4C4D：四相机四维高斯泼溅
4C4D: 4 Camera 4D Gaussian Splatting
针对稀疏视角下 4D 高斯泼溅几何建模弱于外观建模的失衡问题，提出对高斯不透明度施加神经衰减函数，把梯度引导到几何学习上；做稀疏多视角动态重建或 4DGS 时可直接复用这一正则化思路。
原文依据 we introduce a Neural Decaying Function on Gaussian opacities for enhancing the geometric modeling capability of 4D Gaussians
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论本文研究如何从仅由四台便携相机拍摄的视频中恢复四维动态场景。学习建模场景动态以实现时间一致的新视角渲染是计算机图形学中的一项基础任务，而此前的工作往往需要借助包含数十甚至数百个视角的相机阵列进行密集多视角采集。我们提出 4C4D，一个能够从极稀疏相机的视频采集实现高保真四维高斯泼溅的新框架。我们的关键洞见在于：稀疏设置下的几何学习远比外观建模困难。受此观察驱动，我们引入作用于高斯不透明度的神经衰减函数，以增强四维高斯的几何建模能力。该设计通过促使四维高斯泼溅的梯度更多聚焦于几何学习，缓解了四维高斯泼溅中几何与外观建模之间固有的不平衡。在相机重叠程度各异的稀疏视角数据集上的大量实验表明，4C4D 相较先前方法取得了更优性能。
英文原文摘要 1086 字符
This paper tackles the challenge of recovering 4D dynamic scenes from videos captured by as few as four portable cameras. Learning to model scene dynamics for temporally consistent novel-view rendering is a foundational task in computer graphics, where previous works often require dense multi-view captures using camera arrays of dozens or even hundreds of views. We propose 4C4D, a novel framework that enables high-fidelity 4D Gaussian Splatting from video captures of extremely sparse cameras. Our key insight lies that the geometric learning under sparse settings is substantially more difficult than modeling appearance. Driven by this observation, we introduce a Neural Decaying Function on Gaussian opacities for enhancing the geometric modeling capability of 4D Gaussians. This design mitigates the inherent imbalance between geometry and appearance modeling in 4DGS by encouraging the 4DGS gradients to focus more on geometric learning. Extensive experiments across sparse-view datasets with varying camera overlaps show that 4C4D achieves superior performance over prior art.