Evaluation of Pinna Point Cloud Alignment by Means of Non-Rigid Registration Algorithms

Pollack K, Majdak P, Furtado H. Evaluation of Pinna Point Cloud Alignment by Means of Non-Rigid Registration Algorithms. In: AES Conference: 2020 AES International Conference on Audio for Virtual and Augmented Reality (August 2020). Audio Engineering Society; 2020. Paper 10475. Available from: https://aes.org/publications/elibrary-page/?id=21152

@inproceedings{Pollack2020_21152,
  author = {Pollack, Katharina and Majdak, Piotr and Furtado, Hugo},
  title = {{Evaluation of Pinna Point Cloud Alignment by Means of Non-Rigid Registration Algorithms}},
  booktitle = {AES Conference: 2020 AES International Conference on Audio for Virtual and Augmented Reality (August 2020)},
  note = {Paper 10475},
  year = {2020},
  month = aug,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=21152}
}

TY  - CPAPER
TI  - Evaluation of Pinna Point Cloud Alignment by Means of Non-Rigid Registration Algorithms
AU  - Pollack, Katharina
AU  - Majdak, Piotr
AU  - Furtado, Hugo
T2  - AES Conference: 2020 AES International Conference on Audio for Virtual and Augmented Reality (August 2020)
M1  - Paper 10475
PY  - 2020
DA  - 2020/08/06
UR  - https://aes.org/publications/elibrary-page/?id=21152
PB  - Audio Engineering Society
LA  - en
AB  - Recently, there have been advances towards providing personalised binaural audio by means of numerical cal-culations based on a 3D head mesh. Nevertheless, 3D head meshes obtained by non-contact methods such as photogrammetric reconstruction have difficulties in accurately representing the complex biological structure of the pinna. The problems manifest in noisy meshes with holes, confounding the quality of the calculations. These problems can be tackled by applying a template mesh that is deformed to the personalised but noisy target mesh. In this contribution, we review two state-of-the-art non-rigid registration algorithms applied to the deformation of two high-resolution, high-accuracy pinna point clouds. We evaluate the algorithms by discussing their geometric errors, duration of the alignment task, and their robustness to spatial down-sampling.
ER  -