Object Visibility Estimation for Pick-Order Reasoning in Robotic Bin Picking

In warehouse bin picking, a manipulator must decide which object to remove first, because grasping a pinned object often fails or drags neighbouring items out of the bin. Prevailing approaches rely on amodal instance segmentation: the full extent of each partially occluded object is first predicted, and occlusion is inferred from the area ratio between the visible and the completed mask. Completing occluded regions is ill-posed and multi-modal, so shape errors propagate into the pick order while incurring substantial computational cost. We propose a more direct formulation: pick-order reasoning does not require hidden shape, only one visibility-ratio scalar per instance. We attach a lightweight regression head to a frozen segmentation backbone and predict the ratio of visible to complete area directly from instance features. Supervision is obtained at no additional cost from the amodal annotations of existing datasets. We evaluate visibility error, rank correlation and top-layer retrieval on three public benchmarks, and first-grasp success on a physical manipulator. The proposed method yields pick orders on par with representative amodal pipelines while running roughly five to seven times faster than their released implementations.