Eurographics Digital Library

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Recent Submissions

Visual Insights into Memory Behavior of GPU Ray Tracers
(TUprints, 2024-07) von Buelow, Max
Ray tracing is a fundamental rendering technique that typically projects three-dimensional representations of a scene onto a two-dimensional display. This is achieved by perspectively sampling a set of rays into the scene and computing intersections against the relevant geometry. Secondary rays may be sent out from these intersection points, allowing for physically correct global illumination on the reverse photon direction. Real-time rendering has historically used classical rasterization pipelines, which are straightforward to implement on hardware as they form a data-parallel problem projecting the whole scene into the coordinate system of the image. In contrast, task-parallel ray tracing suffers from incoherency between rays. However, recent advances in ray tracing have led to more efficient approaches, resulting in even more efficient embedded hardware implementations. While these approaches are already capable of rendering realistic images, further improvements in run-time performance can compensate for computational time to achieve higher framerates, display resolutions, ray-tracing recursion depths, or reducing the energy footprint of ray-tracing data centers. A fundamental technique for improving ray-tracing performance is the use of bounding-volume hierarchies (BVH), which prevent rays from intersecting the entire scene, especially in occluded or distant regions. In addition to the structural efficiency of a BVH, the primary bottlenecks of GPU ray tracing are memory latency and work distribution. These factors mainly result in more coherent memory accesses, making caching more efficient. Creating programs with the goal of achieving higher caching rates typically requires increased programming efforts and a deep understanding of the hardware, as an additional abstraction layer is introduced, making the memory pipeline less transparent. General-purpose profilers aim to support the implementation process. However, they typically display caching rates based on kernel calls. This is because these values are measured using basic hardware counters that do not distinguish between the context of a memory access. In many cases, it would be useful to have a more detailed representation of memory-related profiling metrics, such as the number of recordings per memory allocation or projections into other domains, such as the framebuffer or the scene geometry. This thesis presents a new method for simulating the GPU memory pipeline accurately. The method uses memory traces exported by dynamic binary instrumentation, which can be applied to any compiled GPU binaries, similar to standard profilers. The exported memory profiles can be used for performance visualization purposes in individual domains, as well as traditional memory profiling metrics that can be displayed in finer granularity than usual. A method for mapping memory metrics onto the original scene is included, allowing users to explore profiling results within the scene domain, making the profiling process more intuitive. In addition, this thesis presents a novel compressed ray-tracing implementation that optimizes its memory footprint by making assumptions about the topological properties of the scene to be rendered. The findings can be used to evaluate and optimize a wide range of ray tracing and ray marching applications in a user-friendly manner.
Improving the efficiency of point cloud data management
(TUprints, 2024-07) Bormann, Pascal
The collection of point cloud data has increased drastically in recent years, which poses challenges for the data management layer. Multi-billion point datasets are commonplace and users are getting accustomed to real-time data exploration in the Web. To make this possible, existing point cloud data management approaches rely on optimized data formats which are time- and resource-intensive to generate. This introduces long wait times before data can be used and frequent data duplication, since these optimized formats are often domain- or application-specific. As a result, data management is a challenging and expensive aspect when developing applications that use point cloud data. We observe that the interaction between applications and the point cloud data management layer can be modeled as a series of queries similar to those found in traditional databases. Based on this observation, we evaluate current point cloud data management using three query metrics: Responsiveness, throughput, and expressiveness. We contribute to the current state of the art by improving these metrics for both the handling of raw files without preprocessing, as well as indexed point clouds. In the domain of unindexed point cloud data, we introduce the concept of ad-hoc queries, which are queries executed ad-hoc on raw point cloud files. We demonstrate that ad-hoc queries can improve query responsiveness significantly as they do not require long wait times for indexing or database imports. Using columnar memory layouts, queries on datasets of up to a billion points can be answered in interactive or near-interactive time, with throughputs of more than one hundred million points per second on unindexed data. A demonstration of an adaptive indexing method shows that spending a few seconds per query on index creation can improve responsiveness by up to an order of magnitude. Our experiments also confirm the importance of high-throughput systems when querying point cloud data, as the overhead of data transmission has a significant effect on the overall query performance. For situations where indexing is mandatory, we demonstrate improvements to the runtime performance of existing point cloud indexing tools. We developed a fast indexer based on task-parallel programming, using Morton indices to efficiently sort and distribute point batches onto worker threads. This system, called Schwarzwald, outperformed existing indexers by up to a factor 9 when it was first published, and still has competitive performance to current out-of-core capable indexers. Additionally we adapted our indexing algorithm for distributed processing in a Cloud-environment and demonstrate that its horizontal scalability allows it to outperform all existing indexers by up to a factor of 3. Lastly we demonstrated point cloud indexing in real-time during Light Detection And Ranging (LiDAR) capturing, based on a similar task-based algorithm but optimized for progressive indexing. Our real-time indexer is able to keep up with current LiDAR sensors in a real-world test, with end-to-end latencies as low as 0.1 seconds. Together, our improvements significantly reduce wait times for working with point cloud data and increase the overall efficiency of the data access layer.
