  Current on-board chips usually have different computing power, which means
multiple training processes are needed for adapting the same learning-based
algorithm to different chips, costing huge computing resources. The situation
becomes even worse for 3D perception methods with large models. Previous
vision-centric 3D perception approaches are trained with regular
grid-represented feature maps of fixed resolutions, which is not applicable to
adapt to other grid scales, limiting wider deployment. In this paper, we
leverage the Polar representation when constructing the BEV feature map from
images in order to achieve the goal of training once for multiple deployments.
Specifically, the feature along rays in Polar space can be easily adaptively
sampled and projected to the feature in Cartesian space with arbitrary
resolutions. To further improve the adaptation capability, we make multi-scale
contextual information interact with each other to enhance the feature
representation. Experiments on a large-scale autonomous driving dataset show
that our method outperforms others as for the good property of one training for
multiple deployments.
