To reduce noise and distortion of a 3D range image obtained from a laser rangefinder, an anisotropic adaptive smoothing method was introduced. The method consisted of stochastic signal estimation and scale-space representation. A feature estimation model was then derived from neighboring pointsand was used to predict the manifold topological relations between those neighboring points. To achieve anisotropic diffusion smoothing, the Mahalanobis distance between the original image and the estimated model was calculated asa similarity measure,which could then be usedtoconstruct a convolution kernel. This method enabled the distortion of the original image to be effectively corrected and noise to be suppressed.It also made the main imagefeatures more apparent. Experimental results indicate that the peak signal-to-noise ratiogain of the adaptive algorithm reached 16.41 dB, and the mean square error was reduced to 66.16% when the noise variance was 4.0×10-4 m2. Our smoothing method can thus improve the quality of noisy 3D range imagesand can provide technical support for 3D sensing and measurement modeling based on laser rangefinders.