Inertia-Informed Orientation Priors for Event-Based Optical Flow Estimation

TL;DR

This paper presents a biologically-inspired hybrid contrast maximization method that integrates inertial cues as orientation priors to enhance event-based optical flow estimation, achieving superior accuracy and robustness.

Contribution

It introduces the use of orientation maps derived from inertial data as priors in the contrast maximization framework for improved optical flow estimation.

Findings

Achieves superior accuracy on MVSEC, DSEC, and ECD datasets.

Improves robustness and convergence of the optical flow estimation.

Outperforms existing state-of-the-art methods.

Abstract

Event cameras, by virtue of their working principle, directly encode motion within a scene. Many learning-based and model-based methods exist that estimate event-based optical flow, however the temporally dense yet spatially sparse nature of events poses significant challenges. To address these issues, contrast maximization (CM) is a prominent model-based optimization methodology that estimates the motion trajectories of events within an event volume by optimally warping them. Since its introduction, the CM framework has undergone a series of refinements by the computer vision community. Nonetheless, it remains a highly non-convex optimization problem. In this paper, we introduce a novel biologically-inspired hybrid CM method for event-based optical flow estimation that couples visual and inertial motion cues. Concretely, we propose the use of orientation maps, derived from camera 3D velocities, as priors to guide the CM process. The orientation maps provide directional guidance and constrain the space of estimated motion trajectories. We show that this orientation-guided formulation leads to improved robustness and convergence in event-based optical flow estimation. The evaluation of our approach on the MVSEC, DSEC, and ECD datasets yields superior accuracy scores over the state of the art.