4 accepted papers
We present a novel large-scale dataset for defect detection in a logistics setting. Recent work on industrial anomaly detection has primarily focused on manufacturing scenarios with highly controlled poses and a limited number of object categories. Existing benchmarks like MVTec-AD (Bergmann et al.,…
We present an integrated approach for perception of unknown articulated objects. To robustly perceive objects and understand interactions, our method tightly integrates pose tracking, shape reconstruction, and the estimation of their kinematic structure. The key insight of our method is that these s
The effectiveness of robot interaction depends on the robot's ability to perform task-relevant actions and on the degree to which it is able to predict the outcomes of these actions. In this paper we argue that the two learning problems - learning actions and learning forward models - must be tightl…
We present a method for multi-class segmentation from RGB-D data in a realistic warehouse picking setting. The method computes pixel-wise probabilities and combines them to find a coherent object segmentation. It reliably segments objects in cluttered scenarios, even when objects are translucent, re…