Optimized KinectFusion Algorithm for 3D Scanning Applications

Optimized KinectFusion Algorithm for 3D Scanning Applications. Alhwarin, F., Schiffer, S., Ferrein, A., & Scholl, I. In Wiebe, S., Gamboa, H., Fred, A. L. N., & i Badia, S. B., editors, Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) , volume 2: BIOIMAGING, pages 50–57, 2018. SciTePress. Best Paper Candidate (Short Listed)

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KinectFusion is an effective way to reconstruct indoor scenes. It takes a depth image stream and uses the iterative closests point (ICP) method to estimate the camera motion. Then it merges the images in a volume to construct a 3D model. The model accuracy is not satisfactory for certain applications such as scanning a human body to provide information about bone structure health. For one reason, camera noise and noise in the ICP method limit the accuracy. For another, the error in estimating the global camera poses accumulates. In this paper, we present a method to optimize KinectFusion for 3D scanning in the above scenarios. We aim to reduce the noise influence on camera pose tracking. The idea is as follows: in our application scenarios we can always assume that either the camera rotates around the object to be scanned or that the object rotates in front of the camera. In both cases, the relative camera/object pose is located on a 3D-circle. Therefore, camera motion can be described as a rotation around a fixed axis passing through a fixed point. Since the axis and the center of rotation are always fixed, the error averaging principle can be utilized to reduce the noise impact and hence to enhance the 3D model accuracy of scanned object.

@inproceedings{Alhwarin0FS18,
  author    = {Faraj Alhwarin and Stefan Schiffer and Alexander Ferrein and Ingrid Scholl},
  editor    = {Sheldon Wiebe and Hugo Gamboa and Ana L. N. Fred and Sergi Berm{\'{u}}dez i Badia},
  title     = {{Optimized KinectFusion Algorithm for 3D Scanning Applications}},
  booktitle = {Proceedings of the 11th International Joint Conference on Biomedical
               Engineering Systems and Technologies ({BIOSTEC} 2018) },
  volume       = {2: BIOIMAGING},
  pages     = {50--57},
  publisher = {SciTePress},
  isbn      = {978-989-758-278-3},
  year      = {2018},
  doi          = {10.5220/0006594700500057},
  url_scitepress = {http://www.scitepress.org/PublicationsDetail.aspx?ID=dZs8lGPb760=&t=1},
  note      = {Best Paper Candidate (Short Listed)},
  keywords  = {BodyScanner},
  abstract     = {KinectFusion is an effective way to reconstruct
                  indoor scenes. It takes a depth image stream and
                  uses the iterative closests point (ICP) method to
                  estimate the camera motion. Then it merges the
                  images in a volume to construct a 3D model. The
                  model accuracy is not satisfactory for certain
                  applications such as scanning a human body to
                  provide information about bone structure health. For
                  one reason, camera noise and noise in the ICP method
                  limit the accuracy. For another, the error in
                  estimating the global camera poses accumulates. In
                  this paper, we present a method to optimize
                  KinectFusion for 3D scanning in the above
                  scenarios. We aim to reduce the noise influence on
                  camera pose tracking. The idea is as follows: in our
                  application scenarios we can always assume that
                  either the camera rotates around the object to be
                  scanned or that the object rotates in front of the
                  camera. In both cases, the relative camera/object
                  pose is located on a 3D-circle. Therefore, camera
                  motion can be described as a rotation around a fixed
                  axis passing through a fixed point. Since the axis
                  and the center of rotation are always fixed, the
                  error averaging principle can be utilized to reduce
                  the noise impact and hence to enhance the 3D model
                  accuracy of scanned object.},
}

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