OMT-Distill: Object-Measure Transport for Heterogeneous 3D Detection Distillation

Knowledge distillation enables LiDAR-only 3D detectors to learn from multimodal teachers. However, differences in encoders, decoders, and detection heads complicate feature alignment and the association of teacher and student predictions. Predicted 3D boxes and class labels have common geometric and semantic meanings, allowing the two prediction sets to be represented as confidence-weighted object measures. We propose Object-Measure Transport (OMT), which combines teacher-location supervision with soft transport over native student objects. Teacher-location supervision extends quality and ordering guidance to responses omitted by student candidate selection. Unbalanced transport distributes supervision through many-to-many object associations, accommodating differences in prediction coverage and confidence. On the nuScenes validation set, OMT-Distill achieves 61.07 mAP and 67.92 NDS with a CenterPoint-Voxel student. OMT-Distill improves detection accuracy across four heterogeneous teacher–student pairs while retaining each student's original LiDAR-only inference architecture.