Shape Retrieval Methods for Architectural 3D Models

This thesis introduces new methods for content-based retrieval of architecture-related 3D models. We thereby consider two different overall types of architectural 3D models. The first type consists of context objects that are used for detailed design and decoration of 3D building model drafts. This includes e.g. furnishing for interior design or barriers and fences for forming the exterior environment. The second type consists of actual building models. To enable efficient content-based retrieval for both model types that is tailored to the user requirements of the architectural domain, type-specific algorithms must be developed. On the one hand, context objects like furnishing that provide similar functions (e.g. seating furniture) often share a similar shape. Nevertheless they might be considered to belong to different object classes from an architectural point of view (e.g. armchair, elbow chair, swivel chair). The differentiation is due to small geometric details and is sometimes only obvious to an expert from the domain. Building models on the other hand are often distinguished according to the underlying floor- and room plans. Topological floor plan properties for example serve as a starting point for telling apart residential and commercial buildings. The first contribution of this thesis is a new meta descriptor for 3D retrieval that combines different types of local shape descriptors using a supervised learning approach. The approach enables the differentiation of object classes according to small geometric details and at the same time integrates expert knowledge from the field of architecture. We evaluate our approach using a database containing arbitrary 3D models as well as on one that only consists of models from the architectural domain. We then further extend our approach by adding a sophisticated shape descriptor localization strategy. Additionally, we exploit knowledge about the spatial relationship of object components to further enhance the retrieval performance. In the second part of the thesis we introduce attributed room connectivity graphs (RCGs) as a means to characterize a 3D building model according to the structure of its underlying floor plans. We first describe how RCGs are inferred from a given building model and discuss how substructures of this graph can be queried efficiently. We then introduce a new descriptor denoted as Bag-of-Attributed-Subgraphs that transforms attributed graphs into a vector-based representation using subgraph embeddings. We finally evaluate the retrieval performance of this new method on a database consisting of building models with different floor plan types. All methods presented in this thesis are aimed at an as automated as possible workflow for indexing and retrieval such that only minimum human interaction is required. Accordingly, only polygon soups are required as inputs which do not need to be manually repaired or structured. Human effort is only needed for offline groundtruth generation to enable supervised learning and for providing information about the orientation of building models and the unit of measurement used for modeling

Context-Based classification of objects in topographic data

Large-scale topographic databases model real world features as vector data objects. These can be point, line or area features. Each of these map objects is assigned to a descriptive class; for example, an area feature might be classed as a building, a garden or a road. Topographic data is subject to continual updates from cartographic surveys and ongoing quality improvement. One of the most important aspects of this is assignment and verification of class descriptions to each area feature. These attributes can be added manually, but, due to the vast volume of data involved, automated techniques are desirable to classify these polygons. Analogy is a key thought process that underpins learning and has been the subject of much research in the field of artificial intelligence (AI). An analogy identifies structural similarity between a well-known source domain and a less familiar target domain. In many cases, information present in the source can then be mapped to the target, yielding a better understanding of the latter. The solution of geometric analogy problems has been a fruitful area of AI research. We observe that there is a correlation between objects in geometric analogy problem domains and map features in topographic data. We describe two topographic area feature classification tools that use descriptions of neighbouring features to identify analogies between polygons: content vector matching (CVM) and context structure matching (CSM). CVM and CSM classify an area feature by matching its neighbourhood context against those of analogous polygons whose class is known. Both classifiers were implemented and then tested on high quality topographic polygon data supplied by Ordnance Survey (Great Britain). Area features were found to exhibit a high degree of variation in their neighbourhoods. CVM correctly classified 85.38% of the 79.03% of features it attempted to classify. The accuracy for CSM was 85.96% of the 62.96% of features it tried to identify. Thus, CVM can classify 25.53% more features than CSM, but is slightly less accurate. Both techniques excelled at identifying the feature classes that predominate in suburban data. Our structure-based classification approach may also benefit other types of spatial data, such as topographic line data, small-scale topographic data, raster data, architectural plans and circuit diagrams

Automatic visual recognition using parallel machines

Invariant features and quick matching algorithms are two major concerns in the area of automatic visual recognition. The former reduces the size of an established model database, and the latter shortens the computation time. This dissertation, will discussed both line invariants under perspective projection and parallel implementation of a dynamic programming technique for shape recognition. The feasibility of using parallel machines can be demonstrated through the dramatically reduced time complexity. In this dissertation, our algorithms are implemented on the AP1000 MIMD parallel machines. For processing an object with a features, the time complexity of the proposed parallel algorithm is O(n), while that of a uniprocessor is O(n2). The two applications, one for shape matching and the other for chain-code extraction, are used in order to demonstrate the usefulness of our methods. Invariants from four general lines under perspective projection are also discussed in here. In contrast to the approach which uses the epipolar geometry, we investigate the invariants under isotropy subgroups. Theoretically speaking, two independent invariants can be found for four general lines in 3D space. In practice, we show how to obtain these two invariants from the projective images of four general lines without the need of camera calibration. A projective invariant recognition system based on a hypothesis-generation-testing scheme is run on the hypercube parallel architecture. Object recognition is achieved by matching the scene projective invariants to the model projective invariants, called transfer. Then a hypothesis-generation-testing scheme is implemented on the hypercube parallel architecture

