Automatic training of a neural net for active stereo 3D reconstruction

Addresses the problem of recovering 3D geometry using an active stereo vision system. Calibration procedures can be adapted to the active stereo configuration, however, considerable effort is required to accurately model and calibrate the kinematics to avoid poor reconstruction. In the active stereo case there will also be errors due to uncertainty in the kinematics of the system. In addition, data collection needs to be automated because active stereo requires significantly more information for calibration. We present a biologically inspired neural network trained to determine the mapping between 3D geometry and stereo image points. To train the network, we have developed a system to automatically collect accurate calibration data. We compare the reconstructed 3D geometry obtained using a kinematic model based approach with our neural network approach.