GS-Blur: A 3D Scene-Based Dataset for Realistic Image Deblurring

• URL: http://arxiv.org/abs/2410.23658v1
• Date: Thu, 31 Oct 2024 06:17:16 GMT
• Title: GS-Blur: A 3D Scene-Based Dataset for Realistic Image Deblurring
• Authors: Dongwoo Lee, Joonkyu Park, Kyoung Mu Lee,
• Abstract summary: We propose GS-Blur, a dataset of synthesized realistic blurry images created using a novel approach. We first reconstruct 3D scenes from multi-view images using 3D Gaussian Splatting (3DGS), then render blurry images by moving the camera view along the randomly generated motion trajectories. By adopting various camera trajectories in reconstructing our GS-Blur, our dataset contains realistic and diverse types of blur, offering a large-scale dataset that generalizes well to real-world blur.
• Score: 50.72230109855628
• License:
• Abstract: To train a deblurring network, an appropriate dataset with paired blurry and sharp images is essential. Existing datasets collect blurry images either synthetically by aggregating consecutive sharp frames or using sophisticated camera systems to capture real blur. However, these methods offer limited diversity in blur types (blur trajectories) or require extensive human effort to reconstruct large-scale datasets, failing to fully reflect real-world blur scenarios. To address this, we propose GS-Blur, a dataset of synthesized realistic blurry images created using a novel approach. To this end, we first reconstruct 3D scenes from multi-view images using 3D Gaussian Splatting (3DGS), then render blurry images by moving the camera view along the randomly generated motion trajectories. By adopting various camera trajectories in reconstructing our GS-Blur, our dataset contains realistic and diverse types of blur, offering a large-scale dataset that generalizes well to real-world blur. Using GS-Blur with various deblurring methods, we demonstrate its ability to generalize effectively compared to previous synthetic or real blur datasets, showing significant improvements in deblurring performance.

Related papers

• EVER: Exact Volumetric Ellipsoid Rendering for Real-time View Synthesis [72.53316783628803]
  We present Exact Volumetric Ellipsoid Rendering (EVER), a method for real-time differentiable emission-only volume rendering. Unlike recentization based approach by 3D Gaussian Splatting (3DGS), our primitive based representation allows for exact volume rendering. We show that our method is more accurate with blending issues than 3DGS and follow-up work on view rendering.
  arXiv  Detail & Related papers  (2024-10-02T17:59:09Z)
• Dual-Camera Smooth Zoom on Mobile Phones [55.4114152554769]
  We introduce a new task, ie, dual-camera smooth zoom (DCSZ) to achieve a smooth zoom preview. The frame models (FI) technique is a potential solution but struggles with ground-truth collection. We suggest a data factory solution where continuous virtual cameras are assembled to generate DCSZ data by rendering reconstructed 3D models of the scene.
  arXiv  Detail & Related papers  (2024-04-07T10:28:01Z)
• Depth-Aware Image Compositing Model for Parallax Camera Motion Blur [4.170640862518009]
  Camera motion introduces spatially varying blur due to the depth changes in the 3D world. We present a simple, yet accurate, Image ting Blur (ICB) model for depth-dependent varying blur.
  arXiv  Detail & Related papers  (2023-03-16T14:15:32Z)
• Towards Real-World Video Deblurring by Exploring Blur Formation Process [53.91239555063343]
  In recent years, deep learning-based approaches have achieved promising success on video deblurring task. The models trained on existing synthetic datasets still suffer from generalization problems over real-world blurry scenarios. We propose a novel realistic blur synthesis pipeline termed RAW-Blur by leveraging blur formation cues.
  arXiv  Detail & Related papers  (2022-08-28T09:24:52Z)
• Realistic Blur Synthesis for Learning Image Deblurring [20.560205377203957]
  We present a novel blur synthesis pipeline that can synthesize more realistic blur. We also present RSBlur, a novel dataset that contains real blurred images and the corresponding sequences of sharp images.
  arXiv  Detail & Related papers  (2022-02-17T17:14:48Z)
• MC-Blur: A Comprehensive Benchmark for Image Deblurring [127.6301230023318]
  In most real-world images, blur is caused by different factors, e.g., motion and defocus. We construct a new large-scale multi-cause image deblurring dataset (called MC-Blur) Based on the MC-Blur dataset, we conduct extensive benchmarking studies to compare SOTA methods in different scenarios.
  arXiv  Detail & Related papers  (2021-12-01T02:10:42Z)
• Urban Radiance Fields [77.43604458481637]
  We perform 3D reconstruction and novel view synthesis from data captured by scanning platforms commonly deployed for world mapping in urban outdoor environments. Our approach extends Neural Radiance Fields, which has been demonstrated to synthesize realistic novel images for small scenes in controlled settings. Each of these three extensions provides significant performance improvements in experiments on Street View data.
  arXiv  Detail & Related papers  (2021-11-29T15:58:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.