Efficient 3-dimensional model reconstruction based on marker encoded fringe projection profilometry

Budianto, B.; Lun, Daniel Pak-Kong

doi:10.1109/icassp.2014.6853661

Cited by 3 publications

(1 citation statement)

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“…The key factor is the use of the phase of the periodic sinusoids (fringes) which is less likely to be influenced by distortions from various sources such as, object's texture, global illumination, ambient light, etc. However, traditional FPP methods can only obtain the modulo-2 or the wrapped phase information of the fringes [6,7]. To solve this problem, traditional FPP methods adopt a phase unwrapping procedure.…”

Section: Introductionmentioning

confidence: 99%

Gabor feature based discriminative dictionary learning for period order detection in fringe projection profilometry

Budianto

Lun

2015

2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

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Fringe projection profilometry (FPP) is a popular method for accurate 3D model reconstruction. In a typical FPP process, a tedious phase unwrapping procedure is often needed to obtain the true phase information of the captured fringe images. However, conventional phase unwrapping algorithms often suffer from the ambiguity problems when the scene contains occlusions or sudden jumps in object's height profile. In this paper, we propose an efficient decoding strategy to overcome the ambiguity problems. A novel coded fringe pattern is employed and a Gabor feature based discriminative dictionary is used to estimate the unknown period order of the wrapped phase. Experimental results show that the proposed method can achieve an accurate 3D reconstruction of objects' model in various adverse conditions where traditional FPP methods often fail to perform.

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Section: Introductionmentioning

confidence: 99%