Occlusion-Robust and Efficient 6D Pose Estimation with Scene-Level Segmentation Refinement and 3D Partial-to-6D Full Point Cloud Transformation

Sukhan Lee, Soojin Lee, Yongjun Yang

2024

Abstract

Accurate estimation of the 6D pose of objects is essential for 3D scene modeling, visual odometry, and map building, as well as robotic manipulation of objects. Recently, various end-to-end deep networks have been proposed for object 6D pose estimation with their accuracies reaching the level of conventional regimes but with much higher efficiency. Despite progress, the accurate yet efficient 6D pose estimation of highly occluded objects in a cluttered scene remains a challenge. In this study, we present an end-to-end deep network framework for 6D pose estimation with particular emphasis on highly occluded objects in a cluttered scene. The proposed framework integrates an occlusion-robust panoptic segmentation network performing scene-level segmentation refinement and a dual associative point autoencoder (AE) directly reconstructing the 6D full camera and object frame-based point clouds corresponding to a captured 3D partial point cloud through latent space association. We evaluated the proposed deep 6D pose estimation framework based on the standard benchmark dataset, LineMod-Occlusion (LMO), and obtained the top-tier performance in the current leaderboard, validating the effectiveness of the proposed approach in terms of efficiency and accuracy.

Paper Citation

in Harvard Style

Lee S., Lee S. and Yang Y. (2024). Occlusion-Robust and Efficient 6D Pose Estimation with Scene-Level Segmentation Refinement and 3D Partial-to-6D Full Point Cloud Transformation. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP; ISBN 978-989-758-679-8, SciTePress, pages 763-771. DOI: 10.5220/0012457700003660

in Bibtex Style

@conference{visapp24,
author={Sukhan Lee and Soojin Lee and Yongjun Yang},
title={Occlusion-Robust and Efficient 6D Pose Estimation with Scene-Level Segmentation Refinement and 3D Partial-to-6D Full Point Cloud Transformation},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP},
year={2024},
pages={763-771},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012457700003660},
isbn={978-989-758-679-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP
TI - Occlusion-Robust and Efficient 6D Pose Estimation with Scene-Level Segmentation Refinement and 3D Partial-to-6D Full Point Cloud Transformation
SN - 978-989-758-679-8
AU - Lee S.
AU - Lee S.
AU - Yang Y.
PY - 2024
SP - 763
EP - 771
DO - 10.5220/0012457700003660
PB - SciTePress