SMURF: Continuous Dynamics for Motion-Deblurring Radiance Fields
Jungho Lee Dogyoon Lee Minhyeok Lee Donghyung Kim Sangyoun Lee
Neural radiance fields (NeRF) has attracted considerable attention for their
exceptional ability in synthesizing novel views with high fidelity. However,
the presence of motion blur, resulting from slight camera movements during
extended shutter exposures, poses a significant challenge, potentially
compromising the quality of the reconstructed 3D scenes. While recent studies
have addressed this issue, they do not consider the continuous dynamics of
camera movements during image acquisition, leading to inaccurate scene
reconstruction. Additionally, these methods are plagued by slow training and
rendering speed. To effectively handle these issues, we propose sequential
motion understanding radiance fields (SMURF), a novel approach that employs
neural ordinary differential equation (Neural-ODE) to model continuous camera
motion and leverages the explicit volumetric representation method for faster
training and robustness to motion-blurred input images. The core idea of the
SMURF is continuous motion blurring kernel (CMBK), a unique module designed to
model a continuous camera movements for processing blurry inputs. Our model,
rigorously evaluated against benchmark datasets, demonstrates state-of-the-art
performance both quantitatively and qualitatively.