  Recently, researchers have been leveraging LiDAR point cloud for higher
accuracy in 3D vehicle detection. Most state-of-the-art methods are deep
learning based, but are easily affected by the number of points generated on
the object. This vulnerability leads to numerous false positive boxes at high
recall positions, where objects are occasionally predicted with few points. To
address the issue, we introduce Penetrated Point Classifier (PPC) based on the
underlying property of LiDAR that points cannot be generated behind vehicles.
It determines whether a point exists behind the vehicle of the predicted box,
and if does, the box is distinguished as false positive. Our straightforward
yet unprecedented approach is evaluated on KITTI dataset and achieved
performance improvement of PointRCNN, one of the state-of-the-art methods. The
experiment results show that precision at the highest recall position is
dramatically increased by 15.46 percentage points and 14.63 percentage points
on the moderate and hard difficulty of car class, respectively.
