Reconstruction of 3D structural semantic points based on multiple camera views

Multi-view can provide more object information than single view and are less susceptible to noise interference. But in the feature matching process, excessive parallax in multi-view can lead to mismatches. And it is difficult to extract features for weakly textured area, which will causes the reconstructed model contain holes. Here, the method for the reconstruction of 3D structural semantic points with multiple camera views is presented. We take the advantage of the multi-view method and 3D feature points to reduce mismatching in the feature matching process. The constraints that provided by structure semantics points are related to object and restrict the distribution of points around object, which can improve the reconstructed model. Besides, the model with 3D feature points can be optimized using semantics and distance information to fill holes and remove noise. The experiment uses eight cameras to test method. The results show that our method can be effective for mismatching and holes. The experiment results prove that our method is effective.