  Recently, significant progress has been made in the study of methods for 3D
reconstruction from multiple images using implicit neural representations,
exemplified by the neural radiance field (NeRF) method. Such methods, which are
based on volume rendering, can model various light phenomena, and various
extended methods have been proposed to accommodate different scenes and
situations. However, when handling scenes with multiple glass objects, e.g.,
objects in a glass showcase, modeling the target scene accurately has been
challenging due to the presence of multiple reflection and refraction effects.
Thus, this paper proposes a NeRF-based modeling method for scenes containing a
glass case. In the proposed method, refraction and reflection are modeled using
elements that are dependent and independent of the viewer's perspective. This
approach allows us to estimate the surfaces where refraction occurs, i.e.,
glass surfaces, and enables the separation and modeling of both direct and
reflected light components. The proposed method requires predetermined camera
poses, but accurately estimating these poses in scenes with glass objects is
difficult. Therefore, we used a robotic arm with an attached camera to acquire
images with known poses. Compared to existing methods, the proposed method
enables more accurate modeling of both glass refraction and the overall scene.
