Abstract
This paper describes a method for inferring threedimensional (3D) plant branch structures that are hidden
under leaves from multi-view observations. Unlike previous geometric approaches that heavily rely on the visibility
of the branches or use parametric branching models, our
method makes statistical inferences of branch structures in
a probabilistic framework. By inferring the probability of
branch existence using a Bayesian extension of image-toimage translation applied to each of multi-view images, our
method generates a probabilistic plant 3D model, which
represents the 3D branching pattern that cannot be directly
observed. Experiments demonstrate the usefulness of the
proposed approach in generating convincing branch structures in comparison to prior approaches