Three-dimensional object registration using wavelet features

Chalfant, Julie; Patrikalakis, Nicholas M.

doi:10.1007/s00366-009-0126-5

Cited by 4 publications

(1 citation statement)

References 78 publications

(98 reference statements)

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“…Gendalf et al [10] use a 3-D integral invariant shape descriptor to detect feature points, which are matched in sets of three items using a branch-andbound algorithm. Other interesting shape descriptors invariant with respect to rigid transformation are used to identify feature points, such as scale invariant feature transforms (SIFT)s [11,12] or Harris corners [12] extracted from reflectance images, 3-D SIFT-like descriptors extracted from triangle meshes approximating the point clouds [13], wavelet features [14], intensity-based range features [15], spin images [16,17], and extended Gaussian images [18].…”

Section: Related Workmentioning

confidence: 99%

Robust surface registration using N-points approximate congruent sets

Yao

Ruggeri

Taddei

et al. 2011

EURASIP J. Adv. Signal Process.

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Scans acquired by 3D sensors are typically represented in a local coordinate system. When multiple scans, taken from different locations, represent the same scene these must be registered to a common reference frame. We propose a fast and robust registration approach to automatically align two scans by finding two sets of N-points, that are approximately congruent under rigid transformation and leading to a good estimate of the transformation between their corresponding point clouds. Given two scans, our algorithm randomly searches for the best sets of congruent groups of points using a RANSAC-based approach. To successfully and reliably align two scans when there is only a small overlap, we improve the basic RANSAC random selection step by employing a weight function that approximates the probability of each pair of points in one scan to match one pair in the other. The search time to find pairs of congruent sets of N-points is greatly reduced by employing a fast search codebook based on both binary and multi-dimensional lookup tables. Moreover, we introduce a novel indicator of the overlapping region quality which is used to verify the estimated rigid transformation and to improve the alignment robustness. Our framework is general enough to incorporate and efficiently combine different point descriptors derived from geometric and texture-based feature points or scene geometrical characteristics. We also present a method to improve the matching effectiveness of texture feature descriptors by extracting them from an atlas of rectified images recovered from the scan reflectance image. Our algorithm is robust with respect to different sampling densities and also resilient to noise and outliers. We demonstrate its robustness and efficiency on several challenging scan datasets with varying degree of noise, outliers, extent of overlap, acquired from indoor and outdoor scenarios.

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