高斯即所需：跨场景尺度的紧凑高斯泼溅

中文摘要 AI 辅助

3D高斯泼溅从一组称为捕获的带位姿照片中，将场景重建为一系列高斯原语的集合。用于表示场景的原语数量影响重建质量、存储和渲染成本。如何在不同捕获尺度下自动选择该数量仍未解决：在标准基准上有效的配置可能使较大的捕获因高斯数量过少而无法重建精细细节。我们观察到，要表示的表面（由捕获的范围和分辨率决定）在训练前已知，而其内容复杂度在训练过程中通过训练视图上的重建质量才变得明显。我们提出TangoGS，它结合了基于捕获的模型规模设定与基于训练的适应性调整：捕获决定模型的尺度，训练反馈决定该尺度内的最终规模。在训练前，TangoGS在扣除重新观察相同场景点的视图后，从捕获的总像素中推导出模型增长的学额。在训练过程中，重建质量指导添加和移除多少高斯。在13个标准基准场景上，TangoGS以比最佳评估基线LeGS少48%的高斯数量，匹配其平均PSNR。在八个大型捕获上，相同配置在必要时自动扩展到更大模型，在评估方法中实现最高平均PSNR：比亚军高0.54 dB，同时使用2.3倍的高斯数量。总之，基于捕获的学额和基于训练质量引导的密度控制，实现了跨场景尺度的最先进质量-规模折衷，无需重新调参。

英文摘要

3D Gaussian Splatting reconstructs a scene as a collection of Gaussian primitives from a set of posed photographs called the capture. The number of primitives used to represent the scene affects reconstruction quality, storage, and rendering cost. How to select this number automatically across capture scales remains unresolved: configurations effective on standard benchmarks can leave larger captures with too few Gaussians to reconstruct fine details. We observe that the surface to represent, given by the capture's extent and resolution, is known before training, whereas its content complexity becomes apparent during training, through the reconstruction quality on the training views. We introduce TangoGS, which combines capture-derived model sizing with training-based adaptation: the capture determines the scale of the model, and training feedback determines its final size within that scale. Before training, TangoGS derives a learning allowance for model growth from the capture's total pixels after discounting views that re-observe the same scene points. During training, reconstruction quality guides how many Gaussians to add and remove. On 13 standard benchmark scenes, TangoGS matches the mean PSNR of the best-performing evaluated baseline, LeGS, with $48\%$ fewer Gaussians. On eight large captures, the same configuration automatically scales to larger models when necessary, achieving the highest mean PSNR among evaluated methods: $0.54$ dB above the runner-up with $2.3\times$ as many Gaussians. Together, capture-derived learning allowances and training-quality guided density control enable a state-of-the-art quality--size compromise across scene scales without retuning.

发表机构

• ETH Zürich（苏黎世联邦理工学院）

机构由 AI 辅助整理，请以论文原文为准。