Panoptic Plant Recognition in 3D Point Clouds: A Dual-Representation Learning Approach with the PP3D Dataset

Abstract

The advancement of Artificial Intelligence (AI) has significantly accelerated progress across various research domains, with growing interest in plant science due to its substantial economic potential. However, the integration of AI with digital vegetation analysis remains underexplored, largely due to the absence of large-scale, real-world plant datasets, which are crucial for advancing this field. To address this gap, we introduce the PP3D dataset—a meticulously labeled collection of about 500 potted plants represented as 3D point clouds, featuring fine-grained annotations for approximately 20 species. The PP3D dataset provides 3D phenotypic data for about 20 plant species spanning model organisms (e.g., Arabidopsis thaliana), potted plants (e.g., Foliage plants, Flowering plants), and horticultural plants (e.g., Solanum lycopersicum), covering most of the common important plant species. Leveraging this dataset, we propose the panoptic plant recognition task, which combines semantic segmentation (stems and leaves) with leaf instance segmentation. To tackle this challenge, we present SCNet, a novel dual-representation learning network designed specifically for plant point cloud segmentation. SCNet integrates two key branches: a cylindrical feature extraction branch for robust spatial encoding and a sequential slice feature extraction branch for detailed structural analysis. By efficiently propagating features between these representations, SCNet achieves superior flexibility and computational efficiency, establishing a new baseline for panoptic plant recognition and paving the way for future AI-driven research in plant science.

Plant phenotyping relevance

3D点群による植物の表現型データセットを構築し、茎・葉の意味分割と葉インスタンス分割の手法を開発・評価しているため、植物フェノタイピング手法が中心である。

Code and data availability

The paper introduces the PP3D plant point cloud dataset and SCNet, but the supplied blocks contain no authors' public URL or repository for the dataset, code, or models. The dataset is described as 'publicly available' yet the text states only representative samples have been released and the full dataset will be 'will

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