A Robust O(n) Solution to the Perspective-n-Point Problem

By Shiqi Li, Chi Xu, Ming Xie. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012

500 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/35c8f1e5bcb424dd8e10ccf4045fd56b0fd0ed7b

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

1. Zachary Teed
2. Jia Deng
3. Weining Lu
4. Zhang Tao
5. Haoyin Zhou
6. Masatoshi Okutomi
7. Pascal Fua
8. Kalle Åström

In Computer Graphics and Computer-Aided Design

1. Jia Deng
2. Zhang Tao
3. Pascal Fua
4. Haoyin Zhou
5. Weining Lu
6. Zachary Teed

In Computer Vision and Pattern Recognition

1. Luc Van Gool
2. Dacheng Tao
3. Mathieu Salzmann
4. Andrea Vedaldi
5. Xiaogang Wang
6. Andrew Zisserman
7. Jitendra Malik
8. Trevor Darrell

Citations in the corpus

• Revisiting the PnP Problem: A Fast, General and Optimal Solution (2013, methodology): “[15] proposed another non-iterative O(n) solution, which estimates the coordinates of two special endpoints and ignores only one nonlinear constraint.”
• Vision-Based Pose Estimation From Points With Unknown Correspondences (2014, methodology): “Similarly, a RANSAC-style framework which is similar with that used in the robust PnP applications[36], is introduced for handling”
• 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].”
• DeepV2D: Video to Depth with Differentiable Structure from Motion (2018, methodology): “We unroll a single iteration of Perspective-n-Point (PnP) (Lepetit et al., 2009; Li et al., 2012) performing Gauss-Newton update over SE3 perturbations to minimize geometric reprojection error.”