Deriving topological relations from topologically augmented direction relation matrices

Topological relations and direction relations represent two pieces of the qualitative spatial reasoning triumvirate. Researchers have previously attempted to use the direction relation matrix to derive a topological relation, finding that no single direction relation matrix can isolate a particular topological relation. In this paper, the technique of topological augmentation is applied to the same problem, identifying a unique topological relation in 28.6% of all topologically augmented direction relation matrices, and furthermore achieving a reduction in a further 40.4% of topologically augmented direction relation matrices when compared to their vanilla direction relation matrix counterpart.

Static locative expressions in Amharic

This paper describes the semantics of static locative expressions in Amharic, particularly the variety spoken in Godʒdʒam. The analysis shows that the semantic category of a site subsumed under Path is exclusively expressed by an adposition. The adpositions can be specific and general locatives. The specific locatives show a specific type of topological relation (for instance, verticality as in tatʃtʃ ‘below, under,’ horizontality as in fit ‘front,’ containment as in wɨst’ ‘in’) between figure and ground entities, but not the general locatives. Besides, static positional verbs encode the conflation of the fact of locatedness with a manner of the positioning of a figure. Based on Talmy’s Motion event typology, the present study has identified that Amharic uses a satellite-framed pattern in static locative constructions exclusively. Moreover, based on Ameka & Levinson’s typology of locative predication, Amharic can be classified under type Ia where a language uses a dummy verb in basic locative construction.

Spatial Topological Relation Analysis for Cluttered Scenes

The spatial topological relations are the foundation of robot operation planning under unstructured and cluttered scenes. Defining complex relations and dealing with incomplete point clouds from the surface of objects are the most difficult challenge in the spatial topological relation analysis. In this paper, we presented the classification of spatial topological relations by dividing the intersection space into six parts. In order to improve accuracy and reduce computing time, convex hulls are utilized to represent the boundary of objects and the spatial topological relations can be determined by the category of points in point clouds. We verified our method on the datasets. The result demonstrated that we have great improvement comparing with the previous method.

AUTOMATIC 3D RECONSTRUCTION OF COMPLEX BUILDINGS FROM INCOMPLETE POINT CLOUDS WITH TOPOLOGICAL-RELATION CONSTRAINTS

Automatic 3D building reconstruction from laser scanning or photogrammetric point clouds has gained increasing attention in the past two decades. Although many efforts have been made, the complexity of buildings and incompletion of point clouds, i.e., data missing, still make it a challenging task for automatic 3D reconstruction of buildings in large-scale urban scenes with various architectural styles. This paper presents an innovative approach for automatic generation of 3D models of complex buildings from even incomplete point clouds. The approach first decomposes the 3D space into multiple space units, including 3D polyhedral cells, facets and edges, where the facets and edges are also encoded with topological-relation constraints. Then, the units and constraints are used together to approximate the buildings. On one hand, by extracting facets from 3D cells and further extracting edges from facets, this approach simplifies complicated topological computations. On the other hand, because this approach models buildings on the basis of polyhedral cells, it can guarantee that the models are manifold and watertight and avoid correcting topological errors. A challenging dataset containing 105 buildings acquired in Central, Hong Kong, was used to evaluate the performance of the proposed approach. The results were compared with two previous methods and the comparisons suggested that the proposed approach outperforms other methods in terms of robustness, regularity, and accuracy of the models, with an average root-mean-square error of less than 0.9 m. The proposed approach is of significance for automatic 3D modelling of buildings for urban applications.