Behnam Maleki

Papers

Total Citations

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About

Behnam Maleki is a researcher whose work lies at the intersection of 3D computer vision and robotics, with a particular focus on non-rigid point cloud registration and dynamic scene reconstruction. His key contributions address a fundamental challenge in robotics: accurately reconstructing deformable objects in motion. In his most-cited work, "SPaM: soft patch matching for non-rigid point cloud registration" (2023), Maleki introduced a novel approach that overcomes the limitations of traditional mesh-based methods, which often compromise registration accuracy and lose critical geometric details due to interpolation and smoothing. By developing a soft patch matching technique, his method preserves fine structural information while enabling robust, non-rigid alignment—a critical capability for applications ranging from robotic manipulation to augmented reality. Though early in his career, his work has already garnered attention, with his flagship paper accumulating 2 citations, signaling growing recognition in the field. Maleki’s research promises to advance the precision and reliability of 3D reconstruction in dynamic environments, offering practical solutions for robots interacting with deformable objects in real-world settings.