Multiple geometry representations for 6D object pose estimation in occluded or truncated scenes

作者：

Highlights：

• A novel 6D object pose estimation method based on multiple geometry representations.

• A two-stage pose regression module is applied to compute the 6D pose of an object.

• Capabilities of handling textureless objects in occluded or truncated scenes.

摘要

•A novel 6D object pose estimation method based on multiple geometry representations.•A two-stage pose regression module is applied to compute the 6D pose of an object.•Capabilities of handling textureless objects in occluded or truncated scenes.

论文关键词：Neural network,Pose estimation,Keypoints,Edge vectors,Symmetry correspondences

论文评审过程：Received 9 January 2022, Revised 4 July 2022, Accepted 13 July 2022, Available online 16 July 2022, Version of Record 29 July 2022.

论文官网地址：https://doi.org/10.1016/j.patcog.2022.108903