1 research outputs found
Estimating general motion and intensity from event cameras
Robotic vision algorithms have become widely used in many consumer products which
enabled technologies such as autonomous vehicles, drones, augmented reality (AR) and
virtual reality (VR) devices to name a few. These applications require vision algorithms
to work in real-world environments with extreme lighting variations and fast moving
objects. However, robotic vision applications rely often on standard video cameras which
face severe limitations in fast-moving scenes or by bright light sources which diminish
the image quality with artefacts like motion blur or over-saturation.
To address these limitations, the body of work presented here investigates the use of
alternative sensor devices which mimic the superior perception properties of human
vision. Such silicon retinas were proposed by neuromorphic engineering, and we focus
here on one such biologically inspired sensor called the event camera which offers a new
camera paradigm for real-time robotic vision. The camera provides a high measurement
rate, low latency, high dynamic range, and low data rate. The signal of the camera is
composed of a stream of asynchronous events at microsecond resolution. Each event
indicates when individual pixels registers a logarithmic intensity changes of a pre-set
threshold size. Using this novel signal has proven to be very challenging in most computer
vision problems since common vision methods require synchronous absolute intensity
information.
In this thesis, we present for the first time a method to reconstruct an image and es-
timation motion from an event stream without additional sensing or prior knowledge of
the scene. This method is based on coupled estimations of both motion and intensity
which enables our event-based analysis, which was previously only possible with severe
limitations. We also present the first machine learning algorithm for event-based unsu-
pervised intensity reconstruction which does not depend on an explicit motion estimation
and reveals finer image details. This learning approach does not rely on event-to-image
examples, but learns from standard camera image examples which are not coupled to the
event data. In experiments we show that the learned reconstruction improves upon our
handcrafted approach. Finally, we combine our learned approach with motion estima-
tion methods and show the improved intensity reconstruction also significantly improves
the motion estimation results. We hope our work in this thesis bridges the gap between
the event signal and images and that it opens event cameras to practical solutions to
overcome the current limitations of frame-based cameras in robotic vision.Open Acces