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Measuring Maxillary Posterior Tooth Movement: A Model Assessment using Palatal and Dental Superimposition

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Neural Residual Correction for 3D Tooth Point Cloud Canonicalization.

Chawalit Chanintonsongkhla^1,2, Varin Chouvatut¹, Chumphol Bunkhumpornpat¹

¹Department of Computer Science, Faculty of Science, Chiang Mai University, Chiang Mai 50200, Thailand.

Journal of Imaging

|June 25, 2026

Summary

This summary is machine-generated.

This study introduces a novel hybrid pipeline for dental point cloud canonicalization, improving accuracy in statistical shape modeling and generative tooth synthesis by combining principal-axis alignment with neural guidance and residual correction.

Keywords:

PointNet artificial intelligence deep learning dental 3D imaging iterative closest point point cloud registration principal component analysis tooth canonicalization

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Area of Science:

Computational anatomy
3D imaging and analysis
Biomedical engineering

Background:

Statistical shape modeling and generative tooth synthesis require dental point clouds in canonical poses.
Canonicalization methods are crucial for standardizing dental data for analysis.
Existing methods face challenges with symmetry and accuracy.

Purpose of the Study:

To compare various canonicalization methods for dental point clouds.
To propose and validate a hybrid pipeline for improved canonicalization.
To address limitations in existing principal component analysis (PCA) based methods.

Main Methods:

Evaluation of seven classical, neural, and hybrid canonicalization methods.
Utilized 9060 upper tooth point clouds from 3DTeethSeg and 1465 external first molars.
Measured alignment using Chamfer Distance (CD Target, CD Template) and geodesic rotation error.

Main Results:

The proposed gPCA-rPointNet (neural-guided PCA with residual refinement) achieved the lowest CD Target and geodesic rotation error.
98.2% of predictions were within 15 degrees of the target pose.
PCA-based methods showed superior performance on an external dataset compared to non-geometric initialization methods.

Conclusions:

A neural orientation guide effectively resolves PCA eigenvector sign ambiguity, preventing 180-degree failures.
Residual correction further minimizes rotation error in canonicalization.
The hybrid pipeline demonstrates consistent canonical pose generation for first molars.