FlexGS: Train Once, Deploy Everywhere with Many-in-One Flexible 3D Gaussian Splatting (2506.04174v1)

Abstract: 3D Gaussian splatting (3DGS) has enabled various applications in 3D scene representation and novel view synthesis due to its efficient rendering capabilities. However, 3DGS demands relatively significant GPU memory, limiting its use on devices with restricted computational resources. Previous approaches have focused on pruning less important Gaussians, effectively compressing 3DGS but often requiring a fine-tuning stage and lacking adaptability for the specific memory needs of different devices. In this work, we present an elastic inference method for 3DGS. Given an input for the desired model size, our method selects and transforms a subset of Gaussians, achieving substantial rendering performance without additional fine-tuning. We introduce a tiny learnable module that controls Gaussian selection based on the input percentage, along with a transformation module that adjusts the selected Gaussians to complement the performance of the reduced model. Comprehensive experiments on ZipNeRF, MipNeRF and Tanks&Temples scenes demonstrate the effectiveness of our approach. Code is available at https://flexgs.github.io.

• The paper introduces an elastic inference method that trains one 3D Gaussian splatting model and deploys it across multiple memory budgets.
• The paper leverages a learnable Gaussian selection module, guided by a Global Importance metric, to adaptively balance rendering quality and memory usage.
• The paper demonstrates notable improvements in PSNR and SSIM on benchmark datasets, validating its approach for AR, VR, and real-time visualization applications.

An Expert Overview of FlexGS: Many-in-One Flexible 3D Gaussian Splatting

The paper "FlexGS: Train Once, Deploy Everywhere with Many-in-One Flexible 3D Gaussian Splatting" introduces a new method for efficiently managing 3D scene representations using an innovative approach called FlexGS. This technique extends upon existing 3D Gaussian Splatting (3DGS) methodologies, offering a mechanism to deploy a trained model across various memory-constrained environments without the need for additional training or fine-tuning.

Key Contributions

1. Elastic Inference: FlexGS innovates by enabling dynamic model compression at inference time. Unlike prior models that required distinct models for different memory budgets, FlexGS uses a single adaptable model. This is achieved through an elastic inference approach that adjusts the number of active Gaussians based on user-specified memory constraints, seamlessly balancing rendering performance and computational cost.
2. Adaptive Gaussian Selection: At the core of FlexGS is a learnable Gaussian selection module, guided by a novel Global Importance (GI) metric. This metric quantifies each Gaussian's significance relative to rendering quality and memory usage based on attributes such as spatial coverage and transmittance. This module boasts efficient Gaussian selection without additional training.
3. Gaussian Transform Field: To further complement the performance of reduced models, FlexGS incorporates a Gaussian Transform Field that learns spatial and geometric adjustments. This adaptation ensures that even with fewer Gaussians, the quality of the rendered scene is maintained across different compression ratios.

Experimental Validation

The applicability of FlexGS has been rigorously tested across several benchmark datasets, including the MipNeRF360, Tanks and Temples, and ZipNeRF scenes. FlexGS demonstrably maintains competitive rendering quality with a fraction of the memory consumption of traditional 3DGS models. Notably, the paper documents significant improvements in PSNR and SSIM metrics across all tested scenarios, particularly at lower Gaussian ratios where compression demands are most stringent.

Implications and Future Directions

The FlexGS framework holds substantial potential for advancing the flexibility and efficiency of 3D scene representations. Its ability to train a model once and deploy it universally across diverse hardware environments mitigates the bottleneck of excessive GPU requirements typical of existing methods. This approach could revolutionize applications in augmented and virtual reality, gaming, and real-time visualizations, where memory constraints and rendering speed are crucial.

Looking forward, FlexGS may inspire further research into generative inference techniques applicable to other neural representation fields, thereby fostering developments in adaptive neural architectures. The integration of real-time feedback mechanisms could enhance the adaptability of models deployed in heterogeneous environments beyond the scope studied in the present framework.

In conclusion, the FlexGS methodology presents a compelling contribution to the evolving landscape of 3D Gaussian splatting, demonstrating how train-once-deploy-everywhere principles can be practically achieved with promising results. It stands as an essential reference for researchers seeking efficient, scalable solutions in 3D representation and rendering.