FLM-Occ reformulates indoor occupancy prediction as feed-forward likelihood maximization over a mixture model with volume-normalized weights, achieving superior accuracy on Occ-ScanNet using only 32 superquadrics.
IEEE Robotics and Automation Letters10(11), 11690–11697 (2025)
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FLM-Occ: Feed-forward Likelihood Maximization for Efficient Indoor Occupancy Prediction
FLM-Occ reformulates indoor occupancy prediction as feed-forward likelihood maximization over a mixture model with volume-normalized weights, achieving superior accuracy on Occ-ScanNet using only 32 superquadrics.