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Item Automatic Step Size Relaxation in Sphere Tracing(The Eurographics Association, 2023) Bán, Róbert; Valasek, Gábor; Babaei, Vahid; Skouras, MelinaWe 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.Item Guiding Light Trees for Many-Light Direct Illumination(The Eurographics Association, 2023) Hamann, Eric; Jung, Alisa; Dachsbacher, Carsten; Babaei, Vahid; Skouras, MelinaPath 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.Item Can GPT-4 Trace Rays(The Eurographics Association, 2024) Feng, Tony Haoran; Wünsche, Burkhard C.; Denny, Paul; Luxton-Reilly, Andrew; Hooper, Steffan; Sousa Santos, Beatriz; Anderson, EikeRay Tracing is a fundamental concept often taught in introductory Computer Graphics courses, and Ray-Object Intersection questions are frequently used as practice for students, as they leverage various skills essential to learning Ray Tracing or Computer Graphics in general, such as geometry and spatial reasoning. Although these questions are useful in teaching practices, they may take some time and effort to produce, as the production procedure can be quite complex and requires careful verification and review. From the recent advancements in Artificial Intelligence, the possibility of automated or assisted exercise generation has emerged. Such applications are unexplored in Ray Tracing education, and if such applications are viable in this area, then it may significantly improve the productivity and efficiency of Computer Graphics instructors. Additionally, Ray Tracing is quite different to the mostly text-based tasks that LLMs have been observed to perform well on, hence it is unclear whether they can cope with these added complexities of Ray Tracing questions, such as visual processing and 3D geometry. Hence we ran some experiments to evaluate the usefulness of leveraging GPT-4 for assistance when creating exercises related to Ray Tracing, more specifically Ray-Object Intersection questions, and we found that an impressive 67% of its generated questions can be used in assessments verbatim, but only 42% of generated model solutions were correct.Item 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, MelinaWe 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.Item 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, MelinaThe 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.Item Axis-Normalized Ray-Box Intersection(The Eurographics Association and John Wiley & Sons Ltd., 2025) Friederichs, Fabian; Benthin, Carsten; Grogorick, Steve; Eisemann, Elmar; Magnor, Marcus; Eisemann, Martin; Bousseau, Adrien; Day, AngelaRay-axis aligned bounding box intersection tests play a crucial role in the runtime performance of many rendering applications, driven not by complexity but mainly by the volume of tests required. While existing solutions were believed to be pretty much optimal in terms of runtime on current hardware, our paper introduces a new intersection test requiring fewer arithmetic operations compared to all previous methods. By transforming the ray we eliminate the need for one third of the traditional bounding-slab tests and achieve a speed enhancement of approximately 13.8% or 10.9%, depending on the compiler.We present detailed runtime analyses in various scenarios.Item Linearly Transformed Spherical Distributions for Interactive Single Scattering with Area Lights(The Eurographics Association and John Wiley & Sons Ltd., 2025) Kt, Aakash; Shah, Ishaan; Narayanan, P. J.; Bousseau, Adrien; Day, AngelaSingle scattering in scenes with participating media is challenging, especially in the presence of area lights. Considerable variance still remains, in spite of good importance sampling strategies. Analytic methods that render unshadowed surface illumination have recently gained interest since they achieve biased but noise-free plausible renderings while being computationally efficient. In this work, we extend the theory of Linearly Transformed Spherical Distributions (LTSDs) which is a well-known analytic method for surface illumination, to work with phase functions. We show that this is non-trivial, and arrive at a solution with in-depth analysis. This enables us to analytically compute in-scattered radiance, which we build on to semi-analytically render unshadowed single scattering. We ground our derivations and formulations on the Volume Rendering Equation (VRE) which paves the way for realistic renderings despite the biased nature of our method. We also formulate ratio estimators for the VRE to work in conjunction with our formulation, enabling the rendering of shadows. We extensively validate our method, analyze its characteristics and demonstrate better performance compared to Monte Carlo single-scattering.Item Neural Two-Level Monte Carlo Real-Time Rendering(The Eurographics Association and John Wiley & Sons Ltd., 2025) Dereviannykh, Mikhail; Klepikov, Dmitrii; Hanika, Johannes; Dachsbacher, Carsten; Bousseau, Adrien; Day, AngelaWe introduce an efficient Two-Level Monte Carlo (subset of Multi-Level Monte Carlo, MLMC) estimator for real-time rendering of scenes with global illumination. Using MLMC we split the shading integral into two parts: the radiance cache integral and the residual error integral that compensates for the bias of the first one. For the first part, we developed the Neural Incident Radiance Cache (NIRC) leveraging the power of tiny neural networks [MRNK21] as a building block, which is trained on the fly. The cache is designed to provide a fast and reasonable approximation of the incident radiance: an evaluation takes 2-25× less compute time than a path tracing sample. This enables us to estimate the radiance cache integral with a high number of samples and by this achieve faster convergence. For the residual error integral, we compute the difference between the NIRC predictions and the unbiased path tracing simulation. Our method makes no assumptions about the geometry, materials, or lighting of a scene and has only few intuitive hyper-parameters. We provide a comprehensive comparative analysis in different experimental scenarios. Since the algorithm is trained in an on-line fashion, it demonstrates significant noise level reduction even for dynamic scenes and can easily be combined with other noise reduction techniques.Item Adaptive Multi-view Radiance Caching for Heterogeneous Participating Media(The Eurographics Association and John Wiley & Sons Ltd., 2025) Stadlbauer, Pascal; Tatzgern, Wolfgang; Mueller, Joerg H.; Winter, Martin; Stojanovic, Robert; Weinrauch, Alexander; Steinberger, Markus; Bousseau, Adrien; Day, AngelaAchieving lifelike atmospheric effects, such as fog, is essential in creating immersive environments and poses a formidable challenge in real-time rendering. Highly realistic rendering of complex lighting interacting with dynamic fog can be very resourceintensive, due to light bouncing through a complex participating media multiple times. We propose an approach that uses a multi-layered spherical harmonics probe grid to share computations temporarily. In addition, this world-space storage enables the sharing of radiance data between multiple viewers. In the context of cloud rendering this means faster rendering and a significant enhancement in overall rendering quality with efficient resource utilization.Item VisibleUS: From Cryosectional Images to Real-Time Ultrasound Simulation(The Eurographics Association, 2025) Casanova-Salas, Pablo; Gimeno, Jesus; Blasco-Serra, Arantxa; González-Soler, Eva María; Escamilla-Muñoz, Laura; Valverde-Navarro, Alfonso Amador; Fernández, Marcos; Portalés, Cristina; Günther, Tobias; Montazeri, ZahraThe VisibleUS project aims to generate synthetic ultrasound images from cryosection images, focusing on the musculoskeletal system. Cryosection images provide a highly accurate representation of real tissue structures without artifacts. Using this rich anatomical data, we developed a ray-tracing-based simulation algorithm that models ultrasound wave propagation, scattering, and attenuation. This results in highly realistic ultrasound images that accurately depict fine anatomical details, such as muscle fibers and connective tissues. The simulation tool has various applications, including generating datasets for training neural networks and developing interactive training tools for ultrasound specialists. Its ability to produce realistic ultrasound images in real time enhances medical education and research, improving both the understanding and interpretation of ultrasound imaging.