In this paper, we discuss noise reduction approaches to improving range images using a nonlinear 2D Kalman filter. First, we propose the nonlinear 2D Kalman filter, which can reduce noise in the range image using an estimated edge vector and a nonlinear function that does not distort sharp edges. Second, we evaluate reduction of the additive noise in a test range image using the mean square error (MSE). Third, we discuss the detection rate and the number of false detections in the estimated range image. Fourth, a simulation example demonstrating the performance of the proposed 2D Kalman filter for a real range image having abrupt changes is presented. Finally, simulation results are presented which show that the estimated image of the nonlinear 2D Kalman filter is effective in reducing the amount of noise, while causing minimal smoothing of the abrupt changes.