3D box method for 3D reconstruction of an object from multi-images

This paper proposes a new method called a 3D box method for estimating the shape of an object from multi-views. This method is useful in those fields, which are needed to recover the 3D information from their images, such as in the field of industries, medical sciences etc. Concept of voting and counting votes from different image views is used to solve the inverse problems of 3D reconstruction of an object from 2D image. The straight line drawn from the lens center of the calibrated camera to an image point of object's silhouettes casts a single vote in each 3D box on its way if it is extended in space. Images are taken from thirty-six positions of a calibrated camera dividing them into four groups as 3x3 cameras in each group, which are positioned on each side of the object. The technique of camera position grouping is used to solve the concavity problems by the detection of occlusion using stereo method and acquiring occlusion free depth. To reduce the large memory size needed to solve in a single-stage, a multi-stage algorithm is developed for getting the accurate shape of the 3D object. Computer simulations are conducted to demonstrate the performance of the algorithm on images of various shapes of objects.