Abstract

The task of detecting the interest points in 3D meshes has typically been handled by geometric methods. These methods, while de- signed according to human preference, can be ill-equipped for handling the variety and sub jectivity in human responses. Different tasks have dif- ferent requirements for interest point detection; some tasks may necessi- tate high precision while other tasks may require high recall. Sometimes points with high curvature may be desirable, while in other cases high curvature may be an indication of noise. Geometric methods lack the required flexibility to adapt to such changes. As a consequence, interest point detection seems to be well suited for machine learning methods that can be trained to match the criteria applied on the annotated training data. In this paper, we formulate interest point detection as a supervised binary classification problem using a random forest as our classifier. We validate the accuracy of our method and compare our results to those of five state of the art methods on a new, standard benchmark.