[ { "id": "komon-2025-dco", "type_id": "inproceedings", "tu_id": null, "repositum_id": "20.500.12708/224640", "title": "Data-Driven Compute Overlays for Interactive Geographic Simulation and Visualization", "date": "2025-12-30", "abstract": "We present interactive data-driven compute overlays for native and web-based 3D geographic map applications based on WebGPU. Our data-driven overlays are generated in a multi-step compute workflow from multiple data sources on the GPU. We demonstrate their potential by showing results from snow cover and avalanche simulations, where simulation parameters can be adjusted interactively and results are visualized instantly. Benchmarks show that our approach can compute large-scale avalanche simulations in milliseconds to seconds, depending on the size of the terrain and the simulation parameters, which is multiple orders of magnitude faster than a state-of-the-art Python implementation.", "authors_et_al": false, "substitute": null, "main_image": { "description": "Rendered 2.5D terrain with avalanche simulation output as overlay, color encodes velocity.", "filetitle": "Teaser image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 2527, "image_height": 1270, "name": "komon-2025-dco-Teaser image.png", "type": "image/png", "size": 3968804, "path": "Publication:komon-2025-dco", "url": "https://www.cg.tuwien.ac.at/research/publications/2025/komon-2025-dco/komon-2025-dco-Teaser image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2025/komon-2025-dco/komon-2025-dco-Teaser image:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1859, 1869, 1013, 1110 ], "booktitle": "2025 IEEE Visualization and Visual Analytics (VIS)", "date_from": "2025-11-01", "date_to": "2025-11-07", "doi": "10.1109/VIS60296.2025.00043", "event": "IEEE VIS 2025", "isbn": "979-8-3315-6613-5", "lecturer": [ 1869 ], "location": "Vienna", "pages": "5", "pages_from": "186", "pages_to": "190", "publisher": "IEEE", "research_areas": [], "keywords": [ "3D geographic visualization", "geographic simulation", "WebGPU" ], "weblinks": [ { "href": "https://arxiv.org/abs/2506.23364", "caption": "Paper preprint", "description": null, "main_file": 1 }, { "href": "https://webigeo.alpinemaps.org/", "caption": "Online demo", "description": null, "main_file": 1 }, { "href": "https://youtu.be/pZq0H_l-8Bs?t=1580", "caption": "Talk recording", "description": null, "main_file": 0 }, { "href": "https://github.com/weBIGeo/webigeo", "caption": "GitHub repository", "description": null, "main_file": 0 } ], "files": [ { "description": "Rendered 2.5D terrain with avalanche simulation output as overlay, color encodes velocity.", "filetitle": "Teaser image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 2527, "image_height": 1270, "name": "komon-2025-dco-Teaser image.png", "type": "image/png", "size": 3968804, "path": "Publication:komon-2025-dco", "url": "https://www.cg.tuwien.ac.at/research/publications/2025/komon-2025-dco/komon-2025-dco-Teaser image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2025/komon-2025-dco/komon-2025-dco-Teaser image:thumb{{size}}.png" } ], "projects_workgroups": [ "d9555" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2025/komon-2025-dco/", "__class": "Publication" }, { "id": "komon-2025-webigeo", "type_id": "inproceedings", "tu_id": null, "repositum_id": "20.500.12708/221523", "title": "weBIGeo: Interaktive Lawinensimulation im Web", "date": "2025-10", "abstract": null, "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "teaser", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 2556, "image_height": 1303, "name": "komon-2025-webigeo-teaser.png", "type": "image/png", "size": 5204641, "path": "Publication:komon-2025-webigeo", "url": "https://www.cg.tuwien.ac.at/research/publications/2025/komon-2025-webigeo/komon-2025-webigeo-teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2025/komon-2025-webigeo/komon-2025-webigeo-teaser:thumb{{size}}.png" }, "sync_repositum_override": "date,projects", "repositum_presentation_id": null, "authors": [ 