We report the theory and implementation of new approaches for the processing of 3D range data in pursuit of library-based object recognition and registration. The image data is obtained from an active LaDAR system (scanned Time-Correlated Single Photon Count or time-gated Burst Illumination Laser) and describes the range and 3D surface characteristics of remote objects at specific views. The reflected laser signal returns are generally embedded in noise and clutter of uncertain origin. We have applied the Markov Chain Monte Carlo (MCMC) methodology, using random sampling of the search space, to evaluate the number, positions and amplitudes of returns in such scenarios. We describe the use of methods for removing outliers and smoothing these time-of-flight generated depth images, based on least median of squares and anisotropic diffusion, respectively. Further, we outline and demonstrate procedures for registration and pose determination of objects from range data. This consists of three phases, namely point feature extraction, pose clustering and registration. The first computes a surface metric facilitating candidate correspondence determination, using the technique of pair-wise geometric histograms. The second is carried out by a leader-based algorithm, which does not require the number of clusters to be pre-specified. The third is an extension of the iterative closest points (ICP) method, being specifically designed for mesh representations. Collectively, these processes allow an object within a scene - described by a 3D range image - to be matched with a preformed model from a database.