Research On Feature Extraction Algorithms For3D Model Retrieval

With the development of3D scanners and modeling software, the number of3D models increases quickly and3D models play important role in many fields, so a high-efficiency3D model retrieval system is necessary. Feature extraction algorithm is the key technology in the3D model retrieval system.3D models are generally classified into two classes:rigid3D models and non-rigid3D models. The rigid3D model databases and non-rigid3D model databases are produced, and they are used in specific fields. There are many applications needing generic3D model databases, which contain both rigid and non-rigid3D models.3D model retrieval mainly contains three steps:model preprocessing, feature extraction and similarity computation. This thesis mainly focuses on the feature extraction, which is carried out through three aspects:rigid3D model feature extraction, non-rigid3D model feature extraction and the generic3D model feature extraction. Our study contains three aspects:how to extract the feature of rigid3D modes with cavity, how to make the feature of non-rigid3D model invariant to scale and how to improve the retrieval accuracy of generic3D model. The main contributions of this thesis are as follows:(1) For rigid3D model retrieval, we propose an integration feature extraction algorithm, which is based on view and transform-based features. The view-based features are from projection images, we extract the exterior contour information of3D models by using view features. We extract the interior structure features of3D models based on transformation. Through the radial integration transformation and the spherical integration transformation, we can extract features from both radial direction and axial direction to fully represent the3D model. We can also retrieve rigid3D model with cavity correctly.(2) For the non-rigid3D model retrieval, we propose a multi-scale local feature extraction algorithm. First, we detect keypoints at multi-scale. In order to improve the reliability of the keypoints, we compute the multiplicity of the keypoints, and we define the final keypoints according to their multiplicity. The purpose of keypoints detection at multi-scale is to aoid skipping any keypoints. We use principal axis curvature ratio to automaticlly select the suitable scales. Then, we extract heat kernel signature features from the keypoints. The heat kernel signatures are invariant to translation and rotation, but they are sensitive to scale variation. Finally, the heat kernel signatures are put into the bag-of-features framework. In the framework, the scale problem is changed into the translation problem, we use histogram quantization technology to solve it.(3) For the generic3D model retrieval, we propose a hybrid feature descriptor, which combines the topological features and the view-based features. We use the multiresolution Reeb graph to represent the topological features and the Spatial Structure Circular Descriptor (SSCD) images to represent the view-based features. The multiresolution reeb graph can represent the global features, and the SSCD images can represent the local features. We render images from topological points, which can overcome the constraint condition. Furthermore, the view-based features have better descriptive power than low-dimension geometrical features, which are used by traditional3D retrieval methods.(4) We design and implement the3D model retrieval system to verify our method. The experimental results show that our feature extraction methods can improve the retrieval accuracy of3D model.