iShape: A First Step Towards Irregular Shape Instance Segmentation

Lei Yang, Yan Zi Wei, Wei Sun, Yisheng HE, Zhenhang Huang, Haibin Huang, Haoqiang Fan

Keywords: instance segmentation, dataset

TL;DR: We introduce dataset and baseline for instance segmentation of irregularly shaped objects.

Abstract: 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. 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.

URL: https://ishape.github.io/

Community Implementations: [ 2 code implementations](https://www.catalyzex.com/paper/arxiv:2109.15068/code)