AI RESEARCH
ESAM++: Efficient Online 3D Perception on the Edge
arXiv CS.CV
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ArXi:2605.29505v1 Announce Type: new Online 3D scene perception in real time is essential for robotics, AR/VR, and autonomous systems, particularly in edge computing scenarios where computational resources are limited and privacy is crucial. Recent state-of-the-art methods like EmbodiedSAM (ESAM) nstrate the promise of online 3D perception by leveraging the Segment Anything Model (SAM) for real-time, fine-grained, and generalized 3D instance segmentation.