In this paper we present results of a study of registered range and reflectance images acquired using a prototype amplitude-modulated cw laser radar. Ranging devices such as laser radars represent new technologies which are being applied in aerospace, nuclear and other hazardous environments where remote inspections, 3D identifications, and measurements are required. However, data acquired using devices of this type may contain non-stationary, signal- dependent noise, range-reflectance crosstalk, and low-reflectance range artifacts. Low level fusion algorithms play an essential role in achieving reliable performance by handling the complex noise, systematic errors, and artifacts. The objective of our study is the development of a stochastic fusion algorithm which takes as its input the registered image pair and produces as its output a reliable description of the underlying physical scene in terms of locally smooth surfaces separated by well-defined depth discontinuities. To construct the algorithm we model each image as a set of coupled Markov random fields representing pixel and several orders of line processes. Within this framework we (i) impose local smoothness constraints, introducing a simple linearity property in place of the usual sums over clique potentials; (ii) fuse the range and reflectance images through line process couplings; and (iii) use nonstationary, signal- dependent variances, adaptive thresholding, and a form of Markov natural selection. We show that the resulting algorithm yields reliable results even in worst-case scenarios.