6 DAILY ICCV Tuesday minus the dynamic offsets, which gives us the camera-induced component. That’s exactly what we need for the bundle adjustment.” Previous methods primarily focused on point tracking for total motion, but the use of an additional network enables motion decoupling. The model is trained on synthetic datasets of dynamic scenes using a deep-learning approach. By decoupling these signals, points on moving objects effectively become ‘pseudo-static’, allowing the classic bundle-adjustment framework to solve the camera pose and geometry. For Weirong, the appeal of this work lies in its everyday potential. “Imagine you have an iPhone and you want to shoot some daily activities for your family – playing tennis or going hiking,” he suggests. “Our method can take any casually shot video and recover the dynamic scenes.” Despite its success, Weirong points out that BA-Track is not a finished solution. Motion decoupling depends on the quality of the learned point tracker, which in turn relies on the volume and quality of the synthetic training data. Resource constraints mean that the model has not yet been trained on the full diversity of real-world scenes. Oral & Award Candidate
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