Dominik Scheuble
Papers
1
Total Citations
25
H-Index
1
About
Dominik Scheuble is a rising researcher at the forefront of computer vision and neural rendering, with a focus on enabling robust perception in adverse weather. His most impactful work, "ScatterNeRF: Seeing Through Fog with Physically-Based Inverse Neural Rendering" (2023), has already garnered 25 citations, marking a significant early-career contribution. In this paper, Scheuble tackles the critical challenge of scattering and attenuation caused by fog, snow, and rain—conditions that severely degrade image quality and hinder the safe operation of autonomous vehicles, drones, and robotics. By integrating physically-based inverse rendering into the Neural Radiance Field (NeRF) framework, ScatterNeRF models light transport through scattering media, effectively "seeing through" fog to reconstruct clear scenes. This work bridges the gap between physics-based vision and neural rendering, offering a principled solution for real-world deployment in inclement weather. Scheuble’s research is not only technically rigorous but also practically vital for advancing autonomous systems that must operate reliably in all conditions. As his citation count grows, he is establishing himself as a key innovator in robust visual perception, with ScatterNeRF serving as a foundational step toward weather-resilient computer vision.
Research Focus
Key Achievements
Top Papers
- 1