# 2D Floor Plan Segmentation Based on Down-sampling

arXiv: https://arxiv.org/abs/2303.13798 · HTML: https://mlsanity.com/paper/2303.13798/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 26 references that are in the map.
- First posted on arXiv 24 Mar 2023.

## Builds on (most cited first, 3 of 3)

- [Adjusting for Chance Clustering Comparison Measures](https://mlsanity.com/paper/1512.01286/) (2015)
- [HouseExpo: A Large-scale 2D Indoor Layout Dataset for Learning-based Algorithms on Mobile Robots](https://mlsanity.com/paper/1903.09845/) (2019)
- [Robust Structure Identification and Room Segmentation of Cluttered Indoor Environments from Occupancy Grid Maps](https://mlsanity.com/paper/2203.03519/) (2022)

## Abstract (arXiv)

In recent years, floor plan segmentation has gained significant attention due to its wide range of applications in floor plan reconstruction and robotics. In this paper, we propose a novel 2D floor plan segmentation technique based on a down-sampling approach. Our method employs continuous down-sampling on a floor plan to maintain its structural information while reducing its complexity. We demonstrate the effectiveness of our approach by presenting results obtained from both cluttered floor plans generated by a vacuum cleaning robot in unknown environments and a benchmark of floor plans. Our technique considerably reduces the computational and implementation complexity of floor plan segmentation, making it more suitable for real-world applications. Additionally, we discuss the appropriate metric for evaluating segmentation results. Overall, our approach yields promising results for 2D floor plan segmentation in cluttered environments.