  Representing and synthesizing novel views in real-world dynamic scenes from
casual monocular videos is a long-standing problem. Existing solutions
typically approach dynamic scenes by applying geometry techniques or utilizing
temporal information between several adjacent frames without considering the
underlying background distribution in the entire scene or the transmittance
over the ray dimension, limiting their performance on static and occlusion
areas. Our approach $\textbf{D}$istribution-$\textbf{D}$riven neural radiance
fields offers high-quality view synthesis and a 3D solution to
$\textbf{D}$etach the background from the entire $\textbf{D}$ynamic scene,
which is called $\text{D}^4$NeRF. Specifically, it employs a neural
representation to capture the scene distribution in the static background and a
6D-input NeRF to represent dynamic objects, respectively. Each ray sample is
given an additional occlusion weight to indicate the transmittance lying in the
static and dynamic components. We evaluate $\text{D}^4$NeRF on public dynamic
scenes and our urban driving scenes acquired from an autonomous-driving
dataset. Extensive experiments demonstrate that our approach outperforms
previous methods in rendering texture details and motion areas while also
producing a clean static background. Our code will be released at
https://github.com/Luciferbobo/D4NeRF.
