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Source channel @githubtrending · Post #15062 · Aug 15

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#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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@repo_science · Post #3567 · 09/06/2023, 07:13 PM

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#python#dataEngineering 📃 Amazon Web Services in Action Description Master essential best practices for deploying and managing applications on Amazon Web Services. This revised bestseller is packed with techniques for building highly available and scalable architectures and automating deployment with Infrastructure as Code. ⚖️1.7 GB 🔗Enlace ----- Main channel: @repo_science Coupons: @freecoupons_reposcience -----

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@repo_science · Post #3495 · 08/03/2023, 01:47 PM

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@repo_science · Post #3084 · 04/21/2023, 02:49 PM

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#apacheSpark#ETL#dataEngineering#Python 🎚 Apache Spark 3 for Data Engineering and Analytics with Python [Video] 🗣David Mngadi 🌐 Inglés ⚖️2.1GB 🔗Link ----- Canal principal:@repo_science Cupones: @freecoupons_reposcience -----

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