# Corners for Layout: End-to-End Layout Recovery from 360 Images

arXiv: https://arxiv.org/abs/1903.08094 · HTML: https://mlsanity.com/paper/1903.08094/

## Key facts

- Cited by 55 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Mar 2019.

## Built on by (most cited first, 30 of 55)

- [UniFuse: Unidirectional Fusion for 360circ Panorama Depth Estimation](https://mlsanity.com/paper/2102.03550/) (2021)
- [OmniFusion: 360 Monocular Depth Estimation via Geometry-Aware Fusion](https://mlsanity.com/paper/2203.00838/) (2022)
- [360MonoDepth: High-Resolution 360deg Monocular Depth Estimation](https://mlsanity.com/paper/2111.15669/) (2021)
- [Review on Panoramic Imaging and Its Applications in Scene Understanding](https://mlsanity.com/paper/2205.05570/) (2022)
- [ACDNet: Adaptively Combined Dilated Convolution for Monocular Panorama Depth Estimation](https://mlsanity.com/paper/2112.14440/) (2021)
- [Deep Learning for Omnidirectional Vision: A Survey and New Perspectives](https://mlsanity.com/paper/2205.10468/) (2022)
- [Guided Co-Modulated GAN for 360deg Field of View Extrapolation](https://mlsanity.com/paper/2204.07286/) (2022)
- [LGT-Net: Indoor Panoramic Room Layout Estimation with Geometry-Aware Transformer Network](https://mlsanity.com/paper/2203.01824/) (2022)
- [PQ-Transformer: Jointly Parsing 3D Objects and Layouts from Point Clouds](https://mlsanity.com/paper/2109.05566/) (2021)
- [A Survey of Representation Learning, Optimization Strategies, and Applications for Omnidirectional Vision](https://mlsanity.com/paper/2502.10444/) (2025)
- [From Semi-supervised to Omni-supervised Room Layout Estimation Using Point Clouds](https://mlsanity.com/paper/2301.13865/) (2023)
- [360 Depth Estimation in the Wild -- The Depth360 Dataset and the SegFuse Network](https://mlsanity.com/paper/2202.08010/) (2022)
- [Disentangling Orthogonal Planes for Indoor Panoramic Room Layout Estimation with Cross-Scale Distortion Awareness](https://mlsanity.com/paper/2303.00971/) (2023)
- [Editable Indoor Lighting Estimation](https://mlsanity.com/paper/2211.03928/) (2022)
- [3D Room Layout Estimation from a Cubemap of Panorama Image via Deep Manhattan Hough Transform](https://mlsanity.com/paper/2207.09291/) (2022)
- [FreDSNet: Joint Monocular Depth and Semantic Segmentation with Fast Fourier Convolutions](https://mlsanity.com/paper/2210.01595/) (2022)
- [3D Scene Geometry Estimation from 360^circ Imagery: A Survey](https://mlsanity.com/paper/2401.09252/) (2024)
- [SSLayout360: Semi-Supervised Indoor Layout Estimation from 360-Degree Panorama](https://mlsanity.com/paper/2103.13696/) (2021)
- [OmniLayout: Room Layout Reconstruction from Indoor Spherical Panoramas](https://mlsanity.com/paper/2104.09403/) (2021)
- [Single-Shot Cuboids: Geodesics-based End-to-end Manhattan Aligned Layout Estimation from Spherical Panoramas](https://mlsanity.com/paper/2102.03939/) (2021)
- [360deg Optical Flow using Tangent Images](https://mlsanity.com/paper/2112.14331/) (2021)
- [SphereDepth: Panorama Depth Estimation from Spherical Domain](https://mlsanity.com/paper/2208.13714/) (2022)
- [Depth-Adapted CNNs for RGB-D Semantic Segmentation](https://mlsanity.com/paper/2206.03939/) (2022)
- [View-aware Salient Object Detection for 360deg Omnidirectional Image](https://mlsanity.com/paper/2209.13222/) (2022)
- [S2Net: Accurate Panorama Depth Estimation on Spherical Surface](https://mlsanity.com/paper/2301.05845/) (2023)
- [360 Layout Estimation via Orthogonal Planes Disentanglement and Multi-view Geometric Consistency Perception](https://mlsanity.com/paper/2312.16268/) (2023)
- [Convolution kernel adaptation to calibrated fisheye](https://mlsanity.com/paper/2402.01456/) (2024)
- [SPVLoc: Semantic Panoramic Viewport Matching for 6D Camera Localization in Unseen Environments](https://mlsanity.com/paper/2404.10527/) (2024)
- [Semantic Room Wireframe Detection from a Single View](https://mlsanity.com/paper/2206.00491/) (2022)
- [360-MLC: Multi-view Layout Consistency for Self-training and Hyper-parameter Tuning](https://mlsanity.com/paper/2210.12935/) (2022)

## Abstract (arXiv)

The problem of 3D layout recovery in indoor scenes has been a core research topic for over a decade. However, there are still several major challenges that remain unsolved. Among the most relevant ones, a major part of the state-of-the-art methods make implicit or explicit assumptions on the scenes -- e.g. box-shaped or Manhattan layouts. Also, current methods are computationally expensive and not suitable for real-time applications like robot navigation and AR/VR. In this work we present CFL (Corners for Layout), the first end-to-end model for 3D layout recovery on 360 images. Our experimental results show that we outperform the state of the art relaxing assumptions about the scene and at a lower cost. We also show that our model generalizes better to camera position variations than conventional approaches by using EquiConvs, a type of convolution applied directly on the sphere projection and hence invariant to the equirectangular distortions. CFL Webpage: https://cfernandezlab.github.io/CFL/