  The analysis of the internal structure of trees is highly important for both
forest experts, biological scientists, and the wood industry. Traditionally,
CT-scanners are considered as the most efficient way to get an accurate inner
representation of the tree. However, this method requires an important
investment and reduces the cost-effectiveness of this operation. Our goal is to
design neural-network-based methods to predict the internal density of the tree
from its external bark shape. This paper compares different image-to-image(2D),
volume-to-volume(3D) and Convolutional Long Short Term Memory based neural
network architectures in the context of the prediction of the defect
distribution inside trees from their external bark shape. Those models are
trained on a synthetic dataset of 1800 CT-scanned look-like volumetric
structures of the internal density of the trees and their corresponding
external surface.
