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        "abstract": "Drones have been widely adopted over the past decades, aiding in critical tasks such as\nsearch and rescue, inspection, and mapping. Autonomous drone scanning could further\nsupport such missions, but remains a significant challenge in indoor environments. This\nthesis explores the feasibility of using a consumer drone for such autonomous indoor 3D\nscanning. Our approach combines a DJI Spark drone, a lightweight Pico Flexx depth\nsensor, and a Raspberry Pi to capture depth data, which is streamed to an external\nserver for real-time processing. The system leverages ROS 2 and InfiniTAM for SLAM\nand map reconstruction, while navigation commands are issued via a smartphone using\nDJI’s Mobile SDK.\nAlthough the system successfully completed a limited autonomous scan, various con-\nstraints—including the drone’s payload capacity, limited sensor range, and hardware\ninstabilities—posed significant challenges. Despite these limitations, a modular software\narchitecture was developed that integrates sensing, mapping, and navigation. This\nframework provides a solid foundation for future work toward fully autonomous indoor\nscanning with more capable hardware. However, generating the next best view and\nfinding a feasible path toward it remain open challenges.",
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        "abstract": "SFA3D:\n\nZeitplan: Auf Mobile portiert bis Ende März / Anfang April.\n\nSAF3D ist mit Pytorch implementiert worden. Folgende Möglichkeiten gibt es um Pytorch Modelle auf Mobile/Android zu portieren:\n\n- Pytorch Mobile: https://pytorch.org/mobile/home/\n\n- Pytorch Flutter Plugins: https://pub.dev/packages/flutter_pytorch oder https://pub.dev/packages/pytorch_mobile\n\n- Deep Java Library: https://djl.ai/\n\n \n\n3D-Multi-Object-Tracker:\n\nZeitplan: Auf Mobile implementiert bis Ende April.\n\nDa 3DMOT ein non-ml Algorithmus ist, kann er in der dann gewählten Sprache implementiert werden.\n\n \n\nPrecog:\n\nZeitplan: Auf Mobile portiert bis Ende Mai.\n\nPrecog habe ich leider nicht zum Laufen bekommen, da ich nicht alle Daten gefunden habe bzw. nicht sicher war welche benötigt werden und wie sie konfiguriert werden sollen.\n\nPrecog verwendet Tensorflow. Dazu habe ich folgendes gefunden:\n\n- Tensorflow Lite: https://www.tensorflow.org/lite\n\n- Tflite Flutter: https://pub.dev/packages/tflite_flutter\n\n \n\nVergleich zwischen Server und Mobile:\n\nZeitplan: Server und Mobile werden Anfang Juni verglichen, wenn bis dahin alles nach Plan läuft.\n\n \n\nSchriftliche Arbeit:\n\nZeitplan: Mit dem Schreiben wird nach dem Vergleichen angefangen und dafür kann der Rest von Juni verwendet werden. Falls sich eines der Schritte herauszögert, dann kann ich die vorlesungsfreie Zeit im Sommer auch verwenden.\n\n",
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        "title": "Parameter-free connectivity for point clouds",
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    {
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        "title": "PPSurf: Combining Patches and Point Convolutions for Detailed Surface Reconstruction",
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        "title": "2D Points Curve Reconstruction Survey and Benchmark",
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    {
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        "title": "Klassifikation Urbaner Punktwolken Mittels 3D CNNs In Kombination mit Rekonstruktion von Gehsteigen",
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        "abstract": "LiDAR devices are able to capture the physical world very accurately. Therefore, they\nare often used for 3D reconstruction. Unfortunately, such data can become extremely\nlarge very quickly and usually only a small part of the point cloud is of interest. Thus,\nthe point cloud is filtered beforehand in order to apply algorithms only on those points\nthat are relevant for it. A semantic information about the points can be used for such a\nfiltering. Semantic segmentation of point clouds is a popular field of research and here\nthere has been a trend towards deep learning in recent years too. However, contrary to\nimages, point clouds are unstructured. Hence, point clouds are often rasterized, but this\nhas to be done, such that the underlying structure is represented well.\nIn this thesis, a 3D Convolutional Neural Network is developed and trained for a semantic\nsegmentation of LiDAR point clouds. Thereby, a point cloud is represented with an\noctree data structure, which makes it easy to rasterize only relevant parts. Since, just\ndense parts of the point cloud, in which important information about the structure is\nlocated, are subdivided further. This allows to simply take nodes of a certain level of the\noctree and rasterize them as data samples.\nThere are many application areas for 3D reconstructions based on point clouds. In an\nurban scenario, these can be for example whole city models or buildings. However, in this\nthesis, the reconstruction of sidewalks is explored. Since, for flood simulations in cities, an\nincrease in height of a few centimeters can make a great difference and information about\nthe curb geometry helps to make them more accurate. In the sidewalk reconstruction\nprocess, the point cloud is filtered first, based on a semantic segmentation of a 3D CNN,\nand then point cloud features are calculated to detect curb points. With these curb\npoints, the geometry of the curb, sidewalk and street are computed.\nTaken all together, this thesis develops a proof-of-concept prototype for semantic point\ncloud segmentation using 3D CNNs and based on that, a curb detection and reconstruction\nalgorithm.",
