BAGS: Blur Agnostic Gaussian Splatting through Multi-Scale Kernel Modeling

• URL: http://arxiv.org/abs/2403.04926v2
• Date: Sun, 24 Mar 2024 20:25:03 GMT
• Title: BAGS: Blur Agnostic Gaussian Splatting through Multi-Scale Kernel Modeling
• Authors: Cheng Peng, Yutao Tang, Yifan Zhou, Nengyu Wang, Xijun Liu, Deming Li, Rama Chellappa,
• Abstract summary: We analyze the robustness of Gaussian-Splatting-based methods against various image blur. We propose Blur Agnostic Gaussian Splatting (BAGS) to address this issue. BAGS introduces additional 2D modeling capacities such that a 3D-consistent and high quality scene can be reconstructed despite image-wise blur.
• Score: 32.493592776662005
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent efforts in using 3D Gaussians for scene reconstruction and novel view synthesis can achieve impressive results on curated benchmarks; however, images captured in real life are often blurry. In this work, we analyze the robustness of Gaussian-Splatting-based methods against various image blur, such as motion blur, defocus blur, downscaling blur, \etc. Under these degradations, Gaussian-Splatting-based methods tend to overfit and produce worse results than Neural-Radiance-Field-based methods. To address this issue, we propose Blur Agnostic Gaussian Splatting (BAGS). BAGS introduces additional 2D modeling capacities such that a 3D-consistent and high quality scene can be reconstructed despite image-wise blur. Specifically, we model blur by estimating per-pixel convolution kernels from a Blur Proposal Network (BPN). BPN is designed to consider spatial, color, and depth variations of the scene to maximize modeling capacity. Additionally, BPN also proposes a quality-assessing mask, which indicates regions where blur occur. Finally, we introduce a coarse-to-fine kernel optimization scheme; this optimization scheme is fast and avoids sub-optimal solutions due to a sparse point cloud initialization, which often occurs when we apply Structure-from-Motion on blurry images. We demonstrate that BAGS achieves photorealistic renderings under various challenging blur conditions and imaging geometry, while significantly improving upon existing approaches.

Related papers

• PF3plat: Pose-Free Feed-Forward 3D Gaussian Splatting [54.7468067660037]
  PF3plat sets a new state-of-the-art across all benchmarks, supported by comprehensive ablation studies validating our design choices. Our framework capitalizes on fast speed, scalability, and high-quality 3D reconstruction and view synthesis capabilities of 3DGS.
  arXiv  Detail & Related papers  (2024-10-29T15:28:15Z)
• 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)
• GAURA: Generalizable Approach for Unified Restoration and Rendering of Arbitrary Views [28.47730275628715]
  We propose a generalizable neural rendering method that can perform high-fidelity novel view synthesis under several degradations. Our method, GAURA, is learning-based and does not require any test-time scene-specific optimization.
  arXiv  Detail & Related papers  (2024-07-11T06:44:37Z)
• HO-Gaussian: Hybrid Optimization of 3D Gaussian Splatting for Urban Scenes [24.227745405760697]
  We propose a hybrid optimization method named HO-Gaussian, which combines a grid-based volume with the 3DGS pipeline. Results on widely used autonomous driving datasets demonstrate that HO-Gaussian achieves photo-realistic rendering in real-time on multi-camera urban datasets.
  arXiv  Detail & Related papers  (2024-03-29T07:58:21Z)
• 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)
• GaussianPro: 3D Gaussian Splatting with Progressive Propagation [49.918797726059545]
  3DGS relies heavily on the point cloud produced by Structure-from-Motion (SfM) techniques. We propose a novel method that applies a progressive propagation strategy to guide the densification of the 3D Gaussians. Our method significantly surpasses 3DGS on the dataset, exhibiting an improvement of 1.15dB in terms of PSNR.
  arXiv  Detail & Related papers  (2024-02-22T16:00:20Z)
• 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)
• Designing a Practical Degradation Model for Deep Blind Image Super-Resolution [134.9023380383406]
  Single image super-resolution (SISR) methods would not perform well if the assumed degradation model deviates from those in real images. This paper proposes to design a more complex but practical degradation model that consists of randomly shuffled blur, downsampling and noise degradations.
  arXiv  Detail & Related papers  (2021-03-25T17:40:53Z)
• Deep Blind Video Super-resolution [85.79696784460887]
  We propose a deep convolutional neural network (CNN) model to solve video SR by a blur kernel modeling approach. The proposed CNN model consists of motion blur estimation, motion estimation, and latent image restoration modules. We show that the proposed algorithm is able to generate clearer images with finer structural details.
  arXiv  Detail & Related papers  (2020-03-10T13:43:24Z)

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.