发表机构
Stargate Studios Malta; University of Malta（马耳他星门工作室; 马耳他大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对无人机摄影测量的局部重建误差问题，提出迭代式混合离散-连续视点规划方法，通过CMA-ES优化生成平衡局部质量与全局鲁棒性的飞行路径，在合成场景上提升了重建精度与完整性。
AI 中文摘要
无人机（UAV）摄影测量需要相机网络提供足够的表面覆盖度、图像重叠度、视差和分辨率，而常规飞行模式往往难以适配场景几何结构，导致局部重建误差。本文提出一种基于代理重建的、面向目标无人机摄影测量的迭代式混合离散-连续视点规划方法。该方法利用基于正面性、成像距离、视差和多视图观测数量的摄影测量启发式规则对采样表面点进行评分，同时从可见性、成对重叠度和图连通性角度评估完整视点集。候选视点在弱观测区域周围生成，通过聚类协方差矩阵自适应进化策略（CMA-ES）优化进行精细化，冗余视点则被移除。最终飞行路径结合了近距细节视点与更广模型覆盖视点，平衡了局部重建质量与全局图像网络鲁棒性。在三个合成场景上的评估表明，与现有无人机路径规划方法相比，所提方法同时提升了重建精度与完整性。
英文摘要
Unmanned aerial vehicle (UAV) photogrammetry requires camera networks that provide sufficient surface coverage, image overlap, parallax, and resolution, yet conventional flight patterns are often poorly adapted to scene geometry resulting in local reconstruction errors. This paper proposes an iterative hybrid discrete-continuous viewpoint planning method for targeted UAV photogrammetry from a proxy reconstruction. The method scores sampled surface points using photogrammetric heuristics based on frontality, imaging distance, parallax, and multi-view observation count, while also evaluating the full viewpoint set in terms of visibility, pairwise overlap, and graph connectivity. Candidate viewpoints are generated around weakly observed regions, refined using clustered Covariance matrix adaptation evolution strategy (CMA-ES) optimisation, and removed when redundant. The final flight path combines close-range detail viewpoints with wider model-coverage viewpoints, balancing local reconstruction quality with global image-network robustness. Evaluation on three synthetic scenes shows that the proposed method improves both reconstruction accuracy and completeness compared with prior UAV path-planning methods.
Comments6 pages, 3 figures, 2 tables. Accepted for publication at the 14th IEEE European Conference on Visual Information Processing (EUVIP 2026)
