  Image-only and pseudo-LiDAR representations are commonly used for monocular
3D object detection. However, methods based on them have shortcomings of either
not well capturing the spatial relationships in neighbored image pixels or
being hard to handle the noisy nature of the monocular pseudo-LiDAR point
cloud. To overcome these issues, in this paper we propose a novel
object-centric voxel representation tailored for monocular 3D object detection.
Specifically, voxels are built on each object proposal, and their sizes are
adaptively determined by the 3D spatial distribution of the points, allowing
the noisy point cloud to be organized effectively within a voxel grid. This
representation is proved to be able to locate the object in 3D space
accurately. Furthermore, prior works would like to estimate the orientation via
deep features extracted from an entire image or a noisy point cloud. By
contrast, we argue that the local RoI information from the object image patch
alone with a proper resizing scheme is a better input as it provides complete
semantic clues meanwhile excludes irrelevant interferences. Besides, we
decompose the confidence mechanism in monocular 3D object detection by
considering the relationship between 3D objects and the associated 2D boxes.
Evaluated on KITTI, our method outperforms state-of-the-art methods by a large
margin. The code will be made publicly available soon.
