Rethinking Pose Refinement in 3D Gaussian Splatting under Pose Prior and Geometric Uncertainty

• URL: http://arxiv.org/abs/2603.16538v1
• Date: Tue, 17 Mar 2026 14:00:41 GMT
• Title: Rethinking Pose Refinement in 3D Gaussian Splatting under Pose Prior and Geometric Uncertainty
• Abstract summary: 3D Splatting (3DGS) has recently emerged as a powerful representation is increasingly used for visual localization and pose refinement.<n>Despite its high-quality differentiable and robustness, 3DGS-based refinement remains highly sensitive to both the initial camera pose and the reconstructed geometry.
• Score: 22.568270316570334
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 3D Gaussian Splatting (3DGS) has recently emerged as a powerful scene representation and is increasingly used for visual localization and pose refinement. However, despite its high-quality differentiable rendering, the robustness of 3DGS-based pose refinement remains highly sensitive to both the initial camera pose and the reconstructed geometry. In this work, we take a closer look at these limitations and identify two major sources of uncertainty: (i) pose prior uncertainty, which often arises from regression or retrieval models that output a single deterministic estimate, and (ii) geometric uncertainty, caused by imperfections in the 3DGS reconstruction that propagate errors into PnP solvers. Such uncertainties can distort reprojection geometry and destabilize optimization, even when the rendered appearance still looks plausible. To address these uncertainties, we introduce a relocalization framework that combines Monte Carlo pose sampling with Fisher Information-based PnP optimization. Our method explicitly accounts for both pose and geometric uncertainty and requires no retraining or additional supervision. Across diverse indoor and outdoor benchmarks, our approach consistently improves localization accuracy and significantly increases stability under pose and depth noise.

Related papers

• EvTrajGS: Accurate and Efficient 3D Gaussian Splatting from Unposed Event Streams [108.45551580182384]
  EvTrajGS is an accurate and efficient 3D Gaussian Splatting framework for unposed event streams.<n>Our method enables reliable joint pose-scene optimization from coarse pose priors, eliminating the need for computationally expensive SLAM-style pipelines.
  arXiv  Detail & Related papers  (2026-08-09T09:00:30Z)
• NoDrift3R: Raymap-Guided Coupling for Drift-Robust Unposed Feed-Forward 3D Reconstruction [25.145305440192875]
  Pose-Free Feed-forward 3D Gaussian Splatting has recently emerged as a powerful paradigm for fast scene reconstruction.<n>However, its performance degrades significantly in long image sequences due to cumulative camera pose estimation drift.<n>We propose a synergistic pose-free framework that explicitly couples geometry and appearance via a Raymap-Guided Coupling Module.
  arXiv  Detail & Related papers  (2026-07-08T09:02:18Z)
• DOC-GS: Dual-Domain Observation and Calibration for Reliable Sparse-View Gaussian Splatting [80.43237927269575]
  In this paper, we revisit sparse-view 3DGS reconstruction from a new perspective.<n>We identify the core challenge as the unobservability of Gaussian primitive reliability.<n>Motivated by this observation, we propose a unified Dual-domain Observation and geometrics in rendered images framework.
  arXiv  Detail & Related papers  (2026-04-08T07:01:24Z)
• Predictive Photometric Uncertainty in Gaussian Splatting for Novel View Synthesis [9.936999820242244]
  We introduce a lightweight, plug-and-play framework for pixel-wise, view-dependent predictive uncertainty estimation.<n>Our method formulates uncertainty as a Bayesian-regularized linear least-squares optimization over reconstruction residuals.<n>This architecture-agnostic approach extracts a per-primitive uncertainty channel without modifying the underlying scene representation.
  arXiv  Detail & Related papers  (2026-03-24T04:21:42Z)
• Gesplat: Robust Pose-Free 3D Reconstruction via Geometry-Guided Gaussian Splatting [21.952325954391508]
  We introduce Gesplat, a 3DGS-based framework that enables robust novel view synthesis and geometrically consistent reconstruction from unposed sparse images.<n>Our approach achieves more robust performance on both forward-facing and large-scale complex datasets compared to other pose-free methods.
  arXiv  Detail & Related papers  (2025-10-11T08:13:46Z)
• GaVS: 3D-Grounded Video Stabilization via Temporally-Consistent Local Reconstruction and Rendering [54.489285024494855]
  Video stabilization is pivotal for video processing, as it removes unwanted shakiness while preserving the original user motion intent.<n>Existing approaches, depending on the domain they operate, suffer from several issues that degrade the user experience.<n>We introduce textbfGaVS, a novel 3D-grounded approach that reformulates video stabilization as a temporally-consistent local reconstruction and rendering' paradigm.
  arXiv  Detail & Related papers  (2025-06-30T15:24:27Z)
• Sparse-view Pose Estimation and Reconstruction via Analysis by Generative Synthesis [25.898616784744377]
  Given a sparse set of observed views, the observations may not provide sufficient direct evidence to obtain complete and accurate 3D.<n>We propose SparseAGS, a method that adapts this analysis-by-synthesis approach by: a) including novel-view-synthesis-based generative priors in conjunction with photometric objectives to improve the quality of the inferred 3D, and b) explicitly reasoning about outliers and using a discrete search with a continuous optimization-based strategy to correct them.
  arXiv  Detail & Related papers  (2024-12-04T18:59:24Z)
• 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.<n>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)
• FrozenRecon: Pose-free 3D Scene Reconstruction with Frozen Depth Models [67.96827539201071]
  We propose a novel test-time optimization approach for 3D scene reconstruction. Our method achieves state-of-the-art cross-dataset reconstruction on five zero-shot testing datasets.
  arXiv  Detail & Related papers  (2023-08-10T17:55:02Z)
• Uncertainty-Aware Adaptation for Self-Supervised 3D Human Pose Estimation [70.32536356351706]
  We introduce MRP-Net that constitutes a common deep network backbone with two output heads subscribing to two diverse configurations. We derive suitable measures to quantify prediction uncertainty at both pose and joint level. We present a comprehensive evaluation of the proposed approach and demonstrate state-of-the-art performance on benchmark datasets.
  arXiv  Detail & Related papers  (2022-03-29T07:14:58Z)

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.