@bachelorsthesis{grossmann-2016-baa, title = "Extracting Sensor Specific Noise Models", author = "Nicolas Grossmann", year = "2017", abstract = "With the growing number of consumer-oriented depth sensors like the Kinect or the newly released Phab2Pro, the question of how precise these sensors are arises. In this thesis we want to evaluate the average noise in the generated depth measurements in both the axial direction and the lateral directions. As part of a two-part project this thesis will view the noise’s development with varying distance and angle. Finally, we will present and evaluate two models describing the noise behavior, with the first being derived from solely this thesis’ measurements and the second one being a combination of the previous model and a model of a colleague. This derived models can be used in a post-processing step to filter the generated depth images.", month = aug, note = "1", address = "Favoritenstrasse 9-11/E193-02, A-1040 Vienna, Austria", school = "Institute of Computer Graphics and Algorithms, Vienna University of Technology ", keywords = "noise model, surface reconstruction, sensor noise", URL = "https://www.cg.tuwien.ac.at/research/publications/2017/grossmann-2016-baa/", } @bachelorsthesis{koeppel-2016-baa, title = "Extracting Noise Models – considering X / Y and Z Noise", author = "Thomas K\"{o}ppel", year = "2017", abstract = "We have developed two different test setups allowing the characterization of noise in X, Y and Z direction for the KinectV2 and the Phab2Pro depth sensors. We have combined these two methods, generating a single noise model allowing a prediction of the amount of noise in specific areas of an image in the three respective directions at a certain distance and rotation. We have conducted two test setups and measured the noise from 900 mm to 3.100 mm for the generation of the noise models. The test setup of this thesis focused on determining the noise in X, Y and Z direction, covering the whole frustum of the respective depth sensor. In this thesis, Z noise was measured against a wall and X and Y noises were measured using a 3D chequerboard that was shifted through the room, allowing the above mentioned coverage of the whole frustum. Along the edges of the cells of the chequerboard, the X and Y noise was measured. The combined model was evaluated by using a solid cube to classify the quality of our noise model.", month = aug, note = "1", address = "Favoritenstrasse 9-11/E193-02, A-1040 Vienna, Austria", school = "Institute of Computer Graphics and Algorithms, Vienna University of Technology ", keywords = "noise model, surface reconstruction, sensor noise", URL = "https://www.cg.tuwien.ac.at/research/publications/2017/koeppel-2016-baa/", } @inproceedings{Radwan-2017-Occ, title = "Cut and Paint: Occlusion-Aware Subset Selection for Surface Processing", author = "Mohamed Radwan and Stefan Ohrhallinger and Elmar Eisemann and Michael Wimmer", year = "2017", abstract = "User-guided surface selection operations are straightforward for visible regions on a convex model. However, concave surfaces present a challenge because self-occlusions require multiple camera positions to get unobstructed views. Therefore, users often have to locate and switch to new unobstructed views in order to continue the operation. Our novel approach enables operations like painting or cutting in a single view, even on the backside of objects and for arbitrary depth complexity, with interactive performance. Continuous projection of a curve drawn in screen space onto the mesh guarantees seamless brush strokes or manifold cuts, unaffected by any occlusions. Our occlusion-aware surface-processing method enables a number of applications in an easy way. As examples, we show continuous painting on the surface, selecting regions for texturing, creating illustrative cutaways from nested models and animation of cutaways.", month = may, publisher = "Canadian Human-Computer Communications Society / Soci{\'e}t{\'e} canadienne du dialogue humain-machine", location = "Edmonton, Alberta, CA", event = "Graphics Interface 2017", doi = "10.20380/GI2017.11", booktitle = "Proceedings of Graphics Interface 2017", pages = "82--89", URL = "https://www.cg.tuwien.ac.at/research/publications/2017/Radwan-2017-Occ/", } @article{forsythe-2016-ccm, title = "Resolution-independent superpixels based on convex constrained meshes without small angles", author = "Jeremy Forsythe and Vitaliy Kurlin and Andrew Fitzgibbon", year = "2016", abstract = "The over-segmentation problem for images is studied in the new resolution-independent formulation when a large image is approximated by a small number of convex polygons with straight edges at subpixel precision. These polygonal superpixels are obtained by refining and extending subpixel edge segments to a full mesh of convex polygons without small angles and with approximation guarantees. Another novelty is the objective error difference between an original pixel-based image and the reconstructed image with a best constant color over each superpixel, which does not need human segmentations. The experiments on images from the Berkeley Segmentation Database show that new meshes are smaller and provide better approximations than the state-of-the-art.", month = dec, journal = "Lecture Notes in Computer Science (LNCS)", volume = "10072", issn = "0302-9743", pages = "223--233", keywords = "superpixels, polygonal mesh, Delaunay triangulation, constrained triangulation, edge detection", URL = "https://www.cg.tuwien.ac.at/research/publications/2016/forsythe-2016-ccm/", } @article{ohrhallinger-2016-sgp, title = "Curve Reconstruction with Many Fewer Samples", author = "Stefan Ohrhallinger and Scott A. Mitchell and Michael Wimmer", year = "2016", abstract = "We consider the problem of sampling points from a collection of smooth curves in the plane, such that the Crust family of proximity-based reconstruction algorithms can rebuild the curves. Reconstruction requires a dense sampling of local features, i.e., parts of the curve that are close in Euclidean distance but far apart geodesically. We show that epsilon<0.47-sampling is sufficient for our proposed HNN-CRUST variant, improving upon the state-of-the-art requirement of epsilon<1/3-sampling. Thus we may reconstruct curves with many fewer samples. We also present a new sampling scheme that reduces the required density even further than epsilon<0.47-sampling. We achieve this by better controlling the spacing between geodesically consecutive points. Our novel sampling condition is based on the reach, the minimum local feature size along intervals between samples. This is mathematically closer to the reconstruction density requirements, particularly near sharp-angled features. We prove lower and upper bounds on reach rho-sampling density in terms of lfs epsilon-sampling and demonstrate that we typically reduce the required number of samples for reconstruction by more than half. ", journal = "Computer Graphics Forum", volume = "35", number = "5", issn = "1467-8659", pages = "167--176", keywords = "sampling condition, curve reconstruction, curve sampling", URL = "https://www.cg.tuwien.ac.at/research/publications/2016/ohrhallinger-2016-sgp/", }