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Static infrastructure dominates occupancy perception, occupying 97.3% of voxels, while dynamic elements barely register, challenging existing benchmarks and methods.
TSFormer redefines 3D visual grounding by leveraging diverse sensor modalities, achieving significant performance gains in outdoor autonomous driving scenarios.
VLMs struggle with object-level counterfactual reasoning, achieving only a fraction of human accuracy in spatial tasks.
StreamPPG achieves real-time rPPG estimation with state-of-the-art accuracy by leveraging privileged information, overcoming the latency barriers of traditional methods.
By injecting 2D instance cues from camera data into the BEV space of 4D radar data, SIFormer overcomes radar's sparse geometry and significantly boosts 3D object detection accuracy.
Overcome the extreme sparsity and noise of 4D radar data with SD4R, a new method that significantly boosts 3D object detection performance.