EndoMD-SLAM: Endoscopic Gaussian Splatting SLAM under Optical Degradation with Memory and Static-Transient Decomposition
- URL: http://arxiv.org/abs/2608.08949v1
- Date: Sun, 09 Aug 2026 22:59:01 GMT
- Title: EndoMD-SLAM: Endoscopic Gaussian Splatting SLAM under Optical Degradation with Memory and Static-Transient Decomposition
- Abstract summary: EndoMD-SLAM is a framework designed to maintain stability under optical degradation.<n>On the tracking side, a memory-driven gating mechanism detects unreliable observations to suspend map updates.<n>On the mapping side, a self-supervised static-transient decomposition isolates visual contaminants into a dedicated transient field.
- Score: 46.23526592715223
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Dense 3D reconstruction is critical for clinical endoscopic navigation and documentation. While Gaussian Splatting SLAM systems show promise in this domain, they fundamentally rely on strict multi-view photometric consistency. In routine procedures, this assumption is severely violated by intermittent optical degradations like moving debris and water flushing. Standard systems erroneously fuse these cameraattached artifacts into the persistent 3D geometry, causing severe tracking drift and irreversible map corruption. To address this limitation, we propose EndoMD-SLAM, a framework designed to maintain stability under optical degradation through specialized tracking and mapping mechanisms. On the tracking side, a memory-driven gating mechanism detects unreliable observations to suspend map updates and utilizes historical keyframes for drift-aware relocalization. On the mapping side, a self-supervised static-transient decomposition isolates visual contaminants into a dedicated transient field. This explicit separation prevents artifacts from structurally entangling with the persistent anatomical map. We curate a degradationfocused benchmark from colonoscopy videos to systematically evaluate these failure modes. Extensive experiments show that while standard baselines fail under severe optical degradation, EndoMD-SLAM preserves geometric integrity, reducing absolute trajectory error by 91% and improving rendering fidelity by 9.9 dB PSNR.
Related papers
- Freezing of Gait Prediction Under Spatial Occlusion: An IMU-Supervised Cross-Modal Distillation Approach [0.0]
Parkinson's disease is a progressive neurodegenerative disorder characterised by gradual deterioration of movement control.<n> automated freezing-of-gait (FOG) detection supports the objective assessment of gait-related motor impairment.<n>Two common approaches are used for FOG prediction: (i) analysing video recordings of the patient's movements and (ii) analysing data collected using inertial measurement unit (IMU) wearable sensors attached to the patient's lower limbs.<n>Video-based approaches may suffer detection errors during continuous turning-in-place tasks because the lower limbs undergo substantial geometric self-occlusion, degrading pose-estimation accuracy.
arXiv Detail & Related papers (2026-09-09T07:29:13Z) - PoInit-of-View: Poisoning Initialization of Views Transfers Across Multiple 3D Reconstruction Systems [55.84012796735184]
Poisoning input views of 3D reconstruction systems has been recently studied.<n>We propose PoInit-of-View, which optimize adversarial perturbations to intentionally introduce cross-view inconsistencies.<n> Experimental results demonstrate the effectiveness of our PoInit-of-View on diverse 3D reconstruction systems and datasets.
arXiv Detail & Related papers (2026-04-17T00:48:12Z) - 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) - 3D Gaussian Splatting for Annular Dark Field Scanning Transmission Electron Microscopy Tomography Reconstruction [13.051560405222775]
ADF-STEM tomography reconstructs nanoscale materials in 3D by integrating multi-view tilt-series images.<n>We introduce a coefficient $$ to stabilize scattering across tilt angles, ensuring consistent denza via scattering view normalization.<n>Experiments on 45-view and 15-view tilt series show that DenZa-Gaussian produces high-fidelity reconstructions and 2D projections that align more closely with original tilts.
arXiv Detail & Related papers (2026-04-06T14:01:43Z) - 3DGS-HPC: Distractor-free 3D Gaussian Splatting with Hybrid Patch-wise Classification [80.43401018217367]
3D Gaussian Splatting (3DGS) has demonstrated remarkable performance in novel view synthesis and 3D scene reconstruction.<n>Its quality often degrades in real-world environments due to transient distractors, such as moving objects and varying shadows.<n>Existing methods rely on semantic cues extracted from pre-trained vision models to identify and suppress these distractors.<n>We propose 3DGS- HPC, a framework that circumvents these limitations by combining two complementary principles.
arXiv Detail & Related papers (2026-03-08T11:16:30Z) - NRGS-SLAM: Monocular Non-Rigid SLAM for Endoscopy via Deformation-Aware 3D Gaussian Splatting [25.805439969022697]
We propose NRGS-SLAM, a monocular non-rigid SLAM system for endoscopy based on 3D Gaussian Splatting.<n>We show that NRGS-SLAM achieves more accurate camera pose estimation (up to 50% reduction in RMSE) and higher-quality photo-realistic reconstructions than state-of-the-art methods.
arXiv Detail & Related papers (2026-02-19T09:03:47Z) - ERGO: Excess-Risk-Guided Optimization for High-Fidelity Monocular 3D Gaussian Splatting [63.138778159026934]
We propose an adaptive optimization framework guided by excess risk decomposition, termed ERGO.<n> ERGO dynamically estimates the view-specific excess risk and adaptively adjust loss weights during optimization.<n>Experiments on the Google Scanned Objects dataset and the OmniObject3D dataset demonstrate the superiority of ERGO over existing state-of-the-art methods.
arXiv Detail & Related papers (2026-02-10T20:44:43Z) - InpaintHuman: Reconstructing Occluded Humans with Multi-Scale UV Mapping and Identity-Preserving Diffusion Inpainting [64.42884719282323]
InpaintHuman is a novel method for generating high-fidelity, complete, and animatable avatars from occluded monocular videos.<n>Our approach employs direct pixel-level supervision to ensure identity fidelity.
arXiv Detail & Related papers (2026-01-05T13:26:02Z) - Occlusion-Aware Self-Supervised Monocular Depth Estimation for Weak-Texture Endoscopic Images [1.1084686909647639]
We propose a self-supervised monocular depth estimation network tailored for endoscopic scenes.<n>Existing methods, though accurate, typically assume consistent illumination.<n>These variations lead to incorrect geometric interpretations and unreliable self-supervised signals.
arXiv Detail & Related papers (2025-04-24T14:12:57Z) - A geometry-aware deep network for depth estimation in monocular
endoscopy [17.425158094539462]
The proposed method is extensively validated across different datasets and clinical images.
The generalizability of the proposed method achieves mean RMSE values of 12.604 (T1-L1), 9.930 (T2-L2), and 13.893 (colon) on the ColonDepth dataset.
arXiv Detail & Related papers (2023-04-20T11:59:32Z)
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.