Model-based reconstruction of featureless objects for automated storage and transportation systems

Three-dimensional (3D) reconstruction from images usually relies on matched point correspondences across images, and the number of detected feature points should be as great as possible to fully describe the object geometry. Featureless objects, such as heaps of raw ore that are of similar color and present very few feature points, however, can usually be seen in many industrial applications, and their 3D reconstruction is essential to developing automated storage and transportation systems. Therefore, we propose a model-based method that utilizes a generic model with an additional reference point to estimate the 3D structure of a featureless object which is the ore pile in our cases. The size, position, and orientation of the estimated object are then optimized, followed by redundant area removal to fit the finer details. We also extend the method to the reconstruction of multiple objects. Experimental results show that the method successfully reconstructs featureless objects having similar colors and textures.