A Clustering Based Denoising Technique for Range Images of Time of Flight Cameras

Schöner, Holger; Moser, Bernhard; Dorrington, Adrian A.; Payne, Andrew D.; Cree, Michael J.; Bauer, Frank; Heise, Bettina

by Holger Schöner, Bernhard Moser, Adrian A. Dorrington, Andrew D. Payne, Michael J. Cree, Frank Bauer, Bettina Heise

Abstract:

A relatively new technique for measuring the 3D structure of visual scenes is provided by time of flight (TOF) cameras. Reflections of modulated light waves are recorded by a parallel pixel array structure. The time series at each pixel of the resulting image stream is used to estimate travelling time and thus range information. This measuring technique results in noise levels with variances changing over several orders of magnitude dependent on the illumination and material parameters. This makes application of traditional global denoising techniques suboptimal. Using free aditional information from the camera we can get local information by clustering which allows for locally adapted smoothing. To illustrate the success of this method, we compare it with pure time averaging and edge preserving smoothing. We show that this mathematical technique works without individual adaptations on two camera systems with highly different noise characteristics.

Reference:

A Clustering Based Denoising Technique for Range Images of Time of Flight Cameras (Holger Schöner, Bernhard Moser, Adrian A. Dorrington, Andrew D. Payne, Michael J. Cree, Frank Bauer, Bettina Heise), In Proceedings of CIMCA08, 2008.

Bibtex Entry:

@inproceedings{schoner_clustering_2008,
	title = {A Clustering Based Denoising Technique for Range Images of Time of Flight Cameras},
	abstract = {A relatively new technique for measuring the 3D structure of visual scenes is provided by time of flight ({TOF}) cameras. Reflections of modulated light waves are recorded by a parallel pixel array structure. The time series at each pixel of the resulting image stream is used to estimate travelling time and thus range information. This measuring technique results in noise levels with variances changing over several orders of magnitude dependent on the illumination and material parameters. This makes application of traditional global denoising techniques suboptimal. Using free aditional information from the camera we can get local information by clustering which allows for locally adapted smoothing. To illustrate the success of this method, we compare it with pure time averaging and edge preserving smoothing. We show that this mathematical technique works without individual adaptations on two camera systems with highly different noise characteristics.},
	booktitle = {Proceedings of {CIMCA}08},
	author = {Schöner, Holger and Moser, Bernhard and Dorrington, Adrian A. and Payne, Andrew D. and Cree, Michael J. and Bauer, Frank and Heise, Bettina},
	month = dec,
	year = {2008},
	keywords = {3d, clustering, denoising, tof},
	file = {download/publications/Schoener2008_CIMCA_ClusteringBasedDenoisingTimeOfFlightCameras.pdf}
}