GRIHA: Synthesizing 2-Dimensional Building Layouts from Images Captured using a Smart Phone
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Abstract (arXiv)
Reconstructing an indoor scene and generating a layout/floor plan in 3D or 2D is a widely known problem. Quite a few algorithms have been proposed in the literature recently. However, most existing methods either use RGB-D images, thus requiring a depth camera, or depending on panoramic photos, assuming that there is little to no occlusion in the rooms. In this work, we proposed GRIHA (Generating Room Interior of a House using ARCore), a framework for generating a layout using an RGB image captured using a simple mobile phone camera. We take advantage of Simultaneous Localization and Mapping (SLAM) to assess the 3D transformations required for layout generation. SLAM technology is built-in in recent mobile libraries such as ARCore by Google. Hence, the proposed method is fast and efficient. It gives the user freedom to generate layout by merely taking a few conventional photos, rather than relying on specialized depth hardware or occlusion-free panoramic images. We have compared GRIHA with other existing methods and obtained superior results. Also, the system is tested on multiple hardware platforms to test the dependency and efficiency.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 33 references that are in the map. First posted on arXiv 15 Mar 2021.
Builds on
10 of 33 references.
References · 10
- 2020Atlas: End-to-End 3D Scene Reconstruction from Posed Imagescited by 206
- 2019The VIA Annotation Software for Images, Audio and Videocited by 167
- 2018High Quality Monocular Depth Estimation via Transfer Learningcited by 133
- 2019HorizonNet: Learning Room Layout with 1D Representation and Pano Stretch Data Augmentationcited by 108
- 2018LayoutNet: Reconstructing the 3D Room Layout from a Single RGB Imagecited by 95
- 2016Pano2CAD: Room Layout From A Single Panorama Imagecited by 20
- 2018Layouts from Panoramic Images with Geometry and Deep Learningcited by 10
- 2019Edge-Semantic Learning Strategy for Layout Estimation in Indoor Environmentcited by 6
- 2019Flat2Layout: Flat Representation for Estimating Layout of General Room Typescited by 4
- 2020Scan2Plan: Efficient Floorplan Generation from 3D Scans of Indoor Scenescited by 1
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