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Existing neural reconstruction schemes such as Neural Radiance Field (NeRF)are largely focused on modeling opaque objects. We present a novel neuralrefractive field(NeReF) to recover wavefront of transparent fluids bysimultaneously estimating the surface position and normal of the fluid front.Unlike prior arts that treat the reconstruction target as a single layer of thesurface, NeReF is specifically formulated to recover a volumetric normal fieldwith its corresponding density field. A query ray will be refracted by NeReFaccording to its accumulated refractive point and normal, and we employ thecorrespondences and uniqueness of refracted ray for NeReF optimization. We showNeReF, as a global optimization scheme, can more robustly tackle refractiondistortions detrimental to traditional methods for correspondence matching.Furthermore, the continuous NeReF representation of wavefront enables viewsynthesis as well as normal integration. We validate our approach on bothsynthetic and real data and show it is particularly suitable for sparsemulti-view acquisition. We hence build a small light field array and experimenton various surface shapes to demonstrate high fidelity NeReF reconstruction.

NeReF: Neural Refractive Field for Fluid Surface Reconstruction and Implicit Representation