# Automatic 3D Registration of Dental CBCT and Face Scan Data using 2D Projection Images

arXiv: https://arxiv.org/abs/2305.10132 · HTML: https://mlsanity.com/paper/2305.10132/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 34 references that are in the map.
- First posted on arXiv 17 May 2023.

## Builds on (most cited first, 11 of 11)

- [PREDATOR: Registration of 3D Point Clouds with Low Overlap](https://mlsanity.com/paper/2011.13005/) (2020)
- [Go-ICP: A Globally Optimal Solution to 3D ICP Point-Set Registration](https://mlsanity.com/paper/1605.03344/) (2016)
- [3DRegNet: A Deep Neural Network for 3D Point Registration](https://mlsanity.com/paper/1904.01701/) (2019)
- [Facial Landmark Detection: a Literature Survey](https://mlsanity.com/paper/1805.05563/) (2018)
- [Style Aggregated Network for Facial Landmark Detection](https://mlsanity.com/paper/1803.04108/) (2018)
- [Max-Margin Object Detection](https://mlsanity.com/paper/1502.00046/) (2015)
- [Shape My Face: Registering 3D Face Scans by Surface-to-Surface Translation](https://mlsanity.com/paper/2012.09235/) (2020)
- [Facial Feature Point Detection: A Comprehensive Survey](https://mlsanity.com/paper/1410.1037/) (2014)
- [Fully automatic integration of dental CBCT images and full-arch intraoral impressions with stitching error correction via individual tooth segmentation and identification](https://mlsanity.com/paper/2112.01784/) (2021)
- [Automatic Registration between Cone-Beam CT and Scanned Surface via Deep-Pose Regression Neural Networks and Clustered Similarities](https://mlsanity.com/paper/1907.12250/) (2019)
- [2D Wasserstein Loss for Robust Facial Landmark Detection](https://mlsanity.com/paper/1911.10572/) (2019)

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

- [Shape-from-Template with Generalised Camera](https://mlsanity.com/paper/2508.13791/) (2025)

## Abstract (arXiv)

This paper presents a fully automatic registration method of dental cone-beam computed tomography (CBCT) and face scan data. It can be used for a digital platform of 3D jaw-teeth-face models in a variety of applications, including 3D digital treatment planning and orthognathic surgery. Difficulties in accurately merging facial scans and CBCT images are due to the different image acquisition methods and limited area of correspondence between the two facial surfaces. In addition, it is difficult to use machine learning techniques because they use face-related 3D medical data with radiation exposure, which are difficult to obtain for training. The proposed method addresses these problems by reusing an existing machine-learning-based 2D landmark detection algorithm in an open-source library and developing a novel mathematical algorithm that identifies paired 3D landmarks from knowledge of the corresponding 2D landmarks. A main contribution of this study is that the proposed method does not require annotated training data of facial landmarks because it uses a pre-trained facial landmark detection algorithm that is known to be robust and generalized to various 2D face image models. Note that this reduces a 3D landmark detection problem to a 2D problem of identifying the corresponding landmarks on two 2D projection images generated from two different projection angles. Here, the 3D landmarks for registration were selected from the sub-surfaces with the least geometric change under the CBCT and face scan environments. For the final fine-tuning of the registration, the Iterative Closest Point method was applied, which utilizes geometrical information around the 3D landmarks. The experimental results show that the proposed method achieved an averaged surface distance error of 0.74 mm for three pairs of CBCT and face scan datasets.