# Edge-Semantic Learning Strategy for Layout Estimation in Indoor Environment

arXiv: https://arxiv.org/abs/1901.00621 · HTML: https://mlsanity.com/paper/1901.00621/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jan 2019.

## Built on by (most cited first, 6 of 6)

- [Polygon Detection for Room Layout Estimation using Heterogeneous Graphs and Wireframes](https://mlsanity.com/paper/2306.12203/) (2023)
- [Semantic Room Wireframe Detection from a Single View](https://mlsanity.com/paper/2206.00491/) (2022)
- [MCTS with Refinement for Proposals Selection Games in Scene Understanding](https://mlsanity.com/paper/2207.03204/) (2022)
- [GH-Feat: Learning Versatile Generative Hierarchical Features from GANs](https://mlsanity.com/paper/2301.05315/) (2023)
- [GRIHA: Synthesizing 2-Dimensional Building Layouts from Images Captured using a Smart Phone](https://mlsanity.com/paper/2103.08297/) (2021)
- [GALoc: Gravity Aligned Wireframes for Depth-Free Monocular Floorplan Localization](https://mlsanity.com/paper/2609.08385/) (2026)

## Abstract (arXiv)

Visual cognition of the indoor environment can benefit from the spatial layout estimation, which is to represent an indoor scene with a 2D box on a monocular image. In this paper, we propose to fully exploit the edge and semantic information of a room image for layout estimation. More specifically, we present an encoder-decoder network with shared encoder and two separate decoders, which are composed of multiple deconvolution (transposed convolution) layers, to jointly learn the edge maps and semantic labels of a room image. We combine these two network predictions in a scoring function to evaluate the quality of the layouts, which are generated by ray sampling and from a predefined layout pool. Guided by the scoring function, we apply a novel refinement strategy to further optimize the layout hypotheses. Experimental results show that the proposed network can yield accurate estimates of edge maps and semantic labels. By fully utilizing the two different types of labels, the proposed method achieves state-of-the-art layout estimation performance on benchmark datasets.