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.

翻译：自动驾驶中, 显性 3D 对象探测是一项重要任务。 在存在自负驱动器的地方, 它可能很容易容易操作。 由于道路平滑和坡度的轻微波动, 这很常见。 由于工业应用方面缺乏洞察力, 开放数据集的现有方法忽视了摄像头, 从而产生信息, 这不可避免地导致探测器容易被摄像外向参数所摄取。 在大多数工业产品自主驾驶的情况下, 物体的扰动非常受欢迎。 为此, 我们提出一种新的方法来捕捉照相机, 以制成不受外部干扰的探测器。 具体地说, 拟议的框架通过探测消失点和地平面变化来预测相机的外部参数。 一个转换器旨在纠正潜在空间中的扰动特征。 这样, 我们的3D 探测器可以不受外向参数变化的影响, 并在现实情况下产生准确的结果, 例如, 水坑和不均匀称道路, 几乎所有现有单向探测器都无法操作。 实验显示我们的方法能够产生最佳的性能, 与其他州- 地势 。