An Information Theoretic Approach for Next Best View Planning in 3-D Reconstruction

We present an algorithm for optimal view point selection for 3D reconstruction of an object using 2D image points. Since the image points are noisy, a Kalman filter is used to obtain the best estimate of the object´s geometry. This Kalman filter allows us to efficiently predict the effect of any given camera position on the uncertainty, and therefore quality, of the estimate. By choosing a suitable optimization criterion, we are able to determine the camera positions which minimize our reconstruction error. We verify our results using two experiments with real images: one experiment uses a calibration pattern for comparison to a ground-truth state, the other reconstructs a real world object