Generalized Image Acquisition and Analysis

A Kaleidoscopic Approach to Surround Geometry and Reflectance Acquisition

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.


BlurTags: Spatially Varying PSF Estimation with Out-of-Focus Patterns

Alexander Reuter, Hans-Peter Seidel, Ivo Ihrke
In: WSCG Communication Papers, 2012


In this paper, we consider the problem of animation reconstruction, i.e., the reconstruction of shape and motion of a deformable object from dynamic 3D scanner data, without using user provided template models. Unlike pre- vious work that addressed this problem, we do not rely on locally conver- gent optimization but present a system that can handle fast motion, tem- porally disrupted input, and can correctly match objects that disappear for extended time periods in acquisition holes due to occlusion. Our approach is motivated by cartography: We first estimate a few landmark correspon- dences, which are extended to a dense matching and then used to recon- struct geometry and motion. We propose a number of algorithmic building blocks: a scheme for tracking landmarks in temporally coherent and inco- herent data, an algorithm for robust estimation of dense correspondences under topological noise, and the integration of local matching techniques to refine the result. We describe and evaluate the individual components and propose a complete animation reconstruction pipeline based on these ideas. We evaluate our method on a number of standard benchmark data sets and show that we can obtain correct reconstructions in situations where other techniques fail completely or require additional user guidance such as a template model.


author = {Alexander Reuter and Hans-Peter Seidel and Ivo Ihrke},
title = {BlurTags: spatially varying PSF estimation with out-of-focus patterns},
booktitle = {Proceedings of WSCG (Communication Papers)},
year = 2012,
pages = "xx--yy",
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