Extended Abstract of Ph.D Thesis Noise modeling and depth calibration for Time-Of-Flight cameras

3D cameras open new perspectives in different application fields such as 3D reconstruction, Augmented Reality and video-surveillance since they provide depth information at high frame-rates. However, they have limitations that affect the accuracy of their measures. In particular for TOF (Time-Of-Flight) cameras, two types of error can be distinguished : stochastic noise of the camera and the depth distortion. This is illustrated in the figure 1 where a depth image of a cube is presented. The pixel (x, y) is the depth measure of the 3D point Q. This depth measure (dTOF ) is compared to the real depth dGT . dTOF corresponds to a distorted measure of dGT in addition to the stochastic noise. In state of the art of TOF cameras, the noise is not well studied and the depth distortion models are difficult to use and don’t guarantee the accuracy required for some applications. The objective of this thesis is to study, to model and to propose an accurate and easy to set up calibration method of these two errors of TOF cameras. For both stochastic noise and depth distortion of TOF cameras, two solutions are proposed. Each of them