Learning Canonical Shape Space for Category-Level 6D Object Pose and Size Estimation

• URL: http://arxiv.org/abs/2001.09322v3
• Date: Sun, 21 Nov 2021 08:38:17 GMT
• Title: Learning Canonical Shape Space for Category-Level 6D Object Pose and Size Estimation
• Authors: Dengsheng Chen and Jun Li and Zheng Wang and Kai Xu
• Abstract summary: We learn canonical shape space (CASS), a unified representation for a large variety of instances of a certain object category. We train a variational auto-encoder (VAE) for generating 3D point clouds in the canonical space from an RGBD image. VAE is trained in a cross-category fashion, exploiting the publicly available large 3D shape repositories.
• Score: 21.7030393344051
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a novel approach to category-level 6D object pose and size estimation. To tackle intra-class shape variations, we learn canonical shape space (CASS), a unified representation for a large variety of instances of a certain object category. In particular, CASS is modeled as the latent space of a deep generative model of canonical 3D shapes with normalized pose. We train a variational auto-encoder (VAE) for generating 3D point clouds in the canonical space from an RGBD image. The VAE is trained in a cross-category fashion, exploiting the publicly available large 3D shape repositories. Since the 3D point cloud is generated in normalized pose (with actual size), the encoder of the VAE learns view-factorized RGBD embedding. It maps an RGBD image in arbitrary view into a pose-independent 3D shape representation. Object pose is then estimated via contrasting it with a pose-dependent feature of the input RGBD extracted with a separate deep neural networks. We integrate the learning of CASS and pose and size estimation into an end-to-end trainable network, achieving the state-of-the-art performance.

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