Dominant points detection for shape analysis

The growing interest in recent years towards the multimedia and the large amount of information exchanged across the network involves the various fields of research towards the study of methods for automatic identification. One of the main objectives is to associate the information content of images, using techniques for identifying composing objects. Among image descriptors, contours reveal are very important because most of the information can be extracted from them and the contour analysis offers a lower computational complexity also. The contour analysis can be restricted to the study of some salient points with high curvature from which it is possible to reconstruct the original contour. The thesis is focused on the polygonal approximation of closed digital curves. After an overview of the most common shape descriptors, distinguished between simple descriptors and external methods, that focus on the analysis of boundary points of objects, and internal methods, which use the pixels inside the object also, a description of the major methods regarding the extraction of dominant points studied so far and the metrics typically used to evaluate the goodness of the polygonal approximation found is given. Three novel approaches to the problem are then discussed in detail: a fast iterative method (DPIL), more suitable for realtime processing, and two metaheuristics methods (GAPA, ACOPA) based on genetic algorithms and Ant Colony Optimization (ACO), more com- plex from the point of view of the calculation, but more precise. Such techniques are then compared with the other main methods cited in literature, in order to assess the performance in terms of computational complexity and polygonal approximation error, and measured between them, in order to evaluate the robustness with respect to affine transformations and conditions of noise. Two new techniques of shape matching, i.e. identification of objects belonging to the same class in a database of images, are then described. The first one is based on the shape alignment and the second is based on a correspondence by ACO, which puts in evidence the excellent results, both in terms of computational time and recognition accuracy, obtained through the use of dominant points. In the first matching algorithm the results are compared with a selection of dominant points generated by a human operator while in the second the dominant points are used instead of a constant sampling of the outline typically used for this kind of approach

Modeling of an automatic CAD-based feature recognition and retrieval system for group technology application

In recent time, many researches have come up with new different approaches and means for Computer-Aided Design (CAD) and Computer-Aided Manufacturing (CAM) integration. Computer-Aided Process Planning (CAPP) is considered to be a bridge that connects these both technologies. CAPP may involve such an important technique as automatic feature extraction - a procedure that is engaged in process plans generation to be used in producing a designed part. Also in terms of CAD, the feature extraction procedure facilitates a cooperative design and process planning within the entire product development process. The main objective of the thesis is to present a new automatic feature extraction and classification system that is able to process mechanical rotational and non-rotational parts from the Opitz Code System point of view. The implemented system takes Standard for Exchange of Product data (STEP) - a neutral product representation format as input and extracts features of parts required for further manufacturing. The STEP format is used to provide geometrical and topological information about machining parts. A methodology to extract shape features was developed based on these geometrical and topological data. As output, the proposed system codes the extracted part features to Opitz Code System. CAD product files were taken from official manufacturers of mechanical parts in order to evaluate the developed system

Digital reconstruction of District Six architecture from archival photographs

Word processed copy.Includes bibliographical references (leaves 88-92).In this thesis we present a strategy for reconstructing instances of District Six Architecture from small sets of old. uncalibrated photographs that are located in the District Six Museum photographic archive. Our reconstruction strategy comprises two major parts. First, we implement a geometry reconstruction framework. based on work by Debevec et al. [1996]. This is used to reconstruct the geometry of a building given as little input as a single photograph. The approach used in this framework requires the user to design a basic model representing the building at hand. using a set of geometric primitives, and then define correspondences between the edges of this model and the edges of the building that are visible in the photographs. This approach is effective, as constraints inherent III the geometry of architectural scenes are exploited through the use of these primitives. The second component of the reconstruction strategy involves texturing the reconstructed models. To accomplish this, we use a combination of the original textures extracted from the photographs, and synthesized textures generated from samples of the original textures. For each face of the reconstructed model, the user is able to use either the original texture material. synthesized material, or a combination of both to create desirable results. Finally, to illustrate the effectiveness of our reconstruction strategy, we consider three example cases of District Six architecture and their reconstructions. All three examples were reconstructed successfully, and using findings from these results, critical analyses of both aspects of our strategy are presented

Evolutionary algorithms in artificial intelligence: a comparative study through applications

