  To boost a detector for single-frame 3D object detection, we present a new
approach to train it to simulate features and responses following a detector
trained on multi-frame point clouds. Our approach needs multi-frame point
clouds only when training the single-frame detector, and once trained, it can
detect objects with only single-frame point clouds as inputs during the
inference. We design a novel Simulated Multi-Frame Single-Stage object Detector
(SMF-SSD) framework to realize the approach: multi-view dense object fusion to
densify ground-truth objects to generate a multi-frame point cloud;
self-attention voxel distillation to facilitate one-to-many knowledge transfer
from multi- to single-frame voxels; multi-scale BEV feature distillation to
transfer knowledge in low-level spatial and high-level semantic BEV features;
and adaptive response distillation to activate single-frame responses of high
confidence and accurate localization. Experimental results on the Waymo test
set show that our SMF-SSD consistently outperforms all state-of-the-art
single-frame 3D object detectors for all object classes of difficulty levels 1
and 2 in terms of both mAP and mAPH.
