  For current object detectors, the scale of the receptive field of feature
extraction operators usually increases layer by layer. Those operators are
called scale-oriented operators in this paper, such as the convolution layer in
CNN, and the set abstraction layer in PointNet++. The scale-oriented operators
are appropriate for 2D images with multi-scale objects, but not natural for 3D
point clouds with multi-density but scale-invariant objects. In this paper, we
put forward a novel density-oriented PointNet (DPointNet) for 3D object
detection in point clouds, in which the density of points increases layer by
layer. In experiments for object detection, the DPointNet is applied to
PointRCNN, and the results show that the model with the new operator can
achieve better performance and higher speed than the baseline PointRCNN, which
verify the effectiveness of the proposed DPointNet.
