AI RESEARCH
Can we Trust Unreliable Voxels? Exploring 3D Semantic Occupancy Prediction under Label Noise
arXiv CS.CV
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ArXi:2603.06279v1 Announce Type: new 3D semantic occupancy prediction is a cornerstone of robotic perception, yet real-world voxel annotations are inherently corrupted by structural artifacts and dynamic trailing effects. This raises a critical but underexplored question: can autonomous systems safely rely on such unreliable occupancy supervision? To systematically investigate this issue, we establish OccNL, the first benchmark dedicated to 3D occupancy under occupancy-asymmetric and dynamic trailing noise.