We are working towards 3D reconstruction of indoor spaces using a pair of HDR
cameras in a stereo vision configuration mounted on an indoor mobile floor
robot that captures various textures and spatial features as 2D images and this
data is simultaneously utilized as a feed to our algorithm which will allow us
to visualize the depth map.

使用一对 HDR 摄像机并在室内移动地面机器人上以立体视觉配置进行拍摄，捕捉各种纹理和空间特征，然后利用我们的算法将数据作为输入，实现室内空间的三维重建和深度图可视化。

利用运动结构进行三维重建

3D reconstruction using Structure for Motion

Cameras digitize real-world scenes as pixel intensity values with a limited
value range given by the available bits per pixel (bpp). High Dynamic Range
(HDR) cameras capture those luminance values in higher resolution through an
increase in the number of bpp. Most displays, however, are limited to 8 bpp.
Naive HDR compression methods lead to a loss of the rich information contained
in those HDR images. In this paper, tone mapping algorithms for thermal
infrared images with 16 bpp are investigated that can preserve this
information. An optimized multi-scale Retinex algorithm sets the baseline. This
algorithm is then approximated with a deep learning approach based on the
popular U-Net architecture. The remaining noise in the images after tone
mapping is reduced implicitly by utilizing a self-supervised deep learning
approach that can be jointly trained with the tone mapping approach in a
multi-task learning scheme. Further discussions are provided on denoising and
deflickering for thermal infrared video enhancement in the context of tone
mapping. Extensive experiments on the public FLIR ADAS Dataset prove the
effectiveness of our proposed method in comparison with the state-of-the-art.

使用优化的多尺度 Retinex 算法和基于 U-Net 架构的深度学习方法，对 16bpp 的热红外图像进行色调映射算法的研究，以及使用自监督深度学习方法减少噪音并提高热红外视频增强质量，并在公共 FLIR ADAS 数据集上证明了所提出的方法效果优于现有技术。