Smooth symmetrical surface rebuilding method based on single image

Abstract

A three-dimensional entity model restructuring method of the invention comprises following steps: inputting an original view of a slippy symmetrical curve surface, and mirror-reversing the original view to obtain a mirror reversing view; determining matched characteristic point pairs in the original view and the mirror reversing view; calculating a camera inner parameter matrix used by an original image; calculating an essence matrix according to the characteristic points between the original image and a mirror reversing image obtained by the inner parameter matrix and the matched characteristic point pairs, decomposing the obtained essence matrix into a rotary matrix and a translation matrix; calculating a three-dimensional coordinate of a corresponding point based on a coordinate of a two-dimensional point of the original image to generate a plane model of an object; obtaining a final slippy curve surface model by minimizing a slippy target function; extracting texture of the original image and mapping the texture on the generated slippy curve surface to obtain the three-dimensional entity model. The method of the invention can be used for restructuring the three-dimensional entity model of stacked objects and curve surfaces from a network, a digital camera or a scanner.

Description

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AI Technical Summary

Benefits of technology

This patented technology helps create an accurate 3D representation for objects that are moving around on both horizontal (X) and vertical directions. It involves reconstructing a new way to make surfaces look smoother or more like reality with its own unique geometry called gravitational centroids. By doing this we can generate better quality images than traditional methods such as ray casting techniques.

Problems solved by technology

This patents discusses different ways to create high quality digital models without requiring complex calculations involving large amounts of data. One approach involves capturing both 2D and 3D views simultanously during imagery capture while minimizing any distortions caused due to external factors like lighting conditions. Another technique called Structured Light Field Representation uses specific mathematical functions to represent shapes accurately within each frame. These technical improvements make this process easier than traditional approaches but still require significant effort before being able to achieve precise depth estimation over all possible perspectives.

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