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This work presents a new depth- and semantics-aware conditional generativemodel, named TITAN-Next, for cross-domain image-to-image translation in amulti-modal setup between LiDAR and camera sensors. The proposed modelleverages scene semantics as a mid-level representation and is able totranslate raw LiDAR point clouds to RGB-D camera images by solely relying onsemantic scene segments. We claim that this is the first framework of its kindand it has practical applications in autonomous vehicles such as providing afail-safe mechanism and augmenting available data in the target image domain.The proposed model is evaluated on the large-scale and challengingSemantic-KITTI dataset, and experimental findings show that it considerablyoutperforms the original TITAN-Net and other strong baselines by 23.7$\%$margin in terms of IoU.

Depth- and Semantics-aware Multi-modal Domain Translation: Generating 3D Panoramic Color Images from LiDAR Point Clouds