通过频域渐进优化，用更少高斯点实现高质量3D重建。
Optimized 3D Gaussian Splatting using Coarse-to-Fine Image Frequency Modulation
- 先粗后精调节图像频域细节，逐步优化场景表示。
- 高斯点数减少62%，训练显存降40%，时间快20%。
- 自动生成多细节层级，适合移动端部署。
新视角合成领域因3D高斯泼溅（3DGS）而革新，实现了高质量场景重建并支持实时渲染。然而，基于3DGS的方法通常需要大量GPU显存和磁盘存储，限制了其在消费级设备上的应用。本文提出Opti3DGS，一种基于图像频率调制的粗到精优化框架，旨在最小化表示场景所需的高斯原语数量，从而降低内存与存储需求。该方法利用图像频域调制，初始采用粗糙场景表示，并在训练过程中逐步调制图像中的频率细节以精细化表示。在基准3DGS上，平均减少62%的高斯点，训练显存需求降低40%，优化时间缩短20%，且不牺牲视觉质量。此外，本方法可无缝集成至多种3DGS技术中，持续减少高斯点数量，同时保持甚至提升视觉质量。值得注意的是，其优化流程天然产生无额外成本的细节层次（LoD）场景表示。结果与代码将公开共享。
原文摘要 · Abstract (English)
The field of Novel View Synthesis has been revolutionized by 3D Gaussian Splatting (3DGS), which enables high-quality scene reconstruction that can be rendered in real-time. 3DGS-based techniques typically suffer from high GPU memory and disk storage requirements which limits their practical application on consumer-grade devices. We propose Opti3DGS, a novel frequency-modulated coarse-to-fine optimization framework that aims to minimize the number of Gaussian primitives used to represent a scene, thus reducing memory and storage demands. Opti3DGS leverages image frequency modulation, initially enforcing a coarse scene representation and progressively refining it by modulating frequency details in the training images. On the baseline 3DGS, we demonstrate an average reduction of 62% in Gaussians, a 40% reduction in the training GPU memory requirements and a 20% reduction in optimization time without sacrificing the visual quality. Furthermore, we show that our method integrates seamlessly with many 3DGS-based techniques, consistently reducing the number of Gaussian primitives while maintaining, and often improving, visual quality. Additionally, Opti3DGS inherently produces a level-of-detail scene representation at no extra cost, a natural byproduct of the optimization pipeline. Results and code will be made publicly available.
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