4 papers
AURORA-KITTI: Any-Weather Depth Completion and Denoising in the Wild
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 im…
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…
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…