A Bayesian Approach for Selective Image-Based Rendering using Superpixels

International audience; Image-Based Rendering (IBR) algorithms generate highquality photo-realistic imagery without the burden of detailedmodeling and expensive realistic rendering. Recentmethods have different strengths and weaknesses, dependingon 3D reconstruction quality and scene content. Eachalgorithm operates with a set of hypotheses about the sceneand the novel views, resulting in different quality/speedtrade-offs in different image regions. We present a principledapproach to select the algorithm with the best quality/speedtrade-off in each region. To do this, we proposea Bayesian approach, modeling the rendering quality, therendering process and the validity of the assumptions ofeach algorithm. We then choose the algorithm to use withMaximum a Posteriori estimation. We demonstrate the utilityof our approach on recent IBR algorithms which useoversegmentation and are based on planar reprojection andshape-preserving warps respectively. Our algorithm selectsthe best rendering algorithm for each superpixel in apreprocessing step; at runtime our selective IBR uses thischoice to achieve significant speedup at equivalent or betterquality compared to previous algorithms.