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

We consider the generic problem of detecting low-level structures in images,
which includes segmenting the manipulated parts, identifying out-of-focus
pixels, separating shadow regions, and detecting concealed objects. Whereas
each such topic has been typically addressed with a domain-specific solution,
we show that a unified approach performs well across all of them. We take
inspiration from the widely-used pre-training and then prompt tuning protocols
in NLP and propose a new visual prompting model, named Explicit Visual
Prompting (EVP). Different from the previous visual prompting which is
typically a dataset-level implicit embedding, our key insight is to enforce the
tunable parameters focusing on the explicit visual content from each individual
image, i.e., the features from frozen patch embeddings and the input's
high-frequency components. The proposed EVP significantly outperforms other
parameter-efficient tuning protocols under the same amount of tunable
parameters (5.7% extra trainable parameters of each task). EVP also achieves
state-of-the-art performances on diverse low-level structure segmentation tasks
compared to task-specific solutions. Our code is available at:
this https URL

本研究提出了一种名为明确视觉提示（EVP）的新型视觉提示模型，通过引入预先训练和提示微调协议，同时从冻结的补丁嵌入和输入的高频成分中进行显式视觉内容的关注，能够有效地解决图像分割、失焦检测、阴影区域和隐藏对象等低级结构检测问题。与领域特定的解决方案相比，EVP 在所有这些问题上都表现出良好的统一性能、更高的检测准确率和更少的可调参数量。