Revisiting the PnP Problem: A Fast, General and Optimal Solution
By Yinqiang Zheng, Yubin Kuang, Shigeki Sugimoto, Kalle Åström, Masatoshi Okutomi. IEEE International Conference on Computer Vision, 2013
381 citations recorded by the source corpus.
Subfields: Aerospace Engineering, Computational Mechanics, Control and Systems Engineering
View Paper: https://www.semanticscholar.org/paper/8c5aaa75759b3beaa7eddc5d991d629c7fa603da
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Aerospace Engineering
- Luca Carlone
- Yun Chang
- Antoni Rosinol
- É. Marchand
- Nathan Hughes
- Jingnan Shi
- Hideaki Uchiyama
- Fabien Spindler
In Computational Mechanics
- Pascal Fua
- Marcus Abate
- Yun Chang
- É. Marchand
- Luca Carlone
- Nathan Hughes
- Mathieu Salzmann
- Fabien Spindler
In Control and Systems Engineering
- Mathieu Salzmann
- Joachim Hugonot
- Yinlin Hu
- Marcus Abate
- Yun Chang
- É. Marchand
- Luca Carlone
- Pascal Fua
Citations in the corpus
- Segmentation-Driven 6D Object Pose Estimation (2018, methodology): “In parallel, increasingly effective PnP methods have been developed to handle noise and mismatches [20, 46, 22, 8].”
- 3D Dynamic Scene Graphs: Actionable Spatial Perception with Places, Objects, and Humans (2020, methodology): “uses a weak perspective camera model) using PnP [115].”
- Kimera: From SLAM to spatial perception with 3D dynamic scene graphs (2021, methodology): “After obtaining the projection, we then compute the location and orientation of the full mesh with respect to the camera using PnP (Zheng et al., 2013) to optimize the camera pose based on the reprojection error of the mesh into the camera frame.”
- Pose Estimation for Augmented Reality: A Hands-On Survey (2016, methodology): “To reject outliers, methods like RANSAC [18] or the use of M-Estimators such as the Tukey estimator [24], [31], [140] are common trends to make the algorithm robust to occlusions and illumination variations.”