True Digital Orthophoto Map (TDOM), a 2D objective representation of the Earth's surface, is an essential geospatial product widely used in urban management, city planning, land surveying, and related applications. However, traditional TDOM generation typically relies on a complex offline photogrammetric pipeline, leading to substantial latency and making it unsuitable for time-critical or real-time scenarios. Moreover, the quality of TDOM may deteriorate due to inaccurate camera poses, imperfect Digital Surface Model (DSM), and incorrect occlusions detection. To address these challenges, this work introduces A-TDOM, a near real-time TDOM generation method built upon On-the-Fly 3DGS (3D Gaussian Splatting) optimization. As each incoming image arrives, its pose and sparse point cloud are computed via On-the-Fly SfM. Newly observed regions are then incrementally reconstructed as additional 3D Gaussians are inserted using a Delaunay triangulated Gaussian sampling and integration and are further optimized via adaptive training iterations and learning rate, especially in previously unseen or coarsely modeled areas. With orthogonal splatting integrated into the rendering pipeline, A-TDOM can actively produce updated TDOM outputs immediately after each 3DGS update. Code is now available at https://github.com/xywjohn/A-TDOM.
翻译:真实数字正射影像图(True Digital Orthophoto Map, TDOM)作为地球表面的二维客观表达,是一种广泛应用于城市管理、城市规划、土地测绘及相关领域的重要地理空间产品。然而,传统的TDOM生成通常依赖于复杂的离线摄影测量流程,导致显著的延迟,使其难以适用于时间敏感或实时场景。此外,由于相机位姿不准确、数字表面模型(Digital Surface Model, DSM)不完善以及遮挡检测错误,TDOM的质量可能下降。为应对这些挑战,本研究提出了A-TDOM——一种基于实时3DGS(3D Gaussian Splatting)优化的近实时TDOM生成方法。随着每幅输入图像的到达,系统通过实时运动恢复结构(On-the-Fly SfM)计算其位姿与稀疏点云。随后,利用Delaunay三角剖分的高斯采样与集成方法,将新观测区域以增量方式重建为新增的3D高斯模型,并通过自适应训练迭代与学习率进行优化,特别是在先前未观测或建模粗糙的区域。通过将正交投影渲染集成至绘制流水线,A-TDOM能够在每次3DGS更新后立即动态生成更新的TDOM输出。代码已发布于https://github.com/xywjohn/A-TDOM。