发表机构
Indian Institute of Technology Bombay（印度理工学院孟买分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对开放词汇无人机感知中移动平台的预测时间不一致问题，提出体素级评估框架，结合度量3D融合研究时间语义稳定性，发现观测持久性是评估长程语义可靠性的关键条件变量。
AI 中文摘要
近期的开放词汇分割模型推动了无人机的语义感知，但移动空中平台对同一物理场景的重复观测仍会出现预测时间不一致的问题。本文通过将逐帧预测与持久世界空间位置关联，结合度量3D融合来研究时间语义稳定性。我们提出一种体素级评估框架，共同表征最终语义一致性、语义信念漂移（Semantic Belief Drift, SBD）、观测持久性（Observation Persistence, OP）及语义不确定性。在UAVid-3D上的实验显示，存在显著的逐帧语义闪烁，且当位置的重复观测支持有限时，高聚合世界空间一致性会夸大时间稳定性。按持久性分层分析表明，重复出现的体素会暴露更大的语义分歧，而信念漂移会随额外证据的积累而降低。该行为在两种分割骨干网络上均被观察到，且在体素分辨率、几何关联和时间采样密度变化时保持一致。减少世界空间重复的条件可提高表观聚合稳定性，表明语义一致性必须与观测支持共同解释。研究结果强调观测持久性是评估长程语义可靠性的关键条件变量。
英文摘要
Recent open-vocabulary segmentation models have advanced semantic perception for UAVs, but predictions from moving aerial platforms can remain temporally inconsistent across repeated observations of the same physical scene. We investigate temporal semantic stability by associating frame-wise predictions with persistent world-space locations through metric 3D fusion. We introduce a voxel-level evaluation framework that jointly characterises final semantic agreement, Semantic Belief Drift (SBD), Observation Persistence (OP), and semantic uncertainty. Experiments on UAVid-3D reveal substantial frame-wise semantic flicker and show that high aggregate world-space agreement can overstate temporal stability when locations have limited repeated-observation support. Persistence-stratified analysis shows that recurrent voxels expose greater semantic disagreement, while belief drift decreases as additional evidence accumulates. This behaviour is observed across two segmentation backbones and remains consistent under variations in voxel resolution, geometric association, and temporal sampling density. Conditions that reduce world-space recurrence can increase apparent aggregate stability, demonstrating that semantic consistency must be interpreted together with observation support. Our findings highlight observation persistence as an essential conditioning variable for evaluating long-horizon semantic reliability.
