Browsing by Author "Stamminger, Marc"

Now showing 1 - 11 of 11
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    Adaptive Temporal Sampling for Volumetric Path Tracing of Medical Data
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Martschinke, Jana; Hartnagel, Stefan; Keinert, Benjamin; Engel, Klaus; Stamminger, Marc; Boubekeur, Tamy and Sen, Pradeep
    Monte-Carlo path tracing techniques can generate stunning visualizations of medical volumetric data. In a clinical context, such renderings turned out to be valuable for communication, education, and diagnosis. Because a large number of computationally expensive lighting samples is required to converge to a smooth result, progressive rendering is the only option for interactive settings: Low-sampled, noisy images are shown while the user explores the data, and as soon as the camera is at rest the view is progressively refined. During interaction, the visual quality is low, which strongly impedes the user's experience. Even worse, when a data set is explored in virtual reality, the camera is never at rest, leading to constantly low image quality and strong flickering. In this work we present an approach to bring volumetric Monte-Carlo path tracing to the interactive domain by reusing samples over time. To this end, we transfer the idea of temporal antialiasing from surface rendering to volume rendering. We show how to reproject volumetric ray samples even though they cannot be pinned to a particular 3D position, present an improved weighting scheme that makes longer history trails possible, and define an error accumulation method that downweights less appropriate older samples. Furthermore, we exploit reprojection information to adaptively determine the number of newly generated path tracing samples for each individual pixel. Our approach is designed for static, medical data with both volumetric and surface-like structures. It achieves good-quality volumetric Monte-Carlo renderings with only little noise, and is also usable in a VR context.
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    Compressed Bounding Volume Hierarchies for Efficient Ray Tracing of Disperse Hair
    (The Eurographics Association, 2018) Martinek, Magdalena; Stamminger, Marc; Binder, Nikolaus; Keller, Alexander; Beck, Fabian and Dachsbacher, Carsten and Sadlo, Filip
    Ray traced human hair is becoming more and more ubiquitous in photorealistic image synthesis. Despite hierarchical data structures for accelerated ray tracing, performance suffers from the bad separability inherent with ensembles of hair strands. We propose a compressed acceleration data structure that improves separability by adaptively subdividing hair fibers. Compression is achieved by storing quantized as well as oriented bounding boxes and an indexing scheme to specify curve segments instead of storing them. We trade memory for speed, as our approach may use more memory, however, in cases of highly curved hair we can double the number of traversed rays per second over prior work. With equal memory we still achieve a speed-up of up to 30%, with equal performance we can reduce memory by up to 30%.
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    CPU-Style SIMD Ray Traversal on GPUs
    (ACM, 2018) Lier, Alexander; Stamminger, Marc; Selgrad, Kai; Patney, Anjul and Niessner, Matthias
    In this paper we describe and evaluate an implementation of CPUstyle SIMD ray traversal on the GPU. We show how spreading moderately wide BVHs (up to a branching factor of eight) across multiple threads in a warp can improve performance while not requiring expensive pre-processing. e presented ray-traversal method exhibits improved traversal performance especially for increasingly incoherent rays.
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    An Efficient Solution to Structured Optimization Problems using Recursive Matrices
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Rückert, Darius; Stamminger, Marc; Steinberger, Markus and Foley, Tim
    We present a linear algebra framework for structured matrices and general optimization problems. The matrices and matrix operations are defined recursively to efficiently capture complex structures and enable advanced compiler optimization. In addition to common dense and sparse matrix types, we define mixed matrices, which allow every element to be of a different type. Using mixed matrices, the low- and high-level structure of complex optimization problems can be encoded in a single type. This type is then analyzed at compile time by a recursive linear solver that picks the optimal algorithm for the given problem. For common computer vision problems, our system yields a speedup of 3-5 compared to other optimization frameworks. The BLAS performance is benchmarked against the MKL library. We achieve a significant speedup in block-SPMV and block-SPMM. This work is implemented and released open-source as a header-only extension to the C++ math library Eigen.
