# BodySLAM: A Generalized Monocular Visual SLAM Framework for Surgical Applications

arXiv: https://arxiv.org/abs/2408.03078 · HTML: https://mlsanity.com/paper/2408.03078/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 50 references that are in the map.
- First posted on arXiv 6 Aug 2024.

## Builds on (most cited first, 20 of 20)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer](https://mlsanity.com/paper/1907.01341/) (2019)
- [NetVLAD: CNN architecture for weakly supervised place recognition](https://mlsanity.com/paper/1511.07247/) (2015)
- [A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation](https://mlsanity.com/paper/1512.02134/) (2015)
- [ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM](https://mlsanity.com/paper/2007.11898/) (2020)
- [InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets](https://mlsanity.com/paper/1606.03657/) (2016)
- [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://mlsanity.com/paper/1703.10593/) (2017)
- [Stereo Correspondence and Reconstruction of Endoscopic Data Challenge](https://mlsanity.com/paper/2101.01133/) (2021)
- [Endo-Depth-and-Motion: Reconstruction and Tracking in Endoscopic Videos using Depth Networks and Photometric Constraints](https://mlsanity.com/paper/2103.16525/) (2021)
- [Self-Supervised Monocular Depth and Ego-Motion Estimation in Endoscopy: Appearance Flow to the Rescue](https://mlsanity.com/paper/2112.08122/) (2021)
- [RetinaGAN: An Object-aware Approach to Sim-to-Real Transfer](https://mlsanity.com/paper/2011.03148/) (2020)
- [SuPer: A Surgical Perception Framework for Endoscopic Tissue Manipulation with Surgical Robotics](https://mlsanity.com/paper/1909.05405/) (2019)
- [Tracking and Mapping in Medical Computer Vision: A Review](https://mlsanity.com/paper/2310.11475/) (2023)
- [SAGE: SLAM with Appearance and Geometry Prior for Endoscopy](https://mlsanity.com/paper/2202.09487/) (2022)
- [SLAM Endoscopy enhanced by adversarial depth prediction](https://mlsanity.com/paper/1907.00283/) (2019)
- [Depth Anything in Medical Images: A Comparative Study](https://mlsanity.com/paper/2401.16600/) (2024)
- [SLAM based Quasi Dense Reconstruction For Minimally Invasive Surgery Scenes](https://mlsanity.com/paper/1705.09107/) (2017)
- [Stomach 3D Reconstruction Based on Virtual Chromoendoscopic Image Generation](https://mlsanity.com/paper/2004.12288/) (2020)
- [SLAM-TKA: Real-time Intra-operative Measurement of Tibial Resection Plane in Conventional Total Knee Arthroplasty](https://mlsanity.com/paper/2208.03945/) (2022)

## Built on by (most cited first, 4 of 4)

- [Towards Dynamic 3D Reconstruction of Hand-Instrument Interaction in Ophthalmic Surgery](https://mlsanity.com/paper/2505.17677/) (2025)
- [BREATH-VL: Vision-Language-Guided 6-DoF Bronchoscopy Localization via Semantic-Geometric Fusion](https://mlsanity.com/paper/2601.03713/) (2026)
- [Endoscopic Depth Estimation Based on Deep Learning: A Survey](https://mlsanity.com/paper/2507.20881/) (2025)
- [Unifying Scale-Aware Depth Prediction and Perceptual Priors for Monocular Endoscope Pose Estimation and Tissue Reconstruction](https://mlsanity.com/paper/2508.11282/) (2025)

## Abstract (arXiv)

Endoscopic surgery relies on two-dimensional views, posing challenges for surgeons in depth perception and instrument manipulation. While Monocular Visual Simultaneous Localization and Mapping (MVSLAM) has emerged as a promising solution, its implementation in endoscopic procedures faces significant challenges due to hardware limitations, such as the use of a monocular camera and the absence of odometry sensors. This study presents BodySLAM, a robust deep learning-based MVSLAM approach that addresses these challenges through three key components: CycleVO, a novel unsupervised monocular pose estimation module; the integration of the state-of-the-art Zoe architecture for monocular depth estimation; and a 3D reconstruction module creating a coherent surgical map. The approach is rigorously evaluated using three publicly available datasets (Hamlyn, EndoSLAM, and SCARED) spanning laparoscopy, gastroscopy, and colonoscopy scenarios, and benchmarked against four state-of-the-art methods. Results demonstrate that CycleVO exhibited competitive performance with the lowest inference time among pose estimation methods, while maintaining robust generalization capabilities, whereas Zoe significantly outperformed existing algorithms for depth estimation in endoscopy. BodySLAM's strong performance across diverse endoscopic scenarios demonstrates its potential as a viable MVSLAM solution for endoscopic applications.