Ray Traced Stochastic Depth Map for Ambient Occlusion
(The Eurographics Association, 2024) Brüll, Felix; Kern, René; Grosch, Thorsten; Haines, Eric; Garces, Elena
Screen-space ambient occlusion is a popular technique for approximating global illumination in real-time rendering. However, it suffers from artifacts due to the lack of information from the depth buffer. A stochastic depth map [VSE21] can be used to retrieve most of the missing information, but it is not suitable for real-time rendering in large scenes. In this paper, we propose a new stochastic depth map acquisition method powered by hardware ray tracing, which shows better performance characteristics than the previous method. We present further improvements that increase the quality and performance of the stochastic depth map generation. Furthermore, the results are almost indistinguishable from a ground truth solution with all depth samples.
Learning Self-Shadowing for Clothed Human Bodies
(The Eurographics Association, 2024) Einabadi, Farshad; Guillemaut, Jean-Yves; Hilton, Adrian; Haines, Eric; Garces, Elena
This paper proposes to learn self-shadowing on full-body, clothed human postures from monocular colour image input, by supervising a deep neural model. The proposed approach implicitly learns the articulated body shape in order to generate self-shadow maps without seeking to reconstruct explicitly or estimate parametric 3D body geometry. Furthermore, it is generalisable to different people without per-subject pre-training, and has fast inference timings. The proposed neural model is trained on self-shadow maps rendered from 3D scans of real people for various light directions. Inference of shadow maps for a given illumination is performed from only 2D image input. Quantitative and qualitative experiments demonstrate comparable results to the state of the art whilst being monocular and achieving a considerably faster inference time. We provide ablations of our methodology and further show how the inferred self-shadow maps can benefit monocular full-body human relighting.
Real-Time Pixel-Perfect Hard Shadows with Leak Tracing
(The Eurographics Association, 2024) Kern, René; Brüll, Felix; Grosch, Thorsten; Haines, Eric; Garces, Elena
Accurate shadows greatly enhance the realism of a rendered image. Shadow mapping is the preferred solution for shadows in real-time applications. However, shadow maps suffer from discretization errors and self-shadowing artifacts, that need custom parameter tuning per scene. Filterable shadow maps such as variance or moment shadow maps solve both issues but introduce light leaking. With the advent of hardware ray tracing, it becomes more realistic to use shadow rays instead of a shadow map. However, distributing a shadow ray is often more expensive than evaluating a shadow map, especially if the ray hits alphatested geometry. We introduce leak tracing, where we use filterable shadow maps techniques on top of default shadow maps and eliminate the light leaks and aliased shadow edges with selective ray tracing. Our algorithm does not need any scene-dependent parameters. We achieve an average speedup ranging from 1.19 to 1.79, with a top speedup of 4.17, depending on the scene and eliminate major performance drops caused by alpha-tested geometry during ray tracing. Our solution is temporally stable and reaches similar quality as pure ray tracing.