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Hazard detection is fundamental for a safe lunar landing. State-of-the-art autonomous lunar hazard detection relies on 2D image-based and 3D Lidar systems. The lunar south pole is challenging for vision-based methods. The low sun inclination and the terrain rich in topographic features create large areas in shadow, hiding the terrain features. The proposed method utilizes a vision transformer (ViT) model, which is a deep learning architecture based on the transformer blocks used in natural language processing, to solve this problem. Our goal is to train the ViT model to extract terrain features information from low-light RGB images. The results show good performances, especially at high altitudes, beating the UNet, one of the most popular convolutional neural networks, in every scenario.
Pubmed ID: 37765902 RIS Download
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Blender is the free open source 3D content creation suite, available for all major operating systems under the GNU General Public License. Because of the overwhelming success of the first open movie project, Ton Roosendaal, the Blender Foundation''s chairman, has established the Blender Institute. This now is the permanent office and studio to more efficiently organize the Blender Foundation goals, but especially to coordinate and facilitate Open Projects related to 3D movies, games or visual effects.
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