# Evaluation of Intra-operative Patient-specific Methods for Point Cloud Completion for Minimally Invasive Liver Interventions

arXiv: https://arxiv.org/abs/2503.11969 · HTML: https://mlsanity.com/paper/2503.11969/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 15 references that are in the map.
- First posted on arXiv 15 Mar 2025.

## Builds on (most cited first, 8 of 8)

- [PCN: Point Completion Network](https://mlsanity.com/paper/1808.00671/) (2018)
- [PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers](https://mlsanity.com/paper/2108.08839/) (2021)
- [GRNet: Gridding Residual Network for Dense Point Cloud Completion](https://mlsanity.com/paper/2006.03761/) (2020)
- [AdaPoinTr: Diverse Point Cloud Completion with Adaptive Geometry-Aware Transformers](https://mlsanity.com/paper/2301.04545/) (2023)
- [Non-Rigid Volume to Surface Registration using a Data-Driven Biomechanical Model](https://mlsanity.com/paper/2005.14695/) (2020)
- [Learning Feature Descriptors for Pre- and Intra-operative Point Cloud Matching for Laparoscopic Liver Registration](https://mlsanity.com/paper/2211.03688/) (2022)
- [Boundary Constraint-free Biomechanical Model-Based Surface Matching for Intraoperative Liver Deformation Correction](https://mlsanity.com/paper/2403.09964/) (2024)
- [Intraoperative Liver Surface Completion with Graph Convolutional VAE](https://mlsanity.com/paper/2009.03871/) (2020)

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

- [Toward Patient-specific Partial Point Cloud to Surface Completion for Pre- to Intra-operative Registration in Image-guided Liver Interventions](https://mlsanity.com/paper/2505.19518/) (2025)

## Abstract (arXiv)

The registration between the pre-operative model and the intra-operative surface is crucial in image-guided liver surgery, as it facilitates the effective use of pre-operative information during the procedure. However, the intra-operative surface, usually represented as a point cloud, often has limited coverage, especially in laparoscopic surgery, and is prone to holes and noise, posing significant challenges for registration methods. Point cloud completion methods have the potential to alleviate these issues. Thus, we explore six state-of-the-art point cloud completion methods to identify the optimal completion method for liver surgery applications. We focus on a patient-specific approach for liver point cloud completion from a partial liver surface under three cases: canonical pose, non-canonical pose, and canonical pose with noise. The transformer-based method, AdaPoinTr, outperforms all other methods to generate a complete point cloud from the given partial liver point cloud under the canonical pose. On the other hand, our findings reveal substantial performance degradation of these methods under non-canonical poses and noisy settings, highlighting the limitations of these methods, which suggests the need for a robust point completion method for its application in image-guided liver surgery.