Osama Abudayyeh

About

Osama Abudayyeh is a rising researcher in autonomous driving and perception systems, with a focus on LiDAR-based environmental understanding. His work centers on developing robust algorithms for real-time 3D scene analysis, particularly through point cloud clustering and depth completion. In his highly cited 2022 paper on "Adaptive DBSCAN LiDAR Point Cloud Clustering," he advanced the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm for autonomous driving, enabling more efficient and accurate object detection and localization—a contribution that has already garnered 35 citations. He further extended his impact with "Guided Depth Completion with Instance Segmentation Fusion," which addresses the critical challenge of sparse LiDAR depth data by fusing instance segmentation to predict missing pixel-level depth information, essential for navigation and 3D reconstruction. With 8 citations to this work, Abudayyeh is demonstrating early-career influence in bridging perception gaps for safe autonomous systems. His research stands at the intersection of computer vision and robotics, promising safer, more reliable self-driving technologies.

Research Focus

Key Achievements

Top Papers

1. 1
   Adaptive DBSCAN LiDAR Point Cloud Clustering For Autonomous Driving Applications

   35 citations · 2022
2. 2
   Guided Depth Completion with Instance Segmentation Fusion in Autonomous Driving Applications

   8 citations · 2022

Key Collaborators

Contact & Links