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Through extensive experimentation with different SOTA models for semantic segmentation, we demonstrate the effectiveness of our approach in overcoming the limitations of small training sets and show how photorealistic synthetic data substantially improves model performance, even on corner cases such as occluded or deformed objects and different lighting conditions, which is crucial to assure the robustness in real-world applications. In addition, we demonstrate the usefulness of this approach with a real-world instance segmentation application together with a ROS-based barrel grasping pipeline for our excavator platform. |
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