[show abstract][hide abstract] ABSTRACT: Image based methods have proved to efficiently render scenes with a higher efficiency than geometry based ap-proaches, mainly because of one of their most important advantages: the bounded complexity by the image resolu-tion, instead of by the number of primitives. Furthermore, due to their parallel and discrete nature, they are highly suitable for GPU implementations. On the other hand, dur-ing the last few years point-based graphics has emerged as a promising complement to other representations. How-ever, with the continuous increase of scene complexity, so-lutions for directly processing and rendering point clouds are in demand. In this paper, algorithms for efficiently ren-dering large point models using image reconstruction tech-niques are proposed. Except for the projection of samples onto screen space, the reconstruction time is bounded only by the screen resolution. The method is also extended to in-terpolate other primitives, such as lines and triangles. In addition, no extra data-structure is required, making the strategy memory efficient.
[show abstract][hide abstract] ABSTRACT: Current state-of-the-art image-based scene reconstruction techniques are capable of generating high-fidelity 3D models when
used under controlled capture conditions. However, they are often inadequate when used in more challenging environments such
as sports scenes with moving cameras. Algorithms must be able to cope with relatively large calibration and segmentation errors
as well as input images separated by a wide-baseline and possibly captured at different resolutions. In this paper, we propose
a technique which, under these challenging conditions, is able to efficiently compute a high-quality scene representation
via graph-cut optimisation of an energy function combining multiple image cues with strong priors. Robustness is achieved
by jointly optimising scene segmentation and multiple view reconstruction in a view-dependent manner with respect to each
input camera. Joint optimisation prevents propagation of errors from segmentation to reconstruction as is often the case with
sequential approaches. View-dependent processing increases tolerance to errors in through-the-lens calibration compared to
global approaches. We evaluate our technique in the case of challenging outdoor sports scenes captured with manually operated
broadcast cameras as well as several indoor scenes with natural background. A comprehensive experimental evaluation including
qualitative and quantitative results demonstrates the accuracy of the technique for high quality segmentation and reconstruction
and its suitability for free-viewpoint video under these difficult conditions.
International Journal of Computer Vision 01/2011; 93:73-100. · 3.62 Impact Factor
[show abstract][hide abstract] ABSTRACT: As-built models and drawings are essential documents used during the operations and maintenance (OM) of buildings for a variety of purposes including the management of facility spaces, equipment, and energy systems. These documents undergo continuous verification and updating procedures both immediately after construction during the initial handover process to reflect construction changes and during occupancy stage for the changes that occur throughout the building's lifespan. Current as-built verification and updating procedures involve largely time consuming on-site surveys, where measurements are taken and recorded manually. In an attempt to streamline this process, the paper investigates the advantages and limitations of using photogrammetric image processing to document and verify actual as-built conditions. A test bed of both the interior and exterior of a university building is used to compare the dimensions generated by automated image processing to dimensions gathered through the manual survey process currently employed by facilities management and strategies for improved accuracy are investigated. Both manual and image-based dimensions are then used to verify dimensions of an existing as-built Building Information Model (BIM). Finally, the potential of the image-based spatial data is assessed for accurately generating 3D models. 2011 Elsevier B.V. All Rights Reserved.
Automation in Construction 01/2012; 21(1):161-171. · 1.82 Impact Factor
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