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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.
Line Integration for Rendering Heterogeneous Emissive Volumes
(The Eurographics Association and John Wiley & Sons Ltd., 2017) Simon, Florian; Hanika, Johannes; Zirr, Tobias; Dachsbacher, Carsten; Zwicker, Matthias and Sander, Pedro
Emissive media are often challenging to render: in thin regions where only few scattering events occur the emission is poorly sampled, while sampling events for emission can be disadvantageous due to absorption in dense regions. We extend the standard path space measurement contribution to also collect emission along path segments, not only at vertices. We apply this extension to two estimators: extending paths via scattering and distance sampling, and next event estimation. In order to do so, we unify the two approaches and derive the corresponding Monte Carlo estimators to interpret next event estimation as a solid angle sampling technique. We avoid connecting paths to vertices hidden behind dense absorbing layers of smoke by also including transmittance sampling into next event estimation. We demonstrate the advantages of our line integration approach which generates estimators with lower variance since entire segments are accounted for. Also, our novel forward next event estimation technique yields faster run times compared to previous next event estimation as it penetrates less deeply into dense volumes.
Rendering 2020 DL Track: Frontmatter
(The Eurographics Association, 2020) Dachsbacher, Carsten; Pharr, Matt; Dachsbacher, Carsten and Pharr, Matt
Improved Half Vector Space Light Transport
(The Eurographics Association and John Wiley & Sons Ltd., 2015) Hanika, Johannes; Kaplanyan, Anton; Dachsbacher, Carsten; Jaakko Lehtinen and Derek Nowrouzezahrai
In this paper, we present improvements to half vector space light transport (HSLT) [KHD14], which make this approach more practical, robust for difficult input geometry, and faster. Our first contribution is the computation of half vector space ray differentials in a different domain than the original work. This enables a more uniform stratification over the image plane during Markov chain exploration. Furthermore, we introduce a new multi chain perturbation in half vector space, which, if combined appropriately with half vector perturbation, makes the mutation strategy both more robust to geometric configurations with fine displacements and faster due to reduced number of ray casts. We provide and analyze the results of improved HSLT and discuss possible applications of our new half vector ray differentials.
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.
Temporal Sample Reuse for Next Event Estimation and Path Guiding for Real-Time Path Tracing
(The Eurographics Association, 2020) Dittebrandt, Addis; Hanika, Johannes; Dachsbacher, Carsten; Dachsbacher, Carsten and Pharr, Matt
Good importance sampling is crucial for real-time path tracing where only low sample budgets are possible. We present two efficient sampling techniques tailored for massively-parallel GPU path tracing which improve next event estimation (NEE) for rendering with many light sources and sampling of indirect illumination. As sampling densities need to vary spatially, we use an octree structure in world space and introduce algorithms to continuously adapt the partitioning and distribution of the sampling budget. Both sampling techniques exploit temporal coherence by reusing samples from the previous frame: For NEE we collect sampled, unoccluded light sources and show how to deduplicate, but also diffuse this information to efficiently sample light sources in the subsequent frame. For sampling indirect illumination, we present a compressed directional quadtree structure which is iteratively adapted towards high-energy directions using samples from the previous frame. The updates and rebuilding of all data structures takes about 1ms in our test scenes, and adds about 6ms at 1080p to the path tracing time compared to using state-of-the-art light hierarchies and BRDF sampling. We show that this additional effort reduces noise in terms of mean squared error by at least one order of magnitude in many situations.
An Interactive Information Visualization Approach to Physically-Based Rendering
(The Eurographics Association, 2016) Simons, Gerard; Ament, Marco; Herholz, Sebastian; Dachsbacher, Carsten; Eisemann, Martin; Eisemann, Elmar; Matthias Hullin and Marc Stamminger and Tino Weinkauf
In this work, we present a novel information visualization tool to gain insight into the light transport in a physically-based rendering setting. The tool consists of a sampling-based data reduction technique, an extended interactive parallel coordinates plot providing an overview of the attributes linked to each light sample, 2D and 3D heat maps to represent different aspects of the rendering process, as well as a three-dimensional view to display and animate the light path transportation throughout the scene. We show several applications including differential light transport visualization for scene analysis, lighting and material optimization, reduction of rendering artifacts, and user-guided importance sampling.
Minimal Convolutional Neural Networks for Temporal Anti Aliasing
(The Eurographics Association, 2023) Herveau, Killian; Piochowiak, Max; Dachsbacher, Carsten; Bikker, Jacco; Gribble, Christiaan
Existing deep learning methods for performing temporal anti aliasing (TAA) in rendering are either closed source or rely on upsampling networks with a large operation count which are expensive to evaluate. We propose a simple deep learning architecture for TAA combining only a few common primitives, easy to assemble and to change for application needs. We use a fully-convolutional neural network architecture with recurrent temporal feedback, motion vectors and depth values as input and show that a simple network can produce satisfactory results. Our architecture template, for which we provide code, introduces a method that adapts to different temporal subpixel offsets for accumulation without increasing the operation count. To this end, convolutional layers cycle through a set of different weights per temporal subpixel offset while their operations remain fixed. We analyze the effect of this method on image quality and present different tradeoffs for adapting the architecture. We show that our simple network performs remarkably better than variance clipping TAA, eliminating both flickering and ghosting without performing upsampling.
Sparse High-degree Polynomials for Wide-angle Lenses
(The Eurographics Association and John Wiley & Sons Ltd., 2016) Schrade, Emanuel; Hanika, Johannes; Dachsbacher, Carsten; Elmar Eisemann and Eugene Fiume
Rendering with accurate camera models greatly increases realism and improves the match of synthetic imagery to real-life footage. Photographic lenses can be simulated by ray tracing, but the performance depends on the complexity of the lens system, and some operations required for modern algorithms, such as deterministic connections, can be difficult to achieve. We generalise the approach of polynomial optics, i.e. expressing the light field transformation from the sensor to the outer pupil using a polynomial, to work with extreme wide angle (fisheye) lenses and aspherical elements. We also show how sparse polynomials can be constructed from the large space of high-degree terms (we tested up to degree 15). We achieve this using a variant of orthogonal matching pursuit instead of a Taylor series when computing the polynomials. We show two applications: photorealistic rendering using Monte Carlo methods, where we introduce a new aperture sampling technique that is suitable for light tracing, and an interactive preview method suitable for rendering with deep images.
Memory-Efficient On-The-Fly Voxelization of Particle Data
(The Eurographics Association, 2015) Zirr, Tobias; Dachsbacher, Carsten; C. Dachsbacher and P. Navrátil
In this paper we present a novel GPU-friendly real-time voxelization technique for rendering homogeneous media that is defined by particles, e.g. fluids obtained from particle-based simulations such as Smoothed Particle Hydrodynamics (SPH). Our method computes view-adaptive binary voxelizations with on-the-fly compression of a tiled perspective voxel grid, achieving higher resolutions than previous approaches. It allows for interactive generation of realistic images, enabling advanced rendering techniques such as ray casting-based refraction and reflection, light scattering and absorption, and ambient occlusion. In contrast to previous methods, it does not rely on preprocessing such as expensive, and often coarse, scalar field conversion or mesh generation steps. Our method directly takes unsorted particle data as input. It can be further accelerated by identifying fully populated simulation cells during simulation. The extracted surface can be filtered to achieve smooth surface appearance.

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