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    Efficient Surfel Fusion Using Normalised Information Distance

    Gallagher, Louis and McDonald, John (2019) Efficient Surfel Fusion Using Normalised Information Distance. In: CVPR Workshops (CVPRW 2019): 3D Scene Understanding for Vision, Graphics, and Robotics., June 2019.

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    We present a new technique that achieves a significant reduction in the quantity of measurements required for a fusion based dense 3D mapping system to converge to an accurate, de-noised surface reconstruction. This is achieved through the use of a Normalised Information Distance metric, that computes the novelty of the information contained in each incoming frame with respect to the reconstruction, and avoids fusing those frames that exceed a redundancy threshold. This provides a principled approach for opitmising the trade-off between surface reconstruction accuracy and the computational cost of processing frames. The technique builds upon the ElasticFusion (EF) algorithm where we report results of the technique’s scalability and the accuracy of the resultant maps by applying it to both the ICL-NUIM [3] and TUM RGB-D [8] datasets. These results demonstrate the capabilities of the approach in performing accurate surface reconstructions whilst utilising a fraction of the frames when compared to the original EF algorithm.

    Item Type: Conference or Workshop Item (Paper)
    Keywords: Efficient Surfel Fusion; Normalised; Information; Distance;
    Academic Unit: Assisting Living & Learning,ALL institute
    Faculty of Science and Engineering > Computer Science
    Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Item ID: 15562
    Depositing User: John McDonald
    Date Deposited: 23 Feb 2022 12:38
    Refereed: Yes
      Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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