In this paper, we propose MoDGS, a new pipeline to render novel-view images
in dynamic scenes using only casually captured monocular videos. Previous
monocular dynamic NeRF or Gaussian Splatting methods strongly rely on the rapid
movement of input cameras to construct multiview consistency but fail to
reconstruct dynamic scenes on casually captured input videos whose cameras are
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 baseline
methods by a significant margin.

在本文中，我们提出了 MoDGS，这是一个新的管道，可以利用仅有的随意捕捉的单目视频生成动态场景的新视角图像。MoDGS 采用了最近的单视图深度估计方法来指导动态场景的学习，并提出了一种新的三维感知初始化方法来学习合理的变形场和新的鲁棒深度损失来指导动态场景几何结构的学习。全面的实验证明，MoDGS 能够从仅有的随意捕捉的单目视频中生成高质量的动态场景新视角图像，优于基线方法的效果显著。

MoDGS: 动态高斯分布从偶然捕获的单目视频

MoDGS: Dynamic Gaussian Splatting from Causually-captured Monocular  Videos

Gated cameras flood-illuminate a scene and capture the time-gated impulse
response of a scene. By employing nanosecond-scale gates, existing sensors are
capable of capturing mega-pixel gated images, delivering dense depth improving
on today's LiDAR sensors in spatial resolution and depth precision. Although
gated depth estimation methods deliver a million of depth estimates per frame,
their resolution is still an order below existing RGB imaging methods. In this
work, we combine high-resolution stereo HDR RCCB cameras with gated imaging,
allowing us to exploit depth cues from active gating, multi-view RGB and
multi-view NIR sensing -- multi-view and gated cues across the entire spectrum.
The resulting capture system consists only of low-cost CMOS sensors and
flood-illumination. We propose a novel stereo-depth estimation method that is
capable of exploiting these multi-modal multi-view depth cues, including the
active illumination that is measured by the RCCB camera when removing the
IR-cut filter. The proposed method achieves accurate depth at long ranges,
outperforming the next best existing method by 39% for ranges of 100 to 220m in
MAE on accumulated LiDAR ground-truth. Our code, models and datasets are
available at this https URL .

通过使用带有闸门的相机和高分辨率立体 HDR RCCB 相机，结合深度估计方法，实现了长距离高精度的深度估计。