Deformable Neural Radiance Fields using RGB and Event Cameras

Modeling Neural Radiance Fields for fast-moving deformable objects from visual data alone is a challenging problem. A major issue arises due to the high deformation and low acquisition rates. To address this problem, we propose to use event cameras that offer very fast acquisition of visual change in an asynchronous manner. In this work, we develop a novel method to model the deformable neural radiance fields using RGB and Event cameras. The proposed method uses the asynchronous stream of events and calibrated sparse RGB frames. In this setup, the pose of the individual events --required to integrate them into the radiance fields-- remains to be unknown. Our method jointly optimizes the pose and the radiance field, in an efficient manner by leveraging the collection of events at once and actively sampling the events during learning. Experiments conducted on both realistically rendered and real-world datasets demonstrate a significant benefit of the proposed method over the state-of-the-art and the compared baseline. This shows a promising direction for modeling deformable neural radiance fields in real-world dynamic scenes. Our code and data will be publicly available.