Registration and entire shape acquisition for grid based active one-shot scanning techniques

Hiroshi Kawasaki, Takuto Hirukawa, Ryo Furukawa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)


One-shot active stereo using structured light is a practical solution for dynamic scene acquisition. Basically, those methods are based on encoding positional information of the pixel into the single projected pattern. A disadvantage of such methods is decreases of the spatial resolution caused by requiring a certain area of the pattern to encode the positional information. Among those methods, grid-based patterns are promising at the point of accuracy and robustness, since triangulation for 3D reconstruction is conducted with light-sectioning method and a line detection is usually a stable image processing. However, no shapes are recovered between the grid lines, and thus, the whole reconstructed shape tends to be sparse. To deal with the problem, integrating multiple shapes that are sequentially captured using registration algorithm such as ICP is one solution. In previous work, we show that naive ICP works poorly for grid-like structured point clouds, and proposed a specialized ICP algorithm for aligning a set of grid-like structured 3D shapes. In this paper, we extend this approach and propose a process for entire shape modeling by capturing objects from all the directions using turn table, and integrating into a single shape using our improved ICP. To achieve this, setting good initial 3D shapes is important. For solution, we interpolation grid shapes to create smooth surface so that common ICP works. Comprehensive experiments are conducted to show the strength of our method compared to common ICP.

Original languageEnglish
Title of host publication2016 23rd International Conference on Pattern Recognition, ICPR 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages7
ISBN (Electronic)9781509048472
Publication statusPublished - Jan 1 2016
Externally publishedYes
Event23rd International Conference on Pattern Recognition, ICPR 2016 - Cancun, Mexico
Duration: Dec 4 2016Dec 8 2016

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651


Other23rd International Conference on Pattern Recognition, ICPR 2016

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition


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