TY - GEN
T1 - A High-Resolution Network-Based Approach for 6D Pose Estimation of Industrial Parts
AU - Fan, Junming
AU - Li, Shufei
AU - Zheng, Pai
AU - Lee, Carman K.M.
N1 - Funding Information:
This research is funded by the Laboratory for Artificial Intelligence in Design (Project Code: RP2-1), Hong Kong Special Administrative Region.
Publisher Copyright:
© 2021 IEEE.
PY - 2021/8/23
Y1 - 2021/8/23
N2 - The estimation of 6D pose of industrial parts is a fundamental problem in smart manufacturing. Traditional approaches mainly focus on matching corresponding key point pairs between observed 2D images and 3D object models via hand-crafted feature descriptors. However, key points are hard to discover from images when the parts are piled up in disorder or occluded by other distractors, e.g., human hands. Although the emerging deep learning-based methods are capable of inferring the poses of occluded parts, the accuracy is not satisfactory largely due to the loss of spatial resolution from multiple downsampling operations inside convolutional neural networks. To overcome this challenge, this paper proposes a 6D pose estimation model consisting of a pose estimator and a pose refiner, by leveraging High-Resolution Networks as the backbone. Experiments are further conducted on a dataset of industrial parts to demonstrate its effectiveness.
AB - The estimation of 6D pose of industrial parts is a fundamental problem in smart manufacturing. Traditional approaches mainly focus on matching corresponding key point pairs between observed 2D images and 3D object models via hand-crafted feature descriptors. However, key points are hard to discover from images when the parts are piled up in disorder or occluded by other distractors, e.g., human hands. Although the emerging deep learning-based methods are capable of inferring the poses of occluded parts, the accuracy is not satisfactory largely due to the loss of spatial resolution from multiple downsampling operations inside convolutional neural networks. To overcome this challenge, this paper proposes a 6D pose estimation model consisting of a pose estimator and a pose refiner, by leveraging High-Resolution Networks as the backbone. Experiments are further conducted on a dataset of industrial parts to demonstrate its effectiveness.
UR - http://www.scopus.com/inward/record.url?scp=85116990231&partnerID=8YFLogxK
U2 - 10.1109/CASE49439.2021.9551495
DO - 10.1109/CASE49439.2021.9551495
M3 - Conference article published in proceeding or book
AN - SCOPUS:85116990231
T3 - IEEE International Conference on Automation Science and Engineering
SP - 1452
EP - 1457
BT - 2021 IEEE 17th International Conference on Automation Science and Engineering, CASE 2021
PB - IEEE Computer Society
T2 - 17th IEEE International Conference on Automation Science and Engineering, CASE 2021
Y2 - 23 August 2021 through 27 August 2021
ER -