Kristiyan Georgiev

Papers

Total Citations

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About

Kristiyan Georgiev is a researcher whose work sits at the intersection of 3D computer vision, geometric modeling, and robotics perception. His primary contributions lie in developing efficient algorithms for real-time 3D scene understanding, with a particular focus on extracting meaningful geometric primitives from raw sensor data. His most influential work, "Real-time 3D scene description using Spheres, Cones and Cylinders" (2016, 7 citations), introduces a novel algorithm capable of fitting complex 3D shapes—cylinders, cones, and spheres—in real time. The key innovation is a constant-time (O(1)) model update per data point, enabling rapid, on-the-fly scene reconstruction from range data. This work is complemented by his earlier research, "3D data classification based on mid-level geometric features" (2011, 4 citations), which pioneered a method for classifying robot environments by first transforming raw 3D point clouds into planar patches. This mid-level representation bridges the gap between low-level sensor data and high-level semantic understanding, a crucial step for autonomous navigation. Though his citation counts are modest, Georgiev’s focus on computational efficiency and geometric abstraction has provided foundational techniques for real-time robotic perception, demonstrating that fast, robust shape extraction is achievable even with limited computational resources.