Urban Neural Surface Reconstruction from Constrained Sparse Aerial Imagery with 3D SAR Fusion

TL;DR

This paper introduces a novel urban neural surface reconstruction framework that fuses 3D SAR data with aerial imagery to improve 3D urban modeling accuracy and robustness in sparse-view scenarios.

Contribution

It is the first to integrate 3D SAR point clouds into neural surface reconstruction for urban environments, addressing geometric ambiguity under limited-view conditions.

Findings

Enhanced reconstruction accuracy with SAR data

Improved robustness in sparse and oblique views

Established a new benchmark dataset for cross-modal 3D reconstruction

Abstract

Neural surface reconstruction (NSR) has recently shown strong potential for urban 3D reconstruction from multi-view aerial imagery. However, existing NSR methods often suffer from geometric ambiguity and instability, particularly under sparse-view conditions. This issue is critical in large-scale urban remote sensing, where aerial image acquisition is limited by flight paths, terrain, and cost. To address this challenge, we present the first urban NSR framework that fuses 3D synthetic aperture radar (SAR) point clouds with aerial imagery for high-fidelity reconstruction under constrained, sparse-view settings. 3D SAR can efficiently capture large-scale geometry even from a single side-looking flight path, providing robust priors that complement photometric cues from images. Our framework integrates radar-derived spatial constraints into an SDF-based NSR backbone, guiding structure-aware ray selection and adaptive sampling for stable and efficient optimization. We also construct the first benchmark dataset with co-registered 3D SAR point clouds and aerial imagery, facilitating systematic evaluation of cross-modal 3D reconstruction. Extensive experiments show that incorporating 3D SAR markedly enhances reconstruction accuracy, completeness, and robustness compared with single-modality baselines under highly sparse and oblique-view conditions, highlighting a viable route toward scalable high-fidelity urban reconstruction with advanced airborne and spaceborne optical-SAR sensing.