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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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djangoproject

@djangoproject · Post #206 · 12/06/2016, 03:28 PM

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http://www.enlistq.com/10-python-idioms-to-help-you-improve-your-code/ If you have ever tried to learn a new language (not a programming language), you know that we always think in our native language before we translate it to the new language. This can lead to you forming some sentences that don’t make sense in the new language but are perfectly normal in your native language. For example, in a lot of languages, you ‘open’ an electronic gadget such as fan, AC or cell phone. When you say that in English, it means to literally open the gadget instead of turning it on. The same is true for programming languages. As we pick up new languages, such as #python, we are using our prior knowledge of programming in another language (q, java, c++ etc) and translating that to python. Many times, your code will work but it won’t be ‘#pretty’ or #fast. In python terms, your code won’t be ‘#pythonic’.

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