View Item 
        •   Utrecht University Student Theses Repository Home
        • UU Theses Repository
        • Theses
        • View Item
        •   Utrecht University Student Theses Repository Home
        • UU Theses Repository
        • Theses
        • View Item
        JavaScript is disabled for your browser. Some features of this site may not work without it.

        Browse

        All of UU Student Theses RepositoryBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

        Flow Compare: Conditional Normalizing Flows for Point Cloud Change Detection

        Thumbnail
        View/Open
        master_thesis_SJ_Galanakis_FlowCompare.pdf (13.89Mb)
        Publication date
        2021
        Author
        Galanakis, S.D.
        Metadata
        Show full item record
        Summary
        Despite significant progress in 3D deep learning for tasks such as classification and semantic segmentation, robust change detection techniques for complex, coloured environments have not been developed. This is in part due to the absence of labelled change detection datasets and the inherent difficulty of constructing such datasets despite the abundance of unlabeled data. Flow Compare is a fully unsupervised approach that leverages expressive generative models with iterative attention trained on multi-temporal coloured point clouds. Change detection is achieved by reframing the problem as anomaly detection given a learnt conditional distribution. Training pairs are formed by co-registered multi-temporal extracts from coloured point cloud scenes. The inherent class imbalance due to the rarity of semantically important change, which is problematic for supervised approaches, is here harnessed to guarantee that relevant changes are considered anomalies under the learnt distribution. This approach shows promise in detecting not only geometric change but also colour change whilst being robust to common semantically unimportant change.
        URI
        https://studenttheses.uu.nl/handle/20.500.12932/41253
        Collections
        • Theses
        Utrecht university logo