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

We present an algorithm for optimal view point selection for 3-D reconstruction of an object using 2-D 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.