NeuralGS: Bridging Neural Fields and 3D Gaussian Splatting for Compact 3D Representations (2503.23162v1)

Abstract: 3D Gaussian Splatting (3DGS) demonstrates superior quality and rendering speed, but with millions of 3D Gaussians and significant storage and transmission costs. Recent 3DGS compression methods mainly concentrate on compressing Scaffold-GS, achieving impressive performance but with an additional voxel structure and a complex encoding and quantization strategy. In this paper, we aim to develop a simple yet effective method called NeuralGS that explores in another way to compress the original 3DGS into a compact representation without the voxel structure and complex quantization strategies. Our observation is that neural fields like NeRF can represent complex 3D scenes with Multi-Layer Perceptron (MLP) neural networks using only a few megabytes. Thus, NeuralGS effectively adopts the neural field representation to encode the attributes of 3D Gaussians with MLPs, only requiring a small storage size even for a large-scale scene. To achieve this, we adopt a clustering strategy and fit the Gaussians with different tiny MLPs for each cluster, based on importance scores of Gaussians as fitting weights. We experiment on multiple datasets, achieving a 45-times average model size reduction without harming the visual quality. The compression performance of our method on original 3DGS is comparable to the dedicated Scaffold-GS-based compression methods, which demonstrate the huge potential of directly compressing original 3DGS with neural fields.

NeuralGS: Compact 3D Representations through Neural Fields and Gaussian Splatting

The paper, "NeuralGS: Bridging Neural Fields and 3D Gaussian Splatting for Compact 3D Representations," presents a novel approach that integrates neural fields with 3D Gaussian splatting to create efficient and compact representations of 3D scenes. The work proposes a method, termed NeuralGS, which addresses the challenges of compressing 3D Gaussian Splating (3DGS) models while maintaining rendering quality.

Methodology

The authors introduce a pruning strategy where 40% of redundant Gaussians are removed, reducing the total number without significantly affecting the quality. Gaussian attributes undergo conversion to half-precision, facilitating a reduction in model size. For processing, the scene is divided into clusters, with the number of clusters $K$ dependent on the scale of the subject—ranging from 6-10 for small objects to 100-140 for outdoor scenes. A tiny MLP is assigned to each cluster and optimized over 60,000 iterations to fit the Gaussian attributes. This setup employs a 5-layer MLP with Tanh activations and a 10-level positional encoding, tightly controlling the dimensionality related to opacity, scale, rotation, color, and spherical harmonics. After the initial fitting, further fine-tuning is implemented over 25,000 iterations using the Adam optimizer.

Results

In empirical tests, the NeuralGS model demonstrates performance that is comparable to existing methods like ScaffoldGS while offering significant improvements in scalability and application breadth. Render quality, as analyzed using quantitative metrics such as PSNR, SSIM, and LPIPS, shows NeuralGS achieving high fidelity with reduced storage requirements. Specifically, tables demonstrate that NeuralGS provides competent figures, with PSNR and SSIM results often near or surpassing existing methods within designated storage parameters (e.g., 16.90MB on Mip-NeRF 360 and 12.98MB on Deep Blending datasets).

Ablation Studies

The paper includes comprehensive ablation studies to delineate the impact of each methodological component, including cluster-based fitting, importance weighting, and frequency loss. Tests indicate a notable improvement in PSNR by introducing frequency loss alone, highlighting the method's significance. Pruning is emphasized as reducing the number of Gaussians enhances fitting precision and therefore model compactness without degrading rendering quality.

Implications

NeuralGS possesses both theoretical and practical implications. By directly combining the simplicity and scalability of 3DGS with the compact neural representations of neural fields, it proposes a flexible model that can be used in various applications, from virtual reality to efficient remote sensing. The provision for public code release suggests a commitment to transparency and community engagement, which could accelerate further refinement and application experimentation.

Future Directions

For future developments, exploring the integration of NeuralGS with other emerging data compression tactics, such as vector quantization, could enhance flexibility and efficiency. Additionally, extending the methodology to function seamlessly with real-time environments could offer significant improvements for interactive applications.

In summary, NeuralGS illustrates how the fusion of neural techniques with established 3D representation systems can achieve substantial compression alongside high-quality rendering, setting the stage for further advancements and practical applications in the field of 3D scene processing.