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        "title": "Points2Surf: Learning Implicit Surfaces from Point Clouds",
        "date": "2020-10-28",
        "abstract": "A key step in any scanning-based asset creation workflow is to convert unordered point clouds to a surface. Classical methods (e.g., Poisson reconstruction) start to degrade in the presence of noisy and partial scans. Hence, deep learning based methods have recently been proposed to produce complete surfaces, even from partial scans. However, such data-driven methods struggle to generalize to new shapes with large geometric and topological variations. We present Points2Surf, a novel patch-based learning framework that produces accurate surfaces directly from raw scans without normals.\n\nLearning a prior over a combination of detailed local patches and coarse global information improves generalization performance and reconstruction accuracy.\n\nOur extensive comparison on both synthetic and real data demonstrates a clear advantage of our method over state-of-the-art alternatives on previously unseen classes (on average, Points2Surf brings down reconstruction error by 30% over SPR and by 270%+ over deep learning based SotA methods) at the cost of longer computation times and a slight increase in small-scale topological noise in some cases. \nOur source code, pre-trained model, and dataset are available on: https://github.com/ErlerPhilipp/points2surf\n",
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        "address": "Cham",
        "booktitle": "Computer Vision -- ECCV 2020",
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        "date_from": "2020-08-24",
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        "doi": "10.1007/978-3-030-58558-7_7",
        "editor": "Vedaldi, Andrea and Bischof, Horst and Brox, Thomas and Frahm, Jan-Michael",
        "event": "ECCV 2020",
        "first_published": "2020-10-28",
        "isbn": "978-3-030-58558-7",
        "journal": "Computer Vision – ECCV 2020",
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        "publisher": "Springer International Publishing",
        "series": "Lecture Notes in Computer Science",
        "volume": "12350",
        "research_areas": [
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            "Modeling"
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        "abstract": "The aim of this bachelor thesis is the development of a Python framework. The main task for this framework is the generation of datasets, which can be further used for surface reconstruction. They are needed for training a neural network, which is then able to reconstruct meshes on its own given a point cloud of a mesh. In order to optimize the training of the neural network, a lot of training data is needed. This framework utilizes multi-processing to achieve a faster generation process in comparison to sequentially generating one mesh after another.\n\nIn addition, the framework is also able to handle any kind of similar pipeline. The user is able to define the steps of such pipeline in an XML document, which then can make calls to arbitrary programs. This fact makes the framework an all-purpose tool for any kind of task that needs to process a lot of data independent from each other.\n\nThe results show a great performance increase when generating datasets. This can be seen in the benchmarks that have been done. The time of execution for a fixed amount of files has been measured with different modes of execution. The custom process pool we developed shows a faster time overall compared to using Python's process pool for each step of the pipeline independently. It is also way faster in comparison to running every step for each file sequentially.",
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        "title": "Pose to Seat: Automated design of body-supporting surfaces",
        "date": "2020-04",
        "abstract": "The design of functional seating furniture is a complicated process which often requires extensive manual design effort and empirical evaluation. We propose a computational design framework for pose-driven automated generation of body-supports which are optimized for comfort of sitting. Given a human body in a specified pose as input, our method computes an approximate pressure distribution that also takes frictional forces and body torques into consideration which serves as an objective measure of comfort. Utilizing this information to find out where the body needs to be supported in order to maintain comfort of sitting, our algorithm can create a supporting mesh suited for a person in that specific pose. This is done in an automated fitting process, using a template model capable of supporting a large variety of sitting poses. The results can be used directly or can be considered as a starting point for further interactive design.",
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    {
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        "title": "Planetary Rendering with Mesh Shaders",
        "date": "2020-02",
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        "repositum_id": null,
        "title": "Extensible Image Classification",
