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Sensor fusion can significantly improve the performance of many computervision tasks. However, traditional fusion approaches are either not data-drivenand cannot exploit prior knowledge nor find regularities in a given dataset orthey are restricted to a single application. We overcome this shortcoming bypresenting a novel deep hierarchical variational autoencoder called FusionVAEthat can serve as a basis for many fusion tasks. Our approach is able togenerate diverse image samples that are conditioned on multiple noisy,occluded, or only partially visible input images. We derive and optimize avariational lower bound for the conditional log-likelihood of FusionVAE. Inorder to assess the fusion capabilities of our model thoroughly, we createdthree novel datasets for image fusion based on popular computer visiondatasets. In our experiments, we show that FusionVAE learns a representation ofaggregated information that is relevant to fusion tasks. The resultsdemonstrate that our approach outperforms traditional methods significantly.Furthermore, we present the advantages and disadvantages of different designchoices.

FusionVAE: A Deep Hierarchical Variational Autoencoder for RGB Image Fusion