  To achieve accurate 3D object detection at a low cost for autonomous driving,
many multi-camera methods have been proposed and solved the occlusion problem
of monocular approaches. However, due to the lack of accurate estimated depth,
existing multi-camera methods often generate multiple bounding boxes along a
ray of depth direction for difficult small objects such as pedestrians,
resulting in an extremely low recall. Furthermore, directly applying depth
prediction modules to existing multi-camera methods, generally composed of
large network architectures, cannot meet the real-time requirements of
self-driving applications. To address these issues, we propose Cross-view and
Depth-guided Transformers for 3D Object Detection, CrossDTR. First, our
lightweight depth predictor is designed to produce precise object-wise sparse
depth maps and low-dimensional depth embeddings without extra depth datasets
during supervision. Second, a cross-view depth-guided transformer is developed
to fuse the depth embeddings as well as image features from cameras of
different views and generate 3D bounding boxes. Extensive experiments
demonstrated that our method hugely surpassed existing multi-camera methods by
10 percent in pedestrian detection and about 3 percent in overall mAP and NDS
metrics. Also, computational analyses showed that our method is 5 times faster
than prior approaches. Our codes will be made publicly available at
https://github.com/sty61010/CrossDTR.
