3D model creation through volumetric fusion of multiple range images

This paper presents a method to fuse multiple noisy range images to obtain a 3-D model of an object. This method projects each range image onto a volumetric grid that is divided into volume elements (voxels). We place a value in each voxel that represents our degree of certainty that that voxel is inside the sensed object. We determine this value by constructing a line from the voxel to the sensor's location and calculating the point that it intersects the range image. The certainty value is determined from the distance from the voxel to the range image intersection point and an estimate of the sensor's noise characteristics. The super Bayesian combination formula is used to fuse the grids created from the individual range images into an overall volumetric grid. We obtain the object model by extracting an isosurface at the value of 1/2 from the volumetric data using a variation of the marching cubes algorithm.