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Source channel @githubtrending · Post #15062 · Aug 15

#python#mllm#point_clouds#scene_understanding#spatial_intelligence SpatialLM is a powerful 3D language model that turns complex 3D point cloud data from videos, RGBD images, or LiDAR into clear, structured 3D scene layouts showing walls, doors, windows, and objects with labels. It works without needing special equipment and can detect user-specified object categories. This helps you understand and analyze indoor spaces better, useful for robotics, navigation, and 3D design. You can run it on your data, visualize results, and even customize detection tasks easily, making 3D scene understanding more accessible and flexible for many applications. https://github.com/manycore-research/SpatialLM

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@emagzinewspars · Post #9391 · 10/21/2025, 08:58 AM

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#The_Science🇺🇸📕[PDF]⬇️ 16 #October2025 #Weekly_Magazines For learning, for free(dom). @backupofmagazines This week’s Science dives deep into the frontiers of #Nanoscopy, visualizing neural circuits with stunning 3D precision. From Arctic #Permafrost preserving ancient RNA viruses to #Ant colonies evolving architectural immunity, the issue spans from the microscopic to the planetary. Groundbreaking work tracks #SeaLevel change over 4.5 million years and explores durable #Hydrogen production membranes and electric control in #2Dmaterials. The “#ColdStorage” feature warns of hidden viral archives in melting ice—a chilling insight into #ClimateScience and future pandemics. A visionary issue linking biology, chemistry, and global sustainability through the lens of innovation.

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