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Adaptively Merging Large-Scale Range Data with Reflectance Properties
March 2005 (vol. 27 no. 3)
pp. 392-405
In this paper, we tackle the problem of geometric and photometric modeling of large intricately shaped objects. Typical target objects we consider are cultural heritage objects. When constructing models of such objects, we are faced with several important issues that have not been addressed in the past—issues that mainly arise due to the large amount of data that has to be handled. We propose two novel approaches to efficiently handle such large amounts of data: A highly adaptive algorithm for merging range images and an adaptive nearest-neighbor search to be used with the algorithm. We construct an integrated mesh model of the target object in adaptive resolution, taking into account the geometric and/or photometric attributes associated with the range images. We use surface curvature for the geometric attributes and (laser) reflectance values for the photometric attributes. This adaptive merging framework leads to a significant reduction in the necessary amount of computational resources. Furthermore, the resulting adaptive mesh models can be of great use for applications such as texture mapping, as we will briefly demonstrate. Additionally, we propose an additional test for the k--d tree nearest-neighbor search algorithm. Our approach successfully omits back-tracking, which is controlled adaptively depending on the distance to the nearest neighbor. Since the main consumption of computational cost lies in the nearest-neighbor search, the proposed algorithm leads to a significant speed-up of the whole merging process. In this paper, we present the theories and algorithms of our approaches with pseudo code and apply them to several real objects, including large-scale cultural assets.

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Index Terms:
Adaptive integration of range images, laser reflectance strength, nearest-neighbor search.
Citation:
Ryusuke Sagawa, Ko Nishino, Katsushi Ikeuchi, "Adaptively Merging Large-Scale Range Data with Reflectance Properties," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 3, pp. 392-405, March 2005, doi:10.1109/TPAMI.2005.46
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