In outdoor scene reconstruction, dynamic occlusions and multiscale structures often undermine multiview consistency and hinder effective gradient accumulation of high-frequency Gaussian primitives, leading to artifacts and the loss of fine details in Gaussian splatting–based radiance field methods. To address these challenges, we propose an uncertainty-aware hierarchical Gaussian splatting framework for outdoor 3D reconstruction. Specifically, our method constructs a hierarchical octree-based spatial representation from the results of aerial triangulation. It introduces level of detail constraints to enable structured management and progressive optimization of Gaussian primitives across different scales. This design effectively alleviates the imbalance in training and the redundant growth of Gaussian primitives commonly observed in multiscale outdoor scenes. In addition, we incorporate an uncertainty prediction mechanism that evaluates the consistency between rendered results and ground-truth images in the feature space, allowing the model to automatically identify dynamically occluded regions and suppress their gradient contributions during optimization. As a result, the adverse impact of dynamic artifacts on static scene modeling is substantially reduced. Experimental results demonstrate that, without incurring significant additional training overhead, our method consistently improves structural consistency and fine-detail reconstruction quality in outdoor scenes, while simultaneously reducing model complexity and maintaining real-time rendering performance. Furthermore, the proposed approach can be seamlessly integrated into multiple mainstream Gaussian splatting frameworks, exhibiting strong robustness and promising potential for practical deployment.
Junxing Yang, Haoran Gao, Chunyu Huang et al.· Journal of Electronic Imagin...· 0 citations
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.
Junxing Yang, Wenya Bian, Xingcheng Liu et al.· IEEE Transactions on Geoscie...· 0 citations