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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 M - Crypto News

@CryptoM · Post #65294 · 04/12/2026, 03:15 PM

🚀 TRON Network Implements Proposal 106 to Enhance Compatibility TRON Network has officially passed Proposal 106 as of April 10 at 20:00 UTC+8. According to ChainCatcher, the mainnet has adjusted the behavior and execution cost of the SELFDESTRUCT instruction. Contracts will only be permanently removed from the blockchain if the instruction is called within the same transaction as the contract's creation. Otherwise, executing the instruction will merely transfer the assets within the contract to a specified address, without deleting the contract itself. Additionally, the energy consumption for SELFDESTRUCT has been changed from 0 to 5000. This adjustment aligns TRON's mechanisms more closely with Ethereum, enhancing the compatibility of the TRON Virtual Machine (TVM). It aims to provide more stable foundational support for multi-chain development and ecosystem adaptation. #TRON#Proposal106#Blockchain#Ethereum#TVM#SELFDESTRUCT#Mainnet#MultiChain#Ecosystem#Crypto#ETH#TRX