LB-NERF: Light Bending Neural Radiance Fields for Transparent Medium

Abstract

Neural radiance fields (NeRFs) have been proposed as methods of novel view synthesis and have been used to address various problems because of its versatility. NeRF can represent colors and densities in 3D space using neural rendering assuming a straight light path. However, a medium with a different refractive index in the scene, such as a transparent medium, causes light refraction and breaks the assumption of the straight path of light. Therefore, the NeRFs cannot be learned consistently across multi-view images. To solve this problem, this study proposes a method to learn consistent radiance fields across multiple viewpoints by introducing the light refraction effect as an offset from the straight line originating from the camera center. The experimental results quantitatively and qualitatively verified that our method can interpolate viewpoints better than the conventional NeRF method when considering the refraction of transparent objects.