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.

翻译：本文提出了一种新的深度与语义感知的条件生成模型，名为TITAN-Next，用于激光雷达与相机传感器在多模态设置下的跨域图像到图像翻译。所提模型利用场景语义作为中层表示，并能够仅依靠语义场景片段将原始激光雷达点云翻译为RGB-D相机图像。我们声称这是首个此类框架，在自动驾驶领域具有实际应用，例如提供故障安全机制以及扩充目标图像域中的可用数据。该模型在大型且具挑战性的Semantic-KITTI数据集上进行了评估，实验结果表明，其在IoU指标上以23.7%的幅度显著优于原始TITAN-Net及其他强基线模型。