Bas-reliefs modelling based on learning deformable 3D models

Bas-relief is a special art-form, which can be used as decorations. Deformable 3D models are usually used in image segmentation and 3D reconstruction, aiming at the reconstruction of certain category especially. To quickly reconstruct different objects of the same category and take the psychological process and visual characteristics of human observation images into account, in this paper, we propose a bas-reliefs generation method for flowers based on deformable 3D models which is a type of parametric model that can deform shapes by changing related parameters. First, we create a dataset by labelling images with keypoints. Then, we use Non Rigid Structure from Motion (NRSfM) algorithm to estimate viewpoints. A deformable 3D model is reconstructed by Visual Hull. Next, we deform the 3D model to a variant of the object according to an input 2D image. After that, we enhance and smoothen the image by applying gamma correction on the gray scale image with gradient. Finally, we calculate height values as details using illumination model and add them to the variant. The experimental results demonstrate that our method can reconstruct bas-reliefs for flowers in different shapes with complex background, which is also proven to be able to extend to other categories, such as birds and fishes.