Paper Info
Title
3D Object Detection and Localization Using Multimodal Point Pair Features
Abstract
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.
Year
DOI
Venue
2012
10.1109/3DIMPVT.2012.53
3D Imaging, Modeling, Processing, Visualization and Transmission
Keywords
Field
DocType
object detection,multimodal point pair features,crucial step,art method,large number,industrial application,depth data,comparable result,localization scheme,hough-like local voting scheme,higher robustness,feature extraction,edge detection
Object detection,Computer vision,Viola–Jones object detection framework,Object-class detection,Pattern recognition,Silhouette,Feature (computer vision),Edge detection,Robustness (computer science),Feature extraction,Artificial intelligence,Mathematics
Conference
ISBN
Citations 
PageRank 
978-1-4673-4470-8
22
0.81
References 
Authors
19
2
Name
Order
Citations
PageRank
Bertram Drost12178.89
Slobodan Ilic2130767.56