Match Normalization: Learning-Based Point Cloud Registration for 6D Object Pose Estimation in the Real World-Reference-Cited by-同舟云学术

Match Normalization: Learning-Based Point Cloud Registration for 6D Object Pose Estimation in the Real World

Published:2024-06 Issue:6 Volume:46 Page:4489-4503
ISSN:0162-8828
Container-title:IEEE Transactions on Pattern Analysis and Machine Intelligence
language:
Short-container-title:IEEE Trans. Pattern Anal. Mach. Intell.

Author:

Dang Zheng¹^ORCID,Wang Lizhou²^ORCID,Guo Yu³^ORCID,Salzmann Mathieu¹^ORCID

Affiliation:

1. Computer Vision Laboratory, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

2. School of Software Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi, China

3. National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, Xi’an, Shaanxi, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/34/10522060/10402084.pdf?arnumber=10402084

Reference78 articles.

1. BOP: Benchmark for 6D Object Pose Estimation

2. Model Based Training, Detection and Pose Estimation of Texture-Less 3D Objects in Heavily Cluttered Scenes

3. Learning 6D Object Pose Estimation Using 3D Object Coordinates

4. CosyPose: Consistent Multi-view Multi-object 6D Pose Estimation

5. BB8: A Scalable, Accurate, Robust to Partial Occlusion Method for Predicting the 3D Poses of Challenging Objects without Using Depth

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