  Instance segmentation for completely occluded objects and dense objects in
robot vision measurement are two challenging tasks. To uniformly deal with
them, this paper proposes a unified coarse-to-fine instance segmentation
framework, CFNet, which uses box prompt-based segmentation foundation models
(BSMs), e.g., Segment Anything Model. Specifically, CFNet first detects
oriented bounding boxes (OBBs) to distinguish instances and provide coarse
localization information. Then, it predicts OBB prompt-related masks for fine
segmentation. CFNet performs instance segmentation with OBBs that only contain
partial object boundaries on occluders to predict occluded object instances,
which overcomes the difficulty of existing amodal instance segmentation methods
in directly predicting occluded objects. In addition, since OBBs only serve as
prompts, CFNet alleviates the over-dependence on bounding box detection
performance of current instance segmentation methods using OBBs for dense
objects. Moreover, to enable BSMs to handle OBB prompts, we propose a novel OBB
prompt encoder. To make CFNet more lightweight, we perform knowledge
distillation on it and introduce a Gaussian label smoothing method for teacher
model outputs. Experiments demonstrate that CFNet outperforms current instance
segmentation methods on both industrial and public datasets. The code is
available at https://github.com/zhen6618/OBBInstanceSegmentation.
