MoDGS：基于深度先验的随意捕获单目视频动态高斯溅射
MoDGS: Dynamic Gaussian Splatting from Casually-captured Monocular Videos with Depth Priors
AI总结：
MoDGS是基于深度先验的新管线，通过3D感知初始化与鲁棒深度损失，从相机静止或缓慢移动的随意单目视频渲染高质量动态场景新视角，性能优于现有方法且代码公开。
摘要：
本文提出MoDGS，一种从随意捕获的单目视频渲染动态场景新视角的新管线。先前的单目动态神经辐射场（NeRF）或高斯溅射（Gaussian Splatting）方法严重依赖输入相机的快速移动来构建多视图一致性，但难以在相机静止或缓慢移动的随意捕获输入视频上重建动态场景。为解决这一具有挑战性的任务，MoDGS采用最新的单视图深度估计方法指导动态场景的学习，提出一种新颖的3D感知初始化方法来学习合理的变形场，还提出一种新的鲁棒深度损失来指导动态场景几何的学习。综合实验表明，MoDGS仅从一段随意捕获的单目视频就能渲染出高质量的动态场景新视角图像，其性能大幅优于最先进的方法，代码现已公开。
英文摘要：
In this paper, we propose MoDGS, a new pipeline to render novel views of dy namic scenes from a casually captured monocular video. Previous monocular dynamic NeRF or Gaussian Splatting methods strongly rely on the rapid move ment of input cameras to construct multiview consistency but struggle to recon struct dynamic scenes on casually captured input videos whose cameras are either static or move slowly. To address this challenging task, MoDGS adopts recent single-view depth estimation methods to guide the learning of the dynamic scene. Then, a novel 3D-aware initialization method is proposed to learn a reasonable deformation field and a new robust depth loss is proposed to guide the learning of dynamic scene geometry. Comprehensive experiments demonstrate that MoDGS is able to render high-quality novel view images of dynamic scenes from just a casually captured monocular video, which outperforms state-of-the-art meth ods by a significant margin. The code is publicly available now.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
