# Depth Augmented and FE Free 3D/2D Liver Registration for Laparoscopic Liver AR

arXiv: https://arxiv.org/abs/2602.17517 · HTML: https://mlsanity.com/paper/2602.17517/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 26 references that are in the map.
- First posted on arXiv 19 Feb 2026.

## Builds on (most cited first, 7 of 7)

- [Depth Anything V2](https://mlsanity.com/paper/2406.09414/) (2024)
- [FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objects](https://mlsanity.com/paper/2312.08344/) (2023)
- [An objective comparison of methods for augmented reality in laparoscopic liver resection by preoperative-to-intraoperative image fusion](https://mlsanity.com/paper/2401.15753/) (2024)
- [Intraoperative Liver Surface Completion with Graph Convolutional VAE](https://mlsanity.com/paper/2009.03871/) (2020)
- [Depth-Driven Geometric Prompt Learning for Laparoscopic Liver Landmark Detection](https://mlsanity.com/paper/2406.17858/) (2024)
- [Subsampled Randomized Fourier GaLore for Adapting Foundation Models in Depth-Driven Liver Landmark Segmentation](https://mlsanity.com/paper/2511.03163/) (2025)
- [LapFM: A Laparoscopic Segmentation Foundation Model via Hierarchical Concept Evolving Pre-training](https://mlsanity.com/paper/2512.08439/) (2025)

## Built on by (most cited first, 1 of 1)

- [Warm-Started Reinforcement Learning for Iterative 3D/2D Liver Registration](https://mlsanity.com/paper/2604.10245/) (2026)

## Abstract (arXiv)

Augmented reality (AR) guidance in laparoscopic liver surgery requires accurate registration of preoperative 3D models to intraoperative 2D video, but remains challenging due to partial visibility, specularities, and tissue deformation. Existing methods often rely on contour-based rigid initialization and finite-element (FE) models for deformable registration, increasing modeling and engineering complexity. We present a depth-augmented, FE-free 3D--2D registration pipeline that combines robust rigid initialization with patient-specific non-rigid refinement. For rigid alignment, we adapt the RefineNet module of FoundationPose to laparoscopic liver scenes by using multi-class contour maps and monocular depth for relative pose refinement. For deformable alignment, we construct a patient-specific statistical deformation model from non-rigid ICP (NICP) correspondences and optimize pose and shape parameters using a coarse-to-fine L-BFGS-B strategy. On a public clinical laparoscopic liver dataset, the proposed method achieves a mean target registration error (TRE) of 14.73,mm under a controlled manual-contour setting designed to isolate registration performance. Ablation studies show that monocular depth improves rigid initialization over contour-only inputs, while tumor-mapping analysis indicates that good surface alignment does not necessarily translate into lower target localization error. On an external dataset without ground truth, the method produces visually plausible overlays for qualitative assessment. These results suggest that depth-augmented pose refinement and FE-free statistical deformation modeling provide a promising alternative to FE-based pipelines for controlled 3D--2D liver registration in surgical AR.