Welcome back to Beyond the Patterns. So today I have the great pleasure to announce Florian

Willemitzer as an invited speaker in our series. Florian is a research assistant professor

at Northwestern University. He graduated from our university where he receives his PhD with

honors summa cum laude in 2017. During his doctoral studies Florian investigated physical

and information theoretical limits of optical 3D sensing and implemented sensors that operate

close to these limits. At Northwestern University Florian and his students develop novel techniques

to overcome traditional resolution limitations and dynamic range restrictions in 3D and 2D

imaging. Moreover Florian's research is focused on new methods to image hidden objects through

scattering media or around corners, high resolution holographic displays and the implementation

of high precision metrology methods in low cost mobile handheld devices.

Florian is currently chair of the OSA COSY conference and has served as a reviewer for

OSA, IEEE, SPIE and the Nature portfolio. His PhD thesis was awarded with the Springer

thesis award for outstanding PhD research. So it's a great pleasure to have you here

Florian and today we will see a presentation by you that is entitled Fundamental Limits

in Computational 3D Imaging from Novel 3D Cameras to Looking Around Corners. I'm very

much looking forward to this presentation and Florian, the stage is yours.

Yeah, thank you Andreas for this very nice introduction and good afternoon to Erlangen

from my side. So first of all I have to say that I'm extremely excited to be invited today

and to be back to my former university even if it's like just virtual this time unfortunately.

So again my name is Florian Willemitzer and I'm currently working as research assistant

professor at Northwestern University in the computational photography lab. And in my talk

today I'm going to talk about the virtue of fundamental limits in computational 3D imaging

and I will show you this virtue at the example of three of my research projects. But before

I start I will shortly introduce Northwestern University and our group. So Northwestern

University is located in Evanston, Illinois which is approximately like a 30-minute drive

away from Chicago and as you can see it has like a lovely campus directly on the shore

of Lake Michigan and here on this image you can even see Chicago in the background. Of

course all the work that I'm going to talk about today was not done in a one-man show

of course. This is always a group effort and which is why I want to give credit to my great

co-workers here especially to our lab director Oliver Kosherd but also to the co-workers

that are not part of our current lab which is shown on this slide here. And for one project

that I'm showing today I especially want to emphasize our colleagues from Prasana Rangarian's

lab at SMU and for the last project that I'm showing today that was done during my PhD

thesis in Erlangen I of course also want to give credit to all my colleagues there and

especially to my PhD advisor Gerd Häusler who basically introduced me in this thinking

in limits that I will share with you today. So why are limits so important? There are

actually many examples to motivate this and I just picked a few where I think many people

in the audience might be familiar with. So what you see here are some examples from computer

vision so you see some examples from gesture estimation, scene understanding in autonomous

mapping, medical imaging even or robotic AR assisted surgery. And if you think a little

bit about how all this has developed in recent years and actually with which speed all this

constantly improves you just get very deeply impressed and very excited. And if you think

a little bit more about this all these exciting developments trigger in turn many exciting

questions. So questions that you could ask can be where does this all end? Does this

just get better and better and can you basically do everything just by improving your algorithm

or finding a data set which is large enough or are there limits of what you can do? And

if yes where are those limits? And trying to answer this question and eventually developing

imaging and display devices that were close at these limits is a central cornerstone of

my research. And by the way the question for limits is by far not only an academic question

so I mean I don't want to sound overly dramatic here but if you just look at these two examples