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In the present paper, we propose a novel 3D shape descriptor by performing multiresolution wavelet analysis on shape orientation. We consider the spatial orientation of the polygon surfaces of a shape as important information and characterize this information by setting view planes. We then analyze these view planes by multiresolution wavelet analysis, a powerful tool used in signal processing, and lower the high resolution to low frequency domains because the high resolution contains too much information, which must be reduced in order to capture the main components. We compare the proposed descriptor to two of the best-performing descriptors on the Princeton Shape Benchmark, Spherical Harmonics Descriptor and Light Field Descriptor, and analyze the performance of the proposed descriptor from several aspects. We also compare the proposed descriptor to the Spherical Wavelet Descriptor, which won the best paper award at SMI06, a near method to our descriptor. The proposed descriptor improves the retrieval performance.