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Item ManyLoDs: Parallel Many-View Level-of-Detail Selection for Real-Time Global Illumination(The Eurographics Association and Blackwell Publishing Ltd., 2011) Holländer, Matthias; Ritschel, Tobias; Eisemann, Elmar; Boubekeur, Tamy; Ravi Ramamoorthi and Erik ReinhardLevel-of-Detail structures are a key component for scalable rendering. Built from raw 3D data, these structures are often defined as Bounding Volume Hierarchies, providing coarse-to-fine adaptive approximations that are well-adapted for many-view rasterization. Here, the total number of pixels in each view is usually low, while the cost of choosing the appropriate LoD for each view is high. This task represents a challenge for existing GPU algorithms. We propose ManyLoDs, a new GPU algorithm to efficiently compute many LoDs from a Bounding Volume Hierarchy in parallel by balancing the workload within and among LoDs. Our approach is not specific to a particular rendering technique, can be used on lazy representations such as polygon soups, and can handle dynamic scenes. We apply our method to various many-view rasterization applications, including Instant Radiosity, Point-Based Global Illumination, and reflection / refraction mapping. For each of these, we achieve real-time performance in complex scenes at high resolutions.Item Geometry and Attribute Compression for Voxel Scenes(The Eurographics Association and John Wiley & Sons Ltd., 2016) Dado, Bas; Kol, Timothy R.; Bauszat, Pablo; Thiery, Jean-Marc; Eisemann, Elmar; Joaquim Jorge and Ming LinVoxel-based approaches are today's standard to encode volume data. Recently, directed acyclic graphs (DAGs) were successfully used for compressing sparse voxel scenes as well, but they are restricted to a single bit of (geometry) information per voxel. We present a method to compress arbitrary data, such as colors, normals, or reflectance information. By decoupling geometry and voxel data via a novel mapping scheme, we are able to apply the DAG principle to encode the topology, while using a palette-based compression for the voxel attributes, leading to a drastic memory reduction. Our method outperforms existing state-of-the-art techniques and is well-suited for GPU architectures. We achieve real-time performance on commodity hardware for colored scenes with up to 17 hierarchical levels (a 128K3 voxel resolution), which are stored fully in core.Item Next Event Estimation++: Visibility Mapping for Efficient Light Transport Simulation(The Eurographics Association and John Wiley & Sons Ltd., 2020) Guo, Jerry Jinfeng; Eisemann, Martin; Eisemann, Elmar; Eisemann, Elmar and Jacobson, Alec and Zhang, Fang-LueMonte-Carlo rendering requires determining the visibility between scene points as the most common and compute intense operation to establish paths between camera and light source. Unfortunately, many tests reveal occlusions and the corresponding paths do not contribute to the final image. In this work, we present next event estimation++ (NEE++): a visibility mapping technique to perform visibility tests in a more informed way by caching voxel to voxel visibility probabilities. We show two scenarios: Russian roulette style rejection of visibility tests and direct importance sampling of the visibility. We show applications to next event estimation and light sampling in a uni-directional path tracer, and light-subpath sampling in Bi-Directional Path Tracing. The technique is simple to implement, easy to add to existing rendering systems, and comes at almost no cost, as the required information can be directly extracted from the rendering process itself. It discards up to 80% of visibility tests on average, while reducing variance by ~20% compared to other state-of-the-art light sampling techniques with the same number of samples. It gracefully handles complex scenes with efficiency similar to Metropolis light transport techniques but with a more uniform convergence.Item Compressed Multiresolution Hierarchies for High-Quality Precomputed Shadows(The Eurographics Association and John Wiley & Sons Ltd., 2016) Scandolo, Leonardo; Bauszat, Pablo; Eisemann, Elmar; Joaquim Jorge and Ming LinThe quality of shadow mapping is traditionally limited by texture resolution. We present a novel lossless compression scheme for high-resolution shadow maps based on precomputed multiresolution hierarchies. Traditional multiresolution trees can compactly represent homogeneous regions of shadow maps at coarser levels, but require many nodes for fine details. By conservatively adapting the depth map, we can significantly reduce the tree complexity. Our proposed method offers high compression rates, avoids quantization errors, exploits coherency along all data dimensions, and is well-suited for GPU architectures. Our approach can be applied for coherent shadow maps as well, enabling several applications, including high-quality soft shadows and dynamic lights moving on fixed-trajectories.Item Texture Browser: Feature-based Texture Exploration(The Eurographics Association and John Wiley & Sons Ltd., 2021) Luo, Xuejiao; Scandolo, Leonardo; Eisemann, Elmar; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana vonTexture is a key characteristic in the definition of the physical appearance of an object and a crucial element in the creation process of 3D artists. However, retrieving a texture that matches an intended look from an image collection is difficult. Contrary to most photo collections, for which object recognition has proven quite useful, syntactic descriptions of texture characteristics is not straightforward, and even creating appropriate metadata is a very difficult task. In this paper, we propose a system to help explore large unlabeled collections of texture images. The key insight is that spatially grouping textures sharing similar features can simplify navigation. Our system uses a pre-trained convolutional neural network to extract high-level semantic image features, which are then mapped to a 2-dimensional location using an adaptation of t-SNE, a dimensionality-reduction technique. We describe an interface to visualize and explore the resulting distribution and provide a series of enhanced navigation tools, our prioritized