Unsupervised Upright Orientation of Man-made Models

Most man-made models can be posed at a unique upright orientation which is consistent to human sense. However, since produced by various techniques, digital man-made models, such as polygon meshes, might be sloped far from the upright orientation. We present a novel unsupervised approach for finding the upright orientation of man-made models by using a low-rank matrix theorem based technique. We propose that projections of the models could be regarded as low-rank matrices when they have been posed at axis-aligned orientations. The models are to be iteratively rotated by using the recently presented TILT technique, in order to ensure that their projections have optimal low-rank observations. After that, the upright orientation can be easily picked up from the six axis-aligned candidate orientations by analysis on geometric properties of the model. The approach does not require any other training set of models and should be regardless of the model quality. A number of experiments will be shown to illustrate the effectiveness and robustness of the proposed approach.