Camera relocalization is a crucial problem in computer vision and robotics.
Recent advancements in neural radiance fields (NeRFs) have shown promise in
synthesizing photo-realistic images. Several works have utilized NeRFs for
refining camera poses, but they do not account for lighting changes that can
affect scene appearance and shadow regions, causing a degraded pose
optimization process. In this paper, we propose a two-staged pipeline that
normalizes images with varying lighting and shadow conditions to improve camera
relocalization. We implement our scene representation upon a hash-encoded NeRF
which significantly boosts up the pose optimization process. To account for the
noisy image gradient computing problem in grid-based NeRFs, we further propose
a re-devised truncated dynamic low-pass filter (TDLF) and a numerical gradient
averaging technique to smoothen the process. Experimental results on several
datasets with varying lighting conditions demonstrate that our method achieves
state-of-the-art results in camera relocalization under varying lighting
conditions. Code and data will be made publicly available.

本文提出了一种基于神经辐射场（NeRF）的两阶段流程，通过标准化具有不同光照和阴影条件的图像来改善相机重定位，并在基于哈希编码的 NeRF 上实现场景表示，进一步利用重新设计的截断动态低通滤波器（TDLF）和数值梯度平均技术来解决网格化 NeRF 中的噪声图像梯度计算问题，实验证明我们的方法在不同光照条件下的相机重定位取得了最先进的结果。

无阴影神经光辐射场中的相机重新定位

Camera Relocalization in Shadow-free Neural Radiance Fields

In order to create an image segmentation method robust to lighting changes,
two novel homogeneity criteria of an image region were studied. Both were
defined using the Logarithmic Image Processing (LIP) framework whose laws model
lighting changes. The first criterion estimates the LIP-additive homogeneity
and is based on the LIP-additive law. It is theoretically insensitive to
lighting changes caused by variations of the camera exposure-time or source
intensity. The second, the LIP-multiplicative homogeneity criterion, is based
on the LIP-multiplicative law and is insensitive to changes due to variations
of the object thickness or opacity. Each criterion is then applied in Revol and
Jourlin's (1997) region growing method which is based on the homogeneity of an
image region. The region growing method becomes therefore robust to the
lighting changes specific to each criterion. Experiments on simulated and on
real images presenting lighting variations prove the robustness of the criteria
to those variations. Compared to a state-of the art method based on the image
component-tree, ours is more robust. These results open the way to numerous
applications where the lighting is uncontrolled or partially controlled.

该研究利用对数图像处理框架中的两种新型均匀性标准，设计了一种稳健的图像分割方法，实验结果表明该方法在光照变化下更具有稳健性。