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Point-based rendering enhancement via deep learning

Point-based rendering enhancement via deep learning Current state-of-the-art point rendering techniques such as splat rendering generally require very high-resolution point clouds in order to create high-quality photo realistic renderings. These can be very time consuming to acquire and oftentimes also require high-end expensive scanners. This paper proposes a novel deep learning-based approach that can generate high-resolution photo realistic point renderings from low-resolution point clouds. More specifically, we propose to use co-registered high-quality photographs as the ground truth data to train the deep neural network for point-based rendering. The proposed method can generate high-quality point rendering images very efficiently and can be used for interactive navigation of large-scale 3D scenes as well as image-based localization. Extensive quantitative evaluations on both synthetic and real datasets show that the proposed method outperforms state-of-the-art methods. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The Visual Computer Springer Journals

Point-based rendering enhancement via deep learning

The Visual Computer , Volume 34 (8) – May 11, 2018

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References (14)

Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer-Verlag GmbH Germany, part of Springer Nature
Subject
Computer Science; Computer Graphics; Computer Science, general; Artificial Intelligence (incl. Robotics); Image Processing and Computer Vision
ISSN
0178-2789
eISSN
1432-2315
DOI
10.1007/s00371-018-1550-6
Publisher site
See Article on Publisher Site

Abstract

Current state-of-the-art point rendering techniques such as splat rendering generally require very high-resolution point clouds in order to create high-quality photo realistic renderings. These can be very time consuming to acquire and oftentimes also require high-end expensive scanners. This paper proposes a novel deep learning-based approach that can generate high-resolution photo realistic point renderings from low-resolution point clouds. More specifically, we propose to use co-registered high-quality photographs as the ground truth data to train the deep neural network for point-based rendering. The proposed method can generate high-quality point rendering images very efficiently and can be used for interactive navigation of large-scale 3D scenes as well as image-based localization. Extensive quantitative evaluations on both synthetic and real datasets show that the proposed method outperforms state-of-the-art methods.

Journal

The Visual ComputerSpringer Journals

Published: May 11, 2018

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