Energy Functions for Global Stereo Matching
By gelautz - Posted on October 22nd, 2007
During the last few years, stereo matching has experienced a significant advance with the introduction of new optimization algorithms. Energy minimization methods based on these optimization schemes currently show the best performance in stereo computation. However, while a lot of research effort has been put into the optimization problem of the energy minimization approach, the fact that the energy functions under consideration might represent an unsatisfactory model for the stereo problem has often been ignored.In the proposed project, we aim at pushing the state-of-the-art in stereo vision by investigating and improving the modelling component of energy minimization techniques. One major contribution to the stereo community is that we will run a competitive performance evaluation among energy functionsthat have been proposed in the literature. Energy functions are typically combinations of several terms that are motivated by the same idea, but differently implemented in each approach. Moreover, the resulting energy functions are minimized using different optimization algorithms. It is therefore difficult to judge why one approach outperforms the other. In the proposed project, we will implement a framework that unifies several energy functions and accounts for their minimization. This framework will serve as the basis for a benchmark test, in which we will use image pairs of real scenes along with ground truth disparities. The goal of our experiments is to identify which components of an energy function result in a performance improvement and which do not. This will lead to a new and deeper understanding of current energy functions and point out those factors that show the highest potential for further improvement. In the second phase of the project, we will use the knowledge gained in our evaluation study to develop novel energy functions. These energy functions will be designed to deliver high-quality disparity maps that improve over the current state-of-the-art. These high-quality disparity maps are vital for a variety of applications, ranging from quality assurance, robotics and virtual reality to promising applications in the entertainment industry such as novel view synthesis.