Daniel Roth
Papers
2
Total Citations
29
H-Index
2
About
Daniel Roth is a leading researcher in 3D computer vision, with a primary focus on category-level 6D object pose estimation. His major contribution is the creation of **HouseCat6D**, a large-scale, multi-modal dataset designed to overcome critical limitations in existing benchmarks. While prior datasets suffered from poor annotation quality and limited pose variety, HouseCat6D provides richly annotated, realistic household scenarios that enable more robust and practical pose estimation models. Roth’s work directly addresses the gap between instance-level and category-level pose estimation, pushing the field toward real-world applications in robotics and augmented reality. The 2024 version of his dataset has already garnered **26 citations**, reflecting its rapid adoption as a foundational resource. By releasing a high-quality, diverse dataset, Roth has empowered researchers to train and evaluate algorithms that generalize across object categories, not just specific instances. His efforts are shaping the next generation of 3D perception systems, making him a key figure in advancing computer vision from controlled labs to everyday environments.
Research Focus
Key Achievements
Top Papers
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