# Vectorizing World Buildings: Planar Graph Reconstruction by Primitive Detection and Relationship Inference

arXiv: https://arxiv.org/abs/1912.05135 · HTML: https://mlsanity.com/paper/1912.05135/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2019.

## Built on by (most cited first, 12 of 12)

- [HEAT: Holistic Edge Attention Transformer for Structured Reconstruction](https://mlsanity.com/paper/2111.15143/) (2021)
- [Structured Outdoor Architecture Reconstruction by Exploration and Classification](https://mlsanity.com/paper/2108.07990/) (2021)
- [Optimized Vectorizing of Building Structures with Switch: High-Efficiency Convolutional Channel-Switch Hybridization Strategy](https://mlsanity.com/paper/2306.15035/) (2023)
- [The P3 Dataset: Pixels, Points and Polygons for Multimodal Building Vectorization](https://mlsanity.com/paper/2505.15379/) (2025)
- [FloorSAM: SAM-Guided Floorplan Reconstruction with Semantic-Geometric Fusion](https://mlsanity.com/paper/2509.15750/) (2025)
- [Primitive Graph Learning for Unified Vector Mapping](https://mlsanity.com/paper/2206.13963/) (2022)
- [CornerFormer: Boosting Corner Representation for Fine-Grained Structured Reconstruction](https://mlsanity.com/paper/2304.07072/) (2023)
- [ZRG: A Dataset for Multimodal 3D Residential Rooftop Understanding](https://mlsanity.com/paper/2304.13219/) (2023)
- [Holistic Geometric Feature Learning for Structured Reconstruction](https://mlsanity.com/paper/2309.09622/) (2023)
- [Unsupervised Roofline Extraction from True Orthophotos for LoD2 Building Model Reconstruction](https://mlsanity.com/paper/2310.01067/) (2023)
- [A-Scan2BIM: Assistive Scan to Building Information Modeling](https://mlsanity.com/paper/2311.18166/) (2023)
- [Polygonizing Roof Segments from High-Resolution Aerial Images Using Yolov8-Based Edge Detection](https://mlsanity.com/paper/2503.09187/) (2025)

## Abstract (arXiv)

This paper tackles a 2D architecture vectorization problem, whose task is to infer an outdoor building architecture as a 2D planar graph from a single RGB image. We provide a new benchmark with ground-truth annotations for 2,001 complex buildings across the cities of Atlanta, Paris, and Las Vegas. We also propose a novel algorithm utilizing 1) convolutional neural networks (CNNs) that detects geometric primitives and infers their relationships and 2) an integer programming (IP) that assembles the information into a 2D planar graph. While being a trivial task for human vision, the inference of a graph structure with an arbitrary topology is still an open problem for computer vision. Qualitative and quantitative evaluations demonstrate that our algorithm makes significant improvements over the current state-of-the-art, towards an intelligent system at the level of human perception. We will share code and data.