Inertia-informed Orientation Priors Enhance Event-Based Optical Flow Estimation for Temporally Dense Motion Analysis

Event cameras capture motion directly, offering advantages over traditional methods, but estimating accurate optical flow from their sparse and temporally dense data remains a significant challenge. Pritam P. Karmokar and William J. Beksi, from The University of Texas at Arlington, address this problem by introducing a new approach to event-based flow estimation that integrates visual and inertial motion cues. Their research centres on refining contrast maximization, a common technique for tracking event trajectories, by incorporating orientation maps derived from camera velocities as guiding priors. This biologically-inspired method improves both the robustness and convergence of flow estimation, ultimately achieving superior accuracy on standard datasets including MVSEC, DSEC, and ECD, and representing a substantial advance in the field of event-based vision.

The research challenges existing methods for estimating motion from event cameras, innovative devices that directly detect changes in a scene. To address limitations in processing sparse and rapidly changing event data, the team introduces a novel approach that incorporates orientation maps, derived from the camera’s 3D velocities, as guiding priors. These maps provide directional information, effectively narrowing down possible motion trajectories and improving the reliability and speed of motion estimation.

MVSEC Dataset and Evaluation Procedures

The research meticulously details the experimental setup, data processing techniques, and evaluation metrics employed in the study, ensuring reproducibility and allowing other researchers to fully understand the approach. The team utilized the MVSEC outdoor dataset, selecting 800 frames spanning 222. 4 to 240. 4 seconds for quantitative analysis. This precise frame selection is crucial for independent verification of the results.

The method relies on creating orientation maps, representing the direction of motion at each pixel, to serve as a guiding prior within the Contrastive Motion framework. This involves projecting 3D camera velocities onto the image plane and creating initial orientation maps. Lens distortion is corrected, and any resulting empty regions are filled using techniques like BORDER_REPLICATE within the OpenCV library. LiDAR data from the DSEC dataset was also employed to estimate ground truth 3D velocities, as this information is not directly provided within the dataset, involving conversion to 3D point clouds, alignment using the Iterative Closest Point algorithm, and Kalman filtering for robustness. A key mathematical justification demonstrates the equivalence of minimizing Mean Squared Error, maximizing the dot product, and minimizing the negative cosine similarity when dealing with normalized orientation vectors. This equivalence validates the choice of loss function used for training and demonstrates interchangeability between different formulations, providing a solid theoretical foundation for the optimization process.

Orientation Priors Enhance Event Camera Motion Estimation

The research introduces Orientation-guided Probabilistic Contrast Maximization (OPCM), a novel method for estimating motion from event cameras. The team addressed the challenge of sparse and temporally dense event data by incorporating orientation maps, derived from 3D camera velocities, as guiding priors within the Contrast Maximization framework. These orientation maps provide directional information, effectively constraining possible motion trajectories and improving the robustness and convergence of flow estimation. 333 with an error percentage of 6. 341, outperforming numerous supervised, semi-supervised, and unsupervised learning approaches. Specifically, with a time step of 4, OPCM’s performance surpasses methods like E-RAFT and DCEIFlow. These results highlight OPCM’s ability to accurately estimate motion from event data, offering a significant advancement in the field of event-based vision.

This research presents a novel method for estimating motion from event cameras, devices that directly encode changes within a scene. The team developed an extension to contrast maximization, introducing guidance from orientation information derived from the camera’s 3D velocities. By incorporating these cues, the method effectively constrains possible motion trajectories, leading to more robust and accurate estimation of event-based optical flow. The approach, inspired by the way biological systems couple visual and vestibular information, achieves stable convergence and improved performance, particularly in areas with limited texture. Evaluation on standard datasets demonstrates that this method surpasses the accuracy of current state-of-the-art techniques.