
Ravi Garg
Australian Institute for Machine Learning
Division of Research and Innovation
Eligible to supervise Masters and PhD - email supervisor to discuss availability.
I am a Computer Vision and Machine Learning Researcher and a Future Making Fellow at Australian Institute of Machine Learning at The University of Adelaide. I obtained my PhD from The University of London in 2014 with the dissertation on "Dense motion capture of deformable surfaces from monocular video", under the guidance of Prof. Lourdes Agapito. I have worked for six years as Sr. Research Associate with Prof Ian Reid at The University of Adelaide. My research incorporated Machine Learning into real-time Localisation and Mapping and introduced Unsupervised Learning frameworks for single view depth and egomotion estimation. Before joining the university as Future Making Fellow, I was working as Sr. Applied Scientist for 2.5 years at Amazon Research with Prof Anton Van Den Hengel.
I am currently interested in 3D Reconstruction, Visual Reasoning and Generative AI for Robotics and Automation.
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Appointments
Date Position Institution name 2023 - ongoing Future Making Fellow University of Adelaide 2020 - 2023 Sr. Applied Scientist Amazon 2014 - 2020 Sr. Research Associate The University of Adelaide -
Education
Date Institution name Country Title 2009 - 2014 Queen Mary University of London United Kingdom PhD -
Research Interests
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Journals
Year Citation 2025 Chng, S. F., Garg, R., Saratchandran, H., & Lucey, S. (2025). Invertible Neural Warp for NeRF. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 15075 LNCS, 405-421.
2021 Lassance, C., Latif, Y., Garg, R., Gripon, V., & Reid, I. (2021). Improved visual localization via graph filtering. Journal of Imaging, 7(2), 20.
Scopus2 WoS12016 Garg, R., Eriksson, A., & Reid, I. (2016). Non-linear Dimensionality Regularizer for Solving Inverse Problems. arXiv preprint arXiv:1603.05015. 2013 Garg, R., Roussos, A., & Agapito, L. (2013). A variational approach to video registration with subspace constraints. International Journal of Computer Vision, 104(3), 286-314.
Scopus111 -
Conference Papers
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Preprint
Year Citation 2024 Chng, S. -F., Garg, R., Saratchandran, H., & Lucey, S. (2024). Invertible Neural Warp for NeRF. 2024 Bethell, A., Garg, R., & Reid, I. (2024). Category Level 6D Object Pose Estimation from a Single RGB Image using
Diffusion.
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Current Higher Degree by Research Supervision (University of Adelaide)
Date Role Research Topic Program Degree Type Student Load Student Name 2024 Co-Supervisor Understanding Generalization and Memorization of Deep Neural Network from Distance and Rank Doctor of Philosophy Doctorate Full Time Mr Jianqiao Zheng 2024 Co-Supervisor Visual Object Tracking and Modelling for Embodied AI. Doctor of Philosophy Doctorate Full Time Mr Adam William Bethell 2024 Principal Supervisor Collaborative object identification for Un-crewed Aerial System (UAS) tasks in complex environments Doctor of Philosophy Doctorate Full Time Mr Andrew Martin Chesson -
Past Higher Degree by Research Supervision (University of Adelaide)
Date Role Research Topic Program Degree Type Student Load Student Name 2017 - 2018 Co-Supervisor Deeply Learned Priors for Geometric Reconstruction Doctor of Philosophy Doctorate Full Time Mr Saroj Weerasekera
Connect With Me
External Profiles