.png){kind=icon}
Return
SS-Pose: Self-Supervised 6-D Object Pose Representation Learning Without Rendering
.png){kind=icon}
PRE
AI
DOI：10.1109/TII.2024.3424591.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Object pose estimation has extensive applications in various industrial scenarios. However, the heavy reliance on dense 6-D annotation and textured object models has become a significant obstacle to the widespread industrial application of 6-D object pose estimation methods. In this work, we present SS-Pose, a self-supervised learning framework for estimating 6-D object poses without annotated 6-D data and textured model. SS-Pose proposes the coordinate system datum reinitializer stage to dynamically establish a sequence-level pose representation datum, and the temporal-spatial constraint resolver module to obtain the self-supervised learning target through interframe constraints. We introduce a one-shot cross-coordinate transformation that establishes the relationship between the 6-D representation and the object poses, which can be further utilized in real-world tasks. We evaluated the proposed SS-Pose on the challenging YCB-Video dataset and texture-less T-LESS dataset. Our approach achieves competitive performance with significantly lower data dependency, making it suitable for visual perception in industrial applications.
Keywords:
Industrial perception
object pose estimation
representation learning
self-supervised learning
Journal
IF:
9.9
Papers:
8.6K
Citations:
6.0W
Organization
No organization information available
Cited Papers
ShareSaveShareSaveShareSaveShareSaveElectrochemically assisted micro localized grafting of aptamers in a microchannel engraved in fluorinated thermoplastic polymer Dyneon THV.png)
RSC Advances
RSC AdvancesIF0
PREAI
ShareSaveShareSaveShareSaveShareSaveNature and landscape planning: Exploring the dynamics of valuation, the case of the Netherlands
PREAI
ShareSave
View more