DeepStereoBrush: Interactive Depth Map Creation - Smolic, Aljosa (My University, 2018)
In this paper, we introduce a novel interactive depth map creation approach for image sequences which uses depth-scribbles as input at user-defined keyframes. These scribbled depth values are then propagated within these keyframes and across the entire sequence using a 3-dimensional geodesic distance transform (3D-GDT). In order to further improve the depth estimation of the intermediate frames, we make use of a convolutional neural network (CNN) in an unconventional manner. Our process is based on online learning which allows us to specifically train a disposable network for each sequence individually using the user generated depth at keyframes along with corresponding RGB images as training pairs. Thus, we actually take advantage of one of the most common issues in deep learning: over-fitting. Furthermore, we integrated this approach into a professional interactive depth map creation application and compared our results against the state of the art in interactive depth map creation.
International Conference on 3D Immersion, 2018, (Received the Lumiere Award for the best scientific paper).
PUBLISHED
Belgium
Keywords: Depth estimation; 2D-to-3D conversion; Deep learning; CNN; Interactive depth map creation; Geodesic distance transform; Creative Technologies; Multimedia & Creativity