Authors:
Steffen Herbort
and
Christian Wöhler
Affiliation:
TU Dortmund, Germany
Keyword(s):
3D Surface Reconstruction, Active Range Scanning, Image-based 3D Reconstruction, Data Fusion, Reflectance Model Estimation.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Geometry and Modeling
;
Image-Based Modeling
;
Motion, Tracking and Stereo Vision
;
Pattern Recognition
;
Software Engineering
;
Stereo Vision and Structure from Motion
Abstract:
3D surface reconstruction data measured with active range scanners typically suffer from high-frequency noise on small scales. This poses a problem for highly demanding surface inspection tasks and all other applications that require a high accuracy of the depth data. One way to achieve increased 3D reconstruction accuracy is the fusion of active range scanning data and photometric image information. Typically, this requires modeling of the surface reflectance behavior, which, in turn, implies the surface to be known with high accuracy to determine valid reflectance parameters as long as no calibration object is available. In this study, we propose an approach that provides a detailed 3D surface reconstruction along with simultaneously estimated parameters of the reflectance model. For 3D surface reconstruction, we employ an algorithm that combines active range scanning data for large-scale accuracy with image-based information for small-scale accuracy. For inferring the reflectance
function, we incorporate the estimation of the reflectance model into a self-consistent computational scheme that successively increases the resolution and thus determines the reflectance parameters based on refined depth information. We present results for a homogeneous dark rough metallic surface,
which is reconstructed based on a single coarse 3D scan and 12 images acquired under different illumination conditions.
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