  Monocular 3D object detection is an important task in autonomous driving. It
can be easily intractable where there exists ego-car pose change w.r.t. ground
plane. This is common due to the slight fluctuation of road smoothness and
slope. Due to the lack of insight in industrial application, existing methods
on open datasets neglect the camera pose information, which inevitably results
in the detector being susceptible to camera extrinsic parameters. The
perturbation of objects is very popular in most autonomous driving cases for
industrial products. To this end, we propose a novel method to capture camera
pose to formulate the detector free from extrinsic perturbation. Specifically,
the proposed framework predicts camera extrinsic parameters by detecting
vanishing point and horizon change. A converter is designed to rectify
perturbative features in the latent space. By doing so, our 3D detector works
independent of the extrinsic parameter variations and produces accurate results
in realistic cases, e.g., potholed and uneven roads, where almost all existing
monocular detectors fail to handle. Experiments demonstrate our method yields
the best performance compared with the other state-of-the-arts by a large
margin on both KITTI 3D and nuScenes datasets.
