  The human brain can effortlessly recognize and localize objects, whereas
current 3D object detection methods based on LiDAR point clouds still report
inferior performance for detecting occluded and distant objects: the point
cloud appearance varies greatly due to occlusion, and has inherent variance in
point densities along the distance to sensors. Therefore, designing feature
representations robust to such point clouds is critical. Inspired by human
associative recognition, we propose a novel 3D detection framework that
associates intact features for objects via domain adaptation. We bridge the gap
between the perceptual domain, where features are derived from real scenes with
sub-optimal representations, and the conceptual domain, where features are
extracted from augmented scenes that consist of non-occlusion objects with rich
detailed information. A feasible method is investigated to construct conceptual
scenes without external datasets. We further introduce an attention-based
re-weighting module that adaptively strengthens the feature adaptation of more
informative regions. The network's feature enhancement ability is exploited
without introducing extra cost during inference, which is plug-and-play in
various 3D detection frameworks. We achieve new state-of-the-art performance on
the KITTI 3D detection benchmark in both accuracy and speed. Experiments on
nuScenes and Waymo datasets also validate the versatility of our method.