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        "abstract": "Struktur des Systems:\nInput: Bereits segmentierte Objekte\n1) Labeling: Input: Objekt(e), Output: Manuelle Zuordnung eines (neuen bzw. korrigierten) Labels pro Objekt\n2) Clustering: Input: Cluster mit Objekten, mit k (mindestens, oder genau zwei) verschiedenen Label, Unterteilung durch k-means Clustering, Output: k Cluster von 'ähnlichen' Objekten pro Label\n3) Training: Input: Objekte mit Label (ihres Clusters), Output: Network, das die Objekte in die derzeit gelabelten Cluster klassifiziert\nDann geht's iterativ mit Schritt 1 weiter - Objekte werden dem User angezeigt und bei Bedarf relabeled, wonach Schritt 2 folgt usw.",
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        "title": "Gaussian-Product Subdivision Surfaces",
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        "abstract": "Probabilistic distribution models like Gaussian mixtures have shown great\npotential for improving both the quality and speed of several geometric operators. This is largely due to their ability to model large fuzzy data using only a reduced set of atomic distributions, allowing for large compression rates at minimal information loss. We introduce a new surface model that utilizes these qualities of Gaussian mixtures for the definition and control of a parametric smooth surface. Our approach is based on an enriched mesh data structure, which describes the probability distribution of spatial surface locations around each vertex via a Gaussian covariance matrix. By incorporating this additional covariance information, we show how to define a smooth surface via a nonlinear probabilistic subdivision operator based on products of Gaussians, which is able to capture rich details at fixed control mesh resolution. This entails new applications in surface reconstruction,\nmodeling, and geometric compression.",
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        "title": "Fast Rotationally Symmetric Direction Fields on 3D Surfaces",
        "date": "2019-06-28",
        "abstract": "We demonstrate the implementation of the Globally Optimal Direction Field algorithm\nby Knöppel et al. as a plugin for a geometry processing software. The plugin constructs\nN-RoSy fields of arbitrary degree by solving a smallest eigenvalue problem. For that, we\nuse a sparse Cholesky solver and the Inverse Power Method. The field can optionally be\naligned to the principal curvature induced by the geometry. We also added the option\nto use the improvements proposed by Pellenard et al. These improvements contain\nconstraints imposed on certain areas of the mesh. A linear least squares approach is\nthen used for solving the over-constrained system. Our main contribution is to clarify\nambiguities we found in these papers, especially regarding the constraints.\n\nWe tested the algorithm using meshes of different common sizes used in 3D modeling\nfor the computation time and ease of usage. Although the algorithm is very fast the\nresponsiveness starts to decline at about 6 * 10^4 polygons. We recommend not to use\nit on huge meshes or detailed 3D scans if fast results are important. The degree of\ncurvature alignment can be difficult to adjust. However, together with fast results,\ndifferent parameter settings can be tested relatively easy.\n\nThe results look very smooth and singularities are often located at geometric features.\nUsing constraints helps to align the field to mesh boundaries, sharp edges or, if it is\nwarped, to the principal curvature directions. Their use is very easy because the results\nare predictable. Only curvature constraints can sometimes be hard to predict and are\nbest used in conjunction with other constraints.",
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        "title": "Profiling and Optimization of Large Biomolecular Scenes",
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        "id": "ohrhallinger_stefan-2018-cgf",
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        "tu_id": null,
        "repositum_id": null,
        "title": "FitConnect: Connecting Noisy 2D Samples by Fitted Neighborhoods",
        "date": "2019-02",
        "abstract": "We propose a parameter-free method to recover manifold connectivity in unstructured 2D point clouds with high noise in terms of the local feature size. This enables us to capture the features which emerge out of the noise. To achieve this, we extend the reconstruction algorithm HNN-Crust, which connects samples to two (noise-free) neighbors and has been proven to output a manifold for a relaxed sampling condition. Applying this condition to noisy samples by projecting their k-nearest neighborhoods onto local circular fits leads to multiple candidate neighbor pairs and thus makes connecting them consistently an NP-hard problem. To solve this efficiently, we design an algorithm that searches that solution space iteratively on different scales of k. It achieves linear time complexity in terms of point count plus quadratic time in the size of noise clusters. Our algorithm FitConnect extends HNN-Crust seamlessly to connect both samples with and without noise, performs as local as the recovered features and can output multiple open or closed piece-wise curves. Incidentally, our method simplifies the output geometry by eliminating all but a representative point from noisy clusters. Since local neighborhood fits overlap consistently, the resulting connectivity represents an ordering of the samples along a manifold. This permits us to simply blend the local fits for denoising with the locally estimated noise extent. Aside from applications like reconstructing silhouettes of noisy sensed data, this lays important groundwork to improve surface reconstruction in 3D. Our open-source algorithm is available online.",
