Point cloud imaging has emerged as an efficient and popular solution to represent immersive visual information. However, the large volume of data generated in the acquisition process reveals the need of efficient compression solutions in order to store and transmit such contents. Several standardization committees are in the process of finalizing efficient compression schemes to cope with the large volume of information that point clouds require. At the same time, recent efforts on learning-based compression approaches have been shown to exhibit good performance in the coding of conventional image and video contents. It is currently an open question how learning-based coding performs when applied to point cloud data. In this study, we extend recent efforts on the matter by exploring neural network implementations for separate, or joint compression of geometric and textural information from point cloud contents. Two alternative architectures are presented and compared; that is, a unified model that learns to encode point clouds in a holistic way, allowing fine-tuning for quality preservation per attribute, and a second paradigm consisting of two cascading networks that are trained separately to encode geometry and color, individually. A baseline configuration from the best-performing option is compared to the MPEG anchor, showing better performance for geometry and competitive performance for color encoding at low bit-rates. Moreover, the impact of a series of parameters is examined on the network performance, such as the selection of input block resolution for training and testing, the color space, and the loss functions. Results provide guidelines for future efforts in learning-based point cloud compression.