DPointNet: A Density-Oriented PointNet for 3D Object Detection in Point Clouds
This work provides an incremental improvement for 3D object detection in point clouds by proposing a new feature extraction operator that is better suited for multi-density point cloud data.
This paper introduces DPointNet, a novel density-oriented PointNet for 3D object detection in point clouds, which addresses the issue of varying point densities rather than object scales. When applied to PointRCNN, DPointNet achieves better performance and higher speed compared to the baseline.
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.