EndoLRMGS: Complete Endoscopic Scene Reconstruction combining Large Reconstruction Modelling and Gaussian Splatting

• URL: http://arxiv.org/abs/2503.22437v1
• Date: Fri, 28 Mar 2025 13:57:12 GMT
• Title: EndoLRMGS: Complete Endoscopic Scene Reconstruction combining Large Reconstruction Modelling and Gaussian Splatting
• Authors: Xu Wang, Shuai Zhang, Baoru Huang, Danail Stoyanov, Evangelos B. Mazomenos,
• Abstract summary: We propose EndoLRMGS, that combines Large Reconstruction Modelling (LRM) and Gaussian Splatting (GS) for complete surgical scene reconstruction.<n>GS reconstructs deformable tissues and LRM generates 3D models for surgical tools while position and scale are subsequently optimized.<n>In experiments on four surgical videos from three public datasets, our method improves the Intersection-over-union (IoU) of tool 3D models in 2D projections by>40%.
• Score: 16.50682401904587
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Complete reconstruction of surgical scenes is crucial for robot-assisted surgery (RAS). Deep depth estimation is promising but existing works struggle with depth discontinuities, resulting in noisy predictions at object boundaries and do not achieve complete reconstruction omitting occluded surfaces. To address these issues we propose EndoLRMGS, that combines Large Reconstruction Modelling (LRM) and Gaussian Splatting (GS), for complete surgical scene reconstruction. GS reconstructs deformable tissues and LRM generates 3D models for surgical tools while position and scale are subsequently optimized by introducing orthogonal perspective joint projection optimization (OPjPO) to enhance accuracy. In experiments on four surgical videos from three public datasets, our method improves the Intersection-over-union (IoU) of tool 3D models in 2D projections by>40%. Additionally, EndoLRMGS improves the PSNR of the tools projection from 3.82% to 11.07%. Tissue rendering quality also improves, with PSNR increasing from 0.46% to 49.87%, and SSIM from 1.53% to 29.21% across all test videos.

