Shape Prior Deformation for Categorical 6D Object Pose and Size Estimation
This addresses the challenge of object pose estimation for robotics and computer vision applications, but it is incremental as it builds on prior shape deformation methods.
The paper tackles the problem of estimating 6D poses and sizes of unseen object instances from RGB-D images by handling intra-class shape variation, and it demonstrates significant outperformance over state-of-the-art methods in experiments on synthetic and real-world datasets.
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 https://github.com/mentian/object-deformnet.