# PolyRoom: Room-aware Transformer for Floorplan Reconstruction

arXiv: https://arxiv.org/abs/2407.10439 · HTML: https://mlsanity.com/paper/2407.10439/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 44 references that are in the map.
- First posted on arXiv 15 Jul 2024.

## Builds on (most cited first, 30 of 30)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes](https://mlsanity.com/paper/1702.04405/) (2017)
- [Masked-attention Mask Transformer for Universal Image Segmentation](https://mlsanity.com/paper/2112.01527/) (2021)
- [Structured3D: A Large Photo-realistic Dataset for Structured 3D Modeling](https://mlsanity.com/paper/1908.00222/) (2019)
- [MapTRv2: An End-to-End Framework for Online Vectorized HD Map Construction](https://mlsanity.com/paper/2308.05736/) (2023)
- [PolyFormer: Referring Image Segmentation as Sequential Polygon Generation](https://mlsanity.com/paper/2302.07387/) (2023)
- [Deep Snake for Real-Time Instance Segmentation](https://mlsanity.com/paper/2001.01629/) (2020)
- [HorizonNet: Learning Room Layout with 1D Representation and Pano Stretch Data Augmentation](https://mlsanity.com/paper/1901.03861/) (2019)
- [PolyTransform: Deep Polygon Transformer for Instance Segmentation](https://mlsanity.com/paper/1912.02801/) (2019)
- [Annotating Object Instances with a Polygon-RNN](https://mlsanity.com/paper/1704.05548/) (2017)
- [Line Segment Detection Using Transformers without Edges](https://mlsanity.com/paper/2101.01909/) (2021)
- [Holistically-Attracted Wireframe Parsing](https://mlsanity.com/paper/2003.01663/) (2020)
- [Connecting the Dots: Floorplan Reconstruction Using Two-Level Queries](https://mlsanity.com/paper/2211.15658/) (2022)
- [SceneCAD: Predicting Object Alignments and Layouts in RGB-D Scans](https://mlsanity.com/paper/2003.12622/) (2020)
- [RoomNet: End-to-End Room Layout Estimation](https://mlsanity.com/paper/1703.06241/) (2017)
- [PolyDiffuse: Polygonal Shape Reconstruction via Guided Set Diffusion Models](https://mlsanity.com/paper/2306.01461/) (2023)
- [Floor-SP: Inverse CAD for Floorplans by Sequential Room-wise Shortest Path](https://mlsanity.com/paper/1908.06702/) (2019)
- [HEAT: Holistic Edge Attention Transformer for Structured Reconstruction](https://mlsanity.com/paper/2111.15143/) (2021)
- [LGT-Net: Indoor Panoramic Room Layout Estimation with Geometry-Aware Transformer Network](https://mlsanity.com/paper/2203.01824/) (2022)
- [FloorPlanCAD: A Large-Scale CAD Drawing Dataset for Panoptic Symbol Spotting](https://mlsanity.com/paper/2105.07147/) (2021)
- [MonteFloor: Extending MCTS for Reconstructing Accurate Large-Scale Floor Plans](https://mlsanity.com/paper/2103.11161/) (2021)
- [F3Loc: Fusion and Filtering for Floorplan Localization](https://mlsanity.com/paper/2403.03370/) (2024)
- [GAT-CADNet: Graph Attention Network for Panoptic Symbol Spotting in CAD Drawings](https://mlsanity.com/paper/2201.00625/) (2022)
- [Structured Outdoor Architecture Reconstruction by Exploration and Classification](https://mlsanity.com/paper/2108.07990/) (2021)

## Built on by (most cited first, 9 of 9)

- [FloorplanVLM: A Vision-Language Model for Floorplan Vectorization](https://mlsanity.com/paper/2602.06507/) (2026)
- [CAGE: Continuity-Aware edGE Network Unlocks Robust Floorplan Reconstruction](https://mlsanity.com/paper/2509.15459/) (2025)
- [FloorSAM: SAM-Guided Floorplan Reconstruction with Semantic-Geometric Fusion](https://mlsanity.com/paper/2509.15750/) (2025)
- [Raster2Seq: Polygon Sequence Generation for Floorplan Reconstruction](https://mlsanity.com/paper/2602.09016/) (2026)
- [SceneAligner: 3D-Grounded Floorplan Localization in the Wild](https://mlsanity.com/paper/2605.22581/) (2026)
- [PlanaReLoc: Camera Relocalization in 3D Planar Primitives via Region-Based Structure Matching](https://mlsanity.com/paper/2603.20818/) (2026)
- [BIMScript: Material-Aware Structured Scene Programs for BIM Ingestion](https://mlsanity.com/paper/2608.21447/) (2026)
- [When Should a Network Emit Geometry, and When Should It Detect It? Readout, Reconciliation, and Representation in Floorplan Vectorization](https://mlsanity.com/paper/2608.25608/) (2026)
- [MvFFN: Multi-view Floor plan Feed-forward Network for Unposed Wide-Baseline Panorama Layout Reconstruction](https://mlsanity.com/paper/2610.14315/) (2026)

## Abstract (arXiv)

Reconstructing geometry and topology structures from raw unstructured data has always been an important research topic in indoor mapping research. In this paper, we aim to reconstruct the floorplan with a vectorized representation from point clouds. Despite significant advancements achieved in recent years, current methods still encounter several challenges, such as missing corners or edges, inaccuracies in corner positions or angles, self-intersecting or overlapping polygons, and potentially implausible topology. To tackle these challenges, we present PolyRoom, a room-aware Transformer that leverages uniform sampling representation, room-aware query initialization, and room-aware self-attention for floorplan reconstruction. Specifically, we adopt a uniform sampling floorplan representation to enable dense supervision during training and effective utilization of angle information. Additionally, we propose a room-aware query initialization scheme to prevent non-polygonal sequences and introduce room-aware self-attention to enhance memory efficiency and model performance. Experimental results on two widely used datasets demonstrate that PolyRoom surpasses current state-of-the-art methods both quantitatively and qualitatively. Our code is available at: https://github.com/3dv-casia/PolyRoom/.