[ { "id": "laager_florian-2014-daa", "type_id": "masterthesis", "tu_id": null, "repositum_id": null, "title": "Robust Registration of Range Images", "date": "2019-01-08", "abstract": "Objects are scanned in 3D by taking overlapping depth images from different perspectives, which then must be registered together to reconstruct the mesh.\n\nMost current work detects a few corresponding points by features in order to find the transform which correctly positions the depth images to each other in 3D. Due to its nature of extracting and matching only a few data, it may not select the best global correspondence, especially if there are several close matches.\n\nIt is also difficult to detect complete failure, since the quality of the guess is not measured, so often manual intervention is necessary. Usually there are multiple depth images and several serial pair-wise registrations, so any errors in the determined transform propagate.\n\nTherefore a better approach to reconsider that problem to use all of the available data and determine only overlaps within an error threshold, e.g. based on confidence from the scanner noise model. This also has applications in shape retrieval, for high-quality partial shape matching. A simplified version of that problem would be to find an overlap of two functions in 2D with the difference in their y-value thresholded in some measure", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "image", "main_file": true, "use_in_gallery": false, "access": "public", "image_width": 729, "image_height": 528, "name": "laager_florian-2014-daa-image.jpg", "type": "image/jpeg", "size": 66878, "path": "Publication:laager_florian-2014-daa", "url": "https://www.cg.tuwien.ac.at/research/publications/2019/laager_florian-2014-daa/laager_florian-2014-daa-image.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2019/laager_florian-2014-daa/laager_florian-2014-daa-image:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1010 ], "date_end": "2019-01-08", "date_start": "2014-06-01", "matrikelnr": "932 0525859", "supervisor": [ 948 ], "research_areas": [ "Geometry" ], "keywords": [ "registration", "point clouds", "shape retrieval" ], "weblinks": [], "files": [ { "description": null, "filetitle": "image", "main_file": true, "use_in_gallery": false, "access": "public", "image_width": 729, "image_height": 528, "name": "laager_florian-2014-daa-image.jpg", "type": "image/jpeg", "size": 66878, "path": "Publication:laager_florian-2014-daa", "url": "https://www.cg.tuwien.ac.at/research/publications/2019/laager_florian-2014-daa/laager_florian-2014-daa-image.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2019/laager_florian-2014-daa/laager_florian-2014-daa-image:thumb{{size}}.png" }, { "description": null, "filetitle": "thesis", "main_file": true, "use_in_gallery": false, "access": "public", "name": "laager_florian-2014-daa-thesis.pdf", "type": "application/pdf", "size": 11268958, "path": "Publication:laager_florian-2014-daa", "url": "https://www.cg.tuwien.ac.at/research/publications/2019/laager_florian-2014-daa/laager_florian-2014-daa-thesis.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2019/laager_florian-2014-daa/laager_florian-2014-daa-thesis:thumb{{size}}.png" } ], "projects_workgroups": [ "Harvest4D" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2019/laager_florian-2014-daa/", "__class": "Publication" }, { "id": "Mayrhauser-2016-Cnc", "type_id": "bachelorthesis", "tu_id": null, "repositum_id": null, "title": "Migration of Surface Curve to Most Concave Isoline", "date": "2016-12", "abstract": "In this paper, I present a solution for migrating a curve on a three dimensional surface to\nthe most concave isoline in its vicinity. Essentially, this problem statement tackles mesh\nsegmentation from a different angle. The search for a suitable segmentation boundary is\nreduced to a shortest path problem.\nFirst, a graph is built using the mesh’s vertices and edges near the input curve. Then,\nthe shortest path is found using the Dijkstra algorithm, whereas a modified weighting\nscheme that makes the passing through of concave edges cheaper, among other factors,\nresults in a path suitable as segmentation boundary.\nThe final algorithm provides segmentation boundaries of a quality similar to existing\nsegmentation algorithms. The runtime generally lies below a second, thus making it\nviable for on the go optimization of the user’s input.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 1587, "image_height": 868, "name": "Mayrhauser-2016-Cnc-image.png", "type": "image/png", "size": 372053, "path": "Publication:Mayrhauser-2016-Cnc", "url": "https://www.cg.tuwien.ac.at/research/publications/2016/Mayrhauser-2016-Cnc/Mayrhauser-2016-Cnc-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2016/Mayrhauser-2016-Cnc/Mayrhauser-2016-Cnc-image:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1348 ], "date_end": "2016-12", "date_start": "2016-07", "matrikelnr": "e0926916", "supervisor": [ 193 ], "research_areas": [ "Geometry" ], "keywords": [], "weblinks": [], "files": [ { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 1587, "image_height": 868, "name": "Mayrhauser-2016-Cnc-image.png", "type": "image/png", "size": 372053, "path": "Publication:Mayrhauser-2016-Cnc", "url": "https://www.cg.tuwien.ac.at/research/publications/2016/Mayrhauser-2016-Cnc/Mayrhauser-2016-Cnc-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2016/Mayrhauser-2016-Cnc/Mayrhauser-2016-Cnc-image:thumb{{size}}.png" }, { "description": null, "filetitle": "thesis", "main_file": false, "use_in_gallery": false, "access": "public", "name": "Mayrhauser-2016-Cnc-thesis.pdf", "type": "application/pdf", "size": 9420121, "path": "Publication:Mayrhauser-2016-Cnc", "url": "https://www.cg.tuwien.ac.at/research/publications/2016/Mayrhauser-2016-Cnc/Mayrhauser-2016-Cnc-thesis.