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ViT-based stereo matching can rival CNNs in detail prediction and generalization to arbitrary resolutions with the right architectural choices.
Forget rigid transforms: CAL2M's elastic sub-map alignment unlocks globally consistent kilometer-scale SLAM with Visual Geometry Foundation Models.
LLMs can now learn to control traffic signals more effectively by debating the best course of action, leading to significant reductions in travel time and congestion.
