ZISVFM: Zero-Shot Object Instance Segmentation in Indoor Robotic Environments With Vision Foundation Models

Abstract

Service robots operating in unstructured environments must effectively recognize and segment unknown objects to enhance their functionality. Traditional supervised learning-based segmentation techniques require extensive annotated datasets, which are impractical for the diversity of objects encountered in real-world scenarios. Unseen object instance segmentation (UOIS) methods aim to address this by training models on synthetic data to generalize to novel objects, but they often suffer from the simulation-to-reality gap. This article proposes a novel approach (ZISVFM) for solving UOIS by leveraging the powerful zero-shot capability of the segment anything model (SAM) and explicit visual representations from a self-supervised vision transformer (ViT). The proposed framework operates in the following three stages: generating object-agnostic mask proposals from colorized depth images using SAM, refining these proposals using attention-based features from the self-supervised ViT to filter nonobject masks, and applying K-Medoids clustering to generate point prompts that guide SAM toward precise object segmentation. Experimental validation on two benchmark datasets and a self-collected dataset demonstrates the superior performance of ZISVFM in complex environments, including hierarchical settings such as cabinets, drawers, and handheld objects.