Event cameras are emerging vision sensors and their advantages are suitable for various applications such as autonomous robots. Contrast maximization (CMax), which provides state‐of‐the‐art accuracy on motion estimation using events, may suffer from an overfitting problem called event collapse. Prior works are computationally expensive or cannot alleviate the overfitting, which undermines the benefits of the CMax framework. A novel, computationally efficient regularizer based on geometric principles to mitigate event collapse is proposed. The experiments show that the proposed regularizer achieves state‐of‐the‐art accuracy results, while its reduced computational complexity makes it two to four times faster than previous approaches. To the best of our knowledge, this regularizer is the only effective solution for event collapse without trading off the runtime. It is hoped that this work opens the door for future applications that unlocks the advantages of event cameras. Project page: https://github.com/tub‐rip/event_collapse
Event cameras are novel bio‐inspired vision sensors that offer advantages over traditional cameras for various applications, such as autonomous systems. The framework of contrast maximization (CMax) provides state‐of‐the‐art accuracy in motion estimation using event cameras. However, it may suffer from an overfitting prob...

A Fast Geometric Regularizer to Mitigate Event Collapse in the Contrast Maximization Framework

10.1002/aisy.202200251