  Autonomous crop monitoring at high spatial and temporal resolution is a
critical problem in precision agriculture. While Structure from Motion and
Multi-View Stereo algorithms can finely reconstruct the 3D structure of a field
with low-cost image sensors, these algorithms fail to capture the dynamic
nature of continuously growing crops. In this paper we propose a 4D
reconstruction approach to crop monitoring, which employs a spatio-temporal
model of dynamic scenes that is useful for precision agriculture applications.
Additionally, we provide a robust data association algorithm to address the
problem of large appearance changes due to scenes being viewed from different
angles at different points in time, which is critical to achieving 4D
reconstruction. Finally, we collected a high quality dataset with ground truth
statistics to evaluate the performance of our method. We demonstrate that our
4D reconstruction approach provides models that are qualitatively correct with
respect to visual appearance and quantitatively accurate when measured against
the ground truth geometric properties of the monitored crops.
