We present the POLAR Traverse Dataset: a dataset of high-fidelity stereo pair
images of lunar-like terrain under polar lighting conditions designed to
simulate a straight-line traverse. Images from individual traverses with
different camera heights and pitches were recorded at 1 m intervals by moving a
suspended stereo bar across a test bed filled with regolith simulant and shaped
to mimic lunar south polar terrain. Ground truth geometry and camera position
information was also recorded. This dataset is intended for developing and
testing software algorithms that rely on stereo or monocular camera images,
such as visual odometry, for use in the lunar polar environment, as well as to
provide insight into the expected lighting conditions in lunar polar regions.

通过极地光照条件下的高保真度立体成对图像的 POLAR Traverse 数据集，模拟直线移动并记录不同相机高度和倾斜度的遍历图像，旨在开发和测试依赖立体或单眼相机图像的软件算法（如视觉里程计）在月球极地环境中的应用，并提供月球极地区域预期光照条件的洞察。

POLAR Traverse 数据集：极端光照条件下模拟月球极地地形穿越的立体相机图像数据集

The POLAR Traverse Dataset: A Dataset of Stereo Camera Images Simulating  Traverses across Lunar Polar Terrain under Extreme Lighting Conditions

Recent work has shown that depth estimation from a stereo pair of images can
be formulated as a supervised learning task to be resolved with convolutional
neural networks (CNNs). However, current architectures rely on patch-based
Siamese networks, lacking the means to exploit context information for finding
correspondence in illposed regions. To tackle this problem, we propose PSMNet,
a pyramid stereo matching network consisting of two main modules: spatial
pyramid pooling and 3D CNN. The spatial pyramid pooling module takes advantage
of the capacity of global context information by aggregating context in
different scales and locations to form a cost volume. The 3D CNN learns to
regularize cost volume using stacked multiple hourglass networks in conjunction
with intermediate supervision. The proposed approach was evaluated on several
benchmark datasets. Our method ranked first in the KITTI 2012 and 2015
leaderboards before March 18, 2018. The codes of PSMNet are available at:
this https URL

该论文提出了一种新的算法 PSMNet，通过空间金字塔池化模块和三维卷积神经网络来提取全局上下文信息并形成代价体积，通过多层次网络及中间监督来优化代价体积，该算法在 KITTI 2012 和 2015 数据集中的表现也十分优秀。