# Online 3D reconstruction and dense tracking in endoscopic videos

arXiv: https://arxiv.org/abs/2409.06037 · HTML: https://mlsanity.com/paper/2409.06037/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 20 references that are in the map.
- First posted on arXiv 9 Sep 2024.

## Builds on (most cited first, 17 of 17)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [RAFT: Recurrent All-Pairs Field Transforms for Optical Flow](https://mlsanity.com/paper/2003.12039/) (2020)
- [Dynamic 3D Gaussians: Tracking by Persistent Dynamic View Synthesis](https://mlsanity.com/paper/2308.09713/) (2023)
- [PointOdyssey: A Large-Scale Synthetic Dataset for Long-Term Point Tracking](https://mlsanity.com/paper/2307.15055/) (2023)
- [TAP-Vid: A Benchmark for Tracking Any Point in a Video](https://mlsanity.com/paper/2211.03726/) (2022)
- [2018 Robotic Scene Segmentation Challenge](https://mlsanity.com/paper/2001.11190/) (2020)
- [Neural Rendering for Stereo 3D Reconstruction of Deformable Tissues in Robotic Surgery](https://mlsanity.com/paper/2206.15255/) (2022)
- [Surgical Data Science -- from Concepts toward Clinical Translation](https://mlsanity.com/paper/2011.02284/) (2020)
- [EndoSurf: Neural Surface Reconstruction of Deformable Tissues with Stereo Endoscope Videos](https://mlsanity.com/paper/2307.11307/) (2023)
- [Learning How To Robustly Estimate Camera Pose in Endoscopic Videos](https://mlsanity.com/paper/2304.08023/) (2023)
- [Neural LerPlane Representations for Fast 4D Reconstruction of Deformable Tissues](https://mlsanity.com/paper/2305.19906/) (2023)
- [Comparative evaluation of instrument segmentation and tracking methods in minimally invasive surgery](https://mlsanity.com/paper/1805.02475/) (2018)
- [Efficient Deformable Tissue Reconstruction via Orthogonal Neural Plane](https://mlsanity.com/paper/2312.15253/) (2023)
- [Semantic-SuPer: A Semantic-aware Surgical Perception Framework for Endoscopic Tissue Identification, Reconstruction, and Tracking](https://mlsanity.com/paper/2210.16674/) (2022)
- [SurgT challenge: Benchmark of Soft-Tissue Trackers for Robotic Surgery](https://mlsanity.com/paper/2302.03022/) (2023)

## Built on by (most cited first, 10 of 10)

- [NFL-BA: Near-Field Light Bundle Adjustment for SLAM in Dynamic Lighting](https://mlsanity.com/paper/2412.13176/) (2024)
- [BridgeSplat: Bidirectionally Coupled CT and Non-Rigid Gaussian Splatting for Deformable Intraoperative Surgical Navigation](https://mlsanity.com/paper/2509.18501/) (2025)
- [Towards Dynamic 3D Reconstruction of Hand-Instrument Interaction in Ophthalmic Surgery](https://mlsanity.com/paper/2505.17677/) (2025)
- [Cholec80-port: A Geometrically Consistent Trocar Port Segmentation Dataset for Robust Surgical Scene Understanding](https://mlsanity.com/paper/2602.17060/) (2026)
- [Online camera-pose-free stereo endoscopic tissue deformation recovery with tissue-invariant vision-biomechanics consistency](https://mlsanity.com/paper/2506.19388/) (2025)
- [Surg-InvNeRF: Invertible NeRF for 3D tracking and reconstruction in surgical vision](https://mlsanity.com/paper/2508.09681/) (2025)
- [arg-VU: Affordance Reasoning with Physics-Aware 3D Geometry for Visual Understanding in Robotic Surgery](https://mlsanity.com/paper/2603.26814/) (2026)
- [Track2Map: Online Deformable SLAM with Motion-Aware Pose Optimization in Robotic Surgery](https://mlsanity.com/paper/2607.08408/) (2026)
- [SurgGMF: Fully Causal Gaussian Motion Forecasting for Anticipatory Surgical Scene Rendering](https://mlsanity.com/paper/2609.34733/) (2026)
- [SCOPE-4D: Endoscopic 4D Geometry Foundation Models](https://mlsanity.com/paper/2610.02343/) (2026)

## Abstract (arXiv)

3D scene reconstruction from stereo endoscopic video data is crucial for advancing surgical interventions. In this work, we present an online framework for online, dense 3D scene reconstruction and tracking, aimed at enhancing surgical scene understanding and assisting interventions. Our method dynamically extends a canonical scene representation using Gaussian splatting, while modeling tissue deformations through a sparse set of control points. We introduce an efficient online fitting algorithm that optimizes the scene parameters, enabling consistent tracking and accurate reconstruction. Through experiments on the StereoMIS dataset, we demonstrate the effectiveness of our approach, outperforming state-of-the-art tracking methods and achieving comparable performance to offline reconstruction techniques. Our work enables various downstream applications thus contributing to advancing the capabilities of surgical assistance systems.