System and method for three-dimensional object reconstruction from two-dimensional images

Abstract

A system and method for three-dimensional acquisition and modeling of a scene using two-dimensional images are provided. The present disclosure provides a system and method for selecting and combining the three-dimensional acquisition techniques that best fit the capture environment and conditions under consideration, and hence produce more accurate three-dimensional models. The system and method provide for acquiring at least two two-dimensional images of a scene (202), applying a first depth acquisition function to the at least two two-dimensional images (214), applying a second depth acquisition function to the at least two two-dimensional images (218), combining an output of the first depth acquisition function with an output of the second depth acquisition function (222), and generating a disparity or depth map from the combined output (224). The system and method also provide for reconstructing a three-dimensional model of the scene from the generated disparity or depth map.

Description

technical field [0001] The present disclosure relates generally to three-dimensional object modeling, and more particularly to systems and methods for three-dimensional (3D) information acquisition from two-dimensional (2D) images that combines multiple 3D acquisition functions , for accurate recovery of 3D information of real-world scenes. Background technique [0002] When a scene is photographed, the resulting video sequence contains implicit information about the three-dimensional (3D) geometry of the scene. Although this implicit information is sufficient for full human perception, for many applications the exact geometry of the 3D scene is required. One class of these applications is when complex data processing techniques are used, such as when generating new views of a scene or when reconstructing 3D geometry for industrial inspection applications. [0003] The process of generating 3D models from single or multiple images is important to many film post-production ...

AI Technical Summary

Problems solved by technology

However, no single technique performs well in all situations, and most of the past methods have been devoted to 3D reconstruction under laboratory conditions that make reconstruction relatively easy.

For real-world scenes, the subject may be in motion, the lighting may be complex, and the depth range may be large

The techniques described above have difficulty handling these real-world conditions

For example, if there is a large depth discontinuity between foreground and background objects, the search range for stereo photo matching must be significantly increased, which may result in unacceptable computational cost as well as additional depth estimation error

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