DeepCinema: Adding Depth with X3D Image-Based Rendering
Abstract. In scientific research, data visualization is crucial for human understanding and insight. Modern experiments and simulations are increasingly growing in scale and complexity; as we approach the Exascale of big data, current methods of interactive 3D scientific visualization become untenable. Cinema has proposed as an Image-Based solution to this problem, where instead of explicit 3D geometry, the model is represented by an image database, through which users navigate among pre-rendered (in situ) screenshots. However, flat images alone cannot fully express the 3D characteristics of a data. Thus, we propose the DeepCinema technique to improve the depth portrayal of Image-Based Rendering using Displacement Maps and shading. We examine the perceptual efficacy of our technique with a 2-Alternative, Forced Choice user study of 60 participants. The within-subjects multi-factorial design compared user depth judgements over: Displacement Map, shading, and the interval of angular perspective for each image. Results show that this method would be useful for the interactive 3D visualization of Big Scientific Data using Web3D Standards.