Event Collapse in Contrast Maximization Frameworks (2207.04007v2)

Abstract: Contrast maximization (CMax) is a framework that provides state-of-the-art results on several event-based computer vision tasks, such as ego-motion or optical flow estimation. However, it may suffer from a problem called event collapse, which is an undesired solution where events are warped into too few pixels. As prior works have largely ignored the issue or proposed workarounds, it is imperative to analyze this phenomenon in detail. Our work demonstrates event collapse in its simplest form and proposes collapse metrics by using first principles of space-time deformation based on differential geometry and physics. We experimentally show on publicly available datasets that the proposed metrics mitigate event collapse and do not harm well-posed warps. To the best of our knowledge, regularizers based on the proposed metrics are the only effective solution against event collapse in the experimental settings considered, compared with other methods. We hope that this work inspires further research to tackle more complex warp models.

• The paper introduces novel flow divergence and area-based deformation metrics to quantify and mitigate event collapse in event-based vision.
• It leverages principles from differential geometry and physics to overcome overfitting, achieving over 90% improvement in endpoint accuracy on standard datasets.
• The proposed regularizers maintain performance on well-posed warps, paving the way for robust, real-time applications in autonomous navigation and motion estimation.

Insights from "Event Collapse in Contrast Maximization Frameworks"

This paper addresses the critical issue of event collapse within the Contrast Maximization (CMax) framework in event-based computer vision. Event cameras capture changes in light intensity asynchronously, allowing for efficient data gathering even in challenging lighting conditions. The CMax framework aligns these captured events via motion and scene parameter estimation, achieving state-of-the-art performance on tasks such as ego-motion and optical flow estimation. However, a significant limitation of CMax occurs when events collapse into a few pixels, a problem known as event collapse.

Core Contributions

The paper's main contributions include a detailed examination of event collapse, proposing two metrics to measure and mitigate this phenomenon: divergence of event transformation flow and area-based deformation. Utilizing principles from differential geometry and physics, the authors offer metrics that can be incorporated as regularizers into the CMax optimization framework.

Metrics Defined:

1. Flow Divergence Regularizer: This measurement conceptualizes the flow of events as a vector field. Divergence within this field indicates sources or sinks, correlating to event accumulation or dispersion. By gauging and penalizing the divergence, the optimization avoids collapsing configurations.
2. Area-Based Deformation Metric: Focused on the geometric deformation of events, this metric assesses the spatial area elements transformed by the CMax process. Deviations measured as area amplification factors reveal collapsing scenarios, enabling the proposed regularizer to adjust the objective function.

Experimental Verification

Experiments employed publicly available datasets, such as MVSEC and DSEC, incorporating both static and dynamic environments, demonstrating the mitigative impact of the regularizers on collapse-enabled warps. Results show that the proposed regularizers resolve the overfitting challenge posed by event collapse, achieving more than 90% gain in endpoint accuracy compared to existing CMax methods without regularization. Notably, these regularizers do not degrade performance when applied to well-posed warps, such as rotational motion, affirming their robustness.

Implications and Future Directions

Addressing event collapse fosters broader applicability and enhancement of the CMax framework in event-based vision. These results suggest robust solutions for real-time event-based vision applications, crucial in autonomous navigation and motion estimation. The proposed methodology can serve as a foundation for handling more intricate warp models and optimizing more complex scenes with varied motions.

For future exploration, the frameworks and regularizers could be adapted to accommodate advanced warp models involving more degrees of freedom, typical in dense optical flow applications. Exploring the application of these metrics across diverse event-based tasks, such as 3D reconstruction and dynamic scene understanding, could further expand the usability of the CMax framework. Overall, the paper establishes a methodical stride towards enhancing event camera data utility while circumventing its intrinsic processing challenges.