Unlike opaque object, novel view synthesis of transparent object is a challenging task, because transparent object refracts light of background causing visual distortions on the transparent object surface along the viewpoint change. Recently introduced Neural Radiance Fields (NeRF) is a view synthesis method. Thanks to its remarkable performance improvement, lots of following applications based on NeRF in various topics have been developed. However, if an object with a different refractive index is included in a scene such as transparent object, NeRF shows limited performance because refracted light ray at the surface of the transparent object is not appropriately considered. To resolve the problem, we propose a NeRF-based method consisting of the following three steps: First, we reconstruct a three-dimensional shape of a transparent object using visual hull. Second, we simulate the refraction of the rays inside of the transparent object according to Snell's law. Last, we sample points through refracted rays and put them into NeRF. Experimental evaluation results demonstrate that our method addresses the limitation of conventional NeRF with transparent objects.

翻译：与不透明物体不同，透明物体的新视角合成是一项具有挑战性的任务，因为透明物体会折射背景光线，导致观察视角变化时透明物体表面出现视觉失真。最近提出的神经辐射场（NeRF）是一种视角合成方法。由于其卓越的性能提升，基于NeRF的各类应用已在多个领域得到发展。然而，当场景中包含折射率不同的透明物体时，NeRF性能受限，原因在于其未能恰当考虑透明物体表面的折射光线。为解决这一问题，我们提出了一种基于NeRF的方法，包含以下三个步骤：首先，利用视觉外壳重建透明物体的三维形状；其次，根据斯涅耳定律模拟光线在透明物体内部的折射；最后，通过折射光线采样点并输入NeRF。实验评估结果表明，我们的方法解决了传统NeRF在处理透明物体时的局限性。