欢迎点云相关产学研的学者和团体加入我们。
笔者评:招聘人员主要进行机载激光点云与其他机载重建的数据进行配准对齐,以及基于点云的场景建模工作。
Student in Computer Science, Engineering Studies or comparable study (m/f): 3D Point Cloud and Surface Registration Using Point Feature
Histograms
Your mission:
When combining the results of airborne laser scanning (LiDAR) and the results of airborne 3D reconstruction from optical sensors, one major problem is the alignment of the two point clouds in 3D space, called point cloud registration. The main idea is usually to identify
corresponding points in both data sets and estimate a rigid body transformation that minimizes the distance between these corresponding points. Finding the corresponding points is not a trivial task, as pure nearest neighbor search in Euclidean space only works for point clouds which are already very well aligned and tends to fail completely, if the offset in both rotation and translation is large.
In this work, Point Feature Histograms (PFH), describing the local neighborhood of each point, should be applied to find corresponding points in bad aligned, noisy and partially overlapping point clouds and to use them for the estimation of the point cloud alignment in an iterative way. The iterative estimation normally is done by using for example the Iterative Closest Point algorithm, with the distance metric exchanged to the one evolved by the Point Feature Histograms. A special case are already triangulated point clouds, where additional information about the local neighborhood of each point can be derived from the mesh and the combination with the abovementioned PFH should be evaluated.
Tasks:
•Getting an overview of the State-of-the-Art algorithms
•Implementing Point Feature Histograms into the existing framework
•Evaluating the accuracy of the algorithms (and their modifications) on ground truth data
Your qualifications:
•Studying computer science, engineering studies or comparable study
•Good knowledge of computer vision algorithms
•Good programming skills in C/C++
•Basic knowledge of Unix / Linux
•Good English skills
•Being able to extend and integrate code in an existing framework and communicate openly
•Self-motivated and independent working
Your benefits:
Look forward to a fulfilling job with an employer who appreciates your commitment and supports your personal and professional development. Disabled applicants with equivalent qualifications will be given preferential treatment.
Student in Computer Science, Engineering Studies or comparable study (m/f): Meshing irregular point clouds in urban areas for 3D modelling
Your mission:
In classical dense stereo matching, the result of two or more 2D input images is a dense point cloud in 3D space. For practical reasons, one often needs to mesh the point cloud to a triangulated isosurface, which then can be textured to produce visually appealing results. The meshing of point clouds can be described as fitting a closed surface, parametrized by e.g. triangles, through the input points. For dense and uniformly sampled point clouds without outliers, this task can be done quite well using established algorithms like for example “Marching Cubes” or “Poisson Surface Reconstruction”.
But when obtaining a 3D point cloud from remote sensing images via dense stereo matching, we face three major problems:
1) Especially in urban areas, normally one or two sides of a building are missing due to occlusion.
2) When merging different point clouds (of the same scene) obtained via multi-view matching, the point cloud normally is quite dense, but at the same time not uniformly sampled and (due to errors in the image registration) prone to some noise. Therefore a 3D point in the real scene often is represented by many representatives slightly different in position.
3) The initial point clouds are far to dense for large-scale applications. A standard aerial stereo image-pair for example results in roughly 30 million points or 60 million triangles for a square kilometer. Fitting a triangulated isosurface to these point clouds is challenging for both of the two first cases. For the third case, the goal is to exploit locally planar structures in the (noisy) point clouds to reduce the input data amount by >95%, while maintaining the same visual quality (detect locally planar surfaces and reduce/re-arrange the mesh).
Tasks:
•Getting an overview of the State-of-the-Art algorithms
•Choosing the best fitting algorithms and implementing them into the existing framework
•Evaluating the accuracy of the algorithms on ground truth data
Your qualifications:
•Studying computer science, engineering studies or comparable study
•Good knowledge of computer vision algorithms
•Good programming skills in C/C++
•Basic knowledge of Unix / Linux
•Good English skills
•Being able to extend and integrate code in an existing framework and communicate openly
•Self-motivated and independent working
Your benefits:
Look forward to a fulfilling job with an employer who appreciates your commitment and supports your personal and professional development. Unser einzigartiges Arbeitsumfeld bietet Ihnen Gestaltungsfreiräume und eine unvergleichbare Infrastruktur, in der Sie Ihre Mission verwirklichen können. Disabled applicants with equivalent qualifications will be given preferential treatment.