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Shanghai Jiao Tong University
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PixelPilot redefines trajectory prediction in autonomous driving by transforming it into scalable 2D tasks, leading to unprecedented generalization across heterogeneous datasets.
Targeted structural completion can slash Gaussian usage by 74% and rendering time by 34%, revolutionizing 3D reconstruction in autonomous driving.
SparseOcc++ achieves a 2.3-point IoU improvement while being 3.9 times faster than previous methods, revolutionizing 3D semantic occupancy prediction.
User-level depression detection can be dramatically improved by routing individuals to specialized experts based on weak semantic priors, rather than relying on a one-size-fits-all classifier.
Overcome the static train-then-freeze paradigm with a new test-time adaptation framework that significantly improves camouflaged object detection in unseen environments.
Achieve spatially grounded natural language descriptions of urban development with PTNet, a new model that understands change semantics better than existing methods.