Registration of stereo-based 3D maps for object modeling: a stochastic yet intelligent solution

A moving object in front of a stereovision system provides range views of its surface. Assuming that we can reconstruct a sequence of partial 3D descriptions of the whole surface, a rigid displacement of the object between successive views can be estimated. This is known as 3D surface registration problem. The closest relevant works are presented and consist in iterative matching and then registration. Unfortunately, they ignore all the particular features and origins of the 3D maps. Hence this paper attacks the registration of stereo based 3D maps. This knowledge leads to use widely the backprojection of the 3D data onto the sensor image planes to model and solve the problem. In contrast to previous approaches, a stochastic scheme iterates to refine a rough estimate and to impose the consistency of the solution both in 3D and image spaces.