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.

翻译：近年来，基于隐式神经表示的多视图三维重建方法（如神经辐射场NeRF）取得了显著进展。这类基于体渲染的方法能够建模多种光学现象，并已衍生出适应不同场景与情境的扩展方法。然而，在处理包含多个玻璃物体（如玻璃展柜中的物体）的场景时，由于多重反射与折射效应的存在，精确建模目标场景仍具挑战性。为此，本文提出一种基于NeRF的玻璃展柜场景建模方法。该方法利用与观察者视角相关及无关的元素分别建模折射与反射，从而能够估计折射发生表面（即玻璃表面），并实现直接光分量与反射光分量的分离与建模。所提方法需要预先确定相机位姿，但玻璃物体场景中精确估计相机位姿十分困难，因此我们采用搭载相机的机械臂采集已知位姿图像。与现有方法相比，本方法能够更精确地建模玻璃折射及整体场景。