# 3D Object Reconstruction with mmWave Radars

arXiv: https://arxiv.org/abs/2504.12348 · HTML: https://mlsanity.com/paper/2504.12348/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Apr 2025.

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

- [Seeing through boxes: Non-Line-of-Sight 3D Reconstruction from Radar Signals](https://mlsanity.com/paper/2605.29098/) (2026)

## Abstract (arXiv)

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.