[ { "id": "herzberger-2024-roh", "type_id": "journalpaper", "tu_id": null, "repositum_id": "20.500.12708/193209", "title": "Residency Octree: a hybrid approach for scalable web-based multi-volume rendering", "date": "2024-01", "abstract": "We present a hybrid multi-volume rendering approach based on a novel Residency Octree that combines the advantages of out-of-core volume rendering using page tables with those of standard octrees. Octree approaches work by performing hierarchical tree traversal. However, in octree volume rendering, tree traversal and the selection of data resolution are intrinsically coupled. This makes fine-grained empty-space skipping costly. Page tables, on the other hand, allow access to any cached brick from any resolution. However, they do not offer a clear and efficient strategy for substituting missing high-resolution data with lower-resolution data. We enable flexible mixed-resolution out-of-core multi-volume rendering by decoupling the cache residency of multi-resolution data from a resolution-independent spatial subdivision determined by the tree. Instead of one-to-one node-to-brick correspondences, each residency octree node is mapped to a set of bricks from different resolution levels. This makes it possible to efficiently and adaptively choose and mix resolutions, adapt sampling rates, and compensate for cache misses. At the same time, residency octrees support fine-grained empty-space skipping, independent of the data subdivision used for caching. Finally, to facilitate collaboration and outreach, and to eliminate local data storage, our implementation is a web-based, pure client-side renderer using WebGPU and WebAssembly. Our method is faster than prior approaches and efficient for many data channels with a flexible and adaptive choice of data resolution.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "teaser", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 2656, "image_height": 912, "name": "herzberger-2024-roh-teaser.png", "type": "image/png", "size": 550157, "path": "Publication:herzberger-2024-roh", "url": "https://www.cg.tuwien.ac.at/research/publications/2024/herzberger-2024-roh/herzberger-2024-roh-teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2024/herzberger-2024-roh/herzberger-2024-roh-teaser:thumb{{size}}.png" }, "sync_repositum_override": "projects,date_from,date_to,event,lecturer,location", "repositum_presentation_id": null, "authors": [ 1857, 226, 5321, 5322, 1933, 166, 818 ], "date_from": "2023-10-22", "date_to": "2023-10-27", "doi": "10.1109/TVCG.2023.3327193", "event": "IEEE VIS 2023", "issn": "1941-0506", "journal": "IEEE Transactions on Visualization and Computer Graphics", "lecturer": [ 1857 ], "location": "Melbourne, Victoria, Australia", "number": "1", "open_access": "yes", "pages": "11", "pages_from": "1380", "pages_to": "1390", "publisher": "IEEE", "volume": "30", "research_areas": [], "keywords": [ "Rendering (computer graphics)", "Octrees", "Spatial resolution", "Graphics processing units", "Data visualization", "Optimization", "Standards", "Volume rendering", "Ray-guided rendering", "Large-scale data", "Out-of-core rendering", "Multi-resolution", "Multi-channel", "Web-Based Visualization" ], "weblinks": [], "files": [ { "description": null, "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "herzberger-2024-roh-paper.pdf", "type": "application/pdf", "size": 8426051, "path": "Publication:herzberger-2024-roh", "url": "https://www.cg.tuwien.ac.at/research/publications/2024/herzberger-2024-roh/herzberger-2024-roh-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2024/herzberger-2024-roh/herzberger-2024-roh-paper:thumb{{size}}.png" }, { "description": null, "filetitle": "teaser", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 2656, "image_height": 912, "name": "herzberger-2024-roh-teaser.png", "type": "image/png", "size": 550157, "path": "Publication:herzberger-2024-roh", "url": "https://www.cg.tuwien.ac.at/research/publications/2024/herzberger-2024-roh/herzberger-2024-roh-teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2024/herzberger-2024-roh/herzberger-2024-roh-teaser:thumb{{size}}.png" } ], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2024/herzberger-2024-roh/", "__class": "Publication" }, { "id": "Troidl_2022", "type_id": "journalpaper", "tu_id": null, "repositum_id": "20.500.12708/192525", "title": "Barrio: Customizable Spatial Neighborhood Analysis and Comparison for Nanoscale Brain Structures", "date": "2022-06-15", "abstract": "High-resolution electron microscopy imaging allows neuroscientists to reconstruct not just entire cells but individual cell sub-structures (i.e., cell organelles) as well. Based on these data, scientists hope to get a better understanding of brain function and development through detailed