Stereo Image Analysis of Non-Lambertian Surfaces |
| |
Authors: | Christian Wöhler Pablo d’Angelo |
| |
Affiliation: | 1.Daimler Group Research, Environment Perception,Ulm,Germany;2.German Aerospace Center (DLR) Oberpfaffenhofen,We?ling,Germany |
| |
Abstract: | Stereo image analysis is based on establishing correspondences between a pair of images by determining similarity measures
for potentially corresponding image parts. Such similarity criteria are only strictly valid for surfaces with Lambertian (diffuse)
reflectance characteristics. Specular reflections are viewpoint dependent and may thus cause large intensity differences at
corresponding image points. In the presence of specular reflections, traditional stereo approaches are often unable to establish
correspondences at all, or the inferred disparity values tend to be inaccurate, or the established correspondences do not
belong to the same physical surface point. The stereo image analysis framework for non-Lambertian surfaces presented in this
contribution combines geometric cues with photometric and polarimetric information into an iterative scheme that allows to
establish stereo correspondences in accordance with the specular reflectance behaviour and at the same time to determine the
surface gradient field based on the known photometric and polarimetric reflectance properties. The described approach yields
a dense 3D reconstruction of the surface which is consistent with all observed geometric and photopolarimetric data. Initially,
a sparse 3D point cloud of the surface is computed by traditional blockmatching stereo. Subsequently, a dense 3D profile of
the surface is determined in the coordinate system of camera 1 based on the shape from photopolarimetric reflectance and depth
technique. A synthetic image of the surface is rendered in the coordinate system of camera 2 using the illumination direction
and reflectance properties of the surface material. Point correspondences between the rendered image and the observed image
of camera 2 are established with the blockmatching technique. This procedure yields an increased number of 3D points of higher
accuracy, compared to the initial 3D point cloud. The improved 3D point cloud is used to compute a refined dense 3D surface
profile. These steps are iterated until convergence of the 3D reconstruction. An experimental evaluation of our method is
provided for areas of several square centimetres of forged and cast iron objects with rough surfaces displaying both diffuse
and significant specular reflectance components, where traditional stereo image analysis largely fails. A comparison to independently
measured ground truth data reveals that the root-mean-square error of the 3D reconstruction results is typically of the order
30–100 μm at a lateral pixel resolution of 86 μm. For two example surfaces, the number of stereo correspondences established
by the specular stereo algorithm is several orders of magnitude higher than the initial number of 3D points. For one example
surface, the number of stereo correspondences decreases by a factor of about two, but the 3D point cloud obtained with the
specular stereo method is less noisy, contains a negligible number of outliers, and shows significantly more surface detail
than the initial 3D point cloud. For poorly known reflectance parameters we observe a graceful degradation of the accuracy
of 3D reconstruction. |
| |
Keywords: | Stereo vision Specular reflectance Shape from shading Shape from polarisation Photometric stereo |
本文献已被 SpringerLink 等数据库收录! |
|