Algorithms for 3D data estimation from single-pixel ToF sensors and stereo vision systems

Depth Map Estimation from stereo devices and time of flight range cameras have been a challenging issues in Computer Vision. Distance Estimations from single-pixel histograms of time of flight sensors are exploited in numerous fields. Beyond the several drawbacks such as degradation caused by strong ambient light, scattered and multi-path possibilities, most of the prediction algorithms could be applied to resolve these problems effectively. As these two different tasks are handled in connection with each other, supervised approaches are considered since they provide more robust results. These results are used to train the model to improve three- dimensional geometry information and against major difficulties such as complicated patterns and objects. These approaches are observed according to their accuracy with help of metrics and get improved their performances. This thesis focuses on the analysis of Time-of-Flight and stereo vision systems for depth map estimation and single-pixel distance prediction. State of art algorithms are compared and implemented with additional strategies which are integrated to minimize the error ratio. The histograms which are obtained from Time of Flight Sensor Simulation are exploited as a dataset for single-pixel distance prediction and after that, NYU Dataset is selected for depth map estimation.