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Found 9 papers, 1 papers with code
Unsupervised Learning of a Hierarchical Spiking Neural Network for Optical Flow Estimation: From Events to Global Motion Perception
Convolutional layers with input synapses characterized by single and multiple transmission delays are employed for feature and local motion perception, respectively; while global motion selectivity emerges in a final fully-connected layer.
Neuromorphic control for optic-flow-based landings of MAVs using the Loihi processor
However, a major challenge for using such processors on robotic platforms is the reality gap between simulation and the real world.
Self-Supervised Learning of Event-Based Optical Flow with Spiking Neural Networks
We focus on the complex task of learning to estimate optical flow from event-based camera inputs in a self-supervised manner, and modify the state-of-the-art ANN training pipeline to encode minimal temporal information in its inputs.
The Artificial Intelligence behind the winning entry to the 2019 AI Robotic Racing Competition
Robotics is the next frontier in the progress of Artificial Intelligence (AI), as the real world in which robots operate represents an enormous, complex, continuous state space with inherent real-time requirements.
NanoFlowNet: Real-time Dense Optical Flow on a Nano Quadcopter
In this work, we present NanoFlowNet, a lightweight convolutional neural network for real-time dense optical flow estimation on edge computing hardware.
Rethinking Event-based Optical Flow: Iterative Deblurring as an Alternative to Correlation Volumes
Inspired by frame-based methods, state-of-the-art event-based optical flow networks rely on the explicit construction of correlation volumes, which are expensive to compute and store, at the same time prohibiting them from estimating high-resolution flow.
Taming Contrast Maximization for Learning Sequential, Low-latency, Event-based Optical Flow
Event cameras have recently gained significant traction since they open up new avenues for low-latency and low-power solutions to complex computer vision problems.
Fully neuromorphic vision and control for autonomous drone flight
Robotic experiments show a successful sim-to-real transfer of the fully learned neuromorphic pipeline.
Low-power event-based face detection with asynchronous neuromorphic hardware
We show that the power consumption of the chip is directly proportional to the number of synaptic operations in the spiking neural network, and we explore the trade-off between power consumption and detection precision with different firing rate regularization, achieving an on-chip face detection mAP[0. 5] of ~0. 6 while consuming only ~20 mW.
