A Benchmark Environment for Neuromorphic Stereo Vision

Without neuromorphic hardware, artificial stereo vision suffers from high resource demands and processing times impeding real-time capability. This is mainly caused by high frame rates, a quality feature for conventional cameras, generating large amounts of redundant data. Neuromorphic visual sensors generate less redundant and more relevant data solving the issue of over- and undersampling at the same time. However, they require a rethinking of processing as established techniques in conventional stereo vision do not exploit the potential of their event-based operation principle. Many alternatives have been recently proposed which have yet to be evaluated on a common data basis. We propose a benchmark environment offering the methods and tools to compare different algorithms for depth reconstruction from two event-based sensors. To this end, an experimental setup consisting of two event-based and one depth sensor as well as a framework enabling synchronized, calibrated data recording is presented. Furthermore, we define metrics enabling a meaningful comparison of the examined algorithms, covering aspects such as performance, precision and applicability. To evaluate the benchmark, a stereo matching algorithm was implemented as a testing candidate and multiple experiments with different settings and camera parameters have been carried out. This work is a foundation for a robust and flexible evaluation of the multitude of new techniques for event-based stereo vision, allowing a meaningful comparison