mlsanityData to 14 Oct 2026

Seamless Augmented Reality Integration in Arthroscopy: A Pipeline for Articular Reconstruction and Guidance

arXiv 2410.00386 ↗First posted 1 Oct 2024cs.CV cs.LGcited by 3

Not on the map. Why?

Abstract (arXiv)

Arthroscopy is a minimally invasive surgical procedure used to diagnose and treat joint problems. The clinical workflow of arthroscopy typically involves inserting an arthroscope into the joint through a small incision, during which surgeons navigate and operate largely by relying on their visual assessment through the arthroscope. However, the arthroscope's restricted field of view and lack of depth perception pose challenges in navigating complex articular structures and achieving surgical precision during procedures. Aiming at enhancing intraoperative awareness, we present a robust pipeline that incorporates simultaneous localization and mapping, depth estimation, and 3D Gaussian splatting to realistically reconstruct intra-articular structures solely based on monocular arthroscope video. Extending 3D reconstruction to Augmented Reality (AR) applications, our solution offers AR assistance for articular notch measurement and annotation anchoring in a human-in-the-loop manner. Compared to traditional Structure-from-Motion and Neural Radiance Field-based methods, our pipeline achieves dense 3D reconstruction and competitive rendering fidelity with explicit 3D representation in 7 minutes on average. When evaluated on four phantom datasets, our method achieves RMSE = 2.21mm reconstruction error, PSNR = 32.86 and SSIM = 0.89 on average. Because our pipeline enables AR reconstruction and guidance directly from monocular arthroscopy without any additional data and/or hardware, our solution may hold the potential for enhancing intraoperative awareness and facilitating surgical precision in arthroscopy. Our AR measurement tool achieves accuracy within 1.59 +/- 1.81mm and the AR annotation tool achieves a mIoU of 0.721.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 17 of its 47 references that are in the map. First posted on arXiv 1 Oct 2024.

Builds on

17 of 47 references.

References · 17

  1. 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,226
  2. 2021NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstructioncited by 1,712
  3. 2023DUSt3R: Geometric 3D Vision Made Easycited by 1,567
  4. 2024Depth Anything: Unleashing the Power of Large-Scale Unlabeled Datacited by 1,360
  5. 2020ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAMcited by 878
  6. 2023CoTracker: It is Better to Track Togethercited by 522
  7. 2016Direct Sparse Odometrycited by 485
  8. 2023Neuralangelo: High-Fidelity Neural Surface Reconstructioncited by 467
  9. 2024GRM: Large Gaussian Reconstruction Model for Efficient 3D Reconstruction and Generationcited by 301
  10. 2023Putting the Object Back into Video Object Segmentationcited by 188
  11. 2024High-quality Surface Reconstruction using Gaussian Surfelscited by 161
  12. 2022Neural Rendering for Stereo 3D Reconstruction of Deformable Tissues in Robotic Surgerycited by 123
5 more
  1. 2023EndoSurf: Neural Surface Reconstruction of Deformable Tissues with Stereo Endoscope Videoscited by 69
  2. 2024FlowMap: High-Quality Camera Poses, Intrinsics, and Depth via Gradient Descentcited by 61
  3. 2024EndoGS: Deformable Endoscopic Tissues Reconstruction with Gaussian Splattingcited by 33
  4. 2023D2NT: A High-Performing Depth-to-Normal Translatorcited by 10
  5. 2023Hand-Eye Calibrationcited by 8

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.