Learning Rank Reduced Interpolation with Principal Component Analysis

In computer vision most iterative optimization algorithms, both sparse and dense, rely on a coarse and reliable dense initialization to bootstrap their optimization procedure. For example, dense optical flow algorithms profit massively in speed and robustness if they are initialized well in the basin of convergence of the used loss function. The same holds true for methods as sparse feature tracking when initial flow or depth information for new features at arbitrary positions is needed. This makes it extremely important to have techniques at hand that allow to obtain from only very few available measurements a dense but still approximative sketch of a desired 2D structure (e.g. depth maps, optical flow, disparity maps, etc.). The 2D map is regarded as sample from a 2D random process. The method presented here exploits the complete information given by the principal component analysis (PCA) of that process, the principal basis and its prior distribution. The method is able to determine a dense reconstruction from sparse measurement. When facing situations with only very sparse measurements, typically the number of principal components is further reduced which results in a loss of expressiveness of the basis. We overcome this problem and inject prior knowledge in a maximum a posterior (MAP) approach. We test our approach on the KITTI and the virtual KITTI datasets and focus on the interpolation of depth maps for driving scenes. The evaluation of the results show good agreement to the ground truth and are clearly better than results of interpolation by the nearest neighbor method which disregards statistical information.Comment: Accepted at Intelligent Vehicles Symposium (IV), Los Angeles, USA, June 201

Motion from Fixation

We study the problem of estimating rigid motion from a sequence of monocular perspective images obtained by navigating around an object while fixating a particular feature point. The motivation comes from the mechanics of the buman eye, which either pursuits smoothly some fixation point in the scene, or "saccades" between different fixation points. In particular, we are interested in understanding whether fixation helps the process of estimating motion in the sense that it makes it more robust, better conditioned or simpler to solve. We cast the problem in the framework of "dynamic epipolar geometry", and propose an implicit dynamical model for recursively estimating motion from fixation. This allows us to compare directly the quality of the estimates of motion obtained by imposing the fixation constraint, or by assuming a general rigid motion, simply by changing the geometry of the parameter space while maintaining the same structure of the recursive estimator. We also present a closed-form static solution from two views, and a recursive estimator of the absolute attitude between the viewer and the scene. One important issue is how do the estimates degrade in presence of disturbances in the tracking procedure. We describe a simple fixation control that converges exponentially, which is complemented by a image shift-registration for achieving sub-pixel accuracy, and assess how small deviations from perfect tracking affect the estimates of motion