  Standard frame-based cameras that sample light intensity frames are heavily
impacted by motion blur for high-speed motion and fail to perceive scene
accurately when the dynamic range is high. Event-based cameras, on the other
hand, overcome these limitations by asynchronously detecting the variation in
individual pixel intensities. However, event cameras only provide information
about pixels in motion, leading to sparse data. Hence, estimating the overall
dense behavior of pixels is difficult. To address such issues associated with
the sensors, we present Fusion-FlowNet, a sensor fusion framework for
energy-efficient optical flow estimation using both frame- and event-based
sensors, leveraging their complementary characteristics. Our proposed network
architecture is also a fusion of Spiking Neural Networks (SNNs) and Analog
Neural Networks (ANNs) where each network is designed to simultaneously process
asynchronous event streams and regular frame-based images, respectively. Our
network is end-to-end trained using unsupervised learning to avoid expensive
video annotations. The method generalizes well across distinct environments
(rapid motion and challenging lighting conditions) and demonstrates
state-of-the-art optical flow prediction on the Multi-Vehicle Stereo Event
Camera (MVSEC) dataset. Furthermore, our network offers substantial savings in
terms of the number of network parameters and computational energy cost.
