11 Computer Vision News Computer Vision News Bangyan Liao Bangyan outlines three key insights for navigating these challenges. First, he proposes a joint approach to solving the coupled subproblems through a novel scheme called soft data association. This reformulates the problem as a quadratically constrained quadratic programming (QCQP) problem, a standard optimization problem in mathematical programming. The second insight involves transforming a non-convex QCQP problem into a convex semidefinite programming (SDP) problem, thereby simplifying the solution process. Finally, to enhance efficiency, he iteratively solves smaller SDP problems rather than one large problem, significantly accelerating the process. As one of only 15 out of almost 2,900 accepted papers to be recognized as Best Paper award candidates, Bangyan reflects on why he thinks his work stood out from the crowd. “I think the reason comes from the fact that we reveal that even longstanding fundamental geometric problems are not entirely solved,” he considers. “It demonstrates that there is still a need for more advanced and powerful optimization algorithms to tackle these classical challenges in the computer vision field.” Looking to the future, Bangyan aims to extend his research beyond the constraints of the Manhattan world and apply convex relaxation techniques to a broader range of applications in computer vision. “Our paper currently focuses on the Manhattan world, which is a relatively strict assumption,” he
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