An Occlusion Compensation Learning Framework for Improving the Rendering Quality of Light Field

Occlusions are common phenomena in light field rendering (LFR) technology applications. The 3-D spatial structures of some features may be missing or incorrect when capturing some samples due to occlusion discontinuities. Most prior works on LFR, however, have neglected occlusions from other objects in 3-D scenes that do not participate in the capturing and rendering of the light field. To improve rendering quality, this report proposes an occlusion probability learning framework (OPLF) based on a deep Boltzmann machine (DBM) to compensate for the occluded information. In the OPLF, an occlusion probability density model is applied to calculate the visibility scores, which are modeled as hidden variables. Additionally, the probability of occlusion is related to the visibility, the camera configuration (i.e., position and direction), and the relationship between the occlusion object and occluded object. Furthermore, a deep probability model based on the OPLF is used for learning the occlusion relationship between the camera and object in multiple layers. The proposed OPLF can optimize the LFR quality. Finally, to verify the claimed performance, we also compare the OPLF with the most advanced occlusion theory and light field reconstruction algorithms. The experimental results show that the proposed OPLF outperforms other known occlusion quantization schemes.