Embodied Neuromorphic Synergy for Lighting-robust Machine Vision [code]

In this research, inspired by the bio-principles governing the human pupillary control pathway, we devise and implement a novel neuromorphic exposure control (NEC) system. This innovation effectively alleviates the longstanding saturation problem that has plagued real-world intelligent machine vision systems operating under highly dynamic lighting conditions.

The NEC resolves this challenge at its core by exploiting bio-principles found in peripheral vision to the computation of a novel trilinear event double integral (TEDI). This approach enables accurate connections between events and frames in the physics space for swift irradiance prediction, ultimately facilitating rapid control parameter updates.

Our experimental results demonstrate the remarkable efficiency, low latency, superior generalization capability, and bio-inspired nature of the NEC in delivering timely and robust neuromorphic synergy for lighting-robust machine vision across a wide range of real-world applications. These applications encompass autonomous driving, mixed-reality, and three-dimensional reconstruction.