NeRF-THO: Neural Radiance Fields for Transparent and Highlighted Objects

Abstract

3-D reconstruction of transparent objects is a pivotal technology extending the application domain in intelligent robotics. However, during the reconstruction process, neural radiance field (NeRF) based on deep learning tends to blur the boundaries of transparent objects and overlook highlights, leading to a decrease in reconstruction performance. To overcome this issue, this article investigates a new NeRF for transparent and highlighted objects (NeRF-THO). First, a multiresolution transparent feature aggregated module is established to process sparse RGB images. Second, a trainable separation-reconstruction module is designed to decompose the scene for modeling by two independent NeRFs. Finally, a carefully designed truncated signed distance function generation network is devised to achieve reasonable separation results and expedite the sampling process. A realistic synthetic dataset of transparent glass bottles is established to evaluate the model’s performance. Experimental results demonstrate that NeRF-THO exhibits superior performance in the quality of 3-D reconstruction.