  We propose a novel rolling shutter bundle adjustment method for neural
radiance fields (NeRF), which utilizes the unordered rolling shutter (RS)
images to obtain the implicit 3D representation. Existing NeRF methods suffer
from low-quality images and inaccurate initial camera poses due to the RS
effect in the image, whereas, the previous method that incorporates the RS into
NeRF requires strict sequential data input, limiting its widespread
applicability. In constant, our method recovers the physical formation of RS
images by estimating camera poses and velocities, thereby removing the input
constraints on sequential data. Moreover, we adopt a coarse-to-fine training
strategy, in which the RS epipolar constraints of the pairwise frames in the
scene graph are used to detect the camera poses that fall into local minima.
The poses detected as outliers are corrected by the interpolation method with
neighboring poses. The experimental results validate the effectiveness of our
method over state-of-the-art works and demonstrate that the reconstruction of
3D representations is not constrained by the requirement of video sequence
input.
