Shape-Optimized Gaussian Splatting for UAV Reconstruction with Manhattan Constraints

The emergence of Gaussian Splatting has ushered in a new phase in large-scale scene geometry reconstruction. Existing research in this field primarily focuses on computational efficiency and often neglects the effective utilization of inherent structural geometric priors. This paper introduces a shape-optimized Gaussian Splatting method based on the Manhattan World Assumption for precise geometric reconstruction from drone imagery. By leveraging the constraints provided by this assumption, a geometry-driven optimization mechanism is developed to guide the deformation and distribution of each Gaussian splat. This ensures improved alignment with the dominant structural directions of the scene. Additionally, dense point clouds are generated from the optimized Gaussian representation, making them more suitable for downstream tasks. The proposed approach maintains high visual fidelity while significantly improving geometric accuracy. Extensive experiments on multiple large-scale scene datasets, including a new benchmark for Jinan City, demonstrate that the method surpasses current state-of-the-art approaches in geometry-oriented evaluation metrics by a considerable margin.