606 research outputs found

    Tessellated Voxelization for Global Illumination using Voxel Cone Tracing

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    Modeling believable lighting is a crucial component of computer graphics applications, including games and modeling programs. Physically accurate lighting is complex and is not currently feasible to compute in real-time situations. Therefore, much research is focused on investigating efficient ways to approximate light behavior within these real-time constraints. In this thesis, we implement a general purpose algorithm for real-time applications to approximate indirect lighting. Based on voxel cone tracing, we use a filtered representation of a scene to efficiently sample ambient light at each point in the scene. We present an approach to scene voxelization using hardware tessellation and compare it with an approach utilizing hardware rasterization. We also investigate possible methods of warped voxelization. Our contributions include a complete and open-source implementation of voxel cone tracing along with both voxelization algorithms. We find similar performance and quality with both voxelization algorithms

    Sparse Volumetric Deformation

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    Volume rendering is becoming increasingly popular as applications require realistic solid shape representations with seamless texture mapping and accurate filtering. However rendering sparse volumetric data is difficult because of the limited memory and processing capabilities of current hardware. To address these limitations, the volumetric information can be stored at progressive resolutions in the hierarchical branches of a tree structure, and sampled according to the region of interest. This means that only a partial region of the full dataset is processed, and therefore massive volumetric scenes can be rendered efficiently. The problem with this approach is that it currently only supports static scenes. This is because it is difficult to accurately deform massive amounts of volume elements and reconstruct the scene hierarchy in real-time. Another problem is that deformation operations distort the shape where more than one volume element tries to occupy the same location, and similarly gaps occur where deformation stretches the elements further than one discrete location. It is also challenging to efficiently support sophisticated deformations at hierarchical resolutions, such as character skinning or physically based animation. These types of deformation are expensive and require a control structure (for example a cage or skeleton) that maps to a set of features to accelerate the deformation process. The problems with this technique are that the varying volume hierarchy reflects different feature sizes, and manipulating the features at the original resolution is too expensive; therefore the control structure must also hierarchically capture features according to the varying volumetric resolution. This thesis investigates the area of deforming and rendering massive amounts of dynamic volumetric content. The proposed approach efficiently deforms hierarchical volume elements without introducing artifacts and supports both ray casting and rasterization renderers. This enables light transport to be modeled both accurately and efficiently with applications in the fields of real-time rendering and computer animation. Sophisticated volumetric deformation, including character animation, is also supported in real-time. This is achieved by automatically generating a control skeleton which is mapped to the varying feature resolution of the volume hierarchy. The output deformations are demonstrated in massive dynamic volumetric scenes

    Animated rendering of cardiac model simulations

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    Heart disease has been the leading cause of death both in the world and the United States in the past decade. Computational cardiac modeling and simulation, especially patient-specific cardiac modeling has been recognized as one of the best ways to improve diagnosis of heart disease by providing insights in individual disease characteristics that cannot be obtained by other means. However presenting the results of cardiac simulations to cardiologists in an interactive manner can considerably improve the utility of cardiac models in understanding the heart function. In this work, we have developed virtual reality and animated volume rendering techniques to render the results of cardiac simulations. We have developed a GPU accelerated algorithm that produces time varying voxelized representation of the quantities of interest in a cardiac model, which can then be interactively rendered in real time. We voxelize the different time frames of the analysis model and transfer the time-varying data to the GPU memory using a flat data structure. This technique allows us to visualize and interact with animation in real time. As a proof-of-concept, we test our method on interactively rendering the simulation results of cardiac biomechanics simulations. We also present the timing results on post-processing and rendering two different cardiac IGA at different resolutions. We achieve an interactive frame rate of over 50 fps for all test cases

    Deep Projective 3D Semantic Segmentation

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    Semantic segmentation of 3D point clouds is a challenging problem with numerous real-world applications. While deep learning has revolutionized the field of image semantic segmentation, its impact on point cloud data has been limited so far. Recent attempts, based on 3D deep learning approaches (3D-CNNs), have achieved below-expected results. Such methods require voxelizations of the underlying point cloud data, leading to decreased spatial resolution and increased memory consumption. Additionally, 3D-CNNs greatly suffer from the limited availability of annotated datasets. In this paper, we propose an alternative framework that avoids the limitations of 3D-CNNs. Instead of directly solving the problem in 3D, we first project the point cloud onto a set of synthetic 2D-images. These images are then used as input to a 2D-CNN, designed for semantic segmentation. Finally, the obtained prediction scores are re-projected to the point cloud to obtain the segmentation results. We further investigate the impact of multiple modalities, such as color, depth and surface normals, in a multi-stream network architecture. Experiments are performed on the recent Semantic3D dataset. Our approach sets a new state-of-the-art by achieving a relative gain of 7.9 %, compared to the previous best approach.Comment: Submitted to CAIP 201

