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.


A Kaleidoscopic Approach to Surround Geometry and Reflectance Acquisition

Ivo Ihrke, Ilya Reshetouski, Alkhazur Manakov, Art Tevs, Michael Wand, Hans-Peter Seidel
In: CVPR Workshop on Computational Cameras and Displays (CCD), 2012


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.


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},
Go to project list