Abstract
Inspired by retinex theory, we propose a novel method for selecting key points from a depth
map of a 3D freeform shape; we also use these key points as a basis for shape registration. To
find key points, first, depths are transformed using the Hotelling method and normalized to reduce
their dependence on a particular viewpoint. Adaptive smoothing is then applied using weights which
decrease with spatial gradient and local inhomogeneity; this preserves local features such as edges
and corners while ensuring smoothed depths are not reduced. Key points are those with locally
maximal depths, faithfully capturing shape. We show how such key points can be used in an efficient
registration process, using two state-of-the-art iterative closest point variants. A comparative study
with leading alternatives, using real range images, shows that our approach provides informative,
expressive, and repeatable points leading to the most accurate registration results.
Original language | English |
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Pages (from-to) | 2126-2142 |
Journal | Pattern Recognition |
Volume | 47 |
Issue number | 6 |
Early online date | 15 Jan 2014 |
DOIs | |
Publication status | Published - Jun 2014 |
Keywords
- Retinex
- Key point
- Freeform shape
- Adaptive smoothing
- Registration