# 4DR P2T: 4D Radar Tensor Synthesis with Point Clouds

arXiv: https://arxiv.org/abs/2502.05550 · HTML: https://mlsanity.com/paper/2502.05550/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 24 references that are in the map.
- First posted on arXiv 8 Feb 2025.

## Builds on (most cited first, 14 of 14)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection](https://mlsanity.com/paper/1711.06396/) (2017)
- [High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs](https://mlsanity.com/paper/1711.11585/) (2017)
- [3D Semantic Segmentation with Submanifold Sparse Convolutional Networks](https://mlsanity.com/paper/1711.10275/) (2017)
- [K-Radar: 4D Radar Object Detection for Autonomous Driving in Various Weather Conditions](https://mlsanity.com/paper/2206.08171/) (2022)
- [Raw High-Definition Radar for Multi-Task Learning](https://mlsanity.com/paper/2112.10646/) (2021)
- [4D Millimeter-Wave Radar in Autonomous Driving: A Survey](https://mlsanity.com/paper/2306.04242/) (2023)
- [Exploring Radar Data Representations in Autonomous Driving: A Comprehensive Review](https://mlsanity.com/paper/2312.04861/) (2023)
- [DPFT: Dual Perspective Fusion Transformer for Camera-Radar-based Object Detection](https://mlsanity.com/paper/2404.03015/) (2024)
- [3DRIMR: 3D Reconstruction and Imaging via mmWave Radar based on Deep Learning](https://mlsanity.com/paper/2108.02858/) (2021)
- [Enhanced K-Radar: Optimal Density Reduction to Improve Detection Performance and Accessibility of 4D Radar Tensor-based Object Detection](https://mlsanity.com/paper/2303.06342/) (2023)
- [RadSimReal: Bridging the Gap Between Synthetic and Real Data in Radar Object Detection With Simulation](https://mlsanity.com/paper/2404.18150/) (2024)

## Built on by (most cited first, 1 of 1)

- [Synthetic FMCW Radar Range Azimuth Maps Augmentation with Generative Diffusion Model](https://mlsanity.com/paper/2601.06228/) (2026)

## Abstract (arXiv)

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.