Neural Radiances Fields (NeRF) and their extensions have shown great success
in representing 3D scenes and synthesizing novel-view images. However, most
NeRF methods take in low-dynamic-range (LDR) images, which may lose details,
especially with nonuniform illumination. Some previous NeRF methods attempt to
introduce high-dynamic-range (HDR) techniques but mainly target static scenes.
To extend HDR NeRF methods to wider applications, we propose a dynamic HDR NeRF
framework, named HDR-HexPlane, which can learn 3D scenes from dynamic 2D images
captured with various exposures. A learnable exposure mapping function is
constructed to obtain adaptive exposure values for each image. Based on the
monotonically increasing prior, a camera response function is designed for
stable learning. With the proposed model, high-quality novel-view images at any
time point can be rendered with any desired exposure. We further construct a
dataset containing multiple dynamic scenes captured with diverse exposures for
evaluation. All the datasets and code are available at
https://guanjunwu.github.io/HDR-HexPlane/.

提出了名为 HDR-HexPlane 的动态 HDR NeRF 框架，可以从具有不同曝光的动态 2D 图像中学习 3D 场景，使用可学习的曝光映射函数和相机响应函数，可以在任意时间点生成具有所需曝光的高质量新视图图像。

动态场景下的快速高动态范围辐射场

Fast High Dynamic Range Radiance Fields for Dynamic Scenes

Recent direct visual odometry and SLAM algorithms have demonstrated
impressive levels of precision. However, they require a photometric camera
calibration in order to achieve competitive results. Hence, the respective
algorithm cannot be directly applied to an off-the-shelf-camera or to a video
sequence acquired with an unknown camera. In this work we propose a method for
online photometric calibration which enables to process auto exposure videos
with visual odometry precisions that are on par with those of photometrically
calibrated videos. Our algorithm recovers the exposure times of consecutive
frames, the camera response function, and the attenuation factors of the sensor
irradiance due to vignetting. Gain robust KLT feature tracks are used to obtain
scene point correspondences as input to a nonlinear optimization framework. We
show that our approach can reliably calibrate arbitrary video sequences by
evaluating it on datasets for which full photometric ground truth is available.
We further show that our calibration can improve the performance of a
state-of-the-art direct visual odometry method that works solely on pixel
intensities, calibrating for photometric parameters in an online fashion in
realtime.

本文提出了一种在线光度标定方法，该方法使用鲁棒的 KLT 特征跟踪获得场景点对应关系，用于非线性优化框架，从而恢复连续帧的曝光时间、摄像机响应函数和传感器污损因子，实现对任意视频序列的可靠标定，并验证了其可以提高基于像素强度完全在线标定光度参数的最先进直接视觉里程计方法的性能。