1859, 1869, 5510, 5511, 5512, 5513, 1013, 1110 ], "ac_number": "AC17717665", "booktitle": "Tagungsband des 6. internationalen Lawinensymposiums", "date_from": "2025-10-18", "date_to": "2025-10-18", "doi": "10.34726/11439", "event": "6. Lawinen Symposium Graz 2025", "lecturer": [ 1859, 1110 ], "location": "Graz", "open_access": "yes", "pages": "4", "pages_from": "150", "pages_to": "153", "research_areas": [ "InfoVis", "Modeling" ], "keywords": [ "Visualisierung", "Lawinen", "3D Karten" ], "weblinks": [ { "href": "https://webigeo.alpinemaps.org/", "caption": "demo", "description": "weBIGeo demo: requires WebGPU-ready browser! ", "main_file": 1 }, { "href": "https://lawinensymposium.naturfreunde.at/", "caption": "Lawinensymposium", "description": "Lawinensymposium", "main_file": 0 } ], "files": [ { "description": null, "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "komon-2025-webigeo-paper.pdf", "type": "application/pdf", "size": 1611394, "path": "Publication:komon-2025-webigeo", "url": "https://www.cg.tuwien.ac.at/research/publications/2025/komon-2025-webigeo/komon-2025-webigeo-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2025/komon-2025-webigeo/komon-2025-webigeo-paper:thumb{{size}}.png" }, { "description": null, "filetitle": "teaser", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 2556, "image_height": 1303, "name": "komon-2025-webigeo-teaser.png", "type": "image/png", "size": 5204641, "path": "Publication:komon-2025-webigeo", "url": "https://www.cg.tuwien.ac.at/research/publications/2025/komon-2025-webigeo/komon-2025-webigeo-teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2025/komon-2025-webigeo/komon-2025-webigeo-teaser:thumb{{size}}.png" } ], "projects_workgroups": [ "d9555", "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2025/komon-2025-webigeo/", "__class": "Publication" }, { "id": "celarek-2025-d3g", "type_id": "journalpaper_notalk", "tu_id": null, "repositum_id": "20.500.12708/219918", "title": "Does 3D Gaussian Splatting Need Accurate Volumetric Rendering?", "date": "2025-05", "abstract": "Since its introduction, 3D Gaussian Splatting (3DGS) has become an important reference method for learning 3D representations of a captured scene, allowing real-time novel-view synthesis with high visual quality and fast training times. Neural Radiance Fields (NeRFs), which preceded 3DGS, are based on a principled ray-marching approach for volumetric rendering. In contrast, while sharing a similar image formation model with NeRF, 3DGS uses a hybrid rendering solution that builds on the strengths of volume rendering and primitive rasterization. A crucial benefit of 3DGS is its performance, achieved through a set of approximations, in many cases with respect to volumetric rendering theory. A naturally arising question is whether replacing these approximations with more principled volumetric rendering solutions can improve the quality of 3DGS. In this paper, we present an in-depth analysis of the various approximations and assumptions used by the original 3DGS solution. We demonstrate that, while more accurate volumetric rendering can help for low numbers of primitives, the power of efficient optimization and the large number of Gaussians allows 3DGS to outperform volumetric rendering despite its approximations.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1013, 5372, 5503, 193, 1650 ], "articleno": "e70032", "doi": "10.1111/cgf.70032", "issn": "1467-8659", "journal": "Computer Graphics Forum", "number": "2", "pages": "12", "publisher": "WILEY", "volume": "44", "research_areas": [], "keywords": [ "CCS Concepts", "Rasterization", "Ray tracing", "Volumetric models", "• Computing methodologies → Image-based rendering" ], "weblinks": [], "files": [], "projects_workgroups": [], "url": "https://www.cg.tuwien.ac.at/research/publications/2025/celarek-2025-d3g/", "__class": "Publication" }, { "id": "eschner-2023-evl", "type_id": "inproceedings", "tu_id": null, "repositum_id": null, "title": "Echtzeitvisualisierung