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    {
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        "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",
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        "abstract": "We propose a novel method for interactive design of well-fitting body-supporting surfaces that is driven by the pressure distribution on the body's surface. \n\nOur main contribution is an interactive modeling system that utilizes captured body poses and computes an importance field that is proportional to the pressure distribution on the body for a given pose. This distribution indicates where the body should be supported in order to easily hold a particular pose, which is one of the measures of comfortable sitting. \t\n\nUsing our approximation, we propose the entire workflow for interactive design of $C^2$ smooth surfaces which serve as seats, or generally, as body supporting furniture for comfortable sitting. Finally, we also provide a design tool for Rhino/Grasshopper that allows for  interactive creation of single designs or entire multi-person sitting scenarios. We also test the tool with design students and present several results. \t\t\n\nOur method aims at interactive design in order to help designers to create appropriate surfaces digitally without additional empirical design passes.",
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        "title": "Integrated Structural-Architectural Design for Interactive Planning",
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        "abstract": "Traditionally, building floorplans are designed by architects with their usability, functionality, and architectural aesthetics in mind, however, the structural properties of the distribution of load-bearing walls and columns are usually not taken into account at this stage. In this paper we propose a novel approach for the design of architectural floorplans by integrating structural layout analysis directly into the planning process. In order to achieve this, we introduce a planning tool which interactively enforces checks for structural stability of the current design, and which on demand proposes how to stabilize it if necessary. Technically, our solution contains an interactive architectural modeling framework as well as a constrained optimization module where both are based on respective architectural rules. Using our tool, an architect can predict already in a very early planning stage which designs are structurally sound such that later changes due to stability reasons can be prevented. We compare manually computed solutions with optimal results of our proposed automated design process in order to show how much our proposed system can help architects to improve the process of laying out structural models optimally.",
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        "title": "Relation-Based Parametrization and Exploration of Shape Collections",
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        "abstract": "With online repositories for 3D models like 3D Warehouse becoming more prevalent and growing ever larger, new possibilities have emerged for both experienced and inexperienced users. These large collections of shapes can provide inspiration for designers or make it possible to synthesize new shapes by combining different parts from already existing shapes, which can be both easy to learn and a fast way of creating new shapes. But exploring large shape collections or searching for particular kinds of shapes can be difficult and time-consuming tasks as well, especially considering that online repositories are often disorganized. In our work, we propose a relation-based way to parametrize shape collections, allowing the user to explore the entire set of shapes based on the variability of spatial arrangements between pairs of parts. The way in which shapes differ from each other is captured automatically, resulting in a small number of exploration parameters. Furthermore, a copy-and-paste system for parts allows the user to change the structure of a shape, making it possible to explore the entire collection from any initial shape.",
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        "abstract": "User-guided surface selection operations are straightforward for visible regions on a convex model. However, concave surfaces present a challenge because self-occlusions require multiple camera positions to get unobstructed views. Therefore, users often have to locate and switch to new unobstructed views in order to continue the operation. Our novel approach enables operations like painting or cutting in a single view, even on the backside of objects and for arbitrary depth complexity, with interactive performance. Continuous projection of a curve drawn in screen space onto the mesh guarantees seamless brush strokes or manifold cuts, unaffected by any occlusions.\n\nOur occlusion-aware surface-processing method enables a number of applications in an easy way. As examples, we show continuous painting on the surface, selecting regions for texturing, creating illustrative cutaways from nested models and animation of cutaways.",
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        "title": "Fast kNN in Screen Space on GPU",
