320 citations · 325 across the 10 of their papers we have counts for
10 papers
TexLiDAR: Automated Text Understanding for Panoramic LiDAR Data
Naor Cohen, Roy Orfaig, Ben-Zion Bobrovsky
Efforts to connect LiDAR data with text, such as LidarCLIP, have primarily focused on embedding 3D point clouds into CLIP text-image space. However, these approaches rely on 3D poi…
NextStop: An Improved Tracker For Panoptic LIDAR Segmentation Data
Nirit Alkalay, Roy Orfaig, Ben-Zion Bobrovsky
4D panoptic LiDAR segmentation is essential for scene understanding in autonomous driving and robotics, combining semantic and instance segmentation with temporal consistency. Curr…
Real-Time 3D Object Detection Using InnovizOne LiDAR and Low-Power Hailo-8 AI Accelerator
Itay Krispin-Avraham, Roy Orfaig, Ben-Zion Bobrovsky
Object detection is a significant field in autonomous driving. Popular sensors for this task include cameras and LiDAR sensors. LiDAR sensors offer several advantages, such as inse…
CLRmatchNet: Enhancing Curved Lane Detection with Deep Matching Process
Sapir Kontente, Roy Orfaig, Ben-Zion Bobrovsky
Lane detection plays a crucial role in autonomous driving by providing vital data to ensure safe navigation. Modern algorithms rely on anchor-based detectors, which are then follow…
Strong-TransCenter: Improved Multi-Object Tracking based on Transformers with Dense Representations
Amit Galor, Roy Orfaig, Ben-Zion Bobrovsky
Transformer networks have been a focus of research in many fields in recent years, being able to surpass the state-of-the-art performance in different computer vision tasks. Howeve…
BoT-SORT: Robust Associations Multi-Pedestrian Tracking
Nir Aharon, Roy Orfaig, Ben-Zion Bobrovsky
The goal of multi-object tracking (MOT) is detecting and tracking all the objects in a scene, while keeping a unique identifier for each object. In this paper, we present a new rob…