  Image outpainting technology generates visually plausible content regardless
of authenticity, making it unreliable to be applied in practice. Thus, we
propose a reliable image outpainting task, introducing the sparse depth from
LiDARs to extrapolate authentic RGB scenes. The large field view of LiDARs
allows it to serve for data enhancement and further multimodal tasks.
Concretely, we propose a Depth-Guided Outpainting Network to model different
feature representations of two modalities and learn the structure-aware
cross-modal fusion. And two components are designed: 1) The Multimodal Learning
Module produces unique depth and RGB feature representations from the
perspectives of different modal characteristics. 2) The Depth Guidance Fusion
Module leverages the complete depth modality to guide the establishment of RGB
contents by progressive multimodal feature fusion. Furthermore, we specially
design an additional constraint strategy consisting of Cross-modal Loss and
Edge Loss to enhance ambiguous contours and expedite reliable content
generation. Extensive experiments on KITTI and Waymo datasets demonstrate our
superiority over the state-of-the-art method, quantitatively and qualitatively.
