Fast Developmental Stereo-Disparity Detectors

Traditional methods for stereo-disparity detection use explicit search between the left and right images. Although such methods are simple and intuitive for understanding, they suffer from degeneracies when the search window contains weak texture. Developmental Networks (DNs) are task-nonspecific and modality-nonspecific learning engines. Because they are general-purpose learners, they have a potential to deal with many types of degeneracies in intelligent systems. This work presents two novel mechanisms to deal with degeneracies: volume dimension and subwindow voting. While developmental stereo-disparity detection has been tested on simulated stereo images in our prior publications, it has never been tested on the real world. This paper reports our system, $3\mathrm{DEye}$, which is the first to have filled this void. The algorithm, software, graphical user interface, training, performance, and update rates on CPU and GPU, respectively, on a Sony G8142 mobile phone are reported. Many deep learning methods that use error back-propagation suffer from the controversy of “post-selection” using the test set [1], to select one from many networks to report. In contrast, all randomly initialized DNs are performance-equivalent, no “post-selection” using test set. Possible future improvements for practical real-world and real-time applications are discussed.