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2016/Mayrhauser-2016-Cnc/Mayrhauser-2016-Cnc-thesis:thumb{{size}}.png" } ], "projects_workgroups": [ "Harvest4D" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2016/Mayrhauser-2016-Cnc/", "__class": "Publication" }, { "id": "WIMMER-2016-HARVEST4D", "type_id": "inproceedings", "tu_id": null, "repositum_id": null, "title": "Harvesting Dynamic 3DWorlds from Commodity Sensor Clouds", "date": "2016-10", "abstract": "The EU FP7 FET-Open project \"Harvest4D: Harvesting Dynamic 3D Worlds from Commodity Sensor Clouds\" deals with the acquisition, processing, and display of dynamic 3D data. Technological progress is offering us a wide-spread availability of sensing devices that deliver different data streams, which can be easily deployed in the real world and produce streams of sampled data with increased density and easier iteration of the sampling process. These data need to be processed and displayed in a new way. The Harvest4D project proposes a radical change in acquisition and processing technology: instead of a goal-driven acquisition that determines the devices and sensors, its methods let the sensors and resulting available data determine the acquisition process. A variety of challenging problems need to be solved: huge data amounts, different modalities, varying scales, dynamic, noisy and colorful data. This short contribution presents a selection of the many scientific results produced by Harvest4D. We will focus on those results that could bring a major impact to the Cultural Heritage domain, namely facilitating the acquisition of the sampled data or providing advanced visual analysis capabilities.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 861, "image_height": 123, "name": "WIMMER-2016-HARVEST4D-image.png", "type": "image/png", "size": 25487, "path": "Publication:WIMMER-2016-HARVEST4D", "url": "https://www.cg.tuwien.ac.at/research/publications/2016/WIMMER-2016-HARVEST4D/WIMMER-2016-HARVEST4D-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2016/WIMMER-2016-HARVEST4D/WIMMER-2016-HARVEST4D-image:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1518, 1519, 823, 1520, 1326, 1521, 1522, 193 ], "booktitle": "Proceedings of the 14th Eurographics Workshop on Graphics and Cultural Heritage", "date_from": "2016-10-05", "date_to": "2016-10-07", "doi": "10.2312/gch.20161378", "editor": "Chiara Eva Catalano and Livio De Luca", "event": "GCH 2016", "isbn": "978-3-03868-011-6", "lecturer": [ 1518 ], "location": "Genova, Italy", "pages_from": "19", "pages_to": "22", "publisher": "Eurographics Association", "research_areas": [ "Geometry", "Rendering" ], "keywords": [ "acquisition", "3d scanning", "reconstruction" ], "weblinks": [], "files": [ { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 861, "image_height": 123, "name": "WIMMER-2016-HARVEST4D-image.png", "type": "image/png", "size": 25487, "path": "Publication:WIMMER-2016-HARVEST4D", "url": "https://www.cg.tuwien.ac.at/research/publications/2016/WIMMER-2016-HARVEST4D/WIMMER-2016-HARVEST4D-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2016/WIMMER-2016-HARVEST4D/WIMMER-2016-HARVEST4D-image:thumb{{size}}.png" }, { "description": null, "filetitle": "paper", "main_file": false, "use_in_gallery": false, "access": "public", "name": "WIMMER-2016-HARVEST4D-paper.pdf", "type": "application/pdf", "size": 11624405, "path": "Publication:WIMMER-2016-HARVEST4D", "url": "https://www.cg.tuwien.ac.at/research/publications/2016/WIMMER-2016-HARVEST4D/WIMMER-2016-HARVEST4D-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2016/WIMMER-2016-HARVEST4D/WIMMER-2016-HARVEST4D-paper:thumb{{size}}.png" } ], "projects_workgroups": [ "Harvest4D" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2016/WIMMER-2016-HARVEST4D/", "__class": "Publication" }, { "id": "SCHUETZ-2016-POT", "type_id": "masterthesis", "tu_id": null, "repositum_id": null, "title": "Potree: Rendering Large Point Clouds in Web Browsers", "date": "2016-09-19", "abstract": "This thesis introduces Potree, a web-based renderer for large point clouds. It allows users\nto view data sets with billions of points, from sources such as LIDAR or photogrammetry,\nin real time in standard web browsers.\nOne of the main advantages of point cloud visualization in web browser is that it\nallows users to share their data sets with clients or the public without the need to install\nthird-party applications and transfer huge amounts of data in advance. The focus on\nlarge point clouds, and a variety of measuring tools, also allows users to use Potree to\nlook at, analyze and validate raw point cloud data, without the need for a time-intensive\nand potentially costly meshing step.\nThe streaming and rendering of billions of points in web browsers, without the need\nto load large amounts of data in advance, is achieved with a hierarchical structure that\nstores subsamples of the original data at different resolutions. A low resolution is stored\nin the root node and with each level, the resolution gradually increases. The structure\nallows Potree to cull regions of the point cloud that are outside the view frustum, and\nto render distant regions at a lower level of detail.\nThe result is an open source point cloud viewer, which was able to render point cloud\ndata sets of up to 597 billion points, roughly 1.6 terabytes after compression, in real time\nin a web browser.", "authors_et_al": false, "substitute": null, "main_image": { "description": "Around 280 million points.\nPoint cloud courtesy of Pix4D.", "filetitle": "Matterhorn", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 906, "image_height": 741, "name": "SCHUETZ-2016-POT-Matterhorn.png", "type": "image/png", "size": 989456, "path": "Publication:SCHUETZ-2016-POT", "url": "https://www.cg.tuwien.ac.at/research/publications/2016/SCHUETZ-2016-POT/SCHUETZ-2016-POT-Matterhorn.