SMURF: Continuous Dynamics for Motion-Deblurring Radiance Fields

TL;DR

SMURF introduces a novel method for modeling continuous camera motion to improve the quality of neural radiance fields reconstructed from motion-blurred images, achieving state-of-the-art results.

Contribution

The paper presents SMURF, a new approach that explicitly models continuous camera motion and uses volumetric representation to handle motion blur in NeRFs.

Findings

Achieves state-of-the-art performance on benchmark datasets.

Effectively models continuous camera motion for motion deblurring.

Robustly reconstructs 3D scenes from motion-blurred images.

Abstract

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. To effectively handle this issue, we propose sequential motion understanding radiance fields (SMURF), a novel approach that models continuous camera motion and leverages the explicit volumetric representation method for robustness to motion-blurred input images. The core idea of the SMURF is continuous motion blurring kernel (CMBK), a module designed to model a continuous camera movements for processing blurry inputs. Our model is evaluated against benchmark datasets and demonstrates state-of-the-art performance both quantitatively and qualitatively.