analysis of local organelle neighborhoods. In-depth analyses require efficient and scalable comparison of a varying number of cell organelles, ranging from two to hundreds of local spatial neighborhoods. Scientists need to be able to analyze the 3D morphologies of organelles, their spatial distributions and distances, and their spatial correlations. We have designed Barrio as a configurable framework that scientists can adjust to their preferred workflow, visualizations, and supported user interactions for their specific tasks and domain questions. Furthermore, Barrio provides a scalable comparative visualization approach for spatial neighborhoods that automatically adjusts visualizations based on the number of structures to be compared. Barrio supports small multiples of spatial 3D views as well as abstract quantitative views, and arranges them in linked and juxtaposed views. To adapt to new domain-specific analysis scenarios, we allow the definition of individualized visualizations and their parameters for each analysis session. We present an in-depth case study for mitochondria analysis in neuronal tissue and demonstrate the usefulness of Barrio in a qualitative user study with neuroscientists.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "Image", "main_file": true, "use_in_gallery": true, "access": "public", "image_width": 1547, "image_height": 789, "name": "Troidl_2022-Image.png", "type": "image/png", "size": 435444, "path": "Publication:Troidl_2022", "url": "https://www.cg.tuwien.ac.at/research/publications/2022/Troidl_2022/Troidl_2022-Image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2022/Troidl_2022/Troidl_2022-Image:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1659, 1932, 166, 1933, 226, 818 ], "cfp": { "name": "Full Papers – EuroVis 2022.pdf", "type": "application/pdf", "error": "0", "size": "560552", "orig_name": "Full Papers – EuroVis 2022.pdf", "ext": "pdf" }, "date_from": "2021-11-11", "date_to": "2022-06-15", "event": "Proceedings Eurographics/IEEE Symposium on Visualization, Eurovis 2022", "first_published": "2022-06-15", "issn": "1467-8659", "journal": "Computer Graphics Forum", "open_access": "yes", "pages": "12", "publisher": "WILEY", "volume": "41", "research_areas": [ "IllVis" ], "keywords": [ "Neuroscience (miscellaneous)", "Computer Graphics", "vizualitation" ], "weblinks": [], "files": [ { "description": null, "filetitle": "Image", "main_file": true, "use_in_gallery": true, "access": "public", "image_width": 1547, "image_height": 789, "name": "Troidl_2022-Image.png", "type": "image/png", "size": 435444, "path": "Publication:Troidl_2022", "url": "https://www.cg.tuwien.ac.at/research/publications/2022/Troidl_2022/Troidl_2022-Image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2022/Troidl_2022/Troidl_2022-Image:thumb{{size}}.png" }, { "description": null, "filetitle": "Paper", "main_file": true, "use_in_gallery": true, "access": "public", "name": "Troidl_2022-Paper.pdf", "type": "application/pdf", "size": 5159703, "path": "Publication:Troidl_2022", "url": "https://www.cg.tuwien.ac.at/research/publications/2022/Troidl_2022/Troidl_2022-Paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2022/Troidl_2022/Troidl_2022-Paper:thumb{{size}}.png" } ], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2022/Troidl_2022/", "__class": "Publication" }, { "id": "raidou_shonan167", "type_id": "misc", "tu_id": null, "repositum_id": null, "title": "NII Shonan Meeting Report No. 167: Formalizing Biological and Medical Visualization", "date": "2020-02", "abstract": "Medicine and biology are among the most important research fields, having a significant impact on humans and their health. For decades, these fields have been highly dependent on visualization—establishing a tight coupling which is crucial for the development of visualization techniques, designed exclusively for the disciplines of medicine and biology. These visualization techniques can be generalized by the term Biological and Medical Visualization—for short,BioMedical Visualization. BioMedical Visualization is not only an enabler for medical diagnosis and treatment, but also an influential component of today’s life science research. Many BioMedical domains can now be studied at various scales and dimensions, with different imaging modalities and simulations, and for a variety of purposes. Accordingly, BioMedical Visualization has also innumerable contributions in industrial applications. However, despite its proven scientific maturity and societal value, BioMedical Visualization is often treated within Computer Science as a mere application subdomain of the broader field of Visualization.To enable BioMedical Visualization to further thrive, it is important to formalize its characteristics independently from the general field of