  Dynamic Neural Radiance Field (NeRF) from monocular videos has recently been
explored for space-time novel view synthesis and achieved excellent results.
However, defocus blur caused by depth variation often occurs in video capture,
compromising the quality of dynamic reconstruction because the lack of sharp
details interferes with modeling temporal consistency between input views. To
tackle this issue, we propose D2RF, the first dynamic NeRF method designed to
restore sharp novel views from defocused monocular videos. We introduce layered
Depth-of-Field (DoF) volume rendering to model the defocus blur and reconstruct
a sharp NeRF supervised by defocused views. The blur model is inspired by the
connection between DoF rendering and volume rendering. The opacity in volume
rendering aligns with the layer visibility in DoF rendering. To execute the
blurring, we modify the layered blur kernel to the ray-based kernel and employ
an optimized sparse kernel to gather the input rays efficiently and render the
optimized rays with our layered DoF volume rendering. We synthesize a dataset
with defocused dynamic scenes for our task, and extensive experiments on our
dataset show that our method outperforms existing approaches in synthesizing
all-in-focus novel views from defocus blur while maintaining spatial-temporal
consistency in the scene.
