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Found 6 papers, 1 papers with code
Lightweight Event-based Optical Flow Estimation via Iterative Deblurring
Inspired by frame-based methods, state-of-the-art event-based optical flow networks rely on the explicit computation of correlation volumes, which are expensive to compute and store on systems with limited processing budget and memory.
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.
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.
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.
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.
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.
