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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2505.16633 |
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| _version_ | 1866910962194317312 |
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| author | Schmuker, Valentin Chan, Alex Hoi Hang Goldluecke, Bastian Waldmann, Urs |
| author_facet | Schmuker, Valentin Chan, Alex Hoi Hang Goldluecke, Bastian Waldmann, Urs |
| contents | In this paper, we present a texture-independent approach to estimate and track 3D joint positions of multiple pigeons. For this purpose, we build upon the existing 3D-MuPPET framework, which estimates and tracks the 3D poses of up to 10 pigeons using a multi-view camera setup. We extend this framework by using a segmentation method that generates silhouettes of the individuals, which are then used to estimate 2D keypoints. Following 3D-MuPPET, these 2D keypoints are triangulated to infer 3D poses, and identities are matched in the first frame and tracked in 2D across subsequent frames. Our proposed texture-independent approach achieves comparable accuracy to the original texture-dependent 3D-MuPPET framework. Additionally, we explore our approach's applicability to other bird species. To do that, we infer the 2D joint positions of four bird species without additional fine-tuning the model trained on pigeons and obtain preliminary promising results. Thus, we think that our approach serves as a solid foundation and inspires the development of more robust and accurate texture-independent pose estimation frameworks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_16633 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Texture- And Shape-Independent 3D Keypoint Estimation in Birds Schmuker, Valentin Chan, Alex Hoi Hang Goldluecke, Bastian Waldmann, Urs Computer Vision and Pattern Recognition In this paper, we present a texture-independent approach to estimate and track 3D joint positions of multiple pigeons. For this purpose, we build upon the existing 3D-MuPPET framework, which estimates and tracks the 3D poses of up to 10 pigeons using a multi-view camera setup. We extend this framework by using a segmentation method that generates silhouettes of the individuals, which are then used to estimate 2D keypoints. Following 3D-MuPPET, these 2D keypoints are triangulated to infer 3D poses, and identities are matched in the first frame and tracked in 2D across subsequent frames. Our proposed texture-independent approach achieves comparable accuracy to the original texture-dependent 3D-MuPPET framework. Additionally, we explore our approach's applicability to other bird species. To do that, we infer the 2D joint positions of four bird species without additional fine-tuning the model trained on pigeons and obtain preliminary promising results. Thus, we think that our approach serves as a solid foundation and inspires the development of more robust and accurate texture-independent pose estimation frameworks. |
| title | Towards Texture- And Shape-Independent 3D Keypoint Estimation in Birds |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.16633 |