Taming Transformers for Realistic Lidar Point Cloud Generation

Diffusion Models (DMs) have achieved State-Of-The-Art (SOTA) results in the Lidar point cloud generation task, benefiting from their stable training and iterative refinement during sampling. However, DMs often fail to realistically model Lidar raydrop noise due to their inherent denoising process. To retain the strength of iterative sampling while enhancing the generation of raydrop noise, we introduce LidarGRIT, a generative model that uses auto-regressive transformers to iteratively sample the range images in the latent space rather than image space. Furthermore, LidarGRIT utilises VQ-VAE to separately decode range images and raydrop masks. Our results show that LidarGRIT achieves superior performance compared to SOTA models on KITTI-360 and KITTI odometry datasets. Code available at:https://github.com/hamedhaghighi/LidarGRIT.

Contrastive Learning-Based Framework for Sim-to-Real Mapping of Lidar Point Clouds in Autonomous Driving Systems

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.