PDRF: Progressively Deblurring Radiance Field for Fast Scene Reconstruction from Blurry Images
Year: 2023
Type: conference-paper
Abstract: We present Progressively Deblurring Radiance Field (PDRF), a novel approach to efficiently reconstruct high quality radiance fields from blurry images. While current State-of-The-Art (SoTA) scene reconstruction methods achieve photo-realistic renderings from clean source views, their performances suffer when the source views are affected by blur, which is commonly observed in the wild. Previous deblurring methods either do not account for 3D geometry, or are computationally intense. To addresses these issues, PDRF uses a progressively deblurring scheme for radiance field modeling, which can accurately model blur with 3D scene context. PDRF further uses an efficient importance sampling scheme that results in fast scene optimization. We perform extensive experiments and show that PDRF is 15X faster than previous SoTA while achieving better performance on both synthetic and real scenes.
Authors: Cheng Peng, Rama Chellappa
Institution: Johns Hopkins University
Language: English
Subfield: Computational Mechanics
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4382457288