  In this work, we present a simple yet effective framework to address the
domain translation problem between different sensor modalities with unique data
formats. By relying only on the semantics of the scene, our modular generative
framework can, for the first time, synthesize a panoramic color image from a
given full 3D LiDAR point cloud. The framework starts with semantic
segmentation of the point cloud, which is initially projected onto a spherical
surface. The same semantic segmentation is applied to the corresponding camera
image. Next, our new conditional generative model adversarially learns to
translate the predicted LiDAR segment maps to the camera image counterparts.
Finally, generated image segments are processed to render the panoramic scene
images. We provide a thorough quantitative evaluation on the SemanticKitti
dataset and show that our proposed framework outperforms other strong baseline
models.
  Our source code is available at
https://github.com/halmstad-University/TITAN-NET
