# Deep Learning on Radar Centric 3D Object Detection

arXiv: https://arxiv.org/abs/2003.00851 · HTML: https://mlsanity.com/paper/2003.00851/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2020.

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

- [Monocular 3D Object Detection: An Extrinsic Parameter Free Approach](https://mlsanity.com/paper/2106.15796/) (2021)
- [Multi-Modal 3D Object Detection in Autonomous Driving: a Survey](https://mlsanity.com/paper/2106.12735/) (2021)
- [Improved Orientation Estimation and Detection with Hybrid Object Detection Networks for Automotive Radar](https://mlsanity.com/paper/2205.02111/) (2022)
- [Self-Supervised Velocity Estimation for Automotive Radar Object Detection Networks](https://mlsanity.com/paper/2207.03146/) (2022)
- [Camera-Radar Perception for Autonomous Vehicles and ADAS: Concepts, Datasets and Metrics](https://mlsanity.com/paper/2303.04302/) (2023)
- [Robust Human Detection under Visual Degradation via Thermal and mmWave Radar Fusion](https://mlsanity.com/paper/2307.03623/) (2023)
- [Graph Query Networks for Object Detection with Automotive Radar](https://mlsanity.com/paper/2511.15271/) (2025)

## Abstract (arXiv)

Even though many existing 3D object detection algorithms rely mostly on camera and LiDAR, camera and LiDAR are prone to be affected by harsh weather and lighting conditions. On the other hand, radar is resistant to such conditions. However, research has found only recently to apply deep neural networks on radar data. In this paper, we introduce a deep learning approach to 3D object detection with radar only. To the best of our knowledge, we are the first ones to demonstrate a deep learning-based 3D object detection model with radar only that was trained on the public radar dataset. To overcome the lack of radar labeled data, we propose a novel way of making use of abundant LiDAR data by transforming it into radar-like point cloud data and aggressive radar augmentation techniques.