von Lawinenrisiko basierend auf hochauflösenden Geodaten", "date": "2023-11-18", "abstract": "Um das Lawinenrisiko auf Touren abzuschätzen, konsultieren Tourengeher·innen typischerweise vorab den aktuellen Lawinenlagebericht (LLB) sowie die Geländeeigenschaften, wie Hangneigung, Höhe und Exposition der geplanten Tour auf einer Karte. Reduktionsmethoden wie Stop-or-Go oder die SnowCard können sowohl bei der Planung als auch vor Ort angewandt werden, um das Risiko abzuschätzen. Bei korrekter Anwendung dieser Methoden könnte ein Großteil der Todesfälle vermieden werden. Die Anwendung umfasst jedoch mehrere kognitiv aufwändige Schritte: Im ersten Schritt müssen Tourengeher·innen die Informationen aus LLB und Karte korrekt verknüpfen und anhand der gewählten Methode interpretieren, um potenziell kritische Regionen entlang der Route vorab identifizieren zu können. Im zweiten Schritt müssen potenziell kritische Regionen auch während der Tour als solche wiedererkannt und vor Ort beurteilt werden. \nUm die Anwendung von Reduktionsmethoden für Wintersportler·innen zu vereinfachen, können die Informationen aus LLB computergestützt mit den Geländeeigenschaften ausgewertet und direkt in einer Karte dargestellt werden. Skitourenguru, beispielsweise, berechnet das Lawinenrisiko entlang vorgegebener Routen und stellt diese in einer 2D Karte dar. Im Vergleich zu 2D Karten erleichtert eine dreidimensionale Darstellung jedoch die Interpretation des Geländes und das Finden von Routen. Unsere Hypothese ist daher, dass eine direkte Visualisierung des Lawinenrisikos auf einer detaillierten 3D Karte die Identifikation von potenziell kritischen Regionen einer Route in der Planungsphase, sowie deren Wiedererkennung während der Tour, erleichtert.\nWir stellen eine integrierte 3D Risikovisualisierung vor, welche Daten aus dem aktuellen LLB mit einem hochauflösenden Geländemodell kombiniert und existierende Reduktionsmethoden in Echtzeit auswertet, um das Ergebnis auf einer interaktiven Webseite zu visualisieren.\n", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 2076, "image_height": 1192, "name": "eschner-2023-evl-.png", "type": "image/png", "size": 4716902, "path": "Publication:eschner-2023-evl", "url": "https://www.cg.tuwien.ac.at/research/publications/2023/eschner-2023-evl/eschner-2023-evl-.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2023/eschner-2023-evl/eschner-2023-evl-:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1653, 1013, 1110 ], "booktitle": "Lawinensymposium 2023", "cfp": { "name": "lawinensymposium_cfp.pdf", "type": "application/pdf", "error": "0", "size": "3319607", "orig_name": "lawinensymposium_cfp.pdf", "ext": "pdf" }, "event": "Lawinensymposium 2023", "lecturer": [ 1653 ], "location": "Graz", "pages_from": "38", "pages_to": "43", "research_areas": [ "InfoVis" ], "keywords": [], "weblinks": [ { "href": "https://alpinemaps.cg.tuwien.ac.at/ ", "caption": "online demo", "description": " Demo version of the avalanche risk visualization ", "main_file": 1 } ], "files": [ { "description": null, "filetitle": "", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 2076, "image_height": 1192, "name": "eschner-2023-evl-.png", "type": "image/png", "size": 4716902, "path": "Publication:eschner-2023-evl", "url": "https://www.cg.tuwien.ac.at/research/publications/2023/eschner-2023-evl/eschner-2023-evl-.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2023/eschner-2023-evl/eschner-2023-evl-:thumb{{size}}.png" }, { "description": null, "filetitle": "paper preprint", "main_file": true, "use_in_gallery": false, "access": "public", "name": "eschner-2023-evl-paper preprint.pdf", "type": "application/pdf", "size": 21355472, "path": "Publication:eschner-2023-evl", "url": "https://www.cg.tuwien.ac.at/research/publications/2023/eschner-2023-evl/eschner-2023-evl-paper preprint.