基于事件的全局最优对比度最大化运动估计
Globally Optimal Contrast Maximisation for Event-based Motion Estimation
摘要：
对比度最大化通过最大化运动补偿事件图像的锐度来估计在事件流中捕获的运动。为了进行对比度最大化，许多以前的作品采用迭代优化算法，如共轭梯度，这需要良好的初始化，以避免收敛到坏的局部最小值。为了缓解这一弱点，我们提出了一种新的全局最优的基于事件的运动估计算法。基于分支定界（BnB），我们的方法解决了事件流上的旋转（3DoF）运动估计，支持视频稳定和姿态估计等实际应用。支撑我们的方法是新的边界函数的对比度最大化，其理论的有效性是严格建立的。我们展示了来自公共数据集的具体例子，其中全局最优解决方案对对比度最大化的成功至关重要。尽管其确切的性质，我们的算法目前能够在300秒内处理50，000个事件输入（局部最优求解器在相同输入上需要30秒），并且有可能使用GPU进一步加速。
英文摘要：
Contrast maximisation estimates the motion captured in an event stream by maximising the sharpness of the motion compensated event image. To carry out contrast maximisation, many previous works employ iterative optimisation algorithms, such as conjugate gradient, which require good initialisation to avoid converging to bad local minima. To alleviate this weakness, we propose a new globally optimal event-based motion estimation algorithm. Based on branch-and-bound (BnB), our method solves rotational (3DoF) motion estimation on event streams, which supports practical applications such as video stabilisation and attitude estimation. Underpinning our method are novel bounding functions for contrast maximisation, whose theoretical validity is rigorously established. We show concrete examples from public datasets where globally optimal solutions are vital to the success of contrast maximisation. Despite its exact nature, our algorithm is currently able to process a 50,000 event input in 300 seconds (a locally optimal solver takes 30 seconds on the same input), and has the potential to be further speeded-up using GPUs.
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