PDRF: Progressively Deblurring Radiance Field for Fast and Robust Scene Reconstruction from Blurry Images

• URL: http://arxiv.org/abs/2208.08049v1
• Date: Wed, 17 Aug 2022 03:42:29 GMT
• Title: PDRF: Progressively Deblurring Radiance Field for Fast and Robust Scene Reconstruction from Blurry Images
• Authors: Cheng Peng, Rama Chellappa
• Abstract summary: We present Progressively Deblurring Radiance Field (PDRF) PDRF is a novel approach to efficiently reconstruct high quality radiance fields from blurry images. We show that PDRF is 15X faster than previous State-of-The-Art scene reconstruction methods.
• Score: 75.87721926918874
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 rendering results from clean source views, their performances suffer when the source views are affected by blur, which is commonly observed for images in the wild. Previous deblurring methods either do not account for 3D geometry, or are computationally intense. To addresses these issues, PDRF, a progressively deblurring scheme in radiance field modeling, accurately models blur by incorporating 3D scene context. PDRF further uses an efficient importance sampling scheme, which results in fast scene optimization. Specifically, PDRF proposes a Coarse Ray Renderer to quickly estimate voxel density and feature; a Fine Voxel Renderer is then used to achieve high quality ray tracing. We perform extensive experiments and show that PDRF is 15X faster than previous SoTA while achieving better performance on both synthetic and real scenes.

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