In order to realize model-based 3D object recognition, first, we propose a geometric feature extraction method based on a novel gaze modeling. In the modeling process, local surface models are independently estimated for parts of range data restricted by several gaze domains. Hence, since features are independently extracted from each gaze domain, inconsistent or incorrect features may be obtained. Therefore we introduce a stochastic method that enables us to integrate such features by evaluating the reliability of each gaze model. Next, we propose a shape descriptor, curvature distribution image (CDI), to achieve object recognition by surface matching. It is generated based on the ratios between surface curvatures. The main contribution of this paper is experimental analysis of the performance of CDIs generated by various generation parameters.