62 citations · 76 across the 10 of their papers we have counts for
5 papers · 1 filter
Self-Supervised Monocular Depth Estimation of Untextured Indoor Rotated Scenes
Benjamin Keltjens, Tom van Dijk, Guido de Croon
Self-supervised deep learning methods have leveraged stereo images for training monocular depth estimation. Although these methods show strong results on outdoor datasets such as K…
Self-Supervised Learning of Event-Based Optical Flow with Spiking Neural Networks
Jesse Hagenaars, Federico Paredes-Vallés, Guido de Croon
The field of neuromorphic computing promises extremely low-power and low-latency sensing and processing. Challenges in transferring learning algorithms from traditional artificial…
Back to Event Basics: Self-Supervised Learning of Image Reconstruction for Event Cameras via Photometric Constancy
F. Paredes-Vallés, G. C. H. E. de Croon
Event cameras are novel vision sensors that sample, in an asynchronous fashion, brightness increments with low latency and high temporal resolution. The resulting streams of events…
How do neural networks see depth in single images?
Tom van Dijk, Guido C. H. E. de Croon
Deep neural networks have lead to a breakthrough in depth estimation from single images. Recent work often focuses on the accuracy of the depth map, where an evaluation on a public…
Fusion of stereo and still monocular depth estimates in a self-supervised learning context
Diogo Martins, Kevin van Hecke, Guido de Croon
We study how autonomous robots can learn by themselves to improve their depth estimation capability. In particular, we investigate a self-supervised learning setup in which stereo…