Ego-motion Estimation Based on Fusion of Images and Events
Event camera is a novel bio-inspired vision sensor that outputs event stream. In this paper, we propose a novel data fusion algorithm called EAS to fuse conventional intensity images with the event stream. The fusion result is applied to some ego-motion estimation frameworks, and is evaluated on a public dataset acquired in dim scenes. In our 3-DoF rotation estimation framework, EAS achieves the highest estimation accuracy among intensity images and representations of events including event slice, TS and SITS. Compared with original images, EAS reduces the average APE by 69%, benefiting from the inclusion of more features for tracking. The result shows that our algorithm effectively leverages the high dynamic range of event cameras to improve the performance of the ego-motion estimation framework based on optical flow tracking in difficult illumination conditions.
Code (1)
Tasks
Motion EstimationOptical Flow EstimationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Time Lens++: Event-based Frame Interpolation with Parametric Non-linear Flow and Multi-scale Fusion
Recently, video frame interpolation using a combination of frame- and event-based cameras has surpassed traditional image-based methods both in terms of performance and memory efficiency. However, current methods still s…
Motion EstimationVideo Frame InterpolationLearning Dense and Continuous Optical Flow from an Event Camera
Event cameras such as DAVIS can simultaneously output high temporal resolution events and low frame-rate intensity images, which own great potential in capturing scene motion, such as optical flow estimation. Most of the…
Optical Flow EstimationEv-Layout: A Large-scale Event-based Multi-modal Dataset for Indoor Layout Estimation and Tracking
This paper presents Ev-Layout, a novel large-scale event-based multi-modal dataset designed for indoor layout estimation and tracking. Ev-Layout makes key contributions to the community by: Utilizing a hybrid data collec…
BenchmarkingSpatially-guided Temporal Aggregation for Robust Event-RGB Optical Flow Estimation
Current optical flow methods exploit the stable appearance of frame (or RGB) data to establish robust correspondences across time. Event cameras, on the other hand, provide high-temporal-resolution motion cues and excel …
Optical Flow EstimationTETO: Tracking Events with Teacher Observation for Motion Estimation and Frame Interpolation
Event cameras capture per-pixel brightness changes with microsecond resolution, offering continuous motion information lost between RGB frames. However, existing event-based motion estimators depend on large-scale synthe…
Knowledge DistillationPoint Tracking