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

2025-02-08

AI Summary

Conventional CFAR clutter suppression in 4D radar point clouds fails to model spatial structure effectively, leading to severe loss of tensor-structured information critical for deep learning. Method: This paper proposes an end-to-end point cloud-to-dense 4D radar tensor synthesis framework. We introduce the first conditional generative adversarial network (cGAN) explicitly designed for 4D radar geometry, integrating signal priors and spatial alignment constraints. Additionally, we propose a point-cloud-density-adaptive percentile filtering (1% or 5%) for preprocessing, which compresses data substantially (e.g., reducing point count by 87% at the 1% threshold) while preserving 96% reconstruction fidelity. Results: Evaluated on the K-Radar dataset, our method achieves PSNR = 30.39 dB and SSIM = 0.96—significantly outperforming prior approaches—and markedly improves both training efficiency and accuracy of downstream tasks.

Technology Category

• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Natural Language Processing: Generation

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

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.

Problem

• Improves clutter removal in 4D Radar
• Generates tensor data for deep learning
• Optimizes point cloud generation methods

Innovation

• Uses 4D Radar Point-to-Tensor model
• Employs conditional generative adversarial network
• Optimizes point cloud generation methods