  3D object detection is one of the most important tasks in 3D vision
perceptual system of autonomous vehicles. In this paper, we propose a novel two
stage 3D object detection method aimed at get the optimal solution of object
location in 3D space based on regressing two additional 3D object properties by
a deep convolutional neural network and combined with cascaded geometric
constraints between the 2D and 3D boxes. First, we modify the existing 3D
properties regressing network by adding two additional components, viewpoints
classification and the center projection of the 3D bounding box s bottom face.
Second, we use the predicted center projection combined with similar triangle
constraint to acquire an initial 3D bounding box by a closed-form solution.
Then, the location predicted by previous step is used as the initial value of
the over-determined equations constructed by 2D and 3D boxes fitting constraint
with the configuration determined with the classified viewpoint. Finally, we
use the recovered physical world information by the 3D detections to filter out
the false detection and false alarm in 2D detections. We compare our method
with the state-of-the-arts on the KITTI dataset show that although conceptually
simple, our method outperforms more complex and computational expensive methods
not only by improving the overall precision of 3D detections, but also
increasing the orientation estimation precision. Furthermore our method can
deal with the truncated objects to some extent and remove the false alarm and
false detections in both 2D and 3D detections.
