This research addresses the problem of automatic extraction of memorial objects from cultural heritage sites represented as scenes of 3D point clouds. Point clouds provide a fine spatial resolution and accurate proxy of the real world. However, how to use them directly is not always obvious. This is especially true for applications where extensive training data or computational resources are not available. In this paper, we present a methodology for automatic segmentation and labelling of cultural heritage objects from 3D point cloud scenes. The proposed methodology is based on machine learning techniques and, in particular, makes use of the concept of transfer learning. Memorial objects are segmented from the scene based on their geometric shape characteristic through a conditional multi-scale partitioning scheme. Then, high-level latent feature descriptors are extracted by a convolutional neural network pre-trained on different 3D object models from a standard dataset (e.g., ModelNet). Based on these descriptors, a classification model (multilayer perceptron) is trained and applied to obtain semantic labels. Experiments demonstrated that the proposed methodology is effective for the extraction and labelling of grave marker objects from cultural heritage sites.