Visualization.Also, several lessons learnt within the context of BioMedical Visualization may be applicable and extensible to other application domains or to the parent field of Visualization. Formalization has become particularly urgent, with the latest advances of BioMedical Visualization—in particular, with respect to dealing with Big Data Visualization, e.g., for the visualization of multi-scale, multi-modal,cohort, or computational biology data. Rapid changes and new opportunities in the field, also regarding the incorporation of Artificial Intelligence with“human-in-the-loop” concepts within the field of Visual Analytics, compel further this formalization. By enabling the BioMedical Visualization community to have intensive discussions on the systematization of current knowledge, we can adequately prepare ourselves for future prospects and challenges, while also contributing to the broader Visualization community.\nDuring this 4-day seminar, which was the 150th NII Shonan meeting to be organized, we brought together 25 visualization experts from diverse institutions,backgrounds and expertise to discuss, identify, formalize, and document the specifics of our field. This has been a great opportunity to cover a range of relevant and contemporary topics, and as a systematic effort towards establishing better fundaments for the field and towards determining novel future challenges.In the upcoming sections of this report, we summarize the content of invited talks and of the eight main topics that were discussed within the working groups during the seminar.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1410, 1248, 818, 951, 590 ], "note": "ISSN 2186-7437", "number": "TR-193-02-2020-1", "open_access": "yes", "research_areas": [ "BioVis", "MedVis" ], "keywords": [], "weblinks": [ { "href": "https://shonan.nii.ac.jp/seminars/167/", "caption": "workshop page", "description": null, "main_file": 0 } ], "files": [ { "description": null, "filetitle": "report", "main_file": false, "use_in_gallery": true, "access": "public", "preview_image_width": 2497, "preview_image_height": 1361, "name": "raidou_shonan167-report.pdf", "type": "application/pdf", "size": 2764706, "path": "Publication:raidou_shonan167", "preview_name": "raidou_shonan167-report:preview.PNG", "preview_type": "image/png", "preview_size": 332855, "url": "https://www.cg.tuwien.ac.at/research/publications/2020/raidou_shonan167/raidou_shonan167-report.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2020/raidou_shonan167/raidou_shonan167-report:thumb{{size}}.png" } ], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2020/raidou_shonan167/", "__class": "Publication" }, { "id": "Muigg_2011_ISI", "type_id": "inproceedings", "tu_id": null, "repositum_id": null, "title": "Interactive Seismic Interpretation with Piecewise Global Energy Minimization", "date": "2011-03", "abstract": "Increasing demands in world-wide energy consumption and oil depletion\r\nof large reservoirs have resulted in the need for exploring\r\nsmaller and more complex oil reservoirs. Planning of the reservoir\r\nvalorization usually starts with creating a model of the subsurface\r\nstructures, including seismic faults and horizons. However, seismic\r\ninterpretation and horizon tracing is a difficult and error-prone task,\r\noften resulting in hours of work needing to be manually repeated.\r\nIn this paper, we propose a novel, interactive workflow for horizon\r\ninterpretation based on well positions, which include additional geological\r\nand geophysical data captured by actual drillings. Instead\r\nof interpreting the volume slice-by-slice in 2D, we propose 3D seismic\r\ninterpretation based on well positions. We introduce a combination\r\nof 2D and 3D minimal cost path and minimal cost surface\r\ntracing for extracting horizons with very little user input. By processing\r\nthe volume based on well positions rather than slice-based,\r\nwe are able to create a piecewise optimal horizon surface at interactive\r\nrates. We have integrated our system into a visual analysis\r\nplatform which supports multiple linked views for fast verification,\r\nexploration and analysis of the extracted horizons. The system is\r\ncurrently being evaluated by our collaborating domain experts.