High-Performance Graphics 2018

Permanent URI for this collection

https://diglib.eg.org/handle/10.2312/2632676
Vancouver, BC, Canada
August 10 – 12, 2018
Anti Aliasing
Adaptive Temporal Antialiasing
[full paper Image] [meta data Image]
Adam Marrs, Josef Spjut, Holger Gruen, Rahul Sathe, and Morgan McGuire
Correlation-Aware Semi-Analytic Visibility for Antialiased Rendering
[full paper Image] [meta data Image]
Cyril Crassin, Chris Wyman, Morgan McGuire, and Aaron Lefohn
Deferred Adaptive Compute Shading
[full paper Image] [meta data Image]
Ian Mallett and Cem Yuksel
Detecting Aliasing Artifacts in Image Sequences Using Deep Neural Networks
[full paper Image] [meta data Image]
Anjul Patney and Aaron Lefohn
Ray Traversal, Transparency, and GPU Computing
Brook GLES Pi: Democratising Accelerator Programming
[full paper Image] [meta data Image]
Matina Maria Trompouki, and Leonidas Kosmidis
Compressed-Leaf Bounding Volume Hierarchies
[full paper Image] [meta data Image]
Carsten Benthin, Ingo Wald, Sven Woop, and Attila T. Áfra
CPU-Style SIMD Ray Traversal on GPUs
[full paper Image] [meta data Image]
Alexander Lier, Marc Stamminger, and Kai Selgrad
Moment Transparency
[full paper Image] [meta data Image]
Brian Sharpe

BibTeX (High-Performance Graphics 2018)
@inproceedings{
10.1145:3231578.3231579,

booktitle = {
Eurographics/ ACM SIGGRAPH Symposium on High Performance Graphics},

editor = {
Patney, Anjul and Niessner, Matthias
},
title = {{
Adaptive Temporal Antialiasing}},

author = {
Marrs, Adam
 and 
Spjut, Josef
 and 
Gruen, Holger
 and 
Sathe, Rahul
 and 
McGuire, Morgan
},
year = {
2018},

publisher = {
ACM},


ISSN = {2079-8679},
ISBN = {978-1-4503-5896-5},

DOI = {
10.1145/3231578.3231579}

}
@inproceedings{
10.1145:3231578.3231584,

booktitle = {
Eurographics/ ACM SIGGRAPH Symposium on High Performance Graphics},

editor = {
Patney, Anjul and Niessner, Matthias
},
title = {{
Correlation-Aware Semi-Analytic Visibility for Antialiased Rendering}},

author = {
Crassin, Cyril
 and 
Wyman, Chris
 and 
McGuire, Morgan
 and 
Lefohn,  Aaron
},
year = {
2018},

publisher = {
ACM},


ISSN = {2079-8679},
ISBN = {978-1-4503-5896-5},

DOI = {
10.1145/3231578.3231584}

}
@inproceedings{
10.1145:3231578.3232160,

booktitle = {
Eurographics/ ACM SIGGRAPH Symposium on High Performance Graphics},

editor = {
Patney, Anjul and Niessner, Matthias
},
title = {{
Deferred Adaptive Compute Shading}},

author = {
Mallett, Ian
 and 
Yuksel, Cem
},
year = {
2018},

publisher = {
ACM},


ISSN = {2079-8679},
ISBN = {978-1-4503-5896-5},

DOI = {
10.1145/3231578.3232160}

}
@inproceedings{
10.1145:3231578.3231580,

booktitle = {
Eurographics/ ACM SIGGRAPH Symposium on High Performance Graphics},

editor = {
Patney, Anjul and Niessner, Matthias
},
title = {{
Detecting Aliasing Artifacts in Image Sequences Using Deep Neural Networks}},

author = {
Patney, Anjul
 and 
Lefohn, Aaron
},
year = {
2018},

publisher = {
ACM},


ISSN = {2079-8679},
ISBN = {978-1-4503-5896-5},

DOI = {
10.1145/3231578.3231580}

}
@inproceedings{
10.1145:3231578.3231582,

booktitle = {
Eurographics/ ACM SIGGRAPH Symposium on High Performance Graphics},