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    Neural Denoising for Path Tracing of Medical Volumetric Data
    (ACM, 2020) Hofmann, Nikolai; Martschinke, Jana; Engel, Klaus; Stamminger, Marc; Yuksel, Cem and Membarth, Richard and Zordan, Victor
    In this paper, we transfer machine learning techniques previously applied to denoising surface-only Monte Carlo renderings to path-traced visualizations of medical volumetric data. In the domain of medical imaging, path-traced videos turned out to be an efficient means to visualize and understand internal structures, in particular for less experienced viewers such as students or patients. However, the computational demands for the rendering of high-quality path-traced videos are very high due to the large number of samples necessary for each pixel. To accelerate the process, we present a learning-based technique for denoising path-traced videos of volumetric data by increasing the sample count per pixel; both through spatial (integrating neighboring samples) and temporal filtering (reusing samples over time). Our approach uses a set of additional features and a loss function both specifically designed for the volumetric case. Furthermore, we present a novel network architecture tailored for our purpose, and introduce reprojection of samples to improve temporal stability and reuse samples over frames. As a result, we achieve good image quality even from severely undersampled input images, as visible in the teaser image.
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    Path-Traced Motion Blur using Motion Trees
    (The Eurographics Association, 2020) Martinek, Magdalena; Thiemann, Philip; Stamminger, Marc; Biasotti, Silvia and Pintus, Ruggero and Berretti, Stefano
    Motion Blur is an important effect of photo-realistic rendering. Distribution ray tracing can simulate motion blur very well by integrating light, both over the spatial and the temporal domain. However, increasing the problem by the temporal dimension entails many challenges, particularly in cinematic multi-bounce path tracing of complex scenes where heavy-weight geometry with complex lighting and even offscreen elements contribute to the final image. In particular, for fast moving objects, undersampling in the time domain results in severe artefacts. In this paper, we propose the Motion Tree, a novel Level-of-Detail data structure for efficient handling of animated objects, that both filters in the spatial and the temporal domain. The Motion Tree is a compact nesting of a temporal interval binary tree for filtering time consecutive data and a sparse voxel octree (SVO) which simplifies spatially nearby data. It is generated during a pre-process and fits nicely into any conventional physically based path tracer. When used in a production-scale environment it significantly reduces memory requirements allowing for a speedup in rendering performance with user control over the degree of impact on quality.
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    Projection Mapping for In-Situ Surgery Planning by the Example of DIEP Flap Breast Reconstruction
    (The Eurographics Association, 2021) Martschinke, Jana; Klein, Vanessa; Kurth, Philipp; Engel, Klaus; Ludolph, Ingo; Hauck, Theresa; Horch, Raymund; Stamminger, Marc; Oeltze-Jafra, Steffen and Smit, Noeska N. and Sommer, Björn and Nieselt, Kay and Schultz, Thomas
    Nowadays, many surgical procedures require preoperative planning, mostly relying on data from 3D imaging techniques like computed tomography or magnetic resonance imaging. However, preoperative assessment of this data is carried out on the PC (using classical CT/MR viewing software) and not on the patient's body itself. Therefore, surgeons need to transfer both their overall understanding of the patient's individual anatomy and also specific markers and labels for important points from the PC to the patient only with the help of imaginative power or approximative measurement. In order to close the gap between preoperative planning on the PC and surgery on the patient, we propose a system to directly project preoperative knowledge to the body surface by projection mapping. As a result, we are able to display both assigned labels and a volumetric and view-dependent view of the 3D data in-situ. Furthermore, we offer a method to interactively navigate through the data and add 3D markers directly in the projected volumetric view. We demonstrate the benefits of our approach using DIEP flap breast reconstruction as an example. By means of a small pilot study, we show that our method outperforms standard surgical planning in accuracy and can easily be understood and utilized even by persons without any medical knowledge.
