A Study of the Human Comprehension of Building Categories Based on Different 3D Building Representations

Tutzauer, Patrick; Becker, Susanne; Fritsch, Dieter; Niese, Till; Deußen, Oliver

doi:10.1127/pfg/2016/0302

Cited by 8 publications

(7 citation statements)

References 5 publications

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“…These rules are defined on the basis of similarity of color, size, and texture and are learned by SVM. Tutzauer et al [41] reveals a study on the human perception of building categories. It summarizes some of the research on two-dimensional (2D) geometric building structures, which aim to totally recognize, generalize, and generate an abstraction of the building.…”

Section: Related Workmentioning

confidence: 99%

Window Detection from UAS-Derived Photogrammetric Point Cloud Employing Density-Based Filtering and Perceptual Organization

et al. 2018

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Point clouds with ever-increasing volume are regular data in 3D city modelling, in which building reconstruction is a significant part. The photogrammetric point cloud, generated from UAS (Unmanned Aerial System) imagery, is a novel type of data in building reconstruction. Its positive characteristics, alongside its challenging qualities, provoke discussions on this theme of research. In this paper, patch-wise detection of the points of window frames on facades and roofs are undertaken using this kind of data. A density-based multi-scale filter is devised in the feature space of normal vectors to globally handle the matter of high volume of data and to detect edges. Color information is employed for the downsized data to remove the inner clutter of the building. Perceptual organization directs the approach via grouping and the Gestalt principles, to segment the filtered point cloud and to later detect window patches. The evaluation of the approach displays a completeness of 95% and 92%, respectively, as well as a correctness of 95% and 96%, respectively, for the detection of rectangular and partially curved window frames in two big heterogeneous cluttered datasets. Moreover, most intrusions and protrusions cannot mislead the window detection approach. Several doors with glass parts and a number of parallel parts of the scaffolding are mistaken as windows when using the large-scale object detection approach due to their similar patterns with window frames. Sensitivity analysis of the input parameters demonstrates that the filter functionality depends on the radius of density calculation in the feature space. Furthermore, successfully employing the Gestalt principles in the detection of window frames is influenced by the width determination of window partitioning.

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Section: Related Workmentioning

confidence: 99%

Window Detection from UAS-Derived Photogrammetric Point Cloud Employing Density-Based Filtering and Perceptual Organization

et al. 2018

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“…Our previous work investigates the perception of different building categories by humans in order to determine which features are important for visual classification (Tutzauer et al, 2016). These features are used to generate more abstract visual representations of the initial 3D models with emphasis on essential structures and simultaneous elimination of unimportant parts, making them more easily understandable for a human user.…”

Section: Importance-based Image Renderingmentioning

confidence: 99%

Neural Networks for the Classification of Building Use From Street-View Imagery

Laupheimer¹,

Tutzauer²,

Haala³

et al. 2018

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Within this paper we propose an end-to-end approach for classifying terrestrial images of building facades into five different utility classes (<i>commercial, hybrid, residential, specialUse, underConstruction</i>) by using Convolutional Neural Networks (CNNs). For our examples we use images provided by Google Street View. These images are automatically linked to a coarse city model, including the outlines of the buildings as well as their respective use classes. By these means an extensive dataset is available for training and evaluation of our Deep Learning pipeline. The paper describes the implemented end-to-end approach for classifying street-level images of building facades and discusses our experiments with various CNNs. In addition to the classification results, so-called Class Activation Maps (CAMs) are evaluated. These maps give further insights into decisive facade parts that are learned as features during the training process. Furthermore, they can be used for the generation of abstract presentations which facilitate the comprehension of semantic image content. The abstract representations are a result of the stippling method, an importance-based image rendering.

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“…As previously mentioned, the interpretation of point clouds is often similar to the human comprehension. A general study about human perception of building constructions based on geometrical building representation is presented in (Tutzauer et al, 2016).…”

Section: Research Objectmentioning

confidence: 99%

Semantic Segmentation of Building Elements Using Point Cloud Hashing

Chizhova

Gurianov

Hess

et al. 2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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For the interpretation of point clouds, the semantic definition of extracted segments from point clouds or images is a common problem. Usually, the semantic of geometrical pre-segmented point cloud elements are determined using probabilistic networks and scene databases. The proposed semantic segmentation method is based on the psychological human interpretation of geometric objects, especially on fundamental rules of primary comprehension. Starting from these rules the buildings could be quite well and simply classified by a human operator (e.g. architect) into different building types and structural elements (dome, nave, transept etc.), including particular building parts which are visually detected. The key part of the procedure is a novel method based on hashing where point cloud projections are transformed into binary pixel representations. A segmentation approach released on the example of classical Orthodox churches is suitable for other buildings and objects characterized through a particular typology in its construction (e.g. industrial objects in standardized enviroments with strict component design allowing clear semantic modelling).

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A Study of the Human Comprehension of Building Categories Based on Different 3D Building Representations

Cited by 8 publications

References 5 publications

Window Detection from UAS-Derived Photogrammetric Point Cloud Employing Density-Based Filtering and Perceptual Organization

Window Detection from UAS-Derived Photogrammetric Point Cloud Employing Density-Based Filtering and Perceptual Organization

Neural Networks for the Classification of Building Use From Street-View Imagery

Semantic Segmentation of Building Elements Using Point Cloud Hashing

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