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Erschienen in:
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2018 | OriginalPaper | Buchkapitel

SRDA: Generating Instance Segmentation Annotation via Scanning, Reasoning and Domain Adaptation

verfasst von : Wenqiang Xu, Yonglu Li, Cewu Lu

Erschienen in: Computer Vision – ECCV 2018

Verlag: Springer International Publishing

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Abstract

Instance segmentation is a problem of significance in computer vision. However, preparing annotated data for this task is extremely time-consuming and costly. By combining the advantages of 3D scanning, reasoning, and GAN-based domain adaptation techniques, we introduce a novel pipeline named SRDA to obtain large quantities of training samples with very minor effort. Our pipeline is well-suited to scenes that can be scanned, i.e. most indoor and some outdoor scenarios. To evaluate our performance, we build three representative scenes and a new dataset, with 3D models of various common objects categories and annotated real-world scene images. Extensive experiments show that our pipeline can achieve decent instance segmentation performance given very low human labor cost.

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Vorheriges Kapitel Deep Multi-task Learning to Recognise Subtle Facial Expressions of Mental States
Nächstes Kapitel Unsupervised Domain Adaptation for 3D Keypoint Estimation via View Consistency
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Metadaten
Titel
SRDA: Generating Instance Segmentation Annotation via Scanning, Reasoning and Domain Adaptation
verfasst von
Wenqiang Xu
Yonglu Li
Cewu Lu
Copyright-Jahr
2018
Buch
Computer Vision – ECCV 2018

Print ISBN: 978-3-030-01257-1

Electronic ISBN: 978-3-030-01258-8

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-030-01258-8_8

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