Carl’s main motivation is not solving a real-world problem. “I do it”, he says, “because I enjoy these problems. That's my main motivation for this, but we know and we've seen fairly recently a couple of papers interested in global structure for motion, in which rotation averaging is a key component. And it's used a lot in SLAM because you can get faster pipelines if you don't have to do bundle adjustment that often, so I think that's like the main benefit of this. And then to be able to get an even more accurate objective function than what we have right now, I think it's a good thing!” But why wasn’t this problem solved until now? Carls doesn’t really know. It feels almost like a trivial problem when you think about it. There is indeed a trivial modification of the old rotation averaging framework that seems like it's going to work and it works in some cases. But Carl was very surprised to see that when he tried it for structure for motion problems, it didn't work. The reason is in the mathematics, he found: “I had to dive into the mathematics and try to understand what went wrong. So I guess nobody really looked into it that much before!” Carl Olsson is a full professor at Lund University. His ICCV 2025 paper was accepted as an oral in the 3D Pose Understanding session today and also as an award candidate paper! This work is about rotation averaging, which is a problem on which Carl has been working for a long time, with enjoyment. In particular Carl and co-authors tried to approximate objective functions like reprojection error and things that we have in vision and try to bring that into the rotation averaging framework, while retaining sort of the ability to do global optimization over these functions. Certifiably Optimal Anisotropic Rotation Averaging 10 DAILY ICCV Wednesday Oral & Award Candidate
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