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Modeling Neural Radiance Fields for fast-moving deformable objects fromvisual data alone is a challenging problem. A major issue arises due to thehigh deformation and low acquisition rates. To address this problem, we proposeto use event cameras that offer very fast acquisition of visual change in anasynchronous manner. In this work, we develop a novel method to model thedeformable neural radiance fields using RGB and event cameras. The proposedmethod uses the asynchronous stream of events and calibrated sparse RGB frames.In our setup, the camera pose at the individual events required to integratethem into the radiance fields remains unknown. Our method jointly optimizesthese poses and the radiance field. This happens efficiently by leveraging thecollection of events at once and actively sampling the events during learning.Experiments conducted on both realistically rendered graphics and real-worlddatasets demonstrate a significant benefit of the proposed method over thestate-of-the-art and the compared baseline.  This shows a promising direction for modeling deformable neural radiancefields in real-world dynamic scenes.

Deformable Neural Radiance Fields using RGB and Event Cameras