ECCV 2022 - Wednesday

15 Angela Dai get out the complete geometry of that environment? How can we understand the individual objects that are observed there, even though they're not seen perfectly? The data is limited. It's imperfect. How can we sort of imbue machines with the same kind of 3D perception that we, as people, do? Were all these developments expected then, or some of these fields or subfields came as a surprise for you? There were definitely some developments that came as a surprise. In hindsight, they made sense, and when they came out, they were a surprise. But that's a sign of good research! Something that a lot of people were doing in this area is that they came and developed, for instance, the first thing probably was these neural coordinate field implicit representations for representing 3D shape geometry. They were very, very effective. They made a lot of sense, and they go back and have ties to traditional geometric representations and implicit representations. That was quite powerful. It's not a perfect representation. It's still a bit of a challenge to see what's the proper way to represent a large scene and not just one object. But, this was cool. Probably everybody knows about NeRF and all of the amazing stuff that you can do with NeRF. And that's, of course, a huge development, particularly from the sort of photorealistic generation side. I think more things have changed since the last time. I was doing my PhD in Stanford and completed it at the end of 2018. I then moved to the Technical University of Munich, where they had these nice opportunities that you could apply for what they called junior research group positions. You can basically apply for funding for yourself and two students, which is actually quite nice. This presents a lot of opportunities to start building up this kind of research group of your own early on, prior to even becoming a professor. So that's what I did in Munich, and I