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2023/eschner-2023-evl/eschner-2023-evl-paper preprint:thumb{{size}}.png" } ], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2023/eschner-2023-evl/", "__class": "Publication" }, { "id": "celarek-2022-gmcn", "type_id": "inproceedings", "tu_id": null, "repositum_id": "20.500.12708/188182", "title": "Gaussian Mixture Convolution Networks", "date": "2022-04", "abstract": "This paper proposes a novel method for deep learning based on the analytical convolution of multidimensional Gaussian mixtures.\nIn contrast to tensors, these do not suffer from the curse of dimensionality and allow for a compact representation, as data is only stored where details exist.\nConvolution kernels and data are Gaussian mixtures with unconstrained weights, positions, and covariance matrices.\nSimilar to discrete convolutional networks, each convolution step produces several feature channels, represented by independent Gaussian mixtures.\nSince traditional transfer functions like ReLUs do not produce Gaussian mixtures, we propose using a fitting of these functions instead.\nThis fitting step also acts as a pooling layer if the number of Gaussian components is reduced appropriately.\nWe demonstrate that networks based on this architecture reach competitive accuracy on Gaussian mixtures fitted to the MNIST and ModelNet data sets.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "teaser", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 1500, "image_height": 1367, "name": "celarek-2022-gmcn-teaser.png", "type": "image/png", "size": 720960, "path": "Publication:celarek-2022-gmcn", "url": "https://www.cg.tuwien.ac.at/research/publications/2022/celarek-2022-gmcn/celarek-2022-gmcn-teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2022/celarek-2022-gmcn/celarek-2022-gmcn-teaser:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1013, 1919, 1650, 951, 193 ], "booktitle": "The Tenth International Conference on Learning Representations (ICLR 2022)", "cfp": { "name": "ICLR2022_Call for Papers.pdf", "type": "application/pdf", "error": "0", "size": "32122", "orig_name": "ICLR2022_Call for Papers.pdf", "ext": "pdf" }, "event": "ICLR | 2022", "lecturer": [ 1013 ], "open_access": "yes", "pages_from": "1", "pages_to": "23", "publisher": "OpenReview.org", "research_areas": [ "Geometry" ], "keywords": [], "weblinks": [ { "href": "https://github.com/cg-tuwien/Gaussian-Mixture-Convolution-Networks", "caption": "Code on github", "description": null, "main_file": 1 }, { "href": "https://openreview.net/forum?id=Oxeka7Z7Hor", "caption": "Paper on OpenReview", "description": null, "main_file": 0 } ], "files": [ { "description": null, "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "celarek-2022-gmcn-paper.pdf", "type": "application/pdf", "size": 5943864, "path": "Publication:celarek-2022-gmcn", "url": "https://www.cg.tuwien.ac.at/research/publications/2022/celarek-2022-gmcn/celarek-2022-gmcn-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2022/celarek-2022-gmcn/celarek-2022-gmcn-paper:thumb{{size}}.png" }, { "description": null, "filetitle": "teaser", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 1500, "image_height": 1367, "name": "celarek-2022-gmcn-teaser.png", "type": "image/png", "size": 720960, "path": "Publication:celarek-2022-gmcn", "url": "https://www.cg.tuwien.ac.at/research/publications/2022/celarek-2022-gmcn/celarek-2022-gmcn-teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2022/celarek-2022-gmcn/celarek-2022-gmcn-teaser:thumb{{size}}.png" } ], "projects_workgroups": [ "rend", "EVOCATION", "3DSpatialization" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2022/celarek-2022-gmcn/", "__class": "Publication" }, { "id": "celarek_adam-2019-qelta", "type_id": "journalpaper", "tu_id": 282852, "repositum_id": null, "title": "Quantifying the Error of Light Transport Algorithms", "date": "2019-07", "abstract": "This paper proposes a new methodology for measuring the error of unbiased physically based rendering algorithms. The