Throughout the course of several years, significant progress has been
made with regard to the accuracy and performance of pair-wise alignment
techniques; however when considering low-resolution scans with minimal pairwise
overlap, and scans with high levels of symmetry, the process of
successfully performing sequential alignments in the object reconstruction
process remains a challenging task. Even with the improvements in surface
point sampling and surface feature correspondence estimation, existing
techniques do not guarantee an alignment between arbitrary point-cloud pairs
due to statistically-driven estimation models. In this paper we define a robust
and intuitive painting-based feature correspondence selection methodology that
can refine input sets for these existing techniques to ensure alignment
convergence. Additionally, we consolidate this painting process into a semiautomated
alignment compilation technique that can be used to ensure the
proper reconstruction of scanned models.
S. Transue and M. Choi, "Intuitive Alignment of Point-Clouds with Painting-Based Feature Correspondence", International Symposium on Visual Computing (ISVC) 2014.
©University of Colorado Denver 2017