POLAR3D: Augmenting NASA's POLAR Dataset for Data-Driven Lunar
  Perception and Rover Simulation
Bo-Hsun Chen Peter Negrut Thomas Liang Nevindu Batagoda Harry Zhang Dan Negrut
We report on an effort that led to POLAR3D, a set of digital assets that
enhance the POLAR dataset of stereo images generated by NASA to mimic lunar
lighting conditions. Our contributions are twofold. First, we have annotated
each photo in the POLAR dataset, providing approximately 23 000 labels for
rocks and their shadows. Second, we digitized several lunar terrain scenarios
available in the POLAR dataset. Specifically, by utilizing both the lunar
photos and the POLAR's LiDAR point clouds, we constructed detailed obj files
for all identifiable assets. POLAR3D is the set of digital assets comprising of
rock/shadow labels and obj files associated with the digital twins of lunar
terrain scenarios. This new dataset can be used for training perception
algorithms for lunar exploration and synthesizing photorealistic images beyond
the original POLAR collection. Likewise, the obj assets can be integrated into
simulation environments to facilitate realistic rover operations in a digital
twin of a POLAR scenario. POLAR3D is publicly available to aid perception
algorithm development, camera simulation efforts, and lunar simulation
exercises.POLAR3D is publicly available at
https://github.com/uwsbel/POLAR-digital.