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Fast Sun-aligned Outdoor Scene Relighting based on TensoRF

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Authors

Chang, Yeonjin; Kim, Yearim; Seo, Seunghyeon; Yi, Jung; Kwak, Nojun

Issue Date
2024-01
Publisher
Institute of Electrical and Electronics Engineers Inc.
Citation
Proceedings - 2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024, pp.3614-3624
Abstract
In this work, we introduce our method of outdoor scene relighting for Neural Radiance Fields (NeRF) named Sun-aligned Relighting TensoRF (SR-TensoRF). SR-TensoRF offers a lightweight and rapid pipeline aligned with the sun, thereby achieving a simplified workflow that eliminates the need for environment maps. Our sun-alignment strategy is motivated by the insight that shadows, unlike viewpoint-dependent albedo, are determined by light direction. We directly use the sun direction as an input during shadow generation, simplifying the requirements of the inference process significantly. Moreover, SR-TensoRF leverages the training efficiency of TensoRF by incorporating our proposed cubemap concept, resulting in notable acceleration in both training and rendering processes compared to existing methods.
URI
https://hdl.handle.net/10371/205127
DOI
https://doi.org/10.1109/WACV57701.2024.00359
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  • Graduate School of Convergence Science & Technology
  • Department of Intelligence and Information
Research Area Feature Selection and Extraction, Object Detection, Object Recognition

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