Non-Penetration Iterative Closest Points for Single-View Multi-Object 6D Pose Estimation

Mengchao Zhang; Kris Hauser

ICRA 2022poster3 citations

Non-Penetration Iterative Closest Points for Single-View Multi-Object 6D Pose Estimation

Abstract

This paper presents a novel iterative closest points (ICP) variant, non-penetration iterative closest points (NPICP), which prevents interpenetration in 6DOF pose optimization and/or joint optimization of multiple object poses. This capability is particularly advantageous in cluttered scenarios, where there are many interactions between objects that constrain the space of valid poses. We use a semi-infinite programming approach to handle non-penetration constraints between complex, non-convex 3D geometries. NPICP is applied to a common use case for ICP as a post-processing method to improve the pose estimation accuracy of a rough guess. The results show that NPICP outperforms ICP, assists in outlier detection, and also outperforms the best result on the IC-BIN dataset in the Benchmark for 6D Object Pose Estimation.

BibTeX

@inproceedings{icra2022_nonpenetrationit,
  title = {Non-Penetration Iterative Closest Points for Single-View Multi-Object 6D Pose Estimation},
  author = {Mengchao Zhang and Kris Hauser},
  booktitle = {ICRA 2022},
  year = {2022}
}