  To fully understand the 3D context of a single image, a visual system must be
able to segment both the visible and occluded regions of objects, while
discerning their occlusion order. Ideally, the system should be able to handle
any object and not be restricted to segmenting a limited set of object classes,
especially in robotic applications. Addressing this need, we introduce a
diffusion model with cumulative occlusion learning designed for sequential
amodal segmentation of objects with uncertain categories. This model
iteratively refines the prediction using the cumulative mask strategy during
diffusion, effectively capturing the uncertainty of invisible regions and
adeptly reproducing the complex distribution of shapes and occlusion orders of
occluded objects. It is akin to the human capability for amodal perception,
i.e., to decipher the spatial ordering among objects and accurately predict
complete contours for occluded objects in densely layered visual scenes.
Experimental results across three amodal datasets show that our method
outperforms established baselines.
