  In this paper, we present a novel point generation model, referred to as
Pillar-based Point Generation Network (PillarGen), which facilitates the
transformation of point clouds from one domain into another. PillarGen can
produce synthetic point clouds with enhanced density and quality based on the
provided input point clouds. The PillarGen model performs the following three
steps: 1) pillar encoding, 2) Occupied Pillar Prediction (OPP), and 3) Pillar
to Point Generation (PPG). The input point clouds are encoded using a pillar
grid structure to generate pillar features. Then, OPP determines the active
pillars used for point generation and predicts the center of points and the
number of points to be generated for each active pillar. PPG generates the
synthetic points for each active pillar based on the information provided by
OPP. We evaluate the performance of PillarGen using our proprietary radar
dataset, focusing on enhancing the density and quality of short-range radar
data using the long-range radar data as supervision. Our experiments
demonstrate that PillarGen outperforms traditional point upsampling methods in
quantitative and qualitative measures. We also confirm that when PillarGen is
incorporated into bird's eye view object detection, a significant improvement
in detection accuracy is achieved.
