Getto, Roman2019-09-202019-09-202019-04-05https://diglib.eg.org:443/handle/10.2312/2632808The amount of 3D objects has grown over the last decades, but we can expect that it will grow much further in the future. 3D objects are also becoming more and more accessible to non-expert users. The growing amount of available 3D data is welcome for everyone working with this type of data, as the creation and acquisition of many 3D objects is still costly. However, the vast majority of available 3D objects are only present as pure polygon meshes. We arguably can not assume to get meta-data and additional semantics delivered together with 3D objects stemming from non-expert or 3D scans of real objects from automatic systems. For this reason content-based retrieval and classification techniques for 3D objects has been developed. Many systems are based on the completely unsupervised case. However, previous work has shown that there are strong possibilities of highly increasing the performance of these tasks by using any type of previous knowledge. In this thesis I use procedural models as previous knowledge. Procedural models describe the construction process of a 3D object instead of explicitly describing the components of the surface. These models can include parameters into the construction process to generate variations of the resulting 3D object. Procedural representations are present in many domains, as these implicit representations are vastly superior to any explicit representation in terms of content generation, flexibility and reusability. Therefore, using a procedural representation always has the potential of outclassing other approaches in many aspects. The usage of procedural models in 3D object retrieval and classification is not highly researched as this powerful representation can be arbitrary complex to create and handle. In the 3D object domain, procedural models are mostly used for highly regularized structures like buildings and trees. However, Procedural models can deeply improve 3D object retrieval and classification, as this representation is able to offer a persistent and reusable full description of a type of object. This description can be used for queries and class definitions without any additional data. Furthermore, the initial classification can be improved further by using a procedural model: A procedural model allows to completely parameterize an unknown object and further identify characteristics of different class members. The only drawback is that the manual design and creation of specialized procedural models itself is very costly. In this thesis I concentrate on the generalization and automation of procedural models for the application in 3D object retrieval and 3D object classification. For the generalization and automation of procedural models I propose to offer different levels of interaction for a user to fulfill the possible needs of control and automation. This thesis presents new approaches for different levels of automation: the automatic generation of procedural models from a single exemplary 3D object. The semi-automatic creation of a procedural model with a sketch-based modeling tool. And the manual definition a procedural model with restricted variation space. The second important step is the insertion of parameters into the procedural model, to define the variations of the resulting 3D object. For this step I also propose several possibilities for the optimal level of control and automation: An automatic parameter detection technique. A semi-automatic deformation based insertion. And an interface for manually inserting parameters by choosing one of the offered insertion principles. It is also possible to manually insert parameters into the procedures if the user needs the full control on the lowest level. To enable the usage of procedural models directly for 3D object retrieval and classification techniques I propose descriptor-based and deep learning based approaches. Descriptors measure the difference of 3D objects. By using descriptors as comparison algorithm, we can define the distance between procedural models and other objects and order these by similarity. The procedural models are sampled and compared to retrieve an optimal object retrieval list. We can also directly use procedural models as data basis for a retraining of a convolutional neural network. By deep learning a set of procedural models we can directly classify new unknown objects without any further large learning database. Additionally, I propose a new multi-layered parameter estimation approach using three different comparison measures to parameterize an unknown object. Hence, an unknown object is not only classified with a procedural model but the approach is also able to gather new information about the characteristics of the object by using the procedural model for the parameterization of the unknown object. As a result, the combination of procedural models with the tasks of 3D object retrieval and classification lead to a meta concept of a holistically seamless system of defining, generating, comparing, identifying, retrieving, recombining, editing and reusing 3D objects.enThesis