The ascension of Unmanned Aerial Vehicles (UAVs) in various fields
necessitates effective UAV image segmentation, which faces challenges due to
the dynamic perspectives of UAV-captured images. Traditional segmentation
algorithms falter as they cannot accurately mimic the complexity of UAV
perspectives, and the cost of obtaining multi-perspective labeled datasets is
prohibitive. To address these issues, we introduce the PPTFormer, a novel
\textbf{P}seudo Multi-\textbf{P}erspective \textbf{T}rans\textbf{former}
network that revolutionizes UAV image segmentation. Our approach circumvents
the need for actual multi-perspective data by creating pseudo perspectives for
enhanced multi-perspective learning. The PPTFormer network boasts Perspective
Decomposition, novel Perspective Prototypes, and a specialized encoder and
decoder that together achieve superior segmentation results through Pseudo
Multi-Perspective Attention (PMP Attention) and fusion. Our experiments
demonstrate that PPTFormer achieves state-of-the-art performance across five
UAV segmentation datasets, confirming its capability to effectively simulate
UAV flight perspectives and significantly advance segmentation precision. This
work presents a pioneering leap in UAV scene understanding and sets a new
benchmark for future developments in semantic segmentation.

通过引入 PPTFormer 网络，我们提出了一种新的伪多视角转换网络，通过创建伪视角来增强多视角学习，并通过 Pseudo Multi-Perspective Attention（PMP Attention）和融合，实现了卓越的无人机图像分割结果。该方法在五个无人机分割数据集上达到了最先进的性能，验证了其能够有效模拟无人机飞行视角并显著提高分割精度，从而在无人机场景理解方面取得了具有开创性的突破，并为语义分割的未来发展树立了新的基准。