Realistic LiDAR Point Cloud

Realistic LiDAR point cloud generation aims to create synthetic LiDAR data that closely mimics real-world sensor readings, addressing the high cost and scarcity of real data. Current research focuses on leveraging advanced generative models, such as diffusion models and neural radiance fields, often incorporating techniques like variational autoencoders and transformers to improve realism and efficiency. This work is crucial for advancing autonomous driving and robotics, enabling more robust training and testing of perception algorithms through the creation of large, diverse, and accurately labeled datasets. The improved realism of generated point clouds also facilitates tasks like scene completion and novel view synthesis.