Browsing by Author "Tessari, Lorenzo"

Now showing 1 - 4 of 4
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    Perceptually Guided Automatic Parameter Optimization for Interactive Visualization
    (The Eurographics Association, 2023) Opitz, Daniel; Zirr, Tobias; Dachsbacher, Carsten; Tessari, Lorenzo; Guthe, Michael; Grosch, Thorsten
    We propose a new reference-free method for automatically optimizing the parameters of visualization techniques such that the perception of visual structures is improved. Manual tuning may require domain knowledge not only in the field of the analyzed data, but also deep knowledge of the visualization techniques, and thus often becomes challenging as the number of parameters that impact the result grows. To avoid this laborious and difficult task, we first derive an image metric that models the loss of perceived information in the processing of a displayed image by a human observer; good visualization parameters minimize this metric. Our model is loosely based on quantitative studies in the fields of perception and biology covering visual masking, photo receptor sensitivity, and local adaptation. We then pair our metric with a generic parameter tuning algorithm to arrive at an automatic optimization method that is oblivious to the concrete relationship between parameter sets and visualization. We demonstrate our method for several volume visualization techniques, where visual clutter, visibility of features, and illumination are often hard to balance. Since the metric can be efficiently computed using image transformations, it can be applied to many visualization techniques and problem settings in a unified manner, including continuous optimization during interactive visual exploration. We also evaluate the effectiveness of our approach in a user study that validates the improved perception of visual features in results optimized using our model of perception.
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    PLOC++ : Parallel Locally-Ordered Clustering for Bounding Volume Hierarchy Construction Revisited
    (ACM Association for Computing Machinery, 2022) Benthin, Carsten; Drabinski, Radoslaw; Tessari, Lorenzo; Dittebrandt, Addis; Josef Spjut; Marc Stamminger; Victor Zordan
    We propose a novel version of the GPU-oriented massively parallel locally-ordered clustering (PLOC) algorithm for constructing bounding volume hierarchies (BVHs). Our method focuses on removing the weaknesses of the original approach by simplifying and fusing different phases, while replacing most performance critical parts by novel and more efficient algorithms. This combination allows for outperforming the original approach by a factor of 1.9 - 2.3×.
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    Stochastic Subsets for BVH Construction
    (The Eurographics Association and John Wiley & Sons Ltd., 2023) Tessari, Lorenzo; Dittebrandt, Addis; Doyle, Michael J.; Benthin, Carsten; Myszkowski, Karol; Niessner, Matthias
    BVH construction is a critical component of real-time and interactive ray-tracing systems. However, BVH construction can be both compute and bandwidth intensive, especially when a large degree of dynamic geometry is present. Different build algorithms vary substantially in the traversal performance that they produce, making high quality construction algorithms desirable. However, high quality algorithms, such as top-down construction, are typically more expensive, limiting their benefit in real-time and interactive contexts. One particular challenge of high quality top-down construction algorithms is that the large working set at the top of the tree can make constructing these levels bandwidth-intensive, due to O(nlog(n)) complexity, limited cache locality, and less dense compute at these levels. To address this limitation, we propose a novel stochastic approach to GPU BVH construction that selects a representative subset to build the upper levels of the tree. As a second pass, the remaining primitives are clustered around the BVH leaves and further processed into a complete BVH. We show that our novel approach significantly reduces the construction time of top-down GPU BVH builders by a factor up to 1.8x, while achieving competitive rendering performance in most cases, and exceeding the performance in others.
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    Temporal Normal Distribution Functions
    (The Eurographics Association, 2020) Tessari, Lorenzo; Hanika, Johannes; Dachsbacher, Carsten; Droske, Marc; Dachsbacher, Carsten and Pharr, Matt
    Specular aliasing can make seemingly simple scenes notoriously hard to render efficiently: small geometric features with high curvature and near specular reflectance result in tiny lighting features which are difficult to resolve at low sample counts per pixel. LEAN and LEADR mapping can be used to convert geometric surface detail to anisotropic surface roughness in a preprocess. In scenes including fluid simulation this problem is particularly apparent with fast moving elements such as spray particles, which are typically represented as participating media in movie rendering. Both approaches, however, are only valid in the far-field regime where the geometric detail is much smaller than a pixel, while the challenge of resolving highlights remains in the meso-scale. Fast motion and the relatively long shutter intervals, commonly used in movie production, lead to strong variation of the surface normals seen under a pixel over time aggravating the problem. Recent specular anti aliasing approaches preintegrate geometric curvature under the pixel footprint for one specific ray to achieve noise free images at low sample counts. We extend these to anisotropic surface roughness and to account for the temporal surface normal variation due to motion blur. We use temporal derivatives to approximate the distribution of the surface normal seen under a pixel over the course of the shutter interval. Furthermore, we discuss how this can afterwards be combined with the surface BSDF in a practical way.