I received my BSc (Hons) and PhD in Computer Science from the University of Otago, and completed my studies 2000. After working for a short time in Christchurch as a software developer I took up a lectureship at The University of Nottingham. In 2006 I returned to New Zealand and worked in commercial research and development at the Geospatial Research Centre and then Areograph Ltd before returning to the Computer Science Department as a lecturer in 2011.
My research interests are in computer vision, and particularly in the reconstruction of 3D scenes from multiple views. While there are a number of outstanding issues, recent advances mean that a wide range of scenes can be reconstructed from images alone. Applications of this technology include terrain modelling from aerial imagery, construction of architectural models, and generation of realistic environments for games and entertainment. I am also interested in related fields such as motion analysis, stereo vision, and image based rendering, as well as applications of computer vision and image processing to the analysis of scientific imagery.
Mills, S., & Regenbrecht, H. (2023). Respecting and protecting cultural values in an Indigenous virtual reality project. IEEE Technology & Society, 42(2), 48-52. doi: 10.1109/MTS.2023.3275628
Baker, L., Ventura, J., Langlotz, T., Gul, S., Mills, S., & Zollmann, S. (2023). Localization and tracking of stationary users for augmented reality. Visual Computer. Advance online publication. doi: 10.1007/s00371-023-02777-2
Venn, L., & Mills, S. (2023). A VR tool for labelling 3D data sets. In W. Q. Yan, M. Nguyen & M. Stommel (Eds.), Image and vision computing: 37th International Conference IVCNZ 2022 revised selected papers: Lecture notes in computer science (Vol. 13836). (pp. 262-271). Cham, Switzerland: Springer. doi: 10.1007/978-3-031-25825-1_19
Regenbrecht, H., Park, N., Duncan, S., Mills, S., Lutz, R., Lloyd-Jones, L., Ott, C., … Whaanga, H. (2022). Ātea presence: Enabling virtual storytelling, presence, and tele-co-presence in an Indigenous setting. IEEE Technology & Society, 41(1), 32-42. doi: 10.1109/MTS.2022.3147525
Liu, J., Mills, S., & McCane, B. (2022). RocNet: Recursive octree network for efficient 3D processing. Computer Vision & Image Understanding, 224, 103555. doi: 10.1016/j.cviu.2022.103555