# Anatomical Landmarks Localization for 3D Foot Point Clouds

arXiv: https://arxiv.org/abs/2110.00937 · HTML: https://mlsanity.com/paper/2110.00937/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 27 references that are in the map.
- First posted on arXiv 3 Oct 2021.

## Builds on (most cited first, 3 of 3)

- [X-ray-transform Invariant Anatomical Landmark Detection for Pelvic Trauma Surgery](https://mlsanity.com/paper/1803.08608/) (2018)
- [Quasi-Newton Solver for Robust Non-Rigid Registration](https://mlsanity.com/paper/2004.04322/) (2020)
- [Attaining human-level performance with atlas location autocontext for anatomical landmark detection in 3D CT data](https://mlsanity.com/paper/1805.08687/) (2018)

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

- [LmPT: Conditional Point Transformer for Anatomical Landmark Detection on 3D Point Clouds](https://mlsanity.com/paper/2602.02808/) (2026)

## Abstract (arXiv)

3D anatomical landmarks play an important role in health research. Their automated prediction/localization thus becomes a vital task. In this paper, we introduce a deformation method for 3D anatomical landmarks prediction. It utilizes a source model with anatomical landmarks which are annotated by clinicians, and deforms this model non-rigidly to match the target model. Two constraints are introduced in the optimization, which are responsible for alignment and smoothness, respectively. Experiments are performed on our dataset and the results demonstrate the robustness of our method, and show that it yields better performance than the state-of-the-art techniques in most cases.