DDF-ISM: Internal Structure Modeling of Human Head Using Probabilistic Directed Distance Field.

Area of Science:

• Computer Graphics
• Medical Imaging
• Computational Anatomy

Background:

• Accurate 3D human head models are crucial for digital avatars and simulations.
• Current methods using explicit representations (point clouds, meshes) are computationally inefficient and struggle with local geometric details.
• There is a need for advanced internal modeling techniques beyond external approximations.

Purpose of the Study:

• To propose a novel method, Directed Distance Field for Internal Structure Modeling (DDF-ISM), for efficient and anatomically accurate 3D internal human head modeling.
• To overcome the limitations of existing explicit modeling approaches.
• To facilitate precise deformation of internal head structures.

Main Methods:

• Developed DDF-ISM, a novel modeling method leveraging a probabilistic Directed Distance Field (DDF) for implicit representation.
• Implemented a probabilistic DDF network to capture local geometric information.
• Utilized a conditioned deformation network guided by local geometry for anatomical accuracy.
• Created a large-scale dataset of human heads with internal structures from CT scans and template models (skull, mandible, brain, head surface).

Main Results:

• DDF-ISM demonstrated superior performance compared to existing methods in modeling quality.
• The proposed method achieved higher efficiency in generating 3D internal head models.
• Evaluations on the large-scale dataset confirmed the effectiveness of DDF-ISM in capturing intricate local geometric features.

Conclusions:

• DDF-ISM offers a significant advancement in 3D internal human head modeling for digital applications.
• The implicit representation and guided deformation approach enhance both accuracy and computational efficiency.
• The developed dataset provides a valuable resource for future research in this domain.