BeSplat: Gaussian Splatting from a Single Blurry Image and Event Stream

• URL: http://arxiv.org/abs/2412.19370v2
• Date: Sun, 05 Jan 2025 21:19:48 GMT
• Title: BeSplat: Gaussian Splatting from a Single Blurry Image and Event Stream
• Authors: Gopi Raju Matta, Reddypalli Trisha, Kaushik Mitra,
• Abstract summary: 3D Gaussian Splatting (3DGS) has effectively addressed key challenges, such as long training times and slow rendering speeds.<n>We demonstrate the recovery of sharp radiance field (Gaussian splats) from a single motion-blurred image and its corresponding event stream.
• Score: 13.649334929746413
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Novel view synthesis has been greatly enhanced by the development of radiance field methods. The introduction of 3D Gaussian Splatting (3DGS) has effectively addressed key challenges, such as long training times and slow rendering speeds, typically associated with Neural Radiance Fields (NeRF), while maintaining high-quality reconstructions. In this work (BeSplat), we demonstrate the recovery of sharp radiance field (Gaussian splats) from a single motion-blurred image and its corresponding event stream. Our method jointly learns the scene representation via Gaussian Splatting and recovers the camera motion through Bezier SE(3) formulation effectively, minimizing discrepancies between synthesized and real-world measurements of both blurry image and corresponding event stream. We evaluate our approach on both synthetic and real datasets, showcasing its ability to render view-consistent, sharp images from the learned radiance field and the estimated camera trajectory. To the best of our knowledge, ours is the first work to address this highly challenging ill-posed problem in a Gaussian Splatting framework with the effective incorporation of temporal information captured using the event stream.

Related papers

• EBAD-Gaussian: Event-driven Bundle Adjusted Deblur Gaussian Splatting [21.46091843175779]
  Event-driven Bundle Adjusted Deblur Gaussian Splatting (EBAD-Gaussian) EBAD-Gaussian reconstructs sharp 3D Gaussians from event streams and severely blurred images. Experiments on synthetic and real-world datasets show that EBAD-Gaussian can achieve high-quality 3D scene reconstruction.
  arXiv  Detail & Related papers  (2025-04-14T09:17:00Z)
• EVolSplat: Efficient Volume-based Gaussian Splatting for Urban View Synthesis [61.1662426227688]
  Existing NeRF and 3DGS-based methods show promising results in achieving photorealistic renderings but require slow, per-scene optimization. We introduce EVolSplat, an efficient 3D Gaussian Splatting model for urban scenes that works in a feed-forward manner.
  arXiv  Detail & Related papers  (2025-03-26T02:47:27Z)
• DehazeGS: Seeing Through Fog with 3D Gaussian Splatting [17.119969983512533]
  We introduce DehazeGS, a method capable of decomposing and rendering a fog-free background from participating media. Experiments on both synthetic and real-world foggy datasets demonstrate that DehazeGS achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2025-01-07T09:47:46Z)
• Splatfacto-W: A Nerfstudio Implementation of Gaussian Splatting for Unconstrained Photo Collections [25.154665328053333]
  We introduce Splatfacto-W, an in-trivial approach that integrates per-Gaussian neural color features and per-image appearance embeddings into an rendering process. Our method improves the Peak Signal-to-Noise Ratio (PSNR) by an average of 5.3 dB compared to 3DGS, enhances training speed by 150 times compared to NeRF-based methods, and achieves a similar rendering speed to 3DGS.
  arXiv  Detail & Related papers  (2024-07-17T04:02:54Z)
• Event3DGS: Event-Based 3D Gaussian Splatting for High-Speed Robot Egomotion [54.197343533492486]
  Event3DGS can reconstruct high-fidelity 3D structure and appearance under high-speed egomotion. Experiments on multiple synthetic and real-world datasets demonstrate the superiority of Event3DGS compared with existing event-based dense 3D scene reconstruction frameworks. Our framework also allows one to incorporate a few motion-blurred frame-based measurements into the reconstruction process to further improve appearance fidelity without loss of structural accuracy.
  arXiv  Detail & Related papers  (2024-06-05T06:06:03Z)
• EvaGaussians: Event Stream Assisted Gaussian Splatting from Blurry Images [36.91327728871551]
  3D Gaussian Splatting (3D-GS) has demonstrated exceptional capabilities in 3D scene reconstruction and novel view synthesis.<n>We introduce Event Stream Assisted Gaussian Splatting (EvaGaussians), a novel approach that integrates event streams captured by an event camera to assist in reconstructing high-quality 3D-GS from blurry images.
  arXiv  Detail & Related papers  (2024-05-29T04:59:27Z)
• SGD: Street View Synthesis with Gaussian Splatting and Diffusion Prior [53.52396082006044]
  Current methods struggle to maintain rendering quality at the viewpoint that deviates significantly from the training viewpoints. This issue stems from the sparse training views captured by a fixed camera on a moving vehicle. We propose a novel approach that enhances the capacity of 3DGS by leveraging prior from a Diffusion Model.
  arXiv  Detail & Related papers  (2024-03-29T09:20:29Z)
• BAD-Gaussians: Bundle Adjusted Deblur Gaussian Splatting [8.380954205255104]
  BAD-Gaussians is a novel approach to handle severe motion-blurred images with inaccurate camera poses. Our method achieves superior rendering quality compared to previous state-of-the-art deblur neural rendering methods.
  arXiv  Detail & Related papers  (2024-03-18T14:43:04Z)
• VastGaussian: Vast 3D Gaussians for Large Scene Reconstruction [59.40711222096875]
  We present VastGaussian, the first method for high-quality reconstruction and real-time rendering on large scenes based on 3D Gaussian Splatting. Our approach outperforms existing NeRF-based methods and achieves state-of-the-art results on multiple large scene datasets.
  arXiv  Detail & Related papers  (2024-02-27T11:40:50Z)
• SplaTAM: Splat, Track & Map 3D Gaussians for Dense RGB-D SLAM [48.190398577764284]
  SplaTAM is an approach to enable high-fidelity reconstruction from a single unposed RGB-D camera. It employs a simple online tracking and mapping system tailored to the underlying Gaussian representation. Experiments show that SplaTAM achieves up to 2x superior performance in camera pose estimation, map construction, and novel-view synthesis over existing methods.
  arXiv  Detail & Related papers  (2023-12-04T18:53:24Z)
• Scaffold-GS: Structured 3D Gaussians for View-Adaptive Rendering [71.44349029439944]
  Recent 3D Gaussian Splatting method has achieved the state-of-the-art rendering quality and speed. We introduce Scaffold-GS, which uses anchor points to distribute local 3D Gaussians. We show that our method effectively reduces redundant Gaussians while delivering high-quality rendering.
  arXiv  Detail & Related papers  (2023-11-30T17:58:57Z)
• Real-Time Radiance Fields for Single-Image Portrait View Synthesis [85.32826349697972]
  We present a one-shot method to infer and render a 3D representation from a single unposed image in real-time. Given a single RGB input, our image encoder directly predicts a canonical triplane representation of a neural radiance field for 3D-aware novel view synthesis via volume rendering. Our method is fast (24 fps) on consumer hardware, and produces higher quality results than strong GAN-inversion baselines that require test-time optimization.
  arXiv  Detail & Related papers  (2023-05-03T17:56:01Z)
• PDRF: Progressively Deblurring Radiance Field for Fast and Robust Scene Reconstruction from Blurry Images [75.87721926918874]
  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.
  arXiv  Detail & Related papers  (2022-08-17T03:42:29Z)

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.