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Now showing 1 - 4 of 4
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    Automatic Step Size Relaxation in Sphere Tracing
    (The Eurographics Association, 2023) Bán, Róbert; Valasek, Gábor; Babaei, Vahid; Skouras, Melina
    We propose a robust auto-relaxed sphere tracing method that automatically scales its step sizes based on data from previous iterations. It possesses a scalar hyperparemeter that is used similarly to the learning rate of gradient descent methods. We show empirically that this scalar degree of freedom has a smaller effect on performance than the step-scale hyperparameters of concurrent sphere tracing variants. Additionally, we compare the performance of our algorithm to these both on procedural and discrete signed distance input and show that it outperforms or performs up to par to the most efficient method, depending on the limit on iteration counts. We also verify that our method takes significantly fewer robustness-preserving sphere trace fallback steps, as it generates fewer invalid, over-relaxed step sizes.
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    Guiding Light Trees for Many-Light Direct Illumination
    (The Eurographics Association, 2023) Hamann, Eric; Jung, Alisa; Dachsbacher, Carsten; Babaei, Vahid; Skouras, Melina
    Path guiding techniques reduce the variance in path tracing by reusing knowledge from previous samples to build adaptive sampling distributions. The Practical Path Guiding (PPG) approach stores and iteratively refines an approximation of the incident radiance field in a spatio-directional data structure that allows sampling the incident radiance. However, due to the limited resolution in both spatial and directional dimensions, this discrete approximation is not able to accurately capture a large number of very small lights. We present an emitter sampling technique to guide next event estimation (NEE) with a global light tree and adaptive tree cuts that integrates into the PPG framework. In scenes with many lights our technique significantly reduces the RMSE compared to PPG with uniform NEE, while adding close to no overhead in scenes with few light sources. The results show that our technique can also aid the incident radiance learning of PPG in scenes with difficult visibility.
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    Tight Bounding Boxes for Voxels and Bricks in a Signed Distance Field Ray Tracer
    (The Eurographics Association, 2023) Hansson-Söderlund, Herman; Akenine-Möller, Tomas; Babaei, Vahid; Skouras, Melina
    We present simple methods to compute tight axis-aligned bounding boxes for voxels and for bricks of voxels in a signed distance function renderer based on ray tracing. Our results show total frame time reductions of 20-31% in a real-time path tracer.
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    Out-of-the-loop Autotuning of Metropolis Light Transport with Reciprocal Probability Binning
    (The Eurographics Association, 2023) Herveau, Killian; Otsu, Hisanari; Dachsbacher, Carsten; Babaei, Vahid; Skouras, Melina
    The performance of Markov Chain Monte Carlo (MCMC) rendering methods depends heavily on the mutation strategies and their parameters. We treat the underlying mutation strategies as black-boxes and focus on their parameters. This avoids the need for tedious manual parameter tuning and enables automatic adaptation to the actual scene. We propose a framework for out-of-the-loop autotuning of these parameters. As a pilot example, we demonstrate our tuning strategy for small-step mutations in Primary Sample Space Metropolis Light Transport. Our σ-binning strategy introduces a set of mutation parameters chosen by a heuristic: the inverse probability of the local direction sampling, which captures some characteristics of the local sampling. We show that our approach can successfully control the parameters and achieve better performance compared to non-adaptive mutation strategies.