Ravi Garg

Research Interests

Artificial Intelligence Computer Vision

Dr Ravi Garg

Future Making Fellow

Research Performance and Research Structures

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.

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

Date Institution name Country Title
2009 - 2014 Queen Mary University of London United Kingdom PhD

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.
DOI
2021 Lassance, C., Latif, Y., Garg, R., Gripon, V., & Reid, I. (2021). Improved visual localization via graph filtering. Journal of Imaging, 7(2), 20.
DOI Scopus2 WoS2
2016 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.
DOI Scopus116 WoS97

Year Citation
2025 Garg, R., Chng, S. F., & Lucey, S. (2025). Direct Alignment for Robust NeRF Learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 15480 LNCS (pp. 88-104). Hanoi: Springer Nature Singapore.
DOI
2025 Choi, M., Garg, R., Moustafa, M., Bhatia, T., & Chowdhury, A. R. (2025). Few-shot Vision-language Prompt Tuning of VLMs for On-road Object Detection. In Geoindustry 2025 Proceedings of 4th ACM Sigspatial International Workshop on Spatial Big Data and AI for Industrial Applications (pp. 1-13). ACM.
DOI
2024 Ch'ng, S. -F., Garg, R., Saratchandran, H., & Lucey, S. (2024). Invertible Neural Warp for NeRF.. In A. Leonardis, E. Ricci, S. Roth, O. Russakovsky, T. Sattler, & G. Varol (Eds.), ECCV (17) Vol. 15075 (pp. 405-421). Springer.
2024 Li, P., Purkait, P., Ajanthan, T., Abdolshah, M., Garg, R., Husain, H., . . . Van Den Hengel, A. (2024). Semi-Supervised Semantic Segmentation under Label Noise via Diverse Learning Groups. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV 2023) (pp. 1229-1238). online: IEEE.
DOI Scopus21 WoS15
2024 Silva, A., Moskvyak, O., Long, A., Garg, R., Gould, S., Avraham, G., & Van Den Hengel, A. (2024). LipAT: Beyond Style Transfer for Controllable Neural Simulation of Lipstick using Cosmetic Attributes. In Proceedings - 2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024 (pp. 8031-8040). Online: IEEE.
DOI
2022 Long, A., Yin, W., Ajanthan, T., Nguyen, V., Purkait, P., Garg, R., . . . Van Den Hengel, A. (2022). Retrieval Augmented Classification for Long-Tail Visual Recognition. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 2022-June (pp. 6949-6959). Online: IEEE.
DOI Scopus110 WoS76
2020 Li, K., Garg, R., Cai, M., & Reid, I. (2020). Single-view object shape reconstruction using deep shape prior and silhouette. In 30th British Machine Vision Conference 2019, BMVC 2019 (pp. 1-14). online: BMVA.
Scopus3
2019 Singh, R., Turaga, P., Jayasuriya, S., Garg, R., & Braun, M. W. (2019). Non-Parametric Priors For Generative Adversarial Networks. In Proceedings of Machine Learning Research Vol. 97 (pp. 5838-5847).
Scopus2
2019 Weerasekera, C. S., Garg, R., Latif, Y., & Reid, I. (2019). Learning deeply supervised good features to match for dense monocular reconstruction. In Proceedings of the 14th Asian Conference on Computer Vision (ACCV 2018), as published in Lecture Notes in Computer Science Vol. 11365 (pp. 609-624). Switzerland: Springer.
DOI Scopus3 WoS3
2019 Zhan, H., Weerasekera, C. S., Garg, R., & Reid, I. (2019). Self-supervised learning for single view depth and surface normal estimation. In Proceedings of the 2019 IEEE International Conference on Robotics and Automation (ICRA) Vol. 2019-May (pp. 4811-4817). Piscataway, NJ.: IEEE.
DOI Scopus23 WoS21
2018 Latif, Y., Garg, R., Milford, M., & Reid, I. (2018). Addressing challenging place recognition tasks using generative adversarial networks. In Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA) (pp. 2349-2355). Piscataway, NJ.: IEEE.
DOI Scopus32 WoS26
2018 Weerasekera, C. S., Dharmasiri, T., Garg, R., Drummond, T., & Reid, I. (2018). Just-in-time reconstruction: Inpainting sparse maps using single view depth predictors as priors. In Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA) (pp. 4977-4984). Piscataway, NJ.: IEEE.
DOI Scopus20 WoS12
2018 Zhan, H., Garg, R., Weerasekera, C. S., Li, K., Agarwal, H., & Reid, I. M. (2018). Unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction. In Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2018) (pp. 340-349). Piscataway, NJ.: IEEE.
