CompGS: Smaller and Faster Gaussian Splatting with Vector Quantization (2311.18159v3)

Abstract: 3D Gaussian Splatting (3DGS) is a new method for modeling and rendering 3D radiance fields that achieves much faster learning and rendering time compared to SOTA NeRF methods. However, it comes with a drawback in the much larger storage demand compared to NeRF methods since it needs to store the parameters for several 3D Gaussians. We notice that many Gaussians may share similar parameters, so we introduce a simple vector quantization method based on K-means to quantize the Gaussian parameters while optimizing them. Then, we store the small codebook along with the index of the code for each Gaussian. We compress the indices further by sorting them and using a method similar to run-length encoding. Moreover, we use a simple regularizer to encourage zero opacity (invisible Gaussians) to reduce the storage and rendering time by a large factor through reducing the number of Gaussians. We do extensive experiments on standard benchmarks as well as an existing 3D dataset that is an order of magnitude larger than the standard benchmarks used in this field. We show that our simple yet effective method can reduce the storage cost for 3DGS by 40 to 50x and rendering time by 2 to 3x with a very small drop in the quality of rendered images.

• The paper presents a novel vector quantization approach that compresses 3D Gaussian Splatting models to reduce storage requirements by 20 times with minimal image quality loss.
• It employs K-means clustering and run-length encoding during training to efficiently quantize similar Gaussian parameters while maintaining real-time rendering speed.
• Experimental results on standard benchmarks and ARKit datasets validate that the method preserves SSIM, PSNR, and LPIPS metrics, enhancing practical AR/VR applications.

Evaluation of "Compact3D: Compressing Gaussian Splat Radiance Field Models with Vector Quantization"

The paper "Compact3D: Compressing Gaussian Splat Radiance Field Models with Vector Quantization" introduces a novel technique for compressing 3D Gaussian Splatting (3DGS) models used for 3D radiance field modeling. This work addresses the significant storage overhead associated with 3DGS, which, while being faster in learning and rendering than NeRF-based methods, demands large storage capacities due to the requirement of storing parameters for several 3D Gaussians.

Overview of Gaussian Splat Radiance Fields

3D Gaussian Splatting offers a different paradigm from the neural radiance fields (NeRF) approaches, focusing on real-time rendering capabilities. It gains an edge over NeRF by allowing real-time scene rendering through a collection of 3D Gaussian shapes characterized by several parameters such as 3D position, color, opacity, and spherical harmonics. Despite these advantages in speed and simplicity, the large parameter set required by 3DGS becomes a bottleneck, necessitating an effective compression technique.

Compression Methodology

The authors propose a vector quantization method using K-means clustering to efficiently compress the Gaussian parameters. This approach capitalizes on the observation that many Gaussians exhibit similar parameter values. The main idea involves quantizing these common parameters, drastically reducing the storage footprint by representing them through a compact codebook and index references rather than storing each set individually.

The paper delineates a detailed method of applying vector quantization during the training phase, emphasizing the use of a straight-through estimator for updating non-quantized parameters to mitigate the learning overhead. The authors compress the stored model further by sorting the Gaussian indices and using run-length encoding, thus optimizing the memory usage during inference and maintaining rendering speed.

Experimental Results

Through extensive experiments across multiple standard and larger benchmarks, such as a newly proposed dataset with 200 scenes derived from the ARKit indoor dataset, the paper demonstrates that this compression technique can reduce the storage requirement by 20 times with minimal impact on image quality. The numerical results underline minimal degradation in metrics such as SSIM, PSNR, and LPIPS when compared to the original 3DGS models, while achieving compression levels akin to NeRF models.

Implications and Future Prospects

The implications of this research are significant, especially for real-time applications involving augmented reality (AR) and virtual reality (VR), where storage efficiency and rendering speed are crucial. The proposed method bridges the gap between high fidelity rendering and practical deployment constraints, making Gaussian Splatting more viable for use in resource-constrained environments like edge devices and AR/VR headsets.

Looking ahead, this paper could stimulate further research into optimizing storage and compute efficiency in 3D radiance field modeling, potentially leading to more sophisticated quantization techniques or alternative modeling paradigms that can provide better trade-offs between model size and rendering performance. Additionally, the introduction of larger-scale benchmarks could drive the development of more robust and scalable methods that go beyond the current limitations of scene complexity and size in 3D modeling.

Conclusion

In conclusion, the "Compact3D" compression technique offers a promising advancement in the field of 3D radiance field modeling, combining the speed of Gaussian Splatting with significant reductions in storage demands. This research marks an important step toward making complex 3D scene rendering more accessible and feasible in real-world applications. Such innovations not only enhance current capabilities but also lay the groundwork for future advancements in efficient 3D modeling techniques.