NeRF-DA: Neural Radiance Fields Deblurring With Active Learning
Sejun Hong, Eunwoo Kim · IEEE Signal Processing Letters · 2024
Neural radiance fields (NeRF) represent multi-view images as 3D scenes, achieving a photo-realistic novel view synthesis quality. However, capturing multi-view images in real-world scenarios is not well aligned and often results in blur or noise. Deblur-NeRF, which uses kernel deformation to improve sharpness, is effective but the quantity of training blur samples and imbalance significantly affect the overall results. In this study, we propose neural radiance fields deblurring with active learning (NeRF-DA), focusing on high-quality blurred images for 3D scene modeling. NeRF-DA uses pool-based active learning with uncertainty estimation to improve model efficiency with a high-quality training set. Subsequently, we deblur the data using the trained model and proceed with NeRF training by selecting the best-sharpened images for querying. Experiments on both camera motion blur and defocus blur demonstrate that NeRF-DA significantly enhances the quality of the existing Deblur-NeRF.