Total3DUnderstanding: Joint Layout, Object Pose and Mesh Reconstruction for Indoor Scenes from a Single Image
Authors: Nie, Y., Han, X., Guo, S., Zheng, Y., Chang, J. and Zhang, J.J.
Journal: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Pages: 52-61
ISSN: 1063-6919
DOI: 10.1109/CVPR42600.2020.00013
Abstract:Semantic reconstruction of indoor scenes refers to both scene understanding and object reconstruction. Existing works either address one part of this problem or focus on independent objects. In this paper, we bridge the gap between understanding and reconstruction, and propose an end-to-end solution to jointly reconstruct room layout, object bounding boxes and meshes from a single image. Instead of separately resolving scene understanding and object reconstruction, our method builds upon a holistic scene context and proposes a coarse-to-fine hierarchy with three components: 1. room layout with camera pose; 2. 3D object bounding boxes; 3. object meshes. We argue that understanding the context of each component can assist the task of parsing the others, which enables joint understanding and reconstruction. The experiments on the SUN RGB-D and Pix3D datasets demonstrate that our method consistently outperforms existing methods in indoor layout estimation, 3D object detection and mesh reconstruction.
https://eprints.bournemouth.ac.uk/33684/
Source: Scopus
Total3DUnderstanding: Joint Layout, Object Pose and Mesh Reconstruction for Indoor Scenes from a Single Image
Authors: Nie, Y., Han, X., Guo, S., Zheng, Y., Chang, J. and Zhang, J.J.
Journal: 2020 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR)
Pages: 52-61
ISSN: 1063-6919
DOI: 10.1109/CVPR42600.2020.00013
https://eprints.bournemouth.ac.uk/33684/
Source: Web of Science (Lite)
Total3DUnderstanding: Joint Layout, Object Pose and Mesh Reconstruction for Indoor Scenes from a Single Image
Authors: Nie, Y., Han, X., Guo, S., Zheng, Y., Chang, J. and Zhang, J.
Conference: IEEE Conference on Computer Vision and Pattern Recognition
Dates: 16-18 June 2020
Abstract:Semantic reconstruction of indoor scenes refers to both scene understanding and object reconstruction. Existing works either address one part of this problem or focus on independent objects. In this paper, we bridge the gap between understanding and reconstruction, and propose an end-to-end solution to jointly reconstruct room layout, object bounding boxes and meshes from a single image. Instead of separately resolving scene understanding and object reconstruction, our method builds upon a holistic scene context and proposes a coarse-to-fine hierarchy with three components: 1. room layout with camera pose; 2. 3D object bounding boxes; 3. object meshes. We argue that understanding the context of each component can assist the task of parsing the others, which enables joint understanding and reconstruction. The experiments on the SUN RGBD and Pix3D datasets demonstrate that our method consistently outperforms existing methods in indoor layout estimation, 3D object detection and mesh reconstruction.
https://eprints.bournemouth.ac.uk/33684/
Source: Manual
Total 3D Understanding: Joint Layout, Object Pose and Mesh Reconstruction for Indoor Scenes from a Single Image
Authors: Nie, Y., Han, X., Guo, S., Zheng, Y., Chang, J. and Zhang, J.J.
Conference: IEEE Conference on Computer Vision and Pattern Recognition
Abstract:Semantic reconstruction of indoor scenes refers to both scene understanding and object reconstruction. Existing works either address one part of this problem or focus on independent objects. In this paper, we bridge the gap between understanding and reconstruction, and propose an end-to-end solution to jointly reconstruct room layout, object bounding boxes and meshes from a single image. Instead of separately resolving scene understanding and object reconstruction, our method builds upon a holistic scene context and proposes a coarse-to-fine hierarchy with three components: 1. room layout with camera pose; 2. 3D object bounding boxes; 3. object meshes. We argue that understanding the context of each component can assist the task of parsing the others, which enables joint understanding and reconstruction. The experiments on the SUN RGBD and Pix3D datasets demonstrate that our method consistently outperforms existing methods in indoor layout estimation, 3D object detection and mesh reconstruction.
https://eprints.bournemouth.ac.uk/33684/
https://www.computer.org/conferences/cfp/CVPR2020
Source: BURO EPrints