# See Further Than CFAR: a Data-Driven Radar Detector Trained by Lidar

arXiv: https://arxiv.org/abs/2402.12970 · HTML: https://mlsanity.com/paper/2402.12970/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2024.

## Built on by (most cited first, 7 of 7)

- [MutualForce: Mutual-Aware Enhancement for 4D Radar-LiDAR 3D Object Detection](https://mlsanity.com/paper/2501.10266/) (2025)
- [Unsupervised Radar Point Cloud Enhancement via Arbitrary LiDAR Guided Diffusion Prior](https://mlsanity.com/paper/2505.09887/) (2025)
- [Automatic Labelling & Semantic Segmentation with 4D Radar Tensors](https://mlsanity.com/paper/2501.11351/) (2025)
- [Toward a Low-Cost Perception System in Autonomous Vehicles: A Spectrum Learning Approach](https://mlsanity.com/paper/2502.01940/) (2025)
- [The Impact of 2D Segmentation Backbones on Point Cloud Predictions Using 4D Radar](https://mlsanity.com/paper/2509.19644/) (2025)
- [4D-RaDiff: Latent Point Diffusion for 4D Radar Point Cloud Generation](https://mlsanity.com/paper/2512.14235/) (2025)
- [Von Mises Based Uncertainty Quantification for Closely Spaced Automotive Radar Targets](https://mlsanity.com/paper/2606.31473/) (2026)

## Abstract (arXiv)

In this paper, we address the limitations of traditional constant false alarm rate (CFAR) target detectors in automotive radars, particularly in complex urban environments with multiple objects that appear as extended targets. We propose a data-driven radar target detector exploiting a highly efficient 2D CNN backbone inspired by the computer vision domain. Our approach is distinguished by a unique cross sensor supervision pipeline, enabling it to learn exclusively from unlabeled synchronized radar and lidar data, thus eliminating the need for costly manual object annotations. Using a novel large-scale, real-life multi-sensor dataset recorded in various driving scenarios, we demonstrate that the proposed detector generates dense, lidar-like point clouds, achieving a lower Chamfer distance to the reference lidar point clouds than CFAR detectors. Overall, it significantly outperforms CFAR baselines detection accuracy.