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We report the theory, construction, and testing of a flexible ocular, on-the-eye microsystem used for ultra-low power object distance sensing suitable for smart adaptive contact lenses. The microsystem determines object distance by vergence angle triangulation. Vergence angle is determined from passive measurements of the earth’s magnetic field at each eye. Vergence measurements were performed every 5-degree interval over 35 degrees in total for each eye to accommodate the entire human visual range. Vergence angle measurements had an RMS error of 1.74 degrees and a distance ranging RMS error of 14.04 mm. The energy requirement per magnetic field measurement was estimated to be approximately 2 μJ per eye.
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Energy-Efficient Adaptive 3D Sensing
Active depth sensing achieves robust depth estimation but is usually limited by the sensing range. Naively increas- ing the optical power can improve sensing range but in- duces eye-safety concerns for many applications, including autonomous robots and augmented reality. In this paper, we propose an adaptive active depth sensor that jointly opti- mizes range, power consumption, and eye-safety. The main observation is that we need not project light patterns to the entire scene but only to small regions of interest where depth is necessary for the application and passive stereo depth es- timation fails. We theoretically compare this adaptive sens- ing scheme with other sensing strategies, such as full-frame projection, line scanning, and point scanning. We show that, to achieve the same maximum sensing distance, the proposed method consumes the least power while having the shortest (best) eye-safety distance. We implement this adaptive sensing scheme with two hardware prototypes, one with a phase-only spatial light modulator (SLM) and the other with a micro-electro-mechanical (MEMS) mirror and diffractive optical elements (DOE). Experimental results validate the advantage of our method and demonstrate its capability of acquiring higher quality geometry adaptively. Please see our project website for video results and code: https://btilmon.github.io/e3d.html
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- Award ID(s):
- 1942444
- PAR ID:
- 10426421
- Date Published:
- Journal Name:
- IEEE Conference on Computer Vision and Pattern Recognition
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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