Multi-View Semantic Labeling of 3D Point Clouds for Automated Plant Phenotyping

Abstract

Semantic labeling of 3D point clouds is important for the derivation of 3D models from real world scenarios in several economic fields such as building industry, facility management, town planning or heritage conservation. In contrast to these most common applications, we describe in this study the semantic labeling of 3D point clouds derived from plant organs by high-precision scanning. Our approach is optimized for the task of plant phenotyping with its very specific challenges and is employing a deep learning framework. Thereby, we report important experiences concerning detailed parameter initialization and optimization techniques. By evaluating our approach with challenging datasets we achieve state-of-the-art results without difficult and time consuming feature engineering as being necessary in traditional approaches to semantic labeling.

Code and data availability

The paper describes a custom dataset of 33 manually labeled grape bunch point clouds and a SnapNet-based model, but no block contains any public deposit, availability statement, or URL for the dataset, code, or trained model. All URLs in the text are citations to prior work.

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