Diffusion-based three-dimensional reconstruction of complex surface using monocular vision.

Area of Science:

• Computer Vision
• Physics
• Optical Engineering

Background:

• Optical diffusion offers advantages for 3D reconstruction, including high-precision depth estimation and monocular vision capabilities.
• Existing methods lack robust mathematical models linking optical diffusion intensity distribution to depth information.

Purpose of the Study:

• To develop a novel mathematical model for 3D reconstruction based on optical diffusion.
• To propose a high-precision 3D reconstruction method utilizing the heat diffusion equation.

Main Methods:

• Analyzing the heat diffusion equation to establish an optical diffusion model.
• Developing a 3D reconstruction technique based on global heat diffusion and depth-information-to-diffusion relationships.
• Validating the method through simulations and experimental setups.

Main Results:

• The proposed method effectively models intensity distribution during optical diffusion.
• Simulations and experiments demonstrate the feasibility and accuracy of the heat diffusion-based 3D reconstruction.
• The method achieves high-precision depth estimation.

Conclusions:

• The heat diffusion equation provides a suitable framework for modeling optical diffusion in 3D reconstruction.
• The developed method offers a novel and effective approach for precise 3D imaging.
• This research bridges the gap between physics principles and optical diffusion-based 3D reconstruction techniques.