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        "title": "LidarScout: Direct Out-of-Core Rendering of Massive Point Clouds",
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        "abstract": "Large-scale terrain scans are the basis for many important tasks, such as topographic mapping, forestry, agriculture, and infrastructure planning. The resulting point cloud data sets are so massive in size that even basic tasks like viewing take hours to days of pre-processing in order to create level-of-detail structures that allow inspecting the data set in their entirety in real time. In this paper, we propose a method that is capable of instantly visualizing massive country-sized scans with hundreds of billions of points. Upon opening the data set, we first load a sparse subsample of points and initialize an overview of the entire point cloud, immediately followed by a surface reconstruction process to generate higher-quality, hole-free heightmaps. As users start navigating towards a region of interest, we continue to prioritize the heightmap construction process to the user's viewpoint. Once a user zooms in closely, we load the full-resolution point cloud data for that region and update the corresponding height map textures with the full-resolution data. As users navigate elsewhere, full-resolution point data that is no longer needed is unloaded, but the updated heightmap textures are retained as a form of medium level of detail. Overall, our method constitutes a form of direct out-of-core rendering for massive point cloud data sets (terabytes, compressed) that requires no preprocessing and no additional disk space. Source code, executable, pre-trained model, and dataset are available at: https://github.com/cg-tuwien/lidarscout",
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        "title": "PPSurf: Combining Patches and Point Convolutions for Detailed Surface Reconstruction",
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    {
        "id": "erler-2020-p2s",
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        "tu_id": null,
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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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        "substitute": null,
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            "description": "We present Points2Surf, a method to reconstruct an accurate implicit surface from a noisy point cloud. Unlike current data-driven surface reconstruction methods like DeepSDF and AtlasNet, it is patch-based, improves detail reconstruction, and unlike Screened Poisson Reconstruction (SPR), a learned prior of low-level patch shapes improves reconstruction accuracy. \nNote the quality of reconstructions, both geometric and topological, against the original surfaces. The ability of Points2Surf to generalize to new shapes makes it the first learning-based approach with significant generalization ability under both geometric and topological variations.",
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        "authors": [
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        "address": "Cham",
        "booktitle": "Computer Vision -- ECCV 2020",
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        "date_from": "2020-08-24",
        "date_to": "2020-08-27",
        "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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        "location": "Glasgow, UK (online)",
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        "pages": "17",
        "pages_from": "108",
        "pages_to": "124",
        "publisher": "Springer International Publishing",
        "series": "Lecture Notes in Computer Science",
        "volume": "12350",
        "research_areas": [
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            "Modeling"
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        "keywords": [
            "surface reconstruction",
            "implicit surfaces",
            "point clouds",
            "patch-based",
            "local and global",
            "deep learning",
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            },
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                "description": "Our processed extract from the Thingi10k dataset that can be used to replicate the results of Points2Surf.",
                "filetitle": "thingi10k",
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            },
            {
                "description": "Our pre-trained model (vanilla version) to reproduce the results.",
                "filetitle": "vanilla_model",
                "main_file": false,
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    {
        "id": "erler_philipp-2017-phd",
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        "repositum_id": null,
        "title": "Smart Surface Reconstruction",
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        "date_end": "2026-02-11",
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        "rigorosum": "2026-02-11",
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        ],
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    {
        "id": "ERLER-2017-HVR",
        "type_id": "masterthesis",
        "tu_id": null,
        "repositum_id": null,
        "title": "Haptic Feedback in Room-Scale VR",
        "date": "2017-07-18",
        "abstract": "Virtual reality (VR) is now becoming a mainstream medium. Current systems like the HTC Vive offer accurate tracking of the HMD and controllers, which allows for highly immersive interactions with the virtual environment. The interactions can be further enhanced by adding feedback. As an example, a controller can vibrate when it is close to a grabbable ball. \n\nAs such interactions are not exhaustingly researched, we conducted a user study. Specifically, we examine:\n\n- grabbing and throwing with controllers in a simple basketball game.\n\n- the influence of haptic and optical feedback on performance, presence, task load, and usability.\n\n- the advantages of VR over desktop for point-cloud editing.\n\nSeveral new techniques emerged from the point-cloud editor for VR. The bi-manual pinch gesture, which extends the handlebar metaphor, is a novel viewing method used to translate, rotate, and scale the point-cloud. Our new rendering technique uses the geometry shader to draw sparse point clouds quickly. The selection volumes at the controllers are our new technique to efficiently select points in point clouds. The resulting selection is visualized in real time.\n\nThe results of the user study show that:\n\n- grabbing with a controller button is intuitive but throwing is not. Releasing a button is a bad metaphor for releasing a grabbed virtual object in order to throw it.\n\n- any feedback is better than none. Adding haptic, optical, or both feedback types to the grabbing improves the user performance and presence. However, only sub-scores like accuracy and predictability are significantly improved. Usability and task load are mostly unaffected by feedback.\n\n- the point-cloud editing is significantly better in VR with the bi-manual pinch gesture and selection volumes than on the desktop with the orbiting camera and lasso selections.\n",
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        "substitute": null,
        "main_image": {
            "description": null,
            "filetitle": "point cloud selection",
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            "access": "public",
            "image_width": 1600,
            "image_height": 900,
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        "authors": [
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        ],
        "date_end": "2017",
        "date_start": "2016",
        "diploma_examina": "2017-08-17",
        "matrikelnr": "01426424",
        "supervisor": [
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            1116
        ],
        "research_areas": [
            "Perception",
            "Rendering"
        ],
        "keywords": [
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        ],
        "weblinks": [
            {
                "href": "https://github.com/ErlerPhilipp/VR_DA",
                "caption": "VR Apps Repo",
                "description": "This is the repo containing the basketball game, the feedback tester and the point cloud editor.",
                "main_file": 1
            },
            {
                "href": "https://bitbucket.org/PhErler/surveyanalyzer/src/master/",
                "caption": "Analyzer Repo",
                "description": "This is the self-written tool to analyze the survey results statistically.",
                "main_file": 1
            },
            {
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