Fluorescent Immersion Range Scanning
The quality of a 3D range scan should not depend on the surface
properties of the object. Most active range scanning techniques,
however, assume a diffuse reflector to allow for a robust detection
of incident light patterns. In our approach we embed the object into
a fluorescent liquid. By analyzing the light rays that become visible
due to fluorescence rather than analyzing their reflections off the
surface, we can detect the intersection points between the projected
laser sheet and the object surface for a wide range of different materials. For transparent objects we can even directly depict a slice
through the object in just one image by matching its refractive index
to the one of the embedding liquid. This enables a direct sampling
of the object geometry without the need for computational reconstruction. This way, a high-resolution 3D volume can be assembled
simply by sweeping a laser plane through the object. We demonstrate the effectiveness of our light sheet range scanning approach
on a set of objects manufactured from a variety of materials and
material mixes, including dark, translucent and transparent objects.
Projects
Art Tevs, Alexander Berner, Michael Wand, Ivo Ihrke, Hans-Peter Seidel
In: Proceedings of EUROGRAPHICS 2011.
Go to project listIn: Proceedings of EUROGRAPHICS 2011.
Abstract
Recently, the problem of intrinsic shape matching has received a lot of attention. A number of algorithms have been proposed, among which random-sampling-based techniques have been particularly successful due to their generality and efficiency. We introduce a new sampling-based shape matching algorithm that uses a planning step to find optimized "landmark" points. These points are matched first in order to maximize the information gained and thus minimize the sampling costs. Our approach makes three main contributions: First, the new technique leads to a significant improvement in performance, which we demonstrate on a number of benchmark scenarios. Second, our technique does not require any keypoint detection. This is often a significant limitation for models that do not show sufficient surface features. Third, we examine the actual numerical degrees of freedom of the matching problem for a given piece of geometry. In contrast to previous results, our estimates take into account unprecise geodesics and potentially numerically unfavorable geometry of general topology, giving a more realistic complexity estimate.
Project Page Bibtex
@INPROCEEDINGS{Tevs11:plansac,
AUTHOR = {Art Tevs and Alexander Berner and Michael Wand and Ivo Ihrke and Hans-Peter Seidel},
EDITOR = {Deussen, Oliver and Chen, Min},
TITLE = {{Intrinsic Shape Matching by Planned Landmark Sampling}},
BOOKTITLE = {Computer Graphics Forum (Proc. EUROGRAPHICS)},
ORGANIZATION = {Eurographics},
PADDRESS = {Oxford, UK},
ADDRESS = {Llandudno, UK},
PUBLISHER = {Blackwell},
YEAR = {2011},
PAGES = {543--552},
}
AUTHOR = {Art Tevs and Alexander Berner and Michael Wand and Ivo Ihrke and Hans-Peter Seidel},
EDITOR = {Deussen, Oliver and Chen, Min},
TITLE = {{Intrinsic Shape Matching by Planned Landmark Sampling}},
BOOKTITLE = {Computer Graphics Forum (Proc. EUROGRAPHICS)},
ORGANIZATION = {Eurographics},
PADDRESS = {Oxford, UK},
ADDRESS = {Llandudno, UK},
PUBLISHER = {Blackwell},
YEAR = {2011},
PAGES = {543--552},
}