BranchPoseNet: Characterizing tree branching with a deep learning-based pose estimation approach
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913513145892864 |
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| author | Puliti, Stefano Fischer, Carolin Astrup, Rasmus |
| author_facet | Puliti, Stefano Fischer, Carolin Astrup, Rasmus |
| contents | This paper presents an automated pipeline for detecting tree whorls in proximally laser scanning data using a pose-estimation deep learning model. Accurate whorl detection provides valuable insights into tree growth patterns, wood quality, and offers potential for use as a biometric marker to track trees throughout the forestry value chain. The workflow processes point cloud data to create sectional images, which are subsequently used to identify keypoints representing tree whorls and branches along the stem. The method was tested on a dataset of destructively sampled individual trees, where the whorls were located along the stems of felled trees. The results demonstrated strong potential, with accurate identification of tree whorls and precise calculation of key structural metrics, unlocking new insights and deeper levels of information from individual tree point clouds. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2409_14755 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | BranchPoseNet: Characterizing tree branching with a deep learning-based pose estimation approach Puliti, Stefano Fischer, Carolin Astrup, Rasmus Computer Vision and Pattern Recognition Quantitative Methods This paper presents an automated pipeline for detecting tree whorls in proximally laser scanning data using a pose-estimation deep learning model. Accurate whorl detection provides valuable insights into tree growth patterns, wood quality, and offers potential for use as a biometric marker to track trees throughout the forestry value chain. The workflow processes point cloud data to create sectional images, which are subsequently used to identify keypoints representing tree whorls and branches along the stem. The method was tested on a dataset of destructively sampled individual trees, where the whorls were located along the stems of felled trees. The results demonstrated strong potential, with accurate identification of tree whorls and precise calculation of key structural metrics, unlocking new insights and deeper levels of information from individual tree point clouds. |
| title | BranchPoseNet: Characterizing tree branching with a deep learning-based pose estimation approach |
| topic | Computer Vision and Pattern Recognition Quantitative Methods |
| url | https://arxiv.org/abs/2409.14755 |