Browsing by Author "Dittebrandt, Addis"

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    Markov Chain Mixture Models for Real-Time Direct Illumination
    (The Eurographics Association and John Wiley & Sons Ltd., 2023) Dittebrandt, Addis; Schüßler, Vincent; Hanika, Johannes; Herholz, Sebastian; Dachsbacher, Carsten; Ritschel, Tobias; Weidlich, Andrea
    We present a novel technique to efficiently render complex direct illumination in real-time. It is based on a spatio-temporal randomized mixture model of von Mises-Fisher (vMF) distributions in screen space. For every pixel we determine the vMF distribution to sample from using a Markov chain process which is targeted to capture important features of the integrand. By this we avoid the storage overhead of finite-component deterministic mixture models, for which, in addition, determining the optimal component count is challenging. We use stochastic multiple importance sampling (SMIS) to be independent of the equilibrium distribution of our Markov chain process, since it cancels out in the estimator. Further, we use the same sample to advance the Markov chain and to construct the SMIS estimator and local Markov chain state permutations avoid the resulting bias due to dependent sampling. As a consequence we require one ray per sample and pixel only. We evaluate our technique using implementations in a research renderer as well as a classic game engine with highly dynamic content. Our results show that it is efficient and quickly readapts to dynamic conditions. We compare to spatio-temporal resampling (ReSTIR), which can suffer from correlation artifacts due to its non-adapting candidate distributions that can deviate strongly from the integrand.While we focus on direct illumination, our approach is more widely applicable and we exemplarily show the rendering of caustics.
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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.