For many years research in artificial intelligence followed a symbolic paradigm which required a level of knowledge described in terms of rules. More recently subsymbolic approaches have been adopted as a suitable means for studying many problems. There are many search mechanisms which can be used to manipulate subsymbolic components, and in recent years general search methods based on models of natural evolution have become increasingly popular. This thesis examines a hybrid symbolic/subsymbolic approach and the application of evolutionary algorithms to a problem from each of the fields of shape representation (finding an iterated function system for an arbitrary shape), natural language dialogue (tuning parameters so that a particular behaviour can be achieved) and speech recognition (selecting the penalties used by a dynamic programming algorithm in creating a word lattice). These problems were selected on the basis that each should have a fundamentally different interactions at the subsymbolic level. Results demonstrate that for the experiments conducted the evolutionary algorithms performed well in most cases. However, the type of subsymbolic interaction that may occur influences the relative performance of evolutionary algorithms which emphasise either top-down (evolutionary programming - EP) or bottom-up (genetic algorithm - GA) means of solution discovery. For the shape representation problem EP is seen to perform significantly better than a GA, and reasons for this disparity are discussed. Furthermore, EP appears to offer a powerful means of finding solutions to this problem, and so the background and details of the problem are discussed at length. Some novel constraints on the problem's search space are also presented which could be used in related work. For the dialogue and speech recognition problems a GA and EP produce good results with EP performing slightly better. Results achieved with EP have been used to improve the performance of a speech recognition system

A Framework for the Semantics-aware Modelling of Objects

The evolution of 3D visual content calls for innovative methods for modelling shapes based on their intended usage, function and role in a complex scenario. Even if different attempts have been done in this direction, shape modelling still mainly focuses on geometry. However, 3D models have a structure, given by the arrangement of salient parts, and shape and structure are deeply related to semantics and functionality. Changing geometry without semantic clues may invalidate such functionalities or the meaning of objects or their parts. We approach the problem by considering semantics as the formalised knowledge related to a category of objects; the geometry can vary provided that the semantics is preserved. We represent the semantics and the variable geometry of a class of shapes through the parametric template: an annotated 3D model whose geometry can be deformed provided that some semantic constraints remain satisfied. In this work, we design and develop a framework for the semantics-aware modelling of shapes, offering the user a single application environment where the whole workflow of defining the parametric template and applying semantics-aware deformations can take place. In particular, the system provides tools for the selection and annotation of geometry based on a formalised contextual knowledge; shape analysis methods to derive new knowledge implicitly encoded in the geometry, and possibly enrich the given semantics; a set of constraints that the user can apply to salient parts and a deformation operation that takes into account the semantic constraints and provides an optimal solution. The framework is modular so that new tools can be continuously added. While producing some innovative results in specific areas, the goal of this work is the development of a comprehensive framework combining state of the art techniques and new algorithms, thus enabling the user to conceptualise her/his knowledge and model geometric shapes. The original contributions regard the formalisation of the concept of annotation, with attached properties, and of the relations between significant parts of objects; a new technique for guaranteeing the persistence of annotations after significant changes in shape's resolution; the exploitation of shape descriptors for the extraction of quantitative information and the assessment of shape variability within a class; and the extension of the popular cage-based deformation techniques to include constraints on the allowed displacement of vertices. In this thesis, we report the design and development of the framework as well as results in two application scenarios, namely product design and archaeological reconstruction

Bimanual Interaction with Clothes. Topology, Geometry, and Policy Representations in Robots

Twardon L. Bimanual Interaction with Clothes. Topology, Geometry, and Policy Representations in Robots. Bielefeld: Universität Bielefeld; 2019.If anthropomorphic robots are to assist people with activities of daily living, they must be able to handle all kinds of everyday objects, including highly deformable ones such as garments. The present thesis begins with a detailed problem analysis of robotic interaction with and perception of clothes. We show that handling items of clothing is very challenging due to their complex dynamics and the vast number of degrees of freedom. As a result of our analysis, we obtain a topological, geometric, and functional description of garments that supports the development of reduced object and task representations. One of the key findings is that the boundary components, which typically correspond with the openings, characterize garments well, both in terms of their topology and their inherent purpose, namely dressing. We present a polygon-based and an interactive method for identifying boundary components using RGB-D vision with application to grasping. Moreover, we propose Active Boundary Component Models (ABCMs), a constraint-based framework for tracking garment openings with point clouds. It is often difficult to maintain an accurate representation of the objects involved in contact-rich interaction tasks such as dressing assistance. Therefore, our policy optimization approach to putting a knit cap on a styrofoam head avoids modeling the details of the garment and its deformations. The experimental results suggest that a heuristic performance measure that takes into account the amount of contact established between the two objects is suitable for the task