| Author(s) |
Bernhard Moser
Frank Bauer
Peter Elbau
Bettina Heise
Holger Schöner
|
| Title |
Denoising techniques for raw 3D data of TOF cameras based on clustering and wavelets |
| Booktitle |
Electronic Proceedings of Electronic Imaging 2008, Three-Dimensional Image Capture and Applications 2008 |
| Typ |
Inproceedings |
| Month |
January |
| Year |
2008 |
| Volume |
6805 |
| Pages |
doi: 10.1117/12.765541 |
| Editor(s) |
B. D. Corner, M. Mochimaru, R. Sitnik |
| Address |
San Jose, California, USA |
| SCCH # |
0743 |
| In order to measure the 3D structure of a number of objects a comparably
new technique in computer vision exists, namely time of flight (TOF) cameras. The
overall principle is rather easy and has been applied using sound or light for a long time
in all kind of sonar and lidar systems. However in this approach one uses modulated light
waves and receives the signals by a parallel pixel array structure. Out of the travelling
time at each pixel one can estimate the depth structure of a distant object. The technique
requires measuring the intensity differences and ratios of several pictures with extremely
high accuracy; therefore one faces in practice rather high noise levels. Object features
as reflectance and roughness influence the measurement results. This leads to partly
high noise levels with variances dependent on the illumination and material parameters.
It can be shown that a reciprocal relation between the variance of the phase and the
squared amplitude of the signals exists. On the other hand, objects can be distinguished
using these dependencies on surface characteristics. It is shown that based on local
variances assigned to separated objects appropriate denoising can be performed based
on Wavelets and edge-preserving smoothing methods. |