書誌事項
- タイトル別名
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- Fast 6D Object Pose Estimation from a RGB-D Image Using Balanced Pose Tree
抄録
<p>In this paper, we propose a fast and robust 6D pose estimation of objects from a RGB-D image. Our proposed method consists of two components: PCOF-MOD (multimodal perspectively cumulated orientation feature) and balanced pose tree. PCOF-MOD is based on the orientation histograms of depth gradient and surface normal vectors, those are extracted on synthesized depth images using randomized 3D pose parameters and 3D CAD data of a target object. Therefore, the model templates of PCOF-MOD explicitly handle a certain range of 3D object pose. Additionally, a large number of templates are organized into a coarse-to-fine 3D pose tree in order to accelerate 6D pose estimation. Predefined polyhedra for viewpoint sampling are prepared for each level of an image pyramid and 3D object pose trees are built so that the number of child nodes of every parent node are almost equal in each pyramid level. In the experimental evaluation of 6D object pose estimation on publicly available RGB-D image dataset, our proposed method showed higher accuracy and comparable speed in comparison with state-of-the-art techniques.</p>
収録刊行物
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- 精密工学会誌
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精密工学会誌 84 (4), 348-355, 2018
公益社団法人 精密工学会
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詳細情報 詳細情報について
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- CRID
- 1390282679807128960
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- NII論文ID
- 130006638835
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- ISSN
- 1882675X
- 09120289
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- 本文言語コード
- ja
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- データソース種別
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- JaLC
- Crossref
- CiNii Articles
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- 抄録ライセンスフラグ
- 使用不可