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Found 11 papers, 6 papers with code
EvShutter: Transforming Events for Unconstrained Rolling Shutter Correction
The simulator permits the use of inexpensive cameras with long exposure to capture high-quality GS images.
Event-based Image Deblurring with Dynamic Motion Awareness
Non-uniform image deblurring is a challenging task due to the lack of temporal and textural information in the blurry image itself.
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.
Multi-Bracket High Dynamic Range Imaging with Event Cameras
Modern high dynamic range (HDR) imaging pipelines align and fuse multiple low dynamic range (LDR) images captured at different exposure times.
Time Lens: Event-Based Video Frame Interpolation
However, while these approaches can capture non-linear motions they suffer from ghosting and perform poorly in low-texture regions with few events.
TimeLens: Event-based Video Frame Interpolation
State-of-the-art frame interpolation methods generate intermediate frames by inferring object motions in the image from consecutive key-frames.
Learning an Event Sequence Embedding for Dense Event-Based Deep Stereo
Based on this module, we design a deep learning-based stereo method for event-based cameras.
Practical Deep Stereo (PDS): Toward applications-friendly deep stereo matching
End-to-end deep-learning networks recently demonstrated extremely good perfor- mance for stereo matching.
Weakly Supervised Learning of Deep Metrics for Stereo Reconstruction
Thirdly, it allows to tune deep metric for a particular stereo system, even if ground truth is not available.
Geometric calibration of Colour and Stereo Surface Imaging System of ESA's Trace Gas Orbiter
There are many geometric calibration methods for "standard" cameras.
Semi-supervised learning of deep metrics for stereo reconstruction
The main contribution of our work is a new semi-supervised method for learning deep metrics from unlabeled stereo images, given coarse information about the scenes and the optical system.
