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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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@kyveannouncements · Post #322 · 06/20/2023, 04:04 PM
💫 With our first mainnet data pool now live, making CosmosHub chain data trustless for the #interchain ecosystem, what blockchain should we start archiving & validating next? 🤔 Tag them in the comments below! 💬👇 https://twitter.com/KYVENetwork/status/1671187001075277825
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@kyveannouncements · Post #356 · 12/18/2023, 04:02 PM
KYVE is partnering with Syntropynet! Taking #interchain data to new bounds 💫 🤝 In the spirit of the #modular era, Syntropy & KYVE are each bringing forward their specialized data solutions to ensure a reliable, full-scope data experience from start to finish for both historical & real-time data streams. Learn more about this exciting partnership ⤵️ https://x.com/KYVENetwork/status/1736776359043539058?s=20
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