  Current multi-view 3D object detection methods often fail to detect objects
in the overlap region properly, and the networks' understanding of the scene is
often limited to that of a monocular detection network. Moreover, objects in
the overlap region are often largely occluded or suffer from deformation due to
camera distortion, causing a domain shift. To mitigate this issue, we propose
using the following two main modules: (1) Stereo Disparity Estimation for Weak
Depth Supervision and (2) Adversarial Overlap Region Discriminator. The former
utilizes the traditional stereo disparity estimation method to obtain reliable
disparity information from the overlap region. Given the disparity estimates as
supervision, we propose regularizing the network to fully utilize the geometric
potential of binocular images and improve the overall detection accuracy
accordingly. Further, the latter module minimizes the representational gap
between non-overlap and overlapping regions. We demonstrate the effectiveness
of the proposed method with the nuScenes large-scale multi-view 3D object
detection data. Our experiments show that our proposed method outperforms
current state-of-the-art models, i.e., DETR3D and BEVDet.
