In this paper, we optimize the placement of a camera in simulation in order to achieve a high success rate for a pose estimation problem. This is achieved by simulating 2D images from a stereo camera in a virtual scene. The stereo images are then used to generate 3D point clouds based on two different methods, namely a single shot stereo matching approach and a multi shot approach using phase shift patterns. After a point cloud is generated, we use a RANSAC-based pose estimation algorithm,

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... relies on feature matching of local 3D descriptors. The object we pose estimate is a tray containing items to be grasped by a robot. The pose estimation is done for different positions of the tray and with different item configuration in the tray, in order to determine the success rate of the pose estimation algorithm for a specific camera placement. Then the camera placement is varied according to different optimization algorithms in order to maximize the success rate. Finally, we evaluate the simulation in a real world scene, to determine whether the optimal camera position found in simulation matches the real scenario.