Remarkable results have been achieved by DCNN based self-supervised depth
estimation approaches. However, most of these approaches can only handle either
day-time or night-time images, while their performance degrades for all-day
images due to large domain shift and the variation of illumination between day
and night images. To relieve these limitations, we propose a domain-separated
network for self-supervised depth estimation of all-day images. Specifically,
to relieve the negative influence of disturbing terms (illumination, etc.), we
partition the information of day and night image pairs into two complementary
sub-spaces: private and invariant domains, where the former contains the unique
information (illumination, etc.) of day and night images and the latter
contains essential shared information (texture, etc.). Meanwhile, to guarantee
that the day and night images contain the same information, the
domain-separated network takes the day-time images and corresponding night-time
images (generated by GAN) as input, and the private and invariant feature
extractors are learned by orthogonality and similarity loss, where the domain
gap can be alleviated, thus better depth maps can be expected. Meanwhile, the
reconstruction and photometric losses are utilized to estimate complementary
information and depth maps effectively. Experimental results demonstrate that
our approach achieves state-of-the-art depth estimation results for all-day
images on the challenging Oxford RobotCar dataset, proving the superiority of
our proposed approach.

通过提出域分离网络，使用白天 - 黑夜图像对进行自监督深度估计， 使用生成对抗网络产生黑夜图片并结合正交性和相似性损失， 实现消除视差差异并获得更好的深度估计， 在 Oxford RobotCar 数据集上证明取得了领先深度估计结果。

基于领域分离的自监督单目深度估计技术在全天候图像中的应用

Self-supervised Monocular Depth Estimation for All Day Images using  Domain Separation

Global localization is an important and widely studied problem for many
robotic applications. Place recognition approaches can be exploited to solve
this task, e.g., in the autonomous driving field. While most vision-based
approaches match an image w.r.t. an image database, global visual localization
within LiDAR-maps remains fairly unexplored, even though the path toward high
definition 3D maps, produced mainly from LiDARs, is clear. In this work we
leverage Deep Neural Network (DNN) approaches to create a shared embedding
space between images and LiDAR-maps, allowing for image to 3D-LiDAR place
recognition. We trained a 2D and a 3D DNN that create embeddings, respectively
from images and from point clouds, that are close to each other whether they
refer to the same place. An extensive experimental activity is presented to
assess the effectiveness of the approach w.r.t. different learning paradigms,
network architectures, and loss functions. All the evaluations have been
performed using the Oxford Robotcar Dataset, which encompasses a wide range of
weather and light conditions.

本研究使用深度神经网络方法来创造一个共享的嵌入空间，将图像和 LiDAR-Map 映射到同一空间中，通过大量实验评估发现，该方法适用于全球定位和场所识别。

通过共享 2D-3D 嵌入空间在 LiDAR 地图中进行全局视觉定位

Global visual localization in LiDAR-maps through shared 2D-3D embedding  space

Large-scale point cloud generated from 3D sensors is more accurate than its
image-based counterpart. However, it is seldom used in visual pose estimation
due to the difficulty in obtaining 2D-3D image to point cloud correspondences.
In this paper, we propose the 2D3D-MatchNet - an end-to-end deep network
architecture to jointly learn the descriptors for 2D and 3D keypoint from image
and point cloud, respectively. As a result, we are able to directly match and
establish 2D-3D correspondences from the query image and 3D point cloud
reference map for visual pose estimation. We create our Oxford 2D-3D Patches
dataset from the Oxford Robotcar dataset with the ground truth camera poses and
2D-3D image to point cloud correspondences for training and testing the deep
network. Experimental results verify the feasibility of our approach.

本研究提出了一种名为 2D3D-MatchNet 的深度网络结构，能够直接从图像和点云中的 2D 和 3D 关键点描述符中匹配和建立 2D-3D 对应关系，并用于视觉位姿估计，结果表明该方法是可行的。