Modeling plants with sensor data

Sensor data, typically images and laser data, are essential to modeling real plants. However, due to the complex geometry of the plants, the measurement data are generally limited, thereby bringing great difficulties in classifying and constructing plant organs, comprising leaves and branches. The paper presents an approach to modeling plants with the sensor data by detecting reliable sharp features, i.e. the leaf apexes of the plants with leaves and the branch tips of the plants without leaves, on volumes recovered from the raw data. The extracted features provide good estimations of correct positions of the organs. Thereafter, the leaves are reconstructed separately by simply fitting and optimizing a generic leaf model. One advantage of the method is that it involves limited manual intervention. For plants without leaves, we develop an efficient strategy for decomposition-based skeletonization by using the tip features to reconstruct the 3D models from noisy laser data. Experiments show that the sharp feature detection algorithm is effective, and the proposed plant modeling approach is competent in constructing realistic models with sensor data.