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2016/SCHUETZ-2016-POT/SCHUETZ-2016-POT-Matterhorn:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1116 ], "date_end": "2016-09-10", "date_start": "2014-08-01", "matrikelnr": "0825723", "supervisor": [ 193 ], "research_areas": [ "Rendering" ], "keywords": [ "point cloud rendering", "WebGL", "LIDAR" ], "weblinks": [ { "href": "https://github.com/potree", "caption": "github repository", "description": null, "main_file": 0 } ], "files": [ { "description": "Around 17 billion points. \nPoint cloud courtesy of Open Topography and PG&E.", "filetitle": " San Luis Obispo County coast", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 2000, "image_height": 990, "name": "SCHUETZ-2016-POT- San Luis Obispo County coast.jpg", "type": "image/jpeg", "size": 386647, "path": "Publication:SCHUETZ-2016-POT", "url": "https://www.cg.tuwien.ac.at/research/publications/2016/SCHUETZ-2016-POT/SCHUETZ-2016-POT- San Luis Obispo County coast.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2016/SCHUETZ-2016-POT/SCHUETZ-2016-POT- San Luis Obispo County coast:thumb{{size}}.png" }, { "description": "Around 280 million points.\nPoint cloud courtesy of Pix4D.", "filetitle": "Matterhorn", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 906, "image_height": 741, "name": "SCHUETZ-2016-POT-Matterhorn.png", "type": "image/png", "size": 989456, "path": "Publication:SCHUETZ-2016-POT", "url": "https://www.cg.tuwien.ac.at/research/publications/2016/SCHUETZ-2016-POT/SCHUETZ-2016-POT-Matterhorn.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2016/SCHUETZ-2016-POT/SCHUETZ-2016-POT-Matterhorn:thumb{{size}}.png" }, { "description": null, "filetitle": "poster", "main_file": true, "use_in_gallery": false, "access": "public", "preview_image_width": null, "preview_image_height": null, "name": "SCHUETZ-2016-POT-poster.pdf", "type": "application/pdf", "size": 18316148, "path": "Publication:SCHUETZ-2016-POT", "preview_name": "SCHUETZ-2016-POT-poster:preview.png", "preview_type": null, "preview_size": null, "url": "https://www.cg.tuwien.ac.at/research/publications/2016/SCHUETZ-2016-POT/SCHUETZ-2016-POT-poster.pdf", "thumb_image_sizes": [] }, { "description": "Point cloud courtesy of Riegl.", "filetitle": "Retz", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 866, "image_height": 533, "name": "SCHUETZ-2016-POT-Retz.png", "type": "image/png", "size": 952898, "path": "Publication:SCHUETZ-2016-POT", "url": "https://www.cg.tuwien.ac.at/research/publications/2016/SCHUETZ-2016-POT/SCHUETZ-2016-POT-Retz.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2016/SCHUETZ-2016-POT/SCHUETZ-2016-POT-Retz:thumb{{size}}.png" }, { "description": null, "filetitle": "thesis", "main_file": true, "use_in_gallery": false, "access": "public", "preview_image_width": 906, "preview_image_height": 741, "name": "SCHUETZ-2016-POT-thesis.pdf", "type": "application/pdf", "size": 95787159, "path": "Publication:SCHUETZ-2016-POT", "preview_name": "SCHUETZ-2016-POT-thesis:preview.png", "preview_type": "image/png", "preview_size": 989456, "url": "https://www.cg.tuwien.ac.at/research/publications/2016/SCHUETZ-2016-POT/SCHUETZ-2016-POT-thesis.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2016/SCHUETZ-2016-POT/SCHUETZ-2016-POT-thesis:thumb{{size}}.png" } ], "projects_workgroups": [ "Harvest4D" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2016/SCHUETZ-2016-POT/", "__class": "Publication" }, { "id": "arikan-2015-dmrt", "type_id": "journalpaper_notalk", "tu_id": null, "repositum_id": null, "title": "Multi-Depth-Map Raytracing for Efficient Large-Scene Reconstruction", "date": "2016-02", "abstract": "With the enormous advances of the acquisition technology over the last years, fast processing and high-quality\nvisualization of large point clouds have gained increasing attention. Commonly, a mesh surface is reconstructed from the point\ncloud and a high-resolution texture is generated over the mesh from the images taken at the site to represent surface materials. However, this global reconstruction and texturing approach becomes impractical with increasing data sizes. Recently, due to its\npotential for scalability and extensibility, a method for texturing a set of depth maps in a preprocessing and stitching them at runtime has been proposed to represent large scenes. However, the rendering performance of this method is strongly dependent on the number of depth maps and their resolution. Moreover, for the proposed scene representation, every single depth map has to be textured by the images, which in practice heavily increases processing costs. In this paper, we present a novel method\nto break these dependencies by introducing an efficient raytracing of multiple depth maps. In a preprocessing phase, we first generate high-resolution textured depth maps by rendering the input points from image cameras and then perform a graph-cut\nbased optimization to assign a small subset of these points to the images. At runtime, we use the resulting point-to-image assignments (1) to identify for each view ray which depth map contains the closest ray-surface intersection and (2) to efficiently\ncompute this intersection point. The resulting algorithm accelerates both the texturing and the rendering of the depth maps by an order of magnitude.", "authors_et_al": false, "substitute": null, "main_image": { "description": "teaser image", "filetitle": "image", "main_file": true, "use_in_gallery": false, "access": "public", "image_width": 2855, "image_height": 1392, "name": "arikan-2015-dmrt-image.jpg", "type": "image/jpeg", "size": 2864240, "path": "Publication:arikan-2015-dmrt", "url": "https://www.cg.tuwien.ac.at/research/publications/2016/arikan-2015-dmrt/arikan-2015-dmrt-image.