Authors:
Sukhan Lee
1
;
Soojin Lee
1
and
Yongjun Yang
2
Affiliations:
1
Department of Artificial Intelligence, Sungkyunkwan University, Suwon, Republic of Korea
;
2
Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Republic of Korea
Keyword(s):
Object 6D Pose, Panoptic Segmentation, Dual Associative Point Autoencoder, Point Cloud, Occluded Object.
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.
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