    Subdivision Surface based One-Piece Representation

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    Subdivision surfaces are capable of modeling and representing complex shapes of arbi-trary topology. However, methods on how to build the control mesh of a complex surfaceare not studied much. Currently, most meshes of complicated objects come from trian-gulation and simplification of raster scanned data points, like the Stanford 3D ScanningRepository. This approach is costly and leads to very dense meshes.Subdivision surface based one-piece representation means to represent the final objectin a design process with only one subdivision surface, no matter how complicated theobject\u27s topology or shape. Hence the number of parts in the final representation isalways one.In this dissertation we present necessary mathematical theories and geometric algo-rithms to support subdivision surface based one-piece representation. First, an explicitparametrization method is presented for exact evaluation of Catmull-Clark subdivisionsurfaces. Based on it, two approaches are proposed for constructing the one-piece rep-resentation of a given object with arbitrary topology. One approach is to construct theone-piece representation by using the interpolation technique. Interpolation is a naturalway to build models, but the fairness of the interpolating surface is a big concern inprevious methods. With similarity based interpolation technique, we can obtain bet-ter modeling results with less undesired artifacts and undulations. Another approachis through performing Boolean operations. Up to this point, accurate Boolean oper-ations over subdivision surfaces are not approached yet in the literature. We presenta robust and error controllable Boolean operation method which results in a one-piecerepresentation. Because one-piece representations resulting from the above two methodsare usually dense, error controllable simplification of one-piece representations is needed.Two methods are presented for this purpose: adaptive tessellation and multiresolutionanalysis. Both methods can significantly reduce the complexity of a one-piece represen-tation and while having accurate error estimation.A system that performs subdivision surface based one-piece representation was im-plemented and a lot of examples have been tested. All the examples show that our ap-proaches can obtain very good subdivision based one-piece representation results. Eventhough our methods are based on Catmull-Clark subdivision scheme, we believe they canbe adapted to other subdivision schemes as well with small modifications

    Point cloud data compression

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    The rapid growth in the popularity of Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR) experiences have resulted in an exponential surge of three-dimensional data. Point clouds have emerged as a commonly employed representation for capturing and visualizing three-dimensional data in these environments. Consequently, there has been a substantial research effort dedicated to developing efficient compression algorithms for point cloud data. This Master's thesis aims to investigate the current state-of-the-art lossless point cloud geometry compression techniques, explore some of these techniques in more detail and then propose improvements and/or extensions to enhance them and provide directions for future work on this topic

    Moxel DAGs: Connecting Material Information to High Resolution Sparse Voxel DAGs

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    As time goes on, the demand for higher resolution and more visually rich images only increases. Unfortunately, creating these more realistic computer graphics is pushing our computational resources to their limits. In realistic rendering, one of the common ways 3D objects are represented is as volumetric elements called voxels. Traditionally, voxel data structures are known for their high memory requirements. One of the standard ways these requirements are minimized is by storing the voxels in a sparse voxel octree (SVO). Very recently, a method called High Resolution Sparse Voxel DAGs was presented that can store binary voxel data orders of magnitudes more efficiently than SVOs. This memory efficiency is achieved by converting the tree into a directed acyclic graph (DAG). The method was also shown to have competitive rendering performance to recent GPU ray tracers. Unfortunately, it does not support storing collections of rendering attributes, commonly called materials. These represent a given object\u27s reflectance properties, and are necessary for calculating its perceived color. We present a method for connecting material information to High Resolution Sparse Voxel DAGs for mid-level scenes, with multiple meshes, and several different materials. This is achieved using an extended Sparse Voxel DAG, called a Moxel DAG, and an external data structure for holding the material information, we call a Moxel Table. Our method is much more memory efficient than traditional SVOs, and only increases in efficiency in comparison when at higher resolutions. Because it stores the equivalent information as SVOs, it achieves the exact same visual quality at the same resolutions
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