# P2PFormer: A Primitive-to-polygon Method for Regular Building Contour Extraction from Remote Sensing Images

arXiv: https://arxiv.org/abs/2406.02930 · HTML: https://mlsanity.com/paper/2406.02930/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 50 references that are in the map.
- First posted on arXiv 5 Jun 2024.

## Builds on (most cited first, 28 of 28)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [Masked-attention Mask Transformer for Universal Image Segmentation](https://mlsanity.com/paper/2112.01527/) (2021)
- [FCOS: Fully Convolutional One-Stage Object Detection](https://mlsanity.com/paper/1904.01355/) (2019)
- [Path Aggregation Network for Instance Segmentation](https://mlsanity.com/paper/1803.01534/) (2018)
- [DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR](https://mlsanity.com/paper/2201.12329/) (2022)
- [YOLACT: Real-time Instance Segmentation](https://mlsanity.com/paper/1904.02689/) (2019)
- [Deep Layer Aggregation](https://mlsanity.com/paper/1707.06484/) (2017)
- [SOLO: Segmenting Objects by Locations](https://mlsanity.com/paper/1912.04488/) (2019)
- [PolarMask: Single Shot Instance Segmentation with Polar Representation](https://mlsanity.com/paper/1909.13226/) (2019)
- [Instances as Queries](https://mlsanity.com/paper/2105.01928/) (2021)
- [Deep Snake for Real-Time Instance Segmentation](https://mlsanity.com/paper/2001.01629/) (2020)
- [PolyTransform: Deep Polygon Transformer for Instance Segmentation](https://mlsanity.com/paper/1912.02801/) (2019)
- [Line Segment Detection Using Transformers without Edges](https://mlsanity.com/paper/2101.01909/) (2021)
- [Topological Map Extraction from Overhead Images](https://mlsanity.com/paper/1812.01497/) (2018)
- [PolyWorld: Polygonal Building Extraction with Graph Neural Networks in Satellite Images](https://mlsanity.com/paper/2111.15491/) (2021)
- [MAP-Net: Multi Attending Path Neural Network for Building Footprint Extraction from Remote Sensed Imagery](https://mlsanity.com/paper/1910.12060/) (2019)
- [E2EC: An End-to-End Contour-based Method for High-Quality High-Speed Instance Segmentation](https://mlsanity.com/paper/2203.04074/) (2022)
- [Multi-Task Learning for Segmentation of Building Footprints with Deep Neural Networks](https://mlsanity.com/paper/1709.05932/) (2017)
- [Machine-learned Regularization and Polygonization of Building Segmentation Masks](https://mlsanity.com/paper/2007.12587/) (2020)
- [BuildMapper: A Fully Learnable Framework for Vectorized Building Contour Extraction](https://mlsanity.com/paper/2211.03373/) (2022)
- [HiT: Building Mapping with Hierarchical Transformers](https://mlsanity.com/paper/2309.09643/) (2023)

## Built on by (most cited first, 5 of 5)

- [IRSAMap:Towards Large-Scale, High-Resolution Land Cover Map Vectorization](https://mlsanity.com/paper/2508.16272/) (2025)
- [Global Collinearity-aware Polygonizer for Polygonal Building Mapping in Remote Sensing](https://mlsanity.com/paper/2505.01385/) (2025)
- [UniVector: Unified Vector Extraction via Instance-Geometry Interaction](https://mlsanity.com/paper/2510.13234/) (2025)
- [Vector Map as Language: Toward Unified Remote Sensing Vector Mapping](https://mlsanity.com/paper/2606.10701/) (2026)
- [PCFootprint: A Large-Scale Dataset and Benchmark for Vectorized Building Footprint Extraction from Aerial LiDAR Point Clouds](https://mlsanity.com/paper/2606.20455/) (2026)

## Abstract (arXiv)

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