Sampling Neural Radiance Fields for Refractive Objects

• URL: http://arxiv.org/abs/2211.14799v1
• Date: Sun, 27 Nov 2022 11:43:21 GMT
• Title: Sampling Neural Radiance Fields for Refractive Objects
• Authors: Jen-I Pan, Jheng-Wei Su, Kai-Wen Hsiao, Ting-Yu Yen, Hung-Kuo Chu
• Abstract summary: In this work, the scene is instead a heterogeneous volume with a piecewise-constant refractive index, where the path will be curved if it intersects the different refractive indices. For novel view synthesis of refractive objects, our NeRF-based framework aims to optimize the radiance fields of bounded volume and boundary from multi-view posed images with refractive object silhouettes. Given the refractive index, we extend the stratified and hierarchical sampling techniques in NeRF to allow drawing samples along a curved path tracked by the Eikonal equation.
• Score: 8.539183778516795
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recently, differentiable volume rendering in neural radiance fields (NeRF) has gained a lot of popularity, and its variants have attained many impressive results. However, existing methods usually assume the scene is a homogeneous volume so that a ray is cast along the straight path. In this work, the scene is instead a heterogeneous volume with a piecewise-constant refractive index, where the path will be curved if it intersects the different refractive indices. For novel view synthesis of refractive objects, our NeRF-based framework aims to optimize the radiance fields of bounded volume and boundary from multi-view posed images with refractive object silhouettes. To tackle this challenging problem, the refractive index of a scene is reconstructed from silhouettes. Given the refractive index, we extend the stratified and hierarchical sampling techniques in NeRF to allow drawing samples along a curved path tracked by the Eikonal equation. The results indicate that our framework outperforms the state-of-the-art method both quantitatively and qualitatively, demonstrating better performance on the perceptual similarity metric and an apparent improvement in the rendering quality on several synthetic and real scenes.

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