# Optical Flow Sensor: A Direction-Selective Bionic Retina Design

arXiv: https://arxiv.org/abs/2607.28686 · HTML: https://mlsanity.com/paper/2607.28686/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 20 references that are in the map.
- First posted on arXiv 30 Jul 2026.

## Builds on (most cited first, 5 of 5)

- [RAFT: Recurrent All-Pairs Field Transforms for Optical Flow](https://mlsanity.com/paper/2003.12039/) (2020)
- [PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume](https://mlsanity.com/paper/1709.02371/) (2017)
- [The Multi Vehicle Stereo Event Camera Dataset: An Event Camera Dataset for 3D Perception](https://mlsanity.com/paper/1801.10202/) (2018)
- [Block-Matching Optical Flow for Dynamic Vision Sensor- Algorithm and FPGA Implementation](https://mlsanity.com/paper/1706.05415/) (2017)
- [Efficient Optical flow and Stereo Vision for Velocity Estimation and Obstacle Avoidance on an Autonomous Pocket Drone](https://mlsanity.com/paper/1612.06702/) (2016)

## Abstract (arXiv)

Optical flow characterizes motion in the visual field and is fundamental to motion perception and tracking in biological and artificial vision systems. Biological retinas extract motion efficiently through local ON/OFF pathways and parallel processing, while conventional frame-based optical flow relies on dense sampling and global computation, resulting in high latency and power consumption. To overcome these limitations, we present a pixel-level Optical Flow Sensor (OFS) integrated circuit. The design combines Dynamic Vision Sensor (DVS) ON/OFF event comparison with time-difference measurement to enable fully parallel optical flow computation on-chip. An optical-flow-specific Address-Event Representation (OF-AER) interface supports low-power, high-throughput readout. revBased on the CMOS-based OFS, we further propose optical memristor-based OFS to reduce sensor power consumption and area overhead. Experimental results show that the proposed OFS achieves a 303× reduction in power consumption compared with FPGA-accelerated DVS systems while maintaining microsecond-level latency. Moreover, by directly outputting optical flow vectors, the OFS reduces output data size by approximately 3.3×, demonstrating strong potential for ultra-high-speed, low-power vision sensing applications.