Neural Implicit Representations for 3D Synthetic Aperture Radar Imaging

2026-02-19

AI Summary

This work addresses the severe reconstruction artifacts in 3D synthetic aperture radar (SAR) imaging caused by insufficient sampling in the Fourier domain. To this end, it introduces— for the first time in this field—a neural implicit surface representation, modeling the signed distance function (SDF) with a neural network to characterize the target’s scattering surface. The approach integrates a physics-driven SAR scattering model with an implicit surface sampling regularization strategy, enabling high-fidelity 3D reconstruction from sparse scattering measurements. Evaluated on both real and simulated data across single-vehicle and multi-vehicle scenarios, the method consistently outperforms existing techniques, achieving state-of-the-art performance in sparse-view 3D SAR imaging.

Technology Category

• Computer Vision: 3D Computer Vision
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Representation Learning

Application Category

• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Synthetic aperture radar (SAR) is a tomographic sensor that measures 2D slices of the 3D spatial Fourier transform of the scene. In many operational scenarios, the measured set of 2D slices does not fill the 3D space in the Fourier domain, resulting in significant artifacts in the reconstructed imagery. Traditionally, simple priors, such as sparsity in the image domain, are used to regularize the inverse problem. In this paper, we review our recent work that achieves state-of-the-art results in 3D SAR imaging employing neural structures to model the surface scattering that dominates SAR returns. These neural structures encode the surface of the objects in the form of a signed distance function learned from the sparse scattering data. Since estimating a smooth surface from a sparse and noisy point cloud is an ill-posed problem, we regularize the surface estimation by sampling points from the implicit surface representation during the training step. We demonstrate the model's ability to represent target scattering using measured and simulated data from single vehicles and a larger scene with a large number of vehicles. We conclude with future research directions calling for methods to learn complex-valued neural representations to enable synthesizing new collections from the volumetric neural implicit representation.

Problem

• Synthetic Aperture Radar
• 3D Imaging
• Ill-posed Inverse Problem
• Sparse Scattering Data
• Tomographic Reconstruction

Innovation

• Neural Implicit Representations
• Synthetic Aperture Radar
• Signed Distance Function
• 3D Imaging
• Surface Scattering