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Masks-to-Skeleton: Multi-View Mask-Based Tree Skeleton Extraction with 3D Gaussian Splatting.

Xinpeng Liu1, Kanyu Xu1, Risa Shinoda1

  • 1Graduate School of Information Science and Technology, The University of Osaka, 1-5 Yamadaoka, Suita 565-0871, Osaka, Japan.

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Summary
This summary is machine-generated.

This study introduces a novel mask-guided graph optimization framework for 3D tree skeleton reconstruction. It bypasses noisy 3D point clouds, improving accuracy and completeness in plant modeling.

Keywords:
Gaussian splattingpoint cloudthree-dimensional reconstructiontree skeletonization

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Area of Science:

  • Computer Vision
  • Computational Geometry
  • Plant Science

Background:

  • Reconstructing 3D tree skeletons from multi-view images is difficult.
  • Existing methods using 3D point clouds struggle with thin branches and low-texture contrast, leading to fragmented data and broken connectivity.

Purpose of the Study:

  • To develop a robust method for accurate 3D tree skeleton reconstruction.
  • To overcome limitations of point-cloud-based approaches by leveraging image segmentation masks.

Main Methods:

  • A mask-guided graph optimization framework is proposed.
  • The 3D skeleton is modeled as a graph, optimized using 3D Gaussian splatting (3DGS) for silhouette rendering and a minimum spanning tree (MST) algorithm for regularization.

Main Results:

  • The new method directly estimates 3D skeletons from multi-view segmentation masks.
  • Demonstrates consistent improvements in completeness and structural accuracy compared to existing methods.

Conclusions:

  • The mask-guided graph optimization framework offers a more reliable approach to 3D tree skeleton reconstruction.
  • This method enhances structural accuracy and completeness, particularly for challenging plant structures.