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2016/arikan-2015-dmrt/arikan-2015-dmrt-image:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 865, 799, 193 ], "doi": "10.1109/TVCG.2015.2430333", "issn": "1077-2626", "journal": "IEEE Transactions on Visualization & Computer Graphics", "number": "2", "pages_from": "1127", "pages_to": "1137", "volume": "22", "research_areas": [ "Rendering" ], "keywords": [], "weblinks": [ { "href": "http://http://dx.doi.org/10.1109/TVCG.2015.2430333", "caption": "doi", "description": null, "main_file": 0 } ], "files": [ { "description": null, "filetitle": "draft", "main_file": true, "use_in_gallery": false, "access": "public", "name": "arikan-2015-dmrt-draft.pdf", "type": "application/pdf", "size": 1697346, "path": "Publication:arikan-2015-dmrt", "url": "https://www.cg.tuwien.ac.at/research/publications/2016/arikan-2015-dmrt/arikan-2015-dmrt-draft.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2016/arikan-2015-dmrt/arikan-2015-dmrt-draft:thumb{{size}}.png" }, { "description": "teaser image", "filetitle": "image", "main_file": true, "use_in_gallery": false, "access": "public", "image_width": 2855, "image_height": 1392, "name": "arikan-2015-dmrt-image.jpg", "type": "image/jpeg", "size": 2864240, "path": "Publication:arikan-2015-dmrt", "url": "https://www.cg.tuwien.ac.at/research/publications/2016/arikan-2015-dmrt/arikan-2015-dmrt-image.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2016/arikan-2015-dmrt/arikan-2015-dmrt-image:thumb{{size}}.png" }, { "description": "[45MB]", "filetitle": "video", "main_file": false, "use_in_gallery": false, "access": "public", "preview_image_width": 1280, "preview_image_height": 720, "name": "arikan-2015-dmrt-video.mp4", "type": "video/mp4", "size": 47571465, "path": "Publication:arikan-2015-dmrt", "preview_name": "arikan-2015-dmrt-video:preview.png", "preview_type": "image/jpeg", "preview_size": 91344, "url": "https://www.cg.tuwien.ac.at/research/publications/2016/arikan-2015-dmrt/arikan-2015-dmrt-video.mp4", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2016/arikan-2015-dmrt/arikan-2015-dmrt-video:thumb{{size}}.png", "video_mp4": "https://www.cg.tuwien.ac.at/research/publications/2016/arikan-2015-dmrt/arikan-2015-dmrt-video:video.mp4" } ], "projects_workgroups": [ "Harvest4D" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2016/arikan-2015-dmrt/", "__class": "Publication" }, { "id": "ohrhallinger-2016-sgp", "type_id": "journalpaper", "tu_id": null, "repositum_id": null, "title": "Curve Reconstruction with Many Fewer Samples", "date": "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.\nWe show that epsilon<0.47-sampling is sufficient for our proposed HNN-CRUST variant, improving upon the \nstate-of-the-art requirement of epsilon<1/3-sampling.\nThus we may reconstruct curves with many fewer samples.\nWe also present a new sampling scheme that reduces the required density even further than epsilon<0.47-sampling.\nWe achieve this by better controlling the spacing between geodesically consecutive points.\nOur novel sampling condition is based on the reach, the minimum local feature size along intervals between samples.\nThis is mathematically closer to the reconstruction density requirements, particularly near sharp-angled features.\nWe 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.\n", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 2000, "image_height": 1200, "name": "ohrhallinger-2016-sgp-image.png", "type": "image/png", "size": 54228, "path": "Publication:ohrhallinger-2016-sgp", "url": "https://www.cg.tuwien.ac.at/research/publications/2016/ohrhallinger-2016-sgp/ohrhallinger-2016-sgp-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2016/ohrhallinger-2016-sgp/ohrhallinger-2016-sgp-image:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 948, 1363, 193 ], "date_from": "2016-06-20", "date_to": "2016-06-24", "event": "Symposium on Geometry Processing", "issn": "1467-8659", "journal": "Computer Graphics Forum", "lecturer": [ 948 ], "location": "Berlin, Germany", "number": "5", "pages_from": "167", "pages_to": "176", "volume": "35", "research_areas": [ "Geometry" ], "keywords": [ "sampling condition", "curve reconstruction", "curve sampling" ], "weblinks": [ { "href": "https://github.com/stefango74/hnn-crust-sgp16", "caption": "Reproducibility Source Code", "description": "This git repository contains all source code and instructions to reproduce the relevant figures and tables of the paper on an Ubuntu 16.04LTS system.", "main_file": 1 }, { "href": "http://dx.doi.org/10.1111/cgf.12973", "caption": "doi", "description": null, "main_file": 0 } ], "files": [ { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 2000, "image_height": 1200, "name": "ohrhallinger-2016-sgp-image.png", "type": "image/png", "size": 54228, "path": "Publication:ohrhallinger-2016-sgp", "url": "https://www.cg.tuwien.ac.at/research/publications/2016/ohrhallinger-2016-sgp/ohrhallinger-2016-sgp-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2016/ohrhallinger-2016-sgp/ohrhallinger-2016-sgp-image:thumb{{size}}.png" }, { "description": null, "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "ohrhallinger-2016-sgp-paper.pdf", "type": "application/pdf", "size": 969324, "path": "Publication:ohrhallinger-2016-sgp", "url": "https://www.cg.tuwien.ac.at/research/publications/2016/ohrhallinger-2016-sgp/ohrhallinger-2016-sgp-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2016/ohrhallinger-2016-sgp/ohrhallinger-2016-sgp-paper:thumb{{size}}.png" }, { "description": "slides", "filetitle": "slides", "main_file": true, "use_in_gallery": false, "access": "public", "name": "ohrhallinger-2016-sgp-slides.pdf", "type": "application/pdf", "size": 6164109, "path": "Publication:ohrhallinger-2016-sgp", "url": "https://www.cg.tuwien.ac.at/research/publications/2016/ohrhallinger-2016-sgp/ohrhallinger-2016-sgp-slides.