An objectness-aware teacher-student framework transfers motion prediction knowledge from synthetic 4D LiDAR data to unlabeled real data, using centroid-offset priors to filter and smooth noisy pseudo-labels.
Pnpnet: End-to-end perception and prediction with tracking in the loop,
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Synthetic-to-Real Translation for Class-Agnostic Motion Prediction
An objectness-aware teacher-student framework transfers motion prediction knowledge from synthetic 4D LiDAR data to unlabeled real data, using centroid-offset priors to filter and smooth noisy pseudo-labels.