Diploma and Master Theses (authored and supervised):
"Real-time Meshing for Noisy Points";
Supervisor: S. Ohrhallinger;
Institut für Computergraphik und Algorithmen,
final examination: 2016-02-11.
The increasing availability of 3D scanning devices in both industrial and entertainment environments (e.g., Microsoft Kinect) creates a demand for fast and reliable resampling and reconstruction techniques. Point clouds, especially raw range images, are often non-uniformly sampled and subject to non-uniform noise levels. Current state-of-the-art techniques often require user-provided parameters that estimate the noise level of the point cloud. This produces sub-optimal results for point sets with varying noise extent. We propose an isotropically fair neighborhood definition which is specifically designed to address non-uniformly sampled point clouds. Our iterative point cloud resampling method estimates and adapts to the local noise level at each sample. This increases the reconstruction quality for point clouds with high noise levels while being completely parameter free. The data structures built during the resampling process are reused to speed up the process of creating a consistent normal orientation. Evaluation of the re- sampling quality shows that our technique outperforms current state-of-the-art methods for varying noise levels and non-uniform sampling. Both the resampling algorithm and the subsequent consistent normal orientation operate locally and can be implemented efficiently in parallel. Our GPU sphere regression implementation outperforms the stan- dard sequential procedure by a factor of 20.
Electronic version of the publication:
Created from the Publication Database of the Vienna University of Technology.