Sotiris Nousias
Papers
1
Total Citations
4
H-Index
1
About
Sotiris Nousias is a researcher at the forefront of 3D computer vision and neural scene representation, with a particular focus on integrating active sensing modalities into the Neural Radiance Field (NeRF) framework. His most cited work, "Transient Neural Radiance Fields for Lidar View Synthesis and 3D Reconstruction" (2023, 4 citations), addresses a critical gap in the field: while NeRFs have revolutionized scene modeling from passive imagery, incorporating lidar or depth sensor data has remained challenging. Nousias’s key contribution lies in developing a novel approach that leverages transient lidar measurements to supervise NeRF training, enabling more accurate geometry reconstruction and novel view synthesis from sparse sensor data. This work bridges the gap between active and passive 3D reconstruction, offering a pathway to robust scene understanding in robotics and autonomous driving. Though early in his career, his research has already garnered attention for tackling the practical limitations of NeRFs in real-world, sensor-rich environments. By pioneering methods that fuse lidar with neural rendering, Nousias is helping to shape the next generation of 3D vision systems that can operate reliably under diverse conditions.
Research Focus
Key Achievements
Top Papers
- 1