The challenge at the center of Weirong’s research is that real-world scenes rarely stand still. Traditional structure-from-motion and SLAM systems, which rely on bundle adjustment, assume a static world, and the classical epipolar geometry that underpins them depends on scene points remaining fixed. Once humans, cars, or animals enter the frame, the mathematics breaks down. Weirong Chen is a second-year PhD student at the Technical University of Munich, supervised by Daniel Cremers and cosupervised by Andrea Vedaldi from the University of Oxford. His paper uses modern learningbased techniques to address the problem of dynamic scene reconstruction from videos. In addition to being accepted for a coveted oral slot at ICCV 2025, it has been shortlisted as a candidate for a Best Paper Award. Ahead of his oral and poster presentations today, Weirong tells us more about his work. Back on Track: Bundle Adjustment for Dynamic Scene Reconstruction 4 DAILY ICCV Tuesday Oral & Award Candidate
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