Abstract
The rise of the Internet of Things, autonomous navigation systems, and wearable devices created a growing need for ultra-compact, low-power, low-latency vision sensors that bridge the physical and digital worlds. Vision sensors capture vast amount of data that require swift processing for semantic scene understanding. However, most computer vision algorithms suffer from large power consumption and latency, necessitating the sacrifice of spatial resolution. Optical systems can potentially address these issues with large parallelism and spatial bandwidth for visual data processing. Particularly, free-space optical systems (encoders) can be easily adapted to conventional imaging systems. This paper details the current state of free-space optical encoders and discusses future opportunities for innovations. We also provide insights on where we can achieve optical advantages for computer vision tasks based on empirical evidences.
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Details
1 Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA (ROR: https://ror.org/00cvxb145) (GRID: grid.34477.33) (ISNI: 0000 0001 2298 6657)
2 Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA (ROR: https://ror.org/00cvxb145) (GRID: grid.34477.33) (ISNI: 0000 0001 2298 6657); Department of Physics, University of Washington, Seattle, WA, USA (ROR: https://ror.org/00cvxb145) (GRID: grid.34477.33) (ISNI: 0000 0001 2298 6657)