We describe an automatic system for fast unattended acquisition of accurate and complete 3D models, called Ro-boScan. The design goal is to reduce the three main bottlenecks in human-assisted 3D scanning: the selection of the range maps to be taken (view planning), the positioning of the scanner in the environment, and the range maps' alignment. The system is designed around a commercial laser-based 3D scanner moved by a robotic arm. The acquisition session is organised in two stages. First, an initial sampling of the surface is performed by the automatic selection of a set of views. Then, some added views are automatically selected, acquired and merged to the initial set in order to fill the surface regions left unsampled. Both the initial set of range maps and the subsequently added ones are post-processed automatically, by using the known scanner positions to initialise the alignment phase. Results of the assessment of the system on real acquisitions are presented and discussed.
Abstract-The efficiency of lossless compression algorithms for fixed-palette images (indexed images) may change if a different indexing scheme is adopted. Many lossless compression algorithms adopt a differential-predictive approach. Hence, if the spatial distribution of the indexes over the image is smooth, greater compression ratios may be obtained. Because of this, finding an indexing scheme that realizes such a smooth distribution is a relevant issue. Obtaining an optimal re-indexing scheme is suspected to be a hard problem and only approximate solutions have been provided in literature. In this paper, we restate the re-indexing problem as a graph optimization problem: an optimal re-indexing corresponds to the heaviest Hamiltonian path in a weighted graph. It follows that any algorithm which finds a good approximate solution to this graph-theoretical problem also provides a good re-indexing. We propose a simple and easy-to-implement approximation algorithm to find such a path. The proposed technique compares favorably with most of the algorithms proposed in literature, both in terms of computational complexity and of compression ratio.Index Terms-Hamiltonian path, indexed images, NP completeness, re-indexing, traveling salesman problem (TSP).
Recommended by Yap-Peng Tan CMOS video cameras with high dynamic range (HDR) output are particularly suitable for driving assistance applications, where lighting conditions can strongly vary, going from direct sunlight to dark areas in tunnels. However, common visualization devices can only handle a low dynamic range, and thus a dynamic range reduction is needed. Many algorithms have been proposed in the literature to reduce the dynamic range of still pictures. Anyway, extending the available methods to video is not straightforward, due to the peculiar nature of video data. We propose an algorithm for both reducing the dynamic range of video sequences and enhancing its appearance, thus improving visual quality and reducing temporal artifacts. We also provide an optimized version of our algorithm for a viable hardware implementation on an FPGA. The feasibility of this implementation is demonstrated by means of a case study.
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