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.

提出了一种新颖的滚动快门捆绑调整方法，用于神经辐射场（NeRF），利用无序滚动快门（RS）图像获取隐式 3D 表示。通过估计相机姿态和速度，恢复 RS 图像的物理形成，从而消除了对顺序数据的输入限制。采用自粗糙到精细的训练策略，利用场景图中成对帧的 RS 对极约束来检测落入局部极小值的相机姿态。通过与相邻姿态的插值方法对检测为异常值的姿态进行修正。实验结果验证了我们的方法在最新工作中的有效性，并证明 3D 表示的重建不受视频序列输入要求的限制。