Radar2Shape: 3D Shape Reconstruction from High-Frequency Radar using Multiresolution Signed Distance Functions
This addresses the problem of accurate 3D shape reconstruction for commercial and aerospace applications, offering a novel method that improves over previous deep learning approaches by handling real-world radar limitations, though it is incremental in adapting diffusion models to this specific domain.
The paper tackles 3D shape reconstruction from high-frequency radar signals, which is complex due to partial observability and limited viewing angles, by introducing Radar2Shape, a denoising diffusion model that correlates radar frequencies with multiresolution shape features, achieving successful reconstruction of arbitrary shapes from partially-observed signals and robust generalization to simulations and real-world data.
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.