REF$^2$-NeRF: Reflection and Refraction aware Neural Radiance Field
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arXiv
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| Format: | Preprint |
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2023
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| _version_ | 1866917643049500672 |
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| author | Kim, Wooseok Fukiage, Taiki Oishi, Takeshi |
| author_facet | Kim, Wooseok Fukiage, Taiki Oishi, Takeshi |
| contents | Recently, significant progress has been made in the study of methods for 3D reconstruction from multiple images using implicit neural representations, exemplified by the neural radiance field (NeRF) method. Such methods, which are based on volume rendering, can model various light phenomena, and various extended methods have been proposed to accommodate different scenes and situations. However, when handling scenes with multiple glass objects, e.g., objects in a glass showcase, modeling the target scene accurately has been challenging due to the presence of multiple reflection and refraction effects. Thus, this paper proposes a NeRF-based modeling method for scenes containing a glass case. In the proposed method, refraction and reflection are modeled using elements that are dependent and independent of the viewer's perspective. This approach allows us to estimate the surfaces where refraction occurs, i.e., glass surfaces, and enables the separation and modeling of both direct and reflected light components. The proposed method requires predetermined camera poses, but accurately estimating these poses in scenes with glass objects is difficult. Therefore, we used a robotic arm with an attached camera to acquire images with known poses. Compared to existing methods, the proposed method enables more accurate modeling of both glass refraction and the overall scene. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_17116 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | REF$^2$-NeRF: Reflection and Refraction aware Neural Radiance Field Kim, Wooseok Fukiage, Taiki Oishi, Takeshi Computer Vision and Pattern Recognition Recently, significant progress has been made in the study of methods for 3D reconstruction from multiple images using implicit neural representations, exemplified by the neural radiance field (NeRF) method. Such methods, which are based on volume rendering, can model various light phenomena, and various extended methods have been proposed to accommodate different scenes and situations. However, when handling scenes with multiple glass objects, e.g., objects in a glass showcase, modeling the target scene accurately has been challenging due to the presence of multiple reflection and refraction effects. Thus, this paper proposes a NeRF-based modeling method for scenes containing a glass case. In the proposed method, refraction and reflection are modeled using elements that are dependent and independent of the viewer's perspective. This approach allows us to estimate the surfaces where refraction occurs, i.e., glass surfaces, and enables the separation and modeling of both direct and reflected light components. The proposed method requires predetermined camera poses, but accurately estimating these poses in scenes with glass objects is difficult. Therefore, we used a robotic arm with an attached camera to acquire images with known poses. Compared to existing methods, the proposed method enables more accurate modeling of both glass refraction and the overall scene. |
| title | REF$^2$-NeRF: Reflection and Refraction aware Neural Radiance Field |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2311.17116 |