Towards Self-Supervised Category-Level Object Pose and Size Estimation
- URL: http://arxiv.org/abs/2203.02884v1
- Date: Sun, 6 Mar 2022 06:02:30 GMT
- Title: Towards Self-Supervised Category-Level Object Pose and Size Estimation
- Authors: Yisheng He, Haoqiang Fan, Haibin Huang, Qifeng Chen, Jian Sun
- Abstract summary: This work presents a self-supervised framework for category-level object pose and size estimation from a single depth image.
We leverage the geometric consistency residing in point clouds of the same shape for self-supervision.
- Score: 121.28537953301951
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This work presents a self-supervised framework for category-level object pose
and size estimation from a single depth image. Unlike previous works that rely
on time-consuming and labor-intensive ground truth pose labels for supervision,
we leverage the geometric consistency residing in point clouds of the same
shape for self-supervision. Specifically, given a normalized category template
mesh in the object-coordinate system and the partially observed object instance
in the scene, our key idea is to apply differentiable shape deformation,
registration, and rendering to enforce geometric consistency between the
predicted and the observed scene object point cloud. We evaluate our approach
on real-world datasets and find that our approach outperforms the simple
traditional baseline by large margins while being competitive with some
fully-supervised approaches.
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