Efficient 3D Surface Super-Resolution via Normal-Based Multimodal Restoration.

Area of Science:

• Computer Vision
• 3D Data Processing
• Geometric Deep Learning

Background:

• High-fidelity 3D surfaces are crucial for applications like medical imaging, virtual reality, and autonomous navigation.
• Existing 3D data representations present challenges in restoring fine geometric details cost-effectively.
• Microgeometry enhancement and computational efficiency are key limitations in current 3D super-resolution methods.

Purpose of the Study:

• To introduce an efficient multimodal normal-based 3D surface super-resolution (mn3DSSR) framework.
• To address challenges in microgeometry enhancement and reduce computational overhead in 3D surface restoration.
• To improve the accuracy and efficiency of 3D surface super-resolution.

Main Methods:

• Construction of a large-scale normal-based multimodal dataset with high data quality and diversity.
• Development of a two-branch multimodal alignment approach and a multimodal split fusion module.
• Introduction of novel normal-induced loss functions for geometric consistency and feature alignment.

Main Results:

• The proposed mn3DSSR framework demonstrates superior performance compared to state-of-the-art super-resolution methods.
• Consistent outperformance across seven benchmark datasets and four different 3D data representations.
• Significant improvements in restoration accuracy coupled with high computational efficiency.

Conclusions:

• The mn3DSSR framework effectively enhances 3D surface microgeometry while maintaining computational efficiency.
• The novel multimodal alignment and fusion strategies mitigate complexity and boost performance.
• The developed normal-induced loss functions ensure geometric consistency, advancing 3D surface super-resolution.