2026· IEEE Transactions on Geoscience and Remote Sensing· Vol 64, pp. 5630221-5630221· 0 citations· 55 references
Abstract
True digital orthophoto maps (TDOMs) accurately represent the true spatial positions and visual appearances of ground objects at the urban scale, and constitute a fundamental remote sensing product for large-scale urban digitization and fine-grained geographic modeling. Conventional TDOM generation methods that rely on digital elevation models (DEMs) or digital surface models (DSMs) are highly susceptible to occlusions and elevation errors in complex urban environments, often resulting in geometric distortions and visual artifacts. Neural radiance field (NeRF)-based approaches can mitigate these issues, but their high computational cost limits practical deployment on large-scale satellite imagery. To address these challenges, we present Tortho–SatGS, a pure vision-based framework that, to the best of our knowledge, is among the first to systematically integrate 3-D Gaussian Splatting (3DGS) into satellite true orthophoto generation. Specifically, we design a 3DGS-based geometric modeling pipeline tailored to satellite imaging geometry and introduce an orthographic rasterization-based rendering scheme to effectively resolve building side facades and edge curvature artifacts, enabling DEM/DSM-free TDOM generation with improved geometric consistency. In addition, a VGG-based perceptual loss is incorporated to complement pixel-level supervision, improving radiometric consistency and fine-grained texture fidelity, particularly in shadowed and low-texture regions. Experimental results on two real-world satellite datasets demonstrate that Tortho–SatGS consistently outperforms conventional methods in terms of geometric accuracy, texture quality, and radiometric consistency. Compared with NeRF-based approaches, our method achieves approximately $27\times $ faster training speed and $15\times $ faster rendering speed, effectively balancing reconstruction accuracy and computational efficiency. These results validate the effectiveness and practical advantages of 3DGS-based approaches for the generation of true orthophotos of satellites.
True digital orthophoto maps (TDOMs) serve as foundational geospatial products for applications in land surveying, urban planning, and emergency management. Conventional TDOM generation relies on differential correction, often resulting in cartographic artifacts such as geometric discontinuities, radiometric inconsistencies, and linear feature misalignments. Although recent methods leverage 3D Gaussian splatting to bypass differential correction, their computational demands hinder real-world deployment. To address these limitations, we propose FastPro-Gaussian, a novel framework enabling rapid high-quality TDOM generation on consumer-grade GPUs. Our contributions are threefold: block-based processing ensuring scalability for large-scale scenes; progressive densification stabilizing model optimization and reducing training iterations; and spherical-to-ellipsoidal Gaussian transformation, accelerating early-stage optimization of structural features (e.g., building edges). Experiments demonstrate that FastPro-Gaussian surpasses commercial solutions (ContextCapture, Metashape, and Pix4Dmapper) in rendering quality for building facades, edges, and roads. Compared to state-of-the-art methods, it achieves comparable TDOM fidelity with over two-fold acceleration in training time (notably more than two times faster than Tortho-GS). These gains in efficiency and efficacy confirm its strong potential for practical deployment in geospatial production pipelines.
Chao Yang, Yapeng Li, Feiyang Liu et al.· Photogrammetric Engineering...· 0 citations
A platform-aware benchmark framework that jointly records visual fidelity, computational cost, metric geometry, product utility, failure behavior, and reproducibility metadata for UAV/aerial, satellite, and hybrid settings is proposed.
Wen-Xuan Fan, Bo Wang, Junqiang Ye et al.· Remote Sensing· 0 citations
Abstract. Satellite imagery offers a distinct advantage in Earth observation by providing expansive coverage and enabling the monitoring of inaccessible regions without physical on-site intervention, serving as a significantly more cost-effective and scalable alternative to traditional aerial or ground-based surveys. The task of 3D reconstruction from multi-view satellite images has therefore been a pivotal point of research at the intersection of photogrammetry and remote sensing. Recently, novel-view synthesis techniques such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) have accelerated the accuracy and speed of topographic modeling. Among these, Earth Observation Gaussian Splatting (EOGS) has emerged as a state-of-the-art approach by adapting 3DGS to handle the unique geometric and radiometric characteristics of satellite data, including Rational Polynomial Coefficients (RPCs) and varying solar conditions. However, the standard EOGS pipeline relies on stochastic initialization, where Gaussians are distributed uniformly within a volumetric bounding box, leading to high computational overhead and dependency on aggressive pruning that can inadvertently remove critical geometric features, particularly in areas with complex urban structures. To address these limitations, we propose Bundle-Adjusted Initialization for Earth Observation Gaussian Splatting, which leverages sparse point clouds from bundle adjustment as geometric priors for Gaussian initialization. Combined with an adaptive densification strategy, our method achieves faster convergence and improved DSM accuracy on the DFC2019 dataset compared to the EOGS baseline.
