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3D Pose Estimation for Bin-picking: ...

Rodrigues, Jose Jeronimo Moreira.

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3D Pose Estimation for Bin-picking: A Data-driven Approach Using Multi-light Images.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	3D Pose Estimation for Bin-picking: A Data-driven Approach Using Multi-light Images./
作者:	Rodrigues, Jose Jeronimo Moreira.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
面頁冊數:	113 p.
附註:	Source: Dissertations Abstracts International, Volume: 80-03, Section: B.
Contained By:	Dissertations Abstracts International80-03B.
標題:	Computer Engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10931980
ISBN:	9780438338753

3D Pose Estimation for Bin-picking: A Data-driven Approach Using Multi-light Images.
Rodrigues, Jose Jeronimo Moreira.

3D Pose Estimation for Bin-picking: A Data-driven Approach Using Multi-light Images. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 113 p.

Source: Dissertations Abstracts International, Volume: 80-03, Section: B.

Thesis (Ph.D.)--Carnegie Mellon University, 2018.

This item must not be sold to any third party vendors.

We study the problem of 3D pose estimation of textureless shiny objects from monocular 2D images, for a bin-picking task. The main challenge of dealing with a shiny object comes from the fact that the object appearance largely changes with its pose and illumination. Therefore, conventional 3D-2D correspondence search usually fails due to the inconsistency of feature descriptors. For a textureless object like a mechanical part, visual feature matching becomes even harder due to the absence of stable texture features. Hierarchical template matching approaches require a larger number of templates to be matched when dealing with shiny objects, due to the drastic appearance changes with pose. In the challenging scenario of a bin-picking task, we must also cope with partial occlusions, shadows and inter-reflections, requiring redoubled effort in matching each template to obtain reliable results, which compromises the attractiveness of such approaches that are usually popular for textureless objects. In this thesis, we develop a purely data-driven method to tackle the pose estimation problem. Motivated by photometric stereo, we develop an imaging system with multiple lights to acquire a multi-light image where channels are obtained by varying illumination directions. In an online stage, we capture multi-light images of a given object in several poses. Then, we use random ferns to cluster the appearance of small patches of the multi-light images, and we store in each cluster the information of possible object poses. At run-time, the patches of the input multi-light image use the clusters information to probabilistically vote on several pose hypotheses. Since our pose hypotheses are a discrete set, we rene the discretized pose into the continuous space, in order to obtain accurate object poses for robotic manipulation. Experiments show that the given method can detect and estimate poses of textureless and shiny objects accurately and robustly within half a second. We further compare our approach with the HALCON commercial software, a highly optimized hierarchical template matching approach developed by MVTec, and show some of the drawbacks of such type of approaches. Finally, we run detection on a different object by simply changing the image database.

ISBN: 9780438338753Subjects--Topical Terms:

1567821
Computer Engineering.

3D Pose Estimation for Bin-picking: A Data-driven Approach Using Multi-light Images.
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520 $a We study the problem of 3D pose estimation of textureless shiny objects from monocular 2D images, for a bin-picking task. The main challenge of dealing with a shiny object comes from the fact that the object appearance largely changes with its pose and illumination. Therefore, conventional 3D-2D correspondence search usually fails due to the inconsistency of feature descriptors. For a textureless object like a mechanical part, visual feature matching becomes even harder due to the absence of stable texture features. Hierarchical template matching approaches require a larger number of templates to be matched when dealing with shiny objects, due to the drastic appearance changes with pose. In the challenging scenario of a bin-picking task, we must also cope with partial occlusions, shadows and inter-reflections, requiring redoubled effort in matching each template to obtain reliable results, which compromises the attractiveness of such approaches that are usually popular for textureless objects. In this thesis, we develop a purely data-driven method to tackle the pose estimation problem. Motivated by photometric stereo, we develop an imaging system with multiple lights to acquire a multi-light image where channels are obtained by varying illumination directions. In an online stage, we capture multi-light images of a given object in several poses. Then, we use random ferns to cluster the appearance of small patches of the multi-light images, and we store in each cluster the information of possible object poses. At run-time, the patches of the input multi-light image use the clusters information to probabilistically vote on several pose hypotheses. Since our pose hypotheses are a discrete set, we rene the discretized pose into the continuous space, in order to obtain accurate object poses for robotic manipulation. Experiments show that the given method can detect and estimate poses of textureless and shiny objects accurately and robustly within half a second. We further compare our approach with the HALCON commercial software, a highly optimized hierarchical template matching approach developed by MVTec, and show some of the drawbacks of such type of approaches. Finally, we run detection on a different object by simply changing the image database.
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