iShape: A First Step Towards Irregular Shape Instance Segmentation

By Lei Yang, Yan Wei, Yisheng He, Wei Sun, Zhenhang Huang, Haibin Huang, Haoqiang Fan. 2021

17 citations recorded by the source corpus.

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

Subfields: Computer Vision and Pattern Recognition, Industrial and Manufacturing Engineering

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Tete Xiao

Citations in the corpus

Segment Anything (2023, background): “-20 0 +20 +40 IoU delta at 1 center point GTEA [34, 63] TrashCan [52] DRAM [24] PIDRay [104] Cityscapes [25] WoodScape [112] IBD [17] EgoHOS [113] Plittersdorf [46] VISOR [28, 27] NDISPark [22, 23] Hypersim [86] OVIS [81] ADE20K [117] iShape [111] ZeroWaste-f [6] STREETS [91] LVIS [44] NDD20 [100] T”
SAM 2: Segment Anything in Images and Videos (2024, unknown): “…DOORS (Pugliatti & Topputo, 2022), DRAM (Cohen et al., 2022), EgoHOS (Zhang et al., 2022), GTEA (Fathi et al., 2011; Li et al., 2015), iShape (Yang et al., 2021a), NDD20 (Trotter et al., 2020), NDISPark (Ciampi et al., 2021, 2022), OVIS (Qi et al., 2022), PPDLS (Minervini et al., 2016),…”