# CAGE: Continuity-Aware edGE Network Unlocks Robust Floorplan Reconstruction

arXiv: https://arxiv.org/abs/2509.15459 · HTML: https://mlsanity.com/paper/2509.15459/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 50 references that are in the map.
- First posted on arXiv 18 Sep 2025.

## Builds on (most cited first, 29 of 29)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [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)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [Swin Transformer V2: Scaling Up Capacity and Resolution](https://mlsanity.com/paper/2111.09883/) (2021)
- [DN-DETR: Accelerate DETR Training by Introducing Query DeNoising](https://mlsanity.com/paper/2203.01305/) (2022)
- [Structured3D: A Large Photo-realistic Dataset for Structured 3D Modeling](https://mlsanity.com/paper/1908.00222/) (2019)
- [BSP-Net: Generating Compact Meshes via Binary Space Partitioning](https://mlsanity.com/paper/1911.06971/) (2019)
- [HorizonNet: Learning Room Layout with 1D Representation and Pano Stretch Data Augmentation](https://mlsanity.com/paper/1901.03861/) (2019)
- [PanoFormer: Panorama Transformer for Indoor 360 Depth Estimation](https://mlsanity.com/paper/2203.09283/) (2022)
- [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)
- [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)
- [FloorNet: A Unified Framework for Floorplan Reconstruction from 3D Scans](https://mlsanity.com/paper/1804.00090/) (2018)
- [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)
- [MonteFloor: Extending MCTS for Reconstructing Accurate Large-Scale Floor Plans](https://mlsanity.com/paper/2103.11161/) (2021)
- [Advancements in Point Cloud Data Augmentation for Deep Learning: A Survey](https://mlsanity.com/paper/2308.12113/) (2023)
- [Pano2CAD: Room Layout From A Single Panorama Image](https://mlsanity.com/paper/1609.09270/) (2016)
- [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)
- [Symbol as Points: Panoptic Symbol Spotting via Point-based Representation](https://mlsanity.com/paper/2401.10556/) (2024)
- [PolyRoom: Room-aware Transformer for Floorplan Reconstruction](https://mlsanity.com/paper/2407.10439/) (2024)
- [FRI-Net: Floorplan Reconstruction via Room-wise Implicit Representation](https://mlsanity.com/paper/2407.10687/) (2024)
- [WAFFLE: Multimodal Floorplan Understanding in the Wild](https://mlsanity.com/paper/2412.00955/) (2024)
- [A Survey of Methods for Converting Unstructured Data to CSG Models](https://mlsanity.com/paper/2305.01220/) (2023)

## Built on by (most cited first, 3 of 3)

- [TLC-Plan: A Two-Level Codebook Based Network for End-to-End Vector Floorplan Generation](https://mlsanity.com/paper/2602.07100/) (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)

## Abstract (arXiv)

We present CAGE (Continuity-Aware edGE) network, a robust framework for reconstructing vector floorplans directly from point-cloud density maps. Traditional corner-based polygon representations are highly sensitive to noise and incomplete observations, often resulting in fragmented or implausible layouts.Recent line grouping methods leverage structural cues to improve robustness but still struggle to recover fine geometric details. To address these limitations,we propose a native edge-centric formulation, modeling each wall segment as a directed, geometrically continuous edge. This representation enables inference of coherent floorplan structures, ensuring watertight, topologically valid room boundaries while improving robustness and reducing artifacts. Towards this design, we develop a dual-query transformer decoder that integrates perturbed and latent queries within a denoising framework, which not only stabilizes optimization but also accelerates convergence. Extensive experiments on Structured3D and SceneCAD show that CAGE achieves state-of-the-art performance, with F1 scores of 99.1% (rooms), 91.7% (corners), and 89.3% (angles). The method also demonstrates strong cross-dataset generalization, underscoring the efficacy of our architectural innovations. Code and pretrained models are available on our project page: https://github.com/ee-Liu/CAGE.git.