GTR: Gaussian Splatting Tracking and Reconstruction of Unknown Objects Based on Appearance and Geometric Complexity
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arXiv
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| Auteurs principaux: | , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866918023572488192 |
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| author | Ikeda, Takuya Zakharov, Sergey Irshad, Muhammad Zubair Opra, Istvan Balazs Iwase, Shun Chen, Dian Tjersland, Mark Lee, Robert Dilly, Alexandre Ambrus, Rares Nishiwaki, Koichi |
| author_facet | Ikeda, Takuya Zakharov, Sergey Irshad, Muhammad Zubair Opra, Istvan Balazs Iwase, Shun Chen, Dian Tjersland, Mark Lee, Robert Dilly, Alexandre Ambrus, Rares Nishiwaki, Koichi |
| contents | We present a novel method for 6-DoF object tracking and high-quality 3D reconstruction from monocular RGBD video. Existing methods, while achieving impressive results, often struggle with complex objects, particularly those exhibiting symmetry, intricate geometry or complex appearance. To bridge these gaps, we introduce an adaptive method that combines 3D Gaussian Splatting, hybrid geometry/appearance tracking, and key frame selection to achieve robust tracking and accurate reconstructions across a diverse range of objects. Additionally, we present a benchmark covering these challenging object classes, providing high-quality annotations for evaluating both tracking and reconstruction performance. Our approach demonstrates strong capabilities in recovering high-fidelity object meshes, setting a new standard for single-sensor 3D reconstruction in open-world environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_11905 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GTR: Gaussian Splatting Tracking and Reconstruction of Unknown Objects Based on Appearance and Geometric Complexity Ikeda, Takuya Zakharov, Sergey Irshad, Muhammad Zubair Opra, Istvan Balazs Iwase, Shun Chen, Dian Tjersland, Mark Lee, Robert Dilly, Alexandre Ambrus, Rares Nishiwaki, Koichi Computer Vision and Pattern Recognition Robotics We present a novel method for 6-DoF object tracking and high-quality 3D reconstruction from monocular RGBD video. Existing methods, while achieving impressive results, often struggle with complex objects, particularly those exhibiting symmetry, intricate geometry or complex appearance. To bridge these gaps, we introduce an adaptive method that combines 3D Gaussian Splatting, hybrid geometry/appearance tracking, and key frame selection to achieve robust tracking and accurate reconstructions across a diverse range of objects. Additionally, we present a benchmark covering these challenging object classes, providing high-quality annotations for evaluating both tracking and reconstruction performance. Our approach demonstrates strong capabilities in recovering high-fidelity object meshes, setting a new standard for single-sensor 3D reconstruction in open-world environments. |
| title | GTR: Gaussian Splatting Tracking and Reconstruction of Unknown Objects Based on Appearance and Geometric Complexity |
| topic | Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2505.11905 |