ARC-NeRF: Area Ray Casting for Broader Unseen View Coverage in Few-shot Object Rendering
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866912312101699584 |
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| author | Seo, Seunghyeon Chang, Yeonjin Yoo, Jayeon Lee, Seungwoo Lee, Hojun Kwak, Nojun |
| author_facet | Seo, Seunghyeon Chang, Yeonjin Yoo, Jayeon Lee, Seungwoo Lee, Hojun Kwak, Nojun |
| contents | Recent advancements in the Neural Radiance Field (NeRF) have enhanced its capabilities for novel view synthesis, yet its reliance on dense multi-view training images poses a practical challenge, often leading to artifacts and a lack of fine object details. Addressing this, we propose ARC-NeRF, an effective regularization-based approach with a novel Area Ray Casting strategy. While the previous ray augmentation methods are limited to covering only a single unseen view per extra ray, our proposed Area Ray covers a broader range of unseen views with just a single ray and enables an adaptive high-frequency regularization based on target pixel photo-consistency. Moreover, we propose luminance consistency regularization, which enhances the consistency of relative luminance between the original and Area Ray, leading to more accurate object textures. The relative luminance, as a free lunch extra data easily derived from RGB images, can be effectively utilized in few-shot scenarios where available training data is limited. Our ARC-NeRF outperforms its baseline and achieves competitive results on multiple benchmarks with sharply rendered fine details. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_10906 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ARC-NeRF: Area Ray Casting for Broader Unseen View Coverage in Few-shot Object Rendering Seo, Seunghyeon Chang, Yeonjin Yoo, Jayeon Lee, Seungwoo Lee, Hojun Kwak, Nojun Computer Vision and Pattern Recognition Recent advancements in the Neural Radiance Field (NeRF) have enhanced its capabilities for novel view synthesis, yet its reliance on dense multi-view training images poses a practical challenge, often leading to artifacts and a lack of fine object details. Addressing this, we propose ARC-NeRF, an effective regularization-based approach with a novel Area Ray Casting strategy. While the previous ray augmentation methods are limited to covering only a single unseen view per extra ray, our proposed Area Ray covers a broader range of unseen views with just a single ray and enables an adaptive high-frequency regularization based on target pixel photo-consistency. Moreover, we propose luminance consistency regularization, which enhances the consistency of relative luminance between the original and Area Ray, leading to more accurate object textures. The relative luminance, as a free lunch extra data easily derived from RGB images, can be effectively utilized in few-shot scenarios where available training data is limited. Our ARC-NeRF outperforms its baseline and achieves competitive results on multiple benchmarks with sharply rendered fine details. |
| title | ARC-NeRF: Area Ray Casting for Broader Unseen View Coverage in Few-shot Object Rendering |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.10906 |