Stochastic Approximation and Rate-Distortion Analysis for Robust Structure and Motion Estimation

Recent research on structure and motion recovery has focused on issues related to sensitivity and robustness of existing techniques. One possible reason is that in practical applications, the underlying assumptions made by existing algorithms are often violated. In this paper, we propose a framework for 3D reconstruction from short monocular video sequences taking into account the statistical errors in reconstruction algorithms. Detailed error analysis is especially important for this problem because the motion between pairs of frames is small and slight perturbations in its estimates can lead to large errors in 3D reconstruction. We focus on the following issues: physical sources of errors, their experimental and theoretical analysis, robust estimation techniques and measures for characterizing the quality of the final reconstruction. We derive a precise relationship between the error in the reconstruction and the error in the image correspondences. The error analysis is used to design a robust, recursive multi-frame fusion algorithm using “stochastic approximation” as the framework since it is capable of dealing with incomplete information about errors in observations. Rate-distortion analysis is proposed for evaluating the quality of the final reconstruction as a function of the number of frames and the error in the image correspondences. Finally, to demonstrate the effectiveness of the algorithm, examples of depth reconstruction are shown for different video sequences.