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2016/ohrhallinger-2016-sgp/ohrhallinger-2016-sgp-slides:thumb{{size}}.png" } ], "projects_workgroups": [ "Harvest4D", "ShapeAcquisition" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2016/ohrhallinger-2016-sgp/", "__class": "Publication" }, { "id": "SCHUETZ-2015-HQP", "type_id": "inproceedings", "tu_id": null, "repositum_id": null, "title": "High-Quality Point Based Rendering Using Fast Single Pass Interpolation", "date": "2015-09", "abstract": "We present a method to improve the visual quality of point cloud renderings through a nearest-neighbor-like interpolation of points. This allows applications to render points at larger sizes in order to reduce holes, without reducing the readability of fine details due to occluding points. The implementation requires only few modifications to existing shaders, making it eligible to be integrated in software applications without major design changes.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 1000, "image_height": 1000, "name": "SCHUETZ-2015-HQP-image.png", "type": "image/png", "size": 1123770, "path": "Publication:SCHUETZ-2015-HQP", "url": "https://www.cg.tuwien.ac.at/research/publications/2015/SCHUETZ-2015-HQP/SCHUETZ-2015-HQP-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2015/SCHUETZ-2015-HQP/SCHUETZ-2015-HQP-image:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1116, 193 ], "booktitle": "Proceedings of Digital Heritage 2015 Short Papers", "date_from": "2015-09-28", "date_to": "2015-10-02", "lecturer": [ 1116 ], "location": "Granada, Spain", "pages_from": "369", "pages_to": "372", "research_areas": [ "Rendering" ], "keywords": [ "point-based rendering" ], "weblinks": [], "files": [ { "description": null, "filetitle": "draft", "main_file": true, "use_in_gallery": false, "access": "public", "name": "SCHUETZ-2015-HQP-draft.pdf", "type": "application/pdf", "size": 1754449, "path": "Publication:SCHUETZ-2015-HQP", "url": "https://www.cg.tuwien.ac.at/research/publications/2015/SCHUETZ-2015-HQP/SCHUETZ-2015-HQP-draft.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2015/SCHUETZ-2015-HQP/SCHUETZ-2015-HQP-draft:thumb{{size}}.png" }, { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 1000, "image_height": 1000, "name": "SCHUETZ-2015-HQP-image.png", "type": "image/png", "size": 1123770, "path": "Publication:SCHUETZ-2015-HQP", "url": "https://www.cg.tuwien.ac.at/research/publications/2015/SCHUETZ-2015-HQP/SCHUETZ-2015-HQP-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2015/SCHUETZ-2015-HQP/SCHUETZ-2015-HQP-image:thumb{{size}}.png" } ], "projects_workgroups": [ "Harvest4D" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2015/SCHUETZ-2015-HQP/", "__class": "Publication" }, { "id": "arikan-2014-pcvis", "type_id": "journalpaper_notalk", "tu_id": null, "repositum_id": null, "title": "Large-Scale Point-Cloud Visualization through Localized Textured Surface Reconstruction", "date": "2014-09", "abstract": "In this paper, we introduce a novel scene representation for the visualization of large-scale point clouds accompanied by a set of high-resolution photographs. Many real-world applications deal with very densely sampled point-cloud data, which are augmented with photographs that often reveal lighting variations and inaccuracies in registration. Consequently, the high-quality\nrepresentation of the captured data, i.e., both point clouds and photographs together, is a challenging and time-consuming task. We propose a two-phase approach, in which the first (preprocessing) phase generates multiple overlapping surface patches and\nhandles the problem of seamless texture generation locally for each patch. The second phase stitches these patches at render-time to produce a high-quality visualization of the data. As a result of the proposed localization of the global texturing problem, our algorithm is more than an order of magnitude faster than equivalent mesh-based texturing techniques. Furthermore, since\nour preprocessing phase requires only a minor fraction of the whole dataset at once, we provide maximum flexibility when dealing with growing datasets.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "image", "main_file": true, "use_in_gallery": false, "access": "public", "image_width": 5356, "image_height": 4850, "name": "arikan-2014-pcvis-image.jpg", "type": "image/jpeg", "size": 6686806, "path": "Publication:arikan-2014-pcvis", "url": "https://www.cg.tuwien.ac.at/research/publications/2014/arikan-2014-pcvis/arikan-2014-pcvis-image.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2014/arikan-2014-pcvis/arikan-2014-pcvis-image:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 865, 799, 614, 356, 193 ], "issn": "1077-2626", "journal": "IEEE Transactions on Visualization & Computer Graphics", "number": "9", "pages_from": "1280", "pages_to": "1292", "volume": "20", "research_areas": [ "Rendering" ], "keywords": [ "image-based rendering", "large-scale models", "color", "surface representation" ], "weblinks": [ { "href": "http://doi.ieeecomputersociety.org/10.1109/TVCG.2014.2312011", "caption": null, "description": null, "main_file": 0 } ], "files": [ { "description": "Acknowledgments datasets", "filetitle": "ackn", "main_file": false, "use_in_gallery": false, "access": "public", "name": "arikan-2014-pcvis-ackn.rtf", "type": "text/rtf", "size": 597, "path": "Publication:arikan-2014-pcvis", "url": "https://www.cg.tuwien.ac.at/research/publications/2014/arikan-2014-pcvis/arikan-2014-pcvis-ackn.rtf", "thumb_image_sizes": [] }, { "description": "[13 MB]", "filetitle": "draft", "main_file": true, "use_in_gallery": false, "access": "public", "name": "arikan-2014-pcvis-draft.pdf", "type": "application/pdf", "size": 13594589, "path": "Publication:arikan-2014-pcvis", "url": "https://www.cg.tuwien.ac.at/research/publications/2014/arikan-2014-pcvis/arikan-2014-pcvis-draft.