This paper represents 3D object recognition, which is an extension of the common feature point-based object recognition, based on novel descriptors utilizing local angles (for shape), gradient orientations (for texture of corners), and color information. First, the proposed algorithm extracts complementary feature points by randomly sampling the positions of the object edges. Then, it generates the proposed descriptors combining local angle patterns, gradient orientations, and color information. After making the descriptors, the method learns a codebook to enable the proposed algorithm to integrate the extracted feature points into a histogram through this codebook. Finally, the method classifies the query histogram based on a classifier. We expect that the proposed algorithm is robust to less textured and similar-shaped objects. The proposed method could be used as a core technology of the initial step of the information retrieval.