Constructing three dimensional models of optically challenging real-world objects

This thesis presents a new range sensing method that is capable of constructing accurate three dimensional models of real-world objects with optically challenging surface materials. Our method utilizes a new range imaging concept called multi-peak range imaging, which stores multiple candidates of range measurements for each point on the object surface. The multiple measurements account for the erroneous structured-light data caused by various surface properties that are not ideal for range sensing. False measurements are iteratively eliminated by applying a series of constraint tests. The constraint tests based on local surface property and local sensor visibility are applied first to individual range data. The constraint tests based on global consistency of coordinates and visibility are then applied to all range data acquired from different viewpoints. We show the effectiveness of our method by constructing three dimensional models of five different optically challenging objects. To evaluate the performance of the constraint tests and to examine the effects of the parameters used in the constraint tests, we acquired the ground truth data by painting those objects to suppress the surface-related properties that cause difficulties in range imaging. Experimental results indicate that our method significantly improves upon the traditional methods for constructing reliable 3D models of optically challenging objects.