Shape2.5D: A Dataset of Texture-Less Surfaces for Depth and Normals Estimation
Reconstructing texture-less surfaces poses unique challenges in computer vision, primarily due to the lack of specialized datasets that cater to the nuanced needs of depth and normals estimation in the absence of textural information. We introduce “Shape2.5D,” a novel, large-sc...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10745503/ |
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| author | Muhammad Saif Ullah Khan Sankalp Sinha Didier Stricker Marcus Liwicki Muhammad Zeshan Afzal |
| author_facet | Muhammad Saif Ullah Khan Sankalp Sinha Didier Stricker Marcus Liwicki Muhammad Zeshan Afzal |
| author_sort | Muhammad Saif Ullah Khan |
| collection | DOAJ |
| description | Reconstructing texture-less surfaces poses unique challenges in computer vision, primarily due to the lack of specialized datasets that cater to the nuanced needs of depth and normals estimation in the absence of textural information. We introduce “Shape2.5D,” a novel, large-scale dataset designed to address this gap. Comprising 1.17 million frames spanning over 39,772 3D models and 48 unique objects, our dataset provides depth and surface normal maps for texture-less object reconstruction. The proposed dataset includes synthetic images rendered with 3D modeling software to simulate various lighting conditions and viewing angles. It also includes a real-world subset comprising 4,672 frames captured with a depth camera. Our comprehensive benchmarks demonstrate the dataset’s ability to support the development of algorithms that robustly estimate depth and normals from RGB images and perform voxel reconstruction. Our open-source data generation pipeline allows the dataset to be extended and adapted for future research. The dataset is publicly available at <uri>https://github.com/saifkhichi96/Shape25D</uri>. |
| format | Article |
| id | doaj-art-16c0a906abd64725bf969fdcb3b43ea8 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-16c0a906abd64725bf969fdcb3b43ea82025-08-20T02:06:50ZengIEEEIEEE Access2169-35362024-01-011217495417496410.1109/ACCESS.2024.349270310745503Shape2.5D: A Dataset of Texture-Less Surfaces for Depth and Normals EstimationMuhammad Saif Ullah Khan0https://orcid.org/0000-0002-7375-807XSankalp Sinha1Didier Stricker2Marcus Liwicki3https://orcid.org/0000-0003-4029-6574Muhammad Zeshan Afzal4Department of Computer Science, Rhineland-Palatinate Technical University of Kaiserslautern-Landau (RPTU), Kaiserslautern, GermanyDepartment of Computer Science, Rhineland-Palatinate Technical University of Kaiserslautern-Landau (RPTU), Kaiserslautern, GermanyDepartment of Computer Science, Rhineland-Palatinate Technical University of Kaiserslautern-Landau (RPTU), Kaiserslautern, GermanyDepartment of Computer Science, Electrical and Space Engineering, Luleå University of Technology, Luleå, SwedenDepartment of Computer Science, Rhineland-Palatinate Technical University of Kaiserslautern-Landau (RPTU), Kaiserslautern, GermanyReconstructing texture-less surfaces poses unique challenges in computer vision, primarily due to the lack of specialized datasets that cater to the nuanced needs of depth and normals estimation in the absence of textural information. We introduce “Shape2.5D,” a novel, large-scale dataset designed to address this gap. Comprising 1.17 million frames spanning over 39,772 3D models and 48 unique objects, our dataset provides depth and surface normal maps for texture-less object reconstruction. The proposed dataset includes synthetic images rendered with 3D modeling software to simulate various lighting conditions and viewing angles. It also includes a real-world subset comprising 4,672 frames captured with a depth camera. Our comprehensive benchmarks demonstrate the dataset’s ability to support the development of algorithms that robustly estimate depth and normals from RGB images and perform voxel reconstruction. Our open-source data generation pipeline allows the dataset to be extended and adapted for future research. The dataset is publicly available at <uri>https://github.com/saifkhichi96/Shape25D</uri>.https://ieeexplore.ieee.org/document/10745503/Texture-less surfacesdepth estimationnormals estimation |
| spellingShingle | Muhammad Saif Ullah Khan Sankalp Sinha Didier Stricker Marcus Liwicki Muhammad Zeshan Afzal Shape2.5D: A Dataset of Texture-Less Surfaces for Depth and Normals Estimation IEEE Access Texture-less surfaces depth estimation normals estimation |
| title | Shape2.5D: A Dataset of Texture-Less Surfaces for Depth and Normals Estimation |
| title_full | Shape2.5D: A Dataset of Texture-Less Surfaces for Depth and Normals Estimation |
| title_fullStr | Shape2.5D: A Dataset of Texture-Less Surfaces for Depth and Normals Estimation |
| title_full_unstemmed | Shape2.5D: A Dataset of Texture-Less Surfaces for Depth and Normals Estimation |
| title_short | Shape2.5D: A Dataset of Texture-Less Surfaces for Depth and Normals Estimation |
| title_sort | shape2 5d a dataset of texture less surfaces for depth and normals estimation |
| topic | Texture-less surfaces depth estimation normals estimation |
| url | https://ieeexplore.ieee.org/document/10745503/ |
| work_keys_str_mv | AT muhammadsaifullahkhan shape25dadatasetoftexturelesssurfacesfordepthandnormalsestimation AT sankalpsinha shape25dadatasetoftexturelesssurfacesfordepthandnormalsestimation AT didierstricker shape25dadatasetoftexturelesssurfacesfordepthandnormalsestimation AT marcusliwicki shape25dadatasetoftexturelesssurfacesfordepthandnormalsestimation AT muhammadzeshanafzal shape25dadatasetoftexturelesssurfacesfordepthandnormalsestimation |