# MLRecon: Robust Markerless Freehand 3D Ultrasound Reconstruction via Coarse-to-Fine Pose Estimation

arXiv: https://arxiv.org/abs/2603.00990 · HTML: https://mlsanity.com/paper/2603.00990/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 25 references that are in the map.
- First posted on arXiv 1 Mar 2026.

## Builds on (most cited first, 13 of 13)

- [SAM 2: Segment Anything in Images and Videos](https://mlsanity.com/paper/2408.00714/) (2024)
- [On the Continuity of Rotation Representations in Neural Networks](https://mlsanity.com/paper/1812.07035/) (2018)
- [FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objects](https://mlsanity.com/paper/2312.08344/) (2023)
- [Real-time Deep Pose Estimation with Geodesic Loss for Image-to-Template Rigid Registration](https://mlsanity.com/paper/1803.05982/) (2018)
- [Deep Motion Network for Freehand 3D Ultrasound Reconstruction](https://mlsanity.com/paper/2207.00177/) (2022)
- [SANSA: Unleashing the Hidden Semantics in SAM2 for Few-Shot Segmentation](https://mlsanity.com/paper/2505.21795/) (2025)
- [Long-term Dependency for 3D Reconstruction of Freehand Ultrasound Without External Tracker](https://mlsanity.com/paper/2310.10248/) (2023)
- [Fine-grained Context and Multi-modal Alignment for Freehand 3D Ultrasound Reconstruction](https://mlsanity.com/paper/2407.04242/) (2024)
- [MoNetV2: Enhanced Motion Network for Freehand 3D Ultrasound Reconstruction](https://mlsanity.com/paper/2506.15835/) (2025)
- [3D Freehand Ultrasound using Visual Inertial and Deep Inertial Odometry for Measuring Patellar Tracking](https://mlsanity.com/paper/2404.15847/) (2024)
- [Enhancing Free-hand 3D Photoacoustic and Ultrasound Reconstruction using Deep Learning](https://mlsanity.com/paper/2502.03505/) (2025)
- [DualTrack: Sensorless 3D Ultrasound needs Local and Global Context](https://mlsanity.com/paper/2509.09530/) (2025)
- [Freehand 3D Ultrasound Imaging: Sim-in-the-Loop Probe Pose Optimization via Visual Servoing](https://mlsanity.com/paper/2510.15668/) (2025)

## Abstract (arXiv)

Freehand 3D ultrasound (US) reconstruction promises volumetric imaging with the flexibility of standard 2D probes, yet existing tracking paradigms face a restrictive trilemma: marker-based systems demand prohibitive costs, inside-out methods require intrusive sensor attachment, and sensorless approaches suffer from severe cumulative drift. To overcome these limitations, we present MLRecon, a robust markerless 3D US reconstruction framework delivering drift-resilient 6D probe pose tracking using a single commodity RGB-D camera. Leveraging the generalization power of vision foundation models, our pipeline enables continuous markerless tracking of the probe, augmented by a vision-guided divergence detector that autonomously monitors tracking integrity and triggers failure recovery to ensure uninterrupted scanning. Crucially, we further propose a dual-stage pose refinement network that explicitly disentangles high-frequency jitter from low-frequency bias, effectively denoising the trajectory while maintaining the kinematic fidelity of operator maneuvers. Experiments demonstrate that MLRecon significantly outperforms competing sensorless and sensor-aided methods, achieving average position errors as low as 0.88 mm on complex trajectories and yielding high-quality 3D reconstructions with sub-millimeter mean surface accuracy. This establishes a new benchmark for low-cost, accessible volumetric US imaging in resource-limited clinical settings.