# Smart Hypothesis Generation for Efficient and Robust Room Layout Estimation

arXiv: https://arxiv.org/abs/1910.12257 · HTML: https://mlsanity.com/paper/1910.12257/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Oct 2019.

## Built on by (most cited first, 10 of 10)

- [From Semi-supervised to Omni-supervised Room Layout Estimation Using Point Clouds](https://mlsanity.com/paper/2301.13865/) (2023)
- [SC-wLS: Towards Interpretable Feed-forward Camera Re-localization](https://mlsanity.com/paper/2210.12748/) (2022)
- [360-DFPE: Leveraging Monocular 360-Layouts for Direct Floor Plan Estimation](https://mlsanity.com/paper/2112.06180/) (2021)
- [Learning to Reconstruct 3D Non-Cuboid Room Layout from a Single RGB Image](https://mlsanity.com/paper/2104.07986/) (2021)
- [MCTS with Refinement for Proposals Selection Games in Scene Understanding](https://mlsanity.com/paper/2207.03204/) (2022)
- [360-MLC: Multi-view Layout Consistency for Self-training and Hyper-parameter Tuning](https://mlsanity.com/paper/2210.12935/) (2022)
- [No More Ambiguity in 360deg Room Layout via Bi-Layout Estimation](https://mlsanity.com/paper/2404.09993/) (2024)
- [RoomStructNet: Learning to Rank Non-Cuboidal Room Layouts From Single View](https://mlsanity.com/paper/2110.00644/) (2021)
- [Structure-preserving Planar Simplification for Indoor Environments](https://mlsanity.com/paper/2408.06814/) (2024)
- [Layout Anything: One Transformer for Universal Room Layout Estimation](https://mlsanity.com/paper/2512.02952/) (2025)

## Abstract (arXiv)

We propose a novel method to efficiently estimate the spatial layout of a room from a single monocular RGB image. As existing approaches based on low-level feature extraction, followed by a vanishing point estimation are very slow and often unreliable in realistic scenarios, we build on semantic segmentation of the input image. To obtain better segmentations, we introduce a robust, accurate and very efficient hypothesize-and-test scheme. The key idea is to use three segmentation hypotheses, each based on a different number of visible walls. For each hypothesis, we predict the image locations of the room corners and select the hypothesis for which the layout estimated from the room corners is consistent with the segmentation. We demonstrate the efficiency and robustness of our method on three challenging benchmark datasets, where we significantly outperform the state-of-the-art.