| dc.description.abstract | In recent years, the developmentof3Dtechnologybasedondeeplearninghasbeenprogressingatan
extremely rapid pace, withtechnologyexpandingfrom2Dplanardomainsto3Dspatialdimensions.
As 3D research advances, many ideas have emerged that leverage the unique capabilities of 3D to
enhance visual representation and applications. For example, there are now techniques to quickly
generate corresponding 3D models from human images, which can be used to realistically depict
human movements and poses. Additionally, 3D reconstruction technology can be used to create
images of people and objects.
However, in the field of deep learning, a significant amount of data is often required for AI
models to learn effectively. The quantity and diversity of datasets greatly influence the subsequent
performance and application effectiveness of AI models. This issue is particularly severe in the
realm of 3Ddeeplearning. Unlike 2D images or audio, where there are abundant datasets available,
3D data is often scarce. Due to the higher complexity of 3D spaces compared to 2D, a single 2D
imageisusuallyinsufficienttoaccuratelyreconstructtheactual3Denvironment. Themostcommon
challenge is how to converge the results to an accurate 3D domain.
To address these issues, this paper constructs a method to establish corresponding 3D objects
from 2D images. It utilizes multiple AI models for data processing and incorporates models with
domain adaptation capabilities. Finally, it employs a loss function to further constrain the generated
results, ensuring that the generated outputs are similar or approximate to real-life objects within a
certain range | en_US |