CLASSIFIED 3D MODEL RETRIEVAL BASED ON CASCADED FUSION OF LOCAL DESCRIPTORS

Konstantinos Mochament1 , Athanasios Tsakiris1 , Dimosthenis Ioannidis1 , Dimitrios Tzovaras1 1 Information Technologies Institute Centre for Research & Technology Hellas, ThermiThessaloniki, Greece 

ABSTRACT 

One of the core tasks in order to perform fast and accurate retrieval results in a content-based search and retrieval 3D system is to determine an efficient and effective method for matching similarities between the 3D models. In this paper the “cascaded fusion of local descriptors” is proposed for efficient retrieval of classified 3D models, based on a 2D coloured logo retrieval methodological approach, suitably modified for the purpose of 3D search and retrieval tasks that are widely used in the augmented reality (AR) and virtual reality (VR) fields. Initially, features from Key points are extracted using different state of the art local descriptor algorithms and then they are joined to constitute the feature tuple for the respective key point. Additionally, a feature vocabulary for each descriptor is created that maps those tuples to the respective vocabularies using distance functions that applied among the newly created tuples of each Point Cloud. Subsequently, an inverted index table is formed that maps the 3D models to each tuple respectively. Therefore, for every query 3D model only the corresponding 3D models are retrieved as these were previously mapped in the inverted index table. Finally, from the retrieved list by comparing the local features frequency of appearance to the first vocabulary, the final re ranked list of the most similar 3D models is produced. 

KEYWORDS 

Point Cloud, local descriptor, features tuple, keypoint, cascaded fusion

More Details : http://aircconline.com/ijcga/V6N1/6116ijcga02.pdf