We re-train several state-of-the-art methods on our benchmark, and the results show a decrease in their accuracy compared to other datasets.
We therefore propose to incorporate RGB information in an event-guided optical flow refinement strategy.
We introduce Deceptive-NeRF, a novel methodology for few-shot NeRF reconstruction, which leverages diffusion models to synthesize plausible pseudo-observations to improve the reconstruction.
To generalize to a new instrument or event class, drawing inspiration from the text-prompt design, we insert an additional query as an audio prompt while freezing the attention mechanism.
The core of our method is a novel propagation strategy for individual objects' radiance fields with a bidirectional photometric loss, enabling an unsupervised partitioning of a scene into salient or meaningful regions corresponding to different object instances.
Event cameras have recently gained in popularity as they hold strong potential to complement regular cameras in situations of high dynamics or challenging illumination.
We present a novel real-time visual odometry framework for a stereo setup of a depth and high-resolution event camera.
1 code implementation • 9 Aug 2021 • Daochen Zha, Zaid Pervaiz Bhat, Yi-Wei Chen, Yicheng Wang, Sirui Ding, Jiaben Chen, Kwei-Herng Lai, Mohammad Qazim Bhat, Anmoll Kumar Jain, Alfredo Costilla Reyes, Na Zou, Xia Hu
Action recognition is an important task for video understanding with broad applications.
We present a new solution to tracking and mapping with an event camera.