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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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Crypto News & Web3 Events | TON Ecosystem

@tonevents_en · Post #1290 · 02/27/2025, 10:44 AM

✉ xQuest — What's Next? 💬The Earn phase in xQuest Launch has officially ended. Over 125,000 users completed various quest tasks: from learning basic xRocket features to mastering trading and staking. This impressive result demonstrates high interest in the new platform. ⏺➕Now the Calculationphase is going, during which the campaign results will be summarize. 💰 After calculations, the Сlaim phase will begin – from March 3rd to March 10th, participants of the first xQuest campaign can claim their earned rewards and share the prize pool of 300,000 $XROCK (~$7750). Trade on xRocket exchange #xQuest#xRocket