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2014/arikan-2014-pcvis/arikan-2014-pcvis-draft:thumb{{size}}.png" }, { "description": null, "filetitle": "image", "main_file": true, "use_in_gallery": false, "access": "public", "image_width": 5356, "image_height": 4850, "name": "arikan-2014-pcvis-image.jpg", "type": "image/jpeg", "size": 6686806, "path": "Publication:arikan-2014-pcvis", "url": "https://www.cg.tuwien.ac.at/research/publications/2014/arikan-2014-pcvis/arikan-2014-pcvis-image.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2014/arikan-2014-pcvis/arikan-2014-pcvis-image:thumb{{size}}.png" }, { "description": "[33 MB]", "filetitle": "video", "main_file": false, "use_in_gallery": false, "access": "public", "preview_image_width": 1280, "preview_image_height": 720, "name": "arikan-2014-pcvis-video.avi", "type": "video/x-msvideo", "size": 33911454, "path": "Publication:arikan-2014-pcvis", "preview_name": "arikan-2014-pcvis-video:preview.png", "preview_type": "image/jpeg", "preview_size": 34237, "url": "https://www.cg.tuwien.ac.at/research/publications/2014/arikan-2014-pcvis/arikan-2014-pcvis-video.avi", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2014/arikan-2014-pcvis/arikan-2014-pcvis-video:thumb{{size}}.png", "video_mp4": "https://www.cg.tuwien.ac.at/research/publications/2014/arikan-2014-pcvis/arikan-2014-pcvis-video:video.mp4" } ], "projects_workgroups": [ "Harvest4D" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2014/arikan-2014-pcvis/", "__class": "Publication" }, { "id": "preiner2014clop", "type_id": "journalpaper", "tu_id": null, "repositum_id": null, "title": "Continuous Projection for Fast L1 Reconstruction", "date": "2014-08", "abstract": "With better and faster acquisition devices comes a demand for fast robust reconstruction algorithms, but no L1-based technique has been fast enough for online use so far. In this paper, we present a novel continuous formulation of the weighted locally optimal projection (WLOP) operator based on a Gaussian mixture describing the input point density. Our method is up to 7 times faster than an optimized GPU implementation of WLOP, and achieves interactive frame rates for moderately sized point clouds. We give a comprehensive quality analysis showing that our continuous operator achieves a generally higher reconstruction quality than its discrete counterpart. Additionally, we show how to apply our continuous formulation to spherical mixtures of normal directions, to also achieve a fast robust normal reconstruction.\n\nProject Page: https://www.cg.tuwien.ac.at/~preiner/projects/clop/", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "thumbnail", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 1080, "image_height": 810, "preview_image_width": 640, "preview_image_height": 480, "name": "preiner2014clop-thumbnail.png", "type": "image/png", "size": 738367, "path": "Publication:preiner2014clop", "preview_name": "preiner2014clop-thumbnail:preview.png", "preview_type": "image/png", "preview_size": 347267, "url": "https://www.cg.tuwien.ac.at/research/publications/2014/preiner2014clop/preiner2014clop-thumbnail.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2014/preiner2014clop/preiner2014clop-thumbnail:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 799, 326, 865, 1184, 193 ], "date_from": "2014-08-10", "date_to": "2014-08-14", "doi": "10.1145/2601097.2601172", "event": "ACM SIGGRAPH 2014", "issn": "0730-0301", "journal": "ACM Transactions on Graphics (Proc. of ACM SIGGRAPH 2014)", "lecturer": [ 799 ], "location": "Vancouver, Canada", "number": "4", "pages_from": "47:1", "pages_to": "47:13", "volume": "33", "research_areas": [ "Geometry" ], "keywords": [ "point set", "Gaussian mixture", "Hierarchical EM", "upsampling", "dynamic reconstruction", "L1 reconstruction" ], "weblinks": [], "files": [ { "description": "compressed quality (7,8 MB)", "filetitle": "paper_small", "main_file": true, "use_in_gallery": false, "access": "public", "name": "preiner2014clop-paper_small.pdf", "type": "application/pdf", "size": 8222514, "path": "Publication:preiner2014clop", "url": "https://www.cg.tuwien.ac.at/research/publications/2014/preiner2014clop/preiner2014clop-paper_small.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2014/preiner2014clop/preiner2014clop-paper_small:thumb{{size}}.png" }, { "description": "high quality (60 MB)", "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "preiner2014clop-paper.pdf", "type": "application/pdf", "size": 62896920, "path": "Publication:preiner2014clop", "url": "https://www.cg.tuwien.ac.at/research/publications/2014/preiner2014clop/preiner2014clop-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2014/preiner2014clop/preiner2014clop-paper:thumb{{size}}.png" }, { "description": "Siggraph 2014 presentation slides", "filetitle": "slides", "main_file": true, "use_in_gallery": false, "access": "public", "name": "preiner2014clop-slides.pptx", "type": "application/zip", "size": 12241698, "path": "Publication:preiner2014clop", "url": "https://www.cg.tuwien.ac.at/research/publications/2014/preiner2014clop/preiner2014clop-slides.pptx", "thumb_image_sizes": [] }, { "description": null, "filetitle": "thumbnail", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 1080, "image_height": 810, "preview_image_width": 640, "preview_image_height": 480, "name": "preiner2014clop-thumbnail.png", "type": "image/png", "size": 738367, "path": "Publication:preiner2014clop", "preview_name": "preiner2014clop-thumbnail:preview.png", "preview_type": "image/png", "preview_size": 347267, "url": "https://www.cg.tuwien.ac.at/research/publications/2014/preiner2014clop/preiner2014clop-thumbnail.