current state of the art includes mean squared error (MSE) based metrics and visual comparisons of equal-time renderings of competing algorithms. Neither is satisfying as MSE does not describe behavior and can exhibit significant variance, and visual comparisons are inherently subjective. Our contribution is two-fold: First, we propose to compute many short renderings instead of a single long run and use the short renderings to estimate MSE expectation and variance as well as per-pixel standard deviation. An algorithm that achieves good results in most runs, but with occasional outliers is essentially unreliable, which we wish to quantify numerically. We use per-pixel standard deviation to identify problematic lighting effects of rendering algorithms. The second contribution is the error spectrum ensemble (ESE), a tool for measuring the distribution of error over frequencies. The ESE serves two purposes: It reveals correlation between pixels and can be used to detect outliers, which offset the amount of error substantially.", "authors_et_al": false, "substitute": null, "main_image": { "description": "Error spectrum ensemble", "filetitle": "image", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 594, "image_height": 372, "name": "celarek_adam-2019-qelta-image.png", "type": "image/png", "size": 39262, "path": "Publication:celarek_adam-2019-qelta", "url": "https://www.cg.tuwien.ac.at/research/publications/2019/celarek_adam-2019-qelta/celarek_adam-2019-qelta-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2019/celarek_adam-2019-qelta/celarek_adam-2019-qelta-image:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1013, 1667, 193, 1666 ], "cfp": { "name": "EGSR-2019_Call-for-Papers.pdf", "type": "application/pdf", "error": "0", "size": "174109", "orig_name": "EGSR-2019_Call-for-Papers.pdf", "ext": "pdf" }, "date_from": "2019-07-10", "date_to": "2019-07-12", "doi": "10.1111/cgf.13775", "event": "Eurographics Symposium on Rendering 2019", "journal": "Computer Graphics Forum", "lecturer": [ 1013 ], "number": "4", "open_access": "yes", "pages_from": "111", "pages_to": "121", "publisher": "The Eurographics Association and John Wiley & Sons Ltd.", "volume": "38", "research_areas": [ "Rendering" ], "keywords": [ "measuring error", "light transport", "global illumination" ], "weblinks": [ { "href": "https://github.com/cg-tuwien/Quantifying-the-Error-of-Light-Transport-Algorithms", "caption": "Git repository", "description": "Git repository with the implementation of the error spectrum ensemble and short rendering generator.", "main_file": 1 } ], "files": [ { "description": "Error spectrum ensemble", "filetitle": "image", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 594, "image_height": 372, "name": "celarek_adam-2019-qelta-image.png", "type": "image/png", "size": 39262, "path": "Publication:celarek_adam-2019-qelta", "url": "https://www.cg.tuwien.ac.at/research/publications/2019/celarek_adam-2019-qelta/celarek_adam-2019-qelta-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2019/celarek_adam-2019-qelta/celarek_adam-2019-qelta-image:thumb{{size}}.png" }, { "description": "Paper", "filetitle": "paper_preprint", "main_file": true, "use_in_gallery": false, "access": "public", "name": "celarek_adam-2019-qelta-paper_preprint.pdf", "type": "application/pdf", "size": 2849560, "path": "Publication:celarek_adam-2019-qelta", "url": "https://www.cg.tuwien.ac.at/research/publications/2019/celarek_adam-2019-qelta/celarek_adam-2019-qelta-paper_preprint.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2019/celarek_adam-2019-qelta/celarek_adam-2019-qelta-paper_preprint:thumb{{size}}.png" }, { "description": "Slides from the presentation at EGSR", "filetitle": "presentation", "main_file": false, "use_in_gallery": false, "access": "public", "name": "celarek_adam-2019-qelta-presentation.pdf", "type": "application/pdf", "size": 3561061, "path": "Publication:celarek_adam-2019-qelta", "url": "https://www.cg.tuwien.ac.at/research/publications/2019/celarek_adam-2019-qelta/celarek_adam-2019-qelta-presentation.