  Neural Radiance Fields (NeRF) has received much attention recently due to its
impressive capability to represent 3D scene and synthesize novel view images.
Existing works usually assume that the input images are captured by a global
shutter camera. Thus, rolling shutter (RS) images cannot be trivially applied
to an off-the-shelf NeRF algorithm for novel view synthesis. Rolling shutter
effect would also affect the accuracy of the camera pose estimation (e.g. via
COLMAP), which further prevents the success of NeRF algorithm with RS images.
In this paper, we propose Unrolling Shutter Bundle Adjusted Neural Radiance
Fields (USB-NeRF). USB-NeRF is able to correct rolling shutter distortions and
recover accurate camera motion trajectory simultaneously under the framework of
NeRF, by modeling the physical image formation process of a RS camera.
Experimental results demonstrate that USB-NeRF achieves better performance
compared to prior works, in terms of RS effect removal, novel view image
synthesis as well as camera motion estimation. Furthermore, our algorithm can
also be used to recover high-fidelity high frame-rate global shutter video from
a sequence of RS images.