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2014/preiner2014clop/preiner2014clop-thumbnail:thumb{{size}}.png" }, { "description": null, "filetitle": "video", "main_file": true, "use_in_gallery": false, "access": "public", "preview_image_width": 1920, "preview_image_height": 1080, "name": "preiner2014clop-video.mp4", "type": "video/mp4", "size": 78627076, "path": "Publication:preiner2014clop", "preview_name": "preiner2014clop-video:preview.png", "preview_type": "image/jpeg", "preview_size": 74242, "url": "https://www.cg.tuwien.ac.at/research/publications/2014/preiner2014clop/preiner2014clop-video.mp4", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2014/preiner2014clop/preiner2014clop-video:thumb{{size}}.png", "video_mp4": "https://www.cg.tuwien.ac.at/research/publications/2014/preiner2014clop/preiner2014clop-video:video.mp4" } ], "projects_workgroups": [ "Harvest4D" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2014/preiner2014clop/", "__class": "Publication" }, { "id": "Radwan-2014-CDR", "type_id": "inproceedings", "tu_id": null, "repositum_id": null, "title": "Efficient Collision Detection While Rendering Dynamic Point Clouds", "date": "2014-05", "abstract": "A recent trend in interactive environments is the use of unstructured\nand temporally varying point clouds. This is driven by both\naffordable depth cameras and augmented reality simulations. One\nresearch question is how to perform collision detection on such\npoint clouds. State-of-the-art methods for collision detection create\na spatial hierarchy in order to capture dynamic point cloud surfaces,\nbut they require O(NlogN) time for N points. We propose\na novel screen-space representation for point clouds which exploits\nthe property of the underlying surface being 2D. In order for dimensionality\nreduction, a 3D point cloud is converted into a series\nof thickened layered depth images. This data structure can be constructed\nin O(N) time and allows for fast surface queries due to\nits increased compactness and memory coherency. On top of that,\nparts of its construction come for free since they are already handled\nby the rendering pipeline. As an application we demonstrate\nonline collision detection between dynamic point clouds. It shows\nsuperior accuracy when compared to other methods and robustness\nto sensor noise since uncertainty is hidden by the thickened boundary.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 1024, "image_height": 1024, "name": "Radwan-2014-CDR-.jpg", "type": "image/jpeg", "size": 1617713, "path": "Publication:Radwan-2014-CDR", "url": "https://www.cg.tuwien.ac.at/research/publications/2014/Radwan-2014-CDR/Radwan-2014-CDR-.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2014/Radwan-2014-CDR/Radwan-2014-CDR-:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 923, 948, 193 ], "booktitle": "Proceedings of the 2014 Graphics Interface Conference", "date_from": "2014-05-07", "date_to": "2014-05-09", "event": "Graphics Interface 2014", "isbn": "978-1-4822-6003-8", "issn": "0713-5424", "lecturer": [ 948 ], "location": "Montreal, Quebec, Canada ", "pages_from": "25", "pages_to": "33", "publisher": "Canadian Information Processing Society", "research_areas": [ "Geometry" ], "keywords": [ "bounding volumes", "layered depth images", "collision detection", "point cloud", "dynamic" ], "weblinks": [], "files": [ { "description": null, "filetitle": "", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 1024, "image_height": 1024, "name": "Radwan-2014-CDR-.jpg", "type": "image/jpeg", "size": 1617713, "path": "Publication:Radwan-2014-CDR", "url": "https://www.cg.tuwien.ac.at/research/publications/2014/Radwan-2014-CDR/Radwan-2014-CDR-.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2014/Radwan-2014-CDR/Radwan-2014-CDR-:thumb{{size}}.png" }, { "description": null, "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "Radwan-2014-CDR-paper.pdf", "type": "application/pdf", "size": 18652471, "path": "Publication:Radwan-2014-CDR", "url": "https://www.cg.tuwien.ac.at/research/publications/2014/Radwan-2014-CDR/Radwan-2014-CDR-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2014/Radwan-2014-CDR/Radwan-2014-CDR-paper:thumb{{size}}.png" }, { "description": null, "filetitle": "slides", "main_file": true, "use_in_gallery": false, "access": "public", "name": "Radwan-2014-CDR-slides.pdf", "type": "application/pdf", "size": 3177669, "path": "Publication:Radwan-2014-CDR", "url": "https://www.cg.tuwien.ac.at/research/publications/2014/Radwan-2014-CDR/Radwan-2014-CDR-slides.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2014/Radwan-2014-CDR/Radwan-2014-CDR-slides:thumb{{size}}.png" } ], "projects_workgroups": [ "Harvest4D" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2014/Radwan-2014-CDR/", "__class": "Publication" }, { "id": "scheiblauer-thesis", "type_id": "phdthesis", "tu_id": null, "repositum_id": null, "title": "Interactions with Gigantic Point Clouds", "date": "2014", "abstract": "During the last decade the increased use of laser range-scanners for sampling the environment has led to gigantic point cloud data sets. Due to the size of such data sets, tasks like viewing, editing, or presenting the data have become a challenge per se, as the point data is too large to fit completely into the main memory of a customary computer system. In order to accomplish these tasks and enable the interaction with gigantic point clouds on consumer grade computer systems, this thesis presents novel methods and data structures for efficiently dealing with point cloud data sets consisting of more than 109 point samples. \r\n\r\nTo be able to access point samples fast that are stored on disk or in memory, they have to be spatially ordered, and for this a data structure is proposed which organizes the points samples in a level-of-detail hierarchy. Point samples stored in this hierarchy cannot only be rendered fast, but can also be edited, for example existing points