25 February 2008 Denoising techniques for raw 3D data of TOF cameras based on clustering and wavelets
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Abstract
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.
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B. Moser, F. Bauer, P. Elbau, B. Heise, H. Schöner, "Denoising techniques for raw 3D data of TOF cameras based on clustering and wavelets", Proc. SPIE 6805, Three-Dimensional Image Capture and Applications 2008, 68050E (25 February 2008); doi: 10.1117/12.765541; https://doi.org/10.1117/12.765541
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