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Visual communication (VC) manages to demonstrate optimal perception effect through reasonable element planning. It has been a more prevalent tendency to introduce 3D image reconstruction into VC activities. Currently, there still lacks reliable solution for this purpose under VC scenes. To deal with this issue, we propose a Kruppa equation-based virtual reconstruction method for 3D images under VC scenes. Firstly, the feature descriptor procedure in scale-invariant feature transform (SIFT) algorithm is introduced into image matching. Then, Harris corner extraction (HCE) algorithm is utilized for selection of feature points. And according to HCE algorithm, we develop a specific direction feature description method. In the proposal, main camera points are calibrated separately, which not only makes the calibration accuracy of the main points higher, but also reduces the unknown parameters of Kruppa equation. Finally, we conduct some experiments to verify efficiency of the proposal. The obtained results show that calibration accuracy of the proposal is 12% higher than other comparison methods, and better virtual reconstruction results can be achieved.

A Kruppa Equation-based Virtual Reconstruction Method for 3D Images Under Visual Communication Scenes