# FloorNet: A Unified Framework for Floorplan Reconstruction from 3D Scans

arXiv: https://arxiv.org/abs/1804.00090 · HTML: https://mlsanity.com/paper/1804.00090/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Mar 2018.

## Built on by (most cited first, 30 of 35)

- [Kimera: from SLAM to Spatial Perception with 3D Dynamic Scene Graphs](https://mlsanity.com/paper/2101.06894/) (2021)
- [NeurAR: Neural Uncertainty for Autonomous 3D Reconstruction with Implicit Neural Representations](https://mlsanity.com/paper/2207.10985/) (2022)
- [Connecting the Dots: Floorplan Reconstruction Using Two-Level Queries](https://mlsanity.com/paper/2211.15658/) (2022)
- [MonteFloor: Extending MCTS for Reconstructing Accurate Large-Scale Floor Plans](https://mlsanity.com/paper/2103.11161/) (2021)
- [Monte Carlo Scene Search for 3D Scene Understanding](https://mlsanity.com/paper/2103.07969/) (2021)
- [PSMNet: Position-aware Stereo Merging Network for Room Layout Estimation](https://mlsanity.com/paper/2203.15965/) (2022)
- [360-DFPE: Leveraging Monocular 360-Layouts for Direct Floor Plan Estimation](https://mlsanity.com/paper/2112.06180/) (2021)
- [Chat2Map: Efficient Scene Mapping from Multi-Ego Conversations](https://mlsanity.com/paper/2301.02184/) (2023)
- [UnLoc: Leveraging Depth Uncertainties for Floorplan Localization](https://mlsanity.com/paper/2509.11301/) (2025)
- [FRI-Net: Floorplan Reconstruction via Room-wise Implicit Representation](https://mlsanity.com/paper/2407.10687/) (2024)
- [QueSTMaps: Queryable Semantic Topological Maps for 3D Scene Understanding](https://mlsanity.com/paper/2404.06442/) (2024)
- [WAFFLE: Multimodal Floorplan Understanding in the Wild](https://mlsanity.com/paper/2412.00955/) (2024)
- [Walk2Map: Extracting Floor Plans from Indoor Walk Trajectories](https://mlsanity.com/paper/2103.00262/) (2021)
- [Floorplan Restoration by Structure Hallucinating Transformer Cascades](https://mlsanity.com/paper/2206.00645/) (2022)
- [MCTS with Refinement for Proposals Selection Games in Scene Understanding](https://mlsanity.com/paper/2207.03204/) (2022)
- [Mutual Scene Synthesis for Mixed Reality Telepresence](https://mlsanity.com/paper/2204.00161/) (2022)
- [AutoEncoding Tree for City Generation and Applications](https://mlsanity.com/paper/2309.15941/) (2023)
- [CAGE: Continuity-Aware edGE Network Unlocks Robust Floorplan Reconstruction](https://mlsanity.com/paper/2509.15459/) (2025)
- [SALVe: Semantic Alignment Verification for Floorplan Reconstruction from Sparse Panoramas](https://mlsanity.com/paper/2406.19390/) (2024)
- [PixCuboid: Room Layout Estimation from Multi-view Featuremetric Alignment](https://mlsanity.com/paper/2508.04659/) (2025)
- [FloorPP-Net: Reconstructing Floor Plans using Point Pillars for Scan-to-BIM](https://mlsanity.com/paper/2106.10635/) (2021)
- [Learning Indoor Layouts from Simple Point-Clouds](https://mlsanity.com/paper/2108.03378/) (2021)
- [HouseLayout3D: A Benchmark and Training-Free Baseline for 3D Layout Estimation in the Wild](https://mlsanity.com/paper/2512.02450/) (2025)
- [Fast SceneScript: Fast and Accurate Language-Based 3D Scene Understanding via Multi-Token Prediction](https://mlsanity.com/paper/2512.05597/) (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)
- [Guessing human intentions to avoid dangerous situations in caregiving robots](https://mlsanity.com/paper/2403.16291/) (2024)
- [Structure-preserving Planar Simplification for Indoor Environments](https://mlsanity.com/paper/2408.06814/) (2024)
- [Topology-Preserving Polygon Augmentation for Segmentation in Structured Visual Domains](https://mlsanity.com/paper/2603.14764/) (2026)
- [FlatLands: Generative Floormap Completion From a Single Egocentric View](https://mlsanity.com/paper/2603.16016/) (2026)

## Abstract (arXiv)

The ultimate goal of this indoor mapping research is to automatically reconstruct a floorplan simply by walking through a house with a smartphone in a pocket. This paper tackles this problem by proposing FloorNet, a novel deep neural architecture. The challenge lies in the processing of RGBD streams spanning a large 3D space. FloorNet effectively processes the data through three neural network branches: 1) PointNet with 3D points, exploiting the 3D information; 2) CNN with a 2D point density image in a top-down view, enhancing the local spatial reasoning; and 3) CNN with RGB images, utilizing the full image information. FloorNet exchanges intermediate features across the branches to exploit the best of all the architectures. We have created a benchmark for floorplan reconstruction by acquiring RGBD video streams for 155 residential houses or apartments with Google Tango phones and annotating complete floorplan information. Our qualitative and quantitative evaluations demonstrate that the fusion of three branches effectively improves the reconstruction quality. We hope that the paper together with the benchmark will be an important step towards solving a challenging vector-graphics reconstruction problem. Code and data are available at https://github.com/art-programmer/FloorNet.