Houjun Sun

Da Li, Chen Yao, Tong Mao et al.

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.

Da Li, Guoqiang Zhao, Chen Yao et al.

Three-dimensional synthetic aperture radar (3D SAR) is an advanced active microwave imaging technology widely utilized in remote sensing area. To achieve high-resolution 3D imaging,3D SAR requires observations from multiple aspects and altitude baselines surrounding the target. However, constrained flight trajectories often lead to sparse observations, which degrade imaging quality, particularly for anisotropic man-made small targets, such as vehicles and aircraft. In the past, compressive sensing (CS) was the mainstream approach for sparse 3D SAR image reconstruction. More recently, deep learning (DL) has emerged as a powerful alternative, markedly boosting reconstruction quality and efficiency. However, existing DL-based methods typically rely solely on high-quality 3D SAR images as supervisory signals to train deep neural networks (DNNs). This unimodal learning paradigm prevents the integration of complementary information from other data modalities, which limits reconstruction performance and reduces target discriminability due to the inherent constraints of electromagnetic scattering. In this paper, we introduce cross-modal learning and propose a Cross-Modal 3D-SAR Reconstruction Network (CMAR-Net) for enhancing sparse 3D SAR images of vehicle targets by fusing optical information. Leveraging cross-modal supervision from 2D optical images and error propagation guaranteed by differentiable rendering, CMAR-Net achieves efficient training and reconstructs sparse 3D SAR images, which are derived from highly sparse-aspect observations, into visually structured 3D vehicle images. Trained exclusively on simulated data, CMAR-Net exhibits robust generalization to real-world data, outperforming state-of-the-art CS and DL methods in structural accuracy within a large-scale parking lot experiment involving numerous civilian vehicles, thereby demonstrating its strong practical applicability.