Abstract
Acquisition of depth and texture with vision sensors finds numerous applications for objects modeling, man-machine interfaces, or robot navigation. One challenge resulting from rich textured 3D datasets resides in the acquisition, management and processing of the large amount of data generated, which often preempts full usage of the information available for autonomous systems to make educated decisions. Most subsampling solutions to reduce dataset’s dimension remain independent from the content of the model and therefore do not optimize the balance between the richness of the measurements and their compression. This paper experimentally evaluates the performance achieved with two computational methods that selectively drive the acquisition of depth measurements over regions of a scene characterized by higher 3D features density, while capitalizing on the knowledge readily available in previously acquired data. Both techniques automatically establish which subsets of measurements contribute most to the representation of the scene, and prioritize their acquisition. The algorithms are validated on datasets acquired from two different RGB-D sensors.
The original version of this chapter was revised: The copyright line was incorrect. This has been corrected. The Erratum to this chapter is available at DOI: 10.1007/978-3-319-02895-8_64
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Payeur, P., Curtis, P., Cretu, AM. (2013). Computational Methods for Selective Acquisition of Depth Measurements: An Experimental Evaluation. In: Blanc-Talon, J., Kasinski, A., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2013. Lecture Notes in Computer Science, vol 8192. Springer, Cham. https://doi.org/10.1007/978-3-319-02895-8_35
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DOI: https://doi.org/10.1007/978-3-319-02895-8_35
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