5 papers
AURORA-KITTI: Any-Weather Depth Completion and Denoising
Yiting Wang, Tim Brödermann, Hamed Haghighi +4
Robust depth completion is fundamental to real-world 3D scene understanding, yet existing RGB--LiDAR fusion methods degrade significantly under adverse weather, where both camera i…
REHEARSE-3D: A Multi-modal Emulated Rain Dataset for 3D Point Cloud De-raining
Abu Mohammed Raisuddin, Jesper Holmblad, Hamed Haghighi +4
Sensor degradation poses a significant challenge in autonomous driving. During heavy rainfall, the interference from raindrops can adversely affect the quality of LiDAR point cloud…
Taming Transformers for Realistic Lidar Point Cloud Generation
Hamed Haghighi, Amir Samadi, Mehrdad Dianati +2
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…
Data-driven Camera and Lidar Simulation Models for Autonomous Driving: A Review from Generative Models to Volume Renderers
Hamed Haghighi, Xiaomeng Wang, Hao Jing +1
Perception sensors, particularly camera and Lidar, are key elements of Autonomous Driving Systems (ADS) that enable them to comprehend their surroundings to informed driving and co…
A Unified Generative Framework for Realistic Lidar Simulation in Autonomous Driving Systems
Hamed Haghighi, Mehrdad Dianati, Valentina Donzella +1
Simulation models for perception sensors are integral components of automotive simulators used for the virtual Verification and Validation (V\&V) of Autonomous Driving Systems (ADS…