FLoD: Integrating Flexible Level of Detail into 3D Gaussian Splatting for Customizable Rendering (2408.12894v2)

Abstract: 3D Gaussian Splatting (3DGS) and its subsequent works are restricted to specific hardware setups, either on only low-cost or on only high-end configurations. Approaches aimed at reducing 3DGS memory usage enable rendering on low-cost GPU but compromise rendering quality, which fails to leverage the hardware capabilities in the case of higher-end GPU. Conversely, methods that enhance rendering quality require high-end GPU with large VRAM, making such methods impractical for lower-end devices with limited memory capacity. Consequently, 3DGS-based works generally assume a single hardware setup and lack the flexibility to adapt to varying hardware constraints. To overcome this limitation, we propose Flexible Level of Detail (FLoD) for 3DGS. FLoD constructs a multi-level 3DGS representation through level-specific 3D scale constraints, where each level independently reconstructs the entire scene with varying detail and GPU memory usage. A level-by-level training strategy is introduced to ensure structural consistency across levels. Furthermore, the multi-level structure of FLoD allows selective rendering of image regions at different detail levels, providing additional memory-efficient rendering options. To our knowledge, among prior works which incorporate the concept of Level of Detail (LoD) with 3DGS, FLoD is the first to follow the core principle of LoD by offering adjustable options for a broad range of GPU settings. Experiments demonstrate that FLoD provides various rendering options with trade-offs between quality and memory usage, enabling real-time rendering under diverse memory constraints. Furthermore, we show that FLoD generalizes to different 3DGS frameworks, indicating its potential for integration into future state-of-the-art developments.

• The paper introduces FLoD, a method that integrates scalable, flexible levels of detail into 3D Gaussian Splatting for high-quality rendering on memory-constrained devices.
• It employs a coarse-to-fine training process with scale constraints and overlap pruning to optimize memory usage while preserving intricate scene details.
• Empirical evaluations on datasets like DL3DV-10K demonstrate superior performance with a PSNR of 31.75 and SSIM of 0.935, outperforming traditional 3DGS approaches.

FLoD: Integrating Flexible Level of Detail into 3D Gaussian Splatting for Customizable Rendering

The paper "FLoD: Integrating Flexible Level of Detail into 3D Gaussian Splatting for Customizable Rendering" by Yunji Seo et al. addresses the inherent memory limitations of 3D Gaussian Splatting (3DGS) in rendering high-fidelity, photorealistic 3D scenes. 3DGS achieves high-quality renderings by utilizing numerous small Gaussians, leading to excessive memory consumption that can be prohibitive for low-cost devices. The goal of this research is to introduce a scalable and flexible solution, termed Flexible Level of Detail (FLoD), that significantly enhances the adaptability of 3DGS-based models without compromising rendering quality.

Introduction and Motivation

Traditional 3DGS models are limited by their high memory requirements, which restricts their deployment on devices with limited GPU memory. Reducing the number of Gaussians to fit within memory constraints typically results in a significant loss of rendering quality. To bridge this gap, the authors propose the integration of LoD concepts within the 3DGS framework. By establishing multiple levels of detail, FLoD allows for customizable rendering quality according to the available hardware capabilities, thereby enabling efficient and real-time rendering across a range of devices from high-end servers to low-cost laptops.

Methodology

Scale Constraint

FLoD introduces a scale constraint $s_\text{min}^{(l)}$ for each level $l$ (where $l \in [1, L_\text{max}]$), which enforces a minimum scale for the Gaussians, ensuring that lower levels represent fewer but larger Gaussians. The scale constraint decreases exponentially across levels, allowing higher levels to capture finer details using smaller Gaussians without an upper scale bound. This approach is formalized as: $$s_\text{min}^{(l)}= \begin{cases} \tau \times \rho^{1-l} & \text{for } 1 \le l < L_\text{max} \ 0 & \text{for } l = L_\text{max} \end{cases}$$ where $\tau$ is the initial scale constraint and $\rho$ is the scale factor.

Level-by-Level Training

The training process for FLoD is performed in a coarse-to-fine manner. The Gaussians at each level are initialized and optimized based on the Gaussians from the preceding level. This method ensures consistency and accuracy in structure across subsequent levels: $$\mathbf{s}^{(l)} = e^{\mathbf{s}_\text{opt} + s_\text{min}^{(l)}}$$ where $\mathbf{s}_\text{opt}$ is the learnable parameter for scale.

Overlap Pruning

To prevent artifacts resulting from excessive overlapping of Gaussians, an overlap pruning mechanism is introduced. Gaussians with average distances to their three nearest neighbors below a pre-defined threshold are removed. This threshold, $d_\text{OP}^{(l)}$, is set as half the scale constraint for the given level.

Results and Analysis

The effectiveness of FLoD is evaluated on multiple datasets, including Tanks and Temples, Mip-NeRF360, and DL3DV-10K. The empirical results demonstrate that FLoD consistently improves rendering quality while allowing flexible memory usage. For instance, on the DL3DV-10K dataset, FLoD achieves a PSNR of 31.75 and SSIM of 0.935, surpassing the performance metrics of existing 3DGS approaches.

Comparative Analysis

Quantitative comparisons with baseline models, such as Octree-GS and Scaffold-GS, indicate that FLoD not only enhances rendering quality but also reduces the number of Gaussians significantly. For instance, in levels 1 to 4, FLoD uses fewer Gaussians compared to Octree-GS, minimizing memory usage and thus enabling rendering on low-memory devices.

Future Implications

The adaptability of FLoD makes it compatible with various 3DGS-based models. This compatibility extends its potential for integration into future state-of-the-art 3D rendering developments. Additionally, by facilitating selective rendering, FLoD ensures that different hardware configurations can efficiently render the same scene without significant loss in detail or quality.

Conclusion

FLoD represents a significant advancement in the field of 3D rendering by integrating flexible levels of detail into 3D Gaussian Splatting. This approach addresses the memory constraints that have traditionally limited the deployment of 3DGS on low-cost devices, thus broadening its applicability. The scale constraint, level-by-level training, and overlap pruning integrate harmoniously to provide high-quality and flexible rendering solutions. Future work may focus on enhancing disk storage efficiency and further optimizing the integration of FLoD with advanced rendering frameworks.