An Asynchronous Kalman Filter for Hybrid Event Cameras
arXiv:2012.05590 · doi:10.1109/ICCV48922.2021.00050
Abstract
Event cameras are ideally suited to capture HDR visual information without blur but perform poorly on static or slowly changing scenes. Conversely, conventional image sensors measure absolute intensity of slowly changing scenes effectively but do poorly on high dynamic range or quickly changing scenes. In this paper, we present an event-based video reconstruction pipeline for High Dynamic Range (HDR) scenarios. The proposed algorithm includes a frame augmentation pre-processing step that deblurs and temporally interpolates frame data using events. The augmented frame and event data are then fused using a novel asynchronous Kalman filter under a unifying uncertainty model for both sensors. Our experimental results are evaluated on both publicly available datasets with challenging lighting conditions and fast motions and our new dataset with HDR reference. The proposed algorithm outperforms state-of-the-art methods in both absolute intensity error (48% reduction) and image similarity indexes (average 11% improvement).
arXiv comment12 pages, 6 figures, published in International Conference on Computer Vision (ICCV) 2021
References in corpus (5)
- 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
- Asynchronous Spatial Image Convolutions for Event Cameras
- Photorealistic Image Reconstruction from Hybrid Intensity and Event based Sensor
- Event Camera Calibration of Per-pixel Biased Contrast Threshold