Search Results for author: Federico Paredes-Vallés

Found 6 papers, 1 papers with code

Lightweight Event-based Optical Flow Estimation via Iterative Deblurring

no code implementations24 Nov 2022 YiLun Wu, Federico Paredes-Vallés, Guido C. H. E. de Croon

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.

Deblurring Optical Flow Estimation

NanoFlowNet: Real-time Dense Optical Flow on a Nano Quadcopter

no code implementations14 Sep 2022 Rik J. Bouwmeester, Federico Paredes-Vallés, Guido C. H. E. de Croon

In this work, we present NanoFlowNet, a lightweight convolutional neural network for real-time dense optical flow estimation on edge computing hardware.

Autonomous Navigation Edge-computing +2

The Artificial Intelligence behind the winning entry to the 2019 AI Robotic Racing Competition

no code implementations30 Sep 2021 Christophe De Wagter, Federico Paredes-Vallés, Nilay Sheth, Guido de Croon

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

no code implementations NeurIPS 2021 Jesse Hagenaars, Federico Paredes-Vallés, Guido de Croon

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.

Optical Flow Estimation Self-Supervised Learning

Neuromorphic control for optic-flow-based landings of MAVs using the Loihi processor

no code implementations1 Nov 2020 Julien Dupeyroux, Jesse Hagenaars, Federico Paredes-Vallés, Guido de Croon

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

1 code implementation28 Jul 2018 Federico Paredes-Vallés, Kirk Y. W. Scheper, Guido C. H. E. de Croon

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.

Event-based vision Optical Flow Estimation

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