  3D object detection based on point clouds has become more and more popular.
Some methods propose localizing 3D objects directly from raw point clouds to
avoid information loss. However, these methods come with complex structures and
significant computational overhead, limiting its broader application in
real-time scenarios. Some methods choose to transform the point cloud data into
compact tensors first and leverage off-the-shelf 2D detectors to propose 3D
objects, which is much faster and achieves state-of-the-art results. However,
because of the inconsistency between 2D and 3D data, we argue that the
performance of compact tensor-based 3D detectors is restricted if we use 2D
detectors without corresponding modification. Specifically, the distribution of
point clouds is uneven, with most points gather on the boundary of objects,
while detectors for 2D data always extract features evenly. Motivated by this
observation, we propose DENse Feature Indicator (DENFI), a universal module
that helps 3D detectors focus on the densest region of the point clouds in a
boundary-aware manner. Moreover, DENFI is lightweight and guarantees real-time
speed when applied to 3D object detectors. Experiments on KITTI dataset show
that DENFI improves the performance of the baseline single-stage detector
remarkably, which achieves new state-of-the-art performance among previous 3D
detectors, including both two-stage and multi-sensor fusion methods, in terms
of mAP with a 34FPS detection speed.
