memory and computational demands of 3D Gaussian Splatting, offering a solution that enables these models to be more efficiently deployed, even on devices with limited resources.

Key Contributions

The paper articulates several key contributions to the field:

1. Gradient and Opacity Aware Pruning (GAP): The paper proposes a pruning strategy that leverages the properties of the gradient and opacity of Gaussians in the optimized scene. By quantifying redundancies and removing less significant Gaussians through iterative pruning, the approach decreases memory requirements and enhances computational efficiency.
2. Quantization-Aware Training (QAT): The incorporation of Learned Step Size Quantization (LSQ) allows for a more hardware-compatible compression strategy. This quantization reduces the dimensionality of model parameters with minimal loss of fidelity, significantly easing storage overheads.
3. Entropy Coding with Morton Ordering: Post-quantization, the implementation of entropy coding further compresses the model by exploiting coherence through the spatial arrangement of Gaussians in Morton order, thereby optimizing storage and increasing efficiency.

Experimental Results

The authors evaluated the ELMGS method on multiple datasets, including Mip-NeRF360, Tanks and Temples, and Deep Blending. Noteworthy empirical findings reveal compression rates up to 38× with minor degradation in performance, as measured by PSNR, SSIM, and LPIPS metrics. ELMGS also achieved significant improvements in rendering speed, nearly tripling FPS rates compared to baseline models.

Theoretical and Practical Implications

The proposed framework's implications extend to practical and theoretical domains. Practically, the compression of 3D Gaussian Splatting models is consequential for applications requiring real-time performance, such as AR/VR environments or low-power devices. By addressing storage and computational resource constraints, ELMGS democratizes the deployment of high-quality rendering techniques beyond traditional high-power computing systems.

Theoretically, the approach sets the stage for further exploration into model efficiency improvements, specifically in rendering tasks. The iterative pruning mechanism highlights a path for future developments in adaptive model reduction techniques that maintain high fidelity while decreasing computational burden.

Speculations on Future Developments

Looking forward, the methodology laid out in ELMGS suggests potential extensions such as enhanced pruning algorithms that dynamically adjust during training to preserve performance while further reducing model size. Moreover, fine-tuning quantization methods could offer even more substantial gains in computational efficiency.

Advancing this line of research might also include exploring domain-specific compression strategies that cater to characteristics inherent to varied 3D environments, thus offering tailored solutions that maximize compression efficiency without compromising rendering quality.

In conclusion, the enhancements proposed by the ELMGS framework offer promising developments in the field of 3D scene rendering. These improvements not only facilitate broader applications of Gaussian Splatting models but also provide a foundation for future innovations that continue to balance efficiency with performance. As 3D rendering technology evolves, such methodologies will be pivotal in ensuring scalable, accessible, and efficient rendering solutions.