Amir Atapour–Abarghouei

Durham University, University of Technology Malaysia

Papers

Total Citations

H-Index

About

Amir Atapour–Abarghouei is a researcher at the forefront of geometric and semantic scene understanding, with a focus on enabling robust perception for autonomous driving and robotic navigation. His work bridges computer vision and artificial intelligence, particularly through multi-task learning frameworks that jointly predict depth, complete sparse depth maps, and interpret semantic scenes. His most cited paper, *Veritatem Dies Aperit* (2019, 42 citations), introduces a temporally consistent depth prediction model that integrates geometric and semantic cues, advancing real-world applications where reliability and coherence over time are critical. Earlier, he contributed to the field of soft computing with a survey on edge detection methods (2009, 26 citations), highlighting the role of AI in solving ill-defined vision problems. Another notable work (2019, 17 citations) proposes a multi-task model for sparse depth completion, further demonstrating his ability to tackle practical challenges in 3D scene understanding. Atapour–Abarghouei’s research is characterized by its applied focus and interdisciplinary approach, making significant strides in how machines perceive and interact with complex environments. His contributions continue to inspire innovations in autonomous systems and robotic vision.