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This result in the classification accuracy rate is unsatisfactory when applied for depth-image-based rendering (DBIR) 3D image identification. In this work, pixel grouping based on histogram shape and nonnegative matrix factorization (NMF) are applied to design DIBR 3D image hashing with better robustness resisting to geometric distortions and higher classification accuracy rate for virtual image identification. Experiments show that the proposed hashing is robust against common signal and geometric distortion attacks, such as additive noise, blurring, JPEG compression, scaling, and rotation. 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