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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URL
https://arxiv.org/abs/2203.02884