3D scene representation has been a central theme of study for a wide range of applications, and the representation of light behavior is one of the relevant topics when producing realistic models. In this work, we create a framework to assess the representation of non-Lambertian scenes by generating a pipeline to create plenoptic point clouds (PPCs) systematically and evaluating them against implicit solutions, such as Neural Radiance Fields (NeRF)-like models. We compare such approaches according to rendering quality and compression efficiency. On the compression side, we propose an encoding scheme for PPC, leveraging the occlusion masks of the points and the Moving Picture Expert Group's (MPEG) Geometry-Based Solid Content Test Model (GeS-TM). Rendering results over the training views show that the uncompressed PPC outperforms 3D Gaussian Splatting (3DGS) by 1.51 dB, on average, for the 8 scenes of the NeRF Synthetic 360 dataset. In compression efficiency, 3DGS outperforms the compressed PPCs by 0.7 dB in BD-PSNR on average. Our occlusion-aware encoding scheme reduces the size of uncompressed PPCs up to 800 times, outperforming current encoding schemes for PPC by 1.9 dB in BD-PSNR.