NASA's POLAR dataset contains approximately 2,600 pairs of high dynamic range stereo photos captured across 13 varied terrain scenarios, including areas with sparse or dense rock distributions, craters, and rocks of different sizes. The purpose of these photos is to spur development in robotics, AI-based perception, and autonomous navigation. Acknowledging a scarcity of lunar images from around the lunar poles, NASA Ames produced on Earth but in controlled conditions images that resemble rover operating conditions from these regions of the Moon. We report on the outcomes of an effort aimed at accomplishing two tasks. In Task 1, we provided bounding boxes and semantic segmentation information for all the images in NASA's POLAR dataset. This effort resulted in 23,000 labels and semantic segmentation annotations pertaining to rocks, shadows, and craters. In Task 2, we generated the digital twins of the 13 scenarios that have been used to produce all the photos in the POLAR dataset. Specifically, for each of these scenarios, we produced individual meshes, texture information, and material properties associated with the ground and the rocks in each scenario. This allows anyone with a camera model to synthesize images associated with any of the 13 scenarios of the POLAR dataset. Effectively, one can generate as many semantically labeled synthetic images as desired -- with different locations and exposure values in the scene, for different positions of the sun, with or without the presence of active illumination, etc. The benefit of this work is twofold. Using outcomes of Task 1, one can train and/or test perception algorithms that deal with Moon images. For Task 2, one can produce as much data as desired to train and test AI algorithms that are anticipated to work in lunar conditions. All the outcomes of this work are available in a public repository for unfettered use and distribution.

翻译：NASA的POLAR数据集包含约2,600对高动态范围立体照片，覆盖13种不同地形场景，包括岩石稀疏或密集分布区域、撞击坑及不同尺寸的岩石。这些照片旨在推动机器人技术、基于人工智能的感知与自主导航领域的发展。鉴于月球极区周边图像稀缺，NASA艾姆斯研究中心在地球受控条件下模拟了月球巡视器在这些区域的操作环境并拍摄了相应图像。本文报告了一项旨在完成两项任务的研究成果。在任务1中，我们为NASA POLAR数据集的所有图像提供了边界框与语义分割信息，共完成23,000个涉及岩石、阴影及撞击坑的标注与语义分割注释。在任务2中，我们构建了用于生成POLAR数据集全部照片的13个场景的数字孪生模型。具体而言，针对每个场景，我们生成了独立网格、纹理信息以及与地面和岩石相关的材质属性。这使得任何拥有相机模型的研究者都能合成与POLAR数据集13个场景中任意场景相关的图像。实际上，研究者可以按需生成任意数量的语义标注合成图像——包括场景中不同位置与曝光值、不同太阳方位、有无主动照明等条件。本工作的优势体现在两方面：利用任务1的成果，可训练和/或测试处理月球图像的感知算法；通过任务2，可生成任意数量的数据来训练和测试预期在月球环境下工作的AI算法。本研究所有成果已在公共存储库中开放，供自由使用与传播。