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    Proxy Painting
    (The Eurographics Association, 2018) Lange, Vanessa; Kurth, Philipp; Keinert, Benjamin; Boss, Martin; Stamminger, Marc; Bauer, Frank; Sablatnig, Robert and Wimmer, Michael
    For archaeologists it is often desireable to present statues in their original coloration. With projection mapping real-world surfaces are augmented by digital content to create compelling alterations of the scene's visual appearance without actually altering or even damaging the object. While there are frequent advances in projection quality, content creation is still a chal- lenging and often unintuitive task, especially for non-experts. In our presented system we combine the advantages of digital content creation such as rapid prototyping with the convenience of an analog workflow. Users paint on smaller versions of the projection mapping target, employing real-world brushes and pencils, while the results are presented live on its large counter- part. We further demonstrate the integration of our system into a state-of-art game engine. By leveraging a powerful rendering and material workflow we make creating compelling materials and lighting situations an intuitive experience.
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    SuBloNet: Sparse Super Block Networks for Large Scale Volumetric Fusion
    (The Eurographics Association, 2021) Rückert, Darius; Stamminger, Marc; Andres, Bjoern and Campen, Marcel and Sedlmair, Michael
    Training and inference of convolutional neural networks (CNNs) on truncated signed distance fields (TSDFs) is a challenging task. Large parts of the scene are usually empty, which makes dense implementations inefficient in terms of memory consumption and compute throughput. However, due to the truncation distance, non-zero values are grouped around the surface creating small dense blocks inside the large empty space. We show that this structure can be exploited by storing the TSDF in a block sparse tensor and then decomposing it into rectilinear super blocks. A super block is a dense 3d cuboid of variable size and can be processed by conventional CNNs. We analyze the rectilinear decomposition and present a formulation for computing the bandwidth-optimal solution given a specific network architecture. However, this solution is NP-complete, therefore we also a present a heuristic approach for fast training and inference tasks. We verify the effectiveness of SuBloNet and report a speedup of 4x towards dense implementations and 1.7x towards state-of-the-art sparse implementations. Using the super block architecture, we show that recurrent volumetric fusion is now possible on large scale scenes. Such a systems is able to reconstruct high-quality surfaces from few noisy depth images.
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    Time‐Warped Foveated Rendering for Virtual Reality Headsets
    (© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Franke, Linus; Fink, Laura; Martschinke, Jana; Selgrad, Kai; Stamminger, Marc; Benes, Bedrich and Hauser, Helwig
    Rendering in real time for virtual reality headsets with high user immersion is challenging due to strict framerate constraints as well as due to a low tolerance for artefacts. Eye tracking‐based foveated rendering presents an opportunity to strongly increase performance without loss of perceived visual quality. To this end, we propose a novel foveated rendering method for virtual reality headsets with integrated eye tracking hardware. Our method comprises recycling pixels in the periphery by spatio‐temporally reprojecting them from previous frames. Artefacts and disocclusions caused by this reprojection are detected and re‐evaluated according to a confidence value that is determined by a newly introduced formalized perception‐based metric, referred to as confidence function. The foveal region, as well as areas with low confidence values, are redrawn efficiently, as the confidence value allows for the delicate regulation of hierarchical geometry and pixel culling. Hence, the average primitive processing and shading costs are lowered dramatically. Evaluated against regular rendering as well as established foveated rendering methods, our approach shows increased performance in both cases. Furthermore, our method is not restricted to static scenes and provides an acceleration structure for post‐processing passes.
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    Visualization Aided Interface Reconstruction
    (The Eurographics Association, 2020) Penk, Dominik; Müller, Jonas; Felfer, Peter; Grosso, Roberto; Stamminger, Marc; Krüger, Jens and Niessner, Matthias and Stückler, Jörg
    Modern atom probe tomography measurements generate large point clouds of atomic locations in solids. A common analysis task in these datasets is to put the location of specific atom types in relation to crystallographic features such as the interface between two crystals (grain boundaries). In cases where these features represent surfaces, their extraction is carried out manually in most cases. In this paper we propose a method for semi automatic extraction of such two dimensional manifold and non-manifold surfaces from a given dataset. We first aid the user to filter the atom data by providing an interactive visualization of the dataset tailored towards enhancing these interfaces. Once a desired set of points representing the interface is found, we provide an automatic surface extraction method to compute an explicit parametric representation of the visualized surface. In case of non-manifold interface structures, this parametric representation is then used to calculate the intersections of the individual manifold parts of the interfaces.