Joint Conditional Random Field of multiple views with online learning for image-based rendering

There are many applications, such as image-based rendering, where multiple views of a scene are considered simultaneously for improved analysis through employing strong correlation among the set of pixels corresponding to the same physical scene point. While being a useful tool for modeling pixel interactions, Markov random field (MRF) models encounter challenges in such cases since they assume strong independence of the observed data for tractability, rendering it difficult to take advantage of having multiple correlated views. In this paper we propose joint conditional random field (CRF) for multiple views in the context of virtual view synthesis in image-based rendering. The model is enabled by the adoption of steerable spatial filters for capturing not only the pixel dependence in a single image but also their correlations among multiple views. Furthermore, a novel on-line learning scheme is proposed for the CRF model, which learns the CRF parameters from the same input data for synthesizing virtual views. This effectively makes the model adaptive to the input and thus optimal results can be expected. Experiments are designed to validate the proposed approach and its effectiveness.