  Accurate depth information is crucial for enhancing the performance of
multi-view 3D object detection. Despite the success of some existing multi-view
3D detectors utilizing pixel-wise depth supervision, they overlook two
significant phenomena: 1) the depth supervision obtained from LiDAR points is
usually distributed on the surface of the object, which is not so friendly to
existing DETR-based 3D detectors due to the lack of the depth of 3D object
center; 2) for distant objects, fine-grained depth estimation of the whole
object is more challenging. Therefore, we argue that the object-wise depth (or
3D center of the object) is essential for accurate detection. In this paper, we
propose a new multi-view 3D object detector named OPEN, whose main idea is to
effectively inject object-wise depth information into the network through our
proposed object-wise position embedding. Specifically, we first employ an
object-wise depth encoder, which takes the pixel-wise depth map as a prior, to
accurately estimate the object-wise depth. Then, we utilize the proposed
object-wise position embedding to encode the object-wise depth information into
the transformer decoder, thereby producing 3D object-aware features for final
detection. Extensive experiments verify the effectiveness of our proposed
method. Furthermore, OPEN achieves a new state-of-the-art performance with
64.4% NDS and 56.7% mAP on the nuScenes test benchmark.
