Shape Prior Deformation for Categorical 6D Object Pose and Size Estimation

By Meng Tian, Marcelo H. Ang Jr., Gim Hee Lee. European Conference on Computer Vision, 2020

296 citations recorded by the source corpus.

We present a novel learning approach to recover the 6D poses and sizes of unseen object instances from an RGB-D image. To handle the intra-class shape variation, we propose a deep network to reconstruct the 3D object model by explicitly modeling the deformation from a pre-learned categorical shape prior. Additionally, our network infers the dense correspondences between the depth observation of the object instance and the reconstructed 3D model to jointly estimate the 6D object pose and size. We design an autoencoder that trains on a collection of object models and compute the mean latent embedding for each category to learn the categorical shape priors. Extensive experiments on both synthetic and real-world datasets demonstrate that our approach significantly outperforms the state of the art. Our code is available at this https URL.

View Paper: https://www.semanticscholar.org/paper/fddcc5d3c2f66e7973974af55a2b3ad7a8de6e42

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Citations in the corpus

FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objects (2023, methodology): “While category-level methods [5, 32, 54, 58, 69] remove these assumptions (instance-wise training and CAD models), they are limited to objects within the predefined category on which they are trained.”