  In the field of architecture, the conversion of single images into 2 and 1/2D
and 3D meshes is a promising technology that enhances design visualization and
efficiency. This paper evaluates four innovative methods: "One-2-3-45," "CRM:
Single Image to 3D Textured Mesh with Convolutional Reconstruction Model,"
"Instant Mesh," and "Image-to-Mesh." These methods are at the forefront of this
technology, focusing on their applicability in architectural design and
visualization. They streamline the creation of 3D architectural models,
enabling rapid prototyping and detailed visualization from minimal initial
inputs, such as photographs or simple sketches.One-2-3-45 leverages a
diffusion-based approach to generate multi-view reconstructions, ensuring high
geometric fidelity and texture quality. CRM utilizes a convolutional network to
integrate geometric priors into its architecture, producing detailed and
textured meshes quickly and efficiently. Instant Mesh combines the strengths of
multi-view diffusion and sparse-view models to offer speed and scalability,
suitable for diverse architectural projects. Image-to-Mesh leverages a
generative adversarial network (GAN) to produce 3D meshes from single images,
focusing on maintaining high texture fidelity and geometric accuracy by
incorporating image and depth map data into its training process. It uses a
hybrid approach that combines voxel-based representations with surface
reconstruction techniques to ensure detailed and realistic 3D models.This
comparative study highlights each method's contribution to reducing design
cycle times, improving accuracy, and enabling flexible adaptations to various
architectural styles and requirements. By providing architects with powerful
tools for rapid visualization and iteration, these advancements in 3D mesh
generation are set to revolutionize architectural practices.
