Light field structured light projection data generation with Blender

Abstract

Light field structured light 3D measurement has gained popularity by merging the advantages of light field and structured light methods. Generating light field structured light dataset is necessary for studying light field 3D reconstruction algorithms, but it is time-consuming and expensive in a real sense, especially for ground truth data. This paper proposes a method to generate light field structured light projection data with Blender simulation. The proposed method allows for the modification of camera and projector settings and parameters, as well as rotating objects. The dataset generated by this method contains 107730 light field structured light images. The label data (ground truth data) including depth and disparity by the 9×9 light field camera array are provided for the performance evaluation of 3D reconstruction algorithms. To the best of our knowledge, it is the first public dataset in the light field structured light projection environment. Diverse 3D reconstruction methods, including deep learning methods, are used to evaluate the proposed data generation method and dataset. The dataset is available at https://github.com/sabaizzz/Light-field-structured-light-dataset.