Efficient monocular pose estimation for complex 3D models

A. Rubio, M. Villamizar, L. Ferraz, A. Penate-Sanchez, A. Ramisa, E. Simo-Serra, A. Sanfeliu, F. Moreno-Noguer

Research output: Contribution to journalConference article

17 Citations (Scopus)


We propose a robust and efficient method to estimate the pose of a camera with respect to complex 3D textured models of the environment that can potentially contain more than 100; 000 points. To tackle this problem we follow a top down approach where we combine high-level deep network classifiers with low level geometric approaches to come up with a solution that is fast, robust and accurate. Given an input image, we initially use a pre-trained deep network to compute a rough estimation of the camera pose. This initial estimate constrains the number of 3D model points that can be seen from the camera viewpoint. We then establish 3D-to-2D correspondences between these potentially visible points of the model and the 2D detected image features. Accurate pose estimation is finally obtained from the 2D-to-3D correspondences using a novel PnP algorithm that rejects outliers without the need to use a RANSAC strategy, and which is between 10 and 100 times faster than other methods that use it. Two real experiments dealing with very large and complex 3D models demonstrate the effectiveness of the approach.

Original languageEnglish
Article number7139372
Pages (from-to)1397-1402
Number of pages6
JournalProceedings - IEEE International Conference on Robotics and Automation
Issue numberJune
Publication statusPublished - 2015 Jun 29
Event2015 IEEE International Conference on Robotics and Automation, ICRA 2015 - Seattle, United States
Duration: 2015 May 262015 May 30

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Efficient monocular pose estimation for complex 3D models'. Together they form a unique fingerprint.

  • Cite this

    Rubio, A., Villamizar, M., Ferraz, L., Penate-Sanchez, A., Ramisa, A., Simo-Serra, E., Sanfeliu, A., & Moreno-Noguer, F. (2015). Efficient monocular pose estimation for complex 3D models. Proceedings - IEEE International Conference on Robotics and Automation, 2015-June(June), 1397-1402. [7139372]. https://doi.org/10.1109/ICRA.2015.7139372