一个可弹性部署的3D高斯点云方法，一次训练适配多种设备。
FlexGS: Train Once, Deploy Everywhere with Many-in-One Flexible 3D Gaussian Splatting
- 根据目标显存大小动态选择并调整高斯点，无需微调。
- 在10%~50%参数量下仍保持90%以上渲染质量。
- 适合移动端、嵌入式等资源受限场景使用。
3D高斯点云（3DGS）凭借高效渲染能力，广泛应用于三维场景表示与新视角合成。然而，其对显存需求较高，限制了在计算资源受限设备上的应用。此前方法多通过剪枝不重要高斯点实现压缩，但通常需微调且难以适应不同设备的内存需求。本文提出一种弹性推理方法：给定目标模型尺寸后，自动选择并转换一组高斯点，在无需额外微调的情况下实现显著渲染性能。引入一个微型可学习模块，根据输入比例控制高斯点选取，配合变换模块提升压缩后模型的表现。在ZipNeRF、MipNeRF和Tanks&Temples数据集上的实验表明该方法有效。代码已开源。
原文摘要 · Abstract (English)
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.
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