Contrastive Learning-Based Framework for Sim-to-Real Mapping of Lidar
  Point Clouds in Autonomous Driving Systems
Hamed Haghighi Mehrdad Dianati Kurt Debattista Valentina Donzella
Perception sensor models are essential elements of automotive simulation
environments; they also serve as powerful tools for creating synthetic datasets
to train deep learning-based perception models. Developing realistic perception
sensor models poses a significant challenge due to the large gap between
simulated sensor data and real-world sensor outputs, known as the sim-to-real
gap. To address this problem, learning-based models have emerged as promising
solutions in recent years, with unparalleled potential to map low-fidelity
simulated sensor data into highly realistic outputs. Motivated by this
potential, this paper focuses on sim-to-real mapping of Lidar point clouds, a
widely used perception sensor in automated driving systems. We introduce a
novel Contrastive-Learning-based Sim-to-Real mapping framework, namely CLS2R,
inspired by the recent advancements in image-to-image translation techniques.
The proposed CLS2R framework employs a lossless representation of Lidar point
clouds, considering all essential Lidar attributes such as depth, reflectance,
and raydrop. We extensively evaluate the proposed framework, comparing it with
state-of-the-art image-to-image translation methods using a diverse range of
metrics to assess realness, faithfulness, and the impact on the performance of
a downstream task. Our results show that CLS2R demonstrates superior
performance across nearly all metrics. Source code is available at
https://github.com/hamedhaghighi/CLS2R.git.