Dylan Campbell

The University of Oxford

Visual Geometry Group

About Me

I am a Research Fellow of the Visual Geometry Group at the University of Oxford, where I am supervised by Andrea Vedaldi, Andrew Zisserman, and João Henriques. Previously I was a Research Fellow of the Australian Centre for Robotic Vision and the Australian National University (ANU), where I was supervised by Stephen Gould. I received my PhD from ANU, supported by Data61/CSIRO, working on geometric vision problems under the supervision of Lars Petersson, Laurent Kneip and Hongdong Li. I hold a BE in Mechatronic Engineering (Hons) from the University of New South Wales, with an honours thesis in robotics completed under the supervision of Mark Whitty.

I have broad research interests within computer vision and robotics, including geometric vision and human-centred vision. In particular, I have investigated the problem of geometric sensor data alignment, such as camera localisation, simultaneous localisation and mapping, and structure from motion, and the underlying geometry and optimisation problems. Currently, I am increasingly looking at problems of recognising, modelling, and predicting human actions, poses and human-object interactions with a view to facilitate robot-human interaction as part of an Australian Centre for Robotic Vision project. I am also investigating how to insert optimisation problems into deep learning systems, including geometric model fitting algorithms, and how to handle hard constraints for these problems.

News

Deep Declarative Networks Workshop (CVPR2020)

The Deep Declarative Networks workshop was held at CVPR2020 and consisted of 6 invited talks and 5 accepted papers with oral presentations. Watch the talks here. Read the papers here.

  • Joined the Visual Geometry Group at the University of Oxford as a Research Fellow under the supervision of Andrea Vedaldi, Andrew Zisserman, and João Henriques.

  • Our Deep Declarative Networks tutorial at ECCV2020 has concluded, but the leave behind an excellent set of reference videos for anyone interested in learning about the topic. Thank you to Itzik for taking the lead on organising it and to all the speakers for their fantastic contributions!

  • Our paper "Solving the Blind Perspective-n-Point Problem End-To-End With Robust Differentiable Geometric Optimization" has been accepted for the 2020 European Conference on Computer Vision (ECCV) as an oral presentation (2% acceptance rate)

  • Our paper "Deep View Synthesis From Colored 3D Point Clouds" has been accepted for the 2020 European Conference on Computer Vision (ECCV) - congratulations Wayne and Zhenbo!

  • The Deep Declarative Networks workshop at CVPR2020 was a great success, with over 100 attendees contributing to the discussion of this emerging field of research - thanks to all who helped organise the event and to our fantastic speakers! Watch the talks here.

  • Our paper "Inferring Temporal Compositions of Actions Using Probabilistic Automata" has been accepted for the 2020 Conference on Computer Vision and Pattern Recognition (CVPR) workshop on Compositionality in Computer Vision (CICV 2020) - congratulations Rodrigo!

  • Our paper "Where am I looking at? Joint Location and Orientation Estimation by Cross-View Matching" has been accepted for the 2020 Conference on Computer Vision and Pattern Recognition (CVPR) - congratulations Yujiao!

2019:

  • We're running a tutorial on Deep Declarative Networks at ECCV 2020 - details to come

  • We're running a workshop on Deep Declarative Networks at CVPR 2020 - details to come

  • Reference code and tutorials for deep declarative networks are now available on GitHub

  • Our paper "The Alignment of the Spheres: Globally-Optimal Spherical Mixture Alignment for Camera Pose Estimation" has been accepted for the 2019 Conference on Computer Vision and Pattern Recognition (CVPR)

2018:

  • Won 1st prize in the IEEE Australia Council Postgraduate Student Paper Competition for our TPAMI article “Globally-Optimal Inlier Set Maximisation for Camera Pose and Correspondence Estimation

  • Accepted a position as Research Fellow with the Australian Centre for Robotic Vision and the Australian National University under the supervision of Stephen Gould

  • Our article "Globally-Optimal Inlier Set Maximisation for Camera Pose and Correspondence Estimation" has been published in Transactions on Pattern Analysis and Machine Intelligence (TPAMI)

  • Commenced research project into heart rate detection using semi-supervised deep learning

  • Completed and submitted PhD thesis "Robust and Optimal Methods for Geometric Sensor Data Alignment"!

  • Submitted article on camera pose estimation to Transactions on Pattern Analysis and Machine Intelligence (TPAMI)

2017:

  • Our paper "Globally-Optimal Inlier Set..." etc has won a Marr Prize Honourable Mention at ICCV 2017!

  • Source code for GOPAC released online

  • Moved site to Black Mountain Laboratories! The reality is more prosaic than the name unfortunately...

  • Our paper "Globally-Optimal Inlier Set Maximisation for Simultaneous Camera Pose and Feature Correspondence" has been accepted for the 2017 International Conference on Computer Vision (ICCV) as an oral presentation (2% acceptance rate)

2016:

  • Source code for GOGMA released online

  • Our paper "GOGMA: Globally-Optimal Gaussian Mixture Alignment" has been accepted for the 2016 Conference on Computer Vision and Pattern Recognition (CVPR) as a spotlight presentation

  • Our article "Go-ICP: A Globally Optimal Solution to 3D ICP Point-Set Registration" has been published in Transactions on Pattern Analysis and Machine Intelligence (TPAMI)

2015:

  • Source code for Support Vector Registration (SVR) released online

  • Our paper "An Adaptive Data Representation for Robust Point-Set Registration and Merging " has been accepted for the 2015 International Conference on Computer Vision (ICCV) [spotlight presentation]