# RoomStructNet: Learning to Rank Non-Cuboidal Room Layouts From Single View

arXiv: https://arxiv.org/abs/2110.00644 · HTML: https://mlsanity.com/paper/2110.00644/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 39 references that are in the map.
- First posted on arXiv 1 Oct 2021.

## Builds on (most cited first, 25 of 25)

- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop](https://mlsanity.com/paper/1506.03365/) (2015)
- [Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition](https://mlsanity.com/paper/1406.4729/) (2014)
- [Dilated Residual Networks](https://mlsanity.com/paper/1705.09914/) (2017)
- [LayoutNet: Reconstructing the 3D Room Layout from a Single RGB Image](https://mlsanity.com/paper/1803.08999/) (2018)
- [IM2CAD](https://mlsanity.com/paper/1608.05137/) (2016)
- [Holistic 3D Scene Parsing and Reconstruction from a Single RGB Image](https://mlsanity.com/paper/1808.02201/) (2018)
- [Corners for Layout: End-to-End Layout Recovery from 360 Images](https://mlsanity.com/paper/1903.08094/) (2019)
- [Cooperative Holistic Scene Understanding: Unifying 3D Object, Layout, and Camera Pose Estimation](https://mlsanity.com/paper/1810.13049/) (2018)
- [RoomNet: End-to-End Room Layout Estimation](https://mlsanity.com/paper/1703.06241/) (2017)
- [Maximum-Margin Structured Learning with Deep Networks for 3D Human Pose Estimation](https://mlsanity.com/paper/1508.06708/) (2015)
- [Physics Inspired Optimization on Semantic Transfer Features: An Alternative Method for Room Layout Estimation](https://mlsanity.com/paper/1707.00383/) (2017)
- [General 3D Room Layout from a Single View by Render-and-Compare](https://mlsanity.com/paper/2001.02149/) (2020)
- [A Coarse-to-Fine Indoor Layout Estimation (CFILE) Method](https://mlsanity.com/paper/1607.00598/) (2016)
- [G-CNN: an Iterative Grid Based Object Detector](https://mlsanity.com/paper/1512.07729/) (2015)
- [GeoLayout: Geometry Driven Room Layout Estimation Based on Depth Maps of Planes](https://mlsanity.com/paper/2008.06286/) (2020)
- [Improving Object Detection with Deep Convolutional Networks via Bayesian Optimization and Structured Prediction](https://mlsanity.com/paper/1504.03293/) (2015)
- [Thinking Outside the Box: Generation of Unconstrained 3D Room Layouts](https://mlsanity.com/paper/1905.03105/) (2019)
- [Smart Hypothesis Generation for Efficient and Robust Room Layout Estimation](https://mlsanity.com/paper/1910.12257/) (2019)
- [Flat2Layout: Flat Representation for Estimating Layout of General Room Types](https://mlsanity.com/paper/1905.12571/) (2019)

## Abstract (arXiv)

In this paper, we present a new approach to estimate the layout of a room from its single image. While recent approaches for this task use robust features learnt from data, they resort to optimization for detecting the final layout. In addition to using learnt robust features, our approach learns an additional ranking function to estimate the final layout instead of using optimization. To learn this ranking function, we propose a framework to train a CNN using max-margin structure cost. Also, while most approaches aim at detecting cuboidal layouts, our approach detects non-cuboidal layouts for which we explicitly estimates layout complexity parameters. We use these parameters to propose layout candidates in a novel way. Our approach shows state-of-the-art results on standard datasets with mostly cuboidal layouts and also performs well on a dataset containing rooms with non-cuboidal layouts.