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2019/celarek_adam-2019-qelta/celarek_adam-2019-qelta-presentation:thumb{{size}}.png" }, { "description": "Additional examples, experiments and similar.", "filetitle": "supplemental_material", "main_file": false, "use_in_gallery": false, "access": "public", "name": "celarek_adam-2019-qelta-supplemental_material.pdf", "type": "application/pdf", "size": 81913248, "path": "Publication:celarek_adam-2019-qelta", "url": "https://www.cg.tuwien.ac.at/research/publications/2019/celarek_adam-2019-qelta/celarek_adam-2019-qelta-supplemental_material.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2019/celarek_adam-2019-qelta/celarek_adam-2019-qelta-supplemental_material:thumb{{size}}.png" }, { "description": "Teaser", "filetitle": "teaser", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 1826, "image_height": 484, "name": "celarek_adam-2019-qelta-teaser.png", "type": "image/png", "size": 407655, "path": "Publication:celarek_adam-2019-qelta", "url": "https://www.cg.tuwien.ac.at/research/publications/2019/celarek_adam-2019-qelta/celarek_adam-2019-qelta-teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2019/celarek_adam-2019-qelta/celarek_adam-2019-qelta-teaser:thumb{{size}}.png" } ], "projects_workgroups": [ "EVOCATION", "OpenData" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2019/celarek_adam-2019-qelta/", "__class": "Publication" }, { "id": "ohrhallinger_stefan-2019-xms", "type_id": "xmascard", "tu_id": null, "repositum_id": null, "title": "X-Mas Card 2019", "date": "2019", "abstract": "See right for correct solution of our connect-the-dots game :-)\nOf course, we not only reconstruct members of our institute but also\nhighly noisy point clouds, additionally denoise the reconstruction,\nand specify the minimum number of samples required for that.\nEduard Gröller\nSee here for the mystery present in the crib: youtu.be/-oVwXaaJNtY\n\nDie Auflösung unseres Punkte-verbinden-Spiels ist hier rechts :-)\nWir rekonstruieren nicht nur Institutsmitglieder, sondern auch\nstark verrauschte Punktewolken, entfernen das Rauschen\nund berechnen die Mindestanzahl der benötigten Messpunkte.\nHier ist das Geschenk in der Krippe zu sehen: youtu.be/-oVwXaaJNtY", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "image", "main_file": true, "use_in_gallery": true, "access": "public", "image_width": 1368, "image_height": 975, "name": "ohrhallinger_stefan-2019-xms-image.png", "type": "image/png", "size": 701936, "path": "Publication:ohrhallinger_stefan-2019-xms", "url": "https://www.cg.tuwien.ac.at/research/publications/2019/ohrhallinger_stefan-2019-xms/ohrhallinger_stefan-2019-xms-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2019/ohrhallinger_stefan-2019-xms/ohrhallinger_stefan-2019-xms-image:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 948, 1013 ], "research_areas": [ "Geometry" ], "keywords": [ "curve reconstruction", "sampling" ], "weblinks": [], "files": [ { "description": null, "filetitle": "image", "main_file": true, "use_in_gallery": true, "access": "public", "image_width": 1368, "image_height": 975, "name": "ohrhallinger_stefan-2019-xms-image.png", "type": "image/png", "size": 701936, "path": "Publication:ohrhallinger_stefan-2019-xms", "url": "https://www.cg.tuwien.ac.at/research/publications/2019/ohrhallinger_stefan-2019-xms/ohrhallinger_stefan-2019-xms-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2019/ohrhallinger_stefan-2019-xms/ohrhallinger_stefan-2019-xms-image:thumb{{size}}.png" }, { "description": null, "filetitle": "pdf", "main_file": true, "use_in_gallery": false, "access": "public", "name": "ohrhallinger_stefan-2019-xms-pdf.pdf", "type": "application/pdf", "size": 1274436, "path": "Publication:ohrhallinger_stefan-2019-xms", "url": "https://www.cg.tuwien.ac.at/research/publications/2019/ohrhallinger_stefan-2019-xms/ohrhallinger_stefan-2019-xms-pdf.