Neuromorphic cameras, or Dynamic Vision Sensors (DVS), are bio-inspired event cameras that measure changes in the image brightness asynchronously and independently at the pixel level. Recently they garnered increasing interest due to their extremely high temporal resolution, wide dynamic range, low power consumption, and high pixel bandwidth. Despite their advantages, most existing 3D event imaging solutions rely on multi-camera configurations, which are costly, complex, and challenging to synchronize. In this work, we present a novel framework for four-dimensional (4D) event imaging utilizing a single static neuromorphic camera to perform stereo imaging without requiring multiple sensors. By optically encoding and spatially multiplexing stereo channels onto the full DVS sensor array we are leveraging inherent sparsity in events to maximize sensor resolution utilization while preserving the core benefits of event cameras.