up. Like to solve the challenge of data: we need data that first captures both the static and dynamic obstacles we want; and second, provides egocentric observations; and third, they need to exhibit such human-like information guidance behaviors, for example, head turning. Boxiao and his co-authors were not able to find any data that has all three. There are a couple of close ones in autonomous driving, but they don't have egocentric observations, or they're synthetic. They have a lot of data, but it’s not very vivid or similar to real-life like dynamic obstacles for navigation. They had to collect their own and they ended up using the Meta Aria glasses as the only collection hardware, which solved the data problem. The main novelty of this work lays in it’s being a solution or a pipeline that brings the navigation robot closer to real world navigation. But what makes Boxiao proud the most? “The major thing I'm proud of,” he answers, “is that this entire pipeline didn't exist before. This is very similar to some of my previous projects in which I had to come up with an entirely new solution, both for the method and the data and evaluation, because we were dealing with a new problem. I'm proud that I personally, as the first author, lead the innovation and development of the entire pipeline.” To learn more about Boxiao’s work, visit Poster Session 6 (Exhibit Hall I) from 14:30 to 16:30 [Poster 24]. 21 DAILY ICCV Thursday Boxiao Pan
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