Point clouds are a new modality for representation of plenoptic content and a popular alternative to create immersive media. Despite recent progress in capture, display, storage, delivery and processing, the problem of a reliable approach to subjectively and objectively assess the quality of point clouds is still largely open. In this study, we extend the state of the art in projection-based objective quality assessment of point cloud imaging by investigating the impact of the number of viewpoints employed to assess the visual quality of a content, while discarding information that does not belong to the object under assessment, such as background color. Additionally, we propose assigning weights to the projected views based on interactivity information, obtained during subjective evaluation experiments. In the experiment that was conducted, human observers assessed a carefully selected collection of typical contents, subject to geometry and color degradations due to compression. The point cloud models were rendered using cubes as primitive elements with adaptive sizes based on local neighborhoods. Our results show that employing a larger number of projected views does not necessarily lead to better predictions of visual quality, while user interactivity information can improve the performance.