  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.
