@thesis{Hallerbach2021, author = {Hallerbach, Dennis}, title = {Development of a toolset and benchmark framework for monocular event-based depth extraction}, publisher = {Technische Hochschule Ingolstadt}, address = {Ingolstadt}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:573-8059}, pages = {XVI, 88}, school = {Technische Hochschule Ingolstadt}, year = {2021}, abstract = {Computing depth is a fundamental problem for both humans and technical systems. Be it for navigation in dynamic environments or simply to grasp objects, depth computation is a central functionality for autonomy. Typically, depth is extracted when having two sensors (i.e. cameras) through matching the perspectives in each of them towards the attended location and is a typical problem in machine vision. When looking at monocular depth extraction, the problem changes and becomes more challenging from both algorithmic and validation perspectives. In this thesis, I address the problem of monocular depth extraction in a comparative setup between traditional (frame-based) cameras and the novel event-based cameras. Targeting practical implementations (i.e. autonomous driving, assistive devices for blind people), I have developed an algorithm benchmarking framework and a physical test-rig for monocular depth computation for both frame-based and event-based cameras. The core contribution of the work is the design and implementation of the test-rig, the software framework for benchmarking the two types of algorithms, together with their calibration and validation. This thesis sets the ground for upcoming projects aiming at evaluating which is the best configuration of camera type and algorithm for monocular depth extraction in practical applications.}, language = {en} }