t-SNE, scalable clustering, and multi-resolution embedding, to further facilitate exploration and retrieval tasks. Finally, we also present the results of a user evaluation that demonstrates the effectiveness of our solution.Item Interactive Indirect Illumination Using Voxel Cone Tracing(The Eurographics Association and Blackwell Publishing Ltd., 2011) Crassin, Cyril; Neyret, Fabrice; Sainz, Miguel; Green, Simon; Eisemann, Elmar; Bing-Yu Chen, Jan Kautz, Tong-Yee Lee, and Ming C. LinIndirect illumination is an important element for realistic image synthesis, but its computation is expensive and highly dependent on the complexity of the scene and of the BRDF of the involved surfaces. While off-line computation and pre-baking can be acceptable for some cases, many applications (games, simulators, etc.) require real-time or interactive approaches to evaluate indirect illumination. We present a novel algorithm to compute indirect lighting in real-time that avoids costly precomputation steps and is not restricted to low-frequency illumination. It is based on a hierarchical voxel octree representation generated and updated on the fly from a regular scene mesh coupled with an approximate voxel cone tracing that allows for a fast estimation of the visibility and incoming energy. Our approach can manage two light bounces for both Lambertian and glossy materials at interactive framerates (25-70FPS). It exhibits an almost scene-independent performance and can handle complex scenes with dynamic content thanks to an interactive octree-voxelization scheme. In addition, we demonstrate that our voxel cone tracing can be used to efficiently estimate Ambient Occlusion.Item Merged Multiresolution Hierarchies for Shadow Map Compression(The Eurographics Association and John Wiley & Sons Ltd., 2016) Scandolo, Leonardo; Bauszat, Pablo; Eisemann, Elmar; Eitan Grinspun and Bernd Bickel and Yoshinori DobashiMultiresolution Hierarchies (MH) and Directed Acyclic Graphs (DAG) are two recent approaches for the compression of highresolution shadow information. In this paper, we introduce Merged Multiresolution Hierarchies (MMH), a novel data structure that unifies both concepts. An MMH leverages both hierarchical homogeneity exploited in MHs, as well as topological similarities exploited in DAG representations. We propose an efficient hash-based technique to quickly identify and remove redundant subtree instances in a modified relative MH representation. Our solution remains lossless and significantly improves the compression rate compared to both preceding shadow map compression algorithms, while retaining the full run-time performance of traditional MH representations.Item Quad-Based Fourier Transform for Efficient Diffraction Synthesis(The Eurographics Association and John Wiley & Sons Ltd., 2018) Scandolo, Leonardo; Lee, Sungkil; Eisemann, Elmar; Jakob, Wenzel and Hachisuka, ToshiyaFar-field diffraction can be evaluated using the Discrete Fourier Transform (DFT) in image space but it is costly due to its dense sampling. We propose a technique based on a closed-form solution of the continuous Fourier transform for simple vector primitives (quads) and propose a hierarchical and progressive evaluation to achieve real-time performance. Our method is able to simulate diffraction effects in optical systems and can handle varying visibility due to dynamic light sources. Furthermore, it seamlessly extends to near-field diffraction. We show the benefit of our solution in various applications, including realistic real-time glare and bloom rendering.Item Hierarchical Stochastic Neighbor Embedding(The Eurographics Association and John Wiley & Sons Ltd., 2016) Pezzotti, Nicola; Höllt, Thomas; Lelieveldt, Boudewijn P. F.; Eisemann, Elmar; Vilanova, Anna; Kwan-Liu Ma and Giuseppe Santucci and Jarke van WijkIn recent years, dimensionality-reduction techniques have been developed and are widely used for hypothesis generation in Exploratory Data Analysis. However, these techniques are confronted with overcoming the trade-off between computation time and the quality of the provided dimensionality reduction. In this work, we address this limitation, by introducing Hierarchical Stochastic Neighbor Embedding (Hierarchical-SNE). Using a hierarchical representation of the data, we incorporate the wellknown mantra of Overview-First, Details-On-Demand in non-linear dimensionality reduction. First, the analysis shows an embedding, that reveals only the dominant structures in the data (Overview). Then, by selecting structures that are visible in the overview, the user can filter the data and drill down in the hierarchy. While the user descends into the hierarchy, detailed visualizations of the high-dimensional structures will lead to new insights. In this paper, we explain how Hierarchical-SNE scales to the analysis of big datasets. In addition, we show its application potential in the visualization of Deep-Learning architectures and the analysis of hyperspectral images.Item Spectral Gradient Sampling for Path Tracing(The Eurographics Association and John Wiley & Sons Ltd., 2018) Petitjean, Victor; Bauszat, Pablo; Eisemann, Elmar; Jakob, Wenzel and Hachisuka, ToshiyaSpectral Monte-Carlo methods are currently the most powerful techniques for simulating light transport with wavelengthdependent phenomena (e.g., dispersion, colored particle scattering, or diffraction gratings). Compared to trichromatic rendering, sampling the spectral domain requires significantly more samples for noise-free images. Inspired by gradient-domain rendering, which estimates image gradients, we propose spectral gradient sampling to estimate the gradients of the spectral distribution inside a pixel. These gradients can be sampled with a significantly lower variance by carefully correlating the path samples of a pixel in the spectral domain, and we introduce a mapping function that shifts paths with wavelength-dependent interactions. We compute the result of each pixel by integrating the estimated gradients over the spectral domain using a onedimensional screened Poisson reconstruction. Our method improves convergence and reduces chromatic noise from spectral sampling, as demonstrated by our implementation within a conventional path tracer.