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title section openreview abstract layout series publisher issn id month tex_title firstpage lastpage page order cycles bibtex_author author date address container-title volume genre issued pdf extras
SA6D: Self-Adaptive Few-Shot 6D Pose Estimator for Novel and Occluded Objects
Poster
gdkKi_F55h
To enable meaningful robotic manipulation of objects in the real-world, 6D pose estimation is one of the critical aspects. Most existing approaches have difficulties to extend predictions to scenarios where novel object instances are continuously introduced, especially with heavy occlusions. In this work, we propose a few-shot pose estimation (FSPE) approach called SA6D, which uses a self-adaptive segmentation module to identify the novel target object and construct a point cloud model of the target object using only a small number of cluttered reference images. Unlike existing methods, SA6D does not require object-centric reference images or any additional object information, making it a more generalizable and scalable solution across categories. We evaluate SA6D on real-world tabletop object datasets and demonstrate that SA6D outperforms existing FSPE methods, particularly in cluttered scenes with occlusions, while requiring fewer reference images.
inproceedings
Proceedings of Machine Learning Research
PMLR
2640-3498
gao23a
0
SA6D: Self-Adaptive Few-Shot 6D Pose Estimator for Novel and Occluded Objects
1572
1595
1572-1595
1572
false
Gao, Ning and Ngo, Vien Anh and Ziesche, Hanna and Neumann, Gerhard
given family
Ning
Gao
given family
Vien Anh
Ngo
given family
Hanna
Ziesche
given family
Gerhard
Neumann
2023-12-02
Proceedings of The 7th Conference on Robot Learning
229
inproceedings
date-parts
2023
12
2