Related papers

• SurGSplat: Progressive Geometry-Constrained Gaussian Splatting for Surgical Scene Reconstruction [7.6065635532326965]
  SurGSplat is a novel paradigm designed to progressively refine 3D Gaussian Splatting (3DGS) through the integration of geometric constraints.<n>By enabling the detailed reconstruction of vascular structures and other critical features, SurGSplat provides surgeons with enhanced visual clarity.<n> Experimental evaluations demonstrate that SurGSplat achieves superior performance in both novel view synthesis (NVS) and pose estimation accuracy.
  arXiv  Detail & Related papers  (2025-06-06T10:02:11Z)
• UAVTwin: Neural Digital Twins for UAVs using Gaussian Splatting [57.63613048492219]
  We present UAVTwin, a method for creating digital twins from real-world environments and facilitating data augmentation for training downstream models embedded in unmanned aerial vehicles (UAVs) This is achieved by integrating 3D Gaussian Splatting (3DGS) for reconstructing backgrounds along with controllable synthetic human models that display diverse appearances and actions in multiple poses.
  arXiv  Detail & Related papers  (2025-04-02T22:17:30Z)
• Feature-EndoGaussian: Feature Distilled Gaussian Splatting in Surgical Deformable Scene Reconstruction [26.358467072736524]
  We introduce Feature-EndoGaussian (FEG), an extension of 3DGS that integrates 2D segmentation cues into 3D rendering to enable real-time semantic and scene reconstruction.<n>FEG achieves superior performance (SSIM of 0.97, PSNR of 39.08, and LPIPS of 0.03) compared to leading methods.
  arXiv  Detail & Related papers  (2025-03-08T10:50:19Z)
• 4DRGS: 4D Radiative Gaussian Splatting for Efficient 3D Vessel Reconstruction from Sparse-View Dynamic DSA Images [49.170407434313475]
  Existing methods often produce suboptimal results or require excessive computation time.<n>We propose 4D radiative Gaussian splatting (4DRGS) to achieve high-quality reconstruction efficiently.<n>4DRGS achieves impressive results in 5 minutes training, which is 32x faster than the state-of-the-art method.
  arXiv  Detail & Related papers  (2024-12-17T13:51:56Z)
• SurgicalGS: Dynamic 3D Gaussian Splatting for Accurate Robotic-Assisted Surgical Scene Reconstruction [18.074890506856114]
  We present SurgicalGS, a dynamic 3D Gaussian Splatting framework specifically designed for surgical scene reconstruction with improved geometric accuracy. Our approach first initialises a Gaussian point cloud using depth priors, employing binary motion masks to identify pixels with significant depth variations and fusing point clouds from depth maps across frames for initialisation. We use the Flexible Deformation Model to represent dynamic scene and introduce a normalised depth regularisation loss along with an unsupervised depth smoothness constraint to ensure more accurate geometric reconstruction.
  arXiv  Detail & Related papers  (2024-10-11T22:46:46Z)
• Enhanced Knee Kinematics: Leveraging Deep Learning and Morphing Algorithms for 3D Implant Modeling [2.752817022620644]
  This study proposes a novel approach using machine learning algorithms and morphing techniques for precise 3D reconstruction of implanted knee models. A convolutional neural network is trained to automatically segment the femur contour of the implanted components. A morphing algorithm generates a personalized 3D model of the implanted knee joint.
  arXiv  Detail & Related papers  (2024-08-02T20:11:04Z)
• FLex: Joint Pose and Dynamic Radiance Fields Optimization for Stereo Endoscopic Videos [79.50191812646125]
  Reconstruction of endoscopic scenes is an important asset for various medical applications, from post-surgery analysis to educational training. We adress the challenging setup of a moving endoscope within a highly dynamic environment of deforming tissue. We propose an implicit scene separation into multiple overlapping 4D neural radiance fields (NeRFs) and a progressive optimization scheme jointly optimizing for reconstruction and camera poses from scratch. This improves the ease-of-use and allows to scale reconstruction capabilities in time to process surgical videos of 5,000 frames and more; an improvement of more than ten times compared to the state of the art while being agnostic to external tracking information
  arXiv  Detail & Related papers  (2024-03-18T19:13:02Z)
• Endo-4DGS: Endoscopic Monocular Scene Reconstruction with 4D Gaussian Splatting [12.333523732756163]
  Dynamic scene reconstruction can significantly enhance downstream tasks and improve surgical outcomes. NeRF-based methods have recently risen to prominence for their exceptional ability to reconstruct scenes. We present Endo-4DGS, a real-time endoscopic dynamic reconstruction approach.
  arXiv  Detail & Related papers  (2024-01-29T18:55:29Z)
• EndoGS: Deformable Endoscopic Tissues Reconstruction with Gaussian Splatting [20.848027172010358]
  We present EndoGS, applying Gaussian Splatting for deformable endoscopic tissue reconstruction. Our approach incorporates deformation fields to handle dynamic scenes, depth-guided supervision with spatial-temporal weight masks, and surface-aligned regularization terms. As a result, EndoGS reconstructs and renders high-quality deformable endoscopic tissues from a single-viewpoint video, estimated depth maps, and labeled tool masks.
  arXiv  Detail & Related papers  (2024-01-21T16:14:04Z)
• BinaryHPE: 3D Human Pose and Shape Estimation via Binarization [99.83378699846767]
  3D human pose and shape estimation (HPE) aims to reconstruct the 3D human body, face, and hands from a single image.<n>We propose BinaryHPE, a novel binarization method designed to estimate the 3D human body, face, and hands parameters efficiently.
  arXiv  Detail & Related papers  (2023-11-24T07:51:50Z)
• 3DSAM-adapter: Holistic adaptation of SAM from 2D to 3D for promptable tumor segmentation [52.699139151447945]
  We propose a novel adaptation method for transferring the segment anything model (SAM) from 2D to 3D for promptable medical image segmentation. Our model can outperform domain state-of-the-art medical image segmentation models on 3 out of 4 tasks, specifically by 8.25%, 29.87%, and 10.11% for kidney tumor, pancreas tumor, colon cancer segmentation, and achieve similar performance for liver tumor segmentation.
  arXiv  Detail & Related papers  (2023-06-23T12:09:52Z)
• Neural LerPlane Representations for Fast 4D Reconstruction of Deformable Tissues [52.886545681833596]
  LerPlane is a novel method for fast and accurate reconstruction of surgical scenes under a single-viewpoint setting. LerPlane treats surgical procedures as 4D volumes and factorizes them into explicit 2D planes of static and dynamic fields. LerPlane shares static fields, significantly reducing the workload of dynamic tissue modeling.
  arXiv  Detail & Related papers  (2023-05-31T14:38:35Z)
• Revisiting 3D Context Modeling with Supervised Pre-training for Universal Lesion Detection in CT Slices [48.85784310158493]
  We propose a Modified Pseudo-3D Feature Pyramid Network (MP3D FPN) to efficiently extract 3D context enhanced 2D features for universal lesion detection in CT slices. With the novel pre-training method, the proposed MP3D FPN achieves state-of-the-art detection performance on the DeepLesion dataset. The proposed 3D pre-trained weights can potentially be used to boost the performance of other 3D medical image analysis tasks.
  arXiv  Detail & Related papers  (2020-12-16T07:11:16Z)

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.