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2019/ohrhallinger_stefan-2019-xms/ohrhallinger_stefan-2019-xms-pdf:thumb{{size}}.png" } ], "projects_workgroups": [ "xmas" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2019/ohrhallinger_stefan-2019-xms/", "__class": "Publication" }, { "id": "CELAREK-2017-QCL", "type_id": "masterthesis", "tu_id": null, "repositum_id": null, "title": "Quantifying the Convergence of Light-Transport Algorithms", "date": "2017-11-14", "abstract": "This work aims at improving methods for measuring the error of unbiased, physically\nbased light-transport algorithms. State-of-the-art papers show algorithmic improvements\nvia error measures like Mean Square Error (MSE) or visual comparison of equal-time\nrenderings. These methods are unreliable since outliers can cause MSE variance and\nvisual comparison is inherently subjective.\nWe introduce a simple proxy algorithm: pure algorithms produce one image corresponding\nto the computation budget N. The proxy, on the other hand, averages N independent\nimages with a computation budget of 1. The proxy algorithm fulfils the preconditions\nfor the Central Limit Theorem (CLT), and hence, we know that its convergence rate is\n(1/N). Since this same convergence rate applies for all methods executed using the\nproxy algorithm, comparisons using variance- or standard-deviation-per-pixel images are\npossible. These per-pixel error images can be routinely computed and allow comparing\nthe render quality of different lighting effects. Additionally, the average of pixel variances\nis more robust against outliers compared to the traditional MSE or comparable metrics\ncomputed for the pure algorithm.\nWe further propose the Error Spectrum Ensemble (ESE) as a new tool for evaluating lighttransport\nalgorithms. It summarizes expected error and outliers over spatial frequencies.\nESE is generated using the data from the proxy algorithm: N error images are computed\nusing a reference, transformed into Fourier power spectra and compressed using radial\naverages. The descriptor is a summary of those radial averages.\nIn the results, we show that standard-deviation images, short equal-time renderings, ESE\nand expected MSE are valuable tools for assessing light-transport algorithms.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 594, "image_height": 372, "name": "CELAREK-2017-QCL-image.png", "type": "image/png", "size": 39262, "path": "Publication:CELAREK-2017-QCL", "url": "https://www.cg.tuwien.ac.at/research/publications/2017/CELAREK-2017-QCL/CELAREK-2017-QCL-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2017/CELAREK-2017-QCL/CELAREK-2017-QCL-image:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1013 ], "date_end": "2017-11-14", "date_start": "2016-01-15", "diploma_examina": "2017-11-14", "matrikelnr": "0926881", "supervisor": [ 193 ], "research_areas": [ "Rendering" ], "keywords": [ "error metric", "global illumination" ], "weblinks": [], "files": [ { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 594, "image_height": 372, "name": "CELAREK-2017-QCL-image.png", "type": "image/png", "size": 39262, "path": "Publication:CELAREK-2017-QCL", "url": "https://www.cg.tuwien.ac.at/research/publications/2017/CELAREK-2017-QCL/CELAREK-2017-QCL-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2017/CELAREK-2017-QCL/CELAREK-2017-QCL-image:thumb{{size}}.png" }, { "description": null, "filetitle": "poster", "main_file": true, "use_in_gallery": false, "access": "public", "name": "CELAREK-2017-QCL-poster.pdf", "type": "application/pdf", "size": 392985, "path": "Publication:CELAREK-2017-QCL", "url": "https://www.cg.tuwien.ac.at/research/publications/2017/CELAREK-2017-QCL/CELAREK-2017-QCL-poster.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2017/CELAREK-2017-QCL/CELAREK-2017-QCL-poster:thumb{{size}}.png" }, { "description": null, "filetitle": "thesis", "main_file": true, "use_in_gallery": false, "access": "public", "name": "CELAREK-2017-QCL-thesis.pdf", "type": "application/pdf", "size": 16734560, "path": "Publication:CELAREK-2017-QCL", "url": "https://www.cg.tuwien.ac.at/research/publications/2017/CELAREK-2017-QCL/CELAREK-2017-QCL-thesis.