View Planning Using Discrete Optimization for 3D Reconstruction of Row Crops

In view planning, the position and orientation of the cameras have been a major contributing factor to the quality of the resulting 3D model. In applications such as precision agriculture, a dense and accurate reconstruction must be obtained quickly while the data is still actionable. Instead of using an arbitrarily large number of images taken from every possible position and orientation in order to cover the desired area of study, a more optimal approach is required. We present an efficient and realistic pipeline, which aims to optimize the positioning of cameras and hence the quality of the 3D reconstruction of a field of row crops. This is achieved with four steps; an initial flight to obtain a sparse point cloud, the fitting of a simple mesh model, the planning of images via a discrete optimization process, and a second flight to obtain the final reconstruction. We demonstrate the effectiveness of our method by comparing it with baseline methods commonly used for agricultural data collection and processing.