High-Performance Graphics 2015
https://dlold.eg.org:443/handle/10.2312/14666
ISBN 978-1-4503-3707-62024-09-07T20:08:46ZMorton Integrals for High Speed Geometry Simplification
https://dlold.eg.org:443/handle/10.2312/14678
Morton Integrals for High Speed Geometry Simplification
Legrand, Hélène; Boubekeur, Tamy
Petrik Clarberg and Elmar Eisemann
Real time geometry processing has progressively reached a performance level that makes a number of signal-inspired primitives practical for on-line applications scenarios. This often comes through the joint design of operators, data structure and even dedicated hardware. Among the major classes of geometric operators, filtering and super-sampling (via tessellation) have been successfully expressed under high-performance constraints. The subsampling operator i.e., adaptive simplification, remains however a challenging case for non-trivial input models. In this paper, we build a fast geometry simplification algorithm over a new concept : Morton Integrals. By summing up quadric error metric matrices along Morton-ordered surface samples, we can extract concurrently the nodes of an adaptive cut in the so-defined implicit hierarchy, and optimize all simplified vertices in parallel. This approach is inspired by integral images and exploits recent advances in high performance spatial hierarchy construction and traversal. As a result, our GPU implementation can downsample a mesh made of several millions of polygons at interactive rates, while providing better quality than uniform simplification and preserving important salient features. We present results for surface meshes, polygon soups and point clouds, and discuss variations of our approach to account for per-sample attributes and alternatives error metrics.
2015-01-01T00:00:00ZGrid-Free Out-Of-Core Voxelization to Sparse Voxel Octrees on GPU
https://dlold.eg.org:443/handle/10.2312/14677
Grid-Free Out-Of-Core Voxelization to Sparse Voxel Octrees on GPU
Pätzold, Martin; Kolb, Andreas
Petrik Clarberg and Elmar Eisemann
In this paper, we present the first grid-free, out-of-core GPU voxelization method. Our method combines efficient parallel triangle voxelization on GPU with out-of-core technologies in order to allow the processing of scenes with large triangle counts at a high resolution. We directly generate the voxelized data in a sparse voxel octree (SVO) representation, without any intermediate grid structure (''grid-free''). We apply triangle preprocessing and avoid atomic operations, thus leading to an optimized balanced GPU workload and efficient parallel triangle processing. Compared to existing out-of-core CPU approaches, we manage a proper handling of voxel attributes, i.e. all triangle attributes contributing to a voxel are accessible when calculating the voxel attribute. We test and compare our approach to state-of-the-art methods and demonstrate its viability in terms of speed, input triangle count, resolution and output quality.
2015-01-01T00:00:00ZCompiling High Performance Recursive Filters
https://dlold.eg.org:443/handle/10.2312/14676
Compiling High Performance Recursive Filters
Chaurasia, Gaurav; Ragan-Kelley, Jonathan; Paris, Sylvain; Drettakis, George; Durand, Frédo
Petrik Clarberg and Elmar Eisemann
Infinite impulse response (IIR) or recursive filters, are essential for image processing because they turn expensive large-footprint convolutions into operations that have a constant cost per pixel regardless of kernel size. However, their recursive nature constrains the order in which pixels can be computed, severely limiting both parallelism within a filter and memory locality across multiple filters. Prior research has developed algorithms that can compute IIR filters with image tiles. Using a divide-and-recombine strategy inspired by parallel prefix sum, they expose greater parallelism and exploit producer-consumer locality in pipelines of IIR filters over multidimensional images. While the principles are simple, it is hard, given a recursive filter, to derive a corresponding tile-parallel algorithm, and even harder to implement and debug it. We show that parallel and locality-aware implementations of IIR filter pipelines can be obtained through program transformations, which we mechanize through a domain-specific compiler. We show that the composition of a small set of transformations suffices to cover the space of possible strategies. We also demonstrate that the tiled implementations can be automatically scheduled in hardwarespecific manners using a small set of generic heuristics. The programmer specifies the basic recursive filters, and the choice of transformation requires only a few lines of code. Our compiler then generates high-performance implementations that are an order of magnitude faster than standard GPU implementations, and outperform hand tuned tiled implementations of specialized algorithms which require orders of magnitude more programming effort-a few lines of code instead of a few thousand lines per pipeline.
2015-01-01T00:00:00ZAdaptively Layered Statistical Volumetric Obscurance
https://dlold.eg.org:443/handle/10.2312/14675
Adaptively Layered Statistical Volumetric Obscurance
Hendrick, Quintjin; Scandolo, Leonardo; Eisemann, Martin; Eisemann, Elmar
Petrik Clarberg and Elmar Eisemann
We accelerate volumetric obscurance, a variant of ambient occlusion, and solve undersampling artifacts, such as banding, noise or blurring, that screen-space techniques traditionally suffer from. We make use of an efficient statistical model to evaluate the occlusion factor in screen-space using a single sample. Overestimations and halos are reduced by an adaptive layering of the visible geometry. Bias at tilted surfaces is avoided by projecting and evaluating the volumetric obscurance in tangent space of each surface point. We compare our approach to several traditional screen-space ambient obscurance techniques and show its competitive qualitative and quantitative performance. Our algorithm maps well to graphics hardware, does not require the traditional bilateral blur step of previous approaches, and avoids typical screen-space related artifacts such as temporal instability due to undersampling.
2015-01-01T00:00:00Z