PointPoseNet: Point Pose Network for Robust 6D Object Pose Estimation

Wei Chen*, Jinming Duan, Hector Basevi, Hyung Jin Chang, Ales Leonardis

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)


In this paper, we propose a novel pipeline to estimate 6D object pose from RGB-D images of known objects present in complex scenes. The pipeline directly operates on raw point clouds extracted from RGB-D scans. Specifically, our method takes the point cloud as input and regresses the point-wise unit vectors pointing to the 3D keypoints. We then use these vectors to generate keypoint hypotheses from which the 6D object pose hypotheses are computed. Finally, we select the best 6D object pose from the hypotheses based on a proposed scoring mechanism with geometry constraints. Extensive experiments show that the proposed method is robust against the variety in object shape and appearance as well as occlusions between objects, and that our method outperforms the state-of-the-art methods on the LINEMOD and Occlusion LINEMOD datasets.

Original languageEnglish
Title of host publication2020 IEEE Winter Conference on Applications of Computer Vision (WACV)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages10
ISBN (Electronic)9781728165530, 9781728165523 (USB)
ISBN (Print)9781728165547 (PoD)
Publication statusPublished - 14 May 2020
Event2020 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2020 - Snowmass Village, United States
Duration: 1 Mar 20205 Mar 2020

Publication series

NameIEEE Workshop on Applications of Computer Vision
ISSN (Print)2472-6737
ISSN (Electronic)2642-9381


Conference2020 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2020
Country/TerritoryUnited States
CitySnowmass Village

Bibliographical note

Publisher Copyright:
© 2020 IEEE.


  • Three-dimensional displays
  • Pose estimation
  • Two dimensional displays
  • Geometry
  • Feature extraction
  • Robustness
  • Pipelines

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Vision and Pattern Recognition


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