# Conv-MPN: Convolutional Message Passing Neural Network for Structured Outdoor Architecture Reconstruction

arXiv: https://arxiv.org/abs/1912.01756 · HTML: https://mlsanity.com/paper/1912.01756/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2019.

## Built on by (most cited first, 25 of 25)

- [HEAT: Holistic Edge Attention Transformer for Structured Reconstruction](https://mlsanity.com/paper/2111.15143/) (2021)
- [Building-GAN: Graph-Conditioned Architectural Volumetric Design Generation](https://mlsanity.com/paper/2104.13316/) (2021)
- [Graph Transformer GANs for Graph-Constrained House Generation](https://mlsanity.com/paper/2303.08225/) (2023)
- [LayoutDETR: Detection Transformer Is a Good Multimodal Layout Designer](https://mlsanity.com/paper/2212.09877/) (2022)
- [The Evolution of Distributed Systems for Graph Neural Networks and their Origin in Graph Processing and Deep Learning: A Survey](https://mlsanity.com/paper/2305.13854/) (2023)
- [MSD: A Benchmark Dataset for Floor Plan Generation of Building Complexes](https://mlsanity.com/paper/2407.10121/) (2024)
- [House-GAN++: Generative Adversarial Layout Refinement Networks](https://mlsanity.com/paper/2103.02574/) (2021)
- [Learning to Extract Building Footprints from Off-Nadir Aerial Images](https://mlsanity.com/paper/2204.13637/) (2022)
- [3D Instance Segmentation of MVS Buildings](https://mlsanity.com/paper/2112.09902/) (2021)
- [Structured Outdoor Architecture Reconstruction by Exploration and Classification](https://mlsanity.com/paper/2108.07990/) (2021)
- [Graph Transformer GANs with Graph Masked Modeling for Architectural Layout Generation](https://mlsanity.com/paper/2401.07721/) (2024)
- [Semi-Supervised Adversarial Recognition of Refined Window Structures for Inverse Procedural Faccade Modeling](https://mlsanity.com/paper/2201.08977/) (2022)
- [AutoEncoding Tree for City Generation and Applications](https://mlsanity.com/paper/2309.15941/) (2023)
- [Prompt-Driven Building Footprint Extraction in Aerial Images with Offset-Building Model](https://mlsanity.com/paper/2310.16717/) (2023)
- [Beyond Grid Data: Exploring Graph Neural Networks for Earth Observation](https://mlsanity.com/paper/2411.03223/) (2024)
- [Computer-Aided Layout Generation for Building Design: A Review](https://mlsanity.com/paper/2504.09694/) (2025)
- [SALVe: Semantic Alignment Verification for Floorplan Reconstruction from Sparse Panoramas](https://mlsanity.com/paper/2406.19390/) (2024)
- [Optimized Vectorizing of Building Structures with Switch: High-Efficiency Convolutional Channel-Switch Hybridization Strategy](https://mlsanity.com/paper/2306.15035/) (2023)
- [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)
- [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)
- [Unified Vector Floorplan Generation via Markup Representation](https://mlsanity.com/paper/2604.04859/) (2026)

## Abstract (arXiv)

This paper proposes a novel message passing neural (MPN) architecture Conv-MPN, which reconstructs an outdoor building as a planar graph from a single RGB image. Conv-MPN is specifically designed for cases where nodes of a graph have explicit spatial embedding. In our problem, nodes correspond to building edges in an image. Conv-MPN is different from MPN in that 1) the feature associated with a node is represented as a feature volume instead of a 1D vector; and 2) convolutions encode messages instead of fully connected layers. Conv-MPN learns to select a true subset of nodes (i.e., building edges) to reconstruct a building planar graph. Our qualitative and quantitative evaluations over 2,000 buildings show that Conv-MPN makes significant improvements over the existing fully neural solutions. We believe that the paper has a potential to open a new line of graph neural network research for structured geometry reconstruction.