Efficiently Linking Real Scenes with Synthetic Data Generation for AI-based Cognitive Robotics and Computer Vision Applications
초록
AI vision models are a driving factor for the potential use case scenarios of cognitive robotics within in the industry and household applications. A large array of methods from semantic environment analysis towards 6D and grasping pose estimation have been proposed based on the latest AI achievements. However, such advancements require further strong and efficient methods w.r.t. training data and AI-architectures, which are capable in synergy to tackle current challenges, precision limits, and scalability beyond domain gaps. In this paper, we discuss these current limits and trends in the related state-of-the-art which are challenging those. Further we discuss our current work in progress on bridging the domain gap between simulations and real world applications by linking those in the training data generation.
저자 (7명)
- Paul Koch — LinkedIn 검색
- Vivek Chavan — LinkedIn 검색
- André Sers — LinkedIn 검색
- Adem Karakurt — LinkedIn 검색
- Paul Hofmann — LinkedIn 검색
- Mohamad Zaher Ziadeh — LinkedIn 검색
- Jörg Krüger — LinkedIn 검색
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