发表机构
Beihang University; Shanghai Artificial Intelligence Laboratory; Shanghai Jiao Tong University; Northwestern Polytechnical University（北京航空航天大学; 上海人工智能实验室; 上海交通大学; 西北工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
OpenFlyScan利用质量模型预测重建缺陷，规划针对性补拍，集成采集与重建，提升消费级无人机城市资产创建效率与质量。
AI 中文摘要
3D高斯泼溅（3DGS）为大规模具身模拟提供了高保真场景，但构建大规模城市资产仍受限于昂贵的设备和滞后的质量反馈。预设的航拍调查可能使复杂表面观测不足，缺陷仅在重建后被发现，导致需要返场和重复处理。我们提出了OpenFlyScan，一个面向消费级无人机的质量引导航拍重建系统，它集成了GS质量模型、重新采集规划器以及一个定制设计的移动应用。该模型从GS渲染误差中学习以预测区域重建质量。基于这些预测，规划器生成补充的重新采集航带，通过应用执行，该应用还支持自动倾斜摄影测量和数据传输，无需机载额外硬件。在真实航拍场景中，该模型有效识别了可能重建不佳的区域。在Expo West现场实验中，针对性的重新采集将额外视角的PSNR提升了10.95 dB。借助消费级无人机，OpenFlyScan集成了采集、针对性重新采集和重建，以支持快速、低成本的城市资产创建。代码和模型将在https://this URL上公开提供。
英文摘要
3D Gaussian Splatting (3DGS) provides high-fidelity scenes for large-scale embodied simulation, but constructing large-scale urban assets remains constrained by expensive equipment and delayed quality feedback. Preset surveys can leave complex surfaces insufficiently observed, with defects discovered only after reconstruction, requiring return visits and repeated processing. We present OpenFlyScan, a quality-guided aerial reconstruction system for consumer drones that integrates a GS quality model, a reacquisition planner, and a custom-designed mobile app. The model learns from GS rendering errors to predict regional reconstruction quality. Based on these predictions, the planner then generates complementary reacquisition strips to be executed through the app, which also supports automated oblique surveys and data transfer without additional hardware on board. Across real aerial scenes, the model effectively identifies regions that are likely to be poorly reconstructed. In the Expo West field experiment, targeted reacquisition improves PSNR at additional views by 10.95 dB. With consumer drones, OpenFlyScan integrates capture, targeted reacquisition, and reconstruction to support rapid, low-cost urban asset creation. Code and models will be made publicly available at https://openflyscan.github.io/.
