BranchPoseNet: Characterizing tree branching with a deep learning-based pose estimation approach

Stefano Puliti, Carolin Fischer, Rasmus Astrup
9/23/2024
cs.CVq-bio.QM

No official code found. Best implementation: stefp/BranchPoseNet (5 stars, last commit 2024-09-24).

Abstract

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.

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Code Implementations(1)

stefp/BranchPoseNetCommunity100%

Repo containing the code to run pose estimation to detect whorls in individual tree point clouds

52Python, Jupyter NotebookAug 22, 20242 years ago

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Cite this paper

@article{puliti2024branchposenet,
  title  = {BranchPoseNet: Characterizing tree branching with a deep learning-based pose estimation approach},
  author = {Stefano Puliti and Carolin Fischer and Rasmus Astrup},
  year   = {2024},
  eprint = {2409.14755},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2409.14755v1}
}

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