Abstract
Drones have become indispensable for reconstructing in-the-wild scenes due to their remarkable agility. Recent advances in radiance-field representations have achieved photorealistic rendering, opening new avenues for 3D reconstruction from drone imagery. However, limited view constraints hinder reliable geometric recovery, while dynamic distractors in the wild violate the multi-view consistency in radiance fields. In the wild, these two challenges are inextricably coupled. To address this coupled dilemma, we propose DroneSplat+, a novel framework that formulates semantics as a unifying hub to simultaneously resolve both issues. DroneSplat+ couples multi-view stereo predictions with segmentation priors to impose semantic constraints that steer Gaussian optimization, enabling accurate geometry recovery under limited-view conditions. To suppress dynamics, DroneSplat+ introduces a dual-elimination strategy that combines adaptive local-global masking and label-based localization to precisely identify and remove dynamic distractors from static scenes. We also provide a drone-captured 3D reconstruction benchmark dataset encompassing both dynamic and static scenes for comprehensive evaluation. Experimental results show that our method outperforms both 3DGS and NeRF baselines in reconstructing in-the-wild drone imagery.