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In the Canadian's lumber industry, simulators are used to predict the lumbersresulting from the sawing of a log at a given sawmill. Giving a log or severallogs' 3D scans as input, simulators perform a real-time job to predict thelumbers. These simulators, however, tend to be slow at processing large volumeof wood. We thus explore an alternative approximation techniques based on theIterative Closest Point (ICP) algorithm to identify the already processed logto which an unseen log resembles the most. The main benefit of the ICP approachis that it can easily handle 3D scans with a variable number of points. Wecompare this ICP-based nearest neighbor predictor, to predictors built usingmachine learning algorithms such as the K-nearest-neighbor (kNN) and RandomForest (RF). The implemented ICP-based predictor enabled us to identify keypoints in using the 3D scans directly for distance calculation. The long-termgoal of this ongoing research is to integrated ICP distance calculations andmachine learning.

An iterative closest point method for measuring the level of similarity of 3d log scans in wood industry