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The detection, characterization, and matching of various 3-D features from visual observations is important for a large variety of applications such as modeling, tracking, recognition or indexing. The existing methods detect features by using either photometric information available with geometric information available with 3-D surfaces. To deal with the low retrieval accuracy problem in complex situation of 3-D objects retrieval, such as 3-D objects rotation and brightness changing, and a 3-D objects retrieval method was proposed. The Harris operator was extended the use of 3-D objects, and an adaptive technique was proposed to determine the neighborhood of a vertex. Then the significant interest points were chosen with the Harris response function value. To construct the global shape features distance histogram of 3-D objects with interest points, the distance histogram was used as the 3-D shape descriptor for 3-D object retrieval. Extensive experimental results demonstrated that the proposed method was robust to affine transformations and distortion transformation such as noise addition. Moreover, the distribution of interest points on the surface of an object remains similar in transformed objects, which is a desirable behavior in applications such as shape matching and object registration.