# Layouts from Panoramic Images with Geometry and Deep Learning

arXiv: https://arxiv.org/abs/1806.08294 · HTML: https://mlsanity.com/paper/1806.08294/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jun 2018.

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

- [A Survey of Representation Learning, Optimization Strategies, and Applications for Omnidirectional Vision](https://mlsanity.com/paper/2502.10444/) (2025)
- [PhotoScene: Photorealistic Material and Lighting Transfer for Indoor Scenes](https://mlsanity.com/paper/2207.00757/) (2022)
- [From Semi-supervised to Omni-supervised Room Layout Estimation Using Point Clouds](https://mlsanity.com/paper/2301.13865/) (2023)
- [3D Scene Geometry Estimation from 360^circ Imagery: A Survey](https://mlsanity.com/paper/2401.09252/) (2024)
- [DarSwin: Distortion Aware Radial Swin Transformer](https://mlsanity.com/paper/2304.09691/) (2023)
- [Indoor Panorama Planar 3D Reconstruction via Divide and Conquer](https://mlsanity.com/paper/2106.14166/) (2021)
- [Single-Shot Cuboids: Geodesics-based End-to-end Manhattan Aligned Layout Estimation from Spherical Panoramas](https://mlsanity.com/paper/2102.03939/) (2021)
- [Panoramic Multimodal Semantic Occupancy Prediction for Quadruped Robots](https://mlsanity.com/paper/2603.13108/) (2026)
- [Monocular Spherical Depth Estimation with Explicitly Connected Weak Layout Cues](https://mlsanity.com/paper/2206.11358/) (2022)
- [GRIHA: Synthesizing 2-Dimensional Building Layouts from Images Captured using a Smart Phone](https://mlsanity.com/paper/2103.08297/) (2021)

## Abstract (arXiv)

In this paper, we propose a novel procedure for 3D layout recovery of indoor scenes from single 360 degrees panoramic images. With such images, all scene is seen at once, allowing to recover closed geometries. Our method combines strategically the accuracy provided by geometric reasoning (lines and vanishing points) with the higher level of data abstraction and pattern recognition achieved by deep learning techniques (edge and normal maps). Thus, we extract structural corners from which we generate layout hypotheses of the room assuming Manhattan world. The best layout model is selected, achieving good performance on both simple rooms (box-type) and complex shaped rooms (with more than four walls). Experiments of the proposed approach are conducted within two public datasets, SUN360 and Stanford (2D-3D-S) demonstrating the advantages of estimating layouts by combining geometry and deep learning and the effectiveness of our proposal with respect to the state of the art.