# EasyVis2: A Real Time Multi-view 3D Visualization System for Laparoscopic Surgery Training Enhanced by a Deep Neural Network YOLOv8-Pose

arXiv: https://arxiv.org/abs/2412.16742 · HTML: https://mlsanity.com/paper/2412.16742/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 50 references that are in the map.
- First posted on arXiv 21 Dec 2024.

## Builds on (most cited first, 21 of 21)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [SAPIEN: A SimulAted Part-based Interactive ENvironment](https://mlsanity.com/paper/2003.08515/) (2020)
- [DensePose: Dense Human Pose Estimation In The Wild](https://mlsanity.com/paper/1802.00434/) (2018)
- [FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objects](https://mlsanity.com/paper/2312.08344/) (2023)
- [RMPE: Regional Multi-person Pose Estimation](https://mlsanity.com/paper/1612.00137/) (2016)
- [DexYCB: A Benchmark for Capturing Hand Grasping of Objects](https://mlsanity.com/paper/2104.04631/) (2021)
- [DeepPose: Human Pose Estimation via Deep Neural Networks](https://mlsanity.com/paper/1312.4659/) (2013)
- [CORe50: a New Dataset and Benchmark for Continuous Object Recognition](https://mlsanity.com/paper/1705.03550/) (2017)
- [T-LESS: An RGB-D Dataset for 6D Pose Estimation of Texture-less Objects](https://mlsanity.com/paper/1701.05498/) (2017)
- [AlphaPose: Whole-Body Regional Multi-Person Pose Estimation and Tracking in Real-Time](https://mlsanity.com/paper/2211.03375/) (2022)
- [Putting the Object Back into Video Object Segmentation](https://mlsanity.com/paper/2310.12982/) (2023)
- [PoseTrack: A Benchmark for Human Pose Estimation and Tracking](https://mlsanity.com/paper/1710.10000/) (2017)
- [Neural Rendering for Stereo 3D Reconstruction of Deformable Tissues in Robotic Surgery](https://mlsanity.com/paper/2206.15255/) (2022)
- [DeepCut: Joint Subset Partition and Labeling for Multi Person Pose Estimation](https://mlsanity.com/paper/1511.06645/) (2015)
- [Falling Things: A Synthetic Dataset for 3D Object Detection and Pose Estimation](https://mlsanity.com/paper/1804.06534/) (2018)
- [MOTSynth: How Can Synthetic Data Help Pedestrian Detection and Tracking?](https://mlsanity.com/paper/2108.09518/) (2021)
- [YOLO-Pose: Enhancing YOLO for Multi Person Pose Estimation Using Object Keypoint Similarity Loss](https://mlsanity.com/paper/2204.06806/) (2022)
- [Robust 6D Object Pose Estimation by Learning RGB-D Features](https://mlsanity.com/paper/2003.00188/) (2020)
- [FFD: Fast Feature Detector](https://mlsanity.com/paper/2012.00859/) (2020)

## Built on by (most cited first, 2 of 2)

- [Sampling Strategies for Efficient Training of Deep Learning Object Detection Algorithms](https://mlsanity.com/paper/2505.18302/) (2025)
- [C-DOG: Multi-View Multi-instance Feature Association Using Connected δ-Overlap Graphs](https://mlsanity.com/paper/2507.14095/) (2025)

## Abstract (arXiv)

EasyVis2 is a system designed to provide hands-free, real-time 3D visualization for laparoscopic surgery. It incorporates a surgical trocar equipped with an array of micro-cameras, which can be inserted into the body cavity to offer an enhanced field of view and a 3D perspective of the surgical procedure. A specialized deep neural network algorithm, YOLOv8-Pose, is utilized to estimate the position and orientation of surgical instruments in each individual camera view. These multi-view estimates enable the calculation of 3D poses of surgical tools, facilitating the rendering of a 3D surface model of the instruments, overlaid on the background scene, for real-time visualization. This study presents methods for adapting YOLOv8-Pose to the EasyVis2 system, including the development of a tailored training dataset. Experimental results demonstrate that, with an identical number of cameras, the new system improves 3D reconstruction accuracy and reduces computation time. Additionally, the adapted YOLOv8-Pose system shows high accuracy in 2D pose estimation.