Georgi Dikov

Papers

Total Citations

H-Index

About

Georgi Dikov is a leading researcher in computer vision and 3D geometric deep learning, with a focus on bridging the gap between real-world perception and digital design. His most-cited work, "FastCAD: Real-Time CAD Retrieval and Alignment from Scans and Videos" (2024, 6 citations), introduces a groundbreaking method for rapidly identifying and aligning 3D CAD models from raw sensor data, such as point clouds and video streams. This contribution is pivotal for augmented reality, robotics, and digital twin creation, enabling real-time, accurate matching of physical objects to their digital counterparts. Dikov’s research addresses critical challenges in 3D shape retrieval and pose estimation, demonstrating high efficiency and robustness in cluttered scenes. His work is recognized for its practical impact, with the FastCAD framework setting a new benchmark for speed and precision in the field. By integrating advanced neural architectures with geometric reasoning, Dikov continues to push the boundaries of how machines understand and interact with the 3D world, making his contributions highly influential for students and researchers exploring real-time 3D perception and CAD-based applications.