Learning Generic Local Shape Properties for Adaptive Super-Sampling
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Date
2022
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
We propose a novel encoder/decoder-based neural network architecture that learns view-dependent shape and appearance of geometry represented by voxel representations. Since the network is trained on local geometry patches, it generalizes to arbitrary models. A geometry model is first encoded into a sparse voxel octree of features learned by a network, and this model representation can then be decoded by another network in-turn for the intended task. We utilize the network for adaptive supersampling in ray-tracing, to predict super-sampling patterns when seeing coarse-scale geometry. We discuss and evaluate the proposed network design, and demonstrate that the decoder network is compact and can be integrated seamlessly into on-chip ray-tracing kernels. We compare the results to previous screen-space super-sampling strategies as well as non-network-based world-space approaches.
Description
@inproceedings{10.2312:egs.20221032,
booktitle = {Eurographics 2022 - Short Papers},
editor = {Pelechano, Nuria and Vanderhaeghe, David},
title = {{Learning Generic Local Shape Properties for Adaptive Super-Sampling}},
author = {Reinbold, Christian and Westermann, Rüdiger},
year = {2022},
publisher = {The Eurographics Association},
ISSN = {1017-4656},
ISBN = {978-3-03868-169-4},
DOI = {10.2312/egs.20221032}
}