Deep Point Cloud Facet Segmentation and Applications in Downsampling and Crop Organ Extraction

Abstract

To address the issues in existing 3D point cloud facet generation networks, specifically, the tendency to produce a large number of empty facets and the uncertainty in facet count, this paper proposes a novel deep learning framework for robust facet segmentation. Based on the generated facet set, two exploratory applications are further developed. First, to overcome the bottleneck where inaccurate empty-facet detection impairs the downsampling performance, a facet-abstracted downsampling method is introduced. By using a learned facet classifier to filter out and discard empty facets, retaining only non-empty surface facets, and fusing point coordinates and local features within each facet, the method achieves significant compression of point cloud data while preserving essential geometric information. Second, to solve the insufficient precision in organ segmentation within crop point clouds, a facet growth-based segmentation algorithm is designed. The network first predicts the edge scores for the facets to determine the seed facets. The facets are then iteratively expanded according to adjacent-facet similarity until a complete organ region is enclosed, thereby enhancing the accuracy of segmentation across semantic boundaries. Finally, the proposed facet segmentation network is trained and validated using a synthetic dataset. Experiments show that, compared with traditional methods, the proposed approach significantly outperforms both downsampling accuracy and instance segmentation performance. In various crop scenarios, it demonstrates excellent geometric fidelity and semantic consistency, as well as strong generalization ability and practical application potential, providing new ideas for in-depth applications of facet-level features in 3D point cloud analysis.

Plant phenotyping relevance

作物3D点群から器官を抽出・分割する画像解析手法を開発し、合成データで検証しており、植物形態の取得が中心的な技術貢献です。

Code and data availability

The paper's crop point cloud dataset (531 tobacco/tomato/sorghum point clouds with instance labels) and FFN code are not publicly deposited; the Data Availability Statement says data are available only on request from the corresponding author, and no public code or model repository URL is given.

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