Masaki Onishi

Rethinking Temporal Feature Propagation for Efficient 3D Object Detection

We propose a scan-by-scan framework for LiDAR 3D object detection that eliminates redundant re-encoding of overlapping historical scans by propagating temporal context through internal memory. This formulation raises two fundamental challenges: inter-step feature misalignment and long-horizon degradation that conventio...