editor = {
Patney, Anjul and Niessner, Matthias
},
title = {{
Brook GLES Pi: Democratising Accelerator Programming}},

author = {
Trompouki, Matina Maria
 and 
Kosmidis, Leonidas
},
year = {
2018},

publisher = {
ACM},


ISSN = {2079-8679},
ISBN = {978-1-4503-5896-5},

DOI = {
10.1145/3231578.3231582}

}
@inproceedings{
10.1145:3231578.3231581,

booktitle = {
Eurographics/ ACM SIGGRAPH Symposium on High Performance Graphics},

editor = {
Patney, Anjul and Niessner, Matthias
},
title = {{
Compressed-Leaf Bounding Volume Hierarchies}},

author = {
Benthin, Carsten
 and 
Wald, Ingo
 and 
Woop, Sven
 and 
Áfra, Attila T.
},
year = {
2018},

publisher = {
ACM},


ISSN = {2079-8679},
ISBN = {978-1-4503-5896-5},

DOI = {
10.1145/3231578.3231581}

}
@inproceedings{
10.1145:3231578.3231583,

booktitle = {
Eurographics/ ACM SIGGRAPH Symposium on High Performance Graphics},

editor = {
Patney, Anjul and Niessner, Matthias
},
title = {{
CPU-Style SIMD Ray Traversal on GPUs}},

author = {
Lier, Alexander
 and 
Stamminger, Marc
 and 
Selgrad, Kai
},
year = {
2018},

publisher = {
ACM},


ISSN = {2079-8679},
ISBN = {978-1-4503-5896-5},

DOI = {
10.1145/3231578.3231583}

}
@inproceedings{
10.1145:3231578.3231585,

booktitle = {
Eurographics/ ACM SIGGRAPH Symposium on High Performance Graphics},

editor = {
Patney, Anjul and Niessner, Matthias
},
title = {{
Moment Transparency}},

