ABSTRACT:During the last years, the demand for indoor models has increased for various purposes. As a provisional step to proceed towards higher dimensional indoor models, powerful and flexible floor plans can be utilised. Therefore, several methods have been proposed that provide automatically generated floor plans from laser point clouds. The prevailing methodology seeks to attain semantic enhancement of a model (e.g. the identification and labelling of its components) built upon already reconstructed (a priori) geometry. In contrast, this paper demonstrates preliminary research on the possibility to directly incorporate semantic knowledge, which is itself derived from the raw data during the extraction, into the geometric modelling process. In this regard, we propose a new method to automatically extract floor plans from raw point clouds. It is based on a hierarchical space partitioning of the data, integrated with primitive selection actuated by object detection. First, planar primitives corresponding to vertical architectural structures are extracted using M-estimator SAmple and Consensus (MSAC). The set of the resulting line segments are refined by a selection process through a novel door detection algorithm, considering optimization of prior information and fitness to the data. The selected lines are used as hyperlines to partition the space into enclosed areas. Finally, a floor plan is extracted from these partitions by Minimum Description Length (MDL) hypothesis ranking. The algorithm is applied on a real mobile laser scanner dataset and the results are evaluated both in terms of door detection and consecutive floor plan extraction.
Commission I, WG I/3KEY WORDS: Registration, 3D Building Models, Aerial Imagery, Geometric Hashing, Model to Image Matching ABSTRACT:In this paper, a new model-to-image framework to automatically align a single airborne image with existing 3D building models using geometric hashing is proposed. As a prerequisite process for various applications such as data fusion, object tracking, change detection and texture mapping, the proposed registration method is used for determining accurate exterior orientation parameters (EOPs) of a single image. This model-to-image matching process consists of three steps: 1) feature extraction, 2) similarity measure and matching, and 3) adjustment of EOPs of a single image. For feature extraction, we proposed two types of matching cues, edged corner points representing the saliency of building corner points with associated edges and contextual relations among the edged corner points within an individual roof. These matching features are extracted from both 3D building and a single airborne image. A set of matched corners are found with given proximity measure through geometric hashing and optimal matches are then finally determined by maximizing the matching cost encoding contextual similarity between matching candidates. Final matched corners are used for adjusting EOPs of the single airborne image by the least square method based on co-linearity equations. The result shows that acceptable accuracy of single image's EOP can be achievable by the proposed registration approach as an alternative to labourintensive manual registration process.
Topological data analysis (TDA) characterizes the global structure of data
based on topological invariants such as persistent homology, whereas convolutional
neural networks (CNNs) are capable of characterizing local features in the global
structure of the data. In contrast, a combined model of TDA and CNN, a family
of multimodal networks, simultaneously takes the image and the corresponding
topological features as the input to the network for classification, thereby significantly
improving the performance of a single CNN. This innovative approach has been
recently successful in various applications. However, there is a lack of explanation
regarding how and why topological signatures, when combined with a CNN, improve
discriminative power. In this paper, we use persistent homology to compute topological
features and subsequently demonstrate both qualitatively and quantitatively the
effects of topological signatures on a CNN model, for which the Grad-CAM analysis
of multimodal networks and topological inverse image map are proposed and
appropriately utilized. For experimental validation, we utilized two famous datasets:
the transient versus bogus image dataset and the HAM10000 dataset. Using Grad-
CAM analysis of multimodal networks, we demonstrate that topological features
enforce the image network of a CNN to focus more on significant and meaningful
regions across images rather than task-irrelevant artifacts such as background noise
and texture.
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