# RadarNeXt: Real-Time and Reliable 3D Object Detector Based On 4D mmWave Imaging Radar

arXiv: https://arxiv.org/abs/2501.02314 · HTML: https://mlsanity.com/paper/2501.02314/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 35 references that are in the map.
- First posted on arXiv 4 Jan 2025.

## Builds on (most cited first, 26 of 26)

- [PointPillars: Fast Encoders for Object Detection from Point Clouds](https://mlsanity.com/paper/1812.05784/) (2018)
- [Center-based 3D Object Detection and Tracking](https://mlsanity.com/paper/2006.11275/) (2020)
- [InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions](https://mlsanity.com/paper/2211.05778/) (2022)
- [YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications](https://mlsanity.com/paper/2209.02976/) (2022)
- [Pillar-based Object Detection for Autonomous Driving](https://mlsanity.com/paper/2007.10323/) (2020)
- [MobileOne: An Improved One millisecond Mobile Backbone](https://mlsanity.com/paper/2206.04040/) (2022)
- [CenterFormer: Center-based Transformer for 3D Object Detection](https://mlsanity.com/paper/2209.05588/) (2022)
- [TJ4DRadSet: A 4D Radar Dataset for Autonomous Driving](https://mlsanity.com/paper/2204.13483/) (2022)
- [3D Object Detection from Images for Autonomous Driving: A Survey](https://mlsanity.com/paper/2202.02980/) (2022)
- [Radar-Camera Fusion for Object Detection and Semantic Segmentation in Autonomous Driving: A Comprehensive Review](https://mlsanity.com/paper/2304.10410/) (2023)
- [LXL: LiDAR Excluded Lean 3D Object Detection with 4D Imaging Radar and Camera Fusion](https://mlsanity.com/paper/2307.00724/) (2023)
- [PillarNeXt: Rethinking Network Designs for 3D Object Detection in LiDAR Point Clouds](https://mlsanity.com/paper/2305.04925/) (2023)
- [RADDet: Range-Azimuth-Doppler based Radar Object Detection for Dynamic Road Users](https://mlsanity.com/paper/2105.00363/) (2021)
- [SMURF: Spatial Multi-Representation Fusion for 3D Object Detection with 4D Imaging Radar](https://mlsanity.com/paper/2307.10784/) (2023)
- [CNN based Road User Detection using the 3D Radar Cube](https://mlsanity.com/paper/2004.12165/) (2020)
- [Lightweight Deep Learning for Resource-Constrained Environments: A Survey](https://mlsanity.com/paper/2404.07236/) (2024)
- [WaterScenes: A Multi-Task 4D Radar-Camera Fusion Dataset and Benchmarks for Autonomous Driving on Water Surfaces](https://mlsanity.com/paper/2307.06505/) (2023)
- [On Mean Absolute Error for Deep Neural Network Based Vector-to-Vector Regression](https://mlsanity.com/paper/2008.07281/) (2020)
- [RadarPillars: Efficient Object Detection from 4D Radar Point Clouds](https://mlsanity.com/paper/2408.05020/) (2024)
- [Talk2Radar: Bridging Natural Language with 4D mmWave Radar for 3D Referring Expression Comprehension](https://mlsanity.com/paper/2405.12821/) (2024)
- [Achelous: A Fast Unified Water-surface Panoptic Perception Framework based on Fusion of Monocular Camera and 4D mmWave Radar](https://mlsanity.com/paper/2307.07102/) (2023)
- [MSSF: A 4D Radar and Camera Fusion Framework With Multi-Stage Sampling for 3D Object Detection in Autonomous Driving](https://mlsanity.com/paper/2411.15016/) (2024)
- [MUFASA: Multi-View Fusion and Adaptation Network with Spatial Awareness for Radar Object Detection](https://mlsanity.com/paper/2408.00565/) (2024)
- [ASY-VRNet: Waterway Panoptic Driving Perception Model based on Asymmetric Fair Fusion of Vision and 4D mmWave Radar](https://mlsanity.com/paper/2308.10287/) (2023)
- [NanoMVG: USV-Centric Low-Power Multi-Task Visual Grounding based on Prompt-Guided Camera and 4D mmWave Radar](https://mlsanity.com/paper/2408.17207/) (2024)
- [A Comprehensive Review of 3D Object Detection in Autonomous Driving: Technological Advances and Future Directions](https://mlsanity.com/paper/2408.16530/) (2024)

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

- [Wavelet-based Multi-View Fusion of 4D Radar Tensor and Camera for Robust 3D Object Detection](https://mlsanity.com/paper/2512.22972/) (2025)

## Abstract (arXiv)

3D object detection is crucial for Autonomous Driving (AD) and Advanced Driver Assistance Systems (ADAS). However, most 3D detectors prioritize detection accuracy, often overlooking network inference speed in practical applications. In this paper, we propose RadarNeXt, a real-time and reliable 3D object detector based on the 4D mmWave radar point clouds. It leverages the re-parameterizable neural networks to catch multi-scale features, reduce memory cost and accelerate the inference. Moreover, to highlight the irregular foreground features of radar point clouds and suppress background clutter, we propose a Multi-path Deformable Foreground Enhancement Network (MDFEN), ensuring detection accuracy while minimizing the sacrifice of speed and excessive number of parameters. Experimental results on View-of-Delft and TJ4DRadSet datasets validate the exceptional performance and efficiency of RadarNeXt, achieving 50.48 and 32.30 mAPs with the variant using our proposed MDFEN. Notably, our RadarNeXt variants achieve inference speeds of over 67.10 FPS on the RTX A4000 GPU and 28.40 FPS on the Jetson AGX Orin. This research demonstrates that RadarNeXt brings a novel and effective paradigm for 3D perception based on 4D mmWave radar.