Learning View-Model Joint Relevance for 3D Object Retrieval

3D object retrieval has attracted extensive research efforts and become an important task in recent years. It is noted that how to measure the relevance between 3D objects is still a difficult issue. Most of the existing methods employ just the model-based or view-based approaches, which may lead to incomplete information for 3D object representation. In this paper, we propose to jointly learn the view-model relevance among 3D objects for retrieval, in which the 3D objects are formulated in different graph structures. With the view information, the multiple views of 3D objects are employed to formulate the 3D object relationship in an object hypergraph structure. With the model data, the model-based features are extracted to construct an object graph to describe the relationship among the 3D objects. The learning on the two graphs is conducted to estimate the relevance among the 3D objects, in which the view/model graph weights can be also optimized in the learning process. This is the first work to jointly explore the view-based and model-based relevance among the 3D objects in a graph-based framework. The proposed method has been evaluated in three data sets. The experimental results and comparison with the state-of-the-art methods demonstrate the effectiveness on retrieval accuracy of the proposed 3D object retrieval method

3D object retrieval and segmentation: various approaches including 2D poisson histograms and 3D electrical charge distributions.

Nowadays 3D models play an important role in many applications: viz. games, cultural heritage, medical imaging etc. Due to the fast growth in the number of available 3D models, understanding, searching and retrieving such models have become interesting fields within computer vision. In order to search and retrieve 3D models, we present two different approaches: one is based on solving the Poisson Equation over 2D silhouettes of the models. This method uses 60 different silhouettes, which are automatically extracted from different viewangles. Solving the Poisson equation for each silhouette assigns a number to each pixel as its signature. Accumulating these signatures generates a final histogram-based descriptor for each silhouette, which we call a SilPH (Silhouette Poisson Histogram). For the second approach, we propose two new robust shape descriptors based on the distribution of charge density on the surface of a 3D model. The Finite Element Method is used to calculate the charge density on each triangular face of each model as a local feature. Then we utilize the Bag-of-Features and concentric sphere frameworks to perform global matching using these local features. In addition to examining the retrieval accuracy of the descriptors in comparison to the state-of-the-art approaches, the retrieval speeds as well as robustness to noise and deformation on different datasets are investigated. On the other hand, to understand new complex models, we have also utilized distribution of electrical charge for proposing a system to decompose models into meaningful parts. Our robust, efficient and fully-automatic segmentation approach is able to specify the segments attached to the main part of a model as well as locating the boundary parts of the segments. The segmentation ability of the proposed system is examined on the standard datasets and its timing and accuracy are compared with the existing state-of-the-art approaches

An automated method mapping parametric features between computer aided design software

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonEnterprise efficiency is limited by data exchange. A product designer might specify the geometry of a product with a Computer Aided Design program, an engineer might re-use that geometry data to calculate physical properties of the product using a Finite Element Analysis program. These different domains place different requirements on the product representation. Representations of product data required for different tasks is dependent on the vendor software associated with those tasks, sharing data between different vendor programs is limited by incompatibility of the vendor formats used. In the case of Computer Aided Design where the virtual form of an object is modelled, no standard data format captures complete model data. Common data standards transfer model surface geometry without capturing the topological elements from which these geometries are constructed. There are prescriptive data representations to allow these features to be specified in a neutral format, but little incentive for vendors to adopt these schemes. Recent efforts instead focus on identifying similar feature elements between different vendor CAD programs, however this approach relies on onerous manual identification requiring frequent revision. This research develops methods to automate the task of mapping relationships between different data format representations. Two independent matching techniques identify similar CAD feature functions between heterogeneous programs. Text similarity and object geometry matching techniques are combined to match the data formats associated with CAD programs. An efficient search for matching function parameters is performed using a genetic algorithm that incorporates semantic data matching and geometry data matching. A greedy semantic matching algorithm is developed that compares with the Doc2vec short text matching technique over the API dataset tested. A SVD geometric surface registration technique is developed that requires fewer calculations than an equivalent Iterative Closest Point method