| Abstract: |
A novel method for recognizing 3D objects in an occluded, cluttered and noisy 2.5D scene, is presented. A ray-triangle intersection algorithm is used to compute distances between a circular sector that does not belong to the object and a triangulated surface. Firstly, for each sector’s point its distance from the object is calculated and stored in a 3D histogram. Secondly, a 2D histogram that counts the 3D histogram’s points whose corresponding distance falls within its distance bins, is formed. Then, the percentages of the bin points that fall within each bin are calculated forming the final descriptor vector. The same procedure is followed for the 2.5D scene. The number of the extracted descriptor vectors is independent to the number of the object’s or scene’s vertices. Experiments proved that the proposed method is fast, robust to noise, occlusion and clutter. |
