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.

本文主要介绍了一种基于神经辐射场(NeRF)的框架，用于优化被限定的带折射物体边界体积的辐射场，以构建折射物体的新视图综合。作者使用分层和分级抽样技术，并使用Eikonal方程跟踪沿曲线路径绘制样本，演示了本框架在多个场景中均比最先进的方法具有更好的感知相似性和渲染质量显着提高。

为折射物采样神经辐射场