TY - GEN
T1 - 3D object pose estimation using viewpoint generative learning
AU - Thachasongtham, Dissaphong
AU - Yoshida, Takumi
AU - De Sorbier, François
AU - Saito, Hideo
PY - 2013/9/26
Y1 - 2013/9/26
N2 - Conventional local features such as SIFT or SURF are robust to scale and rotation changes but sensitive to large perspective change. Because perspective change always occurs when 3D object moves, using these features to estimate the pose of a 3D object is a challenging task. In this paper, we extend one of our previous works on viewpoint generative learning to 3D objects. Given a model of a textured object, we virtually generate several patterns of the model from different viewpoints and select stable keypoints from those patterns. Then our system learns a collection of feature descriptors from the stable keypoints. Finally, we are able to estimate the pose of a 3D object by using these robust features. In our experimental results, we demonstrate that our system is robust against large viewpoint change and even under partial occlusion.
AB - Conventional local features such as SIFT or SURF are robust to scale and rotation changes but sensitive to large perspective change. Because perspective change always occurs when 3D object moves, using these features to estimate the pose of a 3D object is a challenging task. In this paper, we extend one of our previous works on viewpoint generative learning to 3D objects. Given a model of a textured object, we virtually generate several patterns of the model from different viewpoints and select stable keypoints from those patterns. Then our system learns a collection of feature descriptors from the stable keypoints. Finally, we are able to estimate the pose of a 3D object by using these robust features. In our experimental results, we demonstrate that our system is robust against large viewpoint change and even under partial occlusion.
KW - generative learning
KW - pose estimation
KW - stable keypoint
UR - http://www.scopus.com/inward/record.url?scp=84884486752&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84884486752&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-38886-6_48
DO - 10.1007/978-3-642-38886-6_48
M3 - Conference contribution
AN - SCOPUS:84884486752
SN - 9783642388859
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 512
EP - 521
BT - Image Analysis - 18th Scandinavian Conference, SCIA 2013, Proceedings
T2 - 18th Scandinavian Conference on Image Analysis, SCIA 2013
Y2 - 17 June 2013 through 20 June 2013
ER -