| dc.description.abstract | The 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. | en_US |
