UPGS: Unified Pose-aware Gaussian Splatting for Dynamic Scene Deblurring

• URL: http://arxiv.org/abs/2509.00831v2
• Date: Wed, 03 Sep 2025 16:49:23 GMT
• Title: UPGS: Unified Pose-aware Gaussian Splatting for Dynamic Scene Deblurring
• Authors: Zhijing Wu, Longguang Wang,
• Abstract summary: Reconstructing 3D scenes from monocular video often fails due to severe motion blur caused by camera and object motion.<n>We introduce a unified optimization framework by incorporating camera poses as learnable parameters.<n>Our method achieves significant gains in reconstruction quality and pose estimation accuracy over prior dynamic deblurring methods.
• Score: 31.35713139629235
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Reconstructing dynamic 3D scenes from monocular video has broad applications in AR/VR, robotics, and autonomous navigation, but often fails due to severe motion blur caused by camera and object motion. Existing methods commonly follow a two-step pipeline, where camera poses are first estimated and then 3D Gaussians are optimized. Since blurring artifacts usually undermine pose estimation, pose errors could be accumulated to produce inferior reconstruction results. To address this issue, we introduce a unified optimization framework by incorporating camera poses as learnable parameters complementary to 3DGS attributes for end-to-end optimization. Specifically, we recast camera and object motion as per-primitive SE(3) affine transformations on 3D Gaussians and formulate a unified optimization objective. For stable optimization, we introduce a three-stage training schedule that optimizes camera poses and Gaussians alternatively. Particularly, 3D Gaussians are first trained with poses being fixed, and then poses are optimized with 3D Gaussians being untouched. Finally, all learnable parameters are optimized together. Extensive experiments on the Stereo Blur dataset and challenging real-world sequences demonstrate that our method achieves significant gains in reconstruction quality and pose estimation accuracy over prior dynamic deblurring methods.