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        "title": "Resolution-independent superpixels based on convex constrained meshes without small angles",
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        "abstract": "The over-segmentation problem for images is studied in the new resolution-independent formulation when a large image is approximated by a small number of convex polygons with straight edges at subpixel precision. These polygonal superpixels are obtained by refining and extending subpixel edge segments to a full mesh of convex polygons without small angles and with approximation guarantees. Another novelty is the objective error difference between an original pixel-based image and the reconstructed image with a best constant color over each superpixel, which does not need human segmentations. The experiments on images from the Berkeley Segmentation Database show that new meshes are smaller and provide better approximations than the state-of-the-art.",
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        "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.",
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    {
        "id": "aichner-2016-sadf",
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        "repositum_id": null,
        "title": "Interactive Shape-Aware Deformation of 3D Furniture Models",
        "date": "2016-11",
        "abstract": "Resizing of 3D models can be very useful when creating new models or when reusing\nold ones. However, naive resizing can create serious visual artifacts which destroy the\ncharacteristics of an object. In this thesis an algorithm that protects the features of\n3D models during resizing is introduced. It is specialized for furniture models because\nit should be applied to a furniture configurator. We observed that the distortion that\noccurs during scaling is not distributed uniformly across the object. Our algorithm\nautomatically detects the vulnerable parts of a model and then stretches only the non-\nvulnerable ones. Furthermore, the algorithm takes into account that when scaling a\nmesh in a specific direction, the texture has to be adapted as well in order to prevent\nrepresentation errors.",
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        "date_end": "2016-11-21",
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        "research_areas": [
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    {
        "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.",
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            "image_height": 123,
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        "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",
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        "title": "A Survey of Urban Reconstruction",
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        "abstract": "This paper provides a comprehensive overview of urban reconstruction. While there exists a considerable body of literature, this topic is still under very active research. The work reviewed in this survey stems from the following three research communities: computer graphics, computer vision, and photogrammetry and remote sensing. Our goal is to provide a survey that will help researchers to better position their own work in the context of existing solutions, and to help newcomers and practitioners in computer graphics to quickly gain an overview of this vast field. Further, we would like to bring the mentioned research communities to even more interdisciplinary work, since the reconstruction problem itself is by far not solved. ",
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        "title": "Minimising Longest Edge for Closed Surface Construction from Unorganised 3D Point Sets",
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        "abstract": "Given an unorganised 3D point set with just coordinate data, we formulate the problem of closed surface construction as one requiring minimisation of longest edge in triangles, a criterion derivable from Gestalt laws for shape perception. Next we define the Minimum Boundary Complex (BCmin ), which resembles the desired surface Bmin considerably, by slightly relaxing the topological constraint to make it at least two triangles per edge instead of exactly two required by Bmin . A close approximation of BCmin can be computed fast using a greedy algorithm. This provides a very good starting shape which can be transformed by a few steps into the desired shape, close to Bmin. Our method runs in O(n log n) time, with Delaunay Graph construction as largest run-time factor. We show considerable improvement over previous methods, especially for sparse, non-uniform point spacing.\n",
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        "title": "Do We Need Accurate Reconstruction?",
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        "title": "Smooth Levels of Detail",
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        "abstract": "Levels of detail (LODs) are used in interactive computer\ngraphics to avoid overload of the rendering hardware with to high numbers\nof polygons. While conventional methods use a small set of discrete LODs,\nwe introduce a new class of polygonal simplification: Smooth LODs. A very\nlarge number of small details encoded in a data stream allows a progressive\nrefinement of the object from a very coarse approximation to the original\nhigh quality representation. Advantages of the new approach include\nprogressive transmission and encoding suitable for networked applications,\ninteractive selection of any desired quality, and compression of the data\nby incremental and redundancy free encoding.",