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 963, 818, 947, 662, 212, 964, 226 ], "booktitle": "Proceedings of IEEE Pacific Visualization 2011", "date_from": "2011-03-01", "date_to": "2011-03-04", "isbn": " 978-1-61284-935-5 ", "lecturer": [ 963 ], "location": "Hong Kong", "organization": "The 4th IEEE Pacific Visualization Symposium (PacificVis 2011)", "pages_from": "59", "pages_to": "66", "publisher": "IEEE Pacific Visualization 2011", "research_areas": [], "keywords": [], "weblinks": [], "files": [ { "description": "", "filetitle": "Paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "Muigg_2011_ISI-Paper.pdf", "type": "application/pdf", "size": 6377696, "path": "Publication:Muigg_2011_ISI", "url": "https://www.cg.tuwien.ac.at/research/publications/2011/Muigg_2011_ISI/Muigg_2011_ISI-Paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2011/Muigg_2011_ISI/Muigg_2011_ISI-Paper:thumb{{size}}.png" } ], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2011/Muigg_2011_ISI/", "__class": "Publication" }, { "id": "beyer-2009-gpu", "type_id": "phdthesis", "tu_id": null, "repositum_id": null, "title": "GPU-based Multi-Volume Rendering of Complex Data in Neuroscience and Neurosurgery", "date": "2009-10", "abstract": "Recent advances in image acquisition technology and its availability in the medical\r\nand bio-medical fields have lead to an unprecedented amount of high-resolution\r\nimaging data. However, the inherent complexity of this data, caused by its\r\ntremendous size, complex structure or multi-modality poses several challenges\r\nfor current visualization tools. Recent developments in graphics hardware architecture\r\nhave increased the versatility and processing power of today’s GPUs to\r\nthe point where GPUs can be considered parallel scientific computing devices.\r\nThe work in this thesis builds on the current progress in image acquisition\r\ntechniques and graphics hardware architecture to develop novel 3D visualization\r\nmethods for the fields of neurosurgery and neuroscience.\r\nThe first part of this thesis presents an application and framework for planning\r\nof neurosurgical interventions. Concurrent GPU-based multi-volume rendering\r\nis used to visualize multiple radiological imaging modalities, delineating\r\nthe patient’s anatomy, neurological function, and metabolic processes. Additionally,\r\nnovel interaction metaphors are introduced, allowing the surgeon to plan\r\nand simulate the surgial approach to the brain based on the individual patient\r\nanatomy.\r\nThe second part of this thesis focuses on GPU-based volume rendering techniques\r\nfor large and complex EM data, as required in the field of neuroscience.\r\nA new mixed-resolution volume ray-casting approach is presented, which circumvents\r\nartifacts at block boundaries of different resolutions. NeuroTrace is introduced,\r\nan application for interactive segmentation and visualization of neural\r\nprocesses in EM data. EM data is extremely dense, heavily textured and exhibits\r\na complex structure of interconnected nerve cells, making it difficult to achieve\r\nhigh-quality volume renderings. Therefore, this thesis presents a novel on-demand\r\nnonlinear noise removal and edge detection method which allows to enhance important\r\nstructures (e.g., myelinated axons) while de-emphasizing less important\r\nregions of the data. In addition to the methods and concepts described above,\r\nthis thesis tries to bridge the gap between state-of-the-art visualization research\r\nand the use of those visualization methods in actual medical and bio-medical\r\napplications.", "authors_et_al": false, "substitute": null, "main_image": { "description": "", "filetitle": "image", "main_file": true, "use_in_gallery": false, "access": "public", "image_width": 567, "image_height": 534, "name": "beyer-2009-gpu-image.jpg", "type": "image/jpeg", "size": 96953, "path": "Publication:beyer-2009-gpu", "url": "https://www.cg.tuwien.ac.at/research/publications/2009/beyer-2009-gpu/beyer-2009-gpu-image.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2009/beyer-2009-gpu/beyer-2009-gpu-image:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 818 ], "date_end": "2009", "rigorosum": "2009-11-27", "supervisor": [ 226, 231, 166 ], "research_areas": [], "keywords": [], "weblinks": [], "files": [ { "description": "", "filetitle": "image", "main_file": true, "use_in_gallery": false, "access": "public", "image_width": 567, "image_height": 534, "name": "beyer-2009-gpu-image.jpg", "type": "image/jpeg", "size": 96953, "path": "Publication:beyer-2009-gpu", "url": "https://www.cg.tuwien.ac.at/research/publications/2009/beyer-2009-gpu/beyer-2009-gpu-image.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2009/beyer-2009-gpu/beyer-2009-gpu-image:thumb{{size}}.png" }, { "description": "", "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "beyer-2009-gpu-paper.pdf", "type": "application/pdf", "size": 13787283, "path": "Publication:beyer-2009-gpu", "url": "https://www.cg.tuwien.ac.at/research/publications/2009/beyer-2009-gpu/beyer-2009-gpu-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2009/beyer-2009-gpu/beyer-2009-gpu-paper:thumb{{size}}.png" } ], "projects_workgroups": [], "url": "https://www.cg.tuwien.ac.at/research/publications/2009/beyer-2009-gpu/", "__class": "Publication" } ]