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.

我们提出了一种新颖的点云生成模型，称为 Pillar-based Point Generation Network (PillarGen)，它可以将一个域中的点云转换为另一个域中的点云，并生成密度和质量增强的合成点云。该模型通过三个步骤进行：pillar 编码、被占用 pillar 预测和 pillar 到点生成。我们利用自有的雷达数据集评估了 PillarGen 的性能，在短程雷达数据中利用长程雷达数据作为监督实现了密度和质量的提升。实验证明，PillarGen 在定量和定性指标上优于传统的点云上采样方法。我们还证实，将 PillarGen 应用于鸟瞰目标检测可以显著提高检测准确性。