research-article

Ultra-High Resolution SVBRDF Recovery from a Single Image

Authors:

Yanwen GuoAuthors Info & Claims

ACM Transactions on Graphics, Volume 42, Issue 3

Article No.: 33, Pages 1 - 14

https://doi.org/10.1145/3593798

Published: 05 June 2023 Publication History

Abstract

Existing convolutional neural networks have achieved great success in recovering Spatially Varying Bidirectional Surface Reflectance Distribution Function (SVBRDF) maps from a single image. However, they mainly focus on handling low-resolution (e.g., 256 × 256) inputs. Ultra-High Resolution (UHR) material maps are notoriously difficult to acquire by existing networks because (1) finite computational resources set bounds for input receptive fields and output resolutions, and (2) convolutional layers operate locally and lack the ability to capture long-range structural dependencies in UHR images. We propose an implicit neural reflectance model and a divide-and-conquer solution to address these two challenges simultaneously. We first crop a UHR image into low-resolution patches, each of which are processed by a local feature extractor to extract important details. To fully exploit long-range spatial dependency and ensure global coherency, we incorporate a global feature extractor and several coordinate-aware feature assembly modules into our pipeline. The global feature extractor contains several lightweight material vision transformers that have a global receptive field at each scale and have the ability to infer long-term relationships in the material. After decoding globally coherent feature maps assembled by coordinate-aware feature assembly modules, the proposed end-to-end method is able to generate UHR SVBRDF maps from a single image with fine spatial details and consistent global structures.

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Cited By

Vecchio GMartin RRoullier AKaiser ARouffet RDeschaintre VBoubekeur T(2024)ControlMat: A Controlled Generative Approach to Material CaptureACM Transactions on Graphics10.1145/368883043:5(1-17)Online publication date: 27-Aug-2024
https://dl.acm.org/doi/10.1145/3688830
Sinha SGorschlueter FGraf HWeinmann M(2024)DeepMaterialInsights: A Web-based Framework Harnessing Deep Learning for Estimation, Visualization, and Export of Material Assets from ImagesProceedings of the 29th International ACM Conference on 3D Web Technology10.1145/3665318.3677152(1-5)Online publication date: 25-Sep-2024
https://dl.acm.org/doi/10.1145/3665318.3677152
Mao QLiu QLi ZWu LLv BZhang ZHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Cross-reconstructed Augmentation for Dual-target Cross-domain RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657902(2352-2356)Online publication date: 10-Jul-2024
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Index Terms

Ultra-High Resolution SVBRDF Recovery from a Single Image
1. Computing methodologies
  1. Computer graphics
    1. Rendering
      1. Reflectance modeling

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cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 42, Issue 3

June 2023

181 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/3579817

Editor:
Carol O'Sullivan
Trinity College Dublin, Ireland

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Publication History

Published: 05 June 2023

Online AM: 27 April 2023

Revised: 21 February 2023

Accepted: 23 January 2023

Received: 13 October 2022

Published in TOG Volume 42, Issue 3

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Cited By

Vecchio GMartin RRoullier AKaiser ARouffet RDeschaintre VBoubekeur T(2024)ControlMat: A Controlled Generative Approach to Material CaptureACM Transactions on Graphics10.1145/368883043:5(1-17)Online publication date: 27-Aug-2024
https://dl.acm.org/doi/10.1145/3688830
Sinha SGorschlueter FGraf HWeinmann M(2024)DeepMaterialInsights: A Web-based Framework Harnessing Deep Learning for Estimation, Visualization, and Export of Material Assets from ImagesProceedings of the 29th International ACM Conference on 3D Web Technology10.1145/3665318.3677152(1-5)Online publication date: 25-Sep-2024
https://dl.acm.org/doi/10.1145/3665318.3677152
Mao QLiu QLi ZWu LLv BZhang ZHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Cross-reconstructed Augmentation for Dual-target Cross-domain RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657902(2352-2356)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657902
Kavoosighafi BHajisharif SMiandji EBaravdish GCao WUnger J(2024)Deep SVBRDF Acquisition and Modelling: A SurveyComputer Graphics Forum10.1111/cgf.1519943:6Online publication date: 16-Sep-2024
https://doi.org/10.1111/cgf.15199
Vecchio GDeschaintre V(2024)MatSynth: A Modern PBR Materials Dataset2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.02087(22109-22118)Online publication date: 16-Jun-2024
https://doi.org/10.1109/CVPR52733.2024.02087
Chen ZGuo JLai SFu RKong MWang CSun HZhang ZLi CGuo Y(2024)Practical Measurements of Translucent Materials with Inter-Pixel Translucency Prior2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.01978(20932-20942)Online publication date: 16-Jun-2024
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Wang YDu BWang WXu C(2024)Multi-tailed vision transformer for efficient inferenceNeural Networks10.1016/j.neunet.2024.106235174:COnline publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1016/j.neunet.2024.106235
Zhu PLai SChen MGuo JLiu YGuo Y(2023)SVBRDF Reconstruction by Transferring Lighting KnowledgeComputer Graphics Forum10.1111/cgf.1497342:7Online publication date: 5-Nov-2023
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