Event-based 3D reconstruction from neuromorphic retinas

This paper presents a novel NN-ocular 3D reconstruction algorithm for event-based vision data from bio-inspired artificial retina sensors. Artificial retinas capture visual information asynchronously and encode it into streams of asynchronous spike-like pulse signals carrying information on, e.g., temporal contrast events in the scene. The precise time of the occurrence of these visual features are implicitly encoded in the spike timings. Due to the high temporal resolution of the asynchronous visual information acquisition, the output of these sensors is ideally suited for dynamic 3D reconstruction. The presented technique takes full benefit of the event-driven operation, i.e. events are processed individually at the moment they arrive. This strategy allows us to preserve the original dynamics of the scene, hence allowing for more robust 3D reconstructions. As opposed to existing techniques, this algorithm is based on geometric and time constraints alone, making it particularly simple to implement and largely linear.