Journal
IEEE ROBOTICS AND AUTOMATION LETTERS
Volume 7, Issue 2, Pages 1784-1791Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LRA.2022.3142441
Keywords
RGB-D Perception; deep learning for visual perception; perception for grasping and manipulation; category-level 6D pose estimation; object shape estimation
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This letter proposes a category-level 6D object pose and shape estimation approach iCaps, which allows tracking and estimating the poses and shapes of unseen objects. It achieves this by using a category-level auto-encoder network and LatentNet.
This letter proposes a category-level 6D object pose and shape estimation approach iCaps, which allows tracking 6D poses of unseen objects in a category and estimating their 3D shapes. We develop a category-level auto-encoder network using depth images as input, where feature embeddings from the auto-encoder encode poses of objects in a category. The auto-encoder can be used in a particle filter framework to estimate and track 6D poses of objects in a category. By exploiting an implicit shape representation based on signed distance functions, we build a LatentNet to estimate a latent representation of the 3D shape given the estimated pose of an object. Then the estimated pose and shape can be used to update each other in an iterative way. Our category-level 6D object pose and shape estimation pipeline only requires 2D detection and segmentation for initialization. We evaluate our approach on a publicly available dataset and demonstrate its effectiveness. In particular, our method achieves comparably high accuracy on shape estimation.
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