  Monocular 3D object detection aims to detect objects in a 3D physical world
from a single camera. However, recent approaches either rely on expensive LiDAR
devices, or resort to dense pixel-wise depth estimation that causes prohibitive
computational cost. In this paper, we propose an end-to-end trainable monocular
3D object detector without learning the dense depth. Specifically, the grid
coordinates of a 2D box are first projected back to 3D space with the pinhole
model as 3D centroids proposals. Then, a novel object-aware voting approach is
introduced, which considers both the region-wise appearance attention and the
geometric projection distribution, to vote the 3D centroid proposals for 3D
object localization. With the late fusion and the predicted 3D orientation and
dimension, the 3D bounding boxes of objects can be detected from a single RGB
image. The method is straightforward yet significantly superior to other
monocular-based methods. Extensive experimental results on the challenging
KITTI benchmark validate the effectiveness of the proposed method.
