Paper Info
Title
3D Part identification based on local shape descriptors
Abstract
This paper explores 3D object recognition based on local shape descriptor. 3D object recognition is becoming an increasingly important task in modern applications such as computer vision, CAD/CAM, multimedia, molecular biology, robotics, and so on. Compared with general objects, CAD models contain more complicated structures and subtle local features. It is especially challenging to recognize the CAD model from the point clouds which only contain partial data of the model. We adopt the Bag of Words framework to do the partial-to-global 3D CAD retrieval. In this paper the visual words dictionary is constructed based on the spin image local feature descriptor. The method is tested on the Purdue Engineering Benchmark. Furthermore, several experiments are performed to show how the size of query data and the dissimilarity measurement affect the retrieval results.
Year
DOI
Venue
2008
10.1145/1774674.1774700
PerMIS
Keywords
Field
DocType
cad model retrieval,query data,cad model,retrieval result,object recognition,subtle local feature,local shape descriptor,bag of words,cad retrieval,spin image local feature,spin image,partial data,part identification,general object,local shape descriptors,molecular biology,point cloud,computer vision
CAD,Bag-of-words model,Computer vision,Bag-of-words model in computer vision,Computer science,Artificial intelligence,Local feature descriptor,Point cloud,Robotics,Cognitive neuroscience of visual object recognition,Visual Word
Conference
Citations 
PageRank 
References 
8
0.54
12
Authors
3
Name
Order
Citations
PageRank
Xiaolan Li1917.14
Afzal Godil261930.70
Asim Wagan3645.17