[2207.02062] Image Amodal Completion: A Survey
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Computer Science > Computer Vision and Pattern Recognition

arXiv:2207.02062 (cs)
[Submitted on 5 Jul 2022 (v1), last revised 7 Nov 2023 (this version, v3)]

Title:Image Amodal Completion: A Survey

Authors:Jiayang Ao, Qiuhong Ke, Krista A. Ehinger
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Abstract:Existing computer vision systems can compete with humans in understanding the visible parts of objects, but still fall far short of humans when it comes to depicting the invisible parts of partially occluded objects. Image amodal completion aims to equip computers with human-like amodal completion functions to understand an intact object despite it being partially occluded. The main purpose of this survey is to provide an intuitive understanding of the research hotspots, key technologies and future trends in the field of image amodal completion. Firstly, we present a comprehensive review of the latest literature in this emerging field, exploring three key tasks in image amodal completion, including amodal shape completion, amodal appearance completion, and order perception. Then we examine popular datasets related to image amodal completion along with their common data collection methods and evaluation metrics. Finally, we discuss real-world applications and future research directions for image amodal completion, facilitating the reader's understanding of the challenges of existing technologies and upcoming research trends.
Comments: Accepted at Computer Vision and Image Understanding. See this https URL for the final version
Subjects: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
Cite as: arXiv:2207.02062 [cs.CV]
  (or arXiv:2207.02062v3 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2207.02062
Related DOI: https://doi.org/10.1016/j.cviu.2023.103661

Submission history

From: Jiayang Ao [view email]
[v1] Tue, 5 Jul 2022 14:13:22 UTC (6,767 KB)
[v2] Fri, 16 Dec 2022 03:51:02 UTC (6,968 KB)
[v3] Tue, 7 Nov 2023 16:33:33 UTC (6,742 KB)
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