  3D object detection with surrounding cameras has been a promising direction
for autonomous driving. In this paper, we present SimMOD, a Simple baseline for
Multi-camera Object Detection, to solve the problem. To incorporate multi-view
information as well as build upon previous efforts on monocular 3D object
detection, the framework is built on sample-wise object proposals and designed
to work in a two-stage manner. First, we extract multi-scale features and
generate the perspective object proposals on each monocular image. Second, the
multi-view proposals are aggregated and then iteratively refined with
multi-view and multi-scale visual features in the DETR3D-style. The refined
proposals are end-to-end decoded into the detection results. To further boost
the performance, we incorporate the auxiliary branches alongside the proposal
generation to enhance the feature learning. Also, we design the methods of
target filtering and teacher forcing to promote the consistency of two-stage
training. We conduct extensive experiments on the 3D object detection benchmark
of nuScenes to demonstrate the effectiveness of SimMOD and achieve new
state-of-the-art performance. Code will be available at
https://github.com/zhangyp15/SimMOD.
