Completely occluded and dense object instance segmentation (IS) is an important and challenging task. Although current amodal IS methods can predict invisible regions of occluded objects, they are difficult to directly predict completely occluded objects. For dense object IS, existing box-based methods are overly dependent on the performance of bounding box detection. In this paper, we propose CFNet, a coarse-to-fine IS framework for completely occluded and dense objects, which is based on box prompt-based segmentation foundation models (BSMs). 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. To predict completely occluded object instances, CFNet performs IS on occluders and utilizes prior geometric properties, which overcomes the difficulty of directly predicting completely occluded object instances. Furthermore, based on BSMs, CFNet reduces the dependence on bounding box detection performance, improving dense object IS performance. Moreover, we propose a novel OBB prompt encoder for BSMs. To make CFNet more lightweight, we perform knowledge distillation on it and introduce a Gaussian smoothing method for teacher targets. Experimental results demonstrate that CFNet achieves the best performance on both industrial and publicly available datasets.

提出了CFNet模型用于完全遮挡和密集目标的分割任务，该模型基于盒子提示的分割基础模型，首先通过检测有方向的边界框来进行粗略的实例定位，然后通过预测与边界框提示相关的掩码进行精细的分割，并利用先验几何属性来预测完全遮挡的目标实例，同时通过基于盒子提示的分割基础模型减少了对边界框检测性能的依赖，从而提高了密集目标的分割效果。

基于盒状提示的完全遮挡和密集物体实例分割基础模型