This paper presents a fast 3D-High Efficiency Video Coding (3D-HEVC) depth maps intra-frame prediction based on static Coding Unit (CU) splitting decisions trees. This coding approach uses data mining to extract the correlation among the encoder context attributes and to define a split decision tree for each CU level of the depth maps encoding. The decision trees were trained using the information extracted from 3D-HEVC Test Model (3D-HTM) and using the Common Test Conditions (CTC). Each decision tree defines if the current CU must be split into smaller sizes, considering the encoding context through the evaluation of some current encoder attributes. The proposed solution reaches a complexity reduction of 59.0% for depth maps coding with a negligible impact of 0.18% in the encoding efficiency of synthesized views.