Depth modeling mode 1 (DMM-1) processes its prediction unit (PU) without taking into account its neighborhood. However, an extensive analysis of neighbor pattern selection showed that the processing PU could use the same pattern of its neighbors or an adaptation of these patterns. Therefore, this work proposes the DMM-1 fast pattern selector (DFPS) algorithm that includes lightweight and medium-weight DMM-1 pattern predictors. DFPS starts using the lightweight predictor, whose output is compared with a threshold and then the algorithm employs a DMM-1 refinement or the medium-weight predictor. The result of this last predictor is compared against a new threshold, where the encoder decides if the remaining patterns of the DMM-1 algorithm or only its refinement should be evaluated. Experiments evaluating DFPS encoding efficiency in the all-intra mode demonstrate that by reducing 71% and 84% of the DMM-1 patterns’ evaluation, the complexity is reduced 11.7% and 13.4%, respectively, without significantly affecting the quality of the synthesized views.