A Fast Geometric Regularizer to Mitigate Event Collapse in the Contrast Maximization Framework
arXiv:2212.07350 · doi:10.1002/aisy.202200251
Abstract
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. We propose a novel, computationally efficient regularizer based on geometric principles to mitigate event collapse. 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, our regularizer is the only effective solution for event collapse without trading off runtime. We hope our work opens the door for future applications that unlocks the advantages of event cameras.
arXiv comment10 pages, 7 figures, 4 tables. Project page: https://github.com/tub-rip/event collapse
References in corpus (11)
- Event-based Vision: A Survey
- The Event-Camera Dataset and Simulator: Event-based Data for Pose Estimation, Visual Odometry, and SLAM
- The Multi Vehicle Stereo Event Camera Dataset: An Event Camera Dataset for 3D Perception
- Ultimate SLAM? Combining Events, Images, and IMU for Robust Visual SLAM in HDR and High Speed Scenarios
- Event-based Stereo Visual Odometry
- Continuous-Time Visual-Inertial Odometry for Event Cameras
- Secrets of Event-Based Optical Flow
- A compact formula for the derivative of a 3-D rotation in exponential coordinates
- Event-based Imaging Velocimetry -- An Assessment of Event-based Cameras for the Measurement of Fluid Flows
- Multi-Event-Camera Depth Estimation and Outlier Rejection by Refocused Events Fusion
- Event Collapse in Contrast Maximization Frameworks