However, due to inherent limitations in imaging systems, image noise inevitably introduces redundant interference into visual data.
During depth conversion, each image consists of three-dimensional points formed by interleaving lines. Grayscale values determine the relative height of each pixel, and corresponding vertices are translated in 3D space, transforming a flat 2D image into a three-dimensional geometric structure.
When AI processes surface images, attempting to convert 2D data into accurate 3D representations in a noisy environment, misinterpretations arise. Noise in 2D pixels disrupts surface reconstruction, causing originally smooth and regular surfaces to appear rough and randomly textured.
Beyond surface distortions, noise also affects microscopic structures in 3D reconstruction. When stacking a series of 2D cross-sections to form a 3D structure, noise alters morphological characteristics, further compromising accuracy.