ArticleFrontiers in plant science2022
A graph-based approach for simultaneous semantic and instance segmentation of plant 3D point clouds.
Article in Frontiers in plant science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- DBGCN: Dual-branch Graph Convolutional Network for organ instance inference on sparsely labeled 3D plant data.Plant phenomics (Washington, D.C.) · 2026Article
- Cotton field population phenotyping analysis based on 3D Gaussian reconstruction and dynamic spatial constraints.Frontiers in plant science · 2026Article
- From 3DGS scenes to plant traits: a scalable extraction and segmentation framework for muskmelon phenotyping.Frontiers in plant science · 2026Article
- Perceptual graph kernels for image-derived plant trait interaction analysis in precision agriculture.Frontiers in plant science · 2026Article
- Deep learning for three-dimensional (3D) plant phenomics.Plant phenomics (Washington, D.C.) · 2025Review
- MRSliceNet: Multi-Scale Recursive Slice and Context Fusion Network for Instance Segmentation of Leaves from Plant Point Clouds.Plants (Basel, Switzerland) · 2025Article
- Automated 3D Segmentation of Plant Organs via the Plant-MAE: A Self-Supervised Learning Framework.Plant phenomics (Washington, D.C.) · 2025Article
- Measurement of Maize Leaf Phenotypic Parameters Based on 3D Point Cloud.Sensors (Basel, Switzerland) · 2025Article
- Automated measurement of field crop phenotypic traits using UAV 3D point clouds and an improved PointNet+.Frontiers in plant science · 2025Article
- Point clouds segmentation of rapeseed siliques based on sparse-dense point clouds mapping.Frontiers in plant science · 2023Article
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6 authors.
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Abstract
Accurate simultaneous semantic and instance segmentation of a plant 3D point cloud is critical for automatic plant phenotyping. Classically, each organ of the plant is detected based on the local geometry of the point cloud, but the consistency of the global structure of the plant is rarely assessed. We propose a two-level, graph-based approach for the automatic, fast and accurate segmentation of a plant into each of its organs with structural guarantees. We compute local geometric and spectral features on a neighbourhood graph of the points to distinguish between linear organs (main stem, branches, petioles) and two-dimensional ones (leaf blades) and even 3-dimensional ones (apices). Then a quotient graph connecting each detected macroscopic organ to its neighbors is used both to refine the labelling of the organs and to check the overall consistency of the segmentation. A refinement loop allows to correct segmentation defects. The method is assessed on both synthetic and real 3D point-cloud data sets of
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