AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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A multi-source collaborative training and class-aware fusion framework for Sim2Real 3D detection that indicates that a stable, interpretable collaboration among data sources is more valuable than unconstrained aggregation of model outputs.
We present our solution to the LUMPI track of the UCF UrbanTwin Sim2Real LiDAR Challenge at the 6th DriveX Workshop, ECCV 2026. The detector must be trained only on synthetic data and is evaluated on 50 held-out real LiDAR frames; a separate 50-frame synthetic submission is evaluated for point-cloud realism. Our method...
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