can be deleted from the hierarchy or new points can be inserted. Furthermore, the data structure is memory efficient, as it only uses the point samples from the original data set. Therefore, the memory consumption of the point samples on disk, when stored in this data structure, is comparable to the original data set. A second data structure is proposed for selecting points. This data structure describes a volume inside which point samples are considered to be selected, and this has the advantage that the information about a selection does not have to be stored at the point samples. \r\n\r\nIn addition to these two previously mentioned data structures, which represent novel contributions for point data visualization and manipulation, methods for supporting the presentation of point data sets are proposed. With these methods the user experience can be enhanced when navigating through the data. One possibility to do this is by using regional meshes that employ an out-of-core texturing method to show details in the mesoscopic scale on the surface of sampled objects, and which are displayed together with point clouds. Another possibility to increase the user experience is to use graphs in 3D space, which helps users to orient themselves inside point cloud models of large sites, where otherwise it would be difficult to find the places of interest. Furthermore, the quality of the displayed point cloud models can be increased by using a point size heuristics that can mimic a closed surface in areas that would otherwise appear undersampled, by utilizing the density of the rendered points in the different areas of the point cloud model. \r\n\r\nFinally, the use of point cloud models as a tool for archaeological work is proposed. Since it becomes increasingly common to document archaeologically interesting monuments with laser scanners, the number application areas of the resulting point clouds is raising as well. These include, but are not limited to, new views of the monument that are impossible when studying the monument on-site, creating cuts and floor plans, or perform virtual anastylosis. \r\n\r\nAll these previously mentioned methods and data structures are implemented in a single software application that has been developed during the course of this thesis and can be used to interactively explore gigantic point clouds.", "authors_et_al": false, "substitute": null, "main_image": { "description": "Point model of the Wiener Stephansdom consisting of 460 million points", "filetitle": "image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 1116, "image_height": 784, "name": "scheiblauer-thesis-image.png", "type": "image/png", "size": 736820, "path": "Publication:scheiblauer-thesis", "url": "https://www.cg.tuwien.ac.at/research/publications/2014/scheiblauer-thesis/scheiblauer-thesis-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2014/scheiblauer-thesis/scheiblauer-thesis-image:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 614 ], "date_end": "2014-07", "date_start": "2006-12", "reviewer_1": [ 193 ], "reviewer_2": [ 1326 ], "rigorosum": "2014-07-18", "supervisor": [ 193 ], "research_areas": [], "keywords": [ "point-based rendering", "out-of-core rendering", "data structures", "complexity analysis" ], "weblinks": [], "files": [ { "description": "Point model of the Wiener Stephansdom consisting of 460 million points", "filetitle": "image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 1116, "image_height": 784, "name": "scheiblauer-thesis-image.png", "type": "image/png", "size": 736820, "path": "Publication:scheiblauer-thesis", "url": "https://www.cg.tuwien.ac.at/research/publications/2014/scheiblauer-thesis/scheiblauer-thesis-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2014/scheiblauer-thesis/scheiblauer-thesis-image:thumb{{size}}.png" }, { "description": "", "filetitle": "thesis", "main_file": true, "use_in_gallery": false, "access": "public", "name": "scheiblauer-thesis-thesis.pdf", "type": "application/pdf", "size": 83650812, "path": "Publication:scheiblauer-thesis", "url": "https://www.cg.tuwien.ac.at/research/publications/2014/scheiblauer-thesis/scheiblauer-thesis-thesis.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2014/scheiblauer-thesis/scheiblauer-thesis-thesis:thumb{{size}}.png" } ], "projects_workgroups": [ "TERAPOINTS", "Harvest4D" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2014/scheiblauer-thesis/", "__class": "Publication" }, { "id": "WIMMER-2014-DWNT", "type_id": "talk", "tu_id": null, "repositum_id": null, "title": "Do we need the full reconstruction pipeline?", "date": "2014", "abstract": "The traditional cultural heritage documentation pipeline from acquisition using a range scanner to interactive display to the user is a tedious and labor-intensive process. In particular, reconstructing high-quality meshes from large point clouds can be time consuming. In this talk, I will present shortcuts to this pipeline. The first idea is not to reconstruct a mesh at all, but keep the original point cloud as long as possible. I will discuss the challenges in maintaining interactivity and high quality when dealing with the display and manipulation of huge point clouds. The second idea is to reconstruct extremely simple models for regular and man-made structures, using shape analysis and user guidance. These models can be shown in end-user installations and require very few resources for display. ", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 193 ], "date_from": "2014-07-24", "date_to": "2014-07-24", "event": "EU-Korea Conference on Science and Technology", "location": "Vienna, Austria", "research_areas": [], "keywords": [], "weblinks": [], "files": [], "projects_workgroups": [ "Harvest4D" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2014/WIMMER-2014-DWNT/", "__class": "Publication" } ]