3D Object Detection and Localization Using Multimodal Point Pair Features

• Published in: 2012 Second International Conference on 3D Imaging, Modeling, Processing, Visualization & Transmission pp. 9 - 16
• Main Authors: Drost, B., Ilic, S.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2012
• Subjects: Cameras; Clutter; Feature extraction; Image edge detection; Robustness; Solid modeling; Vectors
• ISBN: 1467344702; 9781467344708
• ISSN: 1550-6185
• DOI: 10.1109/3DIMPVT.2012.53

Object detection and localization is a crucial step for inspection and manipulation tasks in robotic and industrial applications. We present an object detection and localization scheme for 3D objects that combines intensity and depth data. A novel multimodal, scale- and rotation-invariant feature is used to simultaneously describe the object's silhouette and surface appearance. The object's position is determined by matching scene and model features via a Hough-like local voting scheme. The proposed method is quantitatively and qualitatively evaluated on a large number of real sequences, proving that it is generic and highly robust to occlusions and clutter. Comparisons with state of the art methods demonstrate comparable results and higher robustness with respect to occlusions.