  Point-cloud based 3D object detectors recently have achieved remarkable
progress. However, most studies are limited to the development of network
architectures for improving only their accuracy without consideration of the
computational efficiency. In this paper, we first propose an autoencoder-style
framework comprising channel-wise compression and decompression via interchange
transfer-based knowledge distillation. To learn the map-view feature of a
teacher network, the features from teacher and student networks are
independently passed through the shared autoencoder; here, we use a compressed
representation loss that binds the channel-wised compression knowledge from
both student and teacher networks as a kind of regularization. The decompressed
features are transferred in opposite directions to reduce the gap in the
interchange reconstructions. Lastly, we present an head attention loss to match
the 3D object detection information drawn by the multi-head self-attention
mechanism. Through extensive experiments, we verify that our method can train
the lightweight model that is well-aligned with the 3D point cloud detection
task and we demonstrate its superiority using the well-known public datasets;
e.g., Waymo and nuScenes.