author = {
Sharpe, Brian
},
year = {
2018},

publisher = {
ACM},


ISSN = {2079-8679},
ISBN = {978-1-4503-5896-5},

DOI = {
10.1145/3231578.3231585}

}

Browse

Recent Submissions

Now showing 1 - 8 of 8
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    Adaptive Temporal Antialiasing
    (ACM, 2018) Marrs, Adam; Spjut, Josef; Gruen, Holger; Sathe, Rahul; McGuire, Morgan; Patney, Anjul and Niessner, Matthias
    We introduce a pragmatic algorithm for real-time adaptive supersampling in games. It extends temporal antialiasing of rasterized images with adaptive ray tracing, and conforms to the constraints of a commercial game engine and today's GPU ray tracing APIs. The algorithm removes blurring and ghosting artifacts associated with standard temporal antialiasing and achieves quality approaching 8× supersampling of geometry, shading, and materials while staying within the 33ms frame budget required of most games.
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    Item
    Correlation-Aware Semi-Analytic Visibility for Antialiased Rendering
    (ACM, 2018) Crassin, Cyril; Wyman, Chris; McGuire, Morgan; Lefohn, Aaron; Patney, Anjul and Niessner, Matthias
    Geometric aliasing is a persistent challenge for real-time rendering. Hardware multisampling remains limited to 8×, analytic coverage fails to capture correlated visibility samples, and spatial and temporal postfiltering primarily target edges of superpixel primitives. We describe a novel semi-analytic representation of coverage designed to make progress on geometric antialiasing for subpixel primitives and pixels containing many edges while handling correlated subpixel coverage. Although not yet fast enough to deploy, it crosses three critical thresholds: image quality comparable to 256× MSAA, faster than 64× MSAA, and constant space per pixel.
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    Deferred Adaptive Compute Shading
    (ACM, 2018) Mallett, Ian; Yuksel, Cem; Patney, Anjul and Niessner, Matthias
    A primary advantage of deferred shading is eliminating wasted shading operations due to overdraw. We present a new algorithm that we call Deferred Adaptive Compute Shading, for providing further reduction in shading computations. Our method hierarchically shades the image while reducing the number of required shading operations to below one shading computation per pixel on average. We determine whether to shade a pixel or approximate it using previously shaded pixels around it, based on an estimate of the image variance at the pixel location. The algorithm is designed to dynamically reconfigure itself to achieve optimal warp coherence and measurable performance gain. We extensively evaluate our algorithm, demonstrating that it produces high-quality results and is robust and highly scalable while providing significant performance improvements in complex scenes.
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    Detecting Aliasing Artifacts in Image Sequences Using Deep Neural Networks
    (ACM, 2018) Patney, Anjul; Lefohn, Aaron; Patney, Anjul and Niessner, Matthias
    In this short paper we present a machine learning approach to detect visual artifacts in rendered image sequences. Specifically, we train a deep neural network using example aliased and antialiased image sequences exported from a real-time renderer. The trained network learns to identify and locate aliasing artifacts in an input sequence, without comparing it against a ground truth. Thus, it is useful as a fully automated tool for evaluating image quality. We demonstrate the effectiveness of our approach in detecting aliasing in several rendered sequences. The trained network correctly predicts aliasing in 64×64×4 animated sequences with more than 90% accuracy for images it hasn't seen before. The output of our network is a single scalar between 0 and 1, which is usable as a quality metric for aliasing. It follows the same trend as (1-SSIM) for images with increasing sample counts.
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    Brook GLES Pi: Democratising Accelerator Programming
    (ACM, 2018) Trompouki, Matina Maria; Kosmidis, Leonidas; Patney, Anjul and Niessner, Matthias
    Nowadays computing is heavily-based on accelerators, however, the cost of the hardware equipment prevents equal access to heterogeneous programming. In this work we present Brook GLES Pi, a port of the accelerator programming language Brook. Our solution, primarily focused on the educational platform Raspberry Pi, allows to teach, experiment and take advantage of heterogeneous programming on any low-cost embedded device featuring an OpenGL ES 2 GPU, democratising access to accelerator programming.
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    Compressed-Leaf Bounding Volume Hierarchies
    (ACM, 2018) Benthin, Carsten; Wald, Ingo; Woop, Sven; Áfra, Attila T.; Patney, Anjul and Niessner, Matthias
    We propose and evaluate what we call Compressed-Leaf Bounding Volume Hierarchies (CLBVH), which strike a balance between compressed and non-compressed BVH layouts. Our CLBVH layout introduces dedicated compressed multi-leaf nodes where most effective at reducing memory use, and uses regular BVH nodes for inner nodes and small, isolated leaves. We show that when implemented within the Embree ray tracing framework, this approach achieves roughly the same memory savings as Embree's compressed BVH layout, while maintaining almost the full performance of its fastest non-compressed BVH.
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    CPU-Style SIMD Ray Traversal on GPUs
    (ACM, 2018) Lier, Alexander; Stamminger, Marc; Selgrad, Kai; Patney, Anjul and Niessner, Matthias
    In this paper we describe and evaluate an implementation of CPUstyle SIMD ray traversal on the GPU. We show how spreading moderately wide BVHs (up to a branching factor of eight) across multiple threads in a warp can improve performance while not requiring expensive pre-processing. e presented ray-traversal method exhibits improved traversal performance especially for increasingly incoherent rays.
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    Moment Transparency
    (ACM, 2018) Sharpe, Brian; Patney, Anjul and Niessner, Matthias
    We introduce moment transparency, a new solution to real-time order-independent transparency. It expands upon existing approximate transmittance function techniques by using moments to capture and reconstruct the transmittance function. Because the momentbased transmittance function can be processed analytically using standard hardware blend operations, it is efficient and overcomes limitations of previous techniques.