SCOUT-SLAM: Structurally-Coupled Dual Uncertainty-Aware 3DGS SLAM in the Wild
- URL: http://arxiv.org/abs/2609.14634v1
- Date: Sun, 13 Sep 2026 16:08:18 GMT
- Title: SCOUT-SLAM: Structurally-Coupled Dual Uncertainty-Aware 3DGS SLAM in the Wild
- Abstract summary: Existing methods rely on the stability of the underlying scene reconstruction to model uncertainty.<n>SCOUT-SLAM is a structurally-coupled dual-uncertainty framework in which both uncertainties are estimated from a shared base network.<n>A low-rank adaptation of this network, trained on multi-view feature consistency, estimates a tracking uncertainty that does not depend solely on the reconstruction quality.
- Score: 5.507970541170981
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recently, 3D Gaussian Splatting SLAM (3DGS-SLAM) has gained significant momentum in simultaneous localization and 3DGS scene reconstruction. In real-world scenarios with rapid camera motion and cluttered dynamic environments, existing methods rely on the stability of the underlying scene reconstruction to model uncertainty. This leads to a circular dependency between camera tracking accuracy and reconstruction quality: reconstruction instabilities degrade uncertainty modeling, which affects accurate camera tracking and static scene reconstruction. To address this, the paper proposes SCOUT-SLAM, a structurally-coupled dual-uncertainty framework in which both uncertainties are estimated from a shared base network. A low-rank adaptation of this network, trained on multi-view feature consistency, estimates a tracking uncertainty that does not depend solely on the reconstruction quality. A spatially-adaptive prior modulates the network's training objective so that reconstruction instability does not inflate uncertainty on static regions, keeping the shared representation intact for both branches. Evaluations on dynamic benchmarks (TUM RGB-D, Bonn Dynamic, Wild-SLAM MoCap) demonstrate that SCOUT-SLAM achieves state-of-the-art camera tracking accuracy and artifact-free static scene reconstruction.
Related papers
- Dynamic-Robust Photometric-Semantic Reconstruction for Open-Vocabulary 3D Scene Understanding [63.599342750566485]
SPAR is a novel joint semantic-geometric encoding architecture that explicitly isolates transient dynamic noise prior to latent space aggregation.<n>Our end-to-end approach achieves exceptional novel view synthesis quality, yielding a PSNR of 22.15 dB and 23.33 dB given only 3 and 4 input views respectively.
arXiv Detail & Related papers (2026-08-29T09:58:24Z) - 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) - DynActiveGS: Active Gaussian Splatting for Dynamic Scene Reconstruction [15.531413683893751]
We present DynActiveGS, a dynamic-aware active reconstruction framework based on 3D Gaussian Splatting (3DGS)<n>The framework incrementally reconstructs a 3D Gaussian scene representation while suppressing motion-corrupted observations.<n>A key component of DynActiveGS is the explicit decomposition of uncertainty into structural uncertainty and motion-induced uncertainty.
arXiv Detail & Related papers (2026-08-02T11:57:49Z) - RU4D-SLAM: Reweighting Uncertainty in Gaussian Splatting SLAM for 4D Scene Reconstruction [8.13353479857245]
4D reconstruction, especially 4D Gaussian splatting, offers a promising direction for addressing these challenges.<n>We propose a robust and efficient framework, namely Reweighting Uncertainty in Gaussian Splatting SLAM (RU4D-SLAM) for 4D scene reconstruction.<n>Our method substantially outperforms state-of-the-art approaches in both trajectory accuracy and 4D scene reconstruction.
arXiv Detail & Related papers (2026-02-24T11:47:43Z) - Towards Geometry-Aware and Motion-Guided Video Human Mesh Recovery [60.51998732898099]
We introduce HMRMamba, a new paradigm for 3D Human Mesh Recovery.<n>It pioneers the use of Structured State Space Models for their efficiency and long-range modeling prowess.<n>Our framework is distinguished by two core contributions. First, the Geometry-Aware Lifting Module, featuring a novel dual-scan Mamba architecture.
arXiv Detail & Related papers (2026-01-29T08:05:02Z) - RoGER-SLAM: A Robust Gaussian Splatting SLAM System for Noisy and Low-light Environment Resilience [4.942278642834429]
RoGER-SLAM is a robust 3DGS SLAM system tailored for noise and low-light resilience.<n>We show that RoGER-SLAM consistently improves trajectory accuracy and reconstruction quality compared with other 3DGS-SLAM systems.
arXiv Detail & Related papers (2025-10-26T09:32:43Z) - Adaptive Relative Pose Estimation Framework with Dual Noise Tuning for Safe Approaching Maneuvers [0.0]
This work presents a complete pipeline integrating advanced computer vision techniques with adaptive nonlinear filtering.<n>A Convolutional Neural Network (CNN), enhanced with image preprocessing, detects structural markers from chaser imagery.<n>A UKF framework is selected for its ability to handle nonlinear relative dynamics to estimate the full relative pose.
arXiv Detail & Related papers (2025-07-22T04:13:03Z) - 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) - HS-SLAM: Hybrid Representation with Structural Supervision for Improved Dense SLAM [38.82194947459594]
NeRF-based SLAM has recently achieved promising results in tracking and reconstruction.<n>We present HS-SLAM to tackle these problems.<n>We propose a hybrid encoding network that combines the complementary strengths of hash-grid, tri-planes, and one-blob.
arXiv Detail & Related papers (2025-03-27T17:59:54Z) - T-3DGS: Removing Transient Objects for 3D Scene Reconstruction [83.05271859398779]
Transient objects in video sequences can significantly degrade the quality of 3D scene reconstructions.<n>We propose T-3DGS, a novel framework that robustly filters out transient distractors during 3D reconstruction using Gaussian Splatting.
arXiv Detail & Related papers (2024-11-29T07:45:24Z) - Event-boosted Deformable 3D Gaussians for Dynamic Scene Reconstruction [50.873820265165975]
We introduce the first approach combining event cameras, which capture high-temporal-resolution, continuous motion data, with deformable 3D-GS for dynamic scene reconstruction.<n>We propose a GS-Threshold Joint Modeling strategy, creating a mutually reinforcing process that greatly improves both 3D reconstruction and threshold modeling.<n>We contribute the first event-inclusive 4D benchmark with synthetic and real-world dynamic scenes, on which our method achieves state-of-the-art performance.
arXiv Detail & Related papers (2024-11-25T08:23:38Z)
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.