Aerial path planning for 3D urban scene reconstruction with dual-task reconstructability learning and adaptive viewpoints selection

Using images captured by Unmanned Aerial Vehicles (UAVs) to perform 3D reconstruction is a cost-effective way to acquire high-quality 3D models for large-scale urban scenes. The challenge, however, has become choosing camera viewpoints and planning flight path accordingly. Existing methods either plan the aerial path heuristically or train a reconstructability predictor, where the accuracy and completeness losses are optimized separately in a two-phase approach, leading to inaccurate reconstruction results and poor generalizability. To address these issues, this paper proposes a dual-task learning framework that establishes the correlation between viewpoint poses and reconstruction quality. In particular, the reconstructability estimation problem is modeled as two subtasks: reconstruction accuracy and reconstruction completeness, allowing both subtasks to be tackled simultaneously within a unified network. The model’s generalizability is improved by the soft parameter sharing and a new dual-loss function with trainable weight parameters. In addition, an adaptive viewpoint optimization strategy is proposed to refine an initial set of viewpoints generated based on the learned reconstructability. Our framework is extensively evaluated on both public datasets and two datasets we collected. Qualitative and quantitative experimental results demonstrate the superiority of our method in both synthetic and real scenes. Our framework achieves consistent improvements over state-of-the-art approaches by an average of 3.6% in F-score with 15% fewer images, and surpasses Oblique Photography with a 6% F-score gain while using 40% less image data. These advancements hold universally across all test scenes, outperforming prior methods in terms of accuracy and completeness. • A dual-task framework to predict reconstruction accuracy and completeness error. • A dual-loss with trainable weight parameters to balance the training of subtasks. • An adaptive viewpoint optimization strategy to achieve better reconstruction results. • State-of-the-art performance in synthetic and real scenes across different datasets.