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