MoBluRF: Motion Deblurring Neural Radiance Fields for Blurry Monocular Video

• URL: http://arxiv.org/abs/2312.13528v3
• Date: Tue, 03 Jun 2025 13:38:16 GMT
• Title: MoBluRF: Motion Deblurring Neural Radiance Fields for Blurry Monocular Video
• Authors: Minh-Quan Viet Bui, Jongmin Park, Jihyong Oh, Munchurl Kim,
• Abstract summary: MoBluRF is a framework for synthesis of sharp-temporal views in blurry monocular video.<n>In the BRI stage, we reconstruct dynamic 3D scenes and jointly initialize the base rays which are used to predict latent sharp rays.<n>In the MDD stage, we introduce a novel Incremental Latent Sharp-rays Prediction (ILSP) approach for the blurry monocular video frames.
• Score: 25.964642223641057
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Neural Radiance Fields (NeRF), initially developed for static scenes, have inspired many video novel view synthesis techniques. However, the challenge for video view synthesis arises from motion blur, a consequence of object or camera movements during exposure, which hinders the precise synthesis of sharp spatio-temporal views. In response, we propose a novel motion deblurring NeRF framework for blurry monocular video, called MoBluRF, consisting of a Base Ray Initialization (BRI) stage and a Motion Decomposition-based Deblurring (MDD) stage. In the BRI stage, we coarsely reconstruct dynamic 3D scenes and jointly initialize the base rays which are further used to predict latent sharp rays, using the inaccurate camera pose information from the given blurry frames. In the MDD stage, we introduce a novel Incremental Latent Sharp-rays Prediction (ILSP) approach for the blurry monocular video frames by decomposing the latent sharp rays into global camera motion and local object motion components. We further propose two loss functions for effective geometry regularization and decomposition of static and dynamic scene components without any mask supervision. Experiments show that MoBluRF outperforms qualitatively and quantitatively the recent state-of-the-art methods with large margins.

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