# SurfSurg6D: Geometry Consistent Dense Correspondence for Textureless Surgical Instrument Pose Estimation

arXiv: https://arxiv.org/abs/2605.25598 · HTML: https://mlsanity.com/paper/2605.25598/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 49 references that are in the map.
- First posted on arXiv 25 May 2026.

## Builds on (most cited first, 27 of 27)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Implicit Neural Representations with Periodic Activation Functions](https://mlsanity.com/paper/2006.09661/) (2020)
- [Contrastive Learning with Hard Negative Samples](https://mlsanity.com/paper/2010.04592/) (2020)
- [FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objects](https://mlsanity.com/paper/2312.08344/) (2023)
- [BOP: Benchmark for 6D Object Pose Estimation](https://mlsanity.com/paper/1808.08319/) (2018)
- [MegaPose: 6D Pose Estimation of Novel Objects via Render & Compare](https://mlsanity.com/paper/2212.06870/) (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)
- [FoundPose: Unseen Object Pose Estimation with Foundation Features](https://mlsanity.com/paper/2311.18809/) (2023)
- [SurfEmb: Dense and Continuous Correspondence Distributions for Object Pose Estimation with Learnt Surface Embeddings](https://mlsanity.com/paper/2111.13489/) (2021)
- [Trans-SVNet: Accurate Phase Recognition from Surgical Videos via Hybrid Embedding Aggregation Transformer](https://mlsanity.com/paper/2103.09712/) (2021)
- [EndoSurf: Neural Surface Reconstruction of Deformable Tissues with Stereo Endoscope Videos](https://mlsanity.com/paper/2307.11307/) (2023)
- [Concurrent Segmentation and Localization for Tracking of Surgical Instruments](https://mlsanity.com/paper/1703.10701/) (2017)
- [Focal Length and Object Pose Estimation via Render and Compare](https://mlsanity.com/paper/2204.05145/) (2022)
- [Pose Estimation for Robot Manipulators via Keypoint Optimization and Sim-to-Real Transfer](https://mlsanity.com/paper/2010.08054/) (2020)
- [SurgPose: a Dataset for Articulated Robotic Surgical Tool Pose Estimation and Tracking](https://mlsanity.com/paper/2502.11534/) (2025)
- [MRC-Net: 6-DoF Pose Estimation with MultiScale Residual Correlation](https://mlsanity.com/paper/2403.08019/) (2024)
- [SurgRIPE challenge: Benchmark of Surgical Robot Instrument Pose Estimation](https://mlsanity.com/paper/2501.02990/) (2025)
- [Differentiable Rendering-based Pose Estimation for Surgical Robotic Instruments](https://mlsanity.com/paper/2503.05953/) (2025)
- [Realistic Surgical Image Dataset Generation Based On 3D Gaussian Splatting](https://mlsanity.com/paper/2407.14846/) (2024)
- [Caveats on the first-generation da Vinci Research Kit: latent technical constraints and essential calibrations](https://mlsanity.com/paper/2210.13598/) (2022)
- [Instrument-Splatting: Controllable Photorealistic Reconstruction of Surgical Instruments Using Gaussian Splatting](https://mlsanity.com/paper/2503.04082/) (2025)
- [Realistic Data Generation for 6D Pose Estimation of Surgical Instruments](https://mlsanity.com/paper/2406.07328/) (2024)
- [Real-time Capable Learning-based Visual Tool Pose Correction via Differentiable Simulation](https://mlsanity.com/paper/2505.08875/) (2025)

## Abstract (arXiv)

Surgical instrument pose estimation provides crucial information for promising applications, including autonomous robotic surgery, skill assessment, and standardization of surgical workflow. However, this task remains highly challenging due to high precision requirements, frequent occlusions, textureless instruments, scarcity of depth information and very limited annotated data. These constraints often lead to unsatisfactory performance when employing general object pose estimation approaches to surgical scenarios. To address these issues, we first construct a new dataset SynSurg6D, to alleviate the data shortage in this task. We further propose SurfSurg6D, a dense-correspondence framework tailored for surgical instrument pose estimation. Experimental results on the SurgRIPE, EndoVis2018 and SurgPose datasets demonstrate that the introduction of our generated dataset SynSurg6D is able to diversify the pose distributions, thus enhancing the performance of existing approaches. Furthermore, SurfSurg6D outperforms existing methods, providing a robust solution for precise and efficient RGB-only pose estimation.