物体姿勢を維持する画像変換を用いたzero-shot姿勢推定

カテゴリ: 論文誌(論文単位)

グループ名: 【C】電子・情報・システム部門

発行日: 2023/12/01

タイトル(英語): Zero-shot Pose Estimation Using Image Translation to Maintain Object Pose

著者名: 藤田　幸平（名城大学 理工学部），田崎　豪（名城大学 理工学部）

著者名(英語): Kohei Fujita (Faculty of Science and Technology, Meijo University), Tsuyoshi Tasaki (Faculty of Science and Technology, Meijo University)

キーワード: 物体の姿勢推定，zero-shot，深層学習　　object pose estimation，zero-shot，deep neural network

要約(英語): In this study, we developed a new method to translate unknown products into known products while maintaining the pose of the products, in order to perform pose estimation of unknown products with zero-shot. Pose estimation methods using Neural Networks (NNs) are highly accurate, but their success rate of pose estimation is significantly decreased for unknown products. In retail stores, products are frequently changed, and it is time consuming to prepare data for each new product. Therefore, the purpose in this study is to improve the success rate of pose estimation with zero-shot. Since the success rate of pose estimation are high for known products, unknown products are translated into known products. However, even if only the appearance of the product is translated into the known product, the success rate of pose estimation cannot be improved because the pose of translated appearance often changes. Therefore, the coarse pose of the product is used as input for image translation NN to translate the product into a known product that maintains its pose. Experimental results showed that using pose-maintaining image translation improved the success rate for the two methods of pose estimation which are state-of-the-art on different datasets.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.143 No.12 （2023） 特集：電気・電子・情報関係学会東海支部連合大会

本誌掲載ページ: 1113-1122 p

原稿種別: 論文／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/143/12/143_1113/_article/-char/ja/