Probabilistic Plant Modeling via Multi-View Image-to-Image Translation
This work addresses a domain-specific challenge in plant modeling for researchers or agricultural applications, but it appears incremental as it builds on existing image-to-image translation methods.
The paper tackles the problem of inferring hidden 3D plant branch structures from multi-view images by using a probabilistic framework based on Bayesian image-to-image translation, resulting in a method that generates convincing branch structures compared to prior approaches.
This paper describes a method for inferring three-dimensional (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-to-image 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.