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Depth prediction is an essential component in the research of unmanned driving. Most existing research works predict depth only based on visible light images or infrared images. However, both visible light images and infrared images have their own advantages and disadvantages, and these two kinds of images contain complementary information when the images are filmed from the same scence. In order to fuse the complementary information and predict depth under various conditions, this paper proposes a convolutional-neural-network-based architecture, called infrared and visible light images fusion network (IVFuseNet), for depth prediction. Specifically, we construct common-feature-fusion subnetwork, full-feature-fusion subnetwork, and high-resolution reconstruction subnetwork, aiming to leverage the complementarity of these two kinds of images. The common-feature-fusion subnetwork adopts a two …