Jiyong Kim, Shuang Song, Rongjun Qin· The International Archives o...· 0 citations
Abstract. High-resolution 3D documentation of cultural heritage sites is essential for their preservation. While terrestrial laser scanning (TLS) remains the gold standard, it is often cost-intensive compared to photogrammetry. This study evaluates three image-based reconstruction techniques, Multi-View Stereo (MVS), Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS), by applying them to a complex scene featuring a chapel and its surrounding vegetation, sensed from an uncrewed aerial vehicle (UAV). A hybrid TLS/MVS model provides a high-accuracy reference. Using identical interior and exterior camera parameters of the 105 UAV-acquired images, we generate dense point clouds with all methods and assess geometric accuracy and completeness using the M3C2 algorithm. Results show that MVS achieves superior accuracy (standard deviation of all M3C2 distances: MVS = 0.11 m, NeRF = 0.15 m), whereas NeRF attains up to 20% higher completeness, particularly in low-texture and vegetation-occluded regions. The 3DGS point cloud was deemed too sparse and was therefore not used for further analysis. The study highlights the potential of NeRFs to recover partially occluded or sparsely textured geometries that are challenging for MVS and suggests a complementary use of both approaches for cost-efficient documentation of cultural heritage.
Frederik Schulte, P. Akwensi, L. Winiwarter· The International Archives o...· 0 citations
High-precision 3D reconstruction of objects with complex surfaces, such as ancient architecture and detailed artworks, requires close-range image acquisition, which remains challenging for Unmanned Aerial Vehicle (UAV) systems. The operational proximity of current UAV workflows is often insufficient to capture fine geometric and textural details, limiting high-fidelity digitization. This paper presents a georeferenced NeRF-based UAV acquisition framework for automated waypoint planning and supervised close-proximity execution. The core of the framework is a path-planning module that operates on a metric geometric prior established through Geographic Neural Radiance Fields (Geo-NeRF), which denotes a georeferenced NeRF modeling pipeline rather than a new NeRF architecture or loss function. By generating waypoints directly on this neural representation and optimizing the flight path via a nearest-neighbor strategy, the proposed framework supports close-proximity image acquisition for static targets under controlled conditions. Empirical validation demonstrates improved close-range flight proximity, photographic accuracy, and 3D reconstruction fidelity compared with the evaluated baselines.
Haoyu Liu, Yizhi Zou, Lu Yang et al.· Drones· 0 citations
Abstract. The proliferation of continuous Neural Radiance Field (NeRF) and 3D Gaussian Splatting (3DGS) has shifted the paradigm of 3D aerial reconstruction from relying solely on geometric stereo matching to inverse rendering optimization. However, while these emerging rendering-based frameworks excel in synthesizing photo-realistic novel views, their capability to extract accurate surfaces in complex aerial scenarios remains ambiguous compared to traditional methods. To establish a clearer understanding, this study presents a comprehensive evaluation of five representative frameworks spanning traditional Structure from Motion (SfM), purely Signed Distance Field (SDF) representations, unstructured 3D Gaussians, hybrid voxel-Gaussians, and strictly explicit sparse voxels. By systematically standardizing identical computational environments, inputs, and unified mesh-extraction pipelines on both real-world airborne LiDAR datasets and synthetic cityscapes, we assess their performance regarding visual fidelity, geometric accuracy, and resource efficiency. The experimental results reveal that while traditional MVS produces the highest overall geometric precision by strictly enforcing multi-view rigid geometry, it is prone to failures in texture-less regions. Among rendering-based representations, a fundamental trade-off exists: highly flexible, unstructured 3DGS achieve highest visual scores but degrade the underlying geometric surfaces; conversely, explicitly structured techniques demonstrate distinct superiority in regularizing topological coherence and floating artifact suppression. Furthermore, we observe that integrating structured voxels avoids the severe memory bottlenecks associated with extracting geometries from chaotic unorganized primitives. These empirical findings emphasize that for large-scale aerial photogrammetry, integrating explicit spatial structuralization into differentiable rendering pipelines is imperative for achieving scalable operations and bridging the geometric accuracy gap with traditional methods.
Shihan Chen, Zhaojin Li, Qingsong Yan et al.· The International Archives o...· 0 citations