Implicit neural representation has opened up new avenues for dynamic scene
reconstruction and rendering. Nonetheless, state-of-the-art methods of dynamic
neural rendering rely heavily on these implicit representations, which
frequently struggle with accurately capturing the intricate details of objects
in the scene. Furthermore, implicit methods struggle to achieve real-time
rendering in general dynamic scenes, limiting their use in a wide range of
tasks. To address the issues, we propose a deformable 3D Gaussians Splatting
method that reconstructs scenes using explicit 3D Gaussians and learns
Gaussians in canonical space with a deformation field to model monocular
dynamic scenes. We also introduced a smoothing training mechanism with no extra
overhead to mitigate the impact of inaccurate poses in real datasets on the
smoothness of time interpolation tasks. Through differential gaussian
rasterization, the deformable 3D Gaussians not only achieve higher rendering
quality but also real-time rendering speed. Experiments show that our method
outperforms existing methods significantly in terms of both rendering quality
and speed, making it well-suited for tasks such as novel-view synthesis, time
synthesis, and real-time rendering.

提出了一个使用可变形的 3D 高斯散射方法对动态场景进行重建和渲染，在实时渲染的速度和渲染质量方面优于现有方法。