论文标题
基于事件的运动估计的全球最佳对比度最大化
Globally Optimal Contrast Maximisation for Event-based Motion Estimation
论文作者
论文摘要
对比度最大化通过最大化运动补偿事件图像的清晰度来估计事件流中捕获的运动。为了实现对比度最大化，许多以前的作品采用了迭代优化算法，例如共轭梯度，这些梯度需要良好的初始化以避免融合到不良的局部最小值。为了减轻这种弱点，我们提出了一种新的基于全球最佳事件的运动估计算法。基于分支机构（BNB），我们的方法在事件流上解决了旋转（3DOF）运动估计，该运动流支持实用应用，例如视频稳定和态度估计。我们的方法的基础是对比最大化的新型边界函数，其理论有效性是严格确定的。我们从公共数据集中展示了具体示例，其中全球最佳解决方案对于对比度最大化至关重要。尽管具有确切的性质，但我们的算法目前仍能够在300秒内处理50,000个事件输入（本地最佳求解器在同一输入上需要30秒），并且有可能使用GPU进一步加速UP。
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.
