  Estimating 3D orientation and translation of objects is essential for
infrastructure-less autonomous navigation and driving. In case of monocular
vision, successful methods have been mainly based on two ingredients: (i) a
network generating 2D region proposals, (ii) a R-CNN structure predicting 3D
object pose by utilizing the acquired regions of interest. We argue that the 2D
detection network is redundant and introduces non-negligible noise for 3D
detection. Hence, we propose a novel 3D object detection method, named SMOKE,
in this paper that predicts a 3D bounding box for each detected object by
combining a single keypoint estimate with regressed 3D variables. As a second
contribution, we propose a multi-step disentangling approach for constructing
the 3D bounding box, which significantly improves both training convergence and
detection accuracy. In contrast to previous 3D detection techniques, our method
does not require complicated pre/post-processing, extra data, and a refinement
stage. Despite of its structural simplicity, our proposed SMOKE network
outperforms all existing monocular 3D detection methods on the KITTI dataset,
giving the best state-of-the-art result on both 3D object detection and Bird's
eye view evaluation. The code will be made publicly available.
