# PanoRoom: From the Sphere to the 3D Layout

arXiv: https://arxiv.org/abs/1808.09879 · HTML: https://mlsanity.com/paper/1808.09879/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Aug 2018.

## Built on by (most cited first, 3 of 3)

- [3D Scene Geometry Estimation from 360^circ Imagery: A Survey](https://mlsanity.com/paper/2401.09252/) (2024)
- [Single-Shot Cuboids: Geodesics-based End-to-end Manhattan Aligned Layout Estimation from Spherical Panoramas](https://mlsanity.com/paper/2102.03939/) (2021)
- [Monocular Spherical Depth Estimation with Explicitly Connected Weak Layout Cues](https://mlsanity.com/paper/2206.11358/) (2022)

## Abstract (arXiv)

We propose a novel FCN able to work with omnidirectional images that outputs accurate probability maps representing the main structure of indoor scenes, which is able to generalize on different data. Our approach handles occlusions and recovers complex shaped rooms more faithful to the actual shape of the real scenes. We outperform the state of the art not only in accuracy of the 3D models but also in speed.