用生成模型提升4D雷达点云转张量的精度与效率
4DR P2T: 4D Radar Tensor Synthesis with Point Clouds
- 基于条件GAN构建4D雷达点云转张量模型
- 在K-Radar数据集上实现30.39dB PSNR和0.96 SSIM
- 1%百分位法平衡数据量与性能，适合深度学习
在四维（4D）雷达点云生成中，通常采用恒定虚警率（CFAR）算法进行杂波去除，但该方法难以充分捕捉目标的空间特征。为此，本文提出4D雷达点到张量（4DR P2T）模型，可生成适用于深度学习的张量数据并最小化测量损失。该方法采用改进的条件生成对抗网络（cGAN），有效处理4D雷达点云数据并生成张量表示。在K-Radar数据集上的实验结果表明，4DR P2T模型达到平均30.39dB的PSNR和0.96的SSIM。此外，对不同点云生成方法的分析显示，5%百分位法整体表现最佳，而1%百分位法在数据量压缩与性能间取得最优平衡，特别适合深度学习应用。
原文摘要 · Abstract (English)
In four-dimensional (4D) Radar-based point cloud generation, clutter removal is commonly performed using the constant false alarm rate (CFAR) algorithm. However, CFAR may not fully capture the spatial characteristics of objects. To address limitation, this paper proposes the 4D Radar Point-to-Tensor (4DR P2T) model, which generates tensor data suitable for deep learning applications while minimizing measurement loss. Our method employs a conditional generative adversarial network (cGAN), modified to effectively process 4D Radar point cloud data and generate tensor data. Experimental results on the K-Radar dataset validate the effectiveness of the 4DR P2T model, achieving an average PSNR of 30.39dB and SSIM of 0.96. Additionally, our analysis of different point cloud generation methods highlights that the 5% percentile method provides the best overall performance, while the 1% percentile method optimally balances data volume reduction and performance, making it well-suited for deep learning applications.
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