Convolutional Legendre Memory Unit (ConvLMU) for Stereo Neuromorphic Event-Based Depth Estimation

Abstract

Event-based computer vision is distinguished by its high temporal resolution and energy efficiency, yet the asynchronous characteristics of event data impede its utilization in conventional embedded computing. In this work, we propose ConvLMU, a neuromorphic design that synergistically integrates Legendre Memory Units (LMUs) and convolutional networks for efficient spatiotemporal processing. Here, we used LMUs to capture long-range temporal dependencies and convolutional layers to provide robust spatial feature extraction for visual depth estimation in 3D from stereo event cameras. Evaluation on the DSEC dataset demonstrates that ConvLMU attains the lowest loss (0.2318) and pixel error rate (0.4904), surpassing the performance of both ConvLSTM (loss: 0.2701; pixel error: 0.7294) and ConvGRU (loss: 0.2818; pixel error: 0.7377). Furthermore, we validated ConvLMU’s enhanced spatial coherence and temporal smoothness using synthetic visual patterns. Our findings substantiate the potential of ConvLMUs as a promising foundation for energy-efficient, neuromorphic-compatible, event-based vision systems.