  As cameras are increasingly deployed in new application domains such as
autonomous driving, performing 3D object detection on monocular images becomes
an important task for visual scene understanding. Recent advances on monocular
3D object detection mainly rely on the ``pseudo-LiDAR'' generation, which
performs monocular depth estimation and lifts the 2D pixels to pseudo 3D
points. However, depth estimation from monocular images, due to its poor
accuracy, leads to inevitable position shift of pseudo-LiDAR points within the
object. Therefore, the predicted bounding boxes may suffer from inaccurate
location and deformed shape. In this paper, we present a novel neighbor-voting
method that incorporates neighbor predictions to ameliorate object detection
from severely deformed pseudo-LiDAR point clouds. Specifically, each feature
point around the object forms their own predictions, and then the ``consensus''
is achieved through voting. In this way, we can effectively combine the
neighbors' predictions with local prediction and achieve more accurate 3D
detection. To further enlarge the difference between the foreground region of
interest (ROI) pseudo-LiDAR points and the background points, we also encode
the ROI prediction scores of 2D foreground pixels into the corresponding
pseudo-LiDAR points. We conduct extensive experiments on the KITTI benchmark to
validate the merits of our proposed method. Our results on the bird's eye view
detection outperform the state-of-the-art performance by a large margin,
especially for the ``hard'' level detection.

Computer Vision and Pattern Recognition