This paper proposes an effective data filtering method to improve the quality of training data, thus enhancing model performance. Additionally, it introduces an image-based event representation that outperforms existing representations.
This paper proposes a novel LiDAR and event camera fusion modality for subterranean (SubT) environments for fast and precise object and human detection in a wide variety of adverse lighting conditions, such as low or no light, high-contrast zones and in the presence of blinding light sources
This work proposes a novel coarse-to-fine framework, named NETwork of Event-based motion Deblurring with STereo event and intensity cameras (St-EDNet), to recover high-quality images directly from the misaligned inputs, consisting of a single blurry image and the concurrent event streams.
blurry image and the concurrent event streams.
This work aims at re-exposing the captured photo in the post-processing, providing a more flexible way to address issues within a unified framework. Specifically, it propose a neural network based image re-exposure framework.
The term “neuromorphic” refers to systems that closely resemble the architecture and dynamics of biological neural networks. From brain-inspired computer chips to sensory devices mimicking human vision and olfaction, neuromorphic computing aims to achieve efficiency levels comparable to biological organisms.