We address the limitations of Deep learning models for 3D geometry segmentation by using Conditional Random fields (CRF). We show that CRFs can take advantage of the neighbouring structure of point clouds to assist the learning of the Deep Learning models (DL). Our hybrid PN-CRF model is able to learn more optimal weights by taking advantage of equal-segmentation assignments to neighbouring points. As a result, it increases the robustness in the model specially for segmentation tasks where correctly detecting the boundaries between segmentations is very important.