다시점 객체 공분할을 이용한 2D-3D 물체 자세 추정 2D-3D Pose Estimation using Multi-view Object Co-segmentation

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We present a region-based approach for accurate pose estimation of small mechanical components. Our algorithm consists of two key phases: Multi-view object co-segmentation and pose estimation. In the first phase, we explain an automatic method to extract binary masks of a target object captured from multiple viewpoints. For initialization, we assume the target object is bounded by the convex volume of interest defined by a few user inputs. The co-segmented target object shares the same geometric representation in space, and has distinctive color models from those of the backgrounds. In the second phase, we retrieve a 3D model instance with correct upright orientation, and estimate a relative pose of the object observed from images. Our energy function, combining region and boundary terms for the proposed measures, maximizes the overlapping regions and boundaries between the multi-view co-segmentations and projected masks of the reference model. Based on high-quality co-segmentations consistent across all different viewpoints, our final results are accurate model indices and pose parameters of the extracted object. We demonstrate the effectiveness of the proposed method using various examples.
Publisher
한국로봇학회
Issue Date
2017
Language
Korean
Keywords

Multi-view object co-segmentation; 2D-3D pose estimation

Citation

로봇학회 논문지, v.12, no.1, pp.33 - 41

ISSN
1975-6291
URI
http://hdl.handle.net/10203/241146
Appears in Collection
EE-Journal Papers(저널논문)
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