SAID-NeRF: Segmentation-AIDed NeRF for Depth Completion of Transparent Objects

Acquiring accurate depth information of transpar- ent objects using off-the-shelf RGB-D cameras is a well-known challenge in Computer Vision and Robotics. Depth estima- tion/completion methods are typically employed and trained on datasets with quality depth labels acquired from either simula- tion, additional sensors or specialized data collection setups and known 3d models. However, acquiring reliable depth informa- tion for datasets at scale is not straightforward, limiting training scalability and generalization. Neural Radiance Fields (NeRFs) are learning-free approaches and have demonstrated wide success in novel view synthesis and shape recovery. However, heuristics and controlled environments (lights, backgrounds, etc) are often required to accurately capture specular surfaces. In this paper, we propose using Visual Foundation Models (VFMs) for segmentation in a zero-shot, label-free way to guide the NeRF reconstruction process for these objects via the simultaneous reconstruction of semantic fields and extensions to increase robustness. Our proposed method Segmentation- AIDed NeRF (SAID-NeRF) shows significant performance on depth completion datasets for transparent objects and robotic grasping.