4,313 research outputs found
PROCEEDINGS OF THE IEEE SPECIAL ISSUE ON APPLICATIONS OF AUGMENTED REALITY ENVIRONMENTS 1 Augmented Reality for Construction Site Monitoring and Documentation
Abstract—Augmented Reality allows for an on-site presentation of information that is registered to the physical environment. Applications from civil engineering, which require users to process complex information, are among those which can benefit particularly highly from such a presentation. In this paper, we will describe how to use Augmented Reality (AR) to support monitoring and documentation of construction site progress. For these tasks, the staff responsible usually requires fast and comprehensible access to progress information to enable comparison to the as-built status as well as to as-planned data. Instead of tediously searching and mapping related information to the actual construction site environment, our AR system allows for the access of information right where it is needed. This is achieved by superimposing progress as well as as-planned information onto the user’s view of the physical environment. For this purpose, we present an approach that uses aerial 3D reconstruction to automatically capture progress information and a mobile AR client for on-site visualization. Within this paper, we will describe in greater detail how to capture 3D, how to register the AR system within the physical outdoor environment, how to visualize progress information in a comprehensible way in an AR overlay and how to interact with this kind of information. By implementing such an AR system, we are able to provide an overview about the possibilities and future applications of AR in the construction industry
Semantic Cross-View Matching
Matching cross-view images is challenging because the appearance and
viewpoints are significantly different. While low-level features based on
gradient orientations or filter responses can drastically vary with such
changes in viewpoint, semantic information of images however shows an invariant
characteristic in this respect. Consequently, semantically labeled regions can
be used for performing cross-view matching. In this paper, we therefore explore
this idea and propose an automatic method for detecting and representing the
semantic information of an RGB image with the goal of performing cross-view
matching with a (non-RGB) geographic information system (GIS). A segmented
image forms the input to our system with segments assigned to semantic concepts
such as traffic signs, lakes, roads, foliage, etc. We design a descriptor to
robustly capture both, the presence of semantic concepts and the spatial layout
of those segments. Pairwise distances between the descriptors extracted from
the GIS map and the query image are then used to generate a shortlist of the
most promising locations with similar semantic concepts in a consistent spatial
layout. An experimental evaluation with challenging query images and a large
urban area shows promising results
An Approach Of Automatic Reconstruction Of Building Models For Virtual Cities From Open Resources
Along with the ever-increasing popularity of virtual reality technology in recent years, 3D city models have been used in different applications, such as urban planning, disaster management, tourism, entertainment, and video games. Currently, those models are mainly reconstructed from access-restricted data sources such as LiDAR point clouds, airborne images, satellite images, and UAV (uncrewed air vehicle) images with a focus on structural illustration of buildings’ contours and layouts. To help make 3D models closer to their real-life counterparts, this thesis research proposes a new approach for the automatic reconstruction of building models from open resources. In this approach, first, building shapes are reconstructed by using the structural and geographic information retrievable from the open repository of OpenStreetMap (OSM). Later, images available from the street view of Google maps are used to extract information of the exterior appearance of buildings for texture mapping onto their boundaries. The constructed 3D environment is used as prior knowledge for the navigation purposes in a self-driving car. The static objects from the 3D model are compared with the real-time images of static objects to reduce the computation time by eliminating them from the detection proces
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