3D Shape Reconstruction from Autonomous Driving Radars


Samah Hussein (École Polytechnique Fédérale de Lausanne (EPFL)), Junfeng Guan (École Polytechnique Fédérale de Lausanne (EPFL), Bosch Research), Swathi Shree Narashiman (École Polytechnique Fédérale de Lausanne (EPFL)), Saurabh Gupta (University of Illinois Urbana-Champaign), Haitham Al Hassanieh (École Polytechnique Fédérale de Lausanne (EPFL))
The 35th British Machine Vision Conference
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Abstract

This paper presents RFconstruct, a framework that enables 3D shape reconstruction using commercial off-the-shelf (COTS) mmWave radars for self-driving scenarios. RFconstruct overcomes radar limitations of low angular resolution, specularity, and sparsity in radar point clouds through a holistic system design that addresses hardware, data processing, and machine learning challenges. The first step is fusing data captured by two radar devices that image orthogonal planes, then performing odometry-aware temporal fusion to generate denser 3D point clouds. RFconstruct then reconstructs 3D shapes of objects using a customized encoder-decoder model that does not require prior knowledge of the object’s bound box. The shape reconstruction performance of RFconstruct is compared against 3D models extracted from a depth camera equipped with a LiDAR. We show that RFconstruct can accurately generate 3D shapes of cars, bikes, and pedestrians.

Citation

@inproceedings{Hussein_2025_BMVC,
author    = {Samah Hussein and Junfeng Guan and Swathi Shree Narashiman and Saurabh Gupta and Haitham Al Hassanieh},
title     = {3D Shape Reconstruction from Autonomous Driving Radars},
booktitle = {36th British Machine Vision Conference 2025, {BMVC} 2025, Sheffield, UK, November 24-27, 2025},
publisher = {BMVA},
year      = {2025},
url       = {https://bmva-archive.org.uk/bmvc/2025/assets/papers/Paper_399/paper.pdf}
}


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