Related papers

• JOintGS: Joint Optimization of Cameras, Bodies and 3D Gaussians for In-the-Wild Monocular Reconstruction [18.636227266388218]
  We present JOintGS, a unified framework that jointly optimize camera extrinsics, human poses, and 3D Gaussian representations.<n>Experiments on NeuMan and EMDB datasets demonstrate that JOintGS achieves superior reconstruction quality.
  arXiv  Detail & Related papers  (2026-02-04T08:33:51Z)
• iGaussian: Real-Time Camera Pose Estimation via Feed-Forward 3D Gaussian Splatting Inversion [62.09575122593993]
  iGaussian is a two-stage feed-forward framework that achieves real-time camera pose estimation through direct 3D Gaussian inversion.<n> Experimental results on the NeRF Synthetic, Mip-NeRF 360, and T&T+DB datasets demonstrate a significant performance improvement over previous methods.
  arXiv  Detail & Related papers  (2025-11-18T05:22:22Z)
• JOGS: Joint Optimization of Pose Estimation and 3D Gaussian Splatting [10.35563602148445]
  We propose a unified framework that jointly optimize 3D Gaussian points and camera poses without requiring pre-calibrated inputs.<n>Our approach iteratively refines 3D Gaussian parameters and updates camera poses through a novel co-optimization strategy.<n>Our approach significantly outperforms existing COLMAP-free techniques in reconstruction quality, and also surpasses the standard COLMAP-based baseline in general.
  arXiv  Detail & Related papers  (2025-10-30T04:00:07Z)
• BSGS: Bi-stage 3D Gaussian Splatting for Camera Motion Deblurring [32.5099324617965]
  Bi-Stage 3D Gaussian Splatting is a novel framework to reconstruct 3D scenes from motion-blurred images.<n>We propose a subframe gradient aggregation strategy to optimize both stages.<n>Experiments verify the effectiveness of our proposed deblurring method and show its superiority over the state of the arts.
  arXiv  Detail & Related papers  (2025-10-14T13:26:56Z)
• PCR-GS: COLMAP-Free 3D Gaussian Splatting via Pose Co-Regularizations [102.0476991174456]
  COLMAP-free 3DGS has attracted increasing attention due to its remarkable performance in reconstructing high-quality 3D scenes from unposed images or videos.<n>We propose PCR-GS, an innovative COLMAP-free 3DGS technique that achieves superior 3D scene modeling and camera pose estimation via camera pose co-regularization.
  arXiv  Detail & Related papers  (2025-07-18T13:09:33Z)
• 3R-GS: Best Practice in Optimizing Camera Poses Along with 3DGS [36.48425755917156]
  3D Gaussian Splatting (3DGS) has revolutionized neural rendering with its efficiency and quality.<n>It heavily depends on accurate camera poses from Structure-from-Motion (SfM) systems.<n>We present 3R-GS, a 3D Gaussian Splatting framework that bridges this gap.
  arXiv  Detail & Related papers  (2025-04-05T22:31:08Z)
• TrackGS: Optimizing COLMAP-Free 3D Gaussian Splatting with Global Track Constraints [40.9371798496134]
  We introduce TrackGS, which incorporates feature tracks to globally constrain multi-view geometry.<n>We also propose minimizing both reprojection and backprojection errors for better geometric consistency.<n>By deriving the gradient of intrinsics, we unify camera parameter estimation with 3DGS training into a joint optimization framework.
  arXiv  Detail & Related papers  (2025-02-27T06:16:04Z)
• FreeSplatter: Pose-free Gaussian Splatting for Sparse-view 3D Reconstruction [69.63414788486578]
  FreeSplatter is a scalable feed-forward framework that generates high-quality 3D Gaussians from uncalibrated sparse-view images.<n>Our approach employs a streamlined transformer architecture where self-attention blocks facilitate information exchange.<n>We develop two specialized variants--for object-centric and scene-level reconstruction--trained on comprehensive datasets.
  arXiv  Detail & Related papers  (2024-12-12T18:52:53Z)
• Look Gauss, No Pose: Novel View Synthesis using Gaussian Splatting without Accurate Pose Initialization [11.418632671254564]
  3D Gaussian Splatting has emerged as a powerful tool for fast and accurate novel-view synthesis from a set of posed input images. We propose an extension to the 3D Gaussian Splatting framework by optimizing the extrinsic camera parameters with respect to photometric residuals. We show results on real-world scenes and complex trajectories through simulated environments.
  arXiv  Detail & Related papers  (2024-10-11T12:01:15Z)
• MotionGS: Exploring Explicit Motion Guidance for Deformable 3D Gaussian Splatting [56.785233997533794]
  We propose a novel deformable 3D Gaussian splatting framework called MotionGS. MotionGS explores explicit motion priors to guide the deformation of 3D Gaussians. Experiments in the monocular dynamic scenes validate that MotionGS surpasses state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-10-10T08:19:47Z)
• PUP 3D-GS: Principled Uncertainty Pruning for 3D Gaussian Splatting [59.277480452459315]
  We propose a principled sensitivity pruning score that preserves visual fidelity and foreground details at significantly higher compression ratios.<n>We also propose a multi-round prune-refine pipeline that can be applied to any pretrained 3D-GS model without changing its training pipeline.
  arXiv  Detail & Related papers  (2024-06-14T17:53:55Z)
• A Construct-Optimize Approach to Sparse View Synthesis without Camera Pose [44.13819148680788]
  We develop a novel construct-and-optimize method for sparse view synthesis without camera poses. Specifically, we construct a solution by using monocular depth and projecting pixels back into the 3D world. We demonstrate results on the Tanks and Temples and Static Hikes datasets with as few as three widely-spaced views.
  arXiv  Detail & Related papers  (2024-05-06T17:36:44Z)
• COLMAP-Free 3D Gaussian Splatting [88.420322646756]
  We propose a novel method to perform novel view synthesis without any SfM preprocessing. We process the input frames in a sequential manner and progressively grow the 3D Gaussians set by taking one input frame at a time. Our method significantly improves over previous approaches in view synthesis and camera pose estimation under large motion changes.
  arXiv  Detail & Related papers  (2023-12-12T18:39:52Z)

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.