Integration of Absolute Orientation Measurements in the KinectFusion Reconstruction Pipeline
KAUST DepartmentCompetitive Research Funds
Computer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Electrical Engineering Program
Visual Computing Center (VCC)
Preprint Posting Date2018-02-12
Online Publication Date2018-12-18
Print Publication Date2018-06
Permanent link to this recordhttp://hdl.handle.net/10754/627172
MetadataShow full item record
AbstractIn this paper, we show how absolute orientation measurements provided by low-cost but high-fidelity IMU sensors can be integrated into the KinectFusion pipeline. We show that integration improves both runtime, robustness and quality of the 3D reconstruction. In particular, we use this orientation data to seed and regularize the ICP registration technique. We also present a technique to filter the pairs of 3D matched points based on the distribution of their distances. This filter is implemented efficiently on the GPU. Estimating the distribution of the distances helps control the number of iterations necessary for the convergence of the ICP algorithm. Finally, we show experimental results that highlight improvements in robustness, a speed-up of almost 12%, and a gain in tracking quality of 53% for the ATE metric on the Freiburg benchmark.
CitationGiancola S, Schneider J, Wonka P, Ghanem BS (2018) Integration of Absolute Orientation Measurements in the KinectFusion Reconstruction Pipeline. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Available: http://dx.doi.org/10.1109/CVPRW.2018.00198.
SponsorsThis work was supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research and the Visual Computing Center (VCC).
Conference/Event name31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018