Bo Chen

Bo Chen

Postdoctoral researcher

Australian Institute for Machine Learning, the University of Adelaide

Biography

I am a postdoctoral researcher at the Australian Institute for Machine Learning (AIML), the University of Adelaide. My research interests lie in the intersection of machine learning and computer vision. Specific topics include: object detection, object pose estimation, differentiable geometric optimisation, domain adaptation, and adversarial learning.

Interests
  • Artificial Intelligence
  • Deep Learning
  • Computer Vision
Education

  • PhD in Artificial Intelligence, 2019

    Monash University

  • MSc in Statistics, 2013

    The University of Queensland

  • BBM in E-Commerce, 2006

    Sun Yat-Sen University

Experience

 
 
 
 
 
Postdoctoral researcher
AIML, the University of Adelaide
Feb 2019 – Present Adelaide, Australia
 
 
 
 
 
Teaching Associate
Monash University
Jul 2017 – Dec 2018 Melbourne, Australia
 
 
 
 
 
Data Analysis Supervisor
Moonbasa
Jul 2011 – Jun 2012 Guangzhou, China
 
 
 
 
 
Assistant Research Executive
Ipsos
Sep 2010 – Feb 2011 Guangzhou, China

Featured Publications

Bo Chen, Alvaro Parra, Jiewei Cao, Nan Li, Tat-Jun Chin
September 2019 In CVPR 2020
End-to-End Learnable Geometric Vision by Backpropagating PnP Optimization
End-to-End Learnable Geometric Vision by Backpropagating PnP Optimization

We propose BPnP, a network module for backpropagation through a PnP optimizer, as if the optimizer were a differentiable function.
PDF Cite Project
Bo Chen, Jiewei Cao, Alvaro Parra, Tat-Jun Chin
September 2019 In ICCVW 2019
Satellite Pose Estimation with Deep Landmark Regression and Nonlinear Pose Refinement
Satellite Pose Estimation with Deep Landmark Regression and Nonlinear Pose Refinement

We proposed an approach to estimate the 6DOF pose of a satellite. Our method won the first place in the Kelvins Pose Estimation Challenge organised by the European Space Agency (ESA).
PDF Cite Project

Publications

SpotGEO dataset
Official dataset of ESA’s Kelvins SpotGEO Challenge
Bo Chen, Daqi Liu, Tat-Jun Chin, Mark Rutten, Dawa Derksen, Marcus Märtens, Moritz von Looz, Gurvan Lecuyer, Dario Izzo
Cite DOI
Anomaly Detection via Neighbourhood Contrast
Relative scores such as Local Outlying Factor and mass ratio have been shown to be better scores than global scores in detecting …
Bo Chen, Kai Ming Ting, Tat-Jun Chin
PDF Cite DOI
Topological Sweep for Multi-Target Detection of Geostationary Space Objects
We propose an algorithm to detect Geostationary space objects using topological sweep.
Daqi Liu, Bo Chen, Tat-Jun Chin, Mark Rutten
PDF Cite
End-to-End Learnable Geometric Vision by Backpropagating PnP Optimization
We propose BPnP, a network module for backpropagation through a PnP optimizer, as if the optimizer were a differentiable function.
Bo Chen, Alvaro Parra, Jiewei Cao, Nan Li, Tat-Jun Chin
PDF Cite Project
End-to-End Learnable Geometric Vision by Backpropagating PnP Optimization
Satellite Pose Estimation with Deep Landmark Regression and Nonlinear Pose Refinement
We proposed an approach to estimate the 6DOF pose of a satellite. Our method won the first place in the Kelvins Pose Estimation Challenge organised by the European Space Agency (ESA).
Bo Chen, Jiewei Cao, Alvaro Parra, Tat-Jun Chin
PDF Cite Project
Satellite Pose Estimation with Deep Landmark Regression and Nonlinear Pose Refinement
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Projects

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Robust Object Pose Estimation
Estimating the 6 degrees of freedom object pose from an RGB image.
Robust Object Pose Estimation
Cactus Mapping
Aerial cactus mapping in the Flinders Ranges
Cactus Mapping
Kelvins SpotGEO Challenge
Spot the GEO satellites challenge co-organised with ESA
Kelvins SpotGEO Challenge
Sattelite Pose Estimation
Estimate the relative attitude and position of a known spacecraft.
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Sattelite Pose Estimation

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