CherryPicker: Semantic Skeletonization and Topological Reconstruction of Cherry Trees

Abstract

In plant phenotyping, accurate trait extraction from 3D point clouds of trees is still an open problem. For automatic modeling and trait extraction of tree organs such as blossoms and fruits, the semantically segmented point cloud of a tree and the tree skeleton are necessary. Therefore, we present CherryPicker, an automatic pipeline that reconstructs photo-metric point clouds of trees, performs semantic segmentation and extracts their topological structure in form of a skeleton. Our system combines several state-of-the-art algorithms to enable automatic processing for further usage in 3D-plant phenotyping applications. Within this pipeline, we present a method to automatically estimate the scale factor of a monocular reconstruction to overcome scale ambiguity and obtain metrically correct point clouds. Furthermore, we propose a semantic skeletonization algorithm build up on Laplacian-based contraction. We also show by weighting different tree organs semantically, our approach can effectively remove artifacts induced by occlusion and structural size variations. CherryPicker obtains high-quality topology reconstructions of cherry trees with precise details.

Plant phenotyping relevance

サクランボ樹木の3D点群から器官の形態・トポロジーを抽出する自動フェノタイピング手法の開発が中心である。

Code and data availability

The paper describes cherry tree image acquisitions, a synthetic Blender tree dataset, and open-source code (PC-Skeletor, Aruco-Estimator), but allowed_urls is empty, so no public URL can be matched to a qualifying paper-specific asset. The cherry tree scans and trained DGCNN/U-Net models are not stated as publicly-dep.

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