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        "id": "Schmalstieg-1996-LOB",
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        "repositum_id": null,
        "title": "Lodestar: An Octree-Based Level of Detail Generator for VRML",
        "date": "1996-10",
        "abstract": "Level of detail generation is important for managing\ngeometric complexity of three-dimensional objects and\nvirtual worlds. However, most algorithms that compute\nlevels of detail do not deal with the special\nrequirements of input data in VRML format. We report\nan algorithm called LODESTAR, based on octree\nquantization that robustly computes simplifications\n                for objects in VRML.",
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    {
        "id": "Traxler-1996-CTB",
        "type_id": "techreport",
        "tu_id": null,
        "repositum_id": null,
        "title": "Calculation of Tight Bounding Volumes for Cyclic CSG-Graphs",
        "date": "1996-01",
        "abstract": "This paper describes how to adapt conventional optimization\ntechniques to cyclic CSG graphs, which are a compact\nrepresentation for the ray tracing of objects defined by\nparallel rewriting systems. For CSG trees a hierarchy of\nbounding volumes is buildt up by a simple recursive algorithm.\nA straight forward transition of this algorithm to CSG graphs\nyields to very huge and thus useless bounding volumes. In this\npaper we introduce an algorithm which calculates tight\nbounding volumes for the nodes of cyclic CSG graphs. This\nmethod can also be applied to CSG trees with explicit\n                transformation nodes or CSG dags.",
        "authors_et_al": false,
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        "authors": [
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        "number": "TR-186-2-96-03",
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        "research_areas": [
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        "keywords": [
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            "Bounding Volumes",
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    {
        "id": "Tobler-1995-AAC",
        "type_id": "techreport",
        "tu_id": null,
        "repositum_id": null,
        "title": "ACSGM -- An adaptive CSG meshing algorithm",
        "date": "1995-12",
        "abstract": "We present a new algorithm, called ACSGM (which is short\nfor Adaptive CSG Mesher), that converts scenes in CSG\nrepresentation into a boundary representation composed of\ntriangles. The algorithm is based on the marching cubes\nalgorithm, but instead of working at a fixed resolution, the\nsize of the cubes used in the meshing process is changed\nadaptively. While the marching cubes algorithm, which\ncalculates the vertices of the triangles of the final mesh\nusing linear interpolation, ACSGM uses ray casting for this\ncomputation. This approach not only produces exact vertices\nbut provides some additional information (e.g.\\ the normal\nvectors in these vertices) that can be used to generate a\nmore accurate approximation of the CSG object by the final\n                mesh.",
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        "authors": [
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            190
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        "number": "TR-186-2-95-13",
        "pages_from": "1",
        "pages_to": "15",
        "research_areas": [
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        "keywords": [
            "CSG",
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    {
        "id": "Tobler-1995-BGD",
        "type_id": "techreport",
        "tu_id": null,
        "repositum_id": null,
        "title": "BABEL: A Generic Data structure for Geometric Modeling",
        "date": "1995-11",
        "abstract": "We present a basic data structure for geometric data which\ncan be adapted to represent common geometry representations\nlike CSG, BSP, aso. The new data structure has been designed\nto be easy to use, and easy to extend. Due to the\nrepresentation of geometric data using a directed acyclic\ngraph, a number of the standard rendering algorithms can be\nused on the data structure in a very straightforward way.\nThe new data structure has been implemented as a C++ library\nand can therefore serve as high-level tool for developing\ngraphics applications, or as an extension for using C++ as a\n                modeling language.",
        "authors_et_al": false,
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        "number": "TR-186-2-95-07",
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        "research_areas": [
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        "keywords": [
            "data structure",
            "C++",
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        "title": "Parametrizing Superquadrics",
        "date": "1995-02",
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        "journal": "Proceedings of WSCG",
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        "location": "University of West Bohemia, Plzen, Czech Republic",
        "note": "TALK: H. Löffelmann 14.2.1995",
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