The use of point clouds to digitally represent three-dimensional objects with both geometry and color attributes is rapidly increasing in several applications. Since storage and transmission of uncompressed point cloud data are often impractical, several lossy compression algorithms have been proposed, each exhibiting specific types of visual distortion. This creates a challenging environment for objective quality metrics, which might be effective only on a restricted number of distortion types and contexts. To evaluate the performance of the most recent objective quality metrics in predicting distortions as perceived by humans, a benchmarking study is conducted using subjective scores from observers examining models distorted with a conventional as well as a learning-based compression method, while rendering them on both a traditional flat monitor and an eye-sensing light field display. The results are then analyzed and conclusions are drawn on the correlation between recent state-of-the-art objective quality metrics and human perception on two different types of visualization devices.