# RadarNet: Exploiting Radar for Robust Perception of Dynamic Objects

arXiv: https://arxiv.org/abs/2007.14366 · HTML: https://mlsanity.com/paper/2007.14366/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jul 2020.

## Built on by (most cited first, 30 of 48)

- [3D Object Detection for Autonomous Driving: A Comprehensive Survey](https://mlsanity.com/paper/2206.09474/) (2022)
- [Simple-BEV: What Really Matters for Multi-Sensor BEV Perception?](https://mlsanity.com/paper/2206.07959/) (2022)
- [RCBEVDet: Radar-camera Fusion in Bird's Eye View for 3D Object Detection](https://mlsanity.com/paper/2403.16440/) (2024)
- [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)
- [Bridging the View Disparity Between Radar and Camera Features for Multi-modal Fusion 3D Object Detection](https://mlsanity.com/paper/2208.12079/) (2022)
- [Exploring Adversarial Robustness of Multi-Sensor Perception Systems in Self Driving](https://mlsanity.com/paper/2101.06784/) (2021)
- [An Application-Driven Conceptualization of Corner Cases for Perception in Highly Automated Driving](https://mlsanity.com/paper/2103.03678/) (2021)
- [HRFuser: A Multi-resolution Sensor Fusion Architecture for 2D Object Detection](https://mlsanity.com/paper/2206.15157/) (2022)
- [CramNet: Camera-Radar Fusion with Ray-Constrained Cross-Attention for Robust 3D Object Detection](https://mlsanity.com/paper/2210.09267/) (2022)
- [MVFusion: Multi-View 3D Object Detection with Semantic-aligned Radar and Camera Fusion](https://mlsanity.com/paper/2302.10511/) (2023)
- [Bi-LRFusion: Bi-Directional LiDAR-Radar Fusion for 3D Dynamic Object Detection](https://mlsanity.com/paper/2306.01438/) (2023)
- [RadarDistill: Boosting Radar-based Object Detection Performance via Knowledge Distillation from LiDAR Features](https://mlsanity.com/paper/2403.05061/) (2024)
- [CRKD: Enhanced Camera-Radar Object Detection with Cross-modality Knowledge Distillation](https://mlsanity.com/paper/2403.19104/) (2024)
- [RCM-Fusion: Radar-Camera Multi-Level Fusion for 3D Object Detection](https://mlsanity.com/paper/2307.10249/) (2023)
- [Exploiting Temporal Relations on Radar Perception for Autonomous Driving](https://mlsanity.com/paper/2204.01184/) (2022)
- [A Survey of Label-Efficient Deep Learning for 3D Point Clouds](https://mlsanity.com/paper/2305.19812/) (2023)
- [MVFAN: Multi-View Feature Assisted Network for 4D Radar Object Detection](https://mlsanity.com/paper/2310.16389/) (2023)
- [RaCFormer: Towards High-Quality 3D Object Detection via Query-based Radar-Camera Fusion](https://mlsanity.com/paper/2412.12725/) (2024)
- [Full-Velocity Radar Returns by Radar-Camera Fusion](https://mlsanity.com/paper/2108.10637/) (2021)
- [DeepFusion: A Robust and Modular 3D Object Detector for Lidars, Cameras and Radars](https://mlsanity.com/paper/2209.12729/) (2022)
- [ADCNet: Learning from Raw Radar Data via Distillation](https://mlsanity.com/paper/2303.11420/) (2023)
- [Radar Voxel Fusion for 3D Object Detection](https://mlsanity.com/paper/2106.14087/) (2021)
- [NVRadarNet: Real-Time Radar Obstacle and Free Space Detection for Autonomous Driving](https://mlsanity.com/paper/2209.14499/) (2022)
- [MUFASA: Multi-View Fusion and Adaptation Network with Spatial Awareness for Radar Object Detection](https://mlsanity.com/paper/2408.00565/) (2024)
- [LiRaFusion: Deep Adaptive LiDAR-Radar Fusion for 3D Object Detection](https://mlsanity.com/paper/2402.11735/) (2024)
- [RCBEVDet++: Toward High-accuracy Radar-Camera Fusion 3D Perception Network](https://mlsanity.com/paper/2409.04979/) (2024)
- [Radars for Autonomous Driving: A Review of Deep Learning Methods and Challenges](https://mlsanity.com/paper/2306.09304/) (2023)
- [Depth Estimation Matters Most: Improving Per-Object Depth Estimation for Monocular 3D Detection and Tracking](https://mlsanity.com/paper/2206.03666/) (2022)
- [Reviewing 3D Object Detectors in the Context of High-Resolution 3+1D Radar](https://mlsanity.com/paper/2308.05478/) (2023)

## Abstract (arXiv)

We tackle the problem of exploiting Radar for perception in the context of self-driving as Radar provides complementary information to other sensors such as LiDAR or cameras in the form of Doppler velocity. The main challenges of using Radar are the noise and measurement ambiguities which have been a struggle for existing simple input or output fusion methods. To better address this, we propose a new solution that exploits both LiDAR and Radar sensors for perception. Our approach, dubbed RadarNet, features a voxel-based early fusion and an attention-based late fusion, which learn from data to exploit both geometric and dynamic information of Radar data. RadarNet achieves state-of-the-art results on two large-scale real-world datasets in the tasks of object detection and velocity estimation. We further show that exploiting Radar improves the perception capabilities of detecting faraway objects and understanding the motion of dynamic objects.