  Current research is primarily dedicated to advancing the accuracy of
camera-only 3D object detectors (apprentice) through the knowledge transferred
from LiDAR- or multi-modal-based counterparts (expert). However, the presence
of the domain gap between LiDAR and camera features, coupled with the inherent
incompatibility in temporal fusion, significantly hinders the effectiveness of
distillation-based enhancements for apprentices. Motivated by the success of
uni-modal distillation, an apprentice-friendly expert model would predominantly
rely on camera features, while still achieving comparable performance to
multi-modal models. To this end, we introduce VCD, a framework to improve the
camera-only apprentice model, including an apprentice-friendly multi-modal
expert and temporal-fusion-friendly distillation supervision. The multi-modal
expert VCD-E adopts an identical structure as that of the camera-only
apprentice in order to alleviate the feature disparity, and leverages LiDAR
input as a depth prior to reconstruct the 3D scene, achieving the performance
on par with other heterogeneous multi-modal experts. Additionally, a
fine-grained trajectory-based distillation module is introduced with the
purpose of individually rectifying the motion misalignment for each object in
the scene. With those improvements, our camera-only apprentice VCD-A sets new
state-of-the-art on nuScenes with a score of 63.1% NDS.
