A new registration method to robustly align a series of sparse 3D data

Optical measurement tasks often require an acquisition of several partial 3D views to collect complete 3D information of an object surface. We consider a sensor that acquires the surface information by taking a series of sparse partial 3D views while being freely moved around the object [1, 2]. Each partial view provides 3D data only along parallel lines. Fig. 1 illustrates this concept. In order to obtain a dense 3D model of the surface all partial views have to be aligned. Existing methods for registration tasks usually detect common surface features and map them onto each other [3, 4, 5]. However, in case of sparse data these methods fail, because insufficient or no neighborhood surface information is available to find common features. We propose a new method that is specifically tailored for the robust registration of sparse 3D data in real time and show measurement results.