Generalized Image Acquisition and Analysis
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
A Kaleidoscopic Approach to Surround Geometry and Reflectance Acquisition
In: CVPR Workshop on Computational Cameras and Displays (CCD), 2012
Abstract
We describe a system for acquiring reflectance fields
of objects without moving parts and without a massively
parallel hardware setup. Our system consists of a set of
planar mirrors which serve to multiply a single camera and
a single projector into a multitude of virtual counterparts.
Using this arrangement, we can acquire reflectance fields
with an average angular sampling rate of about 120+
view/light pairs per surface point. The mirror system allows
for freely programmable illumination with full directional
coverage. We employ this setup to realize a 3D acquisition
system that employs structured illumination to capture the
unknown object geometry, in addition to dense reflectance
sampling. On the software side, we combine state-of-the-art
3D reconstruction algorithms with a reflectance sharing
technique based on non-negative matrix factorization
in order to reconstruct a joint model of geometry and
reflectance. We demonstrate for a number of test scenes
that the kaleidoscopic approach can acquire complex
reflectance properties faithfully. The main limitation is
that the multiplexing approach limits the attainable spatial
resolution, trading it off for improved directional coverage.
Video Bibtex
@InProceedings{Ihrke12,
author = {Ivo Ihrke and Ilya Reshetouski and Alkhazur Manakov and Art Tevs and Michael Wand and Hans-Peter Seidel},
title = "{A Kaleidoscopic Approach to Surround Geometry and Reflectance Acquisition}",
booktitle = {Proceedings of IEEE International Workshop on Computational Cameras and Displays },
pages = "1--8",
year = {2012},
}
author = {Ivo Ihrke and Ilya Reshetouski and Alkhazur Manakov and Art Tevs and Michael Wand and Hans-Peter Seidel},
title = "{A Kaleidoscopic Approach to Surround Geometry and Reflectance Acquisition}",
booktitle = {Proceedings of IEEE International Workshop on Computational Cameras and Displays },
pages = "1--8",
year = {2012},
}