1,920 research outputs found

    Assessment of a photogrammetric approach for urban DSM extraction from tri-stereoscopic satellite imagery

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    Built-up environments are extremely complex for 3D surface modelling purposes. The main distortions that hamper 3D reconstruction from 2D imagery are image dissimilarities, concealed areas, shadows, height discontinuities and discrepancies between smooth terrain and man-made features. A methodology is proposed to improve automatic photogrammetric extraction of an urban surface model from high resolution satellite imagery with the emphasis on strategies to reduce the effects of the cited distortions and to make image matching more robust. Instead of a standard stereoscopic approach, a digital surface model is derived from tri-stereoscopic satellite imagery. This is based on an extensive multi-image matching strategy that fully benefits from the geometric and radiometric information contained in the three images. The bundled triplet consists of an IKONOS along-track pair and an additional near-nadir IKONOS image. For the tri-stereoscopic study a densely built-up area, extending from the centre of Istanbul to the urban fringe, is selected. The accuracy of the model extracted from the IKONOS triplet, as well as the model extracted from only the along-track stereopair, are assessed by comparison with 3D check points and 3D building vector data

    Extracting curve-skeletons from digital shapes using occluding contours

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    Curve-skeletons are compact and semantically relevant shape descriptors, able to summarize both topology and pose of a wide range of digital objects. Most of the state-of-the-art algorithms for their computation rely on the type of geometric primitives used and sampling frequency. In this paper we introduce a formally sound and intuitive definition of curve-skeleton, then we propose a novel method for skeleton extraction that rely on the visual appearance of the shapes. To achieve this result we inspect the properties of occluding contours, showing how information about the symmetry axes of a 3D shape can be inferred by a small set of its planar projections. The proposed method is fast, insensitive to noise, capable of working with different shape representations, resolution insensitive and easy to implement

    A Minimalist Approach to Type-Agnostic Detection of Quadrics in Point Clouds

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    This paper proposes a segmentation-free, automatic and efficient procedure to detect general geometric quadric forms in point clouds, where clutter and occlusions are inevitable. Our everyday world is dominated by man-made objects which are designed using 3D primitives (such as planes, cones, spheres, cylinders, etc.). These objects are also omnipresent in industrial environments. This gives rise to the possibility of abstracting 3D scenes through primitives, thereby positions these geometric forms as an integral part of perception and high level 3D scene understanding. As opposed to state-of-the-art, where a tailored algorithm treats each primitive type separately, we propose to encapsulate all types in a single robust detection procedure. At the center of our approach lies a closed form 3D quadric fit, operating in both primal & dual spaces and requiring as low as 4 oriented-points. Around this fit, we design a novel, local null-space voting strategy to reduce the 4-point case to 3. Voting is coupled with the famous RANSAC and makes our algorithm orders of magnitude faster than its conventional counterparts. This is the first method capable of performing a generic cross-type multi-object primitive detection in difficult scenes. Results on synthetic and real datasets support the validity of our method.Comment: Accepted for publication at CVPR 201

    Variable Resolution & Dimensional Mapping For 3d Model Optimization

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    Three-dimensional computer models, especially geospatial architectural data sets, can be visualized in the same way humans experience the world, providing a realistic, interactive experience. Scene familiarization, architectural analysis, scientific visualization, and many other applications would benefit from finely detailed, high resolution, 3D models. Automated methods to construct these 3D models traditionally has produced data sets that are often low fidelity or inaccurate; otherwise, they are initially highly detailed, but are very labor and time intensive to construct. Such data sets are often not practical for common real-time usage and are not easily updated. This thesis proposes Variable Resolution & Dimensional Mapping (VRDM), a methodology that has been developed to address some of the limitations of existing approaches to model construction from images. Key components of VRDM are texture palettes, which enable variable and ultra-high resolution images to be easily composited; texture features, which allow image features to integrated as image or geometry, and have the ability to modify the geometric model structure to add detail. These components support a primary VRDM objective of facilitating model refinement with additional data. This can be done until the desired fidelity is achieved as practical limits of infinite detail are approached. Texture Levels, the third component, enable real-time interaction with a very detailed model, along with the flexibility of having alternate pixel data for a given area of the model and this is achieved through extra dimensions. Together these techniques have been used to construct models that can contain GBs of imagery data

    A semantic-based platform for the digital analysis of architectural heritage

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    This essay focuses on the fields of architectural documentation and digital representation. We present a research paper concerning the development of an information system at the scale of architecture, taking into account the relationships that can be established between the representation of buildings (shape, dimension, state of conservation, hypothetical restitution) and heterogeneous information about various fields (such as the technical, the documentary or still the historical one). The proposed approach aims to organize multiple representations (and associated information) around a semantic description model with the goal of defining a system for the multi-field analysis of buildings

    Grounding Dynamic Spatial Relations for Embodied (Robot) Interaction

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    This paper presents a computational model of the processing of dynamic spatial relations occurring in an embodied robotic interaction setup. A complete system is introduced that allows autonomous robots to produce and interpret dynamic spatial phrases (in English) given an environment of moving objects. The model unites two separate research strands: computational cognitive semantics and on commonsense spatial representation and reasoning. The model for the first time demonstrates an integration of these different strands.Comment: in: Pham, D.-N. and Park, S.-B., editors, PRICAI 2014: Trends in Artificial Intelligence, volume 8862 of Lecture Notes in Computer Science, pages 958-971. Springe
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