Extracting building contours from remote sensing imagery is a significant
challenge due to buildings' complex and diverse shapes, occlusions, and noise.
Existing methods often struggle with irregular contours, rounded corners, and
redundancy points, necessitating extensive post-processing to produce regular
polygonal building contours. To address these challenges, we introduce a novel,
streamlined pipeline that generates regular building contours without
post-processing. Our approach begins with the segmentation of generic geometric
primitives (which can include vertices, lines, and corners), followed by the
prediction of their sequence. This allows for the direct construction of
regular building contours by sequentially connecting the segmented primitives.
Building on this pipeline, we developed P2PFormer, which utilizes a
transformer-based architecture to segment geometric primitives and predict
their order. To enhance the segmentation of primitives, we introduce a unique
representation called group queries. This representation comprises a set of
queries and a singular query position, which improve the focus on multiple
midpoints of primitives and their efficient linkage. Furthermore, we propose an
innovative implicit update strategy for the query position embedding aimed at
sharpening the focus of queries on the correct positions and, consequently,
enhancing the quality of primitive segmentation. Our experiments demonstrate
that P2PFormer achieves new state-of-the-art performance on the WHU, CrowdAI,
and WHU-Mix datasets, surpassing the previous SOTA PolyWorld by a margin of 2.7
AP and 6.5 AP75 on the largest CrowdAI dataset. We intend to make the code and
trained weights publicly available to promote their use and facilitate further
research.

从遥感图像中提取建筑轮廓是一个重要的挑战，本文介绍了一种新的流线型流程以无需后处理生成规则的建筑轮廓，使用分割通用几何图元并预测它们的顺序，在此基础上，开发了一个利用 Transformer 架构的 P2PFormer，通过引入群体查询的独特表示方法，结合创新的内隐更新策略，提高了图元分割的质量，实验证明 P2PFormer 在多个数据集上表现优于先前的方法。