Front Orientation Detection Algorithm for 3D Man-Made Models Based on Random Forest

The front orientation detection for 3D models is a fundamental task in interior scenes synthesis and reconstruction. The existing method for 3D models front orientation detection algorithm is content-based and the calculation process is complex. To solve this problem, we propose a front orientation detection algorithm for 3D man-made models based on random forest. We first select candidate bases by model simplification and oriented bounding box computation. Next, we present a set of attributes, defined with respect to each candidate base. The attributes are derived using a combination of functional and psychological considerations. Finally, random forest classifier is trained to determine the front orientation. We have tested the proposed method on different kinds of models in interior scenes. The experimental results show that random forest classifier can achieve sound performance with 80% accuracy and outperforms the state-of-the-art methods.