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FlyBy CNN: A 3D surface segmentation framework.

Louis Boumbolo¹, Maxime Dumont², Serge Brosset²

¹University of North Carolina, Chapel Hill, United States.

Proceedings of Spie--The International Society for Optical Engineering

|March 24, 2021

Summary

This summary is machine-generated.

This study introduces FlyBy CNN, a deep learning method for 3D shape segmentation. This novel approach achieves 0.9 accuracy in segmenting intra-oral dental surfaces.

Keywords:

intra oral surface mesh segmentation shape analysis

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Area of Science:

Computer Vision
Deep Learning
Medical Imaging

Background:

Accurate 3D shape segmentation is crucial for applications like medical imaging.
Existing methods may face challenges with complex surface geometries and data acquisition.

Purpose of the Study:

To present FlyBy CNN, a novel deep learning framework for 3D shape segmentation.
To evaluate the efficacy of FlyBy CNN in segmenting intra-oral dental surfaces.

Main Methods:

FlyBy CNN samples 3D object surfaces from multiple viewpoints.
Surface features like normal vectors are extracted to generate 2D images.
2D Convolutional Neural Networks (CNNs), specifically RUNETs, analyze these images for segmentation.

Main Results:

The framework achieves a 0.9 accuracy in the dental application.
Pixel-to-triangle correspondence ensures accurate re-projection of segmentation labels onto the 3D surface.

Conclusions:

FlyBy CNN offers an effective deep learning solution for 3D shape segmentation.
The method demonstrates high accuracy and potential for clinical dental applications.