  In the Canadian's lumber industry, simulators are used to predict the lumbers
resulting from the sawing of a log at a given sawmill. Giving a log or several
logs' 3D scans as input, simulators perform a real-time job to predict the
lumbers. These simulators, however, tend to be slow at processing large volume
of wood. We thus explore an alternative approximation techniques based on the
Iterative Closest Point (ICP) algorithm to identify the already processed log
to which an unseen log resembles the most. The main benefit of the ICP approach
is that it can easily handle 3D scans with a variable number of points. We
compare this ICP-based nearest neighbor predictor, to predictors built using
machine learning algorithms such as the K-nearest-neighbor (kNN) and Random
Forest (RF). The implemented ICP-based predictor enabled us to identify key
points in using the 3D scans directly for distance calculation. The long-term
goal of this ongoing research is to integrated ICP distance calculations and
machine learning.
