From image pair to a computer generated hologram for a real-world scene.

We propose an approach to produce computer generated holograms (CGHs) from image pairs of a real-world scene. The ratio of the three-dimensional (3D) physical size of the object is computed from the image pair to provide the correct depth cue. A multilayer wavefront recording plane method completed with a two-stage occlusion culling process is carried out for wave propagation. Multiple holograms can be generated by propagating the wave toward the desired angles, to cover the circular views that are wider than the viewing angle restricted by the wavelength and pitch size of a single hologram. The impact of the imperfect depth information extracted from the image pair on CGH is examined. The approach is evaluated extensively on image pairs of real-world 3D scenes, and the results demonstrate that the circular-view CGH can be produced from a pair of stereo images using the proposed approach.

Causal Incremental Graph Convolution for Recommender System Retraining.

The real-world recommender system needs to be regularly retrained to keep with the new data. In this work, we consider how to efficiently retrain graph convolution network (GCN)-based recommender models that are state-of-the-art techniques for the collaborative recommendation. To pursue high efficiency, we set the target as using only new data for model updating, meanwhile not sacrificing the recommendation accuracy compared with full model retraining. This is nontrivial to achieve since the interaction data participates in both the graph structure for model construction and the loss function for model learning, whereas the old graph structure is not allowed to use in model updating. Toward the goal, we propose a causal incremental graph convolution (IGC) approach, which consists of two new operators named IGC and colliding effect distillation (CED) to estimate the output of full graph convolution. In particular, we devise simple and effective modules for IGC to ingeniously combine the old representations and the incremental graph and effectively fuse the long- and short-term preference signals. CED aims to avoid the out-of-date issue of inactive nodes that are not in the incremental graph, which connects the new data with inactive nodes through causal inference. In particular, CED estimates the causal effect of new data on the representation of inactive nodes through the control of their collider. Extensive experiments on three real-world datasets demonstrate both accuracy gains and significant speed-ups over the existing retraining mechanism.