# Probabilistic Plant Modeling via Multi-View Image-to-Image Translation

arXiv: https://arxiv.org/abs/1804.09404 · HTML: https://mlsanity.com/paper/1804.09404/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Apr 2018.

## Built on by (most cited first, 3 of 3)

- [Tree-D Fusion: Simulation-Ready Tree Dataset from Single Images with Diffusion Priors](https://mlsanity.com/paper/2407.10330/) (2024)
- [TreeFormer: Single-view Plant Skeleton Estimation via Tree-constrained Graph Generation](https://mlsanity.com/paper/2411.16132/) (2024)
- [Structure-aware Riemannian Growth Fields for 4D Plant Modeling](https://mlsanity.com/paper/2608.13007/) (2026)

## Abstract (arXiv)

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.