  In this paper, we introduce a brand new dataset to promote the study of
instance segmentation for objects with irregular shapes. Our key observation is
that though irregularly shaped objects widely exist in daily life and
industrial scenarios, they received little attention in the instance
segmentation field due to the lack of corresponding datasets. To fill this gap,
we propose iShape, an irregular shape dataset for instance segmentation. iShape
contains six sub-datasets with one real and five synthetics, each represents a
scene of a typical irregular shape. Unlike most existing instance segmentation
datasets of regular objects, iShape has many characteristics that challenge
existing instance segmentation algorithms, such as large overlaps between
bounding boxes of instances, extreme aspect ratios, and large numbers of
connected components per instance. We benchmark popular instance segmentation
methods on iShape and find their performance drop dramatically. Hence, we
propose an affinity-based instance segmentation algorithm, called ASIS, as a
stronger baseline. ASIS explicitly combines perception and reasoning to solve
Arbitrary Shape Instance Segmentation including irregular objects. Experimental
results show that ASIS outperforms the state-of-the-art on iShape. Dataset and
code are available at https://ishape.github.io
