LGT-Net: Indoor Panoramic Room Layout Estimation with Geometry-Aware Transformer Network
3D room layout estimation by a single panorama using deep neural networks has made great progress. However, previous approaches can not obtain efficient geometry awareness of room layout with the only latitude of boundaries or horizon-depth. We present that using horizondepth along with room height...
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| Vydáno v: | Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) s. 1644 - 1653 |
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01.06.2022
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| Abstract | 3D room layout estimation by a single panorama using deep neural networks has made great progress. However, previous approaches can not obtain efficient geometry awareness of room layout with the only latitude of boundaries or horizon-depth. We present that using horizondepth along with room height can obtain omnidirectionalgeometry awareness of room layout in both horizontal and vertical directions. In addition, we propose a planar-geometry aware loss function with normals and gradients of normals to supervise the planeness of walls and turning of corners. We propose an efficient network, LGT-Net, for room layout estimation, which contains a novel Transformer architecture called SWG-Transformer to model geometry relations. SWG-Transformer consists of (Shifted) Window Blocks and Global Blocks to combine the local and global geometry relations. Moreover, we design a novel relative position embedding of Transformer to enhance the spatial identification ability for the panorama. Experiments show that the proposed LGT-Net achieves better performance than current state-of-the-arts (SOTA) on benchmark datasets. The code is publicly available at https://github.com/zhigangjiang/LGT-Net. |
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| AbstractList | 3D room layout estimation by a single panorama using deep neural networks has made great progress. However, previous approaches can not obtain efficient geometry awareness of room layout with the only latitude of boundaries or horizon-depth. We present that using horizondepth along with room height can obtain omnidirectionalgeometry awareness of room layout in both horizontal and vertical directions. In addition, we propose a planar-geometry aware loss function with normals and gradients of normals to supervise the planeness of walls and turning of corners. We propose an efficient network, LGT-Net, for room layout estimation, which contains a novel Transformer architecture called SWG-Transformer to model geometry relations. SWG-Transformer consists of (Shifted) Window Blocks and Global Blocks to combine the local and global geometry relations. Moreover, we design a novel relative position embedding of Transformer to enhance the spatial identification ability for the panorama. Experiments show that the proposed LGT-Net achieves better performance than current state-of-the-arts (SOTA) on benchmark datasets. The code is publicly available at https://github.com/zhigangjiang/LGT-Net. |
| Author | Jiang, Zhigang Xiang, Zhongzheng Xu, Jinhua Zhao, Ming |
| Author_xml | – sequence: 1 givenname: Zhigang surname: Jiang fullname: Jiang, Zhigang email: zigjiang@gmail.com organization: East China Normal University – sequence: 2 givenname: Zhongzheng surname: Xiang fullname: Xiang, Zhongzheng email: even_and_just@126.com organization: Yiwo Technology – sequence: 3 givenname: Jinhua surname: Xu fullname: Xu, Jinhua email: jhxu@cs.ecnu.edu.cn organization: East China Normal University – sequence: 4 givenname: Ming surname: Zhao fullname: Zhao, Ming email: zhaoming@123kanfang.com organization: Yiwo Technology |
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| Snippet | 3D room layout estimation by a single panorama using deep neural networks has made great progress. However, previous approaches can not obtain efficient... |
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| SubjectTerms | 3D from single images; Datasets and evaluation; Deep learning architectures and techniques; Photogrammetry and remote sensing; Recognition: detection categorization Deep learning Estimation Geometry grouping and shape analysis; Vision applications and systems Layout Neural networks retrieval; Robot vision; Scene analysis and understanding; Segmentation Solid modeling Three-dimensional displays |
| Title | LGT-Net: Indoor Panoramic Room Layout Estimation with Geometry-Aware Transformer Network |
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