Please use this identifier to cite or link to this item: http://drsr.daiict.ac.in//handle/123456789/999
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dc.contributor.advisorTatu, Aditya
dc.contributor.authorPandit, Rishabh
dc.date.accessioned2022-05-06T05:39:35Z
dc.date.available2023-02-19T05:39:35Z
dc.date.issued2021
dc.identifier.citationPandit, Rishabh (2021). 3D object Classification. Dhirubhai Ambani Institute of Information and Communication Technology. vi, 32 p. (Acc.No: T00939)
dc.identifier.urihttp://drsr.daiict.ac.in//handle/123456789/999
dc.description.abstractThis thesis deals with the task of classifying 3D Objects. Nowadays, 3D object classification is used in robotics, self-driven cars, etc. To organize any object, we need local and global representation. As humans, we classify objects on the basis of local as well as global descriptors. Our aim is to get a better representation of a 3D object by using local and global features for task of object classification. There are several architectures through which we can get 3D object representations. Here, we use Dynamic graph convolution neural network(DGCNN) for local feature description and global representation. We use the K-means clustering algorithm in order to segment 3D objects, and every segmented cluster is represented by its centroid. We take the centroid as a global descriptor. By combining both local and global features, we try to classify the 3D object. We propose to use the k-means algorithm for obtaining global features. Specifically, centroids of clusters obtained via k-means are used as features giving a global representation of each point. We conduct several experiments to examine the quality of such features obtained for the purpose of object classification.
dc.publisherDhirubhai Ambani Institute of Information and Communication Technology
dc.subject3D Objects
dc.subjectObject classification
dc.subjectDynamic graph convolution neural network(DGCNN)
dc.subjectcentroid
dc.classification.ddc621.367 PAN
dc.title3D object Classification
dc.typeDissertation
dc.degreeM. Tech
dc.student.id201911010
dc.accession.numberT00939
Appears in Collections:M Tech Dissertations

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