Neural Radiance Fields (NeRF) can be optimized to obtain high-fidelity 3D
scene reconstructions of objects and large-scale scenes. However, NeRFs require
accurate camera parameters as input -- inaccurate camera parameters result in
blurry renderings. Extrinsic and intrinsic camera parameters are usually
estimated using Structure-from-Motion (SfM) methods as a pre-processing step to
NeRF, but these techniques rarely yield perfect estimates. Thus, prior works
have proposed jointly optimizing camera parameters alongside a NeRF, but these
methods are prone to local minima in challenging settings. In this work, we
analyze how different camera parameterizations affect this joint optimization
problem, and observe that standard parameterizations exhibit large differences
in magnitude with respect to small perturbations, which can lead to an
ill-conditioned optimization problem. We propose using a proxy problem to
compute a whitening transform that eliminates the correlation between camera
parameters and normalizes their effects, and we propose to use this transform
as a preconditioner for the camera parameters during joint optimization. Our
preconditioned camera optimization significantly improves reconstruction
quality on scenes from the Mip-NeRF 360 dataset: we reduce error rates (RMSE)
by 67% compared to state-of-the-art NeRF approaches that do not optimize for
cameras like Zip-NeRF, and by 29% relative to state-of-the-art joint
optimization approaches using the camera parameterization of SCNeRF. Our
approach is easy to implement, does not significantly increase runtime, can be
applied to a wide variety of camera parameterizations, and can
straightforwardly be incorporated into other NeRF-like models.

我们研究了如何通过改进相机参数优化方法来提高神经辐射场（NeRF）在三维场景重建中的质量，并提出了一种使用白化变换作为相机参数的预处理器来显著改善重建质量的方法。