a b s t r a c tWe present an automatic approach for the reconstruction of parametric 3D building models from indoor point clouds. While recently developed methods in this domain focus on mere local surface reconstructions which enable e.g. efficient visualization, our approach aims for a volumetric, parametric building model that additionally incorporates contextual information such as global wall connectivity. In contrast to pure surface reconstructions, our representation thereby allows more comprehensive use: first, it enables efficient high-level editing operations in terms of e.g. wall removal or room reshaping which always result in a topologically consistent representation. Second, it enables easy taking of measurements like e.g. determining wall thickness or room areas. These properties render our reconstruction method especially beneficial to architects or engineers for planning renovation or retrofitting. Following the idea of previous approaches, the reconstruction task is cast as a labeling problem which is solved by an energy minimization. This global optimization approach allows for the reconstruction of wall elements shared between rooms while simultaneously maintaining plausible connectivity between all wall elements. An automatic prior segmentation of the point clouds into rooms and outside area filters large-scale outliers and yields priors for the definition of labeling costs for the energy minimization. The reconstructed model is further enriched by detected doors and windows. We demonstrate the applicability and reconstruction power of our new approach on a variety of complex real-world datasets requiring little or no parameter adjustment.

We present a novel method for retrieval and classification of 3D building models that is tailored to the specific requirements of architects. In contrast to common approaches our algorithm relies on the interior spatial arrangement of rooms instead of exterior geometric shape. We first represent the internal topological building structure by a Room Connectivity Graph (RCG). Each room is characterized by a node. Connections between rooms like e.g. doors are represented by edges. Nodes and edges are additionally assigned attributes reflecting room and edge properties like e.g area or window size. To enable fast and efficient retrieval and classification with RCGs, we transform the structured graph representation into a vector-based one. We first decompose the RCG into a set of subgraphs. For each subgraph, we compute the similarity to a set of codebook graphs. Aggregating all similarity values finally provides us with a single vector for each RCG which enables fast retrieval and classification. For evaluation, we introduce a classification scheme that was carefully developed following common guidelines in architecture.We finally provide comprehensive experiments showing that the introduced subgraph embeddings yield superior performance compared to state-of-the-art graph retrieval approaches.

Abstract. The PROBADO project is a research effort to develop Digital Library support for non-textual documents. The main goal is to contribute to all parts of the Digital Library workflow from content acquisition over semi-automatic indexing to search and presentation. PROBADO3D is a part of the PROBADO framework designed to support 3D documents, with a focus on the Architectural domain. This demonstration will present a set of specialized user interfaces that were developed for content-based querying in this document domain. Interfaces for Querying in 3D Architectural DataPROBADO3D supports search in metadata space, as well as in content-based space in 3D architectural data comprising models of buildings, and interior and exterior elements. Content-based search relies on domain specific indexing services generating descriptors of the building models during an offline indexing stage. The descriptors include global shape properties as well as connectivity information which describes the layout of rooms within the buildings [1] (cf. Figure 1(a) for an example). For querying in these indexes, specific interfaces have been developed to graphically specify queries for similar content. The following interfaces are currently considered.Querying for Similar Global Shape. Queries for the overall shape of building models are supported by sketching of 3D volumes, or by 2D floor plans. Two editors allow specification of volumes or floor plans. One interface provides a 2D/3D editor based on the Generative Modeling Language (GML [2]) (cf. Figure 1(b)). The other uses the modeling capabilities of Google Sketchup [3] (cf. Figure 1(c)).Querying for Room Configurations. The configuration of rooms inside a building is also an important property which architects like to search for (cf. Figure 1(d)). A graph editor interface allows to input an abstract specification of room connectivity structure (cf. Figure 1(e)). A plan-based interface allows to edit a room sketch (cf. Figure 1(f)).

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