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2017/CELAREK-2017-QCL/CELAREK-2017-QCL-thesis:thumb{{size}}.png" } ], "projects_workgroups": [ "rend", "OpenData" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2017/CELAREK-2017-QCL/", "__class": "Publication" }, { "id": "celarek_adam-2012-rrmro", "type_id": "bachelorthesis", "tu_id": null, "repositum_id": null, "title": "Merging Ray Tracing and Rasterization in Mixed Reality", "date": "2012-11", "abstract": "In mixed reality, virtual objects are inserted into a video stream of a real environment. This technique can be used for many applications including marketing, simulations and cultural heritage. Therefore it is important that the images look plausible. Many applications also have real time constraints.\r\nWith traditional rasterization it is difficult to create realistic reflections and refractions. In ray tracing on the other hand this is a trivial task, but rendering is slow. The solution described in this work uses the graphics card for speeding up ray tracing. Additionally it employs a rasterizer for diffuse surfaces and only traces rays if there is a reflective or refractive surface visible. This works by creating a ray tracing mask using the fast rasterizer in a first step. It holds true for reflective or refractive surfaces and false otherwise. Then all diffuse objects are drawn using the rasterizer. Finally rays are traced on each pixel which is masked as reflective or refractive surface by the ray tracing mask. These rays produce secondary rays which can hit a diffuse surface eventually. In this case the ray tracer takes over the shading. \r\nResults show, that our hybrid rendering method allows high quality reflections and refractions while still having interactive frame rates in mixed reality scenarios.", "authors_et_al": false, "substitute": null, "main_image": { "description": "", "filetitle": "image 2", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 800, "image_height": 600, "name": "celarek_adam-2012-rrmro-image 2.jpg", "type": "image/jpeg", "size": 243591, "path": "Publication:celarek_adam-2012-rrmro", "url": "https://www.cg.tuwien.ac.at/research/publications/2012/celarek_adam-2012-rrmro/celarek_adam-2012-rrmro-image 2.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2012/celarek_adam-2012-rrmro/celarek_adam-2012-rrmro-image 2:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1013 ], "date_end": "2012-11-30", "date_start": "2012-03-01", "matrikelnr": "0926881", "supervisor": [ 193, 736 ], "research_areas": [], "keywords": [ "Refraction", "OptiX", "Augmented Reality", "Reflection" ], "weblinks": [], "files": [ { "description": "", "filetitle": "image 2", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 800, "image_height": 600, "name": "celarek_adam-2012-rrmro-image 2.jpg", "type": "image/jpeg", "size": 243591, "path": "Publication:celarek_adam-2012-rrmro", "url": "https://www.cg.tuwien.ac.at/research/publications/2012/celarek_adam-2012-rrmro/celarek_adam-2012-rrmro-image 2.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2012/celarek_adam-2012-rrmro/celarek_adam-2012-rrmro-image 2:thumb{{size}}.png" }, { "description": "Bachelor Thesis", "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "celarek_adam-2012-rrmro-paper.pdf", "type": "application/pdf", "size": 7983231, "path": "Publication:celarek_adam-2012-rrmro", "url": "https://www.cg.tuwien.ac.at/research/publications/2012/celarek_adam-2012-rrmro/celarek_adam-2012-rrmro-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2012/celarek_adam-2012-rrmro/celarek_adam-2012-rrmro-paper:thumb{{size}}.png" } ], "projects_workgroups": [ "RESHADE" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2012/celarek_adam-2012-rrmro/", "__class": "Publication" } ]