Radar2Shape: 3D Shape Reconstruction from High-Frequency Radar using Multiresolution Signed Distance Functions

TL;DR

Radar2Shape introduces a novel diffusion-based approach for 3D shape reconstruction from high-frequency radar signals, effectively handling partial observations and generalizing across simulation and real-world data.

Contribution

It presents a two-stage multiresolution diffusion model that correlates radar frequencies with shape features, enabling accurate 3D reconstruction from limited radar views.

Findings

Successfully reconstructs arbitrary 3D shapes from partial radar signals

Demonstrates robust generalization to different simulation methods and real data

Provides synthetic datasets to foster future high-frequency radar research

Abstract

Determining the shape of 3D objects from high-frequency radar signals is analytically complex but critical for commercial and aerospace applications. Previous deep learning methods have been applied to radar modeling; however, they often fail to represent arbitrary shapes or have difficulty with real-world radar signals which are collected over limited viewing angles. Existing methods in optical 3D reconstruction can generate arbitrary shapes from limited camera views, but struggle when they naively treat the radar signal as a camera view. In this work, we present Radar2Shape, a denoising diffusion model that handles a partially observable radar signal for 3D reconstruction by correlating its frequencies with multiresolution shape features. Our method consists of a two-stage approach: first, Radar2Shape learns a regularized latent space with hierarchical resolutions of shape features, and second, it diffuses into this latent space by conditioning on the frequencies of the radar signal in an analogous coarse-to-fine manner. We demonstrate that Radar2Shape can successfully reconstruct arbitrary 3D shapes even from partially-observed radar signals, and we show robust generalization to two different simulation methods and real-world data. Additionally, we release two synthetic benchmark datasets to encourage future research in the high-frequency radar domain so that models like Radar2Shape can safely be adapted into real-world radar systems.