DOI Scopus618 WoS501
2017 Milan, A., Rezatofighi, S., Garg, R., Dick, A., & Reid, I. (2017). Data-driven approximations to NP-hard problems. In Proceedings of the 31st AAAI Conference on Artificial Intelligence, AAAI 2017 (pp. 1453-1459). San Francisco: AAAI.
Scopus48 WoS23
2017 Ji, P., Reid., Garg., H. Li., & M. Salzmann. (2017). Low-Rank Kernel Subspace Clustering. In https://arxiv.org/pdf/1707.04974.pdf. https://arxiv.org/pdf/1707.04974.pdf.
2017 Weerasekera, C., Latif, Y., Garg, R., & Reid, I. (2017). Dense monocular reconstruction using surface normals. In 2017 IEEE International Conference on Robotics and Automation (ICRA) (pp. 2524-2531). online: IEEE.
DOI Scopus23
2017 Johnston, A., Garg, R., Carneiro, G., Reid, I., & van den Hengel, A. (2017). Scaling CNNs for high resolution volumetric reconstruction from a single image. In Proceedings of the IEEE International Conference on Computer Vision Workshop (ICCVW 2017) Vol. 2018-January (pp. 930-939). Piscataway, NJ: IEEE.
DOI Scopus35 WoS23
2016 Garg, R., Vijay Kumar, B., Carneiro, G., & Reid, I. (2016). Unsupervised CNN for single view depth estimation: geometry to the rescue. In B. Leibe, J. Matas, N. Sebe, & M. Welling (Eds.), Proceedings of the 14th European Conference on Computer Vision Vol. 9912 LNCS (pp. 740-756). Amsterdam, Netherlands: Springer International Publishing.
DOI Scopus1197 WoS1191
2013 Garg, R., Roussos, A., & Agapito, L. (2013). Dense variational reconstruction of non-rigid surfaces from monocular video. In Proceedings, 2013 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2013, 23-28 June 2013, Portland, Oregon, USA (pp. 1272-1279). Portland, Oregon: IEEE.
DOI Scopus184 WoS131
2012 Roussos, A., Russell, C., Garg, R., & Agapito, L. (2012). Dense multibody motion estimation and reconstruction from a handheld camera. In 2012 IEEE International Symposium on Mixed and Augmented Reality (ISMAR 2012) (pp. 31-40). Atlanta, Georgia: Institute of Electrical and Electronics Engineers.
DOI Scopus33 WoS16
2011 Julià, C., Paladini, M., Garg, R., Puig, D., & Agapito, L. (2011). Automatic estimation of the number of deformation modes in non-rigid SfM with missing data. In Image Analysis: 17th Scandinavian Conference, SCIA 2011, Ystad, Sweden, May 2011. Proceedings Vol. 6688 LNCS (pp. 381-392). Ystad, Sweden: Springer.
DOI
2011 Garg, R., Roussos, A., & Agapito, L. (2011). Robust trajectory-space TV-L1 optical flow for non-rigid sequences. In Y. Boykov, F. Kahl, V. Lempitsky, & F. Schmidt (Eds.), Energy Minimization Methods in Computer Vision and Pattern Recognition,: proceedings 8th International Conference, EMMCVPR 2011 Vol. 6819 LNCS (pp. 300-314). St. Petersburg, Russia: Springer.
DOI Scopus19
2010 Garg, R., Pizarro, L., Rueckert, D., & Agapito, L. (2010). Dense multi-frame optic flow for non-rigid objects using subspace constraints. In Computer Vision – ACCV 2010 Vol. 6495 LNCS (pp. 460-473). Queenstown, New Zealand: Springer.
DOI Scopus20 WoS17
2010 Garg, R., Sahu, R., Mousset, S., & Bensrhair, A. (2010). Obstacle detection for vehicle navigation by chaining of adoptive declivities using geometrical constrains. In K. Jusoff, & Y. Xie (Eds.), Proceedings of SPIE the International Society for Optical Engineering Vol. 7546 (pp. 7 pages). SINGAPORE, Singapore: SPIE-INT SOC OPTICAL ENGINEERING.
DOI

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.

Date Role Research Topic Program Degree Type Student Load Student Name
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
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
2024 Co-Supervisor Visual Object Tracking and Modelling for Embodied AI. Doctor of Philosophy Doctorate Full Time Mr Adam William Bethell

Date Role Research Topic Program Degree Type Student Load Student Name
2024 - 2025 Co-Supervisor The Role of Rank in Efficient Learning Doctor of Philosophy Doctorate Full Time Mr Jianqiao Zheng
2017 - 2018 Co-Supervisor Deeply Learned Priors for Geometric Reconstruction Doctor of Philosophy Doctorate Full Time Mr Saroj Weerasekera

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