Vahid Kiani

Papers

Total Citations

H-Index

About

Vahid Kiani’s research centers on 3D vision and image processing, with a particular focus on efficient data representation for emerging technologies. His most cited work, “Depth image compression using geometrical wavelets” (2014), addresses a critical bottleneck in robotics, mapping, and 3D television: the massive redundancy in depth images at high resolutions and frame rates. By applying geometrical wavelets, Kiani developed a compression method that preserves essential structural information while significantly reducing data size—a contribution that supports real-time applications in autonomous systems and immersive media. Though his citation count is modest, the work’s relevance to the growing fields of free viewpoint video and 3D broadcasting underscores its forward-looking impact. Kiani’s approach bridges the gap between efficient storage and the demands of modern 3D vision, making his research a foundational step for students and engineers tackling data-heavy visual tasks. His work exemplifies how